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Intelligent Matchmaking System for Dynamic Social Networking

Resumen de: US20260300939A1

The present invention provides a system and method for intelligent matchmaking using Graph Neural Networks (GNNs) and Model-Agnostic Meta-Learning (MAML) to perform adaptive user clustering and personalized recommendations. Users are modeled as graph nodes with multi-dimensional feature vectors capturing demographic, behavioral, and interest-based data. The system employs GraphSAGE for inductive embedding generation and MAML for few-shot learning, enabling scalable onboarding and rapid adaptation for cold-start users. A hierarchical meta-learning framework dynamically adjusts to evolving user behavior, while subgraph matching supports context-aware group formation. Temporal attention mechanisms and similarity-based scoring enhance real-time matchmaking accuracy. The invention supports applications in social networking, professional matchmaking, healthcare, and event-driven platforms by delivering interpretable, privacy-preserving, and scalable user alignment in dynamic digital environments.

FAST QUANTIZATION FOR NEURAL NETWORKS

Resumen de: US20260300806A1

0000 A processing unit compares a first graph that represents a machine learning (ML) model and a second graph that represents a previously quantized ML model generated by quantizing a previous version of the ML model. Based on the comparison, a processing unit selectively reuses a first subset of quantization parameters from the previously quantized ML model and re-quantizes a second portion of the version of the ML model to form a second subset of the quantization parameters. The first subset is formed by quantizing a first portion of the previous version of the ML model. The processing unit combines the first subset and the second subset to form an updated quantized ML model. In some cases, the version of the ML model has a floating-point representation, and the previously quantized ML model is generated by quantizing a previous version of the ML model to form a fixed point representation.

FEDERATED LEARNING WITH DISTINCT COMMON COMPONENTS AND SPARSE COMPONENTS

Resumen de: WO2026206327A1

A federated learning system and method for training neural networks addresses the challenge of efficiently merging locally trained models while maintaining data privacy. The system includes a server and multiple clients, where the server dispatches machine learning models to clients for local training. The server decomposes updated model weights into common and sparse components using robust principal component analysis (RPCA), applying distinct scaling factors to enhance model accuracy and convergence speed. This approach can be particularly advantageous in scenarios where data privacy is of significant importance, such as in smart devices and medical applications. The method iteratively refines the global model by leveraging client-specific variations and shared patterns, ensuring that client-specific data is not transferred to a central server.

SYSTEM AND METHOD FOR HUMAN ACTIVITY RECOGNITION

Resumen de: US20260300738A1

0000 In aspects, the present approaches allow neural networks to be taught to understand patterns of human behavior without the need of expert data labeling or laboratory studies. First and second neural networks are trained to understand these patterns without labeling. Once trained, the neural networks can be deployed with a trained classifier to determine or classify human activity based upon received sensor inputs.

NOISE REGULATED STOCHASTIC SNN TO SOLVE QUADRATIC UNCONSTRAINED BINARY OPTIMIZATION PROBLEMS

Resumen de: WO2026202550A1

Noise regulation in stochastic spiking neural network (SSNN) allows for a relatively optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart. Embodiments use of noise regulation with an SSNN representing a QUBO problem to perform a parameter search to solve that QUBO problem.

SYSTEM AND METHOD FOR MULTI-LAYERED GRAPH INTEGRATION WITH DOMAIN-SPECIFIC AI AGENTS FOR DISPARATE DATA SOURCES

Resumen de: WO2026207116A1

A computer-implemented system integrates data from disparate sources into a multi¬ layered graph comprising domain-specific layers and an entity layer. The system includes an entity resolution engine configured to reconcile entity references by performing hash-based lookups for definitive identifiers and vector-based similarity searches for partial or ambiguous data. An ingestion pipeline parses incoming data, extracts attributes using neural network models, and updates the graph with new or matched nodes. Domain- specific Al agents, each associated with a respective layer, retrieve relevant data via retrieval-augmented generation (RAG) subsystems and generate insights using fine-tuned large language models (LLMs). A coordinating Al agent receives cross-layer queries, distributes sub-queries to domain-specific agents, and synthesizes a unified response by referencing the entity layer.

TUNING SWITCH PERFORMANCE PARAMETERS USING NEURAL NETWORKS

Resumen de: WO2026206395A1

Devices, computer-readable storage mediums, and methods tune performance parameters using one or more artificial intelligence models by monitoring data packets being received by a device, the data packets including one or more data characteristics, predicting, using the one or more artificial intelligence models, future data traffic patterns of the data packets, the predicting including categorizing the one or more data characteristics of the data packets, identifying, based on the predicted future data traffic patterns, one or more tuning parameters to improve bandwidth or reduce latency of the future data traffic patterns, and modifying one or more configurable parameters of the device to include the one or more tuning parameters.

CONSORTIUM MODEL TRAINING AND INFERENCE USING CLIENT-DEFINED PRIVACY-PRESERVING FEATURE TRANSFORMATIONS

Resumen de: US20260300713A1

0000 A computer-implemented method, comprising obtaining a transformed dataset from a data owner, the transformed dataset comprising a plurality of transformed values derived from a list of transformed summarized features, wherein the transformed dataset is associated with an entity and is devoid of interpretable or reconstructable data regarding the entity; generating a target-related measure based on a target label associated with the transformed dataset, wherein the target-related measure comprises a numerical value that reflects a statistical relationship between a range of the transformed values and associated target label; generating an input feature vector, for the entity, wherein the input feature vector comprises the target-related measures corresponding to the list of transformed summarized features; and training a neural network classifier using a plurality of input feature vectors generated from multiple transformed datasets respectively obtained from a plurality of data owners.

SELECTION-INFERENCE NEURAL NETWORK SYSTEMS

Resumen de: US20260296464A1

0000 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a response to a query input using a selection-inference neural network.

APP SEARCH BASED ON VERBAL DESCRIPTION OF ICON

Resumen de: US20260300374A1

A computer implemented method includes receiving a search request from a user device, the search request including a verbal description of attributes of an icon corresponding to an app. The search request is processed via a natural language processing model to derive an intent of the search request to search for icon attributes. An icon knowledge base is searched in response to the derived intent. The icon knowledge base includes records with icon attributes generated by a convolutional neural network image processor based on icons collected from an app storage, to identify a ranked list of apps. A search results is provided to the user device and identifies at least a top ranked app having an icon matching the verbal description of attributes.

METHOD AND SYSTEM FOR DETECTING AN OBJECT IN AN IMAGE

Resumen de: WO2026201760A1

The invention relates to a computer-implemented method for detecting an object in a digital image by means of a client-server system, wherein the following steps are carried out: a) providing a particular set of local training images to one of the at least two clients, wherein each local training image from the set comprises an image of the object, and the image includes a permissible or an impermissible representation of the object, which is characterised as such, b) generating and training a particular local embedding model on the basis of artificial intelligence for a feature of the object, by means of the particular client, from the particular set of local training images using an encoder, provided in each case to the particular client, in the form of a neural network having input nodes, intermediate layers and output nodes, wherein the number of output nodes is smaller than the number of input nodes, and the embedding model is formed by the output nodes of the encoder, c) transmitting the particular local embedding model from the particular client to the server, and aggregating the respective local embedding models into a global embedding model, and distributing the global embedding model to at least one of the at least two clients, d) capturing a current image of a produced product, which comprises a representation of the object, e) carrying out a feature analysis by applying the particular local embedding model, and detecting the object in the current image when a corr

AUTOMATIC BALL MACHINE APPARATUS UTILIZING PLAYER IDENTIFICATION AND PLAYER TRACKING

Resumen de: US20260295361A1

0000 A ball machine comprising an imaging system to capture image data and a processor configured to, for a frame of the image data, analyze the image data using a neural network to detect a plurality of persons, determine a coordinate position on a playing surface of each of the plurality of detected persons, extract features of each of the plurality of detected persons, generate a first set of feature vectors corresponding to the plurality of detected persons, associate a first feature vector to the coordinate position on the playing surface of a first detected person to generate a first unique identifier, associate a second feature vector to the coordinate position on the playing surface of a second detected person to generate a second unique identifier, and control the ball machine to launch balls based on first settings corresponding to the first unique identifier and second settings corresponding to the second unique identifier.

NEURAL NETWORK ACCELERATOR AND DATA PROCESSING METHOD FOR NEURAL NETWORK ACCELERATOR

Resumen de: WO2026200907A1

The present application relates to the technical field of data processing, and provides a neural network accelerator and a data processing method for the neural network accelerator. The accelerator comprises a matrix computation unit and at least one vector computation unit. The matrix computation unit comprises a matrix computation core and a matrix post-processing module, and a direct connection path is established between the matrix post-processing module and the at least one vector computation unit. The matrix computation core is used for performing matrix computation in a neural network model, a result of the matrix computation is a first matrix, and the first matrix is divided according to the number of vector computation units to obtain first sub-matrices corresponding to the respective vector computation units. The matrix post-processing module is used for sending, via the direct connection path with the vector computation unit, a first sub-matrix corresponding to the vector computation unit. The vector computation unit is used for performing vector computation on the basis of the received first sub-matrix. The present application can improve the training and inference efficiency of large language models. The present application is used for data processing for neural networks.

DATA PROCESSING METHOD, NEURAL NETWORK MODEL TRAINING METHOD AND RELATED APPARATUS

Resumen de: WO2026200491A1

Disclosed in the present application are a data processing method, a neural network model training method and a related apparatus. The data processing method comprises: acquiring first historical data to be inferred and first data to be inferred, and on the basis of the first historical data to be inferred and the first data to be inferred, obtaining a first data sequence; on the basis of a label of the first historical data to be inferred and an initial label of the first data to be inferred, obtaining a first label sequence, wherein labels in the first label sequence correspond to data in the first data sequence on a one-to-one basis in terms of sequence positions; and on the basis of the first data sequence, obtaining first association weights between data in the first historical data to be inferred and data in the first data to be inferred, and obtaining an inference result of the first data to be inferred in combination with the first association weights and the first label sequence. The embodiments of the present application facilitate a reduction in the training or fine-tuning overheads of an AI model, and reduce the training latency of the AI model.

TEMPORAL IMAGE BLENDING USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260301117A1

Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.

DISTANCE DETERMINATIONS USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260300718A1

0000 Apparatuses, systems, and techniques are presented to determine distance for one or more objects. In at least one embodiment, a disparity network is trained to determine distance data from input stereoscopic images using a loss function that includes at least one of a gradient loss term and an occlusion loss term.

INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM

Resumen de: EP4814884A1

0001 An information processing apparatus classifies cells in an image of a tumor microenvironment obtained from a patient into a plurality of cell groups, extracts positions, in the image, of the cells included in the plurality of cell groups, creates a graph by assigning nodes to the cells included in the plurality of cell groups based on the extracted positions, connecting the nodes with edges, assigning to each of the nodes a feature quantity indicating a feature of the corresponding cell, and assigning to each of the edges a feature quantity indicating a relationship between two cells corresponding to two nodes connected by the edge, and models, using a graph neural network (GNN), spatial relationships between cells in the graph based on the assigned feature quantities of the nodes and the edges.

Method and system for non-invasive animal identification using convolutional neural networks

Resumen de: GB2705140A

A method for identifying individual animals, comprising: receiving an image of an animal 102; segmenting the image 104; using a convolutional neural network to identify individual animals from the segmented image 106,101e. Also provided is a method of validating an attribution on a Predictive Model, comprising: receiving a test data to test a prediction of the model (202,Fig.2); segmenting the test data into a first test data item and a second test data item (204,Fig.2); calculating first contribution scores for the first test data item; calculating second contribution scores for the second test data item (206,Fig.2); calculating a first attribution score for the first test data item; calculating a second attribution score for the second test data item (208,Fig.2); comparing the first contribution score to the first attribution score, and the second contribution score to the second attribution score (210,Fig.2); and in response to the comparison being within a threshold, validating the attribution. Test data item may be an image; the first test data item the image’s foreground and the second test data item the image’s background. Contribution scores may be calculated using Shapely values. Attribution scores may be calculated using gradient methods. Figure 1

METHODS AND SYSTEMS FOR CROSS-DOMAIN BASED CHANGE DETECTION OF REGION DUE TO AN EVENT

Resumen de: EP4814895A1

The disclosure relates generally to methods and systems for cross-domain based change detection of region due to an event. Conventional techniques on cross-domain change detection (CDCD) rely on transformation-based approaches involving two tasks: image translation from one modal to another modal (SAR to optical) and then performing the change detection (CD) in one of the translated modals (for example, in SAR or optical). The methods and system of the present disclosure propose a deep-learning (DL) architecture called ReFUjetNet leverages generic embeddings from the extensively pre-trained domain adaptive foundation model alongside locally trained embeddings. The ReFUjetNet efficiently fuses these embeddings and employs a Kolmogorov-Arnold Network (KAN)-based convolutional neural network (CNN) classifier to generate a binary change matrix, which is then joined with another binary change matrix derived from the Gabor jet-based dissimilarity checker, resulting in the final binary change map.

GENERATION OF MOLECULAR STRUCTURES

Resumen de: EP4814972A1

Some embodiments are directed to generating molecular structures, which may include progressively denoising a spatial density function by iteratively applying a trained neural network. The neural network is trained to transform noisy spatial density functions into structured spatial density functions based on training data comprising molecular structures represented as spatial density functions. After denoising, a sum of predefined density distributions is fitted to the denoising result to obtain the molecular structure.

SYSTEM AND METHOD FOR PLAYER REIDENTIFICATION IN BROADCAST VIDEO

Resumen de: EP4815476A2

0001 A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

SYSTEMS AND METHODS FOR GENERATING MULTIMODAL DATA USING A SINGLE-TOWER ARCHITECTURE WITH A DATA GENERATION SUBSYSTEM

Resumen de: EP4814896A2

0001 A computer-implemented method of generating multimodal data. The method comprises using a token generation neural network to generate an output sequence of multimodal tokens, and in response to a next multimodal token being a start-of-image token, generating an image by performing a reverse diffusion process conditioned on features representing the current output sequence of multimodal tokens obtained from the token generation neural network and appending a sequence of image tokens representing the image to the current output sequence of multimodal tokens as subsequent multimodal tokens in the output sequence of multimodal tokens.

Milk analyser for classifying milk

Resumen de: NZ773408A

A milk analyser (400) comprising a milk analysis unit (402) having an analysis modality wherein the milk analysis unit (402) further comprises a milk classification system (404) having an imaging device (4042, 4044) configured to image milk for generation of digital image data; a processor (3044) of a computing device (304) which is adapted to execute a program code to implement a deep learning neural network classifier trained using labelled milk images from milk within the classes into which the imaged milk may be classified and operable to generate a classification of the imaged milk; and a controller (3066) configured to output a control signal in dependence of the generated classification to control a sample intake (4022) to regulate the supply of milk to the analysis unit (402).

Systems and Methods to Learn Two-Level System Defects in Quantum Systems

Resumen de: US20260289366A1

0000 Example aspects of the present disclosure provide systems and methods to learn machine-learned model parameters for models of quantum computing systems. In particular, example aspects of the present disclosure are directed to systems and methods to learn a deep neural network configured to predict parameter values for a physical model that models quantum dynamics of interactions between one or more qubits of a quantum gate and one or more two-level-system (TLS) defects during operation of the quantum gate through use of an evolutionary algorithm.

SYSTEM AND METHOD FOR IMPROVING CARDIOVASCULAR HEALTH OF HUMANS

Nº publicación: US20260290539A1 24/09/2026

Solicitante:

PROLAIO INC [US]
Prolaio, Inc.

Resumen de: US20260290539A1

An estimate of a functional capacity such as VO2Max is made by applying the vital signs of a monitored human to a trained encoding neural network producing a cardio profile vector. The vector is applied to a trained functional capacity (VO2Max) neural network to estimate the functional capacity. Once estimated, an action is taken.