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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.

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.

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.

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.

A METHOD FOR DESIGNING AND OPTIMIZING AN ELECTRICAL OR ELECTRONIC SYSTEM

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.

Method and System for Processing Sparse Time Series Data

Resumen de: US20260244954A1

Disclosed is a method for processing a sparse time series data. The method comprises receiving a dataset comprising the sparse time series data pertaining to real-world variables of a real-world system; transforming the sparse time series data to enable identifying data elements therein; processing the transformed time series data, for augmenting the transformed time series data and identifying the data elements and causal relationships between the data elements; reconstructing the transformed time series data into an operational model; and applying the operational model and the identified causal relationships to a machine learning algorithm to generate an output pertaining to the real-world variables of the real-world system.

METHOD OF ADVANCED CONFIGURATION SIGNALING FOR MULTIPLE TWO-SIDED MODELS COORDINATION

Resumen de: WO2026171971A1

The present disclosure describes a novel method of using the pre-configured AI/ML (artificial intelligence/machine learning) with cross-RAT model compatibility in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). With AI/ML model applied to radio access network, compatibility of supporting the configured two-sided models is challenging for a single or multiple UEs having different ML operational capabilities and environments. Therefore, model operation (e.g., model training, inferencing, monitoring, updating, etc.) can be set up between network and UE by configuring cross-RAT model compatibility.

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.

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.

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.

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

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.

MACHINE LEARNING MODEL PARAMETER TRANSFER

Resumen de: WO2026172331A1

Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.

PRIVACY PRESERVING WORKFLOW FOR REPRODUCIBLE MACHINE LEARNING TRAINING ITERATIONS

Resumen de: US20260244979A1

0000 Techniques for tracking machine learning model training iterations that preserve privacy, increase security, and reduce memory and other computing resources are disclosed herein. An example computer-implemented method comprises generating one or more hash values corresponding to raw data associated with a machine-learned model training process, the raw data comprising a first raw data point associated with a first key and the one or more hash values comprising a first hash value; storing the first hash values in a database in association with the first key; executing a first processing stage using the first raw data point to generate a processed data point; generating a hash value for the first processed data point; storing the second hash value in association with the first key; training a machine-learned model using processed data to generate a trained machine-learned model; and registering the trained machine-learned model.

MACHINE LEARNING-BASED AUTOMATED WELL LOG QUALITY CHECK AND RECONSTRUCTION

Nº publicación: WO2026173837A1 20/08/2026

Solicitante:

SCHLUMBERGER TECHNOLOGY CORP [US]
SCHLUMBERGER CA LTD [CA]
SERVICES PETROLIERS SCHLUMBERGER [FR]
GEOQUEST SYSTEMS BV [NL]
SCHLUMBERGER TECHNOLOGY CORPORATION
SCHLUMBERGER CANADA LIMITED
SERVICES PETROLIERS SCHLUMBERGER
GEOQUEST SYSTEMS B.V.

Resumen de: WO2026173837A1

A method for processing well log data includes obtaining input data including the well log data. The well log data may include a plurality of datasets. Each dataset of the plurality of datasets includes one or more log curves and is associated with a respective well of one or more wells. The method also includes harmonizing the plurality of datasets using a curated dictionary to produce harmonized datasets. The method further includes reconstructing the one or more log curves of each harmonized dataset of the harmonized datasets to produce reconstructed logs using a supervised machine-learning (ML) model. The method also includes generating normalized datasets based on the harmonized datasets and using log normalization. The method also includes generating an output based on the reconstructed logs and the normalized datasets.