Alerta

SOFTWARE VERSION DEPENDENCY MANAGEMENT SYSTEM USING LARGE LANGUAGE MODEL

Absstract of: US20260211668A1

0000 In an example embodiment, a large language model (LLM) is used to capture semantic relationships between entities so that machine learning techniques can be used to analyze patterns and relationships of entities and dependencies. This includes analyzing version requirements and dependencies among software products. The system is trained to recognize compatibility patterns and understand the impact of product upgrades on dependencies.

GENERATING VIEWS FOR BIAS METRICS AND FEATURE ATTRIBUTION CAPTURED IN MACHINE LEARNING PIPELINES

Absstract of: US20260211980A1

0000 Views may be generated for bias metrics or feature attribution captured in machine learning pipelines. A request to create a view of bias metrics or feature attribution may be received. The bias metrics or feature attribution may have been determined in a machine learning pipeline as part of executing a training job that specified the bias metrics or the feature attribution. A development application may access a data store that stores the bias metrics or the feature attribution determined in the machine learning pipeline. A view based on the bias metrics or feature attribution may be generated and provided.

REAL-TIME MESSAGING OVER A NETWORK USING ELECTRONIC COMMUNICATIONS

Absstract of: US20260212980A1

0000 Techniques are presented for delivering point of care message content, including defining criteria for message content delivered to a user via a graphical user interface during an encounter with a third party client, receiving input data in real-time from the user, and determining, by machine learning, an aspect of therapy or indicator thereof for the third party client. This aspect of therapy or indicator may be absent in the input data. Techniques may further include determining particular message content for the user using the aspect of therapy or indicator and the criteria, and delivering the particular message content to the user during the encounter.

METHOD AND SYSTEM FOR REAL-TIME VEHICLE HEALTH MONITORING AND PREDICTIVE MAINTENANCE

Absstract of: WO2026154516A1

This invention provides a vehicle health monitoring and predictive maintenance system that integrates real-time data analytics, anomaly detection, and hybrid machine learning models. The system utilizes data from vehicle sensors (OBD-II, GPS, and accelerometers) to monitor health, detect irregularities, and predict maintenance needs. A unique combination of ARIMA, SVR, and XGBoost models enables precise forecasting of failures and maintenance requirements, while Isolation Forest ensures efficient anomaly detection. The invention adapts to individual driving behaviours and environmental conditions, generating user- specific maintenance insights and cost-saving recommendations. The generated insights are delivered through user-friendly reports, promoting proactive maintenance strategies that enhance vehicle safety, reduce downtime, and optimize performance. This scalable solution is applicable across industries, including fleet management, autonomous vehicles, and personal vehicle maintenance. By combining IoT, advanced predictive modelling, and real- time insights, this invention addresses limitations in traditional vehicle diagnostics and sets a new standard for predictive maintenance systems.

SYSTEMS AND METHODS FOR SECURITY ANALYSIS OF SINGLE SIGN-ON PROCESSES

Absstract of: US20260214111A1

0000 Systems and methods are disclosed herein for security analysis of a single sign-on (SSO) process. An example method includes detecting a login event initiated by a user device using SSO credentials, and detecting a transaction comprising the SSO credentials involving a third-party service. The example method further includes receiving a first SSO log of user activity from a first SSO provider, and annotating the first SSO log based on the detected transaction to produce an annotated log. The example method also includes determining, using a first machine learning model, an indication of high-risk activity based on the annotated log. The example method also includes generating a first prompt based on the annotated log and the indication of high-risk activity, and generating, using a first language model, a language-based explanation of the high-risk activity.

METHOD AND SYSTEM FOR AI-BASED REAL-TIME ANALYSIS OF ENTITY DATA

Absstract of: US20260212410A1

A system for an automated real-time analysis and generation of predictive entity scoring based on entity data including a processor of a predictive scoring server (PSS) node configured to host a machine learning (ML) module coupled to at least one target user-entity node and to a plurality of remote nodes associated with the at least one target entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire target entity profile data from the at least one target entity node, the target entity profile data including action metrics associated with the at least one target entity and the plurality of remote nodes; perform normalization of the target entity profile data based on the action metrics; parse the normalized data to derive a plurality of classifying features; generate a feature vector based on the plurality of classifying features; ingest the feature vector into the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of predictive scoring parameters from at least one entity scoring predictive model generated by the ML module using outputs of the ANN based on the feature vector; and generate at least one score for the at least one entity node based on the plurality of predictive scoring parameters.

SYSTEM AND METHOD FOR PERFORMING AGENT-BASED SIMULATION WITH LEARNING AGENT MODELS

Absstract of: US20260212196A1

Various methods and processes, apparatuses or systems, and media for performing a multi-agent simulation for capturing reactionary actions by a downstream computer agent are disclosed. The present disclosure provides generating a simulation model with at least two independent computer agents with at least one communication provided between the at least two independent computer agents; initializing each of the at least two independent computer agents; independently performing, via machine learning, reinforcement learning for each of the at least two independent computer agents; executing the simulation model for performing a simulation until a stable state is established in the simulation; initiating at least one exogeneous shock to the simulation model after the stable state is established in the simulation; and capturing reactionary data from the at least two independent computer agents.

TRAINING DATA GENERATION DEVICE, TRAINING DATA GENERATION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM

Absstract of: US20260212179A1

0000 A training data generation device includes at least one processor, in which the at least one processor executes acquisition processing of acquiring data, generation processing of generating an action executable on the data, specification processing of specifying a task that is likely to be achieved by execution of the action using the action generated in the generation processing, and output processing of outputting a pair of the task specified in the specification processing and the action generated in the generation processing as training data used for training of a generative model that generates an action for achieving an input task. This training data further enhances reliable decision making through advanced machine learning techniques.

MANAGING ASSOCIATED IDENTIFIERS IN AI/ML-BASED POSITIONING

Absstract of: US20260213895A1

0000 Example embodiments of the present disclosure are directed to managing associated identifiers (IDs) in Artificial Intelligence Machine Learning (AI/ML) based positioning. A method comprises receiving, from a second apparatus, information comprising a first configuration of a plurality of transmission-reception points (TRPs) and a first association identification, the first association identification associated with at least one TRP of the first configuration of the plurality of TRPs; receiving, from the second apparatus, information comprising a second configuration of a plurality of TRPs, the first association identification associated with at least one TRP of the second configuration of the plurality of TRPs; and determining, for the first association identification, that the at least one TRP of the first configuration of the plurality of TRPs and the at least one TRP of the second configuration of the plurality of TRPs are with consistent physical properties.

INFORMATION PLATFORM SYSTEMS AND METHODS

Absstract of: US20260211963A1

Various aspects describe an information platform for consistently integrating and/or quantifying the underlying principles of ESG into financial analyses, analytical tools, metrics, and/or available information on reviewed companies, business entities, etc. . . . , and further provide integration of analysis with community-based insight, contextual information and tools for readily understanding both. Various embodiments implement machine learning tools for curating data sources and incorporating the data sources into the knowledge platform. The incorporation of AI moderated information sources enables succinct views of often massive information pools, and further provides for transitions between types of information (e.g., qualitative, quantitative, and interactive data source (e.g., engagements, collaborative information, etc.)). The platform facilitates user understanding and can eliminate the need to design and execute complicate queries by allowing users to transition between data types and view to develop better understanding and context of various information sources.

SYSTEMS AND METHODS FOR IDENTIFICATION AND MANAGEMENT OF PERSONALLY IDENTIFIABLE INFORMATION ASSOCIATED WITH ENTERPRISE IT NETWORKS

Absstract of: US20260212226A1

Various examples are provided related to identification of protected information elements associated with unique entities in data files present in data file collections associated with enterprise IT networks. The unique entities can be associated with one or more entity identifications in one or more data files. Computer-generated identification of entity identifications and protected information elements can be conducted, in part, by at least some human review. Information generated accordingly to the disclosed methodology can be used to generate plans for a time and number of human reviewers needed to review data files. Information generated from the processes herein can be configured as user notifications, reports, dashboards, machine learning for subsequent data file analyses, and notifications of unique entities having protected information elements present in one or more data files.

MODULAR SYSTEMS AND METHODS FOR SELECTIVELY ENABLING CLOUD-BASED ASSISTIVE TECHNOLOGIES

Absstract of: US20260212108A1

0000 Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

MODEL CASCADE FOR EFFICIENT RULE EXTRACTION

Absstract of: WO2026155965A1

A method including determining whether to use a functional processing model or a machine-learning processing model and executing the functional processing model or the machine-learning processing model using as input the first input document to transform data in a first input document into the target data format, executing an extraction model using as input the data in the first input document in the target data format to generate first structured data, executing a compilation model using as input a plurality of structured data generated based on data in a set of input documents including the first input document to generate an aggregate structured data, and executing a synthesis model using as input the aggregate structured data and the plurality of structured data to generate an output data structure.

COMPUTER-IMPLEMENTED METHODS, SYSTEMS COMPRISING COMPUTER-READABLE MEDIA, AND ELECTRONIC DEVICES FOR PROVIDING DYNAMIC OPEN BANKING DATA AGGREGATION

Absstract of: WO2026155838A1

A computer-implemented method for providing dynamic open banking data aggregation that includes: applying pre-model rules to data regarding financial institution (FI) accounts to determine that a first subset of the FI accounts will be included in an aggregation batch; inputting the data regarding a second subset of the FI accounts to a machine learning (ML) model to determine that a third subset of the FI accounts will be included in the aggregation batch and that a fourth subset of the FI accounts will not be included in the aggregation batch; based on the determination from the application of the pre-model rules and on the determination from the ML model, respectively, requesting data updates for the first and third subsets from the FI; batching the aggregation batch by processing the data updates for the first and third subsets to produce results; and storing the results to an aggregated data store.

MEHTODS, ARCHITECTURES AND SYSTEMS FOR GENERATING AUDIBLE CONTENT

Absstract of: US20260212201A1

Systems and methods are provided for training an artificial intelligence system and generating audible content for output. The method utilizing a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit, a training input to the system including an input for receiving content for training during the machine learning, and a data plane layer, the data plane layer including an input interface to receive and store data input content from one or more data sources other than the control plane layer, the data input content being subject to transformation into audible content for output. Data input content information is used in synthesizing audible output content at least in part by transforming the data input content into the audible output content.

ARTIFICIAL INTELLIGENCE BASED HEALTH COACHING BASED ON KETONE LEVELS OF PARTICIPANTS

Absstract of: US20260212781A1

A system is disclosed that uses profiles of users, including monitored ketone levels of the users, to assess effectiveness levels of health programs (such as weight loss programs) assigned to the users, and to select health program modifications for the users. The system may use a machine learning (artificial intelligence) algorithm to adaptively learn how to classify users and to select messaging and behavioral modifications for the users. For example, in some embodiments the system classifies the users and provides associated health program recommendations using a computer model trained with expert-classified user data records. As another example, a set of rules may be used to generate the health program recommendations and related messaging, and the set of rules may automatically be modified over time based on feedback data reflective of health program effectiveness levels produced by such rules. In some embodiments the system includes a mobile application that runs on mobile devices of users and communicates wirelessly with breath analysis devices of the users. The mobile application may also communicate with a server-based system that generates the health program recommendations.

SYSTEMS AND METHODS FOR KNOWLEDGE BOUNDARY DETERMINATION

Absstract of: WO2026152315A1

Disclosed herein are systems, methods, and instrumentalities associated with AI knowledge boundary evaluation. An apparatus may receive a question posted to an AI-based intelligent entity such as a robot. The apparatus may, using a machine learning (ML) model such as a large language model (LLM), extract a first knowledge point from the question and determine a first knowledge hierarchy associated with the first knowledge point. The apparatus may further identify a second knowledge point possessed by the robot that corresponds to the first knowledge point, and determine a second knowledge hierarchy associated with the second knowledge point. By comparing the advancement levels of the first knowledge hierarchy and the second knowledge hierarchy, the apparatus may determine whether the question posted to the robot exceeds the knowledge boundary of the robot.

MACHINE-LEARNING FOR CONTENT INTERACTION

Absstract of: US20260212263A1

0000 A system can generate content recommendations and facilitate interactions using machine-learning. The system can receive a request from a provider entity. The system can receive entity data and interaction data associated with a target entity. The system can generate at least a first graph structure and a second graph structure. The system can generate a linked graph structure based on the first graph structure and the second graph structure. The system can determine among a plurality of operations, one or more target operations to perform on data included in the linked graph structure. The system can execute using a trained machine-learning model, the target operations to generate a content recommendation for facilitating an interaction. The system can provide a responsive message based on the content recommendation usable to facilitate the interaction.

System, Method, and Computer Program Product for Efficient Node Embeddings for Use in Predictive Models

Absstract of: US20260212156A1

0000 Described are a system, method, and computer program product for efficient node embeddings for use in predictive models. The method includes receiving graph data associated with a graph comprising a plurality of nodes associated with a plurality of entities and a plurality of edges associated with interactions between entities. The method also includes generating a plurality of node embeddings for the plurality of nodes, and generating a matrix based on each positive pair of nodes and the plurality of node embeddings. The method further includes decomposing the matrix to provide a left unitary matrix, a diagonal matrix, and a right unitary matrix. The method further includes determining a plurality of updated node embeddings for the plurality of nodes based on the left unitary matrix and the diagonal matrix. The method further includes communicating the plurality of updated node embeddings for inputting into a machine learning model to generate a prediction.

METHOD, PROGRAM, AND DEVICE FOR CONSTRUCTING MODEL OPTIMIZED FOR ANALYSIS OF BIO SIGNALS

Absstract of: US20260212211A1

0000 According to embodiments of the present disclosure, there are disclosed a method, program and device for constructing a model optimized for the analysis of biosignals that are performed by a computing device. The method may include: determining the fundamental structure of a machine learning model based on user input; and performing hyperparameter tuning for constructing a machine learning model optimized for the analysis of biosignals based on the determined fundamental structure.

SYSTEMS AND METHODS FOR MACHINE LEARNING BASED EVENT PREDICTION

Absstract of: US20260212259A1

0000 The document relates to systems and methods for machine learning based event prediction. In some embodiments, a computer-implemented method includes generating a configuration file; automatically preparing training data based on the configuration file; automatically generating one or more machine learning models based on the training data; and providing output of model training and evaluation results.

DETECTING UNWANTED SOFTWARE USING A MACHINE LEARNING OPERATION

Absstract of: US2025086093A1

0000 Systems, methods, and software can be used to determine whether a software code is unwanted. In some aspects, a method includes: obtaining, by an electronic device, a set of software features of a software code; obtaining, by the electronic device, a set of user features of a user of the electronic device; and determining, by the electronic device, a classification score of the software code based on the set of software features and the set of the user features, wherein the classification score indicates whether the software code is potentially unwanted for the user.

INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING DEVICE, AND INFORMATION PROCESSING METHOD

Absstract of: EP4779533A1

An Information processing program causing a computer to perform a process including: in classification processing on input graph structure data using a machine learning model (110), acquiring a contribution degree in the classification processing for each of a plurality of partial regions included in graph structure data; and determining an evaluation for the machine learning model (110) based on similarity between the contribution degree and designation information for the partial region of the graph structure data.

MECHANISMS FOR MACHINE LEARNING ENABLEMENT

Absstract of: WO2025059438A1

A method may include receiving by a Rule Engine (RE) service one or more requests to define an RE definition for a Digital Twin (DT) system; communicating with a DT service, a storage service, a Machine Learning (ML) service, or a combination thereof, according to rules specified in the DT system, for processing the one or more requests; and sending, by the RE service, one or more responses with a status to the one or more requests, wherein the one or more response comprise one or more of an overall status of processing the RE definition, an individual status for each event condition, action, state, and/or state transition that was specified in the RE definition, and the status for an interconnection of services between the RE and ML services.

DETERMINING EFFECT OF A DRUG BASED ON BRAIN ACTIVITY AND A TRAINED MACHINE LEARNING MODEL

Nº publicación: EP4776991A1 22/07/2026

Applicant:

PRAXIS PREC MEDICINES INC [US]
Praxis Precision Medicines, Inc.

Absstract of: WO2025059464A1

A computer-implemented method for determining an effect of a drug on a subject, including: obtaining brain activity data of the subject; determining, using a trained machine learning model and the brain activity data of the subject, an effect of the drug administered to the subject, the trained machine learning model trained with brain activity data of subjects treated with at least one of the drug or a second drug and with brain activity data of subjects treated with a placebo; and providing an output indicative of the effect of the drug administered to the subject determined by the trained machine learning model.