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103
results.
Updated on
09/07/2026 [09:57:00]
Absstract of: WO2025045319A1
The invention relates to a method for explaining and/or verifying a behaviour of a neural network trained with a training database, said method having the following steps: - determining sensor information by means of a sensor, so that sensor information is available, - applying the neural network to the sensor information, so that target information is available, - determining similarity information, wherein a comparison is made of the sensor information or part of the sensor information with an information database, so that the similarity information is available, linking the similarity information with the target information, so that a target information tuple is available, on the basis of which the explanation and/or verification of the behaviour of the neural network can be realised.
Absstract of: EP4773045A2
0001 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.
Absstract of: WO2026141769A1
Disclosed are a thermal image depth estimation method and device. According to the present invention, provided is a thermal image depth estimation device comprising a processor, and a memory stored in the processor, wherein the memory stores program instructions executed by the processor so as to: develop a prototype neural network model by using a pre-collected thermal image dataset; train the neural network model on the basis of a preset loss; and optimize and distribute the trained neural network model according to an open neural network exchange (onnx) format.
Absstract of: US20260187922A1
Systems and methods for performing three-dimensional (3D) scene reconstruction based on a set of images. According to one or more embodiments, neural network architectures and machine learning techniques are provided for performing global alignment in latent space to share context information across the input images and reconstructing selective image pairs according to relevant correspondences between images, thereby enabling robust, accurate, and efficient global alignment for a variety of computer vision applications, e.g., 3D reconstruction.
Absstract of: US20260188010A1
An apparatus for generating video descriptions according to an embodiment is a video description generating apparatus based on artificial neural networks, including one or more processors and a memory storing one or more programs executed by the one or more processors, and the apparatus includes a feature generating module that extracts a plurality of preset features from a video and generates one synthetic feature based on the plurality of extracted features and a description generating module that receives the synthetic feature and outputs a description text for the video.
Absstract of: US20260187485A1
Various embodiments of the teachings herein include an anomaly detection method. Some examples include: creating a knowledge graph according to data required to produce a product; acquiring measured values of multiple nodes in the knowledge graph in an upstream inspection process; calculating a characteristic deviation value of each of the multiple nodes based on the measured values of the multiple nodes in the upstream inspection process; calculating a similarity degree between any two nodes in the knowledge graph; and generating anomaly probabilities of respective nodes using a graph neural network according to the characteristic deviation value of each of the multiple nodes and the similarity degree between said any two nodes.
Absstract of: US20260187982A1
A computer-implemented video generation neural network system, configured to determine a value for each of a set of object latent variables by sampling from a respective prior object latent distribution for the object latent variable. The system comprises a trained image frame decoder neural network configured to, for each pixel of each generated image frame and for each generated image frame time step process determined values of the object latent variables to determine parameters of a pixel distribution for each of the object latent variables, combine the pixel distributions for each of the object latent variables to determine a combined pixel distribution, and sample from the combined pixel distribution to determine a value for the pixel and for the time step.
Absstract of: US20260187484A1
Described are techniques of generating and training a neural network that include training multiple models and constructing multiple decision trees with said models. Each decision tree may include additional decision trees at various levels of that decision tree. Each decision tree has a different accuracy indicator due to the unique structuring of each decision tree, and by testing each tree through a testing dataset, the tree with the highest accuracy can be determined.
Absstract of: US20260183087A1
Determine an amount of movement between an “anterior” updated instant and a “posterior” updated instant after the anterior updated instant. At the anterior updated instant, implementing an analysis method to obtain an anterior updated item of spatial information. Analyze a set of several images to obtain spatial information. Create a historical learning database relating to a dental body and the spatial attribute, with a set of historical images all depicting the dental body and an item of spatial information.Training the neural network, by providing the sets of several historical images as input and with the historical spatial information as output, with the spatial attribute defining an ordered sequence of variables in a three-dimensional reference frame. At the posterior updated instant, implement analysis to obtain a posterior updated item of spatial information. Compare the anterior and posterior updated spatial information, to obtain an amount of movement.
Absstract of: US20260187995A1
0000 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. In one aspect, a method includes receiving a text prompt describing a scene; processing the text prompt using a text encoder neural network to generate a contextual embedding of the text prompt; and processing the contextual embedding using a sequence of generative neural networks to generate a final video depicting the scene.
Absstract of: US20260187188A1
0000 A compiler and firmware-generation framework for column-oriented neural-network compute arrays is disclosed. The compiler maps layer operations of a neural network onto a column-based architecture by aligning matrix dimensions to the number of available columns and producing instruction streams that broadcast activation values from each processing row concurrently to all columns while preventing transfer of partial sums between columns. The framework optimizes memory layout, scheduling, and synchronization to preserve the CASCADE dataflow. It can integrate fault-aware remapping and generate microcode for asynchronous or redundant-column configurations. The compiler abstracts the hardware topology, allowing neural-network models to execute efficiently on column-oriented arrays without manual tuning or loss of dataflow integrity.
Absstract of: US20260187990A1
System and methods for generating unified feature maps based on an input image. According to one or more embodiments, neural network architectures and machine learning techniques are provided for extracting features from a variety of domains (e.g., geometric, semantic, and auxiliary) and performing dynamic fusion to integrate the extracted features into robust representations, thereby enabling efficient and generalized matching for a variety of computer vision applications, e.g., 3D reconstruction, object tracking, and image retrieval.
Absstract of: US20260186783A1
0000 Disclosed are a method for generating an instruction sequence, an electronic device, and a storage medium. The method for generating an instruction sequence includes: determining a computation graph corresponding to a neural network model to be compiled and resource status information on hardware executing the instruction sequence; partitioning the computation graph, to determine multiple computation sub-graphs; generating, based on the computation sub-graphs and the resource status information, instruction sub-sequences corresponding respectively to the computation sub-graphs; and determining, based on the instruction sub-sequences, a target instruction sequence corresponding to the neural network model to be compiled.
Absstract of: US20260187451A1
0000 A method for making predictions includes identifying, for each node in the heterogeneous graph structure, a set of node-target paths that connect the node to a target node; assigning, to each of the node-target paths, a path type identifier indicative of a number of edges and corresponding edge types in the associated node-target path; and extracting a semantic tree from the heterogeneous graph structure. The semantic tree includes the target node as a root node and defines a hierarchy of metapaths that each correspond to a subset of the node-target paths in the heterogeneous graph structure assigned to a same path type identifier. The semantic tree is encoded by generating a metapath embedding corresponding to each metapath in the semantic tree. A label is predicted for the target node in the heterogeneous graph structure based on the set of metapath embeddings.
Absstract of: US20260187991A1
0000 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a multi-modal machine learning task on a network input that includes text and an image to generate a network output. One of the systems includes a vision-language model (VLM) neural network. The VLM neural network includes a VLM backbone neural network and an attention-based feature adapter. The VLM neural network has access to an external dataset that stores multiple text items.
Absstract of: US20260187816A1
A system and method of generating a player tracking prediction are described herein. A computing system retrieves a broadcast video feed for a sporting event. The computing system segments the broadcast video feed into a unified view. The computing system generates a plurality of data sets based on the plurality of trackable frames. The computing system calibrates a camera associated with each trackable frame based on the body pose information. The computing system generates a plurality of sets of short tracklets based on the plurality of trackable frames and the body pose information. The computing system connects each set of short tracklets by generating a motion field vector for each player in the plurality of trackable frames. The computing system predicts a future motion of a player based on the player's motion field vector using a neural network.
Absstract of: US20260186092A1
0000 The present disclosure provides an apparatus for restoring the quality of magnetic resonance images based on a deep learning model and a method of controlling the same. The method includes: obtaining a training image corresponding to each magnetic resonance image by applying at least one of a plurality of elements set in connection with the quality of the magnetic resonance image to a magnetic resonance signal corresponding to the magnetic resonance image; obtaining a training dataset including the magnetic resonance image as label data and the obtained training image as input data matching the label data; and training a neural network model based on the training dataset and context data corresponding to the training image. Obtaining the training image includes distorting the magnetic resonance signal by applying the at least one of the plurality of elements and obtaining the training image based on the distorted magnetic resonance signal.
Absstract of: WO2026139108A1
The present invention belongs to the interdisciplinary technical field of medical image processing and artificial intelligence. Disclosed is a medical image reconstruction method based on deep learning. The method comprises: acquiring a medical image data set, and labeling corresponding key anatomical structure information; on the basis of a feature encoder and a graph structure processor, constructing a medical image reconstruction model; the feature encoder performing feature extraction on the medical image data set by means of a convolutional neural network, so as to obtain a multi-scale feature map; the graph structure processor constructing an initial landmark point set of the multi-scale feature map by means of a graph attention network, and analyzing topological relationships between landmark points, so as to obtain an optimized landmark point arrangement; on the basis of the optimized landmark point arrangement, constructing an anatomical structure graph; on the basis of the anatomical structure graph, designing an adaptive loss function to train the medical image reconstruction model; and on the basis of the trained medical image reconstruction model, obtaining a corresponding reconstructed image. The present invention achieves the objective of improving the detail representation of a reconstructed image while maintaining the accuracy of an anatomical structure.
Absstract of: US20260188129A1
0000 A method for perception validation of an aerial vehicle includes: acquiring an image of a ground area with an onboard camera system of the aerial vehicle, generating an above ground altitude (AGL) estimate with a neural network trained to output the AGL estimate in response to the image fed as an input to the neural network, generating a motion estimate or a position estimate based upon sensor data output from a sensor disposed onboard the aerial vehicle, and cross-validating the motion or position estimate against the AGL estimate.
Absstract of: US20260187497A1
A system generates prediction data by processing an input sequence segmented along a time axis based on a predetermined period using neural networks. The neural networks comprise a first neural network configured to apply dilated attention to the input sequence segmented along the time axis based on the predetermined period, and a second neural network configured to apply random partition attention to data arranged along a feature axis.
Absstract of: WO2026138395A1
The present invention provides an object tracking processing method and apparatus, a device, and a medium. The processing method comprises: respectively preprocessing a first-frame image and a current image to generate a corresponding first-frame initial image and a corresponding current initial image; performing feature extraction processing on the first-frame initial image and the current initial image by means of a weight-adaptive fusion encoder to generate input feature data; performing deep-level fusion processing on the first-frame initial image and the current initial image; performing feature extraction processing by means of the weight-adaptive fusion encoder to generate deep feature data; performing attention-weighted fusion processing on the input feature data and the deep feature data by means of the weight-adaptive fusion encoder to generate target feature tensor data; and processing the target feature tensor data by means of a fully convolutional neural network to generate a target image. The object tracking processing method and apparatus, the device, and the medium provided by the present invention improve object tracking performance.
Absstract of: US20260187138A1
In various examples, a technique for multiple object tracking is disclosed that includes generating, using one or more processing units, one or more first encoded image features based on a first image. The technique also includes generating a plurality of first object embeddings based on the first encoded image features, wherein at least one first object embedding of the plurality of first object embeddings corresponds to a different object depicted in the first image. The technique further includes determining, using a first track query of one or more track queries and one or more machine learning operations, a first association between a first track and a first object that corresponds to the at least one first object embedding, wherein the first track corresponds to the first track query. The technique further includes computing, based on the first association, an object trajectory associating the first track with the first object.
Absstract of: WO2026137839A1
The present disclosure relates to the technical field of panel defect detection, and provides a method and system for implementing panel defect detection on the basis of image grayscale equalization. The method comprises: collecting statistics about grayscale values of a panel image by means of image grayscale value statistics collection, so as to obtain a grayscale histogram of the panel image; on the basis of the grayscale histogram of the panel image, using an adaptive histogram equalization algorithm to perform grayscale value equalization processing on the panel image, so as to obtain an equalized image; performing feature labeling on the equalized image, and performing neural network training on the basis of the feature-labeled equalized image, so as to obtain a target detection model; and performing panel defect detection on the equalized image on the basis of the trained target detection model, so as to obtain a defect detection result. By performing grayscale value statistics collection and grayscale value equalization processing on the panel image, defect imaging features can be enhanced, so that the target detection model can accurately perform defect detection and locating, thereby solving the problem that existing Mura defect detection is prone to missed detection.
Absstract of: WO2026138708A1
A multi-device combined use-based identification method and system for raw materials and excipients in a formulation. The method comprises: performing cryogenic polishing on a cross-section of a test sample under analysis to obtain a cross-section under test; using an electron microscope to observe the cross-section under test to obtain an electron microscopy image of the cross-section under test, and determining a region under test on the cross-section under test; using an energy dispersive spectrometer to scan the region under test to obtain an energy spectrum image; on the basis of the electron microscopy image and the energy spectrum image, determining whether there are characteristic elements in the test sample, and if there are characteristic elements, identifying the characteristic elements on the basis of the energy spectrum image; and on the basis of the electron microscopy image and the energy spectrum image, determining whether there are components matching preset morphological characteristics in the test sample, and if there are components matching the preset morphological characteristics, using a pre-constructed convolutional neural network model to perform analysis processing on the electron microscopy image to identify the components matching the preset morphological characteristics. The identification method can more accurately and quickly identify components in a sample under analysis.
Nº publicación: US20260187231A1 02/07/2026
Applicant:
SKSPRUCE TECH CO LTD [CN]
SKSPRUCE TECHNOLOGIES CO., LTD.
Absstract of: US20260187231A1
0000 An anomaly detection system, an anomaly detection method, an electronic device and a storage medium are provided, which relates to the field of artificial intelligence. The anomaly detection system includes a plurality of markers. The plurality of markers are different from each other and correspond to different anomaly detection operations. A log line of a target system may be detected from different dimensions by using the plurality of markers, and marker tokens generated by different markers are obtained. In addition, the anomaly detection system further includes a model marker. The model marker may combine the marker tokens respectively generated by each marker into a token sequence and perform anomaly detection on the token sequence by using a pre-trained neural network model to obtain an overall anomaly score of the target system within a preset time period.
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