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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

Machine learning prediction of ion trap filling functions

Resumen de: GB2705175A

A method for facilitating machine learning prediction of ion trap filling functions is disclosed. The method comprises causing a mass analyser to perform a scan using an injection or accumulation time. The injection time is correlated to a requested or desired ion population size by an ion trap filling function, which may be non-linear. The coefficients of the ion trap filling function are predicted by a machine learning model based on current operational parameters of the mass analyser, e.g. the scan range or width of the mass analyser, pass range or width of an ion trap of the mass analyser, or the number of ion guide injections. The injection time may be scaled using a pre-scan ion flux estimate or an ion species distribution. The machine learning model may be trained in a supervised fashion and updated via backpropagation based on an error between the actual ion population size and the requested ion population size. The machine learning model may be a random forest regressor or a deep learning neural network. A system and a computer program are also claimed. Figure 15

CLINICAL SUPPORT SYSTEM AND ASSOCIATED COMPUTER-IMPLEMENTED METHODS

Resumen de: EP4819077A2

A clinical support system comprises a processor and a display component, wherein: the processor is configured to: receive image data, the image data representing an image of a plurality of cells obtained from a human or animal subject, the image data comprising a plurality of subsets of image data, each subset comprising data representing a portion of the image data corresponding to a respective cell of the plurality of cells; apply a trained deep learning neural network model to each subset of the image data, the deep learning neural network model comprising: a plurality of convolutional neural network layers each comprising a plurality of nodes; and a bottleneck layer comprising no more than ten nodes, wherein the processor is configured to apply the trained deep learning neural network model to each subset of the image data by applying the plurality of CNN layers, and subsequently applying the bottleneck layer, each node of the bottleneck layer of the machine-learning model configured to output a respective activation value for that subset of the image data; for each subset of the image data, derive a dataset comprising no more than three values, the values derived from the activation values of the nodes in the bottleneck layer; and generate instructions, which when executed by the display component of a clinical support system, cause the display component of the computer to display a plot in no more than three dimensions of the respective dataset of each subset of the ima

METHOD FOR ATTENTION-DRIVEN TRAINING OF COMPUTER-IMPLEMENTED NEURAL NETWORKS USING DOMAIN KNOWLEDGE

Resumen de: EP4818980A1

0001 Die vorliegende Erfindung betrifft ein computerimplementiertes aufmerksamkeitsgesteuertes Trainingsverfahren zum Erhalt eines trainierten neuronalen Netzes für die Schädigungserkennung und/oder für die Erkennung mindestens eines Bereichs einer Rissspitze in einer Abbildung eines zu untersuchenden Körpers, ein computerimplementiertes Verfahren für die Schädigungserkennung in Bilddaten eines zu untersuchenden Körpers und dessen Verwendung, ein Prüfsystem, ein Computerprogrammprodukt sowie ein computerlesbares Speichermedium. Hierbei umfasst das Trainingsverfahren die Einbindung von Domänenwisssen zur Steuerung der Aufmerksamkeit während des Trainings.

Driving scene search and driving policy training

Resumen de: GB2705187A

A computer-implemented method for storing and retrieving driving scenes, the method includes: determining one or more natural language descriptions of one or more driving scenes based on inputting, to a large language model, driving scene information of the one or more driving scenes and one or more prompts requesting the one or more natural language descriptions of the one or more driving scenes; creating one or more embeddings of the one or more natural language descriptions using a trained neural network and storing the one or more embeddings; receiving a natural language query associated with a query driving scene; creating an embedding of the natural language query using the trained neural network; selecting, from the one or more driving scenes, one or more similar driving scenes based on the one or more embeddings and the embedding of the natural language query; and outputting the selected one or more similar driving scenes. FIG. 3

NEURAL NETWORK TRAINING TECHNIQUE

Resumen de: US20260300436A1

0000 Apparatuses, systems, and techniques to train neural networks to perform image processing tasks. In at least one embodiment, one or more second neural networks are used to train one or more first neural networks based, at least in part, on a first object type in one or more images and a second object type in the one or more images, in parallel.

IMAGE RECOGNITION AND DETECTION METHOD, IMAGE INFORMATION DETECTION METHOD AND IMAGE PROCESSING SYSTEM

Resumen de: WO2026199807A1

The present invention relates to the technical field of medical instruments. Provided are an image recognition and detection method, an image information detection method and an image processing system. The image recognition and detection method comprises: inputting an image to be tested into a feature extraction unit; acquiring a feature map extracted by the feature extraction unit on the basis of said image; on the basis of the feature map, respectively enabling a first neural network to execute a first prediction process and enabling a second neural network to execute a second prediction process; and acquiring a category classification result about a target object in said image on the basis of a prediction result of the first prediction process, and acquiring quantization information about the target object on the basis of a prediction result of the second prediction process. The present invention can predict karyotypes on the basis of the microscopic morphology of cells in images, and takes into account both the overall presentation of the field of view and microscopic features of individual cells so as to identify titer information, thereby improving the accuracy of karyotype and titer identification and reducing occurrences of incorrect or failed predictions.

Ocular system for deception detection

Resumen de: AU2026233962A1

A method of deception detection, assessing operational risk or optimizing learning is based upon ocular information of a subject by providing a video camera configured to record a close-up view of a subject's eye. The ocular information is processed to identify changes in ocular signals of the subject through the use of convolutional neural networks. Changes in ocular signals are evaluated from the convolutional neural networks by a machine learning algorithm. Results can then be presented in regards to the level of deception, the level of operational risk or the best way to optimize learning. The methods are facilitated by identifying at least one predictive distortion identified in the stroma capturable solely with a visible-spectrum camera correlating to a predicted response in the iris musculature. 20 ep e p

METHOD FOR RECOGNIZING SPACES TO BE OPTIMIZED IN BUILT ENVIRONMENT ON BASIS OF MORPHOLOGICAL HIERARCHY MODEL

Resumen de: WO2026199950A1

A method for recognizing spaces to be optimized in a built environment on the basis of a morphological hierarchy model, comprising collecting and preprocessing data of a built environment, preliminarily performing hierarchical classification of the morphologies of the built environment, correcting the hierarchical classification of the morphologies of the built environment, preliminarily recognizing spaces to be optimized, performing hierarchical recognition of said spaces, and performing recognition result interaction and feedback. According to the method, data collection is performed by means of a laser radar system, an aerial photography unmanned aerial vehicle, a street view collection vehicle, and a low earth orbit satellite, and recognition and display of spaces to be optimized in a built environment are performed on the basis of multi-task Bayesian federated learning, an XGBoost model, a vision transformer, and a convolutional neural network. The described method can facilitate rapid recognition of spaces to be optimized in a built environment in the field of urban planning, and improve the rationality and accuracy of recognition results of said spaces in the built environment by means of morphological hierarchical classification.

NEURAL NETWORK MODEL INFERENCE METHOD AND COMPUTER DEVICE

Resumen de: WO2026199919A1

The present description provides a neural network model inference method and a computer device. A data owner receives N ciphertext neural network models sent by a model owner. The N ciphertext neural network models are obtained by performing obfuscation processing on a first neural network model. A true neural network model comprises a first linear layer, and N-1 fake neural network models obtained by means of obfuscation comprise second linear layers, wherein parameters of the second linear layers are 0. The first linear layer and the N-1 second linear layers are respectively encrypted on the basis of N key shares, wherein the N key shares are generated on the basis of a distributed encryption protocol supporting homomorphic operations. The data owner uses the N ciphertext neural network models to perform inference on target data to obtain the sum of N ciphertext outputs, thereby obtaining an inference result for the target data on the basis of the sum of the N ciphertext outputs. By using the N key shares for encryption and generating the fake neural network models for model obfuscation, the security of the models is improved.

NEURAL NETWORK MODEL INFERENCE METHOD AND COMPUTER DEVICE

Resumen de: WO2026199920A1

A neural network model inference method and a computer device. A data party first receives an encrypted neural network model sent by a model party. A first linear layer of the encrypted neural network model corresponds to a first ciphertext linear layer and N-1 second ciphertext linear layers, the first ciphertext linear layer being obtained by directly encrypting the first linear layer, and the second ciphertext linear layer being obtained by encrypting a linear layer with all zero parameters. The first ciphertext linear layer and the second ciphertext linear layer are respectively encrypted using N key shares, the N key shares being generated on the basis of a distributed encryption protocol supporting homomorphic operations. The data party uses the encrypted neural network model to perform target data inference to obtain an intermediate result output by another layer preceding the first linear layer, and inputs the intermediate result into the first linear layer and the N-1 second linear layers, respectively, to obtain N outputs respectively corresponding to N ciphertext linear layers. An inference result of the target data is obtained according to a sum of the N outputs.

DETECTION AND CLASSIFICATION OF TRAFFIC SIGNS USING CAMERA-RADAR FUSION

Resumen de: US20260299088A1

0000 The disclosed systems and techniques facilitate efficient detection and classification of traffic signs in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle a first set of perspective camera images of an environment and a second set of radar images of the environment. The techniques further include generating, using a first neural network, one or more camera features characterizing the first set of images, generating, using a second neural network, one or more radar features characterizing the second set of images, and processing the one or more camera features and the one or more radar features to obtain an identification of one or more traffic signs in the environment.

PERSISTENT-PHOTOCONDUCTIVITY-BASED BIO-INSPIRED SENSING-MEMORY-COMPUTING INTEGRATED PHOTOELECTRIC DETECTION SYSTEM FOR MOVING TARGET

Resumen de: WO2026199857A1

The present application relates to the fields of bio-inspired intelligent vision and brain-inspired computing. Disclosed is a persistent-photoconductivity-based bio-inspired sensing-memory-computing integrated photoelectric detection system for a moving target. The system comprises a synapse-like bio-inspired photodetector array and a brain-inspired memristor array, which are interconnected, wherein the synapse-like bio-inspired photodetector array applies a fixed bias voltage to a device in each pixel when detecting visual information of the moving target, so as to convert spatial illumination information corresponding to each pixel into a persistently decaying photocurrent, and the synapse-like bio-inspired photodetector array continuously receives, during the persistent decay of the photocurrent, a plurality of frames of illumination information of the moving target, and reads the photocurrent of each pixel after the last frame of illumination information is inputted, so as to determine a feature map that stores all historical moment information; and the brain-inspired memristor array executes deep-neural-network operations on the feature map on the basis of Ohm's law and Kirchhoff's laws, and outputs a current corresponding to a task result label, so as to identify the moving target. The present application can meet the requirements of deep neural networks and complex dynamic vision tasks.

DETECTION OF UNSAFE CABIN CONDITIONS IN AUTONOMOUS VEHICLES

Resumen de: US20260296364A1

0000 Devices, systems and processes for the detection of unsafe cabin conditions that provides a safer passenger experience in autonomous vehicles are described. One example method for enhancing passenger safety includes capturing at least a set of images of one or more passengers in the vehicle, determining, based on the set of images, the occurrence of an unsafe activity in an interior of the vehicle, performing, using a neural network, a classification of the unsafe activity, and performing, based on the classification, one or more responsive actions.

RECURRENT SELF-REPRESENTATION NEURAL NETWORK

Resumen de: US20260300686A1

0000 Disclosed are systems and methods that implement a form of subjective awareness of experience for a neural network and provide the ability to quantitively determine if a neural network is self-aware. The disclosed implementations include a recurrent self-representation system that includes a neural network and an introspective system that is communicatively coupled with the neural network. The introspective system generates and provides recurrent self-representations to the neural network as inputs of an input set. The neural network utilizes those recurrent self-representations to understand its own experience and utilize that understanding to influence the neural networks processing of inputs and generation of outputs.

DISTRIBUTION SYSTEM POWER-FLOW SOLUTION BY HIERARCHICAL ARTIFICIAL NEURAL NETWORKS STRUCTURE

Resumen de: US20260300691A1

Disclosed herein is a method for solving a power flow problem in at least one system. The method includes dividing, by at least one processor, the system into a plurality of clusters, where each of the plurality of clusters has a modular architecture, constructing, by the at least one processor, a hierarchical artificial neural network (ANN) for each of the plurality of clusters, thereby generating a plurality of ANNs, and determining, by the at least one processor by the plurality of ANNs, at least one solution to a power flow problem for (i) each of the plurality of ANNs, and (2) the hierarchical ANN as a whole, where each of the plurality of ANNs is organized hierarchically such that data from at least one lower-level layer is fed into at least one upper layer in accordance with an electric correlation between at least one cluster in the plurality of clusters.

SCALING ELEMENT-WISE OPERATIONS OF A NEURAL NETWORK

Resumen de: US20260300712A1

Neural networks, and in particular deep neural networks, are traditionally resource intensive as they require execution of a significant number of computations in order to generate an output. In an effort to accelerate neural networks, quantization has been employed in which the input/output of certain operations are scaled. However, the degree to which a neural network can be quantized is limited because current scaling methods, which do not ensure consistent scaling for operands, cannot be applied to the commonly used element-wise operations (e.g. addition and subtraction). The present disclosure provides scaling of element-wise operations in a neural network by using scale factors that are shared between operands, thus allowing for greater quantization of neural networks.

Hybrid CPU GPU Execution Framework for Large Language Model Inference with Reinforcement Learning Enhanced Adaptive Computation Time

Resumen de: US20260300708A1

A hybrid CPU-GPU execution framework is disclosed for efficient large language model inference using a reinforcement learning (RL)-enhanced adaptive computation time mechanism. A large language model is fine-tuned with RL to learn when to halt further processing and output an answer, balancing accuracy and computation. This learned policy dynamically skips unnecessary neural network layers or tokens for easy inputs and engages more computation for hard inputs. The system deploys the model across heterogeneous hardware: it allocates heavy computations to GPUs (or other accelerators) and uses CPUs for lighter tasks, guided by the model's policy. The model is compressed (e.g. quantized to 8-bit) to boost CPU performance. The result is an inference system that delivers fast, reasoning-rich responses efficiently, and is scalable from cloud to edge devices by adaptively adjusting both computation depth and hardware usage per query.

ADAPTIVE NEURO-SYMBOLIC CONTEXT FABRIC FOR EXPLAINABLE INTEGRITY IN AI-AGENTIC ENTERPRISE DATA OPERATIONS

Resumen de: US20260300533A1

A computer-implemented system and method providing a four-term Graph-Enhanced Semantic Density Scoring (G-SDS) formula for reducing LLM context window consumption by 60-80% in enterprise master data management (MDM) operations, combined with a SPDZ-protocol federated secure multi-party computation (SMPC) mechanism for binary compliance-vote aggregation across distributed MDM silos, wherein individual silo veto votes remain cryptographically hidden under zero-knowledge proof guarantees while the global veto/approve decision is revealed. The system further comprises a Logical Neural Network (LNN) with AND/OR/NOT/IMPLIES/EQUIV gate neurons maintaining real-valued truth bounds L, U, integrated with a PPO reinforcement learning policy network that dynamically adapts soft-constraint thresholds to minimize false-positive vetoes. A hard symbolic veto gate permanently blocks any transaction violating governance constraints.

PERFORMANCE-GUIDED PARAMETER INITIALIZATION FOR NEURAL NETWORK TRAINING

Resumen de: WO2026207472A1

A method of training a neural network includes generating an initial set of weights which provide a starting point for subsequent gradient-based optimization, the generating comprising applying multiple sets of weights to the neural network, evaluating performance of the multiple sets of weights using a loss function associated with the neural network, iteratively adjusting at least one set of weights based on the evaluated performance without computing gradients of the loss function with respect to the weights, and selecting a set of weights as the initial set of weights based on the iterative adjustments. The gradient-based optimization is then performed using the selected set of weights as the starting point, benefiting from a more advantageous initialization than conventional weight initialization techniques.

SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE AGENTS FOR MULTI-TURN INTERACTIONS

Resumen de: US20260300690A1

Embodiments described herein provide a method for multi-turn generation by an AI agent. The method includes: receiving, by a first neural network based language model, a set of context data including one or more APIs for performing actions, one or more domains, and one or more domain-specific policies; generating, by the first neural network based language model, a multi-turn task including a tuple of (task intent, groundtruth actions, groundtruth outputs); generating, by a second neural network based language model, one or more outputs in response to one or more user questions simulated based on the task intent; and executing, by the second neural network based language model, actions in response to the user questions. The method also includes: forming a training tuple including the user questions, the outputs, and the actions in response to the outputsare consistent with the groundtruth outputs and the actions are consistent with the groundtruth actions.

GRAPH NEURAL NETWORKS

Resumen de: US20260301174A1

0000 A method comprising: generating subgraphs graph based on classification scores corresponding to nodes and obtained by inputting input graph into a graph neural network, GNN, wherein each node in each input graph comprises a plurality of values corresponding respectively to a plurality of features; determining statistical measures of each feature's values in the subgraphs to generate statistical measures for each subgraph, and obtaining for each subgraph classification probabilities corresponding respectively to a plurality of classes; performing k-means clustering on the subgraphs based on the statistical measures to generate a plurality of clusters of subgraphs; generating, for each cluster of subgraphs, a set of surrogate models to approximate the classification probabilities, respectively, of the subgraphs in the cluster, in terms of the statistical measures; and determining, based on the surrogate models of each set of surrogate models, feature importance scores.

METHOD AND DATA PROCESSING SYSTEM FOR LOSSY IMAGE OR VIDEO ENCODING, TRANSMISSION AND DECODING

Resumen de: WO2026207349A1

A method for lossy image or video encoding and transmission, the method including receiving a first image and classification information associated with the first image at a first computer system; with a first neural network, producing a latent representation of the first image using the classification information; transmitting the latent representation of the first image to a second computer system.

DYNAMIC CONTENT WINDOW BASED ON ENVIRONMENT AND CONTEXT

Resumen de: US20260300741A1

A computer system that determines a dynamic content window is described. During operation, the computer system may receive information associated with an environment of an electronic device. Then, the computer system may access a stored context corresponding to the information. Moreover, the computer system may determine a dynamic content window based at least in part on the information and/or the context, where the dynamic content window has a length less than a predefined value. Next, the computer system may generate an input vector based at least in part on the information, the context and the dynamic content window. Furthermore, the computer system may provide, to a pretrained neural network, the input vector and the dynamic content window. Additionally, the computer system may receive, from the pretrained neural network, a response vector, and may provide, addressed to the electronic device, the response vector.

DYNAMIC HARDWARE AWARE MODEL PARTITIONING

Nº publicación: US20260300489A1 01/10/2026

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]
Microsoft Technology Licensing, LLC

Resumen de: US20260300489A1

A data processing system implements a model partitioning framework that partitions the layers of a neural network of an AI model into multiple partitions. Each partition includes a subset of the layers of the neural network architecture and each partition has different security requirements based on whether the layers in the partition are receiving sensitive input data or operating on data from which features of the sensitive input data can be reconstructed. The model partitioning framework determines the security requirements of each of the layers of the neural network architecture and partitioning the neural network architecture into multiple partitions based on the security requirements of the layers included in each partition and the security features of the hardware security domains available for operating each of the partitions. Each partition is operated in a hardware security domain that satisfies the security requirements of that respective partition.