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114
resultados
Última actualización
14/07/2026 [08:18:00]
Resultados 50 a 75 de 114
Resumen de: WO2026137618A1
Disclosed in the present invention are a formation rock and soil parameter testing method based on a controllable neutron source, a ground acquisition computer, a system, and a computer-readable storage medium. The method comprises: acquiring geomechanical and physical property synthetic data and geomechanical and physical property in-situ measured data, and preprocessing same to obtain target rock and soil synthetic data and target rock and soil in-situ measured data; determining a preset dual neural network model, and performing model training on the preset dual neural network model on the basis of the target rock and soil synthetic data and the target rock and soil in-situ measured data to obtain a formation rock and soil parameter prediction model; and acquiring current geomechanical and physical property in-situ measured data, inputting same into the formation rock and soil parameter prediction model, and outputting a formation rock and soil parameter prediction result. In the present invention, by combining a controllable neutron source, cone penetration test, and a data processing method, and introducing a neural network model, effective measurement and accurate analysis of key formation parameters can be implemented, thereby obtaining more accurate formation parameter data.
Resumen de: 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.
Resumen de: EP4769222A2
0001 The invention provides a system and method for training artificial neural networks for solving multiple tasks simultaneously, wherein the artificial neural network comprises at least one capsule layer. The invention also provides a system and a method for solving multiple tasks simultaneously, wherein the artificial neural network comprises at least one capsule layer. The invention further provides additional connected aspects.
Resumen de: EP4769218A1
0001 The invention particularly relates to a system and method that is used artificial intelligence, distributed computing, energy efficiency, optimization, cybersecurity, privacy field and relates to a decentralized system that not only optimizes neural networks for devices with varying capacities but also provides a robust defense mechanism against cybersecurity and privacy threats.
Resumen de: EP4769366A1
0001 The object of the application is a computer-implemented method for generating multimedia games and their launching schedule, in which multimedia game scenarios are made of attributes stored in advance in a database, using a genetic algorithm and deep neural networks, wherein said genetic algorithm is implemented using a bidirectional neural network of the Long Short-Term Memory type, LSTM. 0002 The object of the application is also a system for generating multimedia games, said system being configured and programmed to implement the method according to the application.
Resumen de: EP4769361A2
0001 A system for monitoring shopping baskets (e.g., baskets on human-propelled carts, motorized carts, or hand-carried baskets) can include a computer vision unit that can image a surveillance region (e.g., an exit to a store), determine whether a basket is empty or loaded with merchandise, and assess a potential for theft of the merchandise. The computer vision unit can include a camera and an image processor programmed to execute a computer vision algorithm to identify shopping baskets and determine a load status of the basket. The computer vision algorithm can comprise a neural network. The system can identify an at least partially loaded shopping basket that is exiting the store, without indicia of having paid for the merchandise, and execute an anti-theft action, e.g., actuating an alarm, notifying store personnel, activating a store surveillance system, activating an anti-theft device associated with the basket (e.g., a locking shopping cart wheel), etc.
Resumen de: US20260179249A1
The present disclosure relates to a method for determining position and orientation of a visual sensor and within an environment. The method comprises acquiring a training set of visual data of the environment and an object arranged therein, training an interpolation neural network for estimating one or more synthetic poses using the first set of visual data, and training a convolutional neural network with the first set of visual data. The method comprises acquiring an inspection set of visual data of the environment and an object arranged therein, estimating a coarse pose with the convolutional neural network, and predicting a synthetic image associated with the coarse pose with the interpolation neural network. The method may be performed with data obtained from non-visual sensors.
Resumen de: US20260179315A1
0000 Processing of sensor data from sensor(s). The sensor data are provided as an unordered point cloud. The points of the unordered sequence are then converted into a regular structure using a point-processing neural network and made available for further processing. A transfer device is configured to receive a group of input data elements from the sensors. Each input data element of this group of input data elements includes a point that specifies at least one position. The transfer device also includes a point-processing neural network. This point-processing neural network is configured to map the points of the group of input data elements to a regular output data structure. A processing device is configured to detect an object and/or ascertain properties of an object using the regular output data structure. For the conversion of points of an unordered point cloud to a regular structure, a point-processing neural network is provided.
Resumen de: US20260178899A1
A method for assisting navigation of a mobile system includes obtaining an optical image of a scene acquired by a camera, obtaining a 3D point cloud of the scene acquired by a range-finder, projecting, in 2D into a reference frame of the camera, the 3D points of the 3D point cloud and a measurement uncertainty to provide a depth image and an uncertainty mask of the depth image, determining a semantic map, a depth map and a confidence map of the depth map from the optical image by processing the optical image, the depth image and the uncertainty mask a first convolutional neural network which includes a succession of convolutional layers, each including first to third convolution blocks that estimate respectively a semantic attribute map, a depth attribute map and a confidence attribute map, and determining a scene traversability map by merging the semantic, depth, and confidence maps.
Resumen de: US20260178905A1
According to at least one embodiment, a computer-implemented method of training a neural network for mapping an indoor environment includes: training the neural network in a first stage using a first dataset based on synthetic shapes; and training the neural network in a second stage using a second dataset based on real photographs. The method further includes, for each object of a plurality of objects, collecting a plurality of images of the object, wherein the plurality of images of the object are respectively produced under different lighting conditions; and training the neural network in a third stage using the plurality of images of the object.
Resumen de: US20260178891A1
0000 Described herein are systems and methods for optimizing neural network models for deployment on resource-constrained computing devices through layer-specific quantization. An original neural network model and deployment constraints are received as inputs. The optimization process alternates between a learning phase that updates model weights using task-specific loss functions and a compression phase that determines optimal bitwidth allocations for each layer through multiple-choice knapsack optimization. The compression phase computes quantization errors for different bitwidth options per layer and selects optimal bitwidth combinations while satisfying deployment constraints. The process iteratively updates a penalty parameter and continues until convergence, producing an optimized neural network model with quantized weights and layer-specific bitwidth allocations that maintains performance while meeting size, computational, and latency constraints for the target device.
Resumen de: US20260178882A1
Events may be classified in a staged manner using one or more neural networks and explicit classifiers. For example, a method may be performed which comprises classifying an event using a first explicit classifier, a neural network classifier, and a second explicit classifier. In such a method, classifying the event using the first explicit classifier may provide an initial classification for the event, and that initial classification may be a basis for classifying the event using the neural network classifier. Similarly, classifying the event using the neural network classifier may provide a neural network classification for the event, and the event may be classified using the second explicit classifier on the basis of the neural network classification. When the event is processed by the second explicit classifier, this may provide an output classification, and that output classification may be used as a basis for processing the event.
Resumen de: US20260178958A1
A method may include obtaining a configuration of a quantum circuit comprising n qubits and k quantum gates. The k quantum gates include at least a single-qubit gate or a two-qubit control gate. The method may include constructing a neural network representing the quantum circuit, wherein the neural network includes k+1 layers that include k pairs of adjacent layers, with each pair of the adjacent layers corresponding to one of the k quantum gates. The method may include connecting one or more nodes in each pair of the adjacent layers based on a representation of a corresponding quantum gate of the k quantum gates. The method may include training the neural network using machine learning techniques to obtain an output. The method may include applying the output to the quantum circuit.
Resumen de: US20260179374A1
0000 In various examples, multilabel hierarchical classification of objects for autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more neural networks to classify objects, such as traffic signs, using multilabel classification and/or hierarchical classification. For instance, a multilabel subnetwork of the neural network(s) may classify an object based at least on one or more attributes associated with the object. As such, the output from the multilabel subnetwork may include at least a classification associated with the object and an attribute classification(s) associated with the object. A hierarchical subnetwork of the neural network(s) may also classify the object using one or more class labels, where a class label indicates another classification and/or a class group associated with the object. The systems and methods may then use the classification, the attribute classification(s), and/or the class label(s) to determine a final classification associated with the object.
Resumen de: WO2026129865A1
The present application relates to the field of artificial intelligence, and discloses a large language model (LLM) inference apparatus based on compute-in-memory, an inference system, and an electronic device. The inference apparatus comprises: a storage layer at least used for storage; and a computation layer at least used for computation. The computation layer and the storage layer are stacked by means of hybrid bonding. The computation layer comprises a neural network accelerator based on compute-in-memory. The neural network accelerator comprises an in-memory computing matrix. The in-memory computing matrix is used for performing neural network computation on input feature data and weights from the storage layer. The computation layer is further used for being electrically connected to a main control chip that controls the inference apparatus. The computation layer is further used for performing prefill processing of LLM inference and transmitting data, which is obtained after the prefill processing, to the main control chip for decoding processing of LLM inference, such that the prefill processing is separated from the decoding processing. The inference apparatus provided by the present application supports high bandwidth, has high computational power and low power consumption, and can also resolve the heat dissipation problem of existing LLM inference apparatuses.
Resumen de: WO2026130141A1
Provided are a method and apparatus for establishing a vehicle helmet detection model, a vehicle helmet detection method and apparatus, an electronic device, a computer-readable storage medium and a computer program product. The method for establishing a vehicle helmet detection model comprises: acquiring a vehicle helmet detection data set, the vehicle helmet detection data set comprising a plurality of image samples of a vehicle during driving or stopping; for each image sample among the plurality of image samples, extracting edge information and background information of the image sample, and identifying one or more vehicle helmets in the image sample; and on the basis of the edge information, the background information, and the identified one or more vehicle helmets, training a neural network model to establish a vehicle helmet detection model.
Resumen de: WO2026129760A1
Disclosed in the present invention is a rapid construction method for a knowledge graph of railway bridge design standards, comprising: S1, acquiring data and preprocessing same; and S2, using a BERT pre-training model and a Bi-LSTM model to convert a text sequence in the processed data into an annotated tag sequence, i.e., {y1,y2…yn}; (S3) using conditional random field (CRF) and graph neural network (GNN) technology to identify and annotate an entity, an attribute, and a relationship thereof to form a node and an edge of a knowledge graph, so as to obtain a complete knowledge graph (KG); and (S4) using a dynamic topology optimization algorithm to adjust a structure of the complete knowledge graph (KG) in real time. The method rapidly integrates and optimizes design standard data, reduces data redundancy, and effectively processes data additions and changes, thereby improving knowledge graph construction speed and maintenance efficiency.
Resumen de: US20260179351A1
A system and method of identifying and tracking objects comprises registering an identity of a person who visits an area designated for holding objects, capturing an image of the area designated for holding objects, submitting a version of the image to a deep neural network trained to detect and recognize objects in images like those objects held in the designated area, detecting an object in the version of the image, associating the registered identity of the person with the detected object, retraining the deep neural network using the version of the image if the deep neural network is unable to recognize the detected object, and tracking a location of the detected object while the detected object is in the area designated for holding objects.
Resumen de: WO2026129814A1
Provided in the embodiments of the present application are a processing method for a neural network model, and a secure element and a computing apparatus. The computing apparatus comprises: a storage element, which is used for storing a first neural network code for the first model inference of a neural network model and at least some first network parameters for the first model inference; a secure element, which is used for storing the remaining first network parameters for the first model inference, and/or storing a second neural network code and second network parameters for the second model inference of the neural network model, and executing the second model inference on the basis of the second network parameters and the second neural network code; and a general-purpose computing element, which is used for executing the first model inference on the basis of the first neural network code and the first network parameters. The embodiments of the present application can prevent a neural network model from being physically attacked, thereby improving the security of the neural network model.
Resumen de: US20260179618A1
Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.
Resumen de: US20260179153A1
Aspects extract, from payroll data of employees of an organization, data historically associated to previous instances of certified tax credit eligibility; normalize the extracted data with respect to data type and data value; generate from the normalized extracted data via a neural network classifier multi-class outputs for each employee that indicate strengths of likelihood that each employee is currently eligible for each of a plurality of different tax credits; filter the normalized extracted data by removing portions associated to employees indicated within the multi-class outputs as having no currently eligible likelihood for the different tax credits, thereby generating a remainder set of normalized extracted data associated to remainder eligible ones of the employees; and prioritize application for the tax credits for the remainder eligible employees as a function of respective values and likelihoods of eligibility within the remainder set of normalized extracted data.
Resumen de: US20260179360A1
Embodiments of this application disclose a model structure, a method for training a model, an image enhancement method, and a device, and may be applied to the computer vision field in the artificial intelligence field. The model structure includes: a selection module, a plurality of first neural network layers, a segmentation module, a transformer module, a recombination module, and a plurality of second neural network layers. The model overcomes a limitation that the transformer module can only be used to process a natural language task, and may be applied to a low-level vision task. The model includes the plurality of first/second neural network layers, and different first/second neural network layers correspond to different image enhancement tasks. Therefore, after being trained, the model can be used to process different image enhancement tasks.
Resumen de: US20260178825A1
0000 Disclosed are a method and apparatus for determining a given template of a form used by a filled in instance of that type of form from amongst a great number of form templates (a hundred or more). The given instance is evaluated by a neural network that has been trained by a single example of each template in order to reduce the total number of templates down to a manageable amount. Given a list of closest matching templates, the instance is aligned to each of the closest matching templates. The comparison generates a match score. The form template having the greatest match score is the correct form template. Filtering the instance through a one-shot learning neural network before performing a precise comparison enables the process to scale to any number of template forms.
Resumen de: US20260179222A1
Devices, systems, and methods are provided for recognizing, diagnosing, mapping, sensing, monitoring and/or treating selected areas within a patient's body. The systems, devices and methods may be used to map, detect and/or quantify images and/or physiological parameters collected from the patient. One such system comprises an optical imaging device, such as an endoscope, and a processor coupled to the imaging device. The processor includes a software application configured to recognize the images captured by the optical imaging device and determine if the tissue contains a medical condition and may include an artificial neural network configured to develop at least one set of computer-executable rules useable to recognize the medical condition in the captured tissue images. The systems, devices and methods provided herein allow for a more objective and comprehensive inspection of the targeted areas within a patient so as to improve the diagnosis and ultimate treatment of patients.
Nº publicación: US20260179180A1 25/06/2026
Solicitante:
NVIDIA CORP [US]
NVIDIA Corporation
Resumen de: US20260179180A1
Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.
BOPI
Sede Electrónica
