Alerta

EXECUTION AND SEMANTIC ERROR CORRECTION CAPABILITIES FOR NATURAL LANGUAGE TO LOGICAL FORM MODEL

Absstract of: WO2026030330A1

Techniques are disclosed herein for providing and using a natural language to logical form model having execution and sematic error correction capabilities. In one aspect, a method is disclosed that includes: accessing a set of training examples and generating a set of error correction training examples via an iterative process performed for each training example. The iterative process includes generating an inferred logical form, executing the inferred logical form on a database, when executing the inferred logical form on the database fails, obtaining an execution error message corresponding to the failure, and recording the inferred logical form and the execution error message as part of an execution error example, and populating an error correction prompt template with the execution error example to generate an error correction training example. A machine learning model may then be trained with at least the set of error correction training examples.

Detection of User Interface Imitation

Absstract of: US20260039693A1

Techniques are disclosed relating to generating trained machine learning modules to identify whether user interfaces accessed by a computing device match user interfaces associated with a set of Internet domain names. A server computer system receives a set of Internet domain names and generates screenshots for user interfaces associated with the set of Internet domain names. The server computer system then trains machine learning modules that are customized for the set of Internet domain names using the screenshots. The server then transmits the machine learning modules to the computing device, where the machine learning modules are usable by an application executing on the computing device to identify whether a user interface accessed by the device matches a user interface associated with the set of Internet domain names. Such techniques may advantageously allow servers to identify whether user interfaces are suspicious without introducing latency and increased page load times.

AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM

Absstract of: US20260040099A1

Aspects of the subject disclosure may include, for example, receiving, from a machine learning model, information about an event causing a service degradation in a cellular network, wherein the event is external to the cellular network, determining one or more event categories associated with the event causing the service degradation, determining, based on the one or more event categories, likely affected customers, the likely affected customers being likely to experience the service degradation, determining, by the machine learning model, proper resources for resolution of the service degradation, wherein the determining proper resources is based on the one or more event categories, and dispatching the proper resources for resolution of the service degradation. Other embodiments are disclosed.

INTERACTIVE INTERFACE TASK AUTOMATION UTILIZING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) ACTION MODELS IMPROVED WITH RETRIEVAL-AUGMENTED GENERATION (RAG)

Absstract of: US20260037318A1

This disclosure describes a framework for performing user-requested tasks automatically across an interactive interface using various types of machine learning models. Specifically, this disclosure outlines and describes a task execution system that utilizes a generative artificial intelligence (AI) action model and retrieval-augmented generation (RAG) to complete user-requested actions across an interactive interface. The task execution system solves many of the current limitations of LAMs by using a generative AI action model to determine a session plan, which includes a set of actions for accomplishing stages of the actionable task across the interactive interface, obtaining visual context information of each interactive interface segment, integrates RAG results to improve the accuracy of both the session plan and individual actions, and self-corrects when faced with unexpected obstacles.

MACHINE LEARNING-BASED MODIFICATION OF IMAGE CONTENT

Absstract of: US20260038163A1

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for modifying a captured image. The program and method provide for displaying, by a messaging application, an image captured by a device camera; providing, by the messaging application, a user interface for selecting from among a plurality of content modifiers to modify the image, the plurality of content modifiers including a first content modifier corresponding to a machine learning model trained with a plurality of image pairs, each image pair including a first image and a second image corresponding to a modified version of the first image; receiving user selection of the first content modifier from among the plurality of content modifiers; determining, in response to receiving the user selection, a modified version of the image based on output from the machine learning model; and displaying the modified version of the image.

COMPUTER SYSTEM AND METHOD FOR CLASSIFYING ASSETS IN AUTOMATED AND INDUSTRIAL CONTROL SYSTEMS

Absstract of: EP4687049A1

Classifying one or more assets in an automated and industrial control system (AIC) according to a classification standard. In a computer monitoring tool, a classification query is received for an asset managed by the AIC. Responsive to this classification query, the computer monitor tool retrieves a listing of candidate ontology classes for the queried asset utilizing information received from a semantic data model of known assets. The computer monitor tool then captures, preferably from a database coupled to the AIC, certain classification attribute variables associated with the queried asset. Additionally, the computer monitor tool receives user information describing certain building information associated with the queried asset. The computer monitor tool then generates a computer query configured for requesting results from a machine learning (ML) algorithm indicative of one or more classification standards for the queried asset.

CAPABILITIES AND SAFE PLUGINS

Absstract of: WO2026024342A1

Disclosed are methods for managing execution of plugins of a machine-learning based system. A plugin configuration defines inputs required by the plugin and capabilities provided by the plugin. Capabilities describe the plugin's functionality, such as how the plugin affects the response, what type of content the plugin generates, etc. In some configurations, when responding to a prompt, a collection of relevant plugins is identified. Configurations of these plugins may be analyzed to optimize execution, including determining optimal execution order or enabling parallel execution. Plugin configurations may also be analyzed to improve security by conditionally preventing one plugin from accessing the output of another. Plugin configurations may also be used to inform a client what plugins will run and what results they may yield. This enables the client to optimize and streamline how the response is displayed.

MACHINE LEARNING TECHNIQUES FOR GENERATING VISUALIZATION RECOMMENDATIONS

Absstract of: US20260030528A1

A visualization recommendation system generates recommendation scores for multiple visualizations that combine data attributes of a dataset with visualization configurations. The visualization recommendation system maps meta-features of the dataset to a meta-feature space and configuration attributes of the visualization configurations to a configuration space. The visualization recommendation system generates meta-feature vectors that describe the mapped meta-features, and generates configuration attribute sets that describe the attributes of the visualization configurations. The visualization recommendation system applies multiple scoring models to the meta-feature vectors and configuration attribute sets, including a wide scoring model and a deep scoring model. In some cases, the visualization recommendation system trains the multiple scoring models using the meta-feature vectors and configuration attribute sets.

ENRICHMENT AND RECONCILIATION OF FINANCIAL TRANSACTION DATA

Absstract of: WO2026022822A1

A system is configured to provide data pertaining to a record, performing the following method: (a) obtain, from a first source(s), a first record indicative of an actual financial transaction, paid via the first source; (b) perform enrichment on the first record, thereby determining: a counterparty and/or a financial classification category associated with the first record. The enrichment utilizes machine learning model(s) trained to identify correspondence, of first records indicative of actual financial transactions associated with a business entity, to second data. The second data are obtained from second source(s), distinct from the first source(s); (c) derive an enriched first record, based on the enrichment; and (d) provide the enriched first record.

SYSTEMS AND METHODS FOR PERSONALIZED GUIDANCE

Absstract of: US20260030516A1

Described are systems and method for personalized search results, including a memory storing instructions, a trained machine learning model, and a processor operatively connected to the memory and configured to execute the instructions to perform operations, including receiving the sequence of search queries from a user device associated with a user, predicting the likely next search query from the user by inputting the received sequence of search queries into the trained machine learning model, generating predicted search results by applying the likely next search query, generating the personalized search results by appending the predicted search results to search results from a most recent query of the sequence of queries from the user, and causing the user device to display the personalized search results.

Information Processing Method, Program, and Information Processing Device

Absstract of: US20260030319A1

Provided is an information processing method, etc. that assists a user in interpreting behavior of a generated machine learning model. In the information processing method, a computer executes processing of recording a plurality of sets of an explanatory data vector xn input to an existing machine learning model (21) and an objective data vector yn output from the machine learning model (21) in association with each other, calculating an interpretation matrix A_dagger which is a vector product of an explanatory matrix X in which a plurality of sets of the explanatory data vector xn is arranged and a generalized inverse matrix of an objective matrix Y in which the objective data vector yn is arranged in an order corresponding to the explanatory data vector X, and outputting a chart (41, 42, and 43) related to the interpretation matrix A_dagger.

COMPUTER SYSTEM AND METHOD FOR CLASSIFYING ASSETS IN AUTOMATED AND INDUSTRIAL CONTROL SYSTEMS

Absstract of: US20260029780A1

Classifying one or more assets in an automated and industrial control system (AIC) according to a classification standard. In a computer monitoring tool, a classification query is received for an asset managed by the AIC. Responsive to this classification query, the computer monitor tool retrieves a listing of candidate ontology classes for the queried asset utilizing information received from a semantic data model of known assets. The computer monitor tool then captures, preferably from a database coupled to the AIC, certain classification attribute variables associated with the queried asset. Additionally, the computer monitor tool receives user information describing certain building information associated with the queried asset. The computer monitor tool then generates a computer query configured for requesting results from a machine learning (ML) algorithm indicative of one or more classification standards for the queried asset.

CAPABILITIES AND SAFE PLUGINS

Absstract of: US20260030039A1

Disclosed are methods for managing execution of plugins of a machine-learning based system. A plugin configuration defines inputs required by the plugin and capabilities provided by the plugin. Capabilities describe the plugin’s functionality, such as how the plugin affects the response, what type of content the plugin generates, etc. In some configurations, when responding to a prompt, a collection of relevant plugins is identified. Configurations of these plugins may be analyzed to optimize execution, including determining optimal execution order or enabling parallel execution. Plugin configurations may also be analyzed to improve security by conditionally preventing one plugin from accessing the output of another. Plugin configurations may also be used to inform a client what plugins will run and what results they may yield. This enables the client to optimize and streamline how the response is displayed.

MACHINE LEARNING TECHNIQUES FOR DISCOVERING KEYS IN RELATIONAL DATASETS

Absstract of: AU2024318556A1

Techniques for discovering primary, unique, and/or foreign keys for relational datasets are described. The techniques include profiling the relational datasets to obtain respective data profiles; identifying one or more primary key candidates for a first relational dataset using a first data profile of the first relational dataset and a first trained machine learning model; identifying one or more foreign key proposals for a second relational dataset using the one or more primary key candidates by performing a subset analysis of the second relational dataset with respect to the first relational dataset; identifying one or more foreign key candidates for the second relational dataset using the first data profile, a second data profile of the second relational dataset, and a second trained machine learning model different from the first trained machine learning model; and outputting the at primary key candidate(s) and the foreign key candidate(s).

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, SPAD IMAGING DEVICE, AI MODEL GENERATION METHOD, AI MODEL GENERATION DEVICE, AND INFERENCE PROCESSING DEVICE

Absstract of: EP4686214A1

An information processing device according to the present technology includes a calculation unit that calculates a lower limit value of a length of a count period for making likelihood information equal to or greater than a threshold on the basis of an inference result obtained by inputting a plurality of SPAD images obtained by an SPAD sensor to an AI model obtained by machine learning, the SPAD images having different count values by varying lengths of the count period in which photon counting is performed for each pixel, and likelihood information of the inference result.

METHOD FOR GENERATING A SET OF MANUFACTURING DATA MAKING IT POSSIBLE TO MANUFACTURE A COSMETIC COMPOSITION AND ASSOCIATED DEVICES

Absstract of: WO2026017666A1

The present invention relates to a method for generating a set of manufacturing data of a cosmetic composition capable of complying with at least one acceptability criterion, said method comprising a step of: - obtaining an set to be completed and optionally at least one constraint parameter to be complied with by the chemical composition, and - applying a technique to the set to be completed and optionally the at least one stress parameter to obtain a completed set, the technique including using at least one machine learning model, the at least one machine learning model being capable of completing a set to be completed.

SYSTEM AND METHOD FOR DYNAMIC MULTI-PARTY VERIFICATION OF GENERATIVE ARITIFICIAL INTELLIGENCE SYSTEMS

Absstract of: WO2026019909A1

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative Al systems. The models and associated systems can be deployed in an enterprise and require verification to ensure that cohorts are properly verifying the models and systems and evaluation to ensure that the models and systems operate responsibly and achieve intended outcomes. A dynamic, multi-stakeholder blinded verification process can be employed for the continuous verification and evaluation of machine learning models and the systems that use them. This helps promote unbiased, reproducible verification, evaluation and assessments by preventing potential biases from cohorts form part of the verification process.

SAFETY NET ENGINE FOR MACHINE LEARNING-BASED NETWORK AUTOMATION

Absstract of: US20260025327A1

In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

SYSTEM AND METHOD FOR DYNAMIC MULTI-PARTY VERIFICATION OF GENERATIVE ARITIFICIAL INTELLIGENCE SYSTEMS

Absstract of: US20260025388A1

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative AI systems. The models and associated systems can be deployed in an enterprise and require verification to ensure that cohorts are properly verifying the models and systems and evaluation to ensure that the models and systems operate responsibly and achieve intended outcomes. A dynamic, multi-stakeholder blinded verification process can be employed for the continuous verification and evaluation of machine learning models and the systems that use them. This helps promote unbiased, reproducible verification, evaluation and assessments by preventing potential biases from cohorts form part of the verification process.

DEVICE, SYSTEM AND METHOD FOR FEDERATED LEARNING USING RISK AUDITS

Absstract of: US20260025400A1

A computing device, that is configured to configure a global machine learning model, performs respective electronic risk audits of client devices configured to train respective local machine learning models that correspond to a global machine learning model. Based on respective electronic risk scores of one or more of the client devices, determined via the respective electronic risk audits, the computing device implements one or more parameter privacy adjustment methods on respective parameters received from the client devices prior to using the respective parameters to configure the global machine learning model, wherein respective client devices determined to have higher electronic risk scores have more of the parameter privacy adjustment methods applied than other respective client devices determined to have lower electronic risk scores. The computing device provides, to the client devices, the global machine learning model configured according to the respective parameters as adjusted.

Construction Knowledge Graph

Absstract of: US20260023889A1

An example computing platform is configured to (i) receive a data asset related to a construction project; (ii) determine, via a first machine-learning algorithm, at least one physical location within the construction project to which the received data asset is related; (iii) associate the received data asset with the determined physical location; (iv) based on the determined physical location, determine, via a second machine-learning algorithm, a respective relationship between the received data asset and one or more other data assets related to the construction project; and (v) add the received data asset to a construction knowledge graph as a node that is connected to one or more other respective nodes that represent the one or more other data assets.

EVALUATING ELECTRONIC SUBMISSIONS USING GENERATIVE ARTIFICIAL INTELLIGENCE

Absstract of: US20260023976A1

Aspects of the present disclosure relate to automated evaluation of electronic datasets. Embodiments include receiving one or more rules related to evaluation of electronic datasets. Embodiments further include generating, via an embedding model, embedding representations of the one or more rules. Embodiments further include receiving an electronic dataset. Embodiments further include identifying a rule that is applicable to the electronic dataset based on using a machine learning model configured to search the embedding representations of the one or more rules based on the electronic dataset. Embodiments further include evaluating, using the machine learning model or an additional machine learning model, the electronic dataset based on the identified rule. Embodiments further include using the machine learning model or the additional machine learning model to generate an evaluation summary for the electronic dataset based on determining that an item within the electronic dataset does not comply with the identified rule.

DEFECT-TRIGGERED MACHINE LEARNING-BASED TEST GENERATION AND CONTROL

Absstract of: US20260023678A1

An apparatus comprises at least one processing device configured to generate a first data structure by parsing a support ticket comprising information characterizing defects encountered while operating an information technology asset. The at least one processing device is also configured to process the first data structure utilizing a machine learning model to generate a second data structure specifying a given sequence of test steps for a given test scenario configured for testing of the defects. The at least one processing device is further configured to map the given sequence of test steps in the second data structure to respective application programming interface calls of a test automation framework, each of the application programming interface calls being associated with a functional code test unit of a test code database of the test automation framework, and to execute the given test scenario utilizing the mapped application programming interface calls.

Systems and Methods for Facial Recognition Training Dataset Adaptation with Limited User Feedback in Surveillance Systems

Absstract of: US20260023820A1

Various embodiments provide systems and methods for updating a training dataset so that the generated machine learning model can adapt to both short-term and long-term face variations including, for example, head pose, dressing, lighting conditions, and/or aging.

REDUCING FALSE POSITIVES USING CUSTOMER FEEDBACK AND MACHINE LEARNING

Nº publicación: US20260024101A1 22/01/2026

Applicant:

STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY [US]
State Farm Mutual Automobile Insurance Company

Absstract of: US20260024101A1

A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.