Alerta

MANAGEMENT OF MACHINE LEARNING ENTITY INFERENCE EMULATION IN A CELLULAR NETWORK

Resumen de: WO2024211680A1

A device, a method, a system and one or more computer-readable media. A first example device is to host a management service (MnS) producer for a wireless cellular network. One or more processors of the first device are to receive, from an MnS consumer, a request to perform AI/ML emulation in one or more available machine learning (ML) emulation environments; and send to the MnS consumer one or more instances of an information object class (IOC) associated with the process of the AI/ML emulation. A second example device is to host an MnS consumer. One or more processors of the second device are to send, to an MnS producer, a request to perform AI/ML emulation in one or more available machine learning (ML) emulation environments; and receive, from the MnS producer one or more instances of an information object class (IOC) associated with the process of the AI/ML emulation.

BIOMASS USE ASSISTANCE DEVICE, METHOD, AND PROGRAM

Resumen de: EP4693123A1

A biomass utilization support device: acquires biomass information relating to a biobased material and product information for each of a plurality of products including information about materials configuring the products; uses a machine learning model, which has been trained to estimate appropriate values for replacement amounts in a case of replacing a portion of the materials configuring the products with the biobased material, and the acquired biomass information and product information to estimate the appropriate values for each of the plurality of products; calculates, for each of the plurality of products, environmental impact indicators in a case in which a portion of the materials configuring the products has been replaced with the biobased material at the replacement amounts represented by the estimated appropriate values; and outputs support information listing the estimated appropriate values and the calculated environmental impact indicators.

SYSTEM FOR RESOURCE ALLOCATION IN A HYBRID DISTRIBUTED COMPUTATIONAL ENVIRONMENT

Resumen de: EP4693046A1

Systems, computer program products, and methods are described for resource allocation in a hybrid distributed computational environment. An example system segments a received task into multiple sub-tasks. Upon partitioning the task, each sub-task is assigned to the appropriate computational resource (e.g., CPU, GPU, or QPU), enabling parallel execution of multiple sub-tasks. Both task partitioning and computational resource determination is determined using a machine learning model. Additionally, the machine learning model may continuously monitor the execution of each sub-task by receiving resource utilization information and performance metrics associated with the execution of each sub-task. The resource utilization information and performance metrics may then be used to update the machine learning model.

MACHINE LEARNING MODEL FEATURE SELECTION IN A COMMUNICATION NETWORK

Resumen de: CN120898407A

Embodiments of the present disclosure provide machine learning model feature selection in a communication network. The method includes, in response to a feature selection trigger of a first machine learning model, determining a target input feature set for an analysis task based on contextual information related to the analysis task, the first machine learning model being currently provisioned for performing the analysis task based on a current input feature set, the current input feature set is different from the target input feature set; and causing a second machine learning model to be provisioned to perform an analysis task based on the determined set of target input features. In this manner, the machine learning model may be supplied with an optimized set of input features that is applicable to the current network context and provides an acceptable level of model performance.

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

Resumen de: WO2026029823A1

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.

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

Resumen de: 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.

DATA AGGREGATION AND MODEL TRAINING BASED ON SPARSE DATASETS

Resumen de: WO2026030336A1

A system may access a set of training data and determine a timeframe associated with a positively labeled data item of the training data. A system may generate at least two new positively labeled data items based on the positively labeled data item to generate augmented training data. A system may train a machine learning model by applying the augmented training data as input to a machine learning model, and modifying a weight of the machine learning model.

QUANTUM-SECURE MULTIPARTY DEEP LEARNING

Resumen de: WO2026030526A1

Quantum-secure, multiparty computation enables the joint evaluation of multivariate functions across distributed users while ensuring the privacy of their local inputs. It uses a linear algebra engine that leverages the quantum nature of light for information-theoretically secure multiparty inference using telecommunication components. This linear algebra engine can perform deep learning inference with rigorous upper bounds on the information leakage of both the deep learning model weights and the client's data, enabling double-blind operations. Applied to the MNIST classification task, it performs with classification accuracies exceeding 95% and a leakage of less than 0.1 bit per weight and data symbols. This leakage is an order of magnitude below the minimum bit precision for accurate deep learning using state-of-the- art quantization techniques. Our quantum-secure, multiparty computation lays the foundation for practical quantum-secure computation and unlocks secure cloud deep learning.

DATA AGGREGATION AND MODEL TRAINING BASED ON SPARSE DATASETS

Resumen de: US20260037871A1

A system may access a set of training data and determine a timeframe associated with a positively labeled data item of the training data. A system may generate at least two new positively labeled data items based on the positively labeled data item to generate augmented training data. A system may train a machine learning model by applying the augmented training data as input to a machine learning model, and modifying a weight of the machine learning model.

Contrastive Explanations For Machine Learning Forecasting Models

Resumen de: US20260037842A1

A Contrastive Forecasting Explanation (CFE) tool and technique provides a model-agnostic approach to forecasting explanation. The CFE tool uses an ML-based surrogate forecaster as a surrogate model. The surrogate forecaster includes a time series preprocessor, a simple concept generator, and an ML forecaster. The subsequent interpretation of the predictions of the time series forecaster is based on the behavior of the surrogate forecaster. The CFE tool interprets time series forecasts by identifying the specific temporal concepts impacting predictions and thus generates clear and reliable explanations regardless of model type. The simple concepts and predictions generated by the surrogate model are input into a perturbation-based explainer to produce feature attributions from the surrogate model. An attribution postprocessor aggregates the attributions into more coherent concepts to present a coherent, concise, and interpretable explanation.

Computer Systems and Methods for Generating Predictive Change Events

Resumen de: US20260037890A1

Based on receiving data defining a new data item for a construction project corresponding to a particular category of data items, a computing system (1) automatically: (i) predicts that a change event for the construction project is needed by inputting the new data item into a first machine learning model trained to predict a need for a change event from data items corresponding to certain categories of data items, including the particular category of the new data item, (ii) determines initial recommended data for the predicted change event, and (iii) determines additional data for the predicted change event corresponding to a particular class of additional data by inputting the initial recommended data for the predicted change event into a second machine learning model trained to predict one or more classes of additional data for a change event, and (2) automatically create a data item representing the predicted change event.

Detection of User Interface Imitation

Resumen de: 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

Resumen de: 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)

Resumen de: 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.

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

Resumen de: US20260037505A1

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.

MACHINE LEARNING-BASED MODIFICATION OF IMAGE CONTENT

Resumen de: 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

Resumen de: 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

Resumen de: 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.

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

Resumen de: 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.

Information Processing Method, Program, and Information Processing Device

Resumen de: 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.

CAPABILITIES AND SAFE PLUGINS

Resumen de: 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

Resumen de: 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

Resumen de: 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.

MACHINE LEARNING TECHNIQUES FOR DISCOVERING KEYS IN RELATIONAL DATASETS

Resumen de: 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).

SYSTEMS AND METHODS FOR PERSONALIZED GUIDANCE

Nº publicación: US20260030516A1 29/01/2026

Solicitante:

CAPITAL ONE SERVICES LLC [US]
Capital One Services, LLC

Resumen de: 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.