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129
results.
Updated on
29/10/2026 [08:21:00]
Absstract of: EP4831848A1
Provided are a method and a system for detecting a smart contract state manipulation attack based on a graph neural network, a device, and a medium, belonging to the technical field of blockchain security analysis. The method includes: defining the type of the smart contract state manipulation attack, collecting attack smart contracts and normal smart contracts on a blockchain, and constructing a training data set; positioning a creator of a target smart contract, and constructing a historical external transaction graph; decompiling the target smart contract, and constructing an external call behavior graph; combining the historical external transaction graph with the external call behavior graph, and constructing a creation behavior graph of a smart contract; constructing a classifier of a state manipulation attack; and monitoring a smart contract created in real time, constructing the creation behavior graph, and inputting the creation behavior graph into the classifier to detect the state manipulation attack in real time. Blockchain nodes can be connected, a newly deployed smart contract is acquired in real time, the creation behavior graph of the smart contract is constructed, and the creation behavior graph is input into a graph classification model to detect a state manipulation attack smart contract.
Absstract of: EP4831881A1
System and method for monitoring mixed flows of people and vehicles in rural and natural environments. The system comprises person and vehicle detection devices (2) equipped with a millimetre-wave radar (5) and a data processing unit (8) the purpose of which is to:- classify the moving point cloud (10) of each radar scan using a previously trained artificial neural network (18) that receives as input the distance (12), the azimuth angle (13), the elevation angle (14), the Doppler velocity (15), and the signal-to-noise ratio (16) of each point (11) in the cloud of moving points (10), to yield a list (20) of targets classified by type (23) as a person (24) or a specific vehicle (28, 29, 30) from a list of vehicles (25),- determine a trajectory (27) for each target (21),- count each type (23) of target the trajectory (27) of which crosses a counting line (33).A central server (3) monitors the flows of each type (23) of target using the count information (42).
Absstract of: EP4831935A1
0001 A path loss determination method and apparatus, a storage medium, and an electronic device are provided. The method includes: determining (S202) a superimposed field image of a target grid scene on the basis of a first image, a second image, and an incident field image of the target grid scene; determining (S204) a first loss function on the basis of the incident field image and the superimposed field image, and determining (S204) a second loss function on the basis of the superimposed field image and drive test data of the target grid scene; determining (S206) a target loss function on the basis of the first loss function and the second loss function; and training (S208) a neural network model on the basis of the target loss function, so as to determine the path loss of the target grid scene by means of the trained neural network model.
Absstract of: EP4831877A2
0001 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using self-attention based neural networks. One of the methods includes obtaining one or more images comprising a plurality of pixels; determining, for each image of the one or more images, a plurality of image patches of the image, wherein each image patch comprises a different subset of the pixels of the image; processing, for each image of the one or more images, the corresponding plurality of image patches to generate an input sequence comprising a respective input element at each of a plurality of input positions, wherein a plurality of the input elements correspond to respective different image patches; and processing the input sequences using a neural network to generate a network output that characterizes the one or more images, wherein the neural network comprises one or more self-attention neural network layers.
Absstract of: US20260332790A1
Apparatuses, systems, and techniques to perform compression of video data using neural networks to facilitate video streaming, such as video conferencing. In at least one embodiment, a sender transmits to a receiver a key frame from video data and one or more keypoints identified by a neural network from said video data, and a receiver reconstructs video data using said key frame and one or more received keypoints.
Absstract of: US20260329963A1
A processor-implemented method for reformulating cumulative summation operation in an artificial neural network includes receiving a set of program code. A target operation in the set of program code is identified. The target operation includes a cumulative summation operation. The target operation in the set of program code is reformulated using a first matrix multiplication operation to produce a reformulated target operation. An updated set of program code including the reformulated target operation is output.
Absstract of: WO2026222472A1
An artificial intelligence device (AID) is disclosed that is configured for a specific, predetermined use case and is operable without access to a remote server during normal operation. The AID comprises a processor, a first memory storing at least one AI decision matrix comprising trained neural network weight values, a database storing response templates and vocabulary data, one or more microphones, and a user interface including a touchscreen and speaker. The processor applies the AI decision matrices to user input signals to generate analysis results and constructs responses by querying the database. The AI decision matrices are derived through a supervised training process. The AID may include a self-erase routine triggered by tamper detection, timer expiration, or remote command. A system and methods for operating and training the AID are also disclosed.
Absstract of: WO2026218095A1
The described technology pertains to a system for monitoring and controlling industrial or manufacturing processes, including additive manufacturing processes such as laser powder bed fusion. A sensor subsystem captures process data from at least one sensor, and, in some embodiments, contextual process data is also used. A defect monitoring subsystem comprises a sensor fusion module that fuses the captured data into a unified representation using a neural-network-based data pipeline with variational autoencoder models, and a decision-making module that generates a defect classification output and a defect probability score, and employs a defect detection dictionary to generate a defect detection indication. A process control subsystem may dynamically adjust one or more process parameters based on outputs of the defect monitoring subsystem, and a sensor simulation subsystem may validate proposed control parameter changes by generating synthetic sensor signals.
Absstract of: US20260330826A1
0000 This application relates to the field of sonar technology and discloses a wall recognition method and system for a sonar image, a device, and a storage medium. The method includes: inputting target sonar image data into a pre-trained anomaly detection model for classification prediction of whether each pixel is corresponding to a wall, to obtain a wall recognition result, where training steps for the anomaly detection model include: acquiring training data, using the training data to train an initial model for classification prediction of whether each pixel is corresponding to a wall, to obtain the anomaly detection model, where an anomaly detection network based on a convolutional neural network (CNN) is used for the initial model, and the training data includes sonar image sample data and a target label matrix. accurate presentation of various wall details, and improved wall recognition accuracy.
Absstract of: WO2026218787A1
Disclosed in the present invention is a natural earthquake hazard analysis method based on an artificial neural network, the method comprising: S1, acquiring a target analysis region, and dividing the target analysis region into a plurality of sub-regions; S2, acquiring a historical earthquake dataset corresponding to the target analysis region and performing preprocessing; S3, performing sub-region labeling on the preprocessed dataset to obtain a plurality of focus sub-regions and non-focus sub-regions; S4, acquiring data subsets corresponding to the focus sub-regions and the non-focus sub-regions, and inputting the data subsets into an artificial neural network for feature extraction; S5, calculating the similarities between the non-focus sub-regions and the plurality of focus sub-regions; S6, using the historical earthquake dataset to train a convolutional neural network, so as to obtain an earthquake prediction model; and S7, inputting the data subsets of different regions into the earthquake prediction model for prediction, so as to obtain an earthquake prediction result, and outputting an earthquake hazard analysis report on the basis of the earthquake prediction result. The method realizes earthquake hazard analysis for multiple regions and potential earthquake regions.
Absstract of: US20260330202A1
0000 A system and method of mitigating hallucinations may include receiving an input data signal, comprising a sequence of input data segments, and processing the input data segments through an encoder of a transformer-based model, to generate a respective sequence of encoding vectors. The encoding vectors may be processed through a decoder of the transformer-based model, to generate a set of candidate sequences, each representing a first transformation of the input data signal. A deep Neural Network may be applied on the encoding vectors, to obtain a reference sequence, representing a second transformation of the input data signal. Each of the candidate sequences may be aligned with the reference sequence. For each candidate sequence of the set of candidate sequences, a respective probability score may be computed to indicate a quality of its respective alignment. A candidate sequence may be identified as an hallucination, based on its respective probability score.
Absstract of: US20260330705A1
0000 A computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene.
Absstract of: WO2026221322A1
A real-time artificial ventilation adequacy evaluation feedback system based on image data, according to one embodiment of the present invention, comprises: a photographing module, which photographs a patient and an practitioner so as to generate image data for artificial ventilation; an artificial intelligence module using a pre-learned artificial neural network to recognize movements performed by the practitioner and a response of the patient from the image data and evaluate the adequacy of the artificial ventilation; and an output module for providing real-time feedback about the artificial ventilation on the basis of the adequacy evaluation results.
Absstract of: US20260330820A1
A device for automatically calculating window set information using artificial neural networks according to an embodiment comprises a pre-trained artificial neural network module calculating window set information having a first input information comprising image information as input information, and a window set information extracting window set characteristics from the image information as output information and a feature extractor for extracting a 1-1 feature map and a 1-2 feature map having different scale values for the image information, in the artificial neural network module calculating window set information, wherein the artificial neural network module calculating window set information is generating a 3-1 feature map and a 3-2 feature map by applying convolution to the 1-1 feature map and the 1-2 feature map, respectively, and then determining one of the 3-1 feature map and the 3-2 feature map as a final feature map based on a preset criteria, and then calculating the window set information based on the selected final feature map.
Absstract of: AU2025230958A1
The present invention provides a fully mechanized coal mining face hydraulic support orientation monitoring method and system. The method comprises: once a hydraulic support completes a support moving action, obtaining a monitoring video frame image used for monitoring a fully mechanized coal mining face; on the basis of the monitoring video frame image, extracting a target image, wherein the target image is a local image in the monitoring video frame image comprising a hydraulic support base image and a push rod image; and inputting the target image to a convolutional neural network model obtained by pre-training to obtain a monitoring result, output by the convolutional neural network model, of whether a support loss anomaly has occurred in the hydraulic support in the monitoring video frame image, the convolutional neural network model being used for performing feature extraction on the target image and outputting the monitoring result corresponding to the feature on the basis of the extracted feature. Whether a support loss anomaly has occurred in the hydraulic support can be automatically and accurately recognized, and the problem of system hydraulic support orientation automatic adjustment is solved, thereby reducing the labor cost, and improving the system intelligence level.
Absstract of: US20260330179A1
0000 Embodiments of this specification provide a method and an apparatus for model inference using a cryptographic neural network. The cryptographic neural network includes a plurality of linear layers. The method includes: receiving ciphertext data and key data of a user, where the key data is partial data of an evaluation key of a target format, and the evaluation key of the target format includes a plurality of extended ciphertexts; and inputting the ciphertext data into the cryptographic neural network for cryptographic processing, where the cryptographic processing includes: performing, at any target linear layer in the plurality of linear layers, a ciphertext rotation operation by using a first data part corresponding to a first ciphertext level t in the evaluation key, where the first data part includes only partial data of a single extended ciphertext.
Absstract of: US20260330245A1
0000 The present application discloses a method and an apparatus for determining a brain-inspired application test suite, an electronic device, and a storage medium. The method includes: performing feature analysis on each brain-inspired application included in an initial application test suite to obtain a data representation feature corresponding to each brain-inspired application; computing a computational overhead generated when each brain-inspired application is executed based on a spiking neural network paradigm; determining a data computation feature corresponding to each brain-inspired application according to the computational overhead; performing clustering processing on the brain-inspired applications in the initial application suite based on the data representation feature and the data computation feature, so that applications having similar features are merged into a same group, obtaining several groups whose main features are pairwise orthogonal; and constructing a representative spiking neural network according to a feature vector corresponding to each group.
Absstract of: WO2026218366A1
The present application relates to the technical field of sonar, and discloses a method and system for wall recognition in a sonar image, a device, and a storage medium. The method comprises: inputting target sonar image data into a pre-trained anomaly detection model to perform classification prediction of whether each pixel is a wall, so as to obtain a wall recognition result. Training steps of the anomaly detection model comprise: acquiring training data, and using the training data to train an initial model for classification prediction of whether each pixel is a wall, so as to obtain the anomaly detection model, wherein the initial model uses an anomaly detection network based on a convolutional neural network, and the training data comprises sonar image sample data and a target label matrix. The anomaly detection model in the present application directly performs classification recognition of whether each pixel is a wall. By means of pixel-level classification, a wall can be recognized more accurately, the contour of the wall can be outlined more precisely, and various details of the wall are accurately presented, thereby improving the accuracy of wall recognition.
Absstract of: US20260330230A1
A non-transitory computer-readable medium storing instructions that, when executed by one or more computers, cause the one or more computers to: access a room image of a room for room layout estimation; determine, using an encoder-decoder sub-network of a neural network for estimating a layout of a room and the room image, a plurality of predicted two-dimensional (2D) keypoint maps corresponding to a plurality of room types; determine, using the encoder-decoder sub-network, a classifier sub-network of the neural network connected to the encoder-decoder sub-network, and the room image, a predicted room type from the plurality of room types; determine, using the plurality of predicted 2D keypoint maps and the predicted room type, a plurality of ordered keypoints associated with the predicted room type; and determine, using the plurality of ordered keypoints, a predicted layout of the room in the room image.
Absstract of: US20260330875A1
An image-based search method receives a search input from a user that describes one or more attributes associated with at least one object. The search input is interpreted by applying system attentions and applying a trained neural network that is trained on a training set of syntactical elements and images. A vector is generated based on the interpretation. Images, which may comprise segments of videos, are interpreted by applying system attentions and the trained neural network. The interpretation is applied to generate vectors that correspond to patterns of pixels in the images. The vector associated with the search input is compared to the vectors corresponding to the images and images are selected based on the results of the vector comparisons. Representations of the selected images are provided to the user.
Absstract of: GB2645395A
Methods, systems, and computer readable media are provided for reduced-dose angiography using machine learning (e.g., deep learning) with a concordance metric indicating a degree of similarity between an estimated segmentation of angiographic images generated by a machine learning model and segmented benchmark angiographic images provided as inputs to the machine learning model. Briefly, techniques described herein may use a neural network trained to conserve/preserve angiographic image quality while reducing the angiographic dose of potentially harmful chemical contrast and/or x-ray radiation by employing a concordance metric.
Absstract of: WO2025128327A1
A method for auditing neural network performance may include receiving a first set of input data for the neural network and training the neural network. The method may further include updating an audit model of the neural network based on the training of the neural network and inputting, to the neural network the first set of input data. A first set of output data associated with the first set of input data may be received from the neural network, and an indication of one or more characteristics of the neural network that impacted the first set of output data may be generated based on the updated audit model of the neural network, the first set of input data, and the first set of output data.
Absstract of: EP4826677A1
0001 Computer-implemented method for upsampling a feature map (F) of an image (x), the method comprising: • Obtaining the image (x) and the feature map (F); • Augmenting the image (x) with respective representations (c) of coordinates of pixels of the image, thereby creating an augmented image (au); • Providing the augmented image (au) as input to a neural network (61), wherein the neural network (61) comprises at least one cross-attention layer (CA) that uses the augmented image (au) or a representation (r) thereof as query input and the feature map (F) as key input; • Providing an output (o) of the neural network (60) as upsampled feature map, wherein the output (o) of the neural network (60) is based on an output of the cross-attention layer (CA).
Absstract of: US20260320806A1
0000 This application provides a device wake-up method, a system, and a chip. The method may include: processing a captured first image, determining a motion region image in the first image, and determining, by using a level-1 neural network, whether a target object exists in the motion region image; and when it is determined that the target object exists in the motion region image, performing reconfirmation, by using a level-2 neural network cascaded with the level-1 neural network, on a result output by the level-1 neural network. It can be learned that the target object is determined by using the level-1 neural network and the level-2 neural network, so that a false alarm frequency can be reduced, and system power consumption can be reduced.
Nº publicación: US20260320995A1 15/10/2026
Applicant:
HUAWEI TECH CO LTD [CN]
HUAWEI TECHNOLOGIES CO., LTD.
Absstract of: US20260320995A1
Methods and apparatuses are provided for processing of picture data or picture feature data using a neural network with two or more layers. The present disclosure may be applied in the field of artificial intelligence (AI)-based video or picture compression technologies, and in particular, to the field of neural network-based video compression technologies. According to one or more embodiments, two kinds of data are combined during the processing by the neural network. The two kinds of data are obtained from different stages of processing by the network. Some of the advantages may include greater scalability and a more flexible design of the neural network architecture which may further lead to better encoding/decoding performance.
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