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Prediction of bone based on point cloud

Resumen de: AU2026238109A1

A method for surgical planning includes obtaining, by a computing system, a first point cloud representing a first portion of a bone or a first cloud representing at least a portion of a bone, applying, by the computing system, a point cloud neural network to generate a second point cloud based on the first point cloud, the second point cloud comprising at least one of points representing at least a second portion of the bone or points representing an axis along the bone, and generating, by the computing system, surgical planning information based on the second point cloud. ep e p

ATTRIBUTE-BASED ENCRYPTION FOR SELECTIVE DOCUMENT CONTENT PROTECTION

Resumen de: EP4823042A2

0001 The present disclosure provides a method for securing document content. A document security manager receives a digital document containing original text and image content. The original text content is processed to identify text portions by parsing annotations, analyzing proximate text, extracting content within the determined scope, and determining encryption attributes. The original image content is processed to identify image portions by detecting user-defined selections or employing a neural network to identify sensitive objects. Encrypt content requests are generated including the identified text and image portions with associated attributes and metadata. The requests are transmitted to a cryptographic engine, which returns encrypted content. A partially encrypted document is built by replacing original content with masking symbols, applying visual obfuscation, embedding encrypted content as metadata, and updating document properties to indicate encrypted content.

METHOD FOR GENERATING INTERNAL STRUCTURE OF ORGANISM ON BASIS OF DEEP LEARNING

Resumen de: EP4822642A1

The present application relates to the field of life sciences and medical imaging device technologies, and more particularly, to a deep learning-based method for generating an internal structure of an organism. The method includes the following steps: S1, constructing and training an internal organ generation model of a imaging target; S2, obtaining a three-dimensional body surface contour image of the imaging target; S3, inputting the obtained three-dimensional body surface contour image of the imaging target into the internal organ generation model to obtain a three-dimensional internal organ distribution image; and S4, performing merging on the three-dimensional internal organ distribution image and the three-dimensional body surface contour image, and displaying a result obtained based on the merging. With a deep neural network-based internal organ generation model of the imaging target, the three-dimensional internal organ distribution image of the imaging target can be obtained quickly and accurately simply through obtaining the three-dimensional body surface contour image of the imaging target and inputting the three-dimensional body surface contour image into the internal organ generation model, which simplifies operation steps, realizes automatic imaging, improves an imaging efficiency, and has high applicability.

TECHNIQUES FOR ARTIFICIAL INTELLIGENCE CAPABILITIES AT A NETWORK SWITCH

Resumen de: US20260312885A1

0000 Examples include techniques for artificial intelligence (AI) capabilities at a network switch. These examples include receiving a request to register a neural network for loading to an inference resource located at the network switch and loading the neural network based on information included in the request to support an AI service to be provided by users requesting the AI service.

DEPTH AND MOTION ESTIMATIONS IN MACHINE LEARNING ENVIRONMENTS

Resumen de: US20260311478A1

A mechanism is described for facilitating depth and motion estimation in machine learning environments, according to one embodiment. A method of embodiments, as described herein, includes receiving a frame associated with a scene captured by one or more cameras of a computing device; processing the frame using a deep recurrent neural network architecture, wherein processing includes simultaneously predicating values associated with multiple loss functions corresponding to the frame; and estimating depth and motion based the predicted values.

MODULARIZED HOME ROBOT

Resumen de: US20260307239A1

0000 Implementations of a fully configurable, modularized home robot are described that implement trained neural network classifiers to solve problems of providing home health care and monitoring of the elderly and/or infirm, providing entertainment for the family, providing environmental and safety monitoring coupled with mechanisms to clean the air and remedy indoor climates such as humidity and temperature, and provide a mechanism for caring for pets left at home when the owner is away based upon sensory input.

Video Compression Method and Device with Multi-Scale Implicit Neural Representation

Resumen de: US20260311377A1

0000 A video processing method with multi-scale implicit neural representation includes: using an implicit neural network to convert an original video sequence into network parameters; using the implicit neural network to extract fused encoding features with temporal information and perform spatial decoding, where the fused encoding features are selected based on the original video sequence; constructing a loss function of the implicit neural network based on the original video sequence and a reconstructed video, where the reconstructed video is obtained by reconstructing the original video sequence; using the loss function to iteratively optimize the network parameters, where the final optimized network parameters are used as the video compression results corresponding to the original video sequence. Therefore, by adopting the video processing method, the spatial and temporal structural priors of the video can be fully leveraged, enabling the compression efficiency to reach optimal levels and improving video compression performance.

DOUBLE-LAYER BLOW-MOLDED PLASTIC DETECTION METHOD AND SYSTEM

Resumen de: WO2026208084A1

A double-layer blow-molded plastic detection method, which relates to the field of plastic product production. The method comprises: acquiring dimensional features of a plastic sheet to be tested, and performing screening on the basis of a pressure distribution heat map, so as to obtain differential grid regions (step 101); acquiring a plurality of connected regions consisting of the differential grid regions, performing screening on the connected regions to obtain a target connected region, extracting a mean pressure gradient as a pressure feature of said plastic sheet (step 102); collecting an echo signal set of the target connected region, acquiring an average echo density of the target connected region on the basis of the echo signal set, and using the average echo density as an ultrasonic feature of said plastic sheet (step 103); performing feature-level fusion on the dimensional features, pressure feature and ultrasonic feature of said plastic sheet, so as to obtain a multi-modal feature (step 104); and inputting the multi-modal feature into a convolutional neural network, so as to acquire a wall thickness detection result of said plastic sheet (step 105). Efficient and accurate wall thickness measurement for plastic sheets of different sizes and different materials can be realized. Further provided is a double-layer blow-molded plastic detection system.

SEQUENCE PACKING FOR TRAINING IMAGE PROCESSING NEURAL NETWORKS

Resumen de: US20260311467A1

0000 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image processing neural network. For example, a method can include obtaining a batch of training examples, each training example comprising a respective training image; generating one or more input sequences, wherein each input sequence corresponds to two or more of the training images in the batch and is a sequence of tokens that comprises tokens representing patches from two or more corresponding training images; and processing each input sequence using the image processing neural network to generate a respective training output for each corresponding training image; and training the image processing neural network on a first loss function for the first task using the training outputs for the training images in the batch.

COMPUTER SYSTEM AND METHOD FOR MARITIME SURVEILLANCE AND SATELLITE-BASED EARTH OBSERVATION OF OBJECTS USING NADIR-OBLIQUE IMAGE MATCHING

Resumen de: US20260311486A1

0000 Systems, methods, and computer-readable media for vessel identification are provided. The system includes a data storage device storing: a database of optical oblique images each containing a known candidate vessel having a known unique vessel identifier; and an optical nadir satellite image containing an unknown vessel. The system further includes a processor configured to execute a nadir-oblique image matcher configured to: compare, via a neural network, the nadir image to oblique images in the database including determining a similarity score between the nadir image and a respective oblique image; output, via the neural network, a ranked list of the oblique images based on the determined similarity scores; and assign a known unique vessel identifier to the nadir image based on the ranked list.

SYSTEMS AND METHODS FOR IMPROVED IMAGE PROCESSING

Resumen de: US20260311475A1

0000 A system and method for training (and inference) of a NN model for wide/ultra-wide angle lens object detection and positioning, the NN training module configured to receive one or more images associated with one or more images from the object recognition data set, the data set including one or more bounding box labels associated with the one or more images, apply a customized data augmentation routine on said images to create one or more augmented images in each new iteration of training, provide a predetermined neural network for computer vision model to test and train the neural network for computer vision, predict and obtain a bounding box and confidence score, each being associated with one or more objects in the one or more images from the neural network for computer vision, match ground truth bounding boxes in image space to augmentations from a box data store, apply regression loss on the bounding boxes and compute the cost between predicted values and the label values to determine change in weightings.

CONVOLUTIONAL NEURAL NETWORK STRUCTURE FOR EXPLAINABLE 3D SHAPE LEARNING, AND METHOD AND SYSTEM FOR 3D SHAPE LEARNING USING SAME

Resumen de: US20260311451A1

0000 Disclosed are a convolutional neural network structure for explainable 3D shape learning, and a method and a system for 3D shape learning by using same. A method for shape learning performed by a shape learning system according to an embodiment may comprise: extracting geodesic features and geometric features from three-dimensional shape data; and classifying an object through a convolution operation on the extracted geodesic features and the extracted geometric features with respect to faces constituting the three-dimensional shape data.

TEMPORAL FEATURE SCALING FOR NEURAL NETWORK PROCESSING

Resumen de: US20260311361A1

Systems, devices, and methods described herein relate to feature scaling for neural network processing. In some examples, temporal feature scaling is employed for efficient neural network processing. To process the neural network, a processing system accesses feature maps of a current input frame in a temporal sequence of input frames and scales the feature maps of the current input frame using excitation values generated from a previous input frame in the temporal sequence. The processing system generates new excitation values from the current input frame to perform scaling for a subsequent input frame in the temporal sequence.

TRAINING VISION LANGUAGE NEURAL NETWORKS WITH COMPONENT REUSE

Resumen de: US20260311476A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and training a glued neural network that reuses components taken from a first pre-trained neural network and a second pre-trained neural network. For example, the first pre-trained neural network can be a multi-modal neural network that includes a visual encoder neural network and a small-scale language model neural network. The second pre-trained neural network can be a large-scale language model neural network.

HIGH LIKENESS HIGH COMPLIANCE HUMAN FIGURE GENERATION

Resumen de: WO2026213119A1

Techniques relating to high likeness high compliance human figure generation are disclosed. A method for high likeness high compliance human figure generation may include selecting training images as a basis for an expert learning model, obtaining embeddings for the training images, the embedding being recorded separately for each expert, computing a geometric property of the embedding space for each expert, embedding a received prompt using the same embedding method as for the training images, and generating an output image using a subset of experts selected for highest compliance (e.g., most similarity) to the received prompt, or for other purposes. A system for implementing this method may include a training data ensemble, an image augmentation pipeline, an LLM and other neural networks, a mood and style ensemble of lightweight expert models built on a foundational model, and a tokenizer

OPTICAL SENSING WITH NONLINEAR OPTICAL NEURAL NETWORKS

Resumen de: US20260310953A1

0000 Methods, devices, and systems for optical sensing with nonlinear optical neural network (ONNs) are provided. In one aspect, a method includes: receiving light from a visual scene by a first optical linear layer in a nonlinear ONN apparatus, linearly transforming the light into first optical outputs by the first optical linear layer trained to perform a first optical linear operation, non-linearly generating second optical outputs based on the first optical outputs by an optical nonlinear layer in the ONN apparatus, and linearly transforming the second optical outputs into third optical outputs by a second optical linear layer in the ONN apparatus trained to perform a second optical linear operation. The first optical linear layer, the optical nonlinear layer, and the second optical linear layer are sequentially arranged in series in the ONN apparatus.

METHOD FOR OPERATING NEURAL NETWORK

Resumen de: US20260310951A1

A method is provided and includes operations as below: receiving multiple spike signals in an input layer of a spiking neural network during multiple time steps; counting a corresponding number of spikes in the spike signals for each of the time steps; weighting, in response to the corresponding number of spikes in one of plurality of time steps being greater than a predetermined count value, the spike signals with multiple synaptic weight values to generate multiple synaptic signals; generating a membrane potential by accumulating a number N of the synaptic signals according to a weight distribution of the synaptic weight values; and generating an output spike signal according to the membrane potential.

DATA PROCESSING METHOD AND APPARATUS, ELECTRONIC DEVICE AND STORAGE MEDIUM

Resumen de: US20260310976A1

0000 Disclosed are a data processing method and apparatus, a storage medium, and an electronic device. A neural network is used to perform an inference operation on data to be processed, comprising successively performing M rounds of inference on the data to be processed, wherein the process of the i-th round of inference comprises: at a first stage, performing inference on N2 input data elements, and at a second stage, performing inference on N1 input data elements, the N1 input data elements, which belong to the same batch of input data, being a subset of the N2 input data elements; the inference process of the (i+1)-th round of inference comprises: for N2-N1 input data elements for which the inference is incomplete in the i-th round of inference but for which the output tokens have been acquired, performing inference on the N2-N1 input data elements while taking into account the output tokens.

PERFORMING SEMANTIC SEGMENTATION OF 3D DATA USING DEEP LEARNING

Resumen de: US20260311336A1

0000 A computer-implemented method of training a deep artificial neural network includes receiving a three-dimensional point cloud and training the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the processor, cause the computing system to receive a three-dimensional point cloud and train the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network. In yet another aspect, a non-transitory computer-readable medium includes computer-executable instructions that when executed, cause a computer to receive a three-dimensional point cloud and train the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network.

OPTICAL INFORMATION READING DEVICE

Resumen de: US20260310918A1

To suppress an increase in processing time due to a load of inference processing while improving reading accuracy by the inference processing of machine learning. An optical information reading device includes a processor including: an inference processing part that inputs a code image to a neural network and executes inference processing of generating an ideal image corresponding to the code image; and a decoding processing part that executes first decoding processing of decoding the code image and second decoding processing of decoding the ideal image generated by the inference processing part. The processor executes the inference processing and the first decoding processing in parallel, and executes the second decoding processing after completion of the inference processing.

SYSTEMS AND METHODS FOR ROPE EVALUATION

Resumen de: US20260309639A1

0000 In an embodiment of the disclosed principles, a method of analyzing a rope to estimate its residual break strength (RBS) is provided. The method entails training a multi-layered neural network to recognize rope damage and estimate an RBS for a rope under study by collecting high-resolution visual data of a plurality of sample ropes, extracting visual features of damaged areas of each rope, resolving each visual feature into a damage type and clustering damage type exemplars, breaking each sample rope to determine an actual RBS for each rope and classifying the visual data via determined RBS, specific to product type/damage mode. Job and product data are entered for the rope under study, the rope is paid out, and high-resolution visual data is captured while spooling back the rope. The correlated data is provided to the multi-layered neural network to generate an estimate of RBS for the rope under study.

OBJECT IMAGE COMPLETION

Resumen de: US20260311379A1

Apparatuses, systems, and techniques to generate complete depictions of objects based on a partial depiction of the object. In at least one embodiment, an image of a complete object is generated by one or more neural networks, based on an image of a portion of the object, using an encoder of the one or more neural networks trained using training data generated from output of a decoder of the one or more neural networks.

VIEW GENERATION USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260311378A1

Apparatuses, systems, and techniques are presented to generate image or video content representing at least one point of view. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects from a first point of view based at least in part upon one or more images of the one or more objects from a second point of view.

Neural Network Initialization

Resumen de: US20260310969A1

0000 A neural network representing a controlled space can be initialized by collecting state time series data that affects the controlled space such as weather, and also collecting sensor data from the controlled space at the same time. The time series data is used as input to a neural network that models the controlled space until an area in the neural network equivalent to the sensor is at or near the sensor state at a given time.

PANORAMA GENERATION USING ONE OR MORE NEURAL NETWORKS

Nº publicación: US20260311290A1 08/10/2026

Solicitante:

NVIDIA CORP [US]
NVIDIA Corporation

Resumen de: US20260311290A1

0000 Apparatuses, systems, and techniques are presented to generate panoramas from individual images. In at least one embodiment, one or more generative neural networks are used to generate a spherical panoramic image using features extracted from a single input image.