Alerta

AI INFERENCING TO DETECT ANOMALIES IN IoT SYSTEMS

Resumen de: US20260244953A1

0000 Systems and methods are disclosed herein for monitoring an Internet of Things (IoT) platform. The systems and methods can obtain stack data, profile data, logs, or metrics that are associated with the IoT platform and extract features from each of the stack data, profile data, logs, or metrics. The extracted features from each of the stack data, profile data, logs, or metrics can be input into a separate machine learning model that produces outputs. The outputs can be input into an aggregator to determine whether the IoT platform is anomalous based on the outputs. When at least one output has an anomaly, the systems and methods can determine that the IoT platform is anomalous. The systems and methods can include false positive checks can be performed to ensure accuracy of the determination.

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.

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.

PREDICTING SUBJECTIVE RECOVERY FROM ACUTE EVENTS USING CONSUMER WEARABLES

Resumen de: US20260245733A1

0000 In an aspect, a method for predicting, for a subject, a recovery time from an acute or debilitating event is disclosed. The method may comprise (i) retrieving wearable sensor data from a first time period and a second time period. The first time period may be prior to the acute or debilitating event. The second time period may be after the acute or debilitating event. The method also may comprise (ii) determining the recovery time for the acute or debilitating event at least in part by processing said wearable sensor data from the first time period and the second time period with a trained machine learning algorithm.

SYSTEMS AND METHODS FOR IDENTITY GRAPH BASED FRAUD DETECTION

Resumen de: US20260245097A1

A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud. The method also includes performing, by the commerce platform system, a machine learning model analysis using the feature set and the transaction data to determine a likelihood that the transaction is fraudulent, and performing, by the commerce platforms system, the transaction when the likelihood that the transaction is fraudulent does not satisfy a transaction fraud threshold.

METHOD OF CELL-BASED MODEL CONFIGURATION SIGNALING

Resumen de: WO2026171953A1

The present invention disclosure provides systems and methods for efficiently managing machine learning (ML) operations in communication networks using cell- independent and cell-dependent identification types. ML IDs, encompassing ML condition IDs, model IDs, and dataset IDs, are mapped to these identifiers to enable dynamic adaptability and optimal performance. A mapping relation table is utilized to associate ML IDs with cell-specific and network-wide configurations, facilitating seamless operation across cell boundaries. The invention also includes techniques for periodic and non-periodic feedback-based performance monitoring and signaling flow for mapping relation updates, ensuring enhanced resource utilization and reduced signaling overhead. These configurations enable flexible ML operation management, supporting UE mobility and adaptive decision-making across varying network conditions.

OPTIMIZING ARTIFICIAL INTELLIGENCE GENERATED CODE ON THE COMPUTING CONTINUUM

Resumen de: WO2026173886A1

Systems and methods for optimizing artificial intelligence generated code on the computing continuum. An instruction code that instructs a machine learning model (MLM) can be modified (510) to obtain a modified instruction code that incorporates a dynamic control flow that limits processing of software code based on a target time period. One or more candidate codes based on the modified instruction code can be generated (520). One or more service paths within the dynamic control flow of the one or more candidate codes can be executed (530) to obtain an optimized generated code having detailed responses within a threshold for downstream tasks by asynchronously performing sub-tasks from the one or more service paths to optimal computing nodes.

MAIA Outcome Feedback Computing System

Resumen de: US20260245686A1

0000 MAIA Outcome Feedback Computing System provides users with a real-time, document guidance, interface to upload medical claims, in multiple different formats. Document classification is performed on the uploaded data to determine a document type. Claim features are identified and extracted specific to each contextual document type, forwarded to a pre-approval and feedback manager and used to perform semantic and keyword searches on knowledge databases specific to each document type. The claims, and relevant data are input into a machine learning model, trained to predict medical billing codes using medical claims and generate: a confidence score and results summary for each billing code. Based on a comparison of the confidence score to a threshold, one of a plurality of validation processes is performed on each billing code, results are output to a user. Validated codes may be automatically submitted to third-party insurance providers, monitored for denials, and automatically appealed.

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.

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.

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

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.

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

METHODS AND MECHANISMS FOR USING MACHINE LEARNING TO GENERATE DIAGNOSTIC REPORTS BASED ON ANOMALOUS DATA

Resumen de: WO2026173646A1

A system configured to detect, by a processing device, an anomaly that occurred during a manufacturing process performed by a substrate processing system. A set of prompts is generated based on the anomaly. Each prompt is correlated to specific data obtained from one or more datastores. For each prompt, a respective output of the first trained machine learning model is obtained and a structured prompt is generated based on respective outputs. The structured prompt is provided as input to a second trained machine learning model an output of the second trained machine learning model is obtained. The output of the second trained machine learning model comprising a diagnostic report associated with the anomaly.

SYSTEMS AND METHODS FOR TRAINING MACHINE LEARNING MODELS USING FORWARD VELOCITIES

Resumen de: WO2026173714A1

A computer system for labeling anomalous data for re-training a scoring machine-learning model is provided. The computer system includes a processor programmed to: receive transaction data associated with a plurality of declined transactions; apply a scoring model to the transaction data for the plurality of declined transactions; rank the plurality of declined transactions from low probability to high probability of fraud; apply a labeling model to the transaction data of a set of the plurality of declined transactions, the set including a batch of the declined transactions having higher probability scores assigned thereto; generate, using the labeling model, a precision percentage for the set of the plurality of declined transactions representing a ratio of the declined transactions labeled as fraud by the labeling model relative to the total number of declined transactions included in the set of declined transactions; and refine the precision percentage by examining subsets of the set.

AGENTIC DATA FABRIC WITH PREDICTIVE INTELLIGENCE

Resumen de: WO2026174258A1

Generating profiles that consolidate tenant data with interaction and transaction records from heterogeneous data sources in real time. Executing AI agents including an orchestration agent that orchestrates other agents. Providing a model interface layer that securely connects to an external machine learning model of a third-party agent while enforcing data governance rules, wherein the machine learning model can securely access customer data from the profiles of the multi-tenant platform under contextaware policies. Providing secure access to data from the profiles to the model through the model interface layer without exporting the data into a separate repository, such that the model processes live enterprise data in place. Receiving a predictive output derived from the exported data. Updating a profile by writing the predictive output as a new attribute of that profile, thereby enriching the profile with machine-generated insights in real time to create an enriched profile.

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

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.

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.

TRAINING METHOD AND ELECTRONIC DEVICE FOR QUANTUM MACHINE LEARNING

Resumen de: US20260244971A1

This disclosure proposes a training method for quantum machine learning and an electronic device. The training method includes: configuring a quantum circuit to output probabilities of multiple qubits, where the quantum circuit comprises multiple gates with circuit parameters; mapping the qubits to multiple model parameters of a neural network, where multiple bases are calculated based on the qubits, and the quantity of the bases is greater than or equal to the quantity of the model parameters; inputting data into the neural network and calculating a loss based on the output of the neural network; and updating the circuit parameters in the quantum circuit according to the loss.

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.

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.

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.

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.

CONTRASTIVE EXPLANATION APPROACH FOR MACHINE LEARNING BIAS DETECTION AND MITIGATION

Nº publicación: US20260244983A1 20/08/2026

Solicitante:

OPTUM SERVICES IRELAND LTD [IE]
Optum Services (Ireland) Limited

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.