Alerta

SYSTEMS AND METHODS FOR AUTOFOCUS AND AUTOMATED CELL COUNT USING ARTIFICIAL INTELLIGENCE

Resumen de: US20260177470A1

Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and/or pixel locations of objects based on the seed likelihood image.

IMAGE GENERATION USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260179197A1

Apparatuses, systems, and techniques are presented to generate one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images based, at least in part, on one or noise values.

NEURAL NETWORK CIRCUIT AND NEURAL NETWORK OPERATION METHOD

Resumen de: US20260178895A1

A neural network circuit having multiple neural network operation cores having convolution operation circuits that perform convolution operations and quantization operation circuits that perform quantization operations, wherein the multiple neural network operation cores are connected so as to be able to input and output data.

ANTI-OVERFITTING LIGHTWEIGHT ANOMALY DETECTION NEURAL NETWORK MODEL RETRAINING METHOD

Resumen de: US20260178961A1

Disclosed in the present invention is a lightweight anomaly detection neural network model retraining method with anti-overfitting, which retrains an anomaly detection model based on depth variational autoencoders. When a data distribution changes, a conditional distribution of a hidden state and reconstructed data samples obtained by an encoder and a decoder of the depth variational autoencoders will also change. The present invention uses a mapping function to adjust the conditional distribution of the hidden state and the reconstructed data obtained by the calculation of an old model to adapt to a new data distribution. The mapping function has simple and convex characteristics, and can ensure a fast convergence rate and light overhead in a retraining process on a premise of using a loss function form defined by the present invention. In addition, the present invention provides a rumination module for data enhancement of new observation data to solve a problem of insufficient new observation sample data in an initial period when cloud service characteristics change.

SYSTEM AND METHOD FOR AUTOMATED MULTI-SPEAKER AND MULTI-LINGUAL SPEECH ANALYSIS

Resumen de: US20260178838A1

Exemplary system and methods use a combination of application modules and neural network architecture for multi-speaker and multi-language speech analysis. The exemplary system can receive a natural language input, which it decomposes into plural segments. A sub-group of the plural segments are accumulated in a buffer where each segment representing a period during which voice activity is detected. The sub-groups are analyzed for voice activity of multiple speakers and one or more text segments are generated based on the speakers. A semantic vector for each text segment is generated and stored in vector memory. Relevant data associated with each semantic vector is retrieved from the vector memory based on a similarity measure; and a response including specified information extracted from the one or more text segments is generated based on at least the relevant data.

METHOD AND DEVICE FOR PERFORMING FEDERATED LEARNING IN SATELLITE COMMUNICATION SYSTEM

Resumen de: US20260178932A1

0000 A method for performing federated learning in a satellite communication system includes receiving, at a base station device, association information from each of all satellites within a coverage, setting, one or more satellites available for training among the connected satellites as training satellites based on the received association information, generating, training information for each of the training satellites based on the received association information, transmitting, the generated training information and a global neural network model to each of the training satellites, receiving, each of local neural network models whose training has been completed from the training satellites, and transmitting, the received local neural network models to a server.

GENERATING DOCUMENT-GROUNDED TRAINING DATA FOR GENERATIVE MODELS

Resumen de: US20260178924A1

0000 The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a training dataset for AI agents by using large language models to simulate a conversation between a user and an AI agent. In some embodiments, the disclosed systems determine a synthetic persona by selecting a plurality of characteristics defining the synthetic persona. In some embodiments, the disclosed systems generate a synthetic prompt emulating text input by the synthetic persona utilizing a large language model to process a digital document associated with the synthetic persona. In some embodiments, the disclosed systems generate a synthetic response emulating text generated by an artificial intelligence agent responsive to the text input by the synthetic persona utilizing a second large language model to process the synthetic prompt. In some embodiments, the disclosed systems modify parameters of a neural network using the synthetic prompt and the synthetic response as training data.

DATA-FREE POST-TRAINING QUANTIZATION METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIUM

Resumen de: EP4764966A1

This application provides a data-free post-training quantization method and apparatus, a device, and a storage medium, and relates to the field of neural network technologies. The method includes: obtaining data distribution input by a user, where the data distribution is distribution to which an input activation value of each network layer of a floating-point model conforms; inputting random data into the floating-point model to obtain the input activation value of each network layer; performing statistical analysis on the input activation value of each network layer based on the data distribution, to obtain a data range of the input activation value of each network layer; determining a quantization parameter of the input activation value of each network layer based on endpoint values of the data range; and during inference by using the floating-point model, performing, by using the quantization parameter of the input activation value of each network layer, quantization processing on the input activation value generated during inference of each network layer. According to the solution of this application, quantization processing can be performed on an input activation value in a data-free manner.

DATA PROCESSING METHOD AND APPARATUS

Resumen de: EP4765041A1

A data processing method is provided. The method is applied to image processing and includes: obtaining first data collected by an image sensor; and obtaining spectral information based on the first data by using a neural network model, where the neural network model includes an attention module, and the attention module is configured to determine an attention matrix based on input data, and perform an attention operation based on the attention matrix, where the attention matrix is obtained by performing a first fusion operation on correlation information between different channels of the input data and correlation information of the channels. In this application, a degree of correlation between the different channels and a degree of correlation of the channels may be fused, so that the attention matrix can model both correlation and particularity between the different channels, thereby improving accuracy of spectral signal reconstruction.

GENERATING AUDIO USING AUTO-REGRESSIVE GENERATIVE NEURAL NETWORKS

Resumen de: EP4765100A2

0001 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal; obtaining a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.

METHOD AND ANALYSIS DEVICE FOR CROP YIELD PREDICTION BASED ON GRAPH NEURAL NETWORK

Resumen de: KR20260096403A

0001a 그래프 신경망 기반의 작물 수확량 예측 방법은 분석장치가 타깃 작물의 작물 특성과 참조 작물들의 작물 특성의 유사도를 연산하는 단계, 상기 분석장치가 상기 타깃 작물의 재배 환경 특성과 상기 참조 작물들의 재배 환경 특성의 유사도를 연산하는 단계, 상기 분석장치가 상기 작물 특성에 대한 유사도 및 상기 재배 환경 특성에 대한 유사도를 기준으로 상기 타깃 작물과 상기 참조 작물들 각각의 최종 유사도를 연산하는 단계; 상기 분석장치가 상기 타깃 작물과 상기 참조 작물들을 노드들로 갖고, 상기 타깃 작물과 상기 참조 작물들의 유사도를 에지들로 갖는 유사도 그래프를 생성하는 단계 및 상기 분석장치가 상기 유사도 그래프를 사전에 학습된 그래프 신경망에 입력하여 출력되는 값을 기준으로 상기 타깃 작물의 수확량을 예측하는 단계를 포함한다.

ELECTROCARDIOGRAM-SIGNAL-BASED ASSISTED IDENTIFICATION METHOD, APPARATUS AND SYSTEM FOR ATTENTION DEFICIT HYPERACTIVITY DISORDER

Resumen de: WO2026124091A1

An electrocardiogram-signal-based assisted identification method, apparatus and system for attention deficit hyperactivity disorder. The method comprises: (1) collecting electrocardiogram data of a subject for whom the risk level of attention deficit hyperactivity disorder is required to be assessed, and processing the electrocardiogram data; (2) using a one-dimensional convolutional neural network to perform deep feature extraction on the processed electrocardiogram data of said subject, generating a classification heatmap from a feature map of the convolutional neural network by means of Score-CAM, and extracting a time-domain feature, a frequency-domain feature and a local statistical feature from the generated classification heatmap; and (3) inputting the time-domain feature, the frequency-domain feature and the local statistical feature into a machine learning classifier for classification, so as to obtain an attention deficit hyperactivity disorder risk assessment result of said subject. Deep learning is combined with various machine learning methods, so that the classification performance is improved, and enhanced feature interpretability is also provided.

METHOD AND APPARATUS WITH NEURAL NETWORK DATA PROCESSING

Resumen de: US20260170820A1

0000 A processor-implemented neural network data processing method includes: determining a total number of either one of a first feature value and values less than or equal to the first feature value, in feature data output from a layer of a neural network; determining a quantization parameter based on the determined number; quantizing the feature data based on the determined quantization parameter; and inputting the quantized feature data to a another layer of the neural network connected to the layer.

INTELLIGENT OPTIMIZATION METHOD AND SYSTEM FOR OPERATION OF WASTE COMBUSTION DEVICE, AND MEDIUM

Resumen de: WO2026123973A1

The present application provides an intelligent optimization method and system for the operation of a waste combustion device, and a medium. The method comprises: acquiring a preset number of pieces of historical input variable data and historical output variable data of a waste combustion device, then classifying the historical input variable data and the corresponding historical output variable data to obtain a training data set and a test data set; acquiring key parameters of a preset gradient-boosted tree, and performing training to obtain an updated gradient-boosted tree model; performing calculation on the basis of the updated gradient-boosted tree model to obtain an input variable importance index, and obtaining an optimized input variable by comparing the input variable importance index with a threshold; performing processing by means of a multi-layer BP neural network and a particle swarm optimization algorithm to obtain a thermal efficiency prediction model; processing the thermal efficiency prediction model by means of a reinforcement learning algorithm to obtain optimal input variable feature data. Therefore, intelligent optimization of the waste combustion device is realized by means of the gradient-boosted tree model, the multi-layer BP neural network and the particle swarm optimization algorithm, thereby reducing the power generation costs of the waste combustion device.

SENTIMENT CLASSIFICATION METHOD AND SYSTEM FOR SOCIAL NETWORK DYNAMICS, DEVICE AND STORAGE MEDIUM

Resumen de: WO2026123426A1

A sentiment classification method and system for social network dynamics, a device, and a storage medium. The method comprises: preprocessing a text of social dynamics to obtain a preprocessed data set; on the basis of the data set, constructing a semantic graph comprising word nodes and social dynamics nodes; extracting associated information between the social dynamics on the basis of topic attributes of the social dynamics in the semantic graph and inter-user relationships of users who publish the social dynamics, and establishing a connection relationship between the social dynamics nodes on the basis of the associated information between the social dynamics, so as to obtain a multi-layer social dynamics graph comprising a semantic relationship and a social relationship; and inputting the multi-layer social dynamics graph into an integrated model for processing, to obtain a sentiment classification result of the social dynamics, wherein the integrated model is composed of a hyperbolic learning-based graph convolutional neural network and a large-scale pre-trained language model.

NEURAL NETWORK SCHEDULING MECHANISM

Resumen de: US20260170596A1

An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

NEURAL NETWORK PROCESSOR AND METHOD OF NEURAL NETWORK PROCESSING

Resumen de: US20260170291A1

A neural network processor is provided comprising a plurality of mutually succeeding neural network processor layers is provided. A neural network processor layer therein comprising a plurality of neural network processor elements (1) having a respective state register (2) for storing a state value (X) indicative for their state, as well as an additional state register (4) for storing a value (Q) of a state value change indicator that is indicative for a direction of a previous state change exceeding a threshold value. Neural network processor elements in a neural network processor layer are configured to selectively transmit differential event messages indicative for a change of their state, dependent both on the change of their state value and on the value of their state value change indicator.

TWO-STREAM LSTM METHOD FOR PREDICTING POWER LOAD OF PORT SHORE

Resumen de: US20260171803A1

The present disclosure relates to the technical field of electric power engineering, in particular to a two-stream Long Short-Term Memory (LSTM) method for predicting power load of port shore power. Loads. The method entails collecting longitudinal data to identify factors that affect power load data, performing correlation analysis to classify dominant and auxiliary features power loads; separately modeling the dominant and auxiliary features and generating a fusion feature map; constructing a Bayesian Optimization-Long Short-Term Memory (BO-LSTM) neural network, and inputting a fusion feature map into a two-stream time series learning module, extracting a deep representation of the dominant and auxiliary features, then introducing a channel attention mechanism is to weight a fusion feature vector, and outputting a power load prediction value by a residual correction module. The present disclosure significantly improves the prediction accuracy and robustness, and supports the real-time scheduling of the port shore power system.

HYDROGEL MICROSPHERE SORTING METHOD, ARTIFICIAL ORGAN PREPARATION METHOD, AND SYSTEM AND MEDIUM

Resumen de: WO2026124687A1

A hydrogel microsphere sorting method, an artificial organ preparation method, and a system and a medium. The sorting method comprises the following steps: collecting a hydrogel microsphere image in a microfluidic chip; analyzing the hydrogel microsphere image by means of a neural network, so as to obtain an analysis result; and on the basis of the analysis result, sorting hydrogel microspheres encapsulating target cells, wherein the neural network comprises a feature extraction layer and a multi-scale joint output head, and the hydrogel microsphere image is subjected to convolution by means of the feature extraction layer and is then processed by the multi-scale joint output head, so as to obtain the analysis result. Hydrogel microspheres encapsulating target cells are accurately identified and sorted by using droplet microfluidic technology, and standardized artificial organs can be prepared on the basis of the sorted hydrogel microspheres, thereby significantly improving the functional maturity and reliability of the artificial organs.

SEGMENTATION USING AN UNSUPERVISED NEURAL NETWORK TRAINING TECHNIQUE

Resumen de: US20260170658A1

Systems and methods to detect one or more segments of one or more objects within one or more images based, at least in part, on a neural network trained in an unsupervised manner to infer the one or more segments. Systems and methods to help train one or more neural networks to detect one or more segments of one or more objects within one or more images in an unsupervised manner.

Neural Network-based Predictions of Activity Corresponding to Digital Components

Resumen de: US20260170351A1

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training a neural network. In particular, a network training engine trains the neural network by processing a training dataset that includes one or more sequences of real-world statistical data using feature selection processes and applying Bayesian optimization such that, once the neural network has been trained, the neural network can accurately predict activity of a digital component for one or more time periods.

A COMPUTER-IMPLEMENTED METHOD FOR OPTIMIZING A NEURAL NETWORK MODEL FOR 3D OBJECT DETECTION

Resumen de: WO2026127827A1

A computer-implemented method for optimizing a neural network model for three-dimensional (3D) object detection. The method comprises receiving a pretrained 3D object detection model with multiple neural network layers and computes a layer-wise sparsity allocation across the detection model based on a predefined computational constraint. The layer-wise sparsity allocation is transformed into a layer-wise pruning ratio for each layer using second-order Hessian-based rate-distortion analysis, where the pruning ratio minimizes distortion in detection outputs. The computed pruning ratios are applied to remove redundant weights from each layer of the model, producing a pruned, pretrained 3D object detection model. This method reduces computational complexity while maintaining detection accuracy, making it suitable for real-time 3D perception applications.

GENERATIVE NEURAL NETWORKS WITH EFFECTIVE AUDIO TOKEN PROCESSING

Resumen de: WO2026128699A1

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for processing a received network input that includes audio data using a generative neural network to generate an output sequence that represents a transcription of speech included in the audio data. Then processing the output sequence of output tokens to generate a speech recognition output. One of the described techniques include training the generative neural network to generate outputs that interleave audio and text tokens. Another of the described techniques includes receiving and generating audio at the same time step.

METHOD AND SYSTEM FOR AUGMENTING GRAPH DATA

Resumen de: US20260170295A1

A computer-implemented method for augmenting graph data for use in training a graph neural network (GNN) includes: receiving input data, generating original graph data based on the input data, generating one or more knowledge graphs based on context related inputs, augmenting the original graph data by applying the knowledge graphs to generate augmented graph data, and; training a graph neural network (GNN) using the augmented graph data. The GNN is trained to extract relational data in the input data. One or more knowledge graphs are generated by a large language model (LLM) by prompting the LLM with context related text inputs. The method also includes dynamically merging the one or more knowledge graphs with the original graph, wherein the one or more knowledge graphs are stochastically integrated with the original graph.

CONTROL SYSTEM WITH NEURAL NETWORK PREDICTOR INCORPORATING FIRST PRINCIPLES

Nº publicación: WO2026126063A1 18/06/2026

Solicitante:

IMUBIT ISRAEL LTD [IL]
IMUBIT ISRAEL LTD.

Resumen de: WO2026126063A1

A predictive control system for a plant includes a predictor model trainer and a predictive controller. The predictor model trainer is configured to train a predictor model using a loss function including (i) a first error loss term based on an error between predicted values of controlled variables (CVs) generated by the predictor model and historical values of the CVs in historical state data and (ii) a second error loss term based on the predicted values of the CVs and physical relationships involving the CVs. The predictive controller is configured to control operation of the plant using the trained predictor model.