Alerta

AUTOMATED SOFTWARE APPLICATION PARALLELIZATION, DISTRIBUTION AND EXECUTION ON MULTI-CORE AND MULTI-NODAL DIGITAL SYSTEMS, COMMUNICATION PROTOCOL FOR DISTRIBUTED PROCESSING, AND COORDINATED PARALLELIZATION AND EXECUTION OF SOFTWARE APPLICATIONS

Absstract of: WO2026055302A1

The invention provides for utilization of a machine learning (ML) subsystem to create additional parallel execution on multiple processor cores (CPU, GPU, SPU, etc.) and to further optimize parallel execution within and across nodes of a multi-node system, e.g., through the exchange of objects (instruction, data, task and thread) that define the parallel execution. An abstract object-oriented communication protocol is provided for a multiple node system to enable distribution of objects (thread, task, data, instruction) required for parallel computing of single or multiple applications amongst nodes across any standard network. A method is provided for sharing application parameters (including current and future resource requirements) across a multiple node computing system to enable nodes and aggregates of nodes to further parallelize and further optimize parallel application execution.

COMPUTER SYSTEM AND METHOD FOR PROVIDING A SUBJECT-RELATED DATA DEVELOPMENT PLATFORM

Absstract of: WO2026052797A1

A method (200), performed by a computer system (110) connected to a network (150), comprises receiving (210), by means of a data receiving module (122) of the computer system (110) and from at least one of a plurality of sources connected to the network (150), at least one input data object (162-1 - 162-n, 172-1 - 172-n, 182-1 - 182-n) containing subject-related information according to at least one of a plurality of information types encoded in at least one of a plurality of data formats; processing (220, 300), by means of a data extraction and classification module (124) of the computer system (110), the at least one input data object (162-1 - 162-n, 172-1 - 172-n, 182-1 - 182-n) for standardizing the subject-related information; subjecting (230), by means of a data engineering module (126) of the computer system (110), the subject-related information contained in the processed at least one input data object to a first machine learning model for generating a uniform dataset containing the subject-related information in a uniform structured format; storing (240), by means of a storing module (128) of the computer system (110), the uniform dataset in one or more secured data repositories (140, 181-1 - 181- n) connected to the network (150); and, providing (250), by means of a workspace module (130) of the computer system (110), a secured virtual environment accessible to users (180-1 - 180- n, 190) connected to the network (150), the secured virtual environment enabling impor

USING MACHINE LEARNING ALGORITHMS TO PREDICT TRANSACTIONS THAT MATCH EACH OTHER USING PATTERNS FROM MATCHING FEEDBACK

Absstract of: WO2026054830A1

Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and/or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and/or

VISUAL LOCATION OF AERIAL VEHICLES USING DYNAMIC ALEATORIC UNCERTAINTY

Absstract of: EP4707735A1

Techniques for localizing a vehicle in real time using dynamic uncertainty estimates are presented. The techniques include obtaining a terrain image captured by the vehicle; passing the terrain image to a trained evidential deep learning neural network subsystem, from which a dynamic uncertainty value and a first feature vector are obtained in real time; for each of a plurality of candidate terrain locations, comparing the first feature vector to a respective second feature vector representative of a candidate terrain location, from which a respective similarity score is obtained; for at least one of the plurality of candidate terrain locations, updating in real time, by a recursive Bayesian estimator, a respective location weight based on the dynamic uncertainty value and the respective similarity score; estimating, in real time, a location of the vehicle based on the plurality of location weights; and providing the location of the vehicle.

A BASIC EDUCATION ENHANCEMENT SYSTEM BASED ON DEEP LEARNING

Nº publicación: NL2041009A 09/03/2026

Applicant:

YIBIN RESEARCH INSTITUTE OF SOUTHWEST UNIV [CN]
YIBIN RESEARCH INSTITUTE OF SOUTHWEST UNIVERSITY

Absstract of: NL2041009A

The present invention relates to a system for enhancing basic education based on deep learning, belonging to the field of educational informatization. The system includes: an external information perception module, a perception memory module, a working memory module, a longterm memory module, an activation condition module, an acquisition condition module, a consolidation condition module, a transfer condition module, a quantitative data processing module, and a results visualization module. The quantitative data processing module includes: an SPSS analysis module, a DINA cognitive diagnosis module, a hierarchical analysis module, and a multifaceted Rasch module. The present invention is highly operable and contributes to improving teachers' teaching abilities and professional levels, scientifically promoting the enhancement of basic education quality.