Alerta

METHODS FOR NON-REFERENCE VIDEO-QUALITY PREDICTION

Absstract of: US2025350742A1

A system for non-reference video-quality prediction includes a video-processing block to receive an input bitstream and to generate a first vector, and a neural network to provide a predicted-quality vector after being trained using training data. The training data includes the first vector and a second vector, and elements of the first vector include high-level features extracted from a high-level syntax processing of the input bitstream.

SYSTEMS AND METHODS FOR DETERMINING CONTEXT SWITCHING IN CONVERSATION

Absstract of: US2025348685A1

Systems and methods are described to address shortcomings in a conventional conversation system via a novel technique utilizing artificial neural networks to train the conversation system whether or not to continue context. In some aspects, an interactive media guidance application determines a type of conversation continuity in a natural language conversation comprising first and second queries. The interactive media guidance application determines a first token in the first query and a second token in the second query. The interactive media guidance application identifies entity data for the first and second tokens. The interactive media guidance application retrieves, from a knowledge graph, graph connections between the entity data for the first and second tokens. The interactive media guidance application applies this data as inputs to an artificial neural network. The interactive media guidance application determines an output that indicates the type of conversation continuity between the first and second queries.

METHODS AND SYSTEMS TO DETERMINE SHAPES FOR SEMICONDUCTOR OR FLAT PANEL DISPLAY FABRICATION

Absstract of: US2025347990A1

Methods for calculating a pattern to be manufactured on a substrate include inputting a physical design pattern, determining a plurality of possible neighborhoods for the physical design pattern, generating a plurality of possible mask designs for the physical design pattern, calculating a plurality of possible patterns on the substrate, calculating a variation band from the plurality of possible patterns, and modifying the physical design pattern to reduce the variation band. Embodiments also include inputting a set of parameters for a neural network to calculate a pattern to be manufactured on a substrate, calculating a plurality of patterns to be manufactured on the substrate for the physical design in each possible neighborhood of the plurality of possible neighborhoods, training the neural network with the calculated plurality of patterns, and adjusting the set of parameters to reduce the manufacturing variation for the calculated plurality of patterns to be manufactured on a substrate.

BUILDING MANAGEMENT SYSTEM WITH SUPERVISORY FAULT DETECTION LAYER

Absstract of: US2025348063A1

A method for correcting faults in a building management system (BMS) includes receiving time series data characterizing an operating performance of one or more BMS devices, generating a first fault detection result by processing the time series data using a first fault detection technique, generating a second fault detection result that conflicts with the first fault detection result by processing the time series data using a second fault detection technique different than the first fault detection technique, resolving a conflict between the first fault detection result and the second fault detection result by applying both the first and second fault detection results as inputs to a neural network configured to output an indication of whether a fault condition is occurring in the BMS, and initiating an action to resolve the fault condition in response to the indication indicating that the fault condition is occurring in the BMS.

METHOD AND APPARATUS WITH DECODING IN NEURAL NETWORK FOR SPEECH RECOGNITION

Absstract of: US2025349311A1

A decoding method includes receiving an input sequence corresponding to an input speech at a current time; and in a neural network (NN) for speech recognition, generating an encoded vector sequence by encoding the input sequence, determining reuse tokens from candidate beams of two or more previous times by comparing the candidate beams of the previous times, and decoding one or more tokens subsequent to the reuse tokens based on the reuse tokens and the encoded vector sequence.

THERMAL OPTIMIZATION AND CONTROL IN OPEN-PLAN SPACES USING PHYSICS-INFORMED GRAPH NEURAL NETWORK BASED OPTIMAL CONTROLLER

Absstract of: US2025341329A1

Optimal control of multiple heating, ventilation, and air-conditioning units in an open-plan space demands fast and accurate thermodynamic modeling. Prior methods lack scalability required for effective control in large open-plan offices primarily due to air-mixing interactions. The present disclosure describes a physics-informed graph neural network (PI-GNN) to overcome these challenges. Specifically, thermodynamic interactions are modeled as edges between nodes that represent cells. Further, a modeling approach is used that allows explicit modeling of wall and window surface temperatures which are commonly ignored. The method of present disclosure utilizes PI-GNN as a state-estimator that employs a receding-horizon approach for optimal HVAC control. PI-GNNs are adapted for building HVAC control by incorporating a time-resetting strategy to handle time-dependent ambient conditions and therefore set-points. The method of the present disclosure outperforms a regular PINN model and other baseline control strategies on thermal model accuracy, computation time, energy consumption, and user comfort.

HISTOPATHOLOGY-BASED SOLID TUMOUR ANALYSIS

Absstract of: US2025342974A1

A computer-implemented method is provided, for predicting a risk factor for a patient based on histopathological image analysis. The method includes: receiving at least one histological image of a solid tumour; converting the histological image into a graph representation; processing the graph representation using a neural network, wherein the neural network comprises a graph isomorphism network and a convolutional neural network; and determining the risk factor based on an output of the neural network. Also provided is a method of training one or more neural networks for use in such a method.

DEVICE, METHOD, AND PROGRAM FOR ENHANCING OUTPUT CONTENT THROUGH ITERATIVE GENERATION

Absstract of: US2025342836A1

A method of improving output content through iterative generation is provided. The method includes receiving a natural language input, obtaining user intention information based on the natural language input by using a natural language understanding (NLU) model, setting a target area in base content based on a first user input, determining input content based on the user intention information or a second user input, generating output content related to the base content based on the input content, the target area, and the user intention information by using a neural network (NN) model, generating a caption for the output content by using an image captioning model, calculating similarity between text of the natural language input and the generated output content, and iterating generation of the output content based on the similarity.

CHANNELWISE FEATURE REORIENTATION AND MERGING WITH NEIGHBOURING FEATURES FOR ROBUST LOW COMPLEXITY DNN BASED AUDIO PROCESSING

Absstract of: WO2025228896A1

There is disclosed an apparatus (10) for processing an input information signal (20), comprising: * a feature extractor (100) for extracting a set of features (102) from the input information signal (20), the set of features (102) having a first shape, * a feature block generator (200, CR1) configured to reshape the set of features (102) from a first shape to a second shape by: - segmenting (210) the set of features (102) into at least 3 feature subsets (212, 212a-212h), each feature subset (212, 212a-212h) having a specific shape, - performing a subsampling (220, 220a, 220b) of the feature subsets (212) to generate a plurality of feature blocks (222, 222a-222d) by stacking a selected set of the subsampled feature subsets feature subsets (212, 212a-212h) along a first dimension, - creating (230) a feature hyper-block (332) of the second shape by stacking the plurality of feature blocks (222, 222a-222d) along a second dimension, * a neural network processor (300, D1 ) configured to process the feature hyper block (332) to obtain a first result (380); and * an output post-processor (P2, 700) configured to post-process the result (380) to construct an output information signal (40) and/or to output a classification result.

HISTOPATHOLOGY-BASED SOLID TUMOUR ANALYSIS

Absstract of: EP4645338A1

A computer-implemented method is provided, for predicting a risk factor for a patient based on histopathological image analysis. The method includes: receiving (110) at least one histological image of a solid tumour; converting (150) the histological image into a graph representation; processing (160) the graph representation using a neural network, wherein the neural network comprises a graph isomorphism network and a convolutional neural network; and determining (162) the risk factor based on an output of the neural network. Also provided is a method of training one or more neural networks for use in such a method.

Histopathology-based solid tumour analysis

Absstract of: GB2640747A

The method uses histopathology images preferably of H&E stained tissue samples collected from solid tumours of cancer patients to predict cancer relapse risk levels (known as prognosis) based on artificial intelligence technology. The method is for predicting a risk factor for a patient based on histopathological image analysis. The method includes: receiving 110 at least one histological image of a solid tumour; converting 150 the histological image into a graph representation comprising extracting 120 a region of interest from the histological image and constructing the graph representation based on the region of interest; processing 160 the graph representation using a neural network, wherein the neural network comprises a graph isomorphism network and a convolutional neural network; and determining 162 the risk factor based on an output of the neural network. Also provided is a method of training one or more neural networks for use in such a method. The training relies on a backpropagation algorithm.

CHANNELWISE FEATURE REORIENTATION AND MERGING WITH NEIGHBOURING FEATURES FOR ROBUST LOW COMPLEXITY DNN BASED AUDIO PROCESSING

Absstract of: EP4645165A1

There is disclosed an apparatus (10) for processing an input information signal (20), comprising:a feature extractor (100) for extracting a set of features (102) from the input information signal (20), the set of features (102) having a first shape,a feature block generator (200, CR1) configured to reshape the set of features (102) from a first shape to a second shape by:segmenting (210) the set of features (102) into at least 3 feature subsets (212, 212a-212h), each feature subset (212, 212a-212h) having a specific shape,performing a subsampling (220, 220a, 220b) of the feature subsets (212) to generate a plurality of feature blocks (222, 222a-222d) by stacking a selected set of the subsampled feature subsets feature subsets (212, 212a-212h) along a first dimension,creating (230) a feature hyper-block (332) of the second shape by stacking the plurality of feature blocks (222, 222a-222d) along a second dimension,a neural network processor (300, D1) configured to process the feature hyper block (332) to obtain a first result (380); andan output post-processor (P2, 700) configured to post-process the result (380) to construct an output information signal (40) and/or to output a classification result.

METHOD AND SYSTEM FOR PREDICTING HERB PAIRS COMPATIBILITY OF TRADITIONAL CHINESE MEDICINES

Absstract of: NL2037986A

Disclosed is a method and a system for predicting herb pairs compatibility of traditional 5 Chinese medicines, which comprises the following steps: collecting herb pairs of traditional Chinese medicines in ancient books and preprocessing the data of herb pairs of traditional Chinese medicines, and constructing a traditional Chinese medicine database and a herb pair compatibility database meeting the requirements of data mining; the traditional Chinese medicine database and the compatibility database of herb pairs are represented in network form to obtain 10 the graph network of herb pairs nodes; Learn the embedding feature expression of herb pair compatibility dimension and modern biological information dimension through graph-convolution neural network; based on the embedding feature representation, the final node embedding representation of herb pair compatibility samples are obtained, and the probability distribution of efficacy of herb compatibility combinations are obtained by prediction based on the final node 15 embedding representation. According to the invention, the traditional characteristic dimension and the modern credibility dimension are combined, and the ancient and modern multi-dimensional vector characteristics of herb pairs are considered, so that the information of traditional Chinese medicine can be mined, new herb compatibility combinations can be predicted aiming at efficacy, and a scheme is provided for clinical prescription.

ARTIFICIAL INTELLIGENCE MODELING TO PREDICT ELECTRONIC ACCOUNT DATA

Absstract of: US2025335967A1

Disclosed methods and system describe a server that uses AI modeling to predict negative cash flow at a user level. The server periodically retrieves data associated with the user, the data comprising monetary attributes associated with one or more accounts of the user; executes a deep neural network model trained based upon historical data associated with at least a subset of the users configured to predict a negative cash flow in one or more accounts of the user, a depth of the negative cash flow, and a duration of the negative cash flow; transmits, to a second server, the predicted values, whereby when the second server determines that a likelihood of account needs satisfies a threshold, the second server establishes an electronic communication session with an electronic device of the user; trains the deep neural network when the second server establishes the electronic communication session.

CODING OF AN EVENT IN AN ANALOG DATA FLOW WITH A FIRST EVENT DETECTION SPIKE AND A SECOND DELAYED SPIKE

Absstract of: US2025335754A1

A set of spike pattern data is stored on a set of storage neurons of a neural network. The storing includes storing first parameters indicative of a presence of spikes on respective neurons of the set of storage neurons, and storing, on the set of storage neurons of the neural network, second parameters indicative of a timing of spikes on the respective neurons of the set of storage neurons.

SYSTEM AND METHOD FOR PREDICTING FORMATION IN SPORTS

Absstract of: US2025336208A1

A system and method of predicting a team's formation on a playing surface are disclosed herein. A computing system retrieves one or more sets of event data for a plurality of events. Each set of event data corresponds to a segment of the event. A deep neural network, such as a mixture density network, learns to predict an optimal permutation of players in each segment of the event based on the one or more sets of event data. The deep neural network learns a distribution of players for each segment based on the corresponding event data and optimal permutation of players. The computing system generates a fully trained prediction model based on the learning. The computing system receives target event data corresponding to a target event. The computing system generates, via the trained prediction model, an expected position of each player based on the target event data.

SYSTEMS AND METHODS FOR COMPUTING FEATURING SYNTHETIC COMPUTING OPERATORS AND COLLABORATION

Absstract of: WO2025226986A1

One embodiment is directed to a synthetic engagement system for process-based problem solving, comprising: a computing system comprising one or more operatively coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator in accordance with a predetermined process configuration toward an established requirement based at least in part upon one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to proceed through the predetermined process configuration, and to return result to the human operator selected to at least partially satisfy the established requirement; and wherein each of the one or more synthetic operators is informed by a convolutional neural network informed at least in part by historical actions of a particular actual human operator and a synthetic operator background configuration.

Method and Apparatus for Inferencing By Fusing Pre-processing into Neural Network

Absstract of: KR20250152434A

본 실시예는 신경망에 전처리 기능을 융합한 추론방법 및 장치를 개시한다. 본 실시예에서, 추론 장치는, 다수의 레이어들을 포함하는 딥러닝 기반 신경망을 이용하여 입력 영상으로부터 입력 영상의 특징을 생성하고, 입력 영상의 특징에 기초하여 추론 결과를 생성한다. 신경망의 첫 번째 레이어는 신경망의 트레이닝 과정에서 적용된 입력 정규화를 융합하기 위해, 입력 정규화에 사용된 인자들에 기초하여 정규화된 가중치들 및 바이어스를 포함한다. 신경망의 첫 번째 레이어는 정규화된 가중치들 및 바이어스를 이용하여 입력 영상을 처리한다. 이때, 입력 정규화를 인자들은, 스케일링 인자, 평균 및 표준편차를 포함하고, 트레이닝 과정에서 활용되는 영상 데이터 전체로부터 산정된다.

AI-BASED SMART HEALTH SCREENING SYSTEM FOR IDENTIFYING INDIVIDUALS AT RISK OF DEVELOPING CANCERS, CARDIOVASCULAR DISEASE AND TYPE 2 DIABETES

Absstract of: WO2025219769A1

The invention is an AI-driven health screening system for early detection of cancers, cardiovascular diseases, and type 2 diabetes. It integrates multiple screening methods, using machine learning, deep learning, and natural language processing to analyze genetic and health data. A federated learning framework ensures high prediction accuracy while maintaining data privacy. The system also employs a blockchain-based electronic health record (EHR) for secure data management. AI models, including neural networks and support vector machines, assess risk factors and provide personalized healthcare recommendations. Designed with a three-tier architecture, it supports deployment as a web service, software, or integration into existing programs. The system enhances early disease detection, optimizes healthcare resources, and improves patient outcomes by offering a scalable, efficient, and non-invasive screening solution.

Semantic Segmentation to Identify and Treat Plants in a Field and Verify the Plant Treatments

Absstract of: US2025328760A1

A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Method And Apparatus for Optimizing Process Variables Based on Neural Network Inversion Algorithm

Absstract of: KR20250151661A

신경망 반전 알고리즘 기반 공정 변수 최적화를 위한 방법 및 장치를 개시한다. 본 개시의 일 측면에 의하면, 공정 변수의 최적화를 위한 컴퓨터 구현 방법으로서, 복수의 공정 변수 각각에 대한 초기 값을 포함하는 공정 변수 데이터를 획득하는 과정; 물성 예측 모델을 이용하여 상기 공정 변수 데이터로부터 물성을 예측하는 과정; 상기 예측된 물성과 목표 물성을 비교하여 손실 값을 계산하는 과정; 상기 복수의 공정 변수 각각에 대한 상기 손실 값의 편미분 값을 상기 복수의 공정 변수 각각의 그래디언트로 산출하는 과정; 공정 변수 별로 산출된 그래디언트 및 기 설정된 제약에 기초하여 상기 공정 변수 데이터를 갱신하는 과정; 및 상기 손실 값이 기 정의된 임계값 미만이 될 때까지 상기 물성을 예측하는 과정부터 상기 공정 변수 데이터를 갱신하는 과정까지를 반복 수행하는 과정을 포함하는 방법을 제공한다.

DYNAMIC DETERMINATION OF INFERENCE-TIME PARAMETERS

Absstract of: EP4636650A1

Some embodiments are directed to a method for dynamic determination of inference-time parameters to control the stochastic generation process of a generative neural network. The method may include dynamically determining for an inference request, at least from operational context information, at least one of the inference-time parameters.

SYSTEM AND METHOD FOR VARIATIONAL ANNEALING TO SOLVE FINANCIAL OPTIMIZATION PROBLEMS

Absstract of: AU2024204682A1

A system and method for variational annealing to solve financial optimization problems is provided. The financial optimization problem is encoded as objective function represented in terms of an energy function. An autoregressive neural network is trained to minimize the cost function via variational emulation of classical or quantum annealing. Optimal solutions to the financial optimization problem are obtained after a stopping criterion is set. An optimal solution may be selected according to user defined metrics, and optionally applied to a real-world system associated with the financial optimization problem. A system and method for variational annealing to solve financial optimization problems is provided. The financial optimization problem is encoded as objective function represented in terms of an energy function. An autoregressive neural network is trained to minimize the cost function via variational emulation of classical or quantum annealing. Optimal solutions to the financial optimization problem are obtained after a stopping criterion is set. An optimal solution may be selected according to user defined metrics, and optionally applied to a real-world system associated with the financial optimization problem. ul u l s y s t e m a n d m e t h o d f o r v a r i a t i o n a l a n n e a l i n g t o s o l v e f i n a n c i a l o p t i m i z a t i o n p r o b l e m s i s p r o v i d e d h e f i n a n c i a l o p t i m i z a t i o n p r o b l e m i s e n c o d e d a s o b

TOKEN SELECTION IN TRANSFORMER NEURAL NETWORKS FOR EFFICIENT INFERENCING

Absstract of: US2025322275A1

Certain aspects of the present disclosure provide techniques and apparatus for processing data using a transformer neural network. The method generally includes generating, via a first attention layer of a machine learning model, a first attention map based on an input into the machine learning model; identifying, using a token prediction model, a first subset of tokens in the first attention map more relevant to a second attention layer of the machine learning model and a second subset of tokens in the first attention map less relevant to the second attention layer of the machine learning model; generating, via the second attention layer of the machine learning model, a second attention map based on the first subset of tokens in the first attention map; and generating an inference based on the second attention map and the second subset of tokens in the first attention map.

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR GLOBAL PERSONALIZED RECOMMENDATION

Nº publicación: WO2025217351A1 16/10/2025

Applicant:

VISA INT SERVICE ASS [US]
VISA INTERNATIONAL SERVICE ASSOCIATION

Absstract of: WO2025217351A1

Methods, systems, and computer program products for providing global personalized recommendations are provided. An example method may include generating embeddings for a first plurality of entities based on a first dataset, determining first identifiers of the first plurality of entities included in the first dataset that corresponds to second identifiers of a second plurality of entities included in a second dataset to provide a matched set of entities, wherein the second dataset includes attribute data associated with each entity of the second plurality of entities, generating a graph representation of the second plurality of entities, and wherein the graph includes nodes and each node represents an entity of the second plurality of entities, determining one or more first nodes that lacks data associated with a node embedding, and generating data associated with the node embedding for the one or more first nodes using a graph neural network (GNN) machine learning model.