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EXTENSIBLE MACHINE LEARNING POWERED BEHAVIORAL FRAMEWORK FOR RISK COVERAGE

Resumen de: US2025272617A1

Some aspects of the present disclosure relate to systems, methods and computer readable media for outputting alerts based on potential violations of predetermined standards of behavior. In one example implementation, a computer implemented method includes: training a natural language-based machine learning model to detect at least one risk of a violation condition in an electronic communication between persons, wherein the violation condition is a potential violation of a first predetermined standard of behavior; receiving a lexicon, wherein the lexicon comprises topic data; receiving connection data representing a relationship between the trained machine learning model and the lexicon; detecting, using the trained machine learning model, the lexicon, and the connection data, a potential violation of a second predetermined standard of behavior; and outputting for display an alert indicating the potential violation of the second predetermined standard of behavior.

SYSTEM AND METHOD FOR FEEDBACK-DRIVEN AUTOMATED DRUG DISCOVERY

Resumen de: US2025272582A1

A system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.

SYSTEMS AND METHODS FOR PROGRAMMATIC LABELING OF TRAINING DATA FOR MACHINE LEARNING MODELS VIA CLUSTERING AND LANGUAGE MODEL PROMPTING

Resumen de: AU2023383086A1

Embodiments introduce an approach to semi-automatically generate labels for data based on implementation of a clustering or language model prompting technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. The disclosed methodology is particularly helpful in generating labels or annotations for unstructured data. In some embodiments, the disclosed approach may be used with data in the form of text, images, or other form of unstructured data.

FLEXIBILE ENTITY RESOLUTION NETWORKS

Resumen de: WO2025175313A1

In various embodiments, a computing system is configured to provide a multi-stage cascade of large language models and stage N neural networks that identifies matching data records within a set of data records and then merges the matching data records. More specifically, the computing system can use a combination of domain-agnostic large language models and downstream neural network classifiers to identify matching data records that would otherwise not be possible with other machine learning or rules-based entity resolution systems. In one example, a computing system receives an entity resolution request. The entity resolution request can indicate a first entity and a second entity. For example, a data steward may provide the entity resolution request to help determine whether the entities are the same or different.

MULTI-SCALE SELF-LEARNING

Resumen de: WO2025175190A1

Methods and systems for generating embeddings using a multi-scale machine learning model (also referred to as an "encoder model") and performing various processes (e.g., classification) based on those embeddings are disclosed. Additionally disclosed are contrastive learning methods that can be used to train machine learning models according to embodiments. A data sequence can be separated into one or more sets of data subsequences based on window lengths (e.g., "long", "medium", and "short" window lengths). The machine learning model, comprising a sequence module and one or more window modules, can process the data sequence and the one or more sets of data subsequence to generate a set of partial sequence embeddings and one or more sets of partial window embeddings (which can be generated, in part, using the set of partial sequence embeddings). The one or more sets of window embeddings can be combined to produce an output embedding.

Method, System, And Apparatus For Creating Knowledge Graph In Industrial Field

Resumen de: US2025265479A1

Various embodiments of the teachings herein include a method for creating a knowledge graph in the industrial field. An example includes: obtaining unstructured data from a first source in a sub-field of the industrial field, with knowledge annotations; performing machine learning on the unstructured data to generate a first model adapted to extract knowledge; extracting knowledge from second unstructured data provided by the first source based on the first model, without knowledge annotations; obtaining first structured data and first semi-structured data from a second source in a second sub-field; extracting second knowledge from the first structured data; extracting third knowledge from the first semi-structured data; and building a knowledge graph integrating the first and second sub-field based on the first, second, and third knowledge, represented in the form of triples.

METHOD AND APPARATUS FOR SUPPORT OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML)-BASED POSITIONING

Resumen de: WO2025174154A1

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. According to various examples of the present disclosure, there is provided a location management function (LMF) entity configured to: subscribe to or request artificial intelligence/machine learning (AI/ML) -related services from a network data analytics function (NWDAF) entity in relation to an AI/ML model for determining positioning of a user equipment (UE); and receive, from the NWDAF entity, an indication that training has been performed for the AI/ML model.

EPISODIC OFF-POLICY EVALUATION OF JOINT ACTIONS FOR A MACHINE-LEARNED POLICY FOR AN ONLINE SYSTEM

Resumen de: US2025265478A1

An off-policy evaluation system performs episodic off-policy evaluations to perform off-policy evaluation (OPE) for multiple, joint episodes. For a single episode, a first machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. For a second episode, a second machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. The second machine learning model is evaluated by determining an importance weight for the first model and the second model to determine the inverse propensity score of the second machine learning model.

SYSTEMS AND METHODS FOR INVENTORY MANAGEMENT AND OPTIMIZATION

Resumen de: US2025265546A1

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed in inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

SYSTEMS AND METHODS FOR MODIFYING A DATABASE OF CONTENT RESOURCES USING MACHINE LEARNING

Resumen de: US2025265451A1

Methods and systems for modifying a database of creative content resources using machine learning models. In some aspects, a system may be used to generate new resources and/or modify a subset of resources of the database. The system accesses the database and obtains data indicative of elements and (2) structural specifications for each resource. The system obtains and inputs (1) a user prompt for generating a new creative content resource and (2) a set of rules indicative of standardized assets and structural specifications into a machine learning model to obtain the new creative content resource. The system obtains an indication for replacing a recurring asset included in the new creative content resource with a replacement asset and replaces the recurring asset with the replacement asset in each creative content resource of a subset of creative content resources from the database.

AUTOMATED ARCHITECTURAL SPECIFICATION GENERATION AND HARDWARE IDENTIFICATION

Resumen de: US2025265880A1

A method according to one embodiment includes determining, by a server, a location of a door in an architectural drawing and a room function of a room secured by the door based on an analysis of the architectural drawing, determining, by the server, proper access control hardware to be installed on the door based on the room function, a category of access control hardware, and a predictive machine learning model associated with the category of access control hardware, and generating, by the server, a specification based on the determined proper access control hardware.

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR GENERATING A MACHINE LEARNING MODEL BASED ON ANOMALY NODES OF A GRAPH

Resumen de: WO2024081350A1

Provided are systems that include at least one processor to receive a dataset comprising a set of labeled anomaly nodes, a set of unlabeled anomaly nodes, and a set of normal nodes, randomly sample a node to provide a set of randomly sampled nodes, generate a plurality of new nodes based on the set of labeled anomaly nodes and the set of randomly sampled nodes, combine the plurality of new nodes with the set of labeled anomaly nodes to provide a combined set of labeled anomaly nodes, and train a machine learning model based on an embedding of each labeled anomaly node in the combined set of labeled anomaly nodes, a center of the combined set of labeled anomaly nodes in an embedding space, and a center of the set of normal nodes in the embedding space. Methods and computer program products are also disclosed.

METHOD AND APPARATUS FOR MONITORING MODEL IN BEAM MANAGEMENT BY USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Resumen de: EP4604410A1

Provided are a method and apparatus for monitoring a model in beam management by using artificial intelligence and machine learning. The method may include: in relation to a reference signal configured for a terminal, receiving second reference signal resource set configuration information of the reference signal for monitoring an AI/ML model; on the basis of the second reference signal resource set configuration information, measuring signal strength or signal quality for the reference signal; and reporting the performance result of the AI/ML model by comparing a measured value of the reference signal with a predicted value of the reference signal inferred via the AI/ML model.

METHOD FOR TRAINING A MACHINE LEARNING MODEL FOR DETECTING ROAD USERS IN TRAFFIC SITUATIONS

Resumen de: EP4604078A1

Gemäß verschiedenen Ausführungsformen wird ein Verfahren zum Trainieren eines maschinellen Lernmodells (108, 200) zum Detektieren von Verkehrsteilnehmern in Verkehrssituationen bereitgestellt, aufweisend, für jeden mehrerer aus Sicht eines Fahrzeugs (101) in einer jeweilige Verkehrssituation zu detektierenden Verkehrsteilnehmer in einem Trainingsdatensatz, Ermitteln eines Maßes für das Risiko eines Unfalls des Fahrzeugs (101) mit dem Verkehrsteilnehmer in der jeweiligen Verkehrssituation, Durchführen einer Detektion von Verkehrsteilnehmern in dem Trainingsdatensatz durch das maschinelle Lernmodell, Ermitteln eines Gesamtverlusts der durch das maschinelle Lernmodell durchgeführten Detektion, der für jeden der zu detektierenden Verkehrsteilnehmer einen Detektions-Verlust enthält, wobei der Detektions-Verlust im Gesamtverlust abhängig von einem Wert gewichtet wird, der umso größer ist, je höher das für den Verkehrsteilnehmer ermittelte Maß für das Risiko eines Unfalls des Fahrzeugs (101) mit dem Verkehrsteilnehmer ist und Anpassen des maschinellen Lernmodells zum Reduzieren des Gesamtverlusts.

TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY

Nº publicación: EP4602531A1 20/08/2025

Solicitante:

QUALCOMM INC [US]
QUALCOMM INCORPORATED

Resumen de: CN119895449A

Methods, systems, and devices for wireless communication are described. A machine learning server may generate a set of low-dimensional parameters representing training data for the machine learning server, the training data associated with one or more communication environments or one or more channel environments, or a combination thereof. The machine learning server may receive, from one or more devices within a communication environment or a channel environment or both, a set of low-dimensional parameters representing test data associated with the communication environment or the channel environment or both. The machine learning server may generate a reproducibility metric according to a correlation between the set of parameters representing the training data and the set of parameters representing the test data. The machine learning server may send a message indicating the reproducibility metric to the one or more devices, and the one or more devices may perform a communication procedure based on the reproducibility metric.