Machine learning

METHODS AND SYSTEMS FOR DATA COLLECTION FOR ARTIFICIAL INTELLIGENCE BASED POSITIONING MODEL TRAINING IN A WIRELESS NETWORK

Resumen de: WO2026035512A1

A network device (PRU, WTRU) may receive a request to collect data for artificial intelligence or machine learning (AI/ML) positioning model training, for example from a network data analytics function (NWDAF) and/or from a model training logical function (MTLF) (450b). The request may include an indication of an area of interest, a time window associated with the data for AI/ML positioning model training, a requested number of data samples of the data for AI/ML positioning model training, and/or a data source type of the data for AI/ML positioning model training. The network device may receive the data for AI/ML positioning model training and/or receive location data associated with the one or more WTRUs. The network device may send the location data and the data for AI/ML positioning model training to the NWDAF or the MTLF (485, 495).

MEMORY-CONSTRAINED ATTENTION IN MACHINE LEARNING MODELS

Resumen de: US20260044745A1

Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a machine learning model comprising a plurality of layers, and a set of input data for the machine learning model, are accessed. A combination of hyperparameters for the machine learning model is selected based on the set of input data, comprising selecting, for each respective layer of the plurality of layers, a respective cache size based on the input data. The machine learning model is deployed according to the combination of hyperparameters.

MULTIVARIABLE SERVICE TERMINATION RISK CLASSIFICATION USING MACHINE LEARNING

Resumen de: US20260044803A1

A method can include receiving input data comprising a plurality of features for a plurality of users. A method can including providing the input data to a risk prediction model configured to predict a termination likelihood for each user. In some implementations, the risk prediction model can be a random forest model. A method can include identifying, based on the predicted termination likelihood for each user, an at risk population including users with a termination risk above a threshold amount. A method can include determining, for each user of the at risk population, a profile type of a plurality of profile types. The profile type can describe certain attributes of the user. In some implementations, an end user can select a profile type. A method can include outputting members of the at risk population having the selected profile type.

AUTOMATED TRANSLATIONS FOR AUTONOMOUS CHAT AGENTS

Resumen de: US20260044690A1

Disclosed are various embodiments for automated translations for autonomous chat agents. A build service can send a translation request to a machine translation service, the translation request comprising training data in a first language and the translation request specifying a second language. The build service can then receive translated training data from the machine translation service, the translated training data having been translated from the training data into the second language. Next, the build service can create a translated workflow that comprises a translated machine learning model and a translated intent. Subsequently, the build service can add the translated training data to the translated workflow and train the translated machine learning model using the translated training data.

METHOD AND SYSTEM FOR USING ROBOTIC PROCESS AUTOMATION TO PROVIDE REAL-TIME CASE ASSISTANCE TO CLIENT SUPPORT PROFESSIONALS

Resumen de: US20260044798A1

A case assistant is provided to client support professionals, which utilizes robotic process automation (RPA) technologies to analyze large amounts of data related to historical client cases that are similar to current open cases, data related to skilled experts associated with similar client cases, and data related to business exceptions. Several processes are utilized to provide this data to client support professionals, including a document similarity finder that utilizes a vector data collector, a tokenizer, a stop word remover, a relevance finder, and a similarity finder, several of which utilize a variety of machine learning technologies. Additional processes include a skilled experts finder and a business exceptions finder.

TECHNIQUES FOR PROVIDING INTERACTIVE CLINICAL DECISION SUPPORT FOR DRUG DOSAGE REDUCTION

Resumen de: US20260045348A1

Examples described herein generally relate to recommending drug dosage reductions for a patient. A computer system may generate an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival. The system may receive periodic patient monitoring including at least one drug withdrawal scale score, anxiety scale score, and indicated side effect. The system may determine, using one or more machine learning algorithms, a revised glide path based on a data record for the patient, the at least the drug withdrawal scale score and the at least one anxiety scale score for the patient. The system may recommend at least one medication or therapy for the indicated side effect. The system may determine a prescription adjustment based on the revised glide path.

MACHINE LEARNING FOR VIDEO GAME HELP SESSIONS

Resumen de: US20260042011A1

The disclosed concepts relate to training a machine learning model to provide help sessions during a video game. For instance, prior video game data from help sessions provided by human users can be filtered to obtain training data. Then, a machine learning model can be trained using approaches such as imitation learning, reinforcement learning, and/or tuning of a generative model to perform help sessions. Then, the trained machine learning model can be employed at inference time to provide help sessions to video game players.

SHIP RECOURSE OPTIMIZATION

Resumen de: US20260043656A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.

MACHINE LEARNING FOR VIDEO GAME HELP SESSIONS

Resumen de: WO2026035326A1

The disclosed concepts relate to training a machine learning model to provide help sessions during a video game. For instance, prior video game data from help sessions provided by human users can be filtered to obtain training data. Then, a machine learning model can be trained using approaches such as imitation learning, reinforcement learning, and/or tuning of a generative model to perform help sessions. Then, the trained machine learning model can be employed at inference time to provide help sessions to video game players.

MEMORY-CONSTRAINED ATTENTION IN MACHINE LEARNING

Resumen de: WO2026035335A1

Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a machine learning model comprising a plurality of layers, and a set of input data for the machine learning model, are accessed. A combination of hyperparameters for the machine learning model is selected based on the set of input data, comprising selecting, for each respective layer of the plurality of layers, a respective cache size based on the input data. The machine learning model is deployed according to the combination of hyperparameters.

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.

LEARNING MODEL GENERATION DEVICE, INSPECTION VALUE PREDICTION DEVICE, LEARNING MODEL GENERATION METHOD, INSPECTION VALUE PREDICTION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM

Resumen de: EP4693331A1

This learning model generation device 10 is equipped with a learning model generation unit 11 which, when a function expressing a change in an inspection value obtained by inspecting a person is set, generates a learning model in which the inspection value is the explanatory variable and the parameter is the objective variable, by performing machine learning using inspection values of sample people and parameters of the function for the sample people as training data.

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.

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.

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.

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.

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.

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: 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.

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.

DATA AGGREGATION AND MODEL TRAINING BASED ON SPARSE DATASETS

Nº publicación: WO2026030336A1 05/02/2026

Solicitante:

DE QUILLACQ GONTRAN JEROME [US]
DE QUILLACQ, Gontran, Jerome

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.