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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.

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.

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.

ADVERSARIAL EXAMPLE PURIFICATION METHOD BASED ON CONDITIONAL DIFFUSION MODEL

Resumen de: WO2026123388A1

Disclosed in the present application is an adversarial example purification method based on a conditional diffusion model, comprising the following steps: acquiring a clean example dataset containing clean examples; using a white-box attack algorithm to attack a classification model to generate an adversarial example for each clean example; pairing the clean examples and the adversarial examples in one-to-one correspondence to form a training dataset; acquiring a pre-trained Stable Diffusion model; designing a fine-tuning process and a fine-tuning loss function; S6, presetting the number of iterations and a batch size, and using the training dataset to fine-tune network parameters of a cross-attention layer in the Stable Diffusion model to obtain a fine-tuned conditional diffusion model for adversarial example purification. In the present invention, an adversarial example is inputted as condition information into a UNet neural network to guide model learning, enabling the model to learn features of an adversarial perturbation, and improving computational efficiency, robustness and stability.

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.

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.

COMPUTER-READABLE RECORDING MEDIUM HAVING STORED THEREIN DATA SELECTION PROGRAM, INFORMATION PROCESSING APPARATUS, AND COMPUTER-IMPLEMENTED DATA SELECTION METHOD

Resumen de: US20260170336A1

A non-transitory computer-readable recording medium having stored therein a data selection program that causes a computer to execute a process including determining, for each of a plurality of pieces of graph structure data, a weight in data selection based on the number of adjacent nodes shared by two nodes for a set of the two nodes included in the graph structure data, and selecting training data to be used for training a neural network that predicts a presence or absence of a link between nodes included in the graph structure data input as input data from among the plurality of pieces of graph structure data based on the determined weight and information indicating a presence or absence of a link between nodes for each of one or more sets of two nodes.

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.

MULTI-AGENT REINFORCEMENT LEARNING WITH MATCHMAKING POLICIES

Resumen de: US20260170334A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.

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.

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.

TRAINING NEURAL NETWORKS FOR SENSOR DATA REPRESENTATION LEARNING THROUGH FUTURE FRAME PREDICTION

Resumen de: EP4760597A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining respective sensor data captured by one or more sensors of an autonomous vehicle at each of a sequence of time steps, the sequence of time steps comprising one or more context time steps followed by one or more prediction time steps; generating respective ground truth birds-eye-view (BEV) representations of the respective sensor data for each of the prediction time steps; for each prediction time step, processing the respective sensor data at one or more preceding time steps in the sequence using a future prediction neural network to generate a predicted BEV representation for the prediction time step; and training the future prediction neural network based on, for each prediction time step, an error between the ground truth BEV representation for the prediction time step and the predicted BEV representation for the prediction time step.

CHIP SYSTEM, IMAGE PROCESSING METHOD AND ELECTRONIC DEVICE

Resumen de: EP4760630A1

Embodiments of this application provide a chip system, an image processing method, and an electronic device, and are applied to the field of chip image processing technologies. The chip system includes a graphics processing unit, a neural-network processing unit, and a scheduling circuit. The graphics processing unit is configured to output a first rendered image based on an image subtask. The scheduling circuit is configured to indicate, based on a scheduling subtask, the neural-network processing unit to obtain the first rendered image. The neural-network processing unit is configured to: based on a computing subtask, perform neural network algorithm processing on the first rendered image, and output a computing result. The scheduling circuit is further configured to indicate, based on the scheduling subtask, the graphics processing unit to obtain the computing result. The graphics processing unit is further configured to perform image processing on the computing result based on the image subtask to obtain a second rendered image. In embodiments of this application, image processing is performed based on multi-IP coordination to improve imaging quality. This manner reduces power consumption and processing complexity of the chip system, and is applicable to different chip architectures.

INFRINGEMENT DETECTION METHOD, DEVICE AND SYSTEM

Resumen de: EP4760634A2

A method for detecting an infringement by vehicle operator is described. The method comprises detecting a vehicle; receiving one or more image of at least a part of the vehicle operator; automatically analysing with a neural network the one or more captured received image to detect an infringing act; and providing the one or more captured received images comprising the detected infringing act to thereby detect the infringement. Also described are a system, a device, a computer system and a computer program product all for detecting an infringement by a vehicle operator. The device may comprise one or more flash for illuminating the vehicle or a part thereof with light at a narrow band and one or more camera comprising a narrow band filter that lets through only the wavelengths of light produced by the one or more flash.

Radio frequency digital signal classification

Resumen de: GB2702430A

The invention relates to the field of Radio Frequency (RF) signal classification and recognition, specifically a method of training a Convolutional Neural Network (CNN) and applying the trained CNN for the classification or identification of digitally modulated RF signals. The training comprises the steps of providing time-based training signals, generating IQ representations of those signals, applying k-means clustering to the IQ representations and generating a vector diagram by preserving timing indices of the IQ representations. A combined k-means cluster and vector image is generated (see Figure 3b). Once trained, the model is suitable for classifying digital signals, particularly digitally modulated communication signals. Figs. 2b & 3b

Configurable task prompts for neural network document transcription

Resumen de: GB2702481A

A processor comprises circuits causing a neural network 320 to generate a document transcription 354 of a document image 321 according to a configurable combination of annotation types 325 provided within input 302 to the neural network, wherein the document transcription comprises respective annotations of the annotation types for corresponding portions of content included in the document transcription. The document transcription may comprise a sequence of tokens, wherein the respective annotations correspond to description tokens in the sequence and the portions of content correspond to content tokens. The configurable combination of annotation types may comprise respective indicators that enable or disable individual annotation types of the plurality of annotation types. Annotation corresponding to disabled annotation types are not included in the document transcription. Annotation types may be specified in a multi-dimensional tuple and comprise: bounding boxes, semantic class labels, structured text, or plain text. The neural network may comprise a Vision Transformer (ViT) encoder 322, a compressor 324, and a decoder 326, wherein the document image 321 is input to the ViT encoder and the configurable combination of annotation types 325 is input to the decoder, and wherein the decoder outputs the document transcription. figure 3

TRAINING APPARATUS, ESTIMATION APPARATUS, TRAINING METHOD, ESTIMATION METHOD, AND PROGRAM

Nº publicación: EP4760728A1 17/06/2026

Solicitante:

LG ENERGY SOLUTION LTD [KR]
LG Energy Solution, Ltd.

Resumen de: EP4760728A1

The present disclosure is designed to improve the accuracy of total energy estimation by neural network potential (NNP). An aspect of the present disclosure provides a learning apparatus including a control unit to carry out learning of an estimation model which is a mathematical model for energy estimation of an analysis target atom based on an evaluation target feature quantity indicating a first sum to a u-th sum, wherein the u-th sum is a sum of a three-dimensional (3D) wave function representing a u-th atomic orbital of the evaluation target atom located in a system including one or more atoms in descending order of occupied energy potential, and a 3D wave function of a u-th atomic orbital of each of other atoms in the system within a predetermined range of distances from the evaluation target in descending order of occupied energy potential, wherein in the learning, the estimation model is updated to reduce a difference between the sum of results of executing the estimation model for each atom in the system and the energy of the system estimated by density functional theory.