Alerta

MACHINE LEARNING AGENT WITH SEMANTIC ENTITLEMENT

Absstract of: US20260189557A1

0000 A computing system including one or more processing devices configured to receive a semantic entitlement that semantically specifies an access permission scope of a machine learning (ML) agent included in an ML system. The semantic entitlement has a natural language format. At least in part by processing the semantic entitlement at a generative language model included in the ML system, the one or more processing devices identify one or more resources that are included in the access permission scope indicated in the semantic entitlement. The one or more processing devices grant an ML agent of the plurality of ML agents access to the one or more identified resources. At the ML agent, the one or more processing devices compute an agent output based at least in part on the one or more identified resources. The one or more processing devices output the agent output to an additional computing process.

METHOD FOR ESTIMATING A REMAINING USEFUL LIFE OF A BATTERY, NON-TRANSITORY COMPUTER-READABLE MEDIUM, AND ELECTRONIC ESTIMATION DEVICE

Absstract of: WO2026137061A1

The method (200) comprises extracting (210) a set of selected features from at least one charge-discharge cycle (C) of a battery and estimating (230) the remaining useful life of the battery (1) by applying a machine learning model (140) to the set of selected features. The machine learning model (140) is trained during a preliminary phase (100), comprising: extracting (110) a set of primary features from the charge-discharge cycles (C) of reference batteries; processing (120) the set of primary features by applying an outlier detection process (25) and/or a data smoothing process to a primary feature (F) versus charge-discharge cycles (C) curve (F(C)A); selecting (130) the features from among the processed primary features using correlation between the set of primary features and the remaining useful lives of the reference batteries; and training the machine learning model (140) with a training set of the selected features extracted from multiple charge-discharge cycles (C) of the reference batteries.

TECHNIQUES FOR DETECTING EMERGING PATTERNS IN DATA USED FOR MACHINE LEARNING MODEL-BASED DECISIONS

Absstract of: US20260187524A1

0000 Described are examples for detecting emerging patterns in data. A detection system for detecting patterns outside of supervised machine learning models is provided for determining similarity scores between transactions to detect the emerging patterns. Transactions in the pattern can be reviewed to determine whether to render decisions on the transactions or similar subsequently occurring transactions. A self-correcting detection system is also provided for using machine learning models to correct for emerging patterns in the transaction data.

VIRTUALLY MONITORING GLUCOSE LEVELS IN A PATIENT USING MACHINE LEARNING AND DIGITAL TWIN TECHNOLOGY

Absstract of: US20260182869A1

0000 A patient health management platform implements a machine-learned metabolic model to generate a prediction of a patient's glucose level. The platform implements a short-term prediction model to generate a daily prediction of the patient's glucose level based on nutrition data reported by the patient and sensor data and lab test data collected for the patient. The platform implements a long-term prediction model generate a prediction of the patient's glucose level during an extended time period based on sensor data and lab test data collected for the patient. Using the short-term prediction model, the long-term prediction model, or both, the patient health management platform generates predictions of the patient's glucose level and updates a digital twin of the patient's metabolic profile.

USER SEARCH CATEGORY PREDICTOR

Absstract of: US20260187663A1

0000 Described herein are embodiments for improving search engine results of listings of For Sale Objects (FSOs). A search engine may be improved by implementing rules that resolve ambiguity between listings for different (FSOs) that match the same search inputs. An unsupervised machine learning module may evaluate candidate rules and identify improvements that may not be obvious to a human evaluator. An ecommerce site that combines the improved search engine with the unsupervised machine learning module may dynamically evaluate search results using different candidate rules and iteratively improve search results.

SYSTEMS AND METHODS FOR COORDINATING EXECUTION OF AN ENSEMBLE OF MACHINE LEARNING MODELS

Absstract of: US20260187545A1

System for coordinating execution of an ensemble of machine learning models to determine anatomical structures to target during cancer treatment are described herein. In examples, the systems can coordinate execution of multiple machine learning models based on different types of three-dimensional images of a patient. These images can include positron emission tomography (PET) images, computed tomography (CT) images, and/or other similar images. The outputs of the models can be correlated with one another to quantify locations and volumes of tumor lesions within the patient. In some examples, a tumor stage can be determined based on the quantification of the tumor lesions. This information can then be used to determine one or more optimal treatment plans for the patient.

SYSTEMS AND METHODS OF GENERATING DATASETS FROM HETEROGENEOUS SOURCES FOR MACHINE LEARNING

Absstract of: US20260187495A1

0000 A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

ATTRIBUTION OF IN-STREAM WATER QUALITY VIA MONITORING REPORTING AND VERIFICATION SENSOR-GEOSPATIAL FUSION NETWORKS

Absstract of: AU2025216427A1

Attribution of in-stream water quality via monitoring reporting and verification sensor geospatial fusion networks may be provided by a system comprising a plurality of separate water fixtures, wherein each of the plurality of separate fixtures includes an optical sensor configured to measure a water quality metric of a water source and a data transmission system configured to transmit source data from each of the plurality of water fixtures, respectively, a remote data source configured to transmit remote data which includes survey data about one or more land use metrics, a contamination source detection system configured to receive the source data from the plurality of water fixtures and the remote data from the remote data source and employ a process-based land-surface model ensemble and a machine learning-based model to identify a land-based source of predicted contamination of a water source based upon the remote data and the source data.

UNCERTAINTY LEARNING DEVICE, STORAGE MEDIUM STORING UNCERTAINTY LEARNING PROGRAM, AND UNCERTAINTY LEARNING SYSTEM

Absstract of: US20260187536A1

0000 Included are: a training data acquiring unit that acquires training data created on the basis of operation-related data obtained from a machine device; a noise imparting unit that creates noise-imparted training data in which noise is imparted to the training data acquired by the training data acquiring unit; an outlier detecting unit that calculates an outlier score from the training data acquired by the training data acquiring unit and the noise-imparted training data created by the noise imparting unit; and a model learning unit that calculates a weighted loss function based on the outlier score calculated by the outlier detecting unit, and trains a machine learning model on the basis of the training data and the noise-imparted training data.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO PREDICT CRITICAL CONSTRAINTS

Absstract of: US20260187736A1

A computer-implemented method and computer program product for predicting a required committed capacity of an electric utility are provided. The method includes the steps of: (a) performing a stochastic optimization of raw data to produce a total committed capacity from conventional thermal units as a target data, wherein the raw data comprises grid operating conditions; (b) combining the total committed capacity from conventional thermal units with raw features and engineered features to generate training data; (c) training a machine learning model for predicting the required committed capacity of the electric utility using the generated training data; (d) predicting the required committed capacity of the electric utility using the trained machine learning model; and (e) running an augmented version of a deterministic dispatch optimization model based on the predicted required committed capacity of the electric utility. The computer program performs the aforementioned steps.

DATABASE SYSTEM WITH QUERY OPERATIONS REGARDING MACHINE LEARNING MODELS AND TRAINING THEREOF

Absstract of: US20260187066A1

Within a database system, a computing node obtains a query that includes a training query operation regarding training of a machine learning model, identifies training data, and provides the training query operation and the training data to other computing nodes. Processing core resources (PCRs) of the computing nodes receive the training query operation. The PCRs receive, in a distributed manner, the sets of the training data. The PCRs execute, in substantial parallel, the training query operation on at least a portion of the machine learning model based on respective sub-sets of the sets of the training data to produce a plurality of partial training results. The computing node compiles the plurality of partial training results to produce a training result and, when the training result is favorable, update the machine learning model based on the training result.

MACHINE LEARNING BASED SYSTEM AND METHOD FOR AUTOMATICALLY REDISTRIBUTING DATA

Absstract of: EP4769259A1

A machine learning based (ML-based) method and system for redistributing data, is disclosed. Initially, an input data associated actual bank cash is obtained from data sources. The input data is pre-processed to generate pre-processed data. A month level data associated with the actual bank cash is predicted for a pre-determined horizon based on at least one of: historical cash flow data and seasonality, using machine learning (ML) models on the pre-processed data. At least one of: the month level data to week of month (WOM) level data and the WOM level data to day level data, is redistributed based on a pro-rata configuration using hyperparameters. At least one of: the WOM level data and the day level data, redistributed from the month level data, is provided as an output, to the users on user interfaces associated with electronic devices associated with the users.

MACHINE-LEARNING-BASED SYSTEM AND METHOD FOR AUTOMATICALLY EXTRACTING FIELDS FROM DOCUMENTS

Absstract of: EP4769285A1

0001 A machine-learning based (ML-based) system and method for automatically extracting one or more data fields from one or more documents, are disclosed. The ML-based system includes a document obtaining subsystem to obtain documents, a document pre-processing subsystem to generate pre-processed data, a field identifying subsystem to identify data fields using a trained ML model, and a field extracting subsystem to extract financial information. The ML-based system also comprises an output subsystem to deliver the extracted data to end users via user interfaces. The ML model is trained using historical documents, labelled data fields, and features such as distance-based features, direction-based features, dimension-based features, positional features, and value-based features. The M-based system employs hyperparameter optimization, noise removal, and accuracy assessment mechanisms to enhance performance. This ML-based system provides a scalable, accurate, and automated solution for financial information extraction, ensuring efficiency, adaptability, and seamless integration with enterprise systems.

APPARATUS AND METHOD FOR DESIGNING MULTILAYER FILM

Absstract of: EP4768891A1

An apparatus and method for designing a multilayer film is disclosed. An apparatus for designing a multilayer film may perform: modeling a multilayer film to be designed as a single layer structure having a preset thickness, collecting physical property data with respect to a film corresponding to the single layer structure, reading a value pre-stored in a storage space accessible by an apparatus for designing a multilayer film, and obtaining a feature setting mode for designating different feature setting manners depending on the read value, performing feature setting based on a plurality of physical indicators selected from the physical property data, depending on the feature setting mode, selecting at least one among a plurality of supervised learning models capable of a regression analysis as a machine learning model, predicting the dart impact strength of the multilayer film by using the machine learning model learned by taking the feature as an independent variable, and a dart impact strength of the multilayer film as a target variable, and generating design data for the multilayer film, by combining predicted values for other properties and a predicted value of the dart impact strength, so as to satisfy the design requirements of the multilayer film.

APPARATUS AND METHOD FOR INSPECTING BATTERY

Absstract of: EP4768897A1

0001 A method for inspecting a battery, according to an embodiment of the present invention, relates to a method for inspecting the quality of a battery in a manufacturing process, the method comprising the steps of: acquiring an image capturing at least a portion of the exterior of the battery to preprocess the image; detecting one or more defect candidate regions in the preprocessed image by using one or more detection algorithms; extracting position information of the defect candidate regions and shape feature information of the defect candidate regions; and inputting, into a pre-trained machine learning model, information related to the detection algorithms, the position information of the defect candidate regions, and the shape feature information of the defect candidate regions, to determine whether corresponding defect candidate shapes are defective.

QUALITY PREDICTION MODEL GENERATION METHOD, METAL MATERIAL QUALITY PREDICTION METHOD, METAL MATERIAL MANUFACTURING METHOD, METAL MATERIAL MANUFACTURING CONDITION PRESENTATION METHOD, QUALITY PREDICTION MODEL GENERATION DEVICE, METAL MATERIAL QUALITY PREDICTION DEVICE, METAL MATERIAL MANUFACTURING CONDITION PRESENTATION DEVICE, AND METAL MATERIAL MANUFACTURING SYSTEM

Absstract of: EP4769249A1

A method of generating a quality prediction model includes an acquisition step (S1) of acquiring an explanatory variable selected from manufacturing conditions of each process and an objective variable that is a state of quality defects in the manufactured metal material, a storage step (S3) of storing the explanatory variable and the objective variable in association with each other as training data, calculation steps (S4 to S6) of dividing the training data into groups and testing whether a significant difference exists in the state of quality defects, a search step (S7) of searching for a most significant grouping, and a generation step (S8) of generating the quality prediction model by machine learning using a group according to the most significant grouping found.

TECHNIQUES FOR IMPROVING DECISIONS RENDERED BASED ON MACHINE LEARNING MODEL OUTPUT

Absstract of: EP4769235A1

Described are examples for rendering decisions based on machine learning (ML) model output. A set of segments for a historical set of data for a division of interest, and associated budgets for the decision of interest, can be obtained. For each segment in the set, a budget for incorrect decisions rendered based on output from the ML model can be computed. For each data entry in a current set of data, a current decision can be rendered based on a configured cutoff value and also a shadow decision based on the candidate cutoff value for a segment of the set of segments associated with the data entry. The candidate cutoff value can be promoted to replace the configured cutoff value, for rendering subsequent decisions for a subsequent set of data, based on comparing the shadow decisions for the current set of data based on the budget for incorrect decisions.

METHOD FOR INTELLIGENT ANALYSIS OF IMPORT AND EXPORT HAZARDOUS CHEMICALS BASED ON MACHINE LEARNING

Absstract of: NL4001738A

0001 The present disclosure relates to the technical field of data processing, in particular to a method for intelligent analysis of import and export hazardous chemicals based on machine learning. The method constructs a hazardous chemical knowledge graph and performs path detection on the hazardous chemical knowledge graph to obtain at least one valid path and at least one invalid path; acquires, for any invalid path, a value index of the any invalid path according to a degree of information coverage between intermediate entities of the any invalid path, a similarity feature between adjacent intermediate entities of the any invalid path, and a degree of reliability of each connected segment in the any invalid path; utilizes a value index of each invalid path to screen at least one high-value path among all invalid paths.

NON-TRANSITORY COMPUTER-READABLE MEDIUM, MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE

Absstract of: US20260178983A1

0000 There is provided a non-transitory computer-readable medium storing a calculation program for causing a computer to execute a process. The process includes, training a model by using a loss term corresponding to a degree of continuity or discreteness of a variable to be optimized as a cost function in a search process that incorporates continuous relaxation into a discrete optimization problem, and changing the loss term as the search process progresses.

METHOD, DEVICE AND STORAGE MEDIUM FOR INTERACTION PROCESSING

Absstract of: US20260178889A1

0000 The disclosure provides a method, a device and a storage medium for interaction processing. A method includes: generating, based on context information related to an interaction, task description information for an interaction task with a trained first machine learning model, the task description information at least indicating whether the interaction task is to be performed; generating, in response to the task description information indicating that the interaction task is to be performed, a control instruction for at least one component of a terminal device based on the task description information by using a predetermined association relationship between task description information and control instructions; and controlling, based on the control instruction, the at least one component of the terminal device to perform the interaction task.

METHOD FOR TRAINING A MACHINE LEARNING MODEL WHICH IS DESIGNED TO DETERMINE THE CHARGING TIME FOR BATTERIES OF ELECTRIC VEHICLES

Absstract of: WO2026131066A1

The invention relates to a method for training a machine learning model which is designed to determine the charging time for batteries (104a, 104b, 104c) of electric vehicles (100a, 100b, 100c), having the steps of: providing (200) first training data (202) relating to batteries of a first plurality of electric vehicles; carrying out a first training process (204) on a machine learning model (206) in order to obtain a pre-trained machine learning model (208); providing (224) data (226) which is characteristic of trained individual machine learning models, each of the trained individual machine learning models being obtained by carrying out a second training process (216) on the pre-trained machine learning model (208), respective second training data (214) having been obtained for each of a second plurality of electric vehicles; providing (228) histograms (230) of the respective second training data; adapting (232) the pre-trained machine learning model on the basis of the histograms and on the basis of the data which is characteristic of the trained individual machine learning models in order to obtain one or more trained final machine learning models (234); and providing (236) the one or more trained final machine learning models.

METHODS AND SYSTEMS FOR EXPLAINING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Absstract of: US20260178948A1

In some embodiments, a computing system may generate a first set of importance metrics (e.g., scores or values) for a model. The importance metrics may be generated using an explainable artificial intelligence technique, and an individual importance metric may indicate how influential a corresponding feature is for a decision made by a model. The computing system may determine an important feature and create a modified dataset by removing the important feature from the dataset. The computing system may train the model on the modified dataset and evaluate the performance of the model to determine the effect of removing the feature (e.g., which may indicate how important the feature is to output generated by the model). This process may be repeated for additional features and additional performance metrics may be obtained.

System, Method, and Computer Program Product for Generating an Inference Using a Machine Learning Model Framework

Absstract of: US20260178944A1

Provided is a system for generating an inference based on real-time selection of a machine learning model using a machine learning model framework that includes at least one processor programmed or configured to receive a request for inference, wherein the request includes a payload, select a machine learning model of a plurality of machine learning models based on the request for inference, determine an aggregation of data based on the machine learning model and the payload of the request, transform the aggregation of data into inference data, wherein the inference data has a configuration that is capable of being processed by the machine learning model, and generate an inference based on the inference data using the machine learning model. Methods and computer program products are also provided.

MACHINE LEARNING BASED MODEL FOR DETERMINING EFFECTIVE COMMUNICATION MECHANISM WITH USERS

Absstract of: US20260178941A1

A system uses a machine learning based model to select a channel for communicating with users. The system generates a feature vector based on a user profile of the user. The user profile data includes time series data describing past communications to users and past user actions. The system executes one or more machine learning based models, each machine learning based model configured to receive a feature vector describing a particular user and predict a likelihood of the particular user performing an expected user action responsive to a communication sent via the communication channel. The system selects a communication channel based on the results of the machine learning based models and sends a communication to the user via the selected communication channel.

GENERATING AND MODIFYING ONTOLOGIES FOR MACHINE LEARNING MODELS

Nº publicación: US20260178990A1 25/06/2026

Applicant:

THOMSON REUTERS ENTPR CENTRE GMBH [CH]
Thomson Reuters Enterprise Centre GmbH

Absstract of: US20260178990A1

A method performed by a machine learning system that involves obtaining a first ontology that includes one or more labels. Each label is associated with a sample that includes text. The ML system is configured to use a particular label to retrieve one or more samples associated with the particular label. The method further involves receiving an identification of a label of a first ontology associated with a first machine learning model to share with a second ontology associated with a second machine learning model and sharing the label and the information with the second ontology. The method further involves training the second machine learning model using the shared information associated with the label.