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100
results.
Updated on
20/08/2026 [07:41:00]
Results 50 to 75 of 100
Absstract of: 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.
Absstract of: 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
Absstract of: 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.
Absstract of: AU2024406878A1
Computing systems and methods are disclosed for detecting and tracking a proximal end of a dilator from a surgical video stream, and mitigating the tool's visually distracting effects. In one example system, a memory may store instructions that, when executed by one or more processors, may cause the system to receive, in real-time, image data from an incoming surgical video stream of a field of view of the digital surgical microscope camera. The field of view may show a proximal end of a dilator. The system may generate, using the image data and a trained neural network, a bounding box around a region corresponding to the proximal end of the dilator. A pixel modification algorithm may be applied to the region of the image data corresponding to the proximal end of the dilator. An updated image data may be generated in real-time for an outgoing surgical video stream.
Absstract of: WO2026157121A1
Disclosed in the present invention are a weakly supervised object detection method and system based on misclassification correction, an electronic device, and a storage medium. The method comprises: extracting region features of target candidate locations by using a convolutional neural network; assigning category labels to the extracted region features by means of a classifier; and designing a misclassification correction-driven label assignment module on the basis of confidence differences between categories so as to identify misclassification cases and correct same, and reassigning labels for training of an object detection model. In the method of the present invention, erroneous category labels generated in a training phase are corrected, significantly improving the classification performance of the weakly supervised object detection method.
Absstract of: WO2026157818A1
The present application discloses a method, system, and apparatus for analyzing an event on the basis of a generative task and multimodality, which are used for quickly and accurately ascertaining the progress of an event. The method of the present application comprises: generating structured event information from multi-modal data, and obtaining an event relationship; constructing an event graph; extracting multi-modal features, and using a cross-modal attention mechanism to obtain multi-modal sentiment relationship vectors; on the basis of a preset graph neural network and in view of the event graph, obtaining sentiment feature vectors; calculating a contrastive learning loss value, and obtaining a target sentiment feature on the basis of the contrastive learning loss value; using the cross-modal attention mechanism to generate a target event graph; extracting a relationship feature, and obtaining an event prediction graph in view of attention weights and a preset time series model; extracting a causal chain and performing inference, and obtaining a trigger condition and an emotion factor after the inference; and combining the event prediction graph and the target event graph with a node feature sequence, the trigger condition, and the emotion factor, and obtaining an event analysis result.
Absstract of: 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.
Absstract of: 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.
Absstract of: US20260220451A1
Embodiments described herein provide a method of configuring an artificial intelligence (AI) agent to respond to a user query. The method includes: receiving a user query; generating, by a first neural network based language model, a response to the user query; generating, by the first neural network based language model, a summary of an interaction history; and training, a second neural network based language model, using a dataset including the summary and the interaction history to generate a rating of the summary, an explanation of the rating, and a citation in the interaction history supporting the explanation conditioned on the summary and the interaction history in response to a training query. The method also includes: building, at a server, the AI agent through a first application programming interface (API) to the first neural network based language model and a second API to the trained second neural network based language model.
Absstract of: US20260220470A1
0000 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a neural-symbolic system for reasoning tasks. One of the methods includes performing multiple different search processes in parallel using a symbolic engine and a neural network model to find a solution to a reasoning task, wherein performing the search process includes: processing, using the neural network model, an input including: (i) data for the reasoning task, and (ii) one or more auxiliary data items generated at one or more previous iterations to generate an auxiliary data item at a current iteration; and generating, using the symbolic engine, states for the reasoning task using the auxiliary data items; maintaining a subset of the states for the reasoning task generated during the multiple different search processes; and sharing the subset of the states for the reasoning task across the multiple different search processes.
Absstract of: US20260220425A1
Systems, methods, and computer-readable media are disclosed for providing hierarchical processing in neural network architectures (e.g., transformer-based models, recurrent networks, state space models, memory-augmented networks, and retrieval-augmented systems), which may improve computational efficiency and memory utilization.
Absstract of: WO2026161905A2
A method for long-horizon task planning in an embodied agent environment parses a current observation using a scene parser to generate a scene graph processed by a Graph Neural Network embedder to produce a current scene embedding vector. A lookahead simulator simulates execution of valid actions using a Planning Domain Definition Language-based forward dynamics model to produce action-observation pairs provided to a Large Language Model agent. An experience retriever compares the current scene embedding vector against an experience memory bank and, upon finding a match within a predefined threshold, retrieves a relevant experience comprising a goal, a textual description of a matched state, and a ground-truth action sequence. A loop detector identifies previously visited states and generates a loop warning. The Large Language Model agent processes a structured prompt comprising these components to select and execute an action, while a state-transition graph builder maintains a memory graph of state transitions.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: EP4783073A2
0001 Various embodiments relate to determining a parallel computation scheme for a neural network. A device may receive a computation graph and transform the computation graph into a dataflow graph comprising recursive subgraphs. Each recursive subgraph may comprise a tuple of another recursive subgraph and an operator node, or an empty graph. The device may determine a number of partitioning recursions based on a number of parallel computing devices. For each partitioning recursion, the device may determine costs corresponding to operator nodes, determine a processing order of the recursive subgraphs, and process the recursive subgraphs. To process a recursive subgraph, the device may select a partitioning axis for tensor(s) associated with an operator node of the recursive subgraph. The device may output a partitioning scheme comprising partitioning axes for each tensor associated with the operator nodes. Devices, methods, and computer programs are disclosed.
Absstract of: EP4783068A1
0001 According to an aspect of the present inventive concept there is provided a computer-implemented method (1000) for generating technical explanations of alarms in a supervisory control and data acquisition, SCADA, system for an electrical infrastructure, the method (1000) comprising: providing (1100) a neural network, NN, model trained to detect anomalies in data, wherein the NN model is trained on a training dataset comprising training data based on operational data retrieved by the SCADA system from the electrical infrastructure and comprising data related to a plurality of system parameters of the SCADA system, receiving (1200) run data based on operational data retrieved by the SCADA system from the electrical infrastructure, the run data comprising data related to the plurality of system parameters, detecting (1300), with the NN model, an anomaly in the run data, and generating (1400) a technical explanation of the detected anomaly by means of an interpretability model, wherein the interpretability model applies a model-agnostic interpretable technique to the run data to determine a contribution of the system parameters in the plurality of system parameters to the detection of the anomaly.
Absstract of: WO2025083107A1
The invention relates to a method for categorizing objects detected by n (n = 2, 3...) artificial neural networks in at least one image.
Absstract of: GB2703381A
The invention relates to controlling a vehicle at an entry to a roundabout. The invention includes receiving 201 dynamic data of dynamic characteristics related to how a further vehicle is traversing the roundabout. This is input into a first branch of a dual-branch neural network, which determines 203 a plurality of dynamic data vectors indicative of temporal dynamics of the further vehicle. Static data characteristics associated with the roundabout is input into a second branch of the neural network, and a static data vector indicative of contextual information associated with the roundabout is determined 204. At an attention layer of the neural network, a cross-attention mechanism is applied 205 to the dynamic data vectors and the static data vector to obtain a cross-attended vector. A cross prediction indicating whether the further vehicle will cross in front of the vehicle is then determined 206, and a vehicle control signal is output 207 in dependence thereof. Fig. 2
Absstract of: CN121889838A
A method and apparatus for deep learning. A first input and a second input are accessed. A first embedding of the first input is generated using the bound network. A second embedding of the second input is generated using the bound network. The first embed and the second embed are aggregated to generate a combined embed. The transform function is applied to the combinatorial embedding to generate a transformed combinatorial embedding. The combined embedding of transforms is processed using an unbinding network to extract an embedding of a first transform for the first input and an embedding of a second transform for the second input. An inference function is applied to the embedding of the first transform to generate a first output. An inference function is applied to the embedding of the second transform to generate a second output.
Absstract of: US20260212154A1
Methods and systems for integrating symbolic reasoning with neural network capabilities in artificial intelligence systems, with a particular focus on enabling automated workflow orchestration and agent-based decision making through a neuro-symbolic computation framework.
Absstract of: US20260212659A1
0000 The present invention provides a method for image classification by incorporating a deep neural network embedded with multiscale spatial attention mechanism (MSSAM). The method according to the present invention comprises various stages: Stage I—Data preparation stage; Stage II—Model training stage; Stage III—Evaluation and Testing stage; Stage IV—Iterative optimization stage. During Data preparation stage, data is collected from a large and diverse dataset of images relevant to specific classification task. During the Model training stage, the model architecture is established, appropriate loss function is selected, an optimizer and an initial learning rate is chosen, the model is trained on training dataset, monitoring validation performance and experiments are performed with hyperparameters. During the Evaluation and Testing stage, model's performance is evaluated on the validation set using metrics and during Iterative optimization stage, the optimization process is iterated and continuously monitored for best results.
Absstract of: US20260212250A1
0000 A method may include obtaining a quantum neural network with a plurality of network layers and assigning each network layer of the plurality of network layers to a distinct set of quantum nodes physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture such that adjacent network layers of the plurality of network layers are assigned to sets of quantum nodes on different ones of the two intertwined helices. The method may also include performing one or more quantum computations using the quantum computing architecture.
Nº publicación: US20260212183A1 23/07/2026
Applicant:
AIRBUS SAS [FR]
Airbus SAS
Absstract of: US20260212183A1
A method and an apparatus for compressing a neural network. Circuitry receives a neural network comprising a set of parameters being in at least one floating-point number format of a first precision. The circuitry also applies at least one mathematical model on the neural network to determine errors introduced by using arithmetic of a second precision lower than the first precision and propagation through network layers of the neural network due to using the arithmetic of the second precision instead of the first precision. The circuitry then applies a solver to an optimization problem formulated based on the errors determined by the at least one mathematical model to determine a set of optimized parameters. Finally, the circuitry compresses the neural network based on the set of optimized parameters to output a compressed neural network.
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