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107
results.
Updated on
06/08/2026 [08:15:00]
Absstract of: 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.
Absstract of: EP4787198A1
Embodiments of this application disclose a method for solving a system of nonlinear equations. In the method, during an ith iteration of a first system of nonlinear equations, a neural network model can identify, based on input description information of the ith iteration, system of equations feature information of the first system of nonlinear equations during the ith 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
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: 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: 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: 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: 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: 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: 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: 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: 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.
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: 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: 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: 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.
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: 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: 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: 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.
Nº publicación: EP4781370A1 29/07/2026
Applicant:
EYYES GMBH [AT]
EYYES GmbH
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.
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