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INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

Resumen de: US20260187538A1

An information processing device includes: a machine learning unit to learn a relationship between an evaluation value and a parameter on a basis of a search point of the parameter and an evaluation value of the search point, and predict the evaluation value for a search candidate point of the parameter; and a search progress acquiring unit to acquire progress information indicating progress of a search on a basis of the search point, the evaluation value of the search point, the search candidate point, and the evaluation value of the search candidate point predicted by the machine learning unit.

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

Resumen de: 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 OF GENERATING DATASETS FROM HETEROGENEOUS SOURCES FOR MACHINE LEARNING

Resumen de: 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.

BOOSTING AND MATRIX FACTORIZATION

Resumen de: US20260187197A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting a new machine learning model architecture. In some aspects, the methods include obtaining a training dataset with a plurality of training samples that includes feature variables and output variables. A first matrix is generated using the training dataset which is a sparse representation of the training dataset. Generating the first matrix can include generating a categorical representation of numeric features and an encoded representation of the categorical features. The methods further include generating a second, third and a fourth matrix. Each feature of the first matrix is then represented using a vector that includes a multiple adjustable parameters. The machine learning model can learn by adjusting values of the adjustable parameters using a combination of a loss function the fourth matrix, and the first matrix.

DECISION TREE OF MODELS: USING DECISION TREE MODEL, AND REPLACING THE LEAVES OF THE TREE WITH OTHER MACHINE LEARNING MODELS

Resumen de: US20260187484A1

Described are techniques of generating and training a neural network that include training multiple models and constructing multiple decision trees with said models. Each decision tree may include additional decision trees at various levels of that decision tree. Each decision tree has a different accuracy indicator due to the unique structuring of each decision tree, and by testing each tree through a testing dataset, the tree with the highest accuracy can be determined.

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

Resumen de: 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

Resumen de: 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.

METHODS AND SYSTEMS FOR COMPRESSING AND FINE-TUNING MACHINE LEARNING MODEL

Resumen de: US20260187448A1

0000 The present disclosure relates to a method for compressing and fine-tuning a machine learning model performed by at least one processor. The method includes generating a quantized model by quantizing at least some of parameters of a trained machine learning model, and fine-tuning the quantized model for a target task by fixing a first subset of parameters of the quantized model, and updating only a second subset of the parameters of the quantized model using training data associated with the target task, the second subset of the parameters of the quantized model not including any parameter among the first subset of parameters of the quantized model.

SYSTEM AND METHOD FOR EXTRACTING HIDDEN CUES IN INTERACTIVE COMMUNICATIONS

Resumen de: US20260188340A1

Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process interactive communications between at least two participants. Speech and text, within the interactive communications, are analyzed using machine learning classifiers to extract prosodic, semantic and key phrase cues located within the interactive communications to identify changes to emotion, sentiments and key phrases. A summary of the interactive communications between a first participant and a second participant is generated at least, in-part, based on the extracted prosodic, semantic and key phrase cues and the summary is highlighted based on any of the changes to emotion, the sentiments or the key phrases.

OPERATIONALIZING MACHINE LEARNING MODELS AN INFORMATION TECHNOLOGY AND SECURITY OPERATIONS APPLICATION

Resumen de: US20260187518A1

Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.

ELECTRONIC DEVICE SUPPORTING PIPELINE NETWORK CONDITION DIAGNOSIS AND FAULT PREDICTION AND OPERATING METHOD THEREOF

Resumen de: US20260186479A1

An electronic device may support pipeline network condition diagnosis and fault prediction. The device may receive, as input data, labeled data related to the pipeline network condition diagnosis, and perform machine learning of a support vector machine (SVM), based on a kernel matrix operation and a sequential minimal optimization (SMO) operation on time domain features and frequency domain features of the labeled data. Then the device may perform analysis on unlabeled data related to the pipeline network condition diagnosis using the SVM machine-learned based on the labeled data, and adjust a parameter of the machine-learned SVM based on analysis results of the unlabeled data.

SYSTEM FOR ONLINE PROCESS, CHEMOMETRIC ANALYSIS, USING MACHINE-LEARNING

Resumen de: US20260188437A1

The invention is a system and method for using spectroscopy and precision machine-learning models for accurate chemometric analysis of online process constituents.

PROTECTION OF SENSITIVE INFORMATION IN MACHINE LEARNING MODELS

Resumen de: US20260187526A1

0000 According to an embodiment of the present invention, a computer system partitions a training data set for a machine learning model into a plurality of categories. Data from the plurality of categories is extracted based on density of data elements in the plurality of categories to produce a resulting data set. The resulting data set is divided into a plurality of blocks based on a probability of deletion of data elements in the resulting data set. The machine learning model is incrementally trained using segments from the blocks. Information is removed from the machine learning model by retraining the machine learning model with subsequent data in a corresponding block containing the information to be removed. Embodiments of the present invention further include a method and computer program product for removing information from a machine learning model in substantially the same manner described above.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO PREDICT CRITICAL CONSTRAINTS

Resumen de: 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.

MACHINE LEARNING AGENT WITH SEMANTIC ENTITLEMENT

Resumen de: 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.

MACHINE LEARNING (ML) - ASSISTED PRESENTATION HOSTING PLATFORM

Resumen de: US20260187414A1

0000 Disclosed herein are systems and methods for providing a machine learning (ML)—assisted presentation hosting platform. An example method of preparing ML models of the presentation hosting platform comprises: receiving a first training dataset comprising: a plurality of different presentations having a plurality of different activities, one or more presentation scenarios for each presentation, and one or more devices and/or software that perform the plurality of activities; training a first ML model using the first training dataset to predict presentation scenarios for different types of presentations; receiving a second training dataset comprising a mapping between the one or more presentation scenarios and a plurality of devices and/or software with different configuration, resources, capabilities, and/or load; and training the second ML model using the second training dataset to predict a mapping of the activities of each presentation to devices and/or software of participants of the presentation.

INCREASING DATA SOURCE FUNCTIONALITY THROUGH IMPLEMENTATION OF MACHINE LEARNING AND GENERATIVE ARTIFICIAL INTELLIGENCE

Resumen de: US20260187489A1

Increasing programming functionality of data sources through the use of Artificial Intelligence (AI), specifically Machine Learning (ML) models and Generative AI (GenAI). ML model(s) that have been trained to acquire a knowledge base from a data source are implemented and once acquired, further ML models are implemented that have been trained to identify, based on the knowledge base, opportunities for additional programming functionalities. Once the additional programming functionalities have been determined, the present invention implements GenAI to generate at least a portion of the technology stack associated with the data source. Generating a portion of the technology stack includes one or more rebuilding/revising the data source, generating a new data source, revising use application and/or data source management software and/or generating new use application and/or data source management software.

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

Resumen de: 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.

Artificial Intelligence System for Operation Management

Resumen de: US20260186942A1

Implementations include obtaining a first dataset associated with a negative operation cycle event type; determining, via a first set of machine learning components, a set of causation identifiers corresponding to the negative operation cycle event; determining, via a machine learning classification component, a set of classifications comprising a classification of each causation identifier of the set of causation identifiers; training a second set of machine learning components to classify negative operation cycle events having the negative operation cycle event type according to the set of classifications; determining, based on a second dataset, an event classification associated with a negative operation cycle event; determining at least one rectification operation parameter associated with a rectification operation corresponding to the negative operation cycle event; and outputting assignment content configured to cause a user interface of a user device to present a user interface element associated with the rectification operation.

APPARATUS AND METHOD FOR DESIGNING MULTILAYER FILM

Resumen de: 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.

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

Resumen de: 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.

APPARATUS AND METHOD FOR INSPECTING BATTERY

Resumen de: 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.

TECHNIQUES FOR IMPROVING DECISIONS RENDERED BASED ON MACHINE LEARNING MODEL OUTPUT

Resumen de: 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.

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

Resumen de: 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.

MACHINE LEARNING BASED SYSTEM AND METHOD FOR AUTOMATICALLY REDISTRIBUTING DATA

Nº publicación: EP4769259A1 01/07/2026

Solicitante:

HIGHRADIUS CORP [US]
Highradius Corporation

Resumen de: 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.