https://actuary.info/ai-patent-watch/ Published US patent applications in artificial intelligence and machine learning from tracked insurers, reinsurers, brokers and vendors, still in examination. These are requests, not property: claims are routinely narrowed before anything is granted. en-us Tue, 08 Sep 2026 12:00:00 GMT 1440 https://actuary.info/ai-patent-watch/#pipeline US20260260139A1 Thu, 03 Sep 2026 12:00:00 GMT awaiting examination Describes a system that would generate a base-line probable roof loss confidence score from hail data, then use that score to produce property insurance underwriting, claims and loss mitigation data. How it works: Combines a building's attributes, its roof characteristics and historical weather for its location into a base-line roof loss confidence score, then regenerates the score when a weather event occurs and, where the updated score deviates from the base-line beyond a set threshold, triggers a downstream action. Where it fits: Property insurance underwriting, claims triage and loss mitigation, prioritizing roofs after hail and storm events. Stage: Filed April 2026, published September 2026 and under examination; the claim read here is the original as filed, the broadest version and the one most likely to be narrowed. https://actuary.info/ai-patent-watch/#pipeline US20260245743A1 Thu, 20 Aug 2026 12:00:00 GMT awaiting examination Describes generating synthetic patient records modelled on a nationwide cohort, with the effects of treatments known in advance, so causal methods can be tested against a ground truth without exposing real patient data. How it works: Filters a patient cohort to a treatment group, converts patient trajectories into standardized numeric tables, then trains a generator against a discriminator, weighting rarer features by an identifiability term and a Wasserstein-distance loss so the synthetic set tracks the real distribution while staying distinct from any real patient. Where it fits: Health analytics and research, supplying shareable synthetic cohorts to study treatment effects without exposing real patient data. Stage: Filed February 2026, published August 2026 and under examination; the claim read here is the original as filed, the broadest version and the one most likely to be rejected. https://actuary.info/ai-patent-watch/#pipeline US20260244947A1 Thu, 20 Aug 2026 12:00:00 GMT awaiting examination Describes an AI system that drafts a proposed reply to an inbound customer message, routes it to a representative for feedback, and retrains itself on that feedback, aimed at customer servicing and contact handling. How it works: Derives a query from an inbound message, sends a representative a model-generated proposed reply, records the representative's feedback together with that reply as a historical entry, compiles those entries into a training dataset, and retrains the AI model on it using machine learning. Where it fits: Insurance customer service and claims correspondence, where representative feedback would continuously tune the automated replies. Stage: Filed February 2025, published August 2026 and under examination; the claim read here is the original as filed, not a granted right. https://actuary.info/ai-patent-watch/#pipeline US20260244775A1 Thu, 20 Aug 2026 12:00:00 GMT under examination Proposes deriving user and data-consumption profiles from information management data, then generating access controls from those profiles rather than from manually maintained permission lists. How it works: Ingests information-management data, preprocesses it into user profiles and data-asset consumption profiles, feeds those to generative AI models trained on entitlement-labeled profiles to generate access controls, then checks each incoming request against the requesting user's assigned entitlements to allow or deny access. Where it fits: Enterprise data governance and access control; the abstract describes general-purpose data security, not a specific insurance workflow. Stage: Filed February 2025, published August 2026 and under examination; the claim read here is the original as filed and remains the applicant's request. https://actuary.info/ai-patent-watch/#pipeline US20260244670A1 Thu, 20 Aug 2026 12:00:00 GMT responded Describes parsing an inbound message into keywords, retrieving documents from a matching subject-matter database, and passing them to a generative model that drafts a response and scores each document's relevance, aimed at customer contact handling. How it works: Parses an inbound message into a query, extracts keywords to select a matching subject-matter document database, searches it for responsive documents and scores each for relevance, feeds those documents to a generative AI model to draft a reply, and notifies a representative with selectable document links each shown with its relevancy score. Where it fits: Insurance contact-center triage, retrieving reference documents to draft and rank responses for a human representative. Stage: Filed February 2025, published August 2026 and under examination; the claim read here has already been narrowed by amendment, not granted. https://actuary.info/ai-patent-watch/#pipeline US20260244626A1 Thu, 20 Aug 2026 12:00:00 GMT allowed Describes a review interface that shows a representative an AI-drafted response with linked source documents, captures a response indicator, and retrains the model to display a revised reply, aimed at customer contact handling. How it works: Displays to a representative an AI-drafted reply with document links and a feedback control, takes the assigned response indicator, routes the reply to a subject-matter expert for an updated indicator, records the exchange as a training record, retrains the response model, and shows a revised reply from the retrained model. Where it fits: Insurance customer-service and claims desks, adding an expert-review step to the loop that tunes the automated replies. Stage: Filed February 2025, published August 2026 and under examination; the claim read here was narrowed by amendment during prosecution, not allowed. https://actuary.info/ai-patent-watch/#pipeline US20260236835A1 Thu, 13 Aug 2026 12:00:00 GMT awaiting examination Proposes refining machine learning training labels by first training a classifier on a class-balanced older dataset, then using it to relabel misclassified rare-class cases in a newer dataset before the production model is trained. How it works: Balances an older imbalanced dataset by pairing minority-class samples with an equal, randomly chosen set of majority-class samples, trains a classifier on it, then runs that classifier over a newer dataset to find majority-labelled cases that are really the rare class, relabels them, and trains the production model on the corrected data. Where it fits: General-purpose model training; the claim describes rare-class detection in imbalanced data and names no specific insurance workflow. Stage: Filed August 2025, published August 2026 and under examination; the claim read here is the original as filed, the broadest version and the one most likely to be narrowed. https://actuary.info/ai-patent-watch/#pipeline US20260236743A1 Thu, 13 Aug 2026 12:00:00 GMT awaiting examination Describes an ensemble forecaster that combines autoregressive features with embeddings drawn from sparse datasets, aimed at predictions that draw on several sources rather than a single series. How it works: Takes two or more datasets of different categories, extracts a feature set from each, concatenates them into consolidated input vectors that combine observed historical values with fixed-length embeddings of time-lag sequences, then passes the vectors through a trained neural-network ensemble to generate prediction results. Where it fits: General-purpose time-series forecasting; the abstract describes the model itself and names no specific insurance workflow. Stage: Filed March 2026, published August 2026 and under examination; the claim read here is the original as filed, still the applicant's opening request. https://actuary.info/ai-patent-watch/#pipeline US20260236730A1 Thu, 13 Aug 2026 12:00:00 GMT awaiting examination Seeks to fine-tune a base language model on enterprise data and use it to draft social media content shaped to a user profile, placing generative text inside a carrier's marketing workflow. How it works: Claim 1 fine-tunes a base ML model on training data from many user profiles to produce a set of fine-tuned models, then on a request obtains the user's profile, loads the model tied to it, runs the request through a chatbot trained on that profile's communication style, and returns the generated social media content to the device. Where it fits: Enterprise and agent marketing content generation, not a pricing, underwriting or claims function. Stage: Filed April 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260236275A1 Thu, 13 Aug 2026 12:00:00 GMT awaiting examination Describes assembling a language model prompt from a user's identity and their activity during an authenticated session, so guidance is generated in context rather than drawn from fixed templates. How it works: Claim 1 receives user input during an authorized session, builds a first LLM prompt carrying account identification and a request for additional identifying information, transmits it, receives that information back, then builds a second prompt combining the returned information with a request for personalized information tied to it. Where it fits: Customer servicing and personalized member communications for a financial or insurance provider. Stage: Filed February 2025, docketed and awaiting a first office action, on the original claims as filed, the applicant's broadest version. https://actuary.info/ai-patent-watch/#pipeline US20260236149A1 Thu, 13 Aug 2026 12:00:00 GMT awaiting examination Describes a platform that updates availability determinations for selectable items as configuration and content change, with a machine learning engine producing the availability output. How it works: Claim 1 stores historical availability inputs paired with result outputs, sorts them into per-result ML datasets, receives a new input set from a device, scores a match degree against each dataset, selects the dataset whose match exceeds a preset threshold, and returns that dataset's associated availability result to the device. Where it fits: Availability or eligibility lookups presented to a customer, a servicing rather than pricing function. Stage: Filed April 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260228840A1 Thu, 06 Aug 2026 12:00:00 GMT final rejection Describes combining smart building sensor analytics with claims data in models trained per location to generate loss-related recommendations for a chosen site, aimed at property risk assessment and loss prevention. How it works: Claim 1 as amended receives smart building analytics including electrical sensor data from many buildings plus claims data from an overlapping set, takes a target location, runs both through AI models trained for that location, and outputs building-plan recommendations drawn from buildings whose characteristics are similar to the target. Where it fits: Property loss mitigation and pre-loss risk assessment for insured structures. Stage: Filed May 2025 and under a final rejection: the examiner has rejected the current claims, and this claim was already narrowed during prosecution rather than agreed. https://actuary.info/ai-patent-watch/#pipeline US20260228839A1 Thu, 06 Aug 2026 12:00:00 GMT responded Describes training models on building sensor analytics and claims data to generate a construction plan, with a materials list and drawings, for a building designed to reduce the likelihood of loss at a chosen location. How it works: Claim 1 as amended takes analytics describing each building's materials, features and surroundings plus claims loss data with the event behind each loss, trains AI models to output a plan whose recommended materials and features are customized to reduce loss at a target location, then outputs a materials list and design drawings. Where it fits: Property loss mitigation through risk-informed construction guidance for insured structures. Stage: Filed May 2025, the applicant having responded to an office action, on a claim already narrowed during prosecution rather than the opening version. https://actuary.info/ai-patent-watch/#pipeline US20260228831A1 Thu, 06 Aug 2026 12:00:00 GMT awaiting examination Proposes overlaying recommended devices onto a live camera view of a property by correlating underlay and overlay image layers, so a homeowner sees suggested equipment in place before buying it. How it works: Claim 1 receives underlay layer data for an AR viewer's field of view, calculates the improvement to a home score from placing a recommended device, receives overlay data naming the device and the score improvement, correlates overlay with underlay, and builds an AR display showing the device and the score gain to the user. Where it fits: Home risk-mitigation guidance and loss-prevention engagement for property policyholders. Stage: Filed April 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260228372A1 Thu, 06 Aug 2026 12:00:00 GMT awaiting examination Seeks to train a model on smart building analytics and claims data to generate construction plans for a subdivision whose materials and features are chosen to lower expected loss and improve energy efficiency. How it works: Claim 1 takes building analytics from many buildings plus claims data from an overlapping set, trains AI models to output a plan for a subdivision of enhanced buildings whose materials and features are customized to reduce loss and whose positioning improves energy efficiency, then outputs a materials list and design drawings. Where it fits: Property loss mitigation and pre-loss risk assessment at a subdivision scale. Stage: Filed May 2025, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260220711A1 Thu, 30 Jul 2026 12:00:00 GMT awaiting examination Describes retrieving retirement account data from third-party institutions and passing it through a trained capture model, aimed at discovering and acting on accounts a saver may have lost track of. How it works: Claim 1 takes a transaction request with retirement account identifiers, retrieves plan rules or regulations, feeds the request and rules to a trained smart-contract model to generate contract code, compiles and deploys it to a distributed ledger, sends inputs to the contract, and returns the outputs as a summary to the user. Where it fits: Retirement account transaction execution and recordkeeping for a plan provider. Stage: Filed March 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260220710A1 Thu, 30 Jul 2026 12:00:00 GMT under examination Describes retrieving retirement account data from third-party institutions, structuring it with a capture model and forecasting with a deep learning model, aimed at discovering and managing scattered accounts. How it works: Claim 1 authenticates the user by decrypting credentials with a public key, receives a request to find lost retirement accounts, retrieves the user's identification data, supplies it to several third-party institutions to search for matching accounts, then on a hit provides reset links and saves a returned account identifier and credentials. Where it fits: Account aggregation and onboarding for retirement savers, a servicing function. Stage: Filed March 2026 and under examination, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260220531A1 Thu, 30 Jul 2026 12:00:00 GMT awaiting examination Describes a hybrid machine learning model for risk assessment that would train on a loss function combining discriminative and generative terms to handle missing input values, and output both a risk indicator and the explanatory data behind it. How it works: The model would compute a risk indicator for a target entity from its predictor variables, training by iteratively minimizing a loss function that blends a discriminative and a generative term so it fits even when some input values are unknown, then generate explanatory data linking changes in the score to changes in specific predictors. Where it fits: Automated risk assessment and scoring that gates access to interactive computing services; general risk scoring rather than a named insurance line. Stage: Filed June 2025 and published July 2026; under examination on the claim as originally filed, which has not been narrowed in prosecution yet. https://actuary.info/ai-patent-watch/#pipeline US20260220372A1 Thu, 30 Jul 2026 12:00:00 GMT allowed Describes an information extraction system that tags document chunks with identifiers and asks a language model to cite which chunks it relied on, so each extracted value can be traced, verified, and audited, with an interface for human review and correction. How it works: Splits documents into chunks, each tagged with an identifier pointing back to its source, searches for chunks relevant to a query, prompts a language model to extract the target information and to name which chunks it used, then builds an interface showing each extracted value beside a citation to its source passage. Where it fits: General-purpose extraction from unstructured documents; the abstract describes source traceability and audit rather than a named insurance workflow. Stage: Filed January 2026, published July 2026 and allowed after the applicant narrowed the claim during prosecution; a notice of allowance is not an issued patent. https://actuary.info/ai-patent-watch/#pipeline US20260220371A1 Thu, 30 Jul 2026 12:00:00 GMT allowed Describes a retrieval system that separates a document's tables from its text, indexes each as vector embeddings, and sends only the chunks relevant to a query to a language model, so tabular and prose content are retrieved independently when data elements are extracted. How it works: Receives content containing text and tables, separates them into table chunks and text chunks, converts each chunk into a vector embedding with an index entry, finds the table or text chunk relevant to a prompt through a search of those entries, and stores the language model's response together with the chunk it drew on. Where it fits: General document extraction where tabular data must be retrieved separately from prose; the abstract names no specific insurance workflow. Stage: Filed September 2025, published July 2026 and allowed after the applicant narrowed the claim during prosecution; the issue fee is paid but the patent has not yet issued. https://actuary.info/ai-patent-watch/#pipeline US20260212422A1 Thu, 23 Jul 2026 12:00:00 GMT awaiting examination Seeks to predict claim outcomes from telematics and related data, including which parts of a vehicle were damaged and the expected liability, moving loss estimation ahead of first notice of loss. How it works: Claim 1 obtains accelerometer and GPS data sampled at different rates for a crashed vehicle, resamples both to a common rate, computes vectors and acceleration signals, enriches with external context such as intersection, weather or speed limit, and feeds a classifier trained on historical telematics and outcomes to predict the outcome. Where it fits: Auto claims triage and outcome prediction from post-accident telematics. Stage: Filed November 2025, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260212263A1 Thu, 23 Jul 2026 12:00:00 GMT awaiting examination Describes building linked graph structures from entity and interaction data, then selecting operations for a model to generate a content recommendation that facilitates an interaction between parties. How it works: Claim 1 takes a provider's request to recommend content for an interaction with a target entity, builds two graph structures from the entity's data and interaction data, links them, chooses which of several operations to run on the linked graph, executes them with a trained model to generate a content recommendation, and returns a message. Where it fits: Customer interaction and content targeting; the abstract states no specific insurance function. Stage: Filed June 2025, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260204149A1 Thu, 16 Jul 2026 12:00:00 GMT awaiting examination Describes analysing household sensor data, including device-level electricity use, to identify a condition affecting a resident, aimed at detecting change in a home without direct observation. How it works: Claim 1 analyzes data from home-mounted sensors with a model trained on that home's historical sensor and condition data, determines an abnormal condition of an individual, including detecting an atypical movement pattern from the sensor data, and generates an electronic notification of that condition. Where it fits: Home-based risk monitoring and caregiver alerting, adjacent to health and life risk assessment. Stage: Filed March 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260203648A1 Thu, 16 Jul 2026 12:00:00 GMT awaiting examination Proposes inferring the accuracy and drift of third-party machine learned models from their inputs and outputs, then switching or retraining downstream models when a monitored model degrades. How it works: Claim 1 sends the same unstructured input to an obscured third-party processing system at two times, receives labeled output each time, computes a difference between the two label sets, tests it against a threshold, and when it is met initiates a reconfiguration of a downstream system that consumes the third-party output. Where it fits: Model risk governance and monitoring of vendor models used in insurance analytics. Stage: Filed January 2025, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260203611A1 Thu, 16 Jul 2026 12:00:00 GMT awaiting examination Describes matching an entity's historical risk data to a cluster derived from high-dimensional clustering across many entities, then predicting future risk from that cluster rather than the entity alone. How it works: Claim 1 identifies, from clusters built by high-dimensional clustering over many entities, the target cluster matching an entity's historical risk data, finds that cluster's nearest neighboring clusters, and determines a prediction of future risk for the entity from the target cluster and its neighbors. Where it fits: Risk scoring and access control, applicable to underwriting-style risk assessment. Stage: Filed March 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260203341A1 Thu, 16 Jul 2026 12:00:00 GMT awaiting examination Seeks to segment unstructured text, categorise each segment, then build a prompt per segment so a generative model returns structured fields, aimed at extraction and routing without fixed parsers. How it works: Claim 1 takes a file of customer-interaction text, assigns categories to different portions, derives a field name and description for a portion, builds a prompt from them, passes the prompt and text to an ML model to extract a value, checks it against a constraint, and on satisfaction stores it as metadata linked to the file. Where it fits: Claims and servicing intake, turning unstructured customer text into validated structured fields. Stage: Filed March 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260195820A1 Thu, 09 Jul 2026 12:00:00 GMT awaiting examination Proposes analysing uploaded and labelled images to identify a property type, match it against the reported property, and predict whether a loss should be treated as a total loss. How it works: Claim 1 takes labeled images and a claim identifier, retrieves the claim's total loss score and reported property type, uses an AI model to separate usable from unusable images, identifies the property type from the usable ones, matches it to the reported type, and generates a predicted total loss determination from the score, images and match. Where it fits: Auto or property claims total-loss adjudication from submitted images. Stage: Filed October 2025 and awaiting examination, on a new claim presented after the original claims were cancelled, typically following a rejection. https://actuary.info/ai-patent-watch/#pipeline US20260195721A1 Thu, 09 Jul 2026 12:00:00 GMT awaiting examination Describes scoring agent social media profiles with a media enhancement model, aimed at ranking and improving how individual agents present a carrier's brand online. How it works: Claim 1 scores each agent profile with a trained model that weights follower change in the social media data, partitions the scores into agent groups by a statistical distribution, derives top posts and a per-agent top-posts list by semantic analysis, generates curated tasks to improve the score, and updates them as engagement changes. Where it fits: Agent marketing and social media engagement support, not a pricing or underwriting function. Stage: Filed February 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260195695A1 Thu, 09 Jul 2026 12:00:00 GMT responded Describes governing operating parameters through nested machine learning models built from multiple agents, so personalisation settings are adjusted by the system rather than configured by hand. How it works: Claim 1 as amended receives knowledge data indicating operating parameters, analyzes an input tied to a subset of them with a nested model of several agent models trained on composite knowledge data, determines a compliance action to configure that subset, and generates a recommendation tied to the compliance action. Where it fits: Governance and compliance workflow support for a financial or insurance provider. Stage: Filed January 2025, the applicant having responded to an office action, on a claim already narrowed during prosecution rather than the opening version. https://actuary.info/ai-patent-watch/#pipeline US20260195641A1 Thu, 09 Jul 2026 12:00:00 GMT awaiting examination Seeks to combine geotagged data from user devices with supplemental location data in a model that predicts events and builds a location risk profile from their frequency, aimed at location risk assessment. How it works: Claim 1 takes geotagged data from a user's devices, gathers supplemental data for the tagged locations, feeds both into a model trained to predict event occurrences and recommendations at those locations, builds a risk profile from the frequency of predicted events, and outputs a prediction presentation to the user's interfaces. Where it fits: Location-based risk assessment and loss-prevention alerts for policyholders. Stage: Filed January 2025, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260195431A1 Thu, 09 Jul 2026 12:00:00 GMT awaiting examination Describes an authentication system that generates open-ended questions from known data about a user and uses a large language model to judge whether natural-language answers are correct, incorrect, or incomplete, asking follow-ups before granting access. How it works: Resolves supplied user information to a unique identifier, pulls matching data from data sources, generates authentication questions from it, takes the user's free-text answers, and uses a language model to score each answer against the known ground truth as correct, incorrect, or incomplete before authenticating or asking for more detail. Where it fits: Identity verification and account-access authentication, a control that supports fraud prevention. Stage: Filed March 2026, published July 2026 and awaiting examination; the claim read here is the original as filed, the broadest version and the one most likely to be rejected. https://actuary.info/ai-patent-watch/#pipeline US20260188504A1 Thu, 02 Jul 2026 12:00:00 GMT responded Seeks to route contact centre inputs through a language model that queries healthcare databases and returns responsive information, aimed at pharmacy benefit call handling. How it works: Claim 1 records audio inputs at a call center agent device from members or agents, passes them to LLM circuitry in a neural network, has the LLM develop data requests, accesses several healthcare-related databases for the requested information, identifies the responsive information, and returns it to the agent device to avoid manual searching. Where it fits: Contact-center servicing for a pharmacy benefit manager or health plan. Stage: Filed December 2024, the applicant having responded to an office action, on a new claim presented after the original claims were cancelled, typically following a rejection. https://actuary.info/ai-patent-watch/#pipeline US20260187585A1 Thu, 02 Jul 2026 12:00:00 GMT awaiting examination Proposes locating utility lines by combining LIDAR returns with existing utility records, aimed at establishing where buried or overhead lines run before work near a property begins. How it works: Claim 1 receives LIDAR data from a LIDAR camera and preexisting utility line data, determines a utility line's location from both, builds a 3D model of the surrounding landscape, and displays a representation of that model with a depiction of the utility line. Where it fits: Property risk assessment and inspection support around underground or overhead utilities. Stage: Filed February 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260187571A1 Thu, 02 Jul 2026 12:00:00 GMT final rejection Describes collecting engineering asset data, processing it with learning algorithms, and generating rationalisation actions and reports, aimed at assessing technical debt across a portfolio of systems. How it works: Claim 1 as amended collects data from multiple sources, normalizes it, processes it with an ensemble of AI, generative and reinforcement-learning models to find asset rationalization opportunities, generates proposed actions and a timeline forecast, validates them with adversarial networks, and appends each with multimodal outputs. Where it fits: Internal technology and technical-debt management for the enterprise, no direct insurance function. Stage: Filed December 2024, the applicant having responded to an office action, on a claim already narrowed during prosecution rather than the opening version. https://actuary.info/ai-patent-watch/#pipeline US20260187543A1 Thu, 02 Jul 2026 12:00:00 GMT awaiting examination Describes a shared feature store and standardised training pipeline so models reuse de-sensitised features rather than each team rebuilding them, aimed at reducing duplicated model development work. How it works: Claim 1 generates standardized features from raw external data into a shared feature store, selects input features from it, generates templates for the predictive model, trains models on the selected features and templates, identifies a preferred trained model, registers it and its artifacts, and automates its execution on future data. Where it fits: Model development infrastructure for a health insurer's analytics, not a specific pricing step. Stage: Filed February 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260187533A1 Thu, 02 Jul 2026 12:00:00 GMT awaiting examination Seeks to reduce bias in a risk assessment model by obtaining data on a protected attribute during training and adjusting the model against it, addressing fairness inside the training loop. How it works: Claim 1 trains a risk model on predictor variables and outputs, obtains protected attribute data, computes a bias metric from correlations among a predictor variable, the protected attribute and model outputs, flags bias when it indicates it, modifies and retrains the model, then predicts a risk indicator used to control an entity's access. Where it fits: Fair-lending style bias control in risk scoring, relevant to underwriting model governance. Stage: Filed February 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260187269A1 Thu, 02 Jul 2026 12:00:00 GMT awaiting examination Proposes obfuscating restricted records by aggregating them on shared attributes, so a requester receives grouped values rather than the underlying sensitive rows. How it works: Claim 1 takes a request to access restricted sensitive records, transforms them with a model chosen by the intended use of the aggregated output, groups the records into aggregation segments using a selected segmentation model, and generates aggregated data by combining the records' individual sensitive attributes across those segments. Where it fits: Privacy-preserving data handling for risk and credit analytics, a data-governance function. Stage: Filed February 2026, docketed and awaiting a first office action, on the original claims as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260179361A1 Thu, 25 Jun 2026 12:00:00 GMT awaiting examination Describes scoring how typical a property is against a reference population of comparable properties, and identifying which attributes drive that score, aimed at property risk characterisation. How it works: Obtains imagery of a property in a hazard-exposed region, uses a trained model to extract structural and environmental risk attributes into a vector, selects reference properties sharing the same hazard profile, normalizes each attribute by its variance across that reference set, and computes a typicality metric from the normalized vectors. Where it fits: Property underwriting and catastrophe risk assessment, flagging outlier structures against local peers. Stage: Filed February 2026 and awaiting examination; the claim read here was newly substituted for the cancelled original claims, so examination of it has not begun. https://actuary.info/ai-patent-watch/#pipeline US20260179158A1 Thu, 25 Jun 2026 12:00:00 GMT under examination Seeks to extract home data from inspection reports with a model trained on historical correlations, aimed at turning narrative inspection text into structured input for smart home predictions. How it works: Receives a home inspection report, extracts home data with an AI model trained on correlations between past reports and past home data, stores it across defined fields, finds a field with no value, generates a predicted value for it from historical home data, and stores that prediction. Where it fits: Property underwriting and inspection intake, filling gaps in home records used to assess risk. Stage: Filed November 2025 and awaiting examination; the claim read here is the original as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260179152A1 Thu, 25 Jun 2026 12:00:00 GMT awaiting examination Proposes taking insurance claim complaints through a chatbot that categorises each complaint, aimed at triaging complaint handling by type before a human reviewer sees it. How it works: Identifies a website carrying a complaint, scrapes the text, feeds it to a chatbot using a generative pre-trained transformer or an LSTM, categorizes the complaint by either its tone or the policy issue involved, assembles a report noting that category, and sends it to a complaint administrator's device. Where it fits: Claims and complaint servicing, triaging insurance complaints before they reach a human administrator. Stage: Filed February 2026 and awaiting examination; the claim read here is the original as filed and has not yet been tested by an examiner. https://actuary.info/ai-patent-watch/#pipeline US20260179100A1 Thu, 25 Jun 2026 12:00:00 GMT awaiting examination Describes monitoring a linked user account and acting on the data it returns, with an artificial intelligence platform driving what the monitoring tool does on the account holder's behalf. How it works: Opens a link to a user's device, ingests account data, runs a management tool whose user-specific AI model is trained on that user's historical data, detects an account event that needs handling, generates a response satisfying the event's condition without any contemporaneous input from the user, and transmits it back. Where it fits: Policy and account servicing, automating responses to account events for insurance customers. Stage: Filed December 2025 and awaiting examination; the claim read here is the original as filed, the broadest version on record. https://actuary.info/ai-patent-watch/#pipeline US20260178941A1 Thu, 25 Jun 2026 12:00:00 GMT awaiting examination Describes building a feature vector from a user profile that includes past communications and responses, then selecting which channel to use for the next contact. How it works: Builds a feature vector from a member's profile of past communications and actions, runs a separate model per channel to score how likely the member acts on a message there, each model trained by labeling users responsive or not from their communication and event histories, then selects the top-scoring channel and sends via that channel's API. Where it fits: Health plan member outreach, choosing the contact channel most likely to prompt an action such as care follow-up. Stage: Filed February 2026 and awaiting examination; the claim read here is the original as filed and remains untested by an examiner. https://actuary.info/ai-patent-watch/#pipeline US20260171256A1 Thu, 18 Jun 2026 12:00:00 GMT awaiting examination Describes identifying providers and locations able to perform a referred procedure, aimed at steering a referral toward capable sites at the point a provider submits it. How it works: Receives a referral for a patient to undergo a procedure, determines candidate medical locations or providers weighted by patient location, insurance network, or cost, surfaces those options on the referring provider's display, takes the user's selection, and processes the referral against the selected location or provider. Where it fits: Health plan care management and provider steerage, guiding referrals toward in-network, lower-cost options. Stage: Filed November 2025 and awaiting examination; the claim read here is the original as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260171247A1 Thu, 18 Jun 2026 12:00:00 GMT awaiting examination Describes models that flag medication issues and gaps in a member's care, then propose actions to close them, aimed at intervening before a gap becomes a claim. How it works: Trains machine learning models to flag gaps in care or medication actions, pulls a patient's data from multiple medical databases, formats it into model features, and outputs each finding with a confidence value, then on learning the patient will be seen within a period notifies the provider of the gap or action before or during that visit. Where it fits: Health plan quality and care-gap closure, prompting providers at the point of an upcoming encounter. Stage: Filed November 2025 and awaiting examination; the claim read here is the original as filed and has not been examined. https://actuary.info/ai-patent-watch/#pipeline US20260171121A1 Thu, 18 Jun 2026 12:00:00 GMT awaiting examination Proposes scanning a video file for interactive elements and prompting for the inputs needed to describe them, aimed at bringing interactive video into accessibility compliance. How it works: Analyzes a video executable file to locate where interactive elements will occur during playback, receives label data tying a label to each such location, links every label and its interactive element to the corresponding point in the video, and outputs an enhanced video file with those labels embedded. Where it fits: General accessibility tooling for interactive video; the claim does not tie the mechanism to a specific insurance workflow. Stage: Filed February 2026 and awaiting examination; the claim read here is the original as filed, the broadest version on record. https://actuary.info/ai-patent-watch/#pipeline US20260170555A1 Thu, 18 Jun 2026 12:00:00 GMT final rejection Describes deriving coding signals from engineering data and scoring them against business capabilities to produce a technical debt score for a portfolio of systems. How it works: Trains a neural network on past coding practices and outcomes, normalizes data collected across a technology ecosystem, derives coding signals from code-quality metrics and business capabilities, assigns them weighted scores, classifies those into ranges, then prompts a fine-tuned language model to produce a report explaining the debt score. Where it fits: Enterprise software risk management; the claim describes internal IT governance rather than an insurance workflow. Stage: Filed December 2024 and now under a final rejection; the claim read here was amended during prosecution, meaning the applicant narrowed it, not that the examiner allowed it. https://actuary.info/ai-patent-watch/#pipeline US20260169880A1 Thu, 18 Jun 2026 12:00:00 GMT awaiting examination Seeks to predict an appliance's remaining lifetime from data about that appliance, aimed at anticipating failure in a home before it becomes a claim. How it works: Receives historical data on many appliances including their observed lifetimes, some of it generated by sensors monitoring appliance parameters, trains an AI model to output a predicted remaining lifetime from an appliance's data, and stores the trained model for later use. Where it fits: Homeowners loss prevention and servicing, anticipating appliance failures that drive property claims. Stage: Filed February 2026 and awaiting examination; the claim read here is the original as filed and covers only training and storing the model. https://actuary.info/ai-patent-watch/#pipeline US20260162783A1 Thu, 11 Jun 2026 12:00:00 GMT final rejection Seeks to extract and summarise unstructured medical records with a large language model, aimed at turning clinical documents into reviewable structured content for claim handling. How it works: Receives a set of medical record documents, pre-processes them into structured medical information, then queries a large language model for a summary using a sequence of successively more specific queries, each shaped by the summary the model returned on the previous pass, and hands the result to downstream workflows. Where it fits: Casualty and medical claims handling, condensing records for adjusters and bill review. Stage: Filed December 2024 with a response now on file; the claim read here was amended during prosecution, so the applicant has narrowed it rather than won allowance. https://actuary.info/ai-patent-watch/#pipeline US20260162188A1 Thu, 11 Jun 2026 12:00:00 GMT awaiting examination Describes constructing a customised training dataset before training a model, aimed at predicting and preventing home loss where general-purpose training data fits the risk poorly. How it works: Builds a customer-specific training set by dropping records from a base insurance dataset based on other customers' geographic distance from the target customer and on a temporal constraint, trains a model on that set, and runs the customer's data through it to produce a determination such as probability of loss or an indemnity estimate. Where it fits: Property underwriting and loss prevention, localizing risk models to a customer's area. Stage: Filed December 2025 and awaiting examination; the claim read here is the original as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260162184A1 Thu, 11 Jun 2026 12:00:00 GMT awaiting examination Describes training a model to read an applicant image but exclude factors such as age, sex, ethnicity or race, then set a life or health premium from only the remaining features, aimed at keeping protected characteristics out of an underwriting decision. How it works: Trains a neural network on images to correlate appearance with personal or health characteristics, identifies undesired factors such as age, sex, ethnicity, or race along with their interaction terms, then analyzes an applicant's image while excluding those factors and suggests an insurance premium based only on the remaining characteristics. Where it fits: Life and health underwriting, pricing from an applicant image while attempting to control for protected characteristics. Stage: Filed April 2025 and awaiting examination; the claim read here is the original as filed and has not been examined. https://actuary.info/ai-patent-watch/#pipeline US20260154953A1 Thu, 04 Jun 2026 12:00:00 GMT awaiting examination Describes detecting objects in long-tailed distributions using pseudo-labelled unlabelled data, aimed at recognising rare categories without assembling a large labelled set for each one. How it works: Splits a dataset into frequent head classes and rare tail classes, pre-trains a detection model on the head classes, adapts it to the tail by pseudo-labeling unlabeled images and initializing tail parameters from the pre-trained model, then fine-tunes on a balanced dataset while updating the classifier and regressor and freezing the backbone. Where it fits: General computer-vision tooling; the claim does not tie the method to a specific insurance workflow, though the filer serves risk analytics. Stage: Filed April 2025 and awaiting examination; the claim read here is the original as filed, the broadest version on record. https://actuary.info/ai-patent-watch/#pipeline US20260154769A1 Thu, 04 Jun 2026 12:00:00 GMT awaiting examination Seeks to combine sensor readings with building records in a trained model to characterise risks around a structure, aimed at identifying community-level exposure rather than a single property. How it works: Trains a model on the layout of area security systems, historical scheduled events, and past security occurrences, takes the location and time of a new scheduled event, predicts a potential security occurrence, identifies a person whose commute passes near that location, and sends them a risk-mitigating recommendation timed to the event. Where it fits: Personal-lines risk mitigation and loss prevention, warning insureds away from predicted incident locations. Stage: Filed January 2026 and awaiting examination; the claim read here is the original as filed and remains untested by an examiner. https://actuary.info/ai-patent-watch/#pipeline US20260154532A1 Thu, 04 Jun 2026 12:00:00 GMT awaiting examination Describes a graph convolutional network that produces a risk indicator alongside an account of which predictors drove it, aimed at risk scoring that can be explained to a regulator. How it works: Runs an entity's predictor variables through a power graph convolutional network whose convolutional layer applies an adjacency weight matrix and whose dense layer applies a weight vector, trains those weights under an explainability constraint, produces a risk indicator, and transmits it to control the entity's access. Where it fits: Credit and identity risk scoring, where an explainable score gates access decisions. Stage: Filed April 2024 and awaiting examination; the claim read here is the original as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260154513A1 Thu, 04 Jun 2026 12:00:00 GMT awaiting examination Describes a chatbot that classifies a user into one of two levels from attributes of an open-ended query, then handles higher-level users through further open-ended follow-ups and lower-level users through closed-ended prompts to gather intents before completing a task. How it works: Takes an open-ended query and classifies the user as a first or second level depending on whether attributes of the query cross a threshold; a first-level user is asked further open-ended questions before a fulfillment task runs, while a second-level user gets closed-ended questions to collect the missing intents or entities. Where it fits: Customer-facing chatbot triage; the abstract describes conversational routing, not a specific insurance transaction. Stage: Filed July 2025, published June 2026 and awaiting examination; this claim was newly substituted after the original claims were cancelled, not a granted right. https://actuary.info/ai-patent-watch/#pipeline US20260154332A1 Thu, 04 Jun 2026 12:00:00 GMT under examination Describes a retrieval-augmented generation system that replaces an image in a document with a localization tag marking where it sat, then vectorizes and stores the tagged document so the image can later be searched and retrieved. How it works: A tagging engine locates an image within an electronic document, generates a localization tag recording its position, and swaps the image for that tag; a vector engine then converts the modified document into vectors and stores it in a vector database so the content can be searched and retrieved through retrieval-augmented generation. Where it fits: General-purpose document and image retrieval; the abstract names no insurance workflow. Stage: Filed December 2024, published June 2026 and awaiting examination; the claim read here is the original as filed, the broadest version an examiner is most likely to reject. https://actuary.info/ai-patent-watch/#pipeline US20260148820A1 Thu, 28 May 2026 12:00:00 GMT awaiting examination Describes selecting a diagnostic mapping table from the code and data format on an electronic claim, aimed at reconciling codes that do not correspond across claim systems. How it works: Receives an electronic claim with a diagnostic code in one industry's data format, selects the matching mapping table, resolves the code to a body part and its laterality, feeds those with a categorization table from another format into a trained model to produce an assessment rating, and initiates an action on the claim. Where it fits: Casualty and medical claims adjudication, reconciling diagnostic coding across different industry systems. Stage: Filed January 2026 and awaiting examination; the claim read here is the original as filed and has not been examined. https://actuary.info/ai-patent-watch/#pipeline US20260148320A1 Thu, 28 May 2026 12:00:00 GMT awaiting examination Proposes detecting a failing sump pump and having a chatbot solicit repair or replacement quotes, aimed at closing the loop between a sensor fault and a service booking. How it works: Takes operating data from sump pump sensors that detect water or power interruption, polls a weather server for conditions at the pump's location, merges the two into a dataset, sends it with a prompt to a machine learning chatbot that generates a natural-language fault-detection analysis, and presents that analysis. Where it fits: Homeowners loss prevention, catching sump pump failures that lead to water-damage claims. Stage: Filed January 2026 and awaiting examination; the claim read here is the original as filed, the broadest version on record. https://actuary.info/ai-patent-watch/#pipeline US20260148311A1 Thu, 28 May 2026 12:00:00 GMT awaiting examination Describes monitoring how a user works through an interface and recommending actions to make those interactions more efficient. How it works: Receives an indication of a fault tied to a user interface, reviews the recorded sequence of user inputs, identifies the first input that produced the fault, determines a different input that would have prevented it, and sends the device a notification recommending that alternative input. Where it fits: General user-interface support; the claim does not tie the mechanism to a specific insurance workflow. Stage: Filed January 2026 and awaiting examination; the claim read here was amended during prosecution, meaning the applicant narrowed it, not that an examiner has agreed. https://actuary.info/ai-patent-watch/#pipeline US20260148199A1 Thu, 28 May 2026 12:00:00 GMT under examination Describes an AI model trained on historical lifetimes of similar appliances that would predict a selected appliance's remaining life from retrieved appliance data and present a repair-or-replace recommendation to the user. How it works: Presents a user interface to select an appliance, retrieves that appliance's data, computes a predicted remaining lifetime with an AI model trained on historical lifetimes of similar appliances, generates a recommendation to repair or replace it, and displays the lifetime and recommendation. Where it fits: Homeowners servicing and loss prevention, guiding appliance replacement before a failure causes a claim. Stage: Filed September 2025 and under examination; the claim read here is the original as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260148197A1 Thu, 28 May 2026 12:00:00 GMT awaiting examination Describes passing mixed input data through a model that identifies event details within it and turns them into calendar entries. How it works: Feeds mixed-type input data to a machine learning model that identifies and extracts the details defining an event, detects that those details only partially define it, determines what additional parameters are needed, retrieves them by searching further data sources, and generates a calendar event from the combined parameters. Where it fits: General scheduling and productivity tooling; the claim does not tie the mechanism to a specific insurance workflow. Stage: Filed October 2025 and awaiting examination; the claim read here is the original as filed and remains untested by an examiner. https://actuary.info/ai-patent-watch/#pipeline US20260141644A1 Thu, 21 May 2026 12:00:00 GMT awaiting examination Describes presenting a user's item in a shared virtual environment and generating a simulated event involving that item from sensor data, so two users' devices experience the same simulation. The abstract states no insurance function. How it works: Presents a virtual environment containing an item belonging to a first user, receives sensor data about that item, selects a simulated event involving it based on user input, generates the event using the sensor data, and displays it in the virtual environment to both the first user's and a second user's devices. Where it fits: General virtual-environment simulation; the claim does not tie the mechanism to a specific insurance workflow. Stage: Filed August 2025 and awaiting examination; the claim read here is the original as filed, the broadest version on record. https://actuary.info/ai-patent-watch/#pipeline US20260141632A1 Thu, 21 May 2026 12:00:00 GMT awaiting examination Seeks to build a three-dimensional accident reconstruction from photographs of a vehicle, aimed at reconstructing what happened from claim images rather than from an on-site inspection. How it works: Receives photos of a vehicle from a mobile device, generates an accident-reconstruction model with machine learning models that combine the images with the vehicle's technical specifications, has the model indicate the extent of damage for each region, identifies potential damage, and sends instructions to display it graphically. Where it fits: Auto physical-damage claims, estimating and visualizing vehicle damage from photos. Stage: Filed November 2025 and awaiting examination; the claim read here was newly presented in place of the cancelled original claims, so examination of it has not begun. https://actuary.info/ai-patent-watch/#pipeline US20260141454A1 Thu, 21 May 2026 12:00:00 GMT in examination Seeks to apply generative techniques to captured insurance claim data and produce content used in handling the claim inside a claims application. How it works: Captures claim data from an insurer, generates prompts specifying which AI models to use with the claim data, digitizes the data for a generative AI system, invokes that system through an API call to produce content responsive to the prompts, and stores and returns the generated content to the claims application for further processing. Where it fits: Insurance claims processing, injecting generative AI output into the claim handling workflow. Stage: Filed November 2024 and in examination; the claim read here is an original claim as filed and has not been allowed. https://actuary.info/ai-patent-watch/#pipeline US20260141435A1 Thu, 21 May 2026 12:00:00 GMT awaiting examination Describes a conversational agent that assists a user shopping for a vehicle, built on a machine-learned model trained by a forward-forward method that passes positive then negative data through each layer and adjusts its weights to tune a per-layer goodness metric. How it works: Opens a session through a conversational AI agent, takes input that the user is considering buying a vehicle, computes a personalized insurance cost from information about the user and the vehicle, derives ownership information from the vehicle type and that insurance cost, and presents the ownership information back to the user. Where it fits: Vehicle-shopping assistance that folds a personalized insurance-cost estimate into the ownership picture, supporting auto insurance quoting. Stage: Filed January 2026, published May 2026 and awaiting examination; the claim read here is the original as filed, the broadest version the applicant will hold. https://actuary.info/ai-patent-watch/#pipeline US20260141328A1 Thu, 21 May 2026 12:00:00 GMT final rejection Describes classifying technical debt across an organisation and predicting its risk severity from internal, third-party and operational risk inputs. How it works: Gathers organizational, third-party tool, operational-risk, and prior-assessment data, processes it through neural networks, generative AI models, and reinforcement learning to estimate technical-debt risk severity and likelihood, predicts a potential incident, adjusts network weights from evaluation metrics, and outputs the risk information. Where it fits: Enterprise software and operational risk management; the claim describes internal IT governance rather than an insurance workflow. Stage: Filed November 2024 and now under a final rejection; the claim read here was amended during prosecution, meaning the applicant narrowed it, not that the examiner agreed. https://actuary.info/ai-patent-watch/#pipeline US20260141322A1 Thu, 21 May 2026 12:00:00 GMT responded Seeks to score a water damage claim from its text and images, then match it to the best-fit available adjuster by skill and availability, aimed at routing claims at intake rather than by manual triage. How it works: runs a claim's text through a convolutional network to pull location, damage type and severity, its photos through a recurrent network that tracks damage across a series of images, fuses both into claim ratings, then scores each available adjuster on skill and availability and matches the claim to the best fit. Where it fits: property claims intake and triage, auto-routing a water-damage claim to a suitable handler. Stage: filed November 2024; in examination with claim 1 already amended, so the applicant has narrowed it during prosecution, not had it allowed. https://actuary.info/ai-patent-watch/#pipeline US20260139959A1 Thu, 21 May 2026 12:00:00 GMT awaiting examination Proposes building an accident risk model from vehicle sensor and environmental data for a region, then answering navigation requests against that risk surface rather than travel time alone. How it works: a modeling server collects sensor data from many vehicles plus environmental data for a region, trains an accident-risk model on it, and for a requested trip scores the attributes of several candidate routes by that risk; a mobile device then displays the routes, their risk attributes and a prompt to pick a better time to travel. Where it fits: auto risk assessment and telematics-style routing, though the claim frames it as consumer trip navigation. Stage: filed November 2025; this is a new claim substituted after the original claims were cancelled, typically following a rejection, and it remains in examination. https://actuary.info/ai-patent-watch/#pipeline US20260134679A1 Thu, 14 May 2026 12:00:00 GMT awaiting examination Describes assembling images of a region into a virtual model and presenting it as a navigable environment. How it works: takes a virtual model of a region containing a structure, computes the structure's geographic coordinates, checks them against the coordinates of a customer's property, and on a match builds a 3D environment of the structure and embeds a link from it to that customer's digital record. Where it fits: property underwriting and servicing, connecting a building's 3D view to the matching policyholder file. Stage: filed January 2026; the claim is as filed, the original and broadest version, and sits early in examination before any office action. https://actuary.info/ai-patent-watch/#pipeline US20260134312A1 Thu, 14 May 2026 12:00:00 GMT awaiting examination Seeks to score a medical provider's predicted performance from the claims they have been involved in, combining supervised and unsupervised models, aimed at provider selection in claim management. How it works: feature-engineers historical claims, clusters them into candidate models, then for a new claim runs one model to place the claim in a cluster and a second to place the provider in a cluster, and combines the two into a provider score standing for the provider's predicted performance. Where it fits: claims handling, rating or selecting the provider attached to a claim by predicted performance. Stage: filed October 2025; this is a new claim filed to replace cancelled original claims, usually after a rejection, and remains under examination. https://actuary.info/ai-patent-watch/#pipeline US20260133342A1 Thu, 14 May 2026 12:00:00 GMT allowed Seeks to predict hail frequency at a property by contrasting locations that sustained hail damage with those that did not, aimed at pricing and underwriting hail exposure at address level. How it works: trains one model on historical hail events, including damaging and non-damaging instances, to estimate the frequency of an event exceeding a damaging threshold, and a second on property features to estimate severity, then combines them with a target property's features to predict its damage extent and trigger an action. Where it fits: property catastrophe pricing and underwriting, sizing hail exposure at a specific location. Stage: filed December 2025; claim 1 is currently amended, so the applicant has already narrowed it during prosecution rather than having it allowed. https://actuary.info/ai-patent-watch/#pipeline US20260131802A1 Thu, 14 May 2026 12:00:00 GMT responded Describes a sensor system that reads vehicle acceleration data to classify a condition such as a flat tire, then queries a coverage database by vehicle identifier to select a location-aware response action, oriented to roadside servicing and claims handling. How it works: maps a vehicle's acceleration data to a set of predefined conditions and driving behaviors; when the condition is a flat tire, it queries a database keyed by the vehicle's identifier for its service coverage, then picks response actions from that coverage and the vehicle's location and triggers one. Where it fits: auto telematics and servicing, matching a detected fault to the vehicle's roadside or service coverage. Stage: filed November 2024; claim 1 is currently amended, meaning the applicant has narrowed it in prosecution, not that it has been allowed. https://actuary.info/ai-patent-watch/#pipeline US20260131798A1 Thu, 14 May 2026 12:00:00 GMT under examination Proposes identifying vehicle faults from vibration data captured off a component, aimed at detecting mechanical problems before they present as a claim. How it works: feeds vibration data from a vehicle component into machine-learning models to identify a vehicle problem, then sends a service facility's device commands to display a re-creation of the vehicle event, where that re-creation is built from the vibration data and the identified problem. Where it fits: auto diagnostics and claims servicing, reconstructing a component fault for a repair or service facility. Stage: filed October 2025; this is a new claim presented in place of cancelled original claims, usually after a rejection, and is still in examination. https://actuary.info/ai-patent-watch/#pipeline US20260127678A1 Thu, 07 May 2026 12:00:00 GMT awaiting examination Describes pulling third-party data on a prospective customer and driving an underwriting workflow interface from what that data returns. How it works: takes a new prospect's parameters, pulls third-party data and reads internal cloud records through a stored procedure that evolves the schema into an incremental view, then runs a machine-learning step over all of it to turn a first question workflow into a shorter second one that removes collection screens, cuts questions and prefills fields. Where it fits: underwriting intake and onboarding, tailoring the application questionnaire to each prospect. Stage: filed January 2026; the claim is as filed, its original and broadest form, and sits early in examination before any office action. https://actuary.info/ai-patent-watch/#pipeline US20260127433A1 Thu, 07 May 2026 12:00:00 GMT awaiting examination Describes training on existing room layouts, then recommending where objects should sit in a room from its LIDAR dimensions. How it works: builds room dimensions by measuring LIDAR data from a LIDAR camera, feeds those dimensions plus an object's attributes including its color into a machine-learning model to recommend a placement, then shows the user's own placement alongside the model's so the two can be compared. Where it fits: general-purpose home interior visualization; the claim states no specific insurance workflow. Stage: filed January 2026; the claim is as filed, the original broadest version, and is early in examination with no office action yet. https://actuary.info/ai-patent-watch/#pipeline US20260120353A1 Thu, 30 Apr 2026 12:00:00 GMT under examination Describes a generative diffusion model that turns text describing roof attributes and damage into synthetic images of damaged roofs, proposed to augment training data for roof damage detection models used in property claims and assessment. How it works: takes text describing a roof surface or damage attribute, feeds it to a generative model trained to produce images of damaged roof surfaces, and outputs a synthetic image of a damaged roof, intended to expand scarce training data for damage-detection models. Where it fits: property claims tooling, manufacturing training images for roof-damage assessment models. Stage: filed October 2024; the claim is as filed, its original and broadest form, and remains in examination. https://actuary.info/ai-patent-watch/#pipeline US20260119379A1 Thu, 30 Apr 2026 12:00:00 GMT under examination Describes an enterprise test data management method that maps a data asset, splits a workflow into portioned parallel sub-workflows keyed to sensitive data subsets, and aggregates the results back to the requester. How it works: on a request naming a data asset and a sensitive subset, a machine-learning model maps where that sensitive data sits, the system splits the asset into an optimized number of parallel workflows, runs them while obfuscating the sensitive fields, and aggregates the outputs back to the requester. Where it fits: general enterprise test-data and data-privacy handling; the claim names no insurance workflow. Stage: filed October 2024; the claim is as filed, the original broadest version, and sits in examination. https://actuary.info/ai-patent-watch/#pipeline US20260118550A1 Thu, 30 Apr 2026 12:00:00 GMT allowed Describes training and validating a hail damage frequency model from a set of properties labelled by whether they experienced hail damage, using each property's location and features, which would serve property risk assessment. How it works: predicts hail damage extent at a target location from its property features using two pre-trained models, one estimating event frequency above a damaging threshold from historical damaging and non-damaging instances, the other estimating severity from features of previously damaged properties, then takes an action on the result. Where it fits: property catastrophe pricing and underwriting, sizing hail exposure for specific locations. Stage: filed December 2025; claim 1 is currently amended, so the applicant has already narrowed it in prosecution rather than having it allowed. https://actuary.info/ai-patent-watch/#pipeline US20260118280A1 Thu, 30 Apr 2026 12:00:00 GMT responded Describes pairing a segmentation model with a computer vision model, including a recurrent network across multiple images, to assess roof damage from imagery and output damage cause, severity and possible fraud signals for property claims. How it works: runs a roof image through a segmentation model to get a mask, folds that mask into the image as extra channels carrying segment shape, size or depth labels, then feeds the enriched multichannel image to a second computer-vision model that renders the roof-damage determination. Where it fits: property claims, assessing roof damage from imagery. Stage: filed October 2024; claim 1 is currently amended, meaning the applicant has narrowed it during prosecution, not that it has been allowed. https://actuary.info/ai-patent-watch/#pipeline US20260113249A1 Thu, 23 Apr 2026 12:00:00 GMT awaiting examination Describes using AI models to interface with home IoT devices by inferring each device's protocol, command or data format, storing a device dataset, and answering natural language queries with retrieval augmented generation. How it works: reads a home IoT device's transmission to infer its protocol, command or data format, stores that as a device dataset, then on a natural-language request uses retrieval-augmented generation over the dataset to produce interfacing commands, queries the device for its status or configuration, and answers in natural language. Where it fits: smart-home and property monitoring, giving a natural-language interface to household IoT devices. Stage: filed January 2025; the claim is as filed, its original and broadest form, and remains under examination. https://actuary.info/ai-patent-watch/#pipeline US20260112259A1 Thu, 23 Apr 2026 12:00:00 GMT awaiting examination Describes home sensors, including monitoring of device electricity use, whose data a processor analyzes for anomalies to infer a condition such as a medical one and notify a caregiver, oriented to in-home monitoring. How it works: captures data from home sensors and runs it through a model trained on that home's data to identify an individual's patterns, then periodically sends a caregiver's device a snapshot report of those patterns and how they have changed from the prior report. Where it fits: health and life servicing, monitoring an individual at home for a caregiver, such as aging in place. Stage: filed December 2025; the claim is as filed, the original broadest version, and is early in examination before any office action. https://actuary.info/ai-patent-watch/#pipeline US20260111968A1 Thu, 23 Apr 2026 12:00:00 GMT under examination Describes training a model per chatbot plus a routing model that sends each query to the right one, aimed at directing customer questions to the assistant best suited to answer. How it works: parses a query to identify a category and two sub-categories, routes different portions of it to at least two specialized chatbot models chosen by those sub-categories, generates a combined response from them within the conversation, and trains the selected models by tying the response back to their chatbots. Where it fits: customer-service and servicing chat, splitting a query across specialized assistants; the claim is not insurance-specific. Stage: filed June 2025; this is a new claim substituted for cancelled original claims, usually after a rejection, and remains in examination. https://actuary.info/ai-patent-watch/#pipeline US20260111855A1 Thu, 23 Apr 2026 12:00:00 GMT awaiting examination Describes reading waste and recycling sensor data near a bin to determine fill level, aimed at waste handling and energy efficiency in a monitored property. How it works: collects energy data from a user's electric devices, builds an energy-consumption profile of their patterns, picks a monitoring plan by matching that profile to a reference pattern with threshold values, then compares later energy readings to those thresholds and alerts the user's device when a device's operational-health status changes. Where it fits: home and property monitoring, flagging appliance or device health from energy use; the claim is not insurance-specific. Stage: filed December 2025; the claim is as filed, its original and broadest form, and remains in examination. https://actuary.info/ai-patent-watch/#pipeline US20260111828A1 Thu, 23 Apr 2026 12:00:00 GMT awaiting examination Seeks to detect and weight electronic links between an employee and external actors through interaction data mining, aimed at assessing exposure arising from those connections. How it works: capturing devices tied to an employee's unit collect the employee's account parameters and their interaction data; an accumulation module extracts the external actors, then a weighting unit with a machine-learning component measures each interaction and assigns weighting factors that score the strength of each employee-to-actor link. Where it fits: insider and connection risk assessment; the claim frames it broadly, not as a specific insurance line. Stage: filed September 2025; the claim is as filed, its original and broadest form, and sits in examination. https://actuary.info/ai-patent-watch/#pipeline US20260111427A1 Thu, 23 Apr 2026 12:00:00 GMT under examination Describes matching a member's stated criteria and characteristics against provider records to recommend providers, aimed at steering members toward a suitable clinician. How it works: precomputes a vector of a member's characteristics, and for a provider request retrieves candidate providers each as a vector carrying quality and cost factors, then scores each provider against the member vector and the member's criteria to return an ordered, member-specific provider list. Where it fits: health provider search and network steerage, ranking providers by fit, cost and quality for a member. Stage: filed October 2025; the claim is as filed, its original and broadest form, and remains under examination. https://actuary.info/ai-patent-watch/#pipeline US20260106901A1 Thu, 16 Apr 2026 12:00:00 GMT responded Describes generating an execution script that exercises an interface function under several security policies, aimed at testing whether an externally callable interface stays compliant. How it works: generates a script that exercises an API's externally invocable functions under several security policies, runs it to capture policy-execution outputs, evaluates how far the API complies with those policies, and either alerts when compliance falls below a threshold or logs the execution details otherwise. Where it fits: general API security governance and compliance monitoring; the claim names no insurance workflow. Stage: filed October 2024; claim 1 is currently amended, so the applicant has narrowed it in prosecution rather than having it allowed. https://actuary.info/ai-patent-watch/#pipeline US20260106008A1 Thu, 16 Apr 2026 12:00:00 GMT responded Seeks to count and verify dispensed prescription product from camera images at the dispensing site, aimed at checking a fill against the prescription without a second physical count. How it works: a receptacle holds a pharmaceutical while a camera, spectrometer, olfactory sensor and audio sensor each capture data; a processor extracts a feature from each and a machine-learning model fuses them into a recognition-confidence value for a pharmaceutical type, returning an indicator the user acts on. Where it fits: pharmacy and health operations, identifying or verifying a dispensed pharmaceutical. Stage: filed October 2024; claim 1 is currently amended, meaning the applicant has narrowed it during prosecution, not that it has been allowed. https://actuary.info/ai-patent-watch/#pipeline US20260105509A1 Thu, 16 Apr 2026 12:00:00 GMT awaiting examination Describes determining a product's status and reconciling it against retrieved descriptions, aimed at identifying products where catalogue records disagree. How it works: takes a selected product with its category and description, looks up its status in a database, retrieves descriptions of other products in the same category, and feeds those plus the product's description to a machine-learning model that outputs correlations, from which it recommends replacement products. Where it fits: general product matching and replacement recommendation; the claim states no insurance workflow. Stage: filed October 2025; the claim is as filed, its original and broadest form, and remains in examination. https://actuary.info/ai-patent-watch/#pipeline US20260105504A1 Thu, 16 Apr 2026 12:00:00 GMT awaiting examination Describes retrieving product information, detecting where it contains proprietary content, and generating a description from what remains. How it works: retrieves a product's information including its existing description, detects proprietary content in it, removes that content, then feeds the scrubbed information to a machine-learning model that generates a second description conforming to specified characteristics. Where it fits: general automated content generation with proprietary-data removal; the claim names no insurance workflow. Stage: filed October 2025; the claim is as filed, its original and broadest form, and remains under examination. https://actuary.info/ai-patent-watch/#pipeline US20260105162A1 Thu, 16 Apr 2026 12:00:00 GMT awaiting examination Seeks to derive risk signals from an entity's own data and its interaction history to judge whether an identity has been manipulated, aimed at synthetic and altered identity fraud. How it works: derives a risk signal for a target entity with an AI model, builds one graph of the entity's identity data and another of its interaction history, links them, applies the risk signal to the combined graph to compute a risk indicator, and blocks the entity from the environment when it signals malicious behavior. Where it fits: fraud detection and access control, screening entities for identity manipulation. Stage: filed December 2025; the claim is as filed, its original and broadest form, and sits early in examination. https://actuary.info/ai-patent-watch/#pipeline US20260094215A1 Thu, 02 Apr 2026 12:00:00 GMT awaiting examination Describes training on historical claim processing records to produce commercial and shared-economy insights, aimed at exposures that sit outside conventional personal or commercial lines. How it works: trains a primary model to tell a claim-processing request from a claim query, do similarity matching against historical claims for automated decisions, and pick specialist secondary models; at run time it either outputs a claim decision or routes sub-queries to the secondary models and returns their answers. Where it fits: commercial claims processing and automated decisioning, including shared-economy exposures. Stage: filed December 2025; the claim is as filed, its original and broadest form, and remains under examination. https://actuary.info/ai-patent-watch/#pipeline US20260094128A1 Thu, 02 Apr 2026 12:00:00 GMT under examination Describes a system that trains damage classification models on historical repair records, verifies that submitted photos of a damaged object are usable, then infers object type, damage type, repair amount and repair time to support claim estimating. How it works: Trains damage classification models on historical damage and repair data, builds a digital view of a submitted object, checks each image is properly captured and asks the user to reshoot any that are not, then identifies object and damage type and runs selected models to output a repair amount and time. Where it fits: Auto claim intake and repair estimating from policyholder photos. Stage: Filed July 2023 and under examination; the claim read here was already narrowed by amendment, not the original. https://actuary.info/ai-patent-watch/#pipeline US20260093847A1 Thu, 02 Apr 2026 12:00:00 GMT awaiting examination Describes a gateway that screens an incoming prompt for personally identifiable information and only when the prompt is PII-free routes it to a selected large language model and returns the output, governing enterprise use of generative AI. How it works: Receives a prompt at an image or text component, runs a trained model to determine a PII status and returns a PII response, then only when the prompt is PII-free takes a selection of a large language model, checks that model's status, transmits the prompt to it and returns its output through an interface. Where it fits: Governs how an insurer's staff can send data to generative AI tools. Stage: Filed December 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260093375A1 Thu, 02 Apr 2026 12:00:00 GMT awaiting examination Describes a virtual reality training system that reads sensor data while a trainee interacts with a client avatar, then applies a trained model to generate a scripted coaching message shown to the trainee during the exercise. How it works: Presents a virtual environment with a client avatar to a trainee's device, receives sensor data from the trainee and client devices during an interaction, feeds that data into a trained machine learning model that outputs an instructional message containing scripted text for the trainee to say, and displays it on the trainee's device. Where it fits: Simulated coaching for agents or claims staff who deal with customers. Stage: Filed September 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260087229A1 Thu, 26 Mar 2026 12:00:00 GMT awaiting examination Describes a platform that turns transcripts and audiovisual recordings into tokens and prompts a trained neural network to produce a requested artifact such as a call summary or requirements document, with a chatbot to query the result. How it works: Processes a set of video frames into tokens, captures an output-type instruction naming the artifact wanted, has a trained neural network generate that artifact conditioned on project type, user role, domain or technology stack, and displays it beside a chatbot whose context is set to the artifact so a user query searches it. Where it fits: General document and meeting-artifact generation; no specific insurance function is described. Stage: Filed September 2024 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260080489A1 Thu, 19 Mar 2026 12:00:00 GMT awaiting examination Describes a system for storing a policyholder's financial and account information and later guiding a trusted individual through the estate using a chatbot that simulates a characteristic of the account owner. How it works: Receives information identifying a user authorized to access a digital asset, instructs an electronic device to connect to a digital asset service, receives a request to use the service, establishes a virtual communication session with the device, and during the session provides a simulation of a characteristic of the account's owner. Where it fits: Estate and beneficiary servicing tied to life or financial policies. Stage: Filed November 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260077771A1 Thu, 19 Mar 2026 12:00:00 GMT awaiting examination Describes a device that reads in-cabin sensor data to gauge occupant distraction from torso posture and its weighted duration, then computes a probability of distraction at a particular geographic location for driving-risk assessment. How it works: One module receives interior sensor data, where distraction severity scales with the degree of motion of the occupant's torso posture and its weighted duration, and a second module combines that data with vehicle location data to generate distraction data representing the probability of occupant distraction at a particular location. Where it fits: Telematics-based driving risk scoring for usage-based auto pricing. Stage: Filed November 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260075144A1 Thu, 12 Mar 2026 12:00:00 GMT awaiting examination Describes a contact-center framework in which a server aggregates interaction data across representatives' sessions and returns operating instructions that drive a representative's device to start a call or take up a deferred task. How it works: A server determines interaction data from actions in two separate representative-customer sessions, generates operating instructions from that combined data, and transmits them to a representative device, causing it to either start an additional session through an external provider or begin a deferred work item from an internal provider. Where it fits: Contact-center operations for insurance customer service. Stage: Filed November 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260073244A1 Thu, 12 Mar 2026 12:00:00 GMT awaiting examination Describes a federated knowledge-base platform that resolves a search request to the certified version of a document held in one of several underlying systems, using a stored snapshot reference to locate and retrieve it. How it works: A consumer device requests a certified version of a document, the federated platform matches it to a snapshot reference identifying the storage location within a particular knowledge-base system, retrieves that identifier, uses it to obtain the certified version from that system, and builds and displays an interface showing the document. Where it fits: Document governance across systems; no specific insurance function is described. Stage: Filed September 2024 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260057674A1 Thu, 26 Feb 2026 12:00:00 GMT awaiting examination Describes a mixed-reality headset with depth and motion sensing that builds a 3-D model of a fire-damaged site as the wearer moves through it and runs AI spatial and safety models over the scene for claim investigation. How it works: A head-mounted see-through display with a time-of-flight sensor, camera and inertial units captures distances to surface points from one location, computes a point-cloud portion, tracks the wearer's movement to a second location, captures and computes a second portion, and merges them into a 3-D wire-mesh model of the environment for analysis. Where it fits: On-site property and fire loss investigation for claims. Stage: Filed October 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260051405A1 Thu, 19 Feb 2026 12:00:00 GMT under examination Describes a health-plan system that applies a member's health data to an observation model to identify conditions, feeds those to service models that generate a treatment plan, and notifies both a provider and the member of a recommended appointment. How it works: Receives member health data, applies it to an observation model trained to identify the member's conditions from historical data, passes those conditions to service models trained to generate treatment plans, takes a recommended plan with an appointment, and sends appointment notifications to both a care-provider device and the member's device. Where it fits: Care management and utilization steering for a health plan. Stage: Filed August 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260051022A1 Thu, 19 Feb 2026 12:00:00 GMT awaiting examination Describes a two-network method for generating per-pixel semantic predictions from imagery: a first CNN trained on a sparse grid of pixels, from which a second, modified CNN is derived to predict labels at higher pixel density. How it works: Defines a first CNN over a mask of pixels spaced s apart and trains it on image crops against target labels by minimizing prediction error, then builds a second CNN from the first's weights with a reduced final-layer stride and dilated later layers, and runs it on a full image to output semantic predictions at higher density than the mask. Where it fits: Aerial-imagery feature extraction feeding property risk assessment; the claim itself is a general image-segmentation technique. Stage: Filed August 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260050996A1 Thu, 19 Feb 2026 12:00:00 GMT awaiting examination Describes an AI system that turns roof imagery and non-image data into vector representations and compares them against prior vectors to detect whether a roof has changed or to estimate its age, supporting property risk assessment. How it works: Receives geospatial data tiles each carrying a temporal identifier and location, processes a subset to derive attribute data, generates attribute keys from the identifier and processing configuration, stores the attributes by key, and on a property request selects the overlapping tile, computes its key and returns its stored attributes. Where it fits: Roof condition and age inputs for property underwriting and renewals. Stage: Filed August 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260044835A1 Thu, 12 Feb 2026 12:00:00 GMT under examination Describes a system that estimates the remaining lifespan of a home's plumbing from residential records via a trained model, then revises the estimate after the homeowner reports taking recommended actions. How it works: Receives plumbing and property aspects from residential records, has a trained model estimate each aspect's impact to predict the plumbing's remaining lifespan, issues a notification with that lifespan and recommended actions, then on user input that actions were taken computes a lifespan modification factor and issues an updated estimate. Where it fits: Property risk assessment and loss prevention for homeowners cover. Stage: Filed October 2024 and under examination; the claim read here was already narrowed by amendment, not the original. https://actuary.info/ai-patent-watch/#pipeline US20260044778A1 Thu, 12 Feb 2026 12:00:00 GMT awaiting examination Describes a system that processes several types of residential data through a trained model to produce a plumbing impact score representing the remaining useful life of a home's plumbing, and triggers a follow-up action. How it works: Receives several different types of residential data about a plumbing system, has a trained machine learning model estimate each type's impact on remaining useful life and generate a residential plumbing impact score predicting that life, and initiates an action in response to the score. Where it fits: Property risk assessment and maintenance prompts for homeowners cover. Stage: Filed October 2024 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260038056A1 Thu, 05 Feb 2026 12:00:00 GMT awaiting examination Describes a system that trains and retrains models on property parameters and telematics data to estimate a property's damage likelihood, identify the factors driving it and recommend mitigating actions. How it works: Trains a model on property parameters, receives telematics data from sensors on property devices, retrains the model on that data, outputs a likelihood of damage from how the devices are functioning, determines the damage factors that raise that likelihood, and outputs recommended actions to mitigate it. Where it fits: Loss prevention and property risk assessment for homeowners cover. Stage: Filed October 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260038037A1 Thu, 05 Feb 2026 12:00:00 GMT allowed Describes a method for approving a loan tied to a property by retrieving customer information from two blockchains, verifying it cryptographically, determining an approval status and recording that status on-chain. How it works: Receives a loan request tied to a property, retrieves first customer information from a block of one blockchain and second information from another, verifies the second via a cryptographic key-pair by reaching consensus with a remote node, determines an approval status, writes it into a new block and verifies it with a second key-pair. Where it fits: Mortgage approval workflows; the claim centers on blockchain verification rather than an insurance function. Stage: Filed August 2025; the application is at the allowed stage on the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260037838A1 Thu, 05 Feb 2026 12:00:00 GMT awaiting examination Describes a server that validates the inputs to a predictive model against a specification file, imputes valid values when the inputs do not conform, and then executes the model to return results. How it works: Receives a request to run a predictive model with a set of inputs, validates the inputs against required ranges or types in the model's specification file, and when they do not conform computes valid values, revises the model's package to include them and an identifier, executes the model within that revised package and returns the output. Where it fits: Operational plumbing for running actuarial or predictive models on request; no specific insurance line is described. Stage: Filed August 2025 and awaiting examination; the original claims were cancelled and this was submitted as a new claim in their place. https://actuary.info/ai-patent-watch/#pipeline US20260036715A1 Thu, 05 Feb 2026 12:00:00 GMT awaiting examination Describes a method that chains machine learning models to estimate wind speed and report frequency for a location and then derive damage frequency and severity metrics, supporting catastrophe risk assessment. How it works: Receives a location, has a wind-speed model and a wind-report-frequency model estimate those values, feeds the values plus location feature data into a damage-frequency model to produce a damage-frequency metric, and feeds a separate feature set into a damage-severity model to produce a damage-severity metric. Where it fits: Wind and catastrophe risk assessment for property pricing and underwriting. Stage: Filed July 2024 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260031234A1 Thu, 29 Jan 2026 12:00:00 GMT under examination Describes training a causal language model on tokenized, chronologically ordered clinical event codes to predict outcomes, and generating a synthetic dataset for evaluation. How it works: Obtains a training set of coded data, preprocesses it by chronologically ordering each individual's event codes, inserting delimiter tokens to separate intra-event, inter-event and per-individual data, and tokenizing with a fixed vocabulary, then trains a causal language model to predict the next code as a probability distribution. Where it fits: Outcome prediction from medical claim and clinical codes for a health plan. Stage: Filed July 2025 and under examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260030685A1 Thu, 29 Jan 2026 12:00:00 GMT awaiting examination Describes a method that trains a model and then retrains it to identify and exclude undesired factors such as age, sex or race from its output when analyzing an applicant's image or audio during underwriting. How it works: Trains a first model whose output includes one or more undesired factors, identifies those factors in the output, retrains the model to identify and exclude them to produce a second model, and runs the second model to analyze an individual's images or audio while excluding those factors from its output. Where it fits: Underwriting from image or audio while suppressing protected-class factors. Stage: Filed October 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260030299A1 Thu, 29 Jan 2026 12:00:00 GMT final rejection Describes a framework that turns a natural language query into a SQL query using a large language model trained on an application-specific data dictionary, runs it through an API, and returns a natural language response. How it works: Builds a per-application data dictionary of each object's name, type, size and relationships, uses a large language model trained on it to turn a natural language query into a SQL query, calls a no-contract API to run it, and depending on whether the response answers the query returns a natural language answer or generates a second SQL query. Where it fits: Natural language access to insurer data stores; no specific line of business is described. Stage: Filed July 2024 and standing at a final rejection; the claim read here was already narrowed by amendment, not the original. https://actuary.info/ai-patent-watch/#pipeline US20260024154A1 Thu, 22 Jan 2026 12:00:00 GMT awaiting examination Describes a system that opens a conversation session with a beneficiary, verifies the customer's identity from a certifying digital file, and processes a claim-related digital file as part of estate handling. How it works: Receives a digital file certifying an event and a request to access a customer's asset, establishes a conversation session with the beneficiary, verifies that the identity in the request matches the customer's actual identity, and on a match generates a second file and returns information indicating the action needed to process it. Where it fits: Claim intake and estate handling for beneficiaries on a policy. Stage: Filed September 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260024101A1 Thu, 22 Jan 2026 12:00:00 GMT awaiting examination Describes a method that generates fraud-classification rules with a trained model and applies them to account data to identify the type of fraud, aimed at reducing false positive alerts. How it works: A trained machine learning program, trained on data indicating types of fraud associated with transactions or accounts, generates fraud-classification rules, and the system accesses a particular account's data and applies those rules to output a classification naming the type of fraud associated with that account. Where it fits: Fraud detection and alert tuning in claims or financial servicing. Stage: Filed September 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260023830A1 Thu, 22 Jan 2026 12:00:00 GMT awaiting examination Describes a method that classifies a webpage's text fields into functional groups from their metadata and behavior, then selects a matching behavioral analysis algorithm to flag authentication issues or fraud. How it works: Monitors a webpage's text fields, receives their field data and metadata, builds feature vectors of the fields' contextual patterns, classifies the fields into functional groups, selects a behavioral analysis algorithm from those groups, processes the field data with it, and outputs an indication of user authentication or potential fraud. Where it fits: Identity verification and fraud screening in online journeys. Stage: Filed September 2025 and awaiting examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260023639A1 Thu, 22 Jan 2026 12:00:00 GMT under examination Describes a method that extracts a website's click data, processes it into weighted n-grams, and uses a trained analytics engine to diagnose which user-experience problem from a candidate list the site has. How it works: Receives a list of potential user-experience problems, extracts click data from a website, processes it into weighted grams whose weights come from click timing or webpage frequency or into forward and reverse grams, and uses a trained analytics engine to diagnose which listed problem the site has. Where it fits: Diagnosing digital self-service issues on an insurer's website. Stage: Filed July 2025 and under examination; these are the original claims as filed. https://actuary.info/ai-patent-watch/#pipeline US20260011151A1 Thu, 08 Jan 2026 12:00:00 GMT awaiting examination Describes a mixed reality and AI fire investigation system that builds a 3-D model of a fire-damaged location, runs AI fire and safety analyses, and layers data onto the model, supporting claims investigation. How it works: A time-of-flight sensor on a head-mounted see-through display captures distances to surface points as the wearer moves, an inertial measurement unit tracks that movement between locations, and the device stitches successive point-cloud portions into a 3-D wire mesh model of the fire-damaged environment. Where it fits: Property claims investigation and loss adjustment for fire-damaged structures. Stage: Filed September 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20260010860A1 Thu, 08 Jan 2026 12:00:00 GMT awaiting examination Describes a system that collects enterprise data, applies AI to analyze it, predicts stability, identifies dependencies, and generates actionable tasks to maintain equilibrium across a technology ecosystem. How it works: It collects logging and monitoring data across the ecosystem's functional layers, scores each layer's stability with one machine learning model and rolls those into an overall health score with a second, then uses a generative AI model to synthesize tickets and epics as remediation tasks and refreshes its data sources on event triggers. Where it fits: Enterprise technology operations and reliability rather than a specific insurance workflow. Stage: Filed July 2024, awaiting examination, written from a claim the applicant has already amended during prosecution. https://actuary.info/ai-patent-watch/#pipeline US20260010759A1 Thu, 08 Jan 2026 12:00:00 GMT awaiting examination Describes an AI engine that takes a user prompt together with a user profile or a market, industry, or behavior report and generates console data and an information element in response, serving general information generation. How it works: A user interaction module passes a prompt from the interface to a neural console, which feeds the prompt plus at least one source such as a user profile, market performance report or industry report into a generative model to produce console data, and the module turns that data into an information element displayed back to the user. Where it fits: Customer-facing financial and retirement account guidance. Stage: Filed July 2024, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20260004361A1 Thu, 01 Jan 2026 12:00:00 GMT awaiting examination Describes an augmented reality system that overlays recommended device updates onto a view of a structure and displays an improved home score, which would support property risk assessment and loss prevention. How it works: The method takes camera data of a device near a structure, identifies the device, determines a recommended repair or replacement, calculates how that change would improve a home score, then overlays the recommendation and the score improvement onto the live view through an AR viewer. Where it fits: Property underwriting and policyholder loss prevention. Stage: Filed September 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20260004328A1 Thu, 01 Jan 2026 12:00:00 GMT under examination Describes a device that builds a driver profile from vehicle telematics, combines it with contextual environment data, and generates offerings to influence driving behavior, supporting auto pricing and servicing. How it works: From a driver profile built on historical telematics the device selects an offering and a driving-behavior recommendation carrying an incentive, displays them to the driver, then checks current trip data to see whether the recommendation was followed and sends a confirmation that the incentive was earned when it was. Where it fits: Usage-based auto insurance pricing and policyholder engagement. Stage: Filed September 2025, under examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20260004279A1 Thu, 01 Jan 2026 12:00:00 GMT allowed Describes a digital wallet that draws data from a user's linked electronic accounts and applies a machine learning model to generate recommendations, shown in a wallet app integrated with decentralized web technology. How it works: The system gathers data from sources tied to a set of electronic accounts linked to a user's digital wallet, runs it through at least one machine learning model to generate an account-related recommendation, and displays that recommendation inside a wallet application integrated with decentralized web technology. Where it fits: Consumer financial and retirement account servicing; the abstract names no specific insurance workflow. Stage: Filed September 2025, applicant has responded to the examiner, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20260004222A1 Thu, 01 Jan 2026 12:00:00 GMT under examination Proposes training a projection model on historical process and performance metrics to predict a project trend and visualize it alongside current metric readings as new data arrives. How it works: An application tracks process and performance metrics for a project, trains a projection model by grouping historical indicators into fixed-length trend windows and learning from the windows that precede them, then applies that model to a window of live metrics to generate and chart a predicted performance trend alongside the current readings. Where it fits: General project performance analytics; the abstract states no specific insurance workflow. Stage: Filed June 2025, under examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20260004076A1 Thu, 01 Jan 2026 12:00:00 GMT awaiting examination Describes turning time-ordered process events into emoji sequences, converting those into feature vectors, and feeding them to a machine learning technique to prepare unstructured data for analysis. How it works: The method identifies the time-ordered events that occurred during a process, encodes each event as a categorical value to form a sequence in that time order, and generates a graphical representation of the sequence; the broader disclosure uses such sequences as feature vectors for machine learning. Where it fits: Preparing unstructured operational or claims process data for statistical analysis; no specific line is named. Stage: Filed September 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250390825A1 Thu, 25 Dec 2025 12:00:00 GMT responded Describes a generative AI assistant that extracts and summarizes information from sources such as knowledge graphs, websites and loss reports to answer risk relationship queries, aimed at underwriting support. How it works: A back-end server answers a risk relationship query by having a deep-learning and NLP extractor summarize information from sources such as knowledge graphs, websites and historical loss reports, then passing it to neuro-symbolic language model agents that generate a response carrying alerts and suggestions from the risk data store. Where it fits: Commercial underwriting decision support. Stage: Filed October 2024, applicant has responded and the claim has already been amended during prosecution. https://actuary.info/ai-patent-watch/#pipeline US20250390418A1 Thu, 25 Dec 2025 12:00:00 GMT awaiting examination Proposes prompting an AI model to write network security testing code, then running that code to scan a network for devices and vulnerabilities and report them to a user. How it works: On receiving a security vulnerability announcement the system prompts an ML chatbot to generate testing code for that vulnerability, receives the code, and executes it to scan the network for devices, identify devices affected by the announced vulnerability, and report them to a user. Where it fits: Internal cybersecurity operations; the abstract states no insurance-specific workflow. Stage: Filed August 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250389542A1 Thu, 25 Dec 2025 12:00:00 GMT final rejection Describes a cross-platform app that displays trip assignments and, once a user selects them, generates an optimal route using an AI model trained on historical trip records. How it works: The app displays trip assignments from multiple provider systems, takes the worker's selection of a set of assignments each carrying a trip type and an origin and destination, and feeds those into an AI model trained on historical records of concurrently performed trips to generate and display one optimal route across them. Where it fits: Gig-economy driver platforms; the abstract names no specific insurance workflow. Stage: Filed May 2025, applicant has responded and the claim has already been amended during prosecution. https://actuary.info/ai-patent-watch/#pipeline US20250385880A1 Thu, 18 Dec 2025 12:00:00 GMT awaiting examination Describes an AI module that transcribes a spoken statement, infers its intent, recommends and analyzes a response, and produces a spoken reply delivered through a selected chatbot, aimed at contact handling. How it works: An orchestration device receives a customer statement, determines its intent, selects one or more chatbots from a pool based on that intent to start an audio conversation, while an AI module monitors the exchange and drives a representative-facing interface that displays the conversation data. Where it fits: Customer service and claims call handling. Stage: Filed May 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250384877A1 Thu, 18 Dec 2025 12:00:00 GMT awaiting examination Describes an AI module that transcribes a spoken statement, infers its intent, recommends and analyzes a response, and produces a spoken reply delivered through a selected chatbot, aimed at contact handling. How it works: The system receives a caller's spoken statement, detects pauses to divide it into separate utterances, identifies an intent for each, selects a chatbot per utterance based on its intent, and generates a single audio response from the combined outputs of the selected chatbots. Where it fits: Automated customer service and call handling. Stage: Filed May 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250384875A1 Thu, 18 Dec 2025 12:00:00 GMT awaiting examination Describes an AI module that transcribes a spoken statement, infers its intent, recommends and analyzes a response, and produces a spoken reply delivered through a selected chatbot, aimed at contact handling. How it works: An AI module transcribes a user's spoken statement to text, augments it by determining its intent, produces recommendations for a response, analyzes the augmented text against those recommendations, then generates audio-response data and plays it back by having a selected chatbot execute it. Where it fits: Automated customer service and call handling. Stage: Filed May 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250384497A1 Thu, 18 Dec 2025 12:00:00 GMT under examination Proposes generating a guided building assessment plan, collecting a user's unstructured observations, and using an AI model to compose a structured inspection report from building data and those observations. How it works: The system takes building data with structural information, determines a layout, generates a guided plan posing questions about the structure to collect a user's unstructured observations, then feeds the data, observations and responses into an AI model that generates a structured inspection report. Where it fits: Residential property inspection and underwriting. Stage: Filed July 2024, under examination, written from a claim the applicant has already amended during prosecution. https://actuary.info/ai-patent-watch/#pipeline US20250384397A1 Thu, 18 Dec 2025 12:00:00 GMT under examination Describes training models on historical data to identify a customer's intent, generate an engagement output, and select a communication channel to deliver it, aimed at customer engagement. How it works: The platform reads an individual's intent with a selected model, picks output-generation models to produce a customer engagement message, uses channel models to choose the channel most likely to provoke a response, has an enterprise device format and display it, and retrains itself on new interaction data seeded from a labelled set. Where it fits: Customer engagement, marketing and retention. Stage: Filed May 2025, under examination, written from a new claim substituted after the original claims were cancelled. https://actuary.info/ai-patent-watch/#pipeline US20250384267A1 Thu, 18 Dec 2025 12:00:00 GMT awaiting examination Describes a two-pass clustering method for large datasets that would group records, refine the groups using derived cluster features, and select training samples from the result to train a neural network that computes a risk indicator for an entity. How it works: The method would first split a dataset into a chosen number of clusters, derive special features for those clusters, then re-cluster on those features; a neural network is trained on samples drawn from the refined clusters and applied to an entity's predictor variables to compute a risk indicator in response to a query. Where it fits: Building and querying risk scoring models over large populations; general risk assessment rather than a named insurance line. Stage: Filed June 2025 and published December 2025; pending examination on the originally filed claim, not yet narrowed during prosecution. https://actuary.info/ai-patent-watch/#pipeline US20250384186A1 Thu, 18 Dec 2025 12:00:00 GMT awaiting examination Proposes feeding real-world agent position data into a machine learning simulation that outputs synthetic traffic movement metrics for a chosen geographic area and time frame. How it works: The system feeds real-world agent-based position data for a selected geographic area and timeframe into a machine learning model, generates a simulated environment with movement constraints and a variation of it, and outputs metrics describing synthetic driver movement over time on a map, including metrics for the varied scenario. Where it fits: Auto risk modeling and scenario analysis. Stage: Filed June 2024, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250378717A1 Thu, 11 Dec 2025 12:00:00 GMT allowed Describes collecting device data such as location, road type and ambient noise to detect a stopped vehicle and estimate the likelihood it needs roadside assistance. How it works: The method detects when a vehicle moves from a non-highway to a highway road and raises sensor sampling, then compares its speed against a threshold and against the aggregate speed of nearby vehicles to calculate an emergency probability, and when that probability crosses a threshold it initiates roadside assistance or contacts the driver. Where it fits: Roadside assistance dispatch and auto claims servicing. Stage: Filed May 2025 and allowed but not yet granted, written from a new claim substituted after the original claims were cancelled. https://actuary.info/ai-patent-watch/#pipeline US20250377917A1 Thu, 11 Dec 2025 12:00:00 GMT awaiting examination Proposes using data models to select the highest-confidence support data object for resolving a malfunction on a networked home device, seeking to raise first-time resolution rates. How it works: While a transmission request runs behind a waiting-message panel, the apparatus renders in a second panel a predicted operational support data object, selected as the highest-confidence resource for the malfunction, as a link that retrieves a third-party support resource, and selecting that link terminates the underlying transmission process. Where it fits: Connected-device and warranty support servicing. Stage: Filed April 2025, awaiting examination, written from a new claim substituted after the original claims were cancelled. https://actuary.info/ai-patent-watch/#pipeline US20250372087A1 Thu, 04 Dec 2025 12:00:00 GMT awaiting examination Describes transcribing a spoken statement, labeling its words, detecting splits, dividing it into multiple intents, and generating a response, aimed at contact handling. How it works: The system receives a spoken statement, builds a constituency tree of its grammar, detects split points by parsing that tree against grammar rules, divides the statement into separate utterances at those splits, identifies an intent for each utterance, and generates a response based on the set of intents. Where it fits: Automated customer service and call routing. Stage: Filed August 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250371632A1 Thu, 04 Dec 2025 12:00:00 GMT allowed Proposes detecting a flood event in a structure and directing an ML chatbot to file a flood reimbursement claim with the insurer by phone, converting text output into voice. How it works: The system detects a flood event in a structure and transmits information about the structure and a prompt to a machine learning chatbot, causing the chatbot to request flood remediation services over a telephone call by converting its text output into a voice output. Where it fits: Flood claims handling and loss mitigation. Stage: Filed August 2025 and allowed but not yet granted, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250370776A1 Thu, 04 Dec 2025 12:00:00 GMT awaiting examination Describes applying unit performance data to trained models to produce a predictive performance data set for an analytical unit and rendering it as virtual widgets on a device screen. How it works: The method receives performance data for an analytical unit, applies it to one or more trained performance-analysis models to generate a predictive performance data set, builds renderable virtual widgets representing that data, and displays them on a user device screen. Where it fits: General performance analytics; the abstract states no specific insurance workflow. Stage: Filed May 2025, awaiting examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250370725A1 Thu, 04 Dec 2025 12:00:00 GMT allowed Proposes chunking legacy code into prompts for a neural network to generate replacement code, adjusting input chunk and output window sizes to limit truncation during migration. How it works: The engine breaks source-language code into normalized chunks kept under a neural network's token limit, builds a prompt that preserves code and data dependencies, applies a trained converter to produce target-language code, and when the output exceeds a size threshold it lowers it and re-prompts on a reselected subset to avoid truncation. Where it fits: Software modernization and developer tooling; the abstract states no insurance-specific use. Stage: Filed May 2024 and allowed but not yet granted, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250364138A1 Thu, 27 Nov 2025 12:00:00 GMT under examination Describes using optical character recognition to extract text from a historical record and applying a natural language model against authorization guidelines to decide whether a treatment meets approval criteria. How it works: The method OCRs the patient's historical record into text, decides whether the treatment has guidelines to analyze by machine learning, then identifies the authorization criteria and a matching NLP model, runs that model over the extracted text to find relevant record data, and authorizes the treatment when the data meets the criteria. Where it fits: Health insurance prior authorization and utilization management. Stage: Filed April 2025, under examination, written from the claim as originally filed. https://actuary.info/ai-patent-watch/#pipeline US20250363566A1 Thu, 27 Nov 2025 12:00:00 GMT responded Proposes building a model relating historical liability amounts to personal, telematics and environmental data, then applying it to a candidate user's current data, relevant to auto risk assessment. How it works: The device builds a machine learning model from training datasets of historical vehicle driving data, collects current data for a candidate driver, folds it into updated training datasets to continuously retrain the model, and runs the updated model to output a current coverage level for the driver including a likelihood of an accident. Where it fits: Auto insurance underwriting and pricing. Stage: Filed August 2025, applicant has responded and the claim has already been amended during prosecution. https://actuary.info/ai-patent-watch/#pipeline US20250358339A1 Thu, 20 Nov 2025 12:00:00 GMT awaiting examination Describes a browser-based thick-client framework for a contact center that distributes processes across a device and provides shared queuing and communication services with an integrated model implementation. How it works: The client app signals availability, the system checks the status of two candidate service providers, selects one based on those statuses, sends it a request, and processes the returned data object back through the client. Where it fits: Contact center servicing and communication routing, applicable to insurer call-center operations. Stage: Filed July 2025 and in examination; the claim read is the original as filed. https://actuary.info/ai-patent-watch/#pipeline US20250356428A1 Thu, 20 Nov 2025 12:00:00 GMT under examination Proposes estimating vehicle damage from images by finding similar damaged vehicles, scoring the likelihood a part is damaged, and deciding whether to include it in a repair estimate. How it works: From a damage image and the vehicle type, the method picks a matching machine learning model, finds stored images of comparable damaged vehicles, estimates the probability a given component is damaged, and when that probability clears a threshold computes a repair or replacement cost. Where it fits: Automated auto physical damage claims estimation and adjuster support. Stage: Filed July 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250356139A1 Thu, 20 Nov 2025 12:00:00 GMT awaiting examination Describes chaining three large language models, one to screen and transform the prompt, one to generate, and one to check the output for hallucinations and harmful content. How it works: A first language model receives the user prompt and produces a revised prompt, a second model generates a response from that revised prompt, and a third model reviews and where needed rewrites that response to remove inaccuracy or harmful content before it reaches the user. Where it fits: General AI safety and output control; the abstract states no specific insurance workflow. Stage: Filed August 2025; the original claims were cancelled and this claim substituted, typically following a rejection. https://actuary.info/ai-patent-watch/#pipeline US20250353519A1 Thu, 20 Nov 2025 12:00:00 GMT under examination Proposes generating driver notifications through audio, visual or tactile channels from vehicle sensor and external data about road conditions, hazards, points of interest and related insurance offers. How it works: The system ingests evacuation data and geographic evacuation-zone data from external sources, uses onboard sensors to determine that the vehicle qualifies as eligible within that zone, and transmits a notification of available evacuation funds to the driver. Where it fits: Policyholder servicing and catastrophe-response communication for auto or property insurers. Stage: Filed May 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250348967A1 Thu, 13 Nov 2025 12:00:00 GMT under examination Describes using a trained model to determine potential risks for a building from sensor and database data, then generating a risk profile and mitigation alerts or recommendations. How it works: In the claim, the system builds a 3D electronic model of part of a city that includes planned future construction, runs a virtual simulation of a weather event across it, determines a potential risk to a building from that simulation, and updates the model to mark the risk at that building's location. Where it fits: Property underwriting, catastrophe risk assessment and loss mitigation. Stage: Filed July 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250347824A1 Thu, 13 Nov 2025 12:00:00 GMT awaiting examination Describes training and validating a hail damage frequency model from properties that did and did not experience hail damage, using each property's location and features, to support property pricing and catastrophe risk assessment. How it works: The method identifies a set of properties, selects a reduced subset of their available features, trains a hail damage frequency model on that subset, validates it, compares it against other model instances trained on different feature subsets, and selects the best-performing subset and model for production. Where it fits: Property catastrophe pricing and underwriting for hail-exposed risks. Stage: Filed July 2025 and in examination; the claim read has already been narrowed by amendment during prosecution, not granted. https://actuary.info/ai-patent-watch/#pipeline US20250342526A1 Thu, 06 Nov 2025 12:00:00 GMT under examination Proposes predicting a debtor's recovery value from receivables, payment history and credit data, then assigning the individual to a collection segment to guide recovery strategy and benchmark collection efforts across clients. How it works: Input data on an individual is fed to a hyperdimensional model trained on historical inputs and actual recovery values; the model returns a predicted recovery value, the individual is assigned to a segment sent to the client, and the later actual recovery is fed back to retrain the model. Where it fits: Debt collection prioritization; adjacent to insurer subrogation and recovery operations. Stage: Filed May 2025; the original claims were cancelled and this claim substituted in their place, typically after a rejection. https://actuary.info/ai-patent-watch/#pipeline US20250342404A1 Thu, 06 Nov 2025 12:00:00 GMT awaiting examination Describes a guided interface that walks a user through selecting data, choosing an algorithm and training a machine learning model, then distributing the trained model from a cloud server to client devices for predictive analytics. How it works: Through a guided interface the tool takes a user's inputs to pull a dataset from a database, determine a correlated subset tied to a chosen data object, select a machine learning algorithm, build a model generation tool for that object, and deploy the tool on a server. Where it fits: General predictive-analytics model building; no insurance-specific workflow is stated. Stage: Filed July 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250336002A1 Thu, 30 Oct 2025 12:00:00 GMT under examination Describes extracting, classifying and verifying content from images of insurance claim documents, then applying verified content to the claim or issuing a denial, to automate claims document handling. How it works: seeks to extract content from claim document images, classify the document, verify the content, then apply it to the claim or deny it. Where it fits: would serve claims processing and document intake for insurance. Stage: filed July 2025, under examination, where the claims are commonly narrowed before any allowance. https://actuary.info/ai-patent-watch/#pipeline US20250329197A1 Thu, 23 Oct 2025 12:00:00 GMT under examination Proposes estimating probable causes and value of damage to an object from descriptive and third-party data using machine classifiers, with confidence metrics and scene-diagram interfaces, to support claims damage and liability analysis. How it works: The system draws a geographic scene containing a vehicle built from its operational data and damage indicators plus movable objects; after the user drags objects to new positions it regenerates the scene and produces event interpretation data carrying a confidence metric. Where it fits: Auto claims accident reconstruction and liability determination. Stage: Filed June 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250328686A1 Thu, 23 Oct 2025 12:00:00 GMT awaiting examination Describes storing personal information with per-item privacy settings and, on an access request, comparing the request's attributes to those settings to release only the approved information, as a privacy-governed data access mechanism. How it works: The system stores each individual's personal information with its own privacy settings, receives an access request carrying one or more attributes, compares those attributes against the stored settings, determines which items are approved for disclosure, and transmits just those items to the requestor. Where it fits: Consent-governed personal data access; no insurance-specific workflow is stated. Stage: Filed April 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250328634A1 Thu, 23 Oct 2025 12:00:00 GMT final rejection Proposes a trained AI model that curates cyber threat intelligence objects from a distributed ledger by relevance and recommends a threat practice in response to a queried threat type, to support security operations. How it works: Given a cyber threat type submitted by a node with distributed-ledger access, a trained AI model identifies threat intelligence objects that were contributed by nodes or model-generated, scores each for relevance, and returns a curated set together with a recommended threat-response practice. Where it fits: Cyber security threat intelligence sharing; adjacency to cyber insurance risk is not stated. Stage: Filed April 2024; the claim has already been narrowed by amendment during prosecution, which does not mean the examiner has agreed. https://actuary.info/ai-patent-watch/#pipeline US20250322662A1 Thu, 16 Oct 2025 12:00:00 GMT awaiting examination Describes generating a home recommendation model from images of a home environment and user input, then surfacing home recommendations on a device, as a property-focused recommendation mechanism. How it works: A mobile device with a camera captures images of a home environment and a related user input; a separate modeling device receives them, builds a home recommendation model with machine learning from the images and input, identifies recommendations, and sends them back to the device to display. Where it fits: Homeowner servicing and property loss-prevention guidance. Stage: Filed April 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250322200A1 Thu, 16 Oct 2025 12:00:00 GMT under examination Proposes applying a machine learning model to a policy's data structure and attributes to assign that policy to one of several application instances in a policy administration system, as an infrastructure routing mechanism. How it works: The system identifies a policy's data structure, gathers its attributes, and applies a machine learning model trained on prior policy-to-instance assignments to assign that data structure to one of several application instances of the policy administration system. Where it fits: Back-office policy administration and workload routing for insurers. Stage: Filed April 2024 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250315868A1 Thu, 09 Oct 2025 12:00:00 GMT awaiting examination Describes deriving a surface dimension from a mobile image using standardized reference objects, then computing settlement and repair outputs, to support claims estimation. How it works: The system takes an image, generates bounding boxes resized to a neural network's required dimensions, runs them through the network to find a standardized reference object, derives the real dimensions of a target object from the reference object's pixel size, and transmits those dimensions. Where it fits: Property and auto claims measurement, settlement and repair estimation. Stage: Filed June 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250315844A1 Thu, 09 Oct 2025 12:00:00 GMT under examination Proposes comparing the location of the device used for a card transaction against the authorized cardholder's geolocation and blocking the transaction on a mismatch, to reduce false positive fraud alerts. How it works: The method receives transaction data from the device used for an online purchase and determines its location, obtains a record from a second device tied to the same user to determine where the user actually was, and updates the transaction's status based on whether the two locations agree. Where it fits: Payment fraud detection and reduction of false-positive declines. Stage: Filed June 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250315739A1 Thu, 09 Oct 2025 12:00:00 GMT awaiting examination Describes generating multiple resampled imbalanced datasets, building an ensemble of models from them and comparing false positive error rates, to lower the false positive rate of a machine learning process. How it works: Starting from an imbalanced fraud dataset and its baseline false-positive rate, the method resamples it into multiple new datasets each with a distinct positive-to-negative ratio, trains a separate fraud model on each, and ensembles a subset of their outputs to achieve a lower false-positive rate. Where it fits: Fraud detection model development where positive cases are rare. Stage: Filed June 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250310284A1 Thu, 02 Oct 2025 12:00:00 GMT under examination Describes a chatbot that negotiates terms for one party using a supplied parameter such as priority terms or a negotiating style, judges whether proposed terms are acceptable, and generates counter-terms to send to the other party when they are not. How it works: Retrieves a negotiating parameter set by one contracting party, such as a prioritized product list, a set of priority terms, or a negotiating style, uses the AI chatbot to test whether prospective terms are acceptable against it, and where they are not, generates counter-terms from the parameter and sends them to the other party. Where it fits: Automated negotiation of contractual or policy terms between parties; the abstract does not tie it to a specific insurance line. Stage: Filed June 2025, published October 2025 and under examination with a non-final office action mailed; the claim read here is the original as filed. https://actuary.info/ai-patent-watch/#pipeline US20250308263A1 Thu, 02 Oct 2025 12:00:00 GMT awaiting examination Proposes categorizing in-vehicle image sensor data into driver postures and normalizing them across drivers and camera positions for storage, as a foundation for driver behavior monitoring. How it works: The system receives data from in-vehicle image sensors, categorizes it into driver postures representing driver movements, rotates and scales those postures so they are standardized across different drivers and camera positions, and stores the normalized postures in a database. Where it fits: Telematics and usage-based auto insurance driver-behavior analysis. Stage: Filed June 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250307768A1 Thu, 02 Oct 2025 12:00:00 GMT awaiting examination Describes using LIDAR and AI to track store inventory, check whether ordered goods remain available and overlay their location on a generated store map, as a retail inventory and mapping mechanism. How it works: From LIDAR data the method generates a virtual map of a store, determines where the still-available goods from an electronic order sit, overlays those locations on a map showing the aisles, and on confirmation that items were picked up or delivered charges a virtual pay account. Where it fits: Retail inventory and order fulfillment; no insurance workflow is stated. Stage: Filed June 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250307722A1 Thu, 02 Oct 2025 12:00:00 GMT awaiting examination Proposes using a machine learning algorithm to forecast when vehicle demand will exceed supply in a car-sharing application and prompting users to supply a vehicle, as a fleet balancing mechanism. How it works: The method compiles historical vehicle-sharing supply and demand data, predicts for a given date and location that demand will exceed supply, and on that basis directs a vehicle to drive to a parking location near that place on that date. Where it fits: Vehicle-sharing fleet balancing; no insurance workflow is stated. Stage: Filed June 2025; the original claims were cancelled and this claim substituted, typically after a rejection. https://actuary.info/ai-patent-watch/#pipeline US20250307646A1 Thu, 02 Oct 2025 12:00:00 GMT awaiting examination Describes a combined convolutional and LSTM neural network that scores mobile-device kinematic data over time to predict whether a trigger event is an impact, to support collision detection. How it works: Kinematic variables from a mobile device's movement over a time window are fed to a model whose CNN layer extracts features per time segment, an LSTM layer consumes those in sequence, and a prediction layer produces a score used to classify the event as a type of impact, output with timing. Where it fits: Telematics crash detection and automated first notice of loss for auto insurers. Stage: Filed March 2024 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250307279A1 Thu, 02 Oct 2025 12:00:00 GMT under examination Proposes using a generative AI model to infer a user's personalization characteristics, including information retention, and build a personalized graphical interface, framed around telematics data analysis. How it works: A generative AI model identifies the user from a user action, determines personalization characteristics linked to the user's information retention rate, and generates a personalized graphical interface whose visual graphics are shaped by those characteristics. Where it fits: Personalized customer-facing interfaces; an insurance-specific use is only loosely implied. Stage: Filed June 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250307277A1 Thu, 02 Oct 2025 12:00:00 GMT under examination Describes prompting machine learning and large language models with service-channel transcripts and natural language questions over a benefits database to generate omnichannel interaction insights and agent recommendations, for member service. How it works: The method collects transcripts from digital service channels, generates a channel-specific prompt for each from the channel type and extracted metadata, feeds them to machine learning models for insights, and integrates those with member healthcare data using sentiment analysis to display analytical insights. Where it fits: Health plan member service operations and benefits support. Stage: Filed November 2024 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250307024A1 Thu, 02 Oct 2025 12:00:00 GMT awaiting examination Describes a multi-agent large language model architecture in which a primary agent checks a user's access entitlements, selects only the tools that user is permitted to use, plans a multi-step response and delegates each step to a secondary agent. How it works: On a user request, an identity server looks up the user's access entitlements; a primary LLM agent uses them to select only the tools that user may access, builds a multi-tier plan assigning each action to a tool, then calls a dedicated secondary LLM agent per tool and assembles their responses into a reply. Where it fits: Governed multi-agent LLM tooling where user permissions constrain which capabilities run; general enterprise AI infrastructure, not an insurance-specific function. Stage: Filed February 2025 and published October 2025; under examination on the claim as originally filed, not yet narrowed in prosecution. https://actuary.info/ai-patent-watch/#pipeline US20250306244A1 Thu, 02 Oct 2025 12:00:00 GMT allowed Proposes chaining hail size, hail frequency, damage frequency and damage severity models for a location to estimate hail damage risk, to support property pricing and catastrophe exposure assessment. How it works: For a given location the method trains hail size, hail frequency and damage severity models that can discount older data, determines the location's hail size and frequency, applies a damage frequency model to feature data including those, and applies a damage severity model to property-feature data to estimate severity. Where it fits: Property hail catastrophe pricing, underwriting and loss estimation. Stage: Filed June 2025; the claim has already been narrowed by amendment during prosecution, which does not mean the examiner has agreed. https://actuary.info/ai-patent-watch/#pipeline US20250301205A1 Thu, 25 Sep 2025 12:00:00 GMT under examination Describes extracting a content object from a selected segment of visual media and generating a relevance object for display, as a general media content extraction mechanism. How it works: The system receives a segment selection for a piece of visual media, identifies the segment from the temporal indicator tied to that selection, extracts a content data object from a portion of the segment, generates a relevance data object from it, and displays that object to the user. Where it fits: General video content extraction; no insurance workflow is stated. Stage: Filed March 2025 and in examination; reflects the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250299231A1 Thu, 25 Sep 2025 12:00:00 GMT awaiting examination Proposes measuring an object from a mobile image via standardized reference objects and predicting other objects likely in the room, then estimating a repair cost, to support claims estimation. How it works: The system takes an image, forms bounding boxes matched to a neural network's dimensions, reduces their quality and transposes them onto a standardized image, derives each object's real dimensions from its pixel size, and infers which objects are in the room from those dimensions and objects predicted for that room type. Where it fits: Property claims room contents assessment and repair cost estimation. Stage: Filed January 2025; the original claims were cancelled and this claim substituted in their place, typically after a rejection. https://actuary.info/ai-patent-watch/#pipeline US20250299129A1 Thu, 25 Sep 2025 12:00:00 GMT under examination Describes predictive models that forecast future resource needs across channels and generate, adjust or approve a resource offer set as demand and value change over time, as a general resource allocation mechanism. How it works: Retrieves offer-generation input data plus a benchmark and portfolio target data set, applies them to an offer-generation model to produce an offer set that meets the targets, renders an adjustment interface for one user to revise it, then routes the revised set to a second user's approval interface and stores it with the resulting status. Where it fits: General resource-offer generation and approval; the benchmark and portfolio targets could touch insurance pricing or underwriting, though the abstract stays domain-neutral. Stage: Filed December 2024 and published September 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250299108A1 Thu, 25 Sep 2025 12:00:00 GMT awaiting examination Proposes two machine learning models that predict the likelihood and expected count of avoidable negative health events for each member, then target outreach campaigns accordingly, for population health management. How it works: Trains a model on historical census, lifestyle and employment profile data, then for each person builds a feature vector from that data and computes a score representing the predicted time until they transition to retirement, sorts people into bins by that score, and automatically sends bin-specific campaign material to each group. Where it fits: Would support outreach targeting in life and retirement or health servicing, distributing campaigns by predicted score. Stage: Filed June 2025 and published September 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250298820A1 Thu, 25 Sep 2025 12:00:00 GMT awaiting examination Describes classifying an incoming email's request type, extracting entities from it and matching them against database records to generate a prepopulated processing request, to automate correspondence intake. How it works: Reads an email at a mail server, runs a classifier to determine its request type, extracts entities from the free-form text when the type is supported, scores the extraction confidence, and above a threshold looks the entities up in a database and builds a new processing request prepopulated from a matching record before deleting the email. Where it fits: Would automate intake and routing of email-based requests such as claims or policy servicing correspondence. Stage: Filed June 2025 and published September 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250296530A1 Thu, 25 Sep 2025 12:00:00 GMT under examination Proposes computing a vehicle fingerprint from sensor data across operating modes and alerting when later sensor readings deviate from it, as an anomaly-based unauthorized access detection mechanism. How it works: Generates a fingerprint from the vehicle's dynamics sensor data recorded during authorized operation, then for later data computes a difference score between the expected and actual vehicle characteristic, updating the fingerprint when the score stays below a threshold and issuing an alert of potential unauthorized use when it does not. Where it fits: Would serve auto telematics and anti-theft risk assessment for insurers. Stage: Filed April 2025 and published September 2025; in examination, written from a new claim the applicant substituted after the original claims were cancelled. https://actuary.info/ai-patent-watch/#pipeline US20250286913A1 Thu, 11 Sep 2025 12:00:00 GMT allowed Describes a computer agent that gathers internet-facing information about an entity to assess its cyber security risk, then automatically recommends policy setting changes and network changes to reduce that risk, aimed at cyber underwriting and risk assessment. How it works: Uses an agent to collect Internet-accessible information, infers which of that circumstantial data pertains to a given entity and assigns a certainty level that weights its influence on the risk estimate, then determines a network or security-policy change and sends the resulting recommendation to a client system. Where it fits: Would support cyber insurance risk assessment and underwriting of an organization's exposure. Stage: Filed May 2025 and published September 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250284819A1 Thu, 11 Sep 2025 12:00:00 GMT under examination Proposes a tool that prompts a user to describe a proposed generative AI solution, then scores its overall value including the likelihood of a data compromising event, and ranks it against other candidate solutions being considered. How it works: Displays a template collecting the components of a proposed generative AI use case, computes a use score capturing the deployment's overall value together with the likelihood of a data-compromising event, and outputs a priority report comparing that score against other GenAI solutions under consideration. Where it fits: Serves AI governance and risk prioritization; the abstract does not tie it to a specific insurance workflow. Stage: Filed February 2025 and published September 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250278653A1 Thu, 04 Sep 2025 12:00:00 GMT awaiting examination Describes converting gates of a quantum feature map into variational data-encoding gates and running machine learning to fit their parameters, a general-purpose quantum machine learning technique with no insurance function stated in the abstract. How it works: Generates a quantum feature map corresponding to a quantum circuit of multiple gates, applies variational data-encoding parameterization to turn at least one gate into a variational data-encoded gate, and runs machine-learning processes over the feature map to determine a value for that gate's variational parameter. Where it fits: The abstract describes a general quantum machine-learning technique and does not specify an insurance workflow. Stage: Filed August 2024 and published September 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250278457A1 Thu, 04 Sep 2025 12:00:00 GMT under examination Describes machine learning models that predict denials of medical claims and approvals of resubmitted claims, with automatic clustering that routes claims to workflow queues, aimed at claims processing and resource allocation. How it works: Processes payer remit data to label each historical claim as approved or denied, isolates the denied claims, and applies a resubmission-probability model, trained on historical claim and remit data and validated against an error threshold, to predict each denied claim's likelihood of approval if resubmitted. Where it fits: Would serve health insurance claims processing, denial management and resubmission workflows. Stage: Filed December 2024 and published September 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250272756A1 Thu, 28 Aug 2025 12:00:00 GMT under examination Proposes detecting client devices near a weather event, soliciting response data from them by messaging, and estimating how many claim records a locality will generate so that services can be allocated, aimed at claims handling and catastrophe response. How it works: Combines weather data with IoT sensor readings to detect a weather event, identifies nearby users from profile data, activates an app that asks each user event-specific questions, then runs a classification engine over the responses and existing records to estimate the number of new records expected and allocates services accordingly. Where it fits: Would support catastrophe claims intake and resource allocation for insurers after weather events. Stage: Filed February 2024 and published August 2025; in examination, written from a claim the applicant has already narrowed during prosecution. https://actuary.info/ai-patent-watch/#pipeline US20250272699A1 Thu, 28 Aug 2025 12:00:00 GMT under examination Describes querying a database of user licenses, detecting which are in a renewal period, notifying those users, and pre-populating a renewal application for their approval, aimed at agent and adjuster licensing administration. How it works: Feeds a candidate user's profile into a model trained on sample roles, practice locations and license data, infers the person's role or location of practice, compares their licenses against those held by similar profiles, and flags a potential licensure discrepancy when a peer-held license is missing before notifying the user. Where it fits: Would support agent and producer licensing compliance and renewal management. Stage: Filed May 2025 and published August 2025; in examination, written from the original claim as filed. https://actuary.info/ai-patent-watch/#pipeline US20250272160A1 Thu, 28 Aug 2025 12:00:00 GMT awaiting examination Describes identifying computing nodes in an inactive state and scheduling machine learning sub-tasks to run on them, then collating the results, a general-purpose resource-utilisation method with no insurance function stated in the abstract. How it works: Uses a resource model to estimate a sub-task's resource requirement, schedules that sub-task to run on a computing node during the node's inactive state, collates the finished sub-task into the completed task, and generates a notification when the task is done. Where it fits: The abstract describes general compute-resource scheduling and does not specify an insurance workflow. Stage: Filed May 2025 and published August 2025; in examination, written from the original claim as filed.