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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.

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.

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.

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.

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.

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.

MACHINE LEARNING BASED SYSTEM AND METHOD FOR AUTOMATICALLY REDISTRIBUTING DATA

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.

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.

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.

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

Nº publicación: NL4001738A 30/06/2026

Solicitante:

TECHNICAL CENTER OF QINGDAO CUSTOMS [CN]
TECHNICAL CENTER OF QINGDAO CUSTOMS

Resumen de: 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.