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APPARATUS AND METHODS FOR GENERATING STRUCTURED DATA OUTPUTS

Resumen de: US20260244949A1

0000 Apparatus for generating structured data outputs and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive entity data associated with an entity, the entity data including projection data and location-based data, determine at least a selection criterion as a function of the entity data, receive from a data repository a plurality of metrics as a function of the at least a selection criterion, select at least an output parameter by applying the at least a selection criterion to a plurality of output parameters, as a function of the plurality of metrics, and synthesize, using an output generation machine-learning model trained on output generation training data, a structured data output as a function of the at least an output parameter, wherein the structured data output includes a plurality of event handler graphics.

CONTRASTIVE EXPLANATION APPROACH FOR MACHINE LEARNING BIAS DETECTION AND MITIGATION

Resumen de: US20260244983A1

Various embodiments of the present disclosure provide a contrastive explanation approach for machine learning bias detection and mitigation that improves the functionality of a computer in various aspects. The techniques comprise receiving a target identifier, a feature vector, a prediction, and a target bias feature; determining a comparison feature vector; determining a first contribution score for a first feature, determining a subset of features from the first set of features, determining a set of divergent features, determining a bias indicator, and initiating a computing action.

DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS USING CONTAINER ORCHETRATION SYSTEM

Resumen de: US20260244981A1

An illustrative embodiment provides a computer-implemented method. The method comprises using a processor set to create a headless service and a number of pods for a container orchestration system. Each pod from the number of pods comprises a number of containers for performing tasks. The processor set transfers a set of training data from a cloud object storage service to the number of pods from the container orchestration system. The processor set trains a machine learning model using the set of training data. The machine learning model is trained in a distributed manner using the headless service and the number of pods for the container orchestration system, and the container orchestration system divides training task for the machine learning model into a number of portions of the training task and each pod from the number of pods performs a portion of the training task to train the machine learning model.

MACHINE LEARNING MODEL MINIATURIZATION AND DEPLOYMENT

Resumen de: US20260244925A1

Devices, methods, and systems for automated machine learning model miniaturization and deployment are described herein. One method includes determining device specifications for a number of devices, selecting a machine learning model for the number of devices based on a function to be performed by the number of devices, selecting a miniaturization model for the machine learning model based on the function, selecting configuration settings for the miniaturization model for the number of devices, generating corresponding miniaturized machine learning models for the number of devices utilizing the selected configuration settings, and deploying the corresponding miniaturized machine learning models to each the number of devices based on the function and the device specifications associated with the number of devices.

LARGE SCALE DATA MODELING USING MACHINE LEARNING

Resumen de: US20260245105A1

0000 Examples described herein provide a computer-implemented method for large-scale data modeling using machine learning. The method includes receiving migration data from multiple countries. The method further includes integrating the migration data into a multi-modular machine learning model by: performing agent-based modeling of the migration data, performing network analysis on the migration data, and performing labor market analysis on the migration data. The method further includes performing web scraping on online resources to extract real-time or near-real-time migration-related information. The method further includes performing multi-group confirmatory factor analysis on the multi-modular machine learning model to identify underlying constructs behind how different countries shape foreign policy and migration quotas. The method further includes generating real-time suggestions for policymakers to optimize migration policies based on the multi-modular machine learning model and the real-time or near-real-time migration-related information.

Configuring Applications Based On A User'S Wakefulness State

Resumen de: US20260240490A1

0000 Techniques for configuring one or more applications based on a detected wakefulness state of a user are disclosed. A system trains and applies a machine learning model to wakefulness data to compute a wakefulness state of a user. The system obtains the wakefulness data from wearable devices worn by the user and environmental devices in a user's environment. The system configures applications and/or devices based on the computed wakefulness state of the user. The system configures the ability of devices or applications to generate visual, audible, or tactile notifications in response to determining that a user is awake or asleep.

METHOD FOR OPERATING A BEAM DEVICE, COMPUTER PROGRAM AND BEAM DEVICE FOR CARRYING OUT THE METHOD AS WELL AS A METHOD FOR GENERATING A TRAINING DATA SET AND A METHOD FOR TRAINING A MACHINE LEARNING MODEL

Resumen de: US20260246526A1

The system described herein relates to operating a beam device for obtaining information about an object. Moreover, the invention relates to a computer program product having a program code, which, when executed, controls the beam device in such a way that the method for operating the beam device is carried out. Additionally, the invention relates to a method for generating a training data set for a processing unit and/or for a machine learning model. Furthermore, the invention relates to a method for training a machine learning model of a beam device. The processing unit determines which machine learning model of a plurality of machine learning models is to be used for determining control values of control parameters. The control values of the control parameters are used to operate the control unit for generating the information about the object.

Automated analytical content generation using large language models

Resumen de: AU2025271014A1

Aspects of the present disclosure relate to automated analytical content generation. Embodiments include receiving data from one or more data sources. Embodiments further include extracting trends from the data using a heuristic algorithm. Embodiments further include providing an input based on the extracted trends to a generative machine learning model that has been configured to generate content based on extracted trends. Embodiments further include receiving, from the generative machine learning model based on the input, content that represents the extracted trends. Embodiments further include displaying the content via a user interface. ov o v RECEIVE DATA FROM ONE OR MORE DATA SOURCES EXTRACT TRENDS FROM THE DATA USING A HEURISTIC ALGORITHM PROVIDE AN INPUT BASED ON THE EXTRACTED TRENDS TO A GENERATIVE MACHINE LEARNING MODEL THAT HAS BEEN CONFIGURED TO GENERATE CONTENT BASED ON EXTRACTED RECEIVE, FROM THE GENERATIVE MACHINE LEARNING MODEL BASED ON THE INPUT, CONTENT THAT REPRESENTS THE EXTRACTED DISPLAY THE CONTENT VIA A USER INTERFACE RECEIVE DATA FROM ONE OR MORE DATA SOURCES ov o v

EFFICIENTLY UPDATABLE MACHINE LEARNING

Resumen de: US20260244996A1

0000 A method of generating machine learning predictions by an efficiently updatable ensemble of machine learning models includes identifying, in response to an inference request, one or more machine learning models of the ensemble that are available for generating a prediction. An aggregated prediction is generated in response to the inference request. The aggregated prediction aggregates individual predictions generated by the one or more machine leaning models of the ensemble identified as available to generate a prediction. Responsive to determining that less than all of the ensemble of machine learning models are available, a performance guarantee based on the individual predictions is generated. The aggregated prediction is output in response to the performance guarantee satisfying a predetermined threshold.

PROGRAMMABLE LOGIC CONTROLLER, INFERENCE EXECUTION SYSTEM, INFERENCE EXECUTION METHOD, AND RECORDING MEDIUM

Resumen de: US20260244181A1

0000 A programmable logic controller includes a control application to generate execution instruction information indicating an instruction to execute an inference, a plurality of inference applications each to execute the inference and be capable of generating an inference result, and a machine learning platform to identify, in response to the execution instruction information transmitted from the control application, an inference application satisfying a predetermined rule from the plurality of inference applications to cause the identified inference application to execute the inference, and transmit an inference result generated by the identified inference application to the control application.

RANKING AND MARKING MESSAGES IN A GROUP-BASED COMMUNICATION SYSTEM USING MACHINE LEARNING TECHNIQUES

Resumen de: US20260244950A1

Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, a user may save posts for later (e.g., to reply to a message at a later time, to complete a task associated with a message at a later time). The system may use a machine learning model to determine to automatically mark a post for later for a user, for example, based on a set of features including at least a semantic embedding of the post. Additionally, or alternatively, the system may use a machine learning model to determine an order for displaying items (e.g., posts, reminders, files) within a user view (e.g., a later tab, a drafts tab, a threads tab, a files tab) for a user via a user interface. The system may update one or more machine learning models based on how users interact with the posts, user views, or both.

STATEFUL TRAINING OF MACHINE LEARNING MODELS IN WIRELESS NETWORKING

Resumen de: US20260244985A1

There is provided a user equipment apparatus that includes at least one processor and at least one memory storing instructions. The instructions, when executed by the at least one processor, cause the user equipment apparatus at least to: access a usable machine learning (ML) model; receive, from a network apparatus, a freeze-to-adaptive ratio value; determine, based on the freeze-to-adaptive ratio, a frozen portion of the current ML model to not train and an adaptive portion of the usable ML model to train; access a performance measure for the usable ML model; retrain the adaptive portion of the usable ML model to provide a retrained ML model; determine a performance measure for the retrained ML model; and select one of the retrained ML model or the usable ML model based on the performance measure of the usable ML model and the performance measure of the retrained ML model.WO

TIME-CONTINUOUS ANNOTATION PROCESSING SYSTEM AND METHODS FOR USE THEREWITH

Resumen de: US20260244991A1

A time-continuous annotation processing system operates by: sending a segment of time-continuous audiovisual (A/V) data to a first plurality of client devices; receiving subjective time-continuous annotation data corresponding to the segment of time-continuous A/V data, the subjective time-continuous annotation data indicating time-continuous annotated values of a subjective parameter varying over a time period of the segment of time-continuous A/V data; determining, via a subjective gold standard analysis tool, when there is a single ordinal agreement between the subjective time-continuous annotation data received from the first plurality of client devices, and in response: generating, via the subjective gold standard analysis tool, a first ordinal time-continuous gold standard annotation based on the ordinal agreement between the subjective time-continuous annotation data received from the first plurality of client devices; and constructing, via an A/V training data set post processing tool, an annotated time-continuous A/V training dataset to include the first ordinal time-continuous gold standard annotation and the segment of time-continuous A/V data; determining, via the subjective gold standard analysis tool, when there are a plurality of differing ordinal agreements of the subjective time-continuous annotation data received from the first plurality of client devices, and in response: generating, via the subjective gold standard analysis tool, a first plurality of diffe

MODEL FILE LOADING METHOD AND SYSTEM, COMPUTER DEVICE, AND STORAGE MEDIUM

Resumen de: WO2026170718A1

The present application relates to the technical field of machine learning, and discloses a model file loading method and system, a computer device, and a storage medium. The method comprises: when an inference service starts, acquiring an update request; if it is detected that the update request carries a local storage path of a model file, using the local storage path as a target storage volume of a target scheduling unit; mounting the target storage volume in an init container of the target scheduling unit, and generating a target mount item of the init container; generating response information on the basis of the target storage volume and the target mount item, wherein the response information is used for updating the target scheduling unit to establish a communication link between the updated target scheduling unit and a local directory; and sending the response information to a first slave node, so that the first slave node updates the target scheduling unit, and loads the model file on the basis of the communication link. The present application can solve problems such as high transmission delay, redundant occupation of hard disk resources, and namespace limitations during model file loading.

AUGMENTED-INTELLIGENCE BASED PERSONALIZED VIRTUAL PATIENT SIMULATOR

Resumen de: US20260245740A1

A system, comprising at least one cardiac sensor adapted to measure a hemodynamic profile of a patient; and a computing node operatively coupled to the cardiac sensor and configured to perform the steps of reading a hemodynamic profile of a patient; based on the hemodynamic profile, tuning a plurality of parameters of a cardiovascular model to create a digital twin of the patient; augmenting the hemodynamic profile of the patient with at least one parameter generated from the digital twin; providing the augmented hemodynamic profile to a pretrained machine learning model and receiving therefrom a patient profile; and outputting the patient profile for clinical decision support.

METHODS AND SYSTEMS FOR PREDICTING SEQUENTIAL ACTIONS USING DEEP GENERATIVE MODELS

Resumen de: US20260244936A1

Methods and systems for implementing an action prediction framework associated with a user are described. An action prediction model generates a plurality of synthetic action sequences and corresponding synthetic end states for the user, based on a sequence of historical actions and corresponding historical end states. A plurality of pathways are projected through the plurality of synthetic action sequences, for assisting the user in arriving at a desired end state. During a training phase, a machine learning model is trained to learn a plurality of implicit features related to user behavior, for generating the synthetic action sequences and pathways. During an inference phase, the action prediction framework identifies waypoints associated with recommended user or system actions for assisting the user in reaching the end state more efficiently. The disclosed methods and systems may enable robust and efficient sequential action prediction while minimizing resource consumption associated with computationally expensive foundation models.

SYSTEM AND METHOD FOR COLD-START MACHINE LEARNING MODELS

Resumen de: US20260245109A1

Provided are computer-implemented methods and systems for generating a prediction using a cold-start model, including: providing at least one data set from at least one data source, the at least one data set comprising historical transactional data for a first plurality of individuals, and contextual data for a second plurality of individuals, the first plurality of individuals comprising a subset of the second plurality of individuals; determining at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generating a cold start prediction comprising at least one candidate identifier; generating at least one attribution value based on the at least one feature of the at least one activity; and generating an explainable prediction. Also provided are computer-implemented methods and systems for generating a cold-start model.

A SYSTEM AND METHODS FOR TRAINING AI-MODELS FOR ELECTRICAL AND ELECTRONIC SYSTEM DESIGNING

Resumen de: WO2026172366A1

The present invention relates to methods and systems for training machine learning and artificial intelligence models for electronic and electrical system design. A first method includes receiving input data associated with electronic and electrical systems, extracting design parameters, configuring a baseline machine learning model based on a first design configuration, and iteratively refining the model by generating candidate design configurations, computing loss metrics, and determining a second design configuration exceeding a predefined threshold. An optimization module generates an optimization score defining improvement, and the model is updated when the score exceeds a predefined optimization threshold. A second method trains artificial intelligence models using multi-modal datasets including design, simulation, manufacturing, test, and reliability data, preprocessing the datasets, defining training tasks, training models using multi-task learning objectives, and performing transfer learning for specific application domains. A system provides an integrated platform with data registration, dataset ingestion, model selection, training optimization, and model registry interfaces for deploying AI models to electronic design tools.

SYSTEMS AND METHODS FOR PREDICTING FORMATION PROPERTY CHANGES AND USES THEREOF

Resumen de: WO2026170381A1

Machine learning methods may be used in formation stimulation. Such methods may include, for example: providing a training dataset comprising injection training parameters and formation training parameters and formation property alteration training parameters, wherein the formation property alteration training parameters are provided based on a first formation simulation of a first geological formation; training a machine-learning (ML) algorithm, using the training dataset to provide a trained ML model that predicts formation property alteration parameters.

RNA SIGNATURE TEST FOR DIAGNOSIS OF THORACIC AORTIC ANEURYSM DISEASE

Resumen de: WO2026174031A1

A method for identifying RNA patterns in diagnosis and treatment of thoracic aortic aneurysm disease includes performing RNA sequencing on samples; analyzing the RNA sequencing data with the performance of differential gene expression analysis focusing on differentially regulated pathways to reveal complex regulatory mechanisms; developing a machine learning model to integrate pathway-level interactions; and combining pathway- specific analysis of the RNA patterns with the machine learning model to generate predictions identifying patients likely to be susceptible to thoracic aortic aneurysm disease.

Apparatus and methods for generating data intelligence

Resumen de: US20260244980A1

Apparatus for generating data intelligence and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive digital records, each of which includes a plurality of reference attributes, receive query data including a plurality of query attributes, identify one or more relevant digital records by matching one or more query attributes with one or more reference attributes, generate, using an output generation machine-learning model, one or more output data structures as a function of the one or more relevant digital records, calculate an intelligence metric as a function of each output data structure of the one or more output data structures, wherein the intelligence metric includes an estimated likelihood of positive outcome, and select at least a recommended output data structure as a function of the one or more intelligence metrics.

AUTOMATED WORKFLOW FOR SOCIAL DETERMINANTS OF HEALTH DATA MANAGEMENT

Resumen de: US20260245690A1

0000 The technology disclosed teaches a system and methods for generating a personalized care plan based on social determinants of health. The method further comprises pre-processing unstructured patient data corresponding to a patient to generate structured patient data and processing the structured patient data using a machine learning model, wherein the machine learning model is pre-trained to generate output data including at least one of a barrier to care, a disease risk factor, a discrepancy in the structured patient data, a risk score, and a recommended SDoH intervention. The method further includes creating a personalized care plan for the patient, based on the output data, including a personalized resource recommendation, wherein the personalized resource recommendation identifies an action plan responsive to an identified barrier to care.

METHOD AND SYSTEM FOR PREDICTING EIMERIA MAXIMA INFECTION OR PREVALENCE IN ANIMALS

Resumen de: WO2026171696A1

The method (900) for predicting Eimeria maxima infection or prevalence in animals comprises the steps of: - providing (905) a plurality of features and a plurality of empirically measured biomarker data; - training (910), using as input the plurality of features and historical biomarker data, a machine learning model to associate predetermined labels indicating whether the set of animals have Eimeria maxima infection or prevalence to said input; - receiving (915), measured biomarker data corresponding to one or more animals, wherein the measured biomarker data indicates blood concentrations of one or more biomarkers in the one or more animals; providing (920), the biomarker data as input to the trained machine learning model; receiving (925) at least one predetermined label indicating whether the one or more animals are positive for Eimeria maxima infection or prevalence or susceptible to mMX prevalence; and providing (930), upon a computer interface, at least one predetermined label received.

ORTHOPEDIC SURGICAL PLANNING SYSTEM FOR AUTONOMOUS ROBOTIC SURGERY

Resumen de: WO2026173966A1

Systems and methods may provide orthopedic surgical planning for autonomous robotic surgery. For example, a method can include displaying a user interface including a surgical planning program. The method can include receiving patient information at the surgical planning program, the patient information indicating that a patient has a sagittal plane knee malalignment. The method can include generating, using a machine learning trained model of the surgical planning program, a sagittal plane knee surgical plan based on the patient information, the sagittal plane knee surgical plan being specific to the sagittal plane knee malalignment and including a resection recommendation. The method can include displaying the sagittal plane knee surgical plan on the user interface for use with a robotic arm.

A METHOD FOR DESIGNING AND OPTIMIZING AN ELECTRICAL OR ELECTRONIC SYSTEM

Nº publicación: WO2026172367A1 20/08/2026

Solicitante:

YERUVA ARAVIND RAJ [IN]
YERUVA, Aravind Raj

Resumen de: WO2026172367A1

The present invention relates to methods for designing and optimizing electrical or electronic systems using machine learning. The method comprises receiving input data associated with a desired electrical and electronic system including a design configuration and at least one performance target, extracting a plurality of design parameters and design rules from the input data, initializing at least one machine learning model based on the extracted design parameters, determining one or more candidate design configurations by the machine learning model, simulating each candidate design configuration to generate simulation results associated with performance metrics, evaluating the simulation results against design rules to determine optimization metrics, and iteratively refining the extracted design parameters and model weights in response to the optimization metrics until a predetermined number of iteration cycles is completed. The candidate design configuration of the current iteration cycle is selected as the desired design configuration. The method enables automated multi-disciplinary design optimization across electrical, mechanical, thermal, and manufacturing engineering domains.