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GRAPH DATA REPRESENTATION SYSTEMS AND METHODS FOR ELIGIBILITY DETERMINATION AND/OR MONITORING

Resumen de: US2025328787A1

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing an inclusion of an entity for an event. In accordance with one embodiment, a method is provided that includes: determining whether a graph representation data object comprises an inbound edge connecting an entity node representing the entity with an event node representing the event; and responsive to determining the graph representation data object comprises the inbound edge, performing an action involving inclusion of the entity for the event. The inbound edge is generated via an inbound edge generator machine learning model configured to: traverse entity and/or inclusion edges of the graph representation data object to identify inclusion and entity edges connected, generate an entity score data object for the entity based at least in part on the inclusion edges, and responsive to the data object satisfying a threshold, generate the inbound edge.

IDENTIFICATION AND MITIGATION OF PERFORMANCE ISSUES OF ENTITIES AND AUTOMATED COMPONENTS

Resumen de: US2025328783A1

Systems and methods are described for identifying and resolving performance issues of automated components. The automated components are segmented into groups by applying a K-means clustering algorithm thereto based on segmentation feature values respectively associated therewith, wherein an initial set of centroids for the K-means clustering algorithm is selected by applying a set of context rules to the automated components. Then, for each group, a performance ranking is generated based at least on a set of performance feature values associated with each of the automated components in the group and a feature importance value for each of the performance features. The feature importance values are determined by training a machine learning based classification model to classify automated components into each of the groups, wherein the training is performed based on the respective performance feature values of the automated components and the respective groups to which they were assigned.

SYSTEMS AND METHODS FOR BATTERY PERFORMANCE PREDICTION

Resumen de: US2025328780A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery performance prediction. One of the methods includes actions of receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property of at least two battery tests. Each battery test includes applying pulses on the battery cell during a battery cycle. The battery test data is provided as input to a machine learning system to predict battery cell performance. The machine learning system includes a machine learning model that has been trained using training data includes test data of battery cells that reached respective end of life (EOL) cycles. In response, a prediction result for the battery cell is automatically generated by the machine learning model. The prediction result indicates an EOL cycle of the battery cell. An action is taken based on the prediction result.

MULTISTAGE FEED RANKING SYSTEM WITH METHODOLOGY PROVIDING SCALABLE MULTI-OBJECTIVE MODEL APPROXIMATION

Resumen de: US2025328821A1

Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model-that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system-with the more lightweight, single objective model-that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

Benchmarking Algorithms for Data Quality Monitoring

Resumen de: US2025328505A1

In a general aspect, benchmarking for data quality monitoring is described. In some embodiments, a system identifies a base data set to be used as input to a machine learning (ML) model. The system generates a modified base data set by causing synthetic anomaly injection operations to be performed on data of the base data set. The system causes the ML model to run, using the base data set as input, to determine a first output of the ML model, and to run, using the modified base data set as input, to determine a second output of the ML model. The system determines a set of performance metrics representing performance of the ML model at detecting data anomalies and outputs a representation of the set of performance metrics.

ENHANCING ARTIFICIAL INTELLIGENCE CHATBOTS WITH SEARCH FUNCTIONALITY

Resumen de: US2025328535A1

Methods, systems, and apparatus, including computer-readable media, for enhancing artificial intelligence chatbots with search functionality. In some implementations, a system stores a search index for data sets, where the search index describes data objects of the data sets and values for the data objects in the data sets. The system receives a user prompt to a chatbot and searches for data objects and values that are relevant to the user prompt, including using the search index to search for data objects and values of one or more data sets that the chatbot is configured to access. The system uses one or more results obtained using the search index to generate a chatbot response to the user prompt, including providing the one or more results to an artificial intelligence and/or machine learning (AI/ML) model. The system provides the chatbot response as a response to the user prompt.

Computer-Implemented Method and System for Search-Based Behavior Planning for an Ego Vehicle

Resumen de: US2025326404A1

A computer-implemented method is for search-based behavior planning for an ego vehicle in a traffic scenario involving at least one further participant. A scenario representation of the traffic scenario is generated based on aggregated scenario-specific information in order to generate, using a deep learning based planning component, a tree structure including multiple sequences of scenario representations for N>1 consecutive planning time increments i, i∈{0, . . . , N}. At least one one-shot prediction is also generated for at least one possible development of the traffic scenario for M>1 consecutive prediction time increments in order to associate the individual sequences of the tree structure with at least one such one-shot prediction. The subsequent scenario representations are generated in individual planning time increments i, i∈{1, . . . , N}, each based on at least one such one-shot prediction.

AGENT PROMPT GENERATION USING TOOL DESCRIPTION EMBEDDINGS

Resumen de: WO2025221523A1

Methods, systems, and apparatuses include receiving, via a conversational interface, user input from a user of an online system. A user input embedding is generated for the user input. A vector store is retrieved including tool description embeddings. A similarity search is performed using the user input embedding and the tool description embeddings. A set of tool descriptions is determined using results of the similarity search. A prompt is generated using the set of tool descriptions and the user input. Machine learning agents are applied to the prompt to cause the machine learning agents to use tools associated with the set of tool descriptions. A response to the prompt is received, from the machine learning agents, in response to the machine learning agents using the tools. An output to the user input based on the response is sent, via the conversational interface, to the user of the online system.

BANDWIDTH PREDICTION USING MACHINE LEARNING

Resumen de: WO2025221398A1

Systems, methods, and apparatus, including computer-readable media, for bandwidth prediction using machine learning. In some implementations, a device detects a series of requests for streaming media content. The device generates a set of feature values based on times that the requests for the streaming media content were issued. The device provides the set of feature values as input to a machine learning model that has been trained to predict a time that a future request for media content will be issued. The device receives output of the machine learning model that indicates a predicted time of a subsequent request for the streaming media content or a predicted time to request bandwidth allocation for the subsequent request. Based on the output generated by the machine learning model, the device sends a bandwidth allocation request to allocate bandwidth to transmit data in a wireless network.

MACHINE LEARNING MODEL ADMINISTRATION AND OPTIMIZATION

Nº publicación: EP4634837A1 22/10/2025

Solicitante:

C3 AI INC [US]
C3.ai, Inc

Resumen de: US2025190475A1

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.