Alerta

DILATOR PROXIMAL END BLOCKER IN A SURGICAL VISUALIZATION

Resumen de: 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.

WEAKLY SUPERVISED OBJECT DETECTION METHOD BASED ON MISCLASSIFICATION CORRECTION, AND RELATED DEVICE

Resumen de: 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.

METHOD, SYSTEM, AND APPARATUS FOR ANALYZING EVENT ON THE BASIS OF GENERATIVE TASK AND MULTIMODALITY

Resumen de: 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.

SYSTEMS AND METHODS FOR VERIFYING TEXT-GENERATING NEURAL NETWORK MODELS

Resumen de: 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.

NEURO-SYMBOLIC SYSTEM FOR SOLVING REASONING TASKS

Resumen de: 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.

HIERARCHICAL MEMORY ATTENTION NETWORK WITH MULTI-COMPONENT WEIGHTED ATTENTION AND ADAPTIVE RESOURCE ALLOCATION

Resumen de: 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.

LONG-HORIZON EMBODIED TASK PLANNING VIA GRAPH-INFORMED ACTION GENERATION

Resumen de: 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.

Systems and Methods for Training Neural Networks and Related Machine Learning Models

Resumen de: US20260220467A1

0000 Systems and methods for training-concurrent compression of network models are illustrated. One embodiment includes a method for training-concurrent pruning, performed by processors. The method obtains a pretrained network comprising initial values for a potential function and weights. The method trains a network based upon the pretrained network and training samples, wherein training the network comprises calculating loss for a current network using a loss function; updating a gradient of the potential function at the current time iteration according to a gradient of the loss; updating an inverse mapping of the gradient of the potential function; creating an updated network including updated weights for a next time iteration according to the gradient of the potential function and the inverse mapping of the gradient of the potential function, wherein the updated weights is obtained by pruning the weights of the current network; and storing the updated weights.

SYSTEM AND METHOD TO LEVERAGE AGENTIC FOUNDATIONAL MODELS TO ENHANCE GRAPH NEURAL NETWORKS IN MOBILE COMMUNICATION

Resumen de: US20260220419A1

Enhanced network graphs and graph neural networks (GNNs) are disclosed. In a system that includes a radio access network and a radio intelligent controller (RIC), the RIC is configured to receive multi-modal data along with other data. Semantic features from the multi-modal data are combined with features from the other data. The combined features are used to enhance a network graph of the network and to train a GNN whose feature embeddings are enhanced with the multi-modal data. Decisions made in the network may be based on the feature embeddings, which account for multi-modal data and network inter-dependencies.

POST-FACTO DETERMINATION OF CONTRIBUTIONS OF SOURCE DATA TO OUTPUTS OF GENERATIVE AI MODELS

Resumen de: WO2026161749A1

A system includes: a neural network trained to extract claims from an output response generated by a large language model, a graph structure stored in a database, the graph structure including a plurality claims, entities, events, concepts, or beliefs and relationship links between the plurality of claims, the claims being extracted from a plurality of source data, and a contribution engine configured to determine contribution scores of the source data used to train the large language model to generate the output response.

ACCELERATING MULTI-VIEW SEARCH WITH DYNAMIC VIEW ROUTING

Resumen de: US20260220430A1

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for selecting relevant documents. A system processes a query using a query encoder neural network to generate query embeddings. A router neural network processes the query embeddings to generate a respective score for each embedding. The system selects a representative query embedding based on the scores and utilizes the representative query embedding and stored document embeddings to generate a relevance score for each of a plurality of documents. The system then selects a subset of the documents as relevant to the query based on the relevance scores.

RISK MITIGATION IN NETWORKS

Resumen de: US20260222420A1

Generalized risk management is disclosed. When an event related to a network is identified, a request is generated and sent to a prompt generator. The prompt generator generates a prompt based on the request, key performance indicators of the network, and a semantic state of the network, which is generated by a first model. The prompt is input to a second model and the second model identifies risks associated with the event and/or generates a recommended potential solution to a potential incident caused by the event should the event occur. An agent may evaluate the risks and/or the recommended potential solution and send a command to the network to mitigate the risks and/or resolve the incident should the incident occur. The prompt generator also generates the prompt using a graph neural network and/or a knowledge base. The network incident management can be automated and/or include a human-in-the-loop.

SELF-CLASSIFICATION OF NEURAL NETWORKS

Resumen de: WO2025083107A1

The invention relates to a method for categorizing objects detected by n (n = 2, 3...) artificial neural networks in at least one image.

Controlling a vehicle at an entry to a roundabout

Resumen de: 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

PARALLEL COMPUTING SCHEME GENERATION FOR NEURAL NETWORKS

Resumen de: 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.

A NEURAL NETWORK SYSTEM WITH MULTIPLE INPUTS AND MULTIPLE OUTPUTS

Resumen de: 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.

METHOD AND SYSTEM FOR GENERATING TECHNICAL EXPLANATIONS OF ALARMS IN A SCADA SYSTEM

Resumen de: 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.

METHOD AND APPARATUS FOR COMPRESSING A NEURAL NETWORK, ELECTRONIC DEVICE, AND AIRCRAFT

Nº publicación: EP4783071A1 29/07/2026

Solicitante:

AIRBUS SAS [FR]
Airbus S.A.S.

Resumen de: EP4783071A1

Proposed is a method and an apparatus (100) for compressing a neural network. The processing circuitry (110) is configured to receive a neural network comprising a set of parameters being in at least one floating-point number format of a first precision. Further, the processing circuitry (110) is configured to apply 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 processing circuitry (110) is further configured to apply 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. Further, the processing circuitry (110) is configured to compress the neural network based on the set of optimized parameters to output a compressed neural network.