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NEURAL NETWORK CACHE EVICTION TO AVOID RESTORE

Resumen de: US20260228135A1

0000 Apparatuses, systems, and techniques to identify information to evict from a Key-Value (KV) cache. In at least one embodiment, information stored within one or more large language model (LLM) KV caches may be identified to cause an indication to generated of information stored within the one or more LLM KV caches that can be evicted without causing other information stored within the one or more LLM KV caches to be restored to the one or more LLM KV caches.

SYSTEM AND METHOD FOR LEVERAGING QUANTUM ALGORITHMS TO PROCESS NETWORK SIMULATIONS

Resumen de: US20260228502A1

Embodiments of the invention are directed to systems, methods, and computer program products for processing network simulations. In some embodiments, the method includes identifying, from one or more datasets, a plurality of microsegments; generating a graph neural network (GNN) comprising a plurality of nodes and a plurality of edges, where each node is associated with a microsegment; using a generative artificial intelligence (AI) engine to generate a marker token for each node, where the marker token is associated with an exposure level; executing at least one quantum algorithm, where executing the at least one quantum algorithm comprises processing a plurality of simulations; and based on one or more outputs of the quantum algorithm, causing a user device to execute a remedial action.

TRAINING QUANTUM GENERATIVE NETWORKS BASED ON MARGINAL AND JOINT DISTRIBUTIONS

Resumen de: US20260228591A1

0000 In an embodiment, a parameterized quantum circuit is initialized on a quantum computer for a Quantum Neural Network (QNN) with trainable parameters to learn a first conditional distribution of a training dataset. A marginal distribution is loaded on a first set of qubits and the first joint distribution on a second set of qubits. The QNN is operated on the marginal distribution to predict the first conditional distribution. The QNN is operated on the marginal distribution and first conditional distribution to generate a second conditional distribution for the input data and second output data. A second joint distribution is generated from the second conditional distribution and loaded onto a third set of qubits. Joint quantum measurements are extracted from the second and first joint distributions to train the QNN to learn the first conditional distribution.

TRANSFORMER-BASED SHAPE MATCHING ACROSS IMAGES

Resumen de: WO2026165126A1

A system for matching shapes between images includes a transformer-based neural network with an encoder processing input images and a decoder processing a query shape from a first image. A parallel decoding module estimates corresponding shapes in a second image based on processed feature maps and query shapes from the first image. The system may include a recursive zoom-in module that iteratively refines the estimated corresponding shapes by zooming into regions around initial estimates to achieve sub-pixel accuracy. The transformer-based neural network can be trained using a data generation process that adapts based on training loss curves.

NOISE-RESISTANT NEURAL NETWORK INFERENCING

Resumen de: US20260228571A1

0000 Apparatuses, systems, and techniques to perform inferencing using one or more neural networks with improved noise immunity are disclosed. In at least one embodiment, one or more neural networks may generate inferences, where these inferences may include measurably consistent information from the same input data irrespective of changes in environmental conditions.

GENERALIZED ENERGY DISTANCE DIFFUSION MODELS

Resumen de: WO2026165379A1

A method for generating a data item using a generative neural network model comprises, at each of a plurality of iterations, the generative neural network model processing a network input based on a current version of a data representation, to form a network output. The generative neural network is trained to form the network output as a sample from a target probability distribution over the representation space. The data representation is a vector defined in a representation space, and in each iteration, the network output is used to generate an updated data representation. The data item is generated based on the updated data representation generated in the last iteration.

OBJECT DETECTION FROM SYNTHETIC APERTURE RADAR USING A COMPLEX-VALUED CONVOLUTIONAL NEURAL NETWORK

Resumen de: US2025180734A1

Systems and methods are provided for object detection. A radar interface receives complex-valued data representing a region of interest from a synthetic aperture radar system. A complex-valued convolutional neural network includes a plurality of convolutional layers and provides an output indicating if objects are present in the region of interest. Each convolutional layer includes a complex-valued kernel that is applied to an input. The kernel includes a first set of weights that is applied to each of real and imaginary components of the input to provide respective first and second convolution products and a second set of weights applied to each of real and imaginary components of the input to provide respective third and fourth convolution products. A difference between the first and fourth convolution products provides a real output component and a sum of the second and third convolution products provides an imaginary output component.

METHOD FOR SOLVING NONLINEAR EQUATION SET, AND RELATED DEVICE

Resumen de: EP4787198A1

Embodiments of this application disclose a method for solving a system of nonlinear equations. In the method, during an i^th iteration of a first system of nonlinear equations, a neural network model can identify, based on input description information of the i^th iteration, system of equations feature information of the first system of nonlinear equations during the i^th iteration, to efficiently determine a first convergence policy that is appropriate for the first system of nonlinear equations in the current iteration process. In this way, a target convergence policy is determined according to the first convergence policy, and the first system of nonlinear equations is iteratively solved efficiently and accurately according to the target convergence policy, so that performance of iterative solving on a system of nonlinear equations is improved (for example, a solving speed is improved and solving time consumption is reduced), and a generalization problem caused by a convergence policy determined based on expert experience and according to a default convergence policy is avoided, thereby resolving a problem that a convergence policy determined in different scenarios has a poor effect and is difficult to converge when a system of nonlinear equations is solved.

CATEGORIZATION WITH GRAPH NEURAL NETWORK AND LANGUAGE MODEL

Resumen de: EP4769175A1

0001 Certain aspects of the disclosure provide techniques for categorization by a device. An example method includes receiving input information regarding a plurality of classification targets and a plurality of categories for classification of the plurality of classification targets; generating a plurality of embeddings for the input information using a first model, the plurality of embeddings including: a set of first embeddings associated with the plurality of classification targets, and a set of second embeddings associated with the plurality of categories; determining that a similarity score for the set of first embeddings and the set of second embeddings fails to satisfy a threshold; generating, based on the similarity score failing to satisfy the threshold and using a graph neural network (GNN), a classification of the plurality of classification targets in accordance with the plurality of categories; and outputting information regarding the classification.

METHOD AND APPARATUS FOR PROCESSING CT IMAGE AND METHOD AND APPARATUS FOR INSPECTING INTERNATIONAL EXPRESS DELIVERY

Resumen de: US20260219417A1

0000 Provided are a method and an apparatus for processing a CT image and a method and an apparatus for inspecting the international express delivery. The method for processing the CT image includes a pre-processing step where uniform sampling and coordinate normalization are performed on three-dimensional data of the CT image to acquire pre-processed data, an object instance acquisition step where object instance data acquired after instance segmentation combined with semantic information is acquired for the pre-processed data, and a structured feature acquisition step where structured features of the object instance data are acquired using a feature extractor trained based on a neural network.

TITER PROCESSING METHOD AND SYSTEM BASED ON DEEP LEARNING

Resumen de: US20260220770A1

0000 The present application discloses a titer processing method and system based on deep learning, which belong to the field of machine learning, and comprises: obtaining a first image of non-syphilis treponemal serological test results; judging the clarity of the test image and outputting a second image; applying a convolutional neural network on the second image for detecting circular targets and outputting the center coordinates and radius of the circular targets; segmenting the second image according to the center coordinates and radius of the detected circular targets to extract circular target regions; sorting the extracted circular target regions according to preset sorting rules; using a convolutional neural network to classify the sorted circular target regions and outputting a negative or positive result; calculating a corresponding titer of the negative or positive result.

OPTIMIZED PLACEMENT FOR EFFICIENCY FOR ACCELERATED DEEP LEARNING

Resumen de: US20260219967A1

0000 Techniques in optimized placement for efficiency for accelerated deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element comprises a compute element to execute programmed instructions using the data and a router to route the wavelets. The routing is in accordance with virtual channel specifiers of the wavelets and controlled by routing configuration information of the router. A software stack determines optimized placement based on a description of a neural network. The determined placement is used to configure the routers including usage of the respective colors. The determined placement is used to configure the compute elements including the respective programmed instructions each is configured to execute.

NEURAL NETWORK TRAINING METHOD AND DEFECT DETECTION METHOD AND APPARATUS

Resumen de: US20260220931A1

A method of training a neural network is provided. The method includes steps of obtaining labeled defect information of an object and a training image set collected from the object, and obtaining, based on the training image set, feature image sets for representing a plurality of features of the object; inputting the feature image sets into the neural network, utilizing attention mechanism modules in the neural network to carry out local attention generation and global attention generation with respect to the feature image sets, respectively, so as to generate processing results, and creating, based on the processing results, training defect information of the object; and comparing the training defect information of the object and the labeled defect information of the object, so as to train the neural network and adjust parameters of the neural network.

Scenario-based Cyber Network Topology Generation System and Method

Resumen de: US20260222449A1

An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An AI-based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy-related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

Method, apparatus and system for encoding and decoding tensors

Resumen de: AU2025200211A1

41663381_1 Abstract METHOD, APPARATUS AND SYSTEM FOR ENCODING AND DECODING A system and method of decoding a bitstream to produce tensors for use by a network portion. The method comprises decoding a plurality of pictures from the bitstream, wherein each picture contains a feature map for the network portion and the decoding may or may not conform to behaviour of a particular implementation of neural network operations, with associated output from the decoder indicating the conformance status of the decoder output. Abstract METHOD, APPARATUS AND SYSTEM FOR ENCODING AND DECODING A system and method of decoding a bitstream to produce tensors for use by a network portion. The method comprises decoding a plurality of pictures from the bitstream, wherein each picture contains a feature map for the network portion and the decoding may or may not conform to behaviour of a particular implementation of neural network operations, with associated output from the decoder indicating the conformance status of the decoder output. 41663381_1 an a n 45362676_1 FCM_CPS FCM_VMPS FCM_RSD FCM_CVD FCM_RSD FCM_CVD FCM_SPS FCM_PPS FCM_PH 1022c 1012c 1022 1012b 1022b VMPS_ID CPS_ID AU (POC #0) AU (POC #1) I slice P slice 10212 10214 Fig. 10 Start NAL unit NAL unit code header payload 10232 10234 an a n ( # ) u n i t

AUGMENTING DEEP NEURAL NETWORKS WITH RESIDUAL MEMORIZATION

Resumen de: US20260221132A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task. In particular, the machine learning task is performed by augmenting a trained neural network with residual memorization.

SYSTEM AND METHOD FOR ESTIMATING THE METASTATIC STATUS OF A SENTINEL LYMPH NODE

Resumen de: US20260215754A1

A computer-implemented method for estimating the metastatic status of a sentinel lymph node. The method comprises: acquiring an ultrasound image of the primary tumor; segmenting said ultrasound image, wherein segmenting said ultrasound image comprises determining a region of interest, ROI, comprising an intratumoral region of the primary tumor and a peritumoral region of the primary tumor and wherein segmenting said ultrasound image comprises performing a semantic segmentation process using a CNN that was trained to determine said intratumoral region of the primary tumor; extracting a plurality of characteristics of said image from said ROI using a pre-trained convolutional neural network, CNN; selecting one or more characteristics of said plurality of characteristics; and classifying the metastatic status of said sentinel lymph node based on said one or more characteristics.

SOMATIC VARIANT PREDICTION

Resumen de: US20260221225A1

0000 Method and systems for prediction of somatic mutations based on data of a potentially tumorous sample, by receiving nucleic acid sequencing data of the potentially tumorous sample; transforming the nucleic acid sequencing data into an image-like representation; processing the image-like representation by a trained neural network to predict somatic mutations in the nucleic acid sequencing data.

SYSTEMS AND METHODS FOR INTERFACING LIVING BIOLOGICAL NEURAL NETWORKS

Resumen de: US20260220445A1

0000 Disclosed herein are systems and methods for closed-loop drug and/or environmental evaluation. In various aspects, described herein is a neuro-interface platform comprising: a neurophysiological unit comprising a multielectrode array (MEA) disposed in a chamber with biological or synthetic tissue comprising neurons. A first electrode is configured to detect an efferent signal from the neurons at a recording region corresponding to neuronal activity, and a second electrode is configured to provide electrical stimulation to the neurons at a stimulation region. The platform further includes a sensorimotor unit comprising a test device, the test device being configured to be selectively controlled based on a measurement of the efferent signal and to concurrently sense an afferent signal from a sensor coupled to the test device. The platform also includes an interface unit configured to electrically couple the neurophysiological unit and the sensorimotor unit.

ELECTRONIC APPARATUS FOR PREDICTING SYSTEMIC BIOLOGICAL AGE AND OPERATION METHOD THEREOF

Resumen de: WO2026160517A1

An electronic apparatus for predicting systemic biological age, according to an embodiment of the present invention, comprises a memory and a processor that predicts systemic biological age, wherein the processor can acquire an eye image of an examinee, process the eye image by using an artificial neural network model that has been established, and generate a predicted result for the systemic biological age of the examinee.

FINGERPRINT CARRIER STATE RECOGNITION METHOD AND APPARATUS, ELECTRONIC DEVICE, AND STORAGE MEDIUM

Resumen de: WO2026156746A1

Embodiments of the present application provide a fingerprint carrier state recognition method and apparatus, an electronic device, and a storage medium. The fingerprint carrier state recognition method comprises: acquiring a current fingerprint image frame; inputting feature information of the current fingerprint image frame, time information corresponding to the current fingerprint image frame, and a latent variable corresponding to the current fingerprint image frame into a recurrent neural network model to obtain carrier state information outputted by the recurrent neural network model and a latent variable corresponding to a next fingerprint image frame, wherein a fingerprint carrier is a medium carrying a fingerprint corresponding to a fingerprint image; and determining the state of the fingerprint carrier on the basis of the carrier state information. In the fingerprint carrier state recognition method provided by the present application, carrier state recognition is performed with reference to the time information of the current fingerprint image frame and a latent variable obtained from a historical fingerprint image, so that a high recognition accuracy rate can be achieved.

SYSTEMS, METHODS AND TESTING APPARATUS FOR ASSESSING PLATELET SAMPLES USING MACHINE LEARNING FOR CANCER ESTIMATION

Resumen de: WO2026156436A1

An artificial intelligence-based method for detecting a disease comprises a meta- classification model comprising a plurality of base classification models each generating a respective output and wherein each of such outputs is input to a meta-model, the meta-model comprising an artificial neural network. Variants are described in respect of the meta-model stacked ensemble pipeline, dimensionality / complexity control, and specific architectures designed for tracking disease trajectory through identifying gene pathway crosstalk. The method is particularly suitable for detecting cancer in blood samples comprising tumor educated platelets.

Proxy-Based Multi-Modal Learning For Render Time Prediction

Resumen de: US20260220485A1

0000 Techniques for predicting render times are disclosed. A system accesses auxiliary rendered outputs that were generated from a coarse render pass of a 3D scene description using a set of hardware resources. The system accesses system performance metrics of the set of hardware resources associated with the coarse render pass. The system encodes visual feature representations from the auxiliary rendered outputs using a convolutional neural network. The system also encodes the system performance metrics and a set of rendering configuration parameters associated with a target rendering operation. The system generates, using a multi-modal fusion model, a fused representation of the encoded visual feature representations, the encoded system performance metrics, and the encoded first set of rendering configuration parameters, using an attention-based mechanism that weights contributions of the inputs to the model. The system predicts a render time for the target rendering operation based on the fused representation.

GENERATING SPATIAL-TEMPORAL REPRESENTATIONS OF DIGITAL FILES USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Resumen de: US20260220198A1

Methods, apparatus, and processor-readable storage media for generating spatial-temporal representations of digital files using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining one or more spatial properties within at least a portion of at least one digital file and at least one sequential order of data within the at least a portion of the digital file(s); generating at least one graph representation of the at least a portion of the digital file(s) based on the one or more spatial properties and the sequential order(s) of data; encoding parts of the graph representation(s) using at least one graph neural network in conjunction with one or more spatial-temporal transformers; generating a spatial-temporal feature representation of the digital file(s) by aggregating a plurality of the encoded parts of the graph representation(s); and performing one or more automated actions based on the spatial-temporal feature representation of the digital file(s).

SYSTEMS AND METHODS FOR EFFICIENT MODEL EXECUTION ON MACHINE-LEARNING ACCELERATORS

Nº publicación: US20260220439A1 30/07/2026

Solicitante:

GOOGLE LLC [US]
Google LLC

Resumen de: US20260220439A1

Methods, systems, and apparatus for reducing latency of configuration of image sensor and image signal processor. A computing system can include a machine learning (ML) processing engine that can process denoising diffusion ML models for execution on statically compiled ML accelerators. The system can determine that a partitioned graph representation, which includes connected subgraphs that each represent at least one layer of the neural network of the ML model, forms a directed acyclic graph. The system can insert cache nodes in the graph representation, where each cache node corresponds to a respective subgraph and is configured to cache output of the respective subgraph. The system can generate an execution dataflow graph including a plurality of iterations of the partitioned graph representation, where, at one or more iterations during model inference operations, the execution dataflow graph uses inputs from cache nodes and excludes execution of subgraphs corresponding to the cache nodes.