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99
resultados
Última actualización
14/08/2026 [10:05:00]
Resumen de: AU2025214687A1
Disclosed herein are sequencing systems and sequencing methods for training neural networks and for utilizing the trained neural networks for sequencing analysis after acquiring flow cell images using the sequencing systems. The sequencing systems disclosed herein can include Field-Programmable Gate Array (FPGAs), artificial intelligence (AI) chips, or a combination thereof.
Resumen de: AU2026208040A1
Abstract Computer-implemented method transmits a predictive model from a first device to a second device. The first device receives data values of a data stream comprising sensor data collected from one or more sensor devices in an industrial environment. Using the received data values, the first device generates and refines a first predictive model for predicting future data values, including determining and adjusting predictive model parameters based on newly received data values. The first device transmits the predictive model parameters to the second device. The second device receives the predictive model parameters and parameterizes a second predictive model using the received parameters. The second predictive model predicts future values of the first device. Based at least in part on the predicted future values, the second device causes a physical control action to be performed with respect to a physical component in the industrial environment. The predictive model parameters may be updated and retransmitted during operation. Abstract to FIG. 3 Self Organizing Network Coding to FIG. 2 Data Pool Predictive Trained Diagnostics Maintenance Models Data Rights Config. Semantic Semantic System Pool Management Automation Processing Pricing Policy Decision User Order Analysis Automation Engine Analytics Private Cloud Ad and Anticipation Location Based Customer Storage Content Targeting of State Services Applications Analytics Expert Pattern Speech Other Services Dynamic Systems
Resumen de: EP4790443A1
The present disclosure provides an apparatus for restoring the quality of magnetic resonance images based on a deep learning model and a method of controlling the same. The method includes: obtaining a training image corresponding to each magnetic resonance image by applying at least one of a plurality of elements set in connection with the quality of the magnetic resonance image to a magnetic resonance signal corresponding to the magnetic resonance image; obtaining a training dataset including the magnetic resonance image as label data and the obtained training image as input data matching the label data; and training a neural network model based on the training dataset and context data corresponding to the training image. Obtaining the training image includes distorting the magnetic resonance signal by applying the at least one of the plurality of elements and obtaining the training image based on the distorted magnetic resonance signal.
Resumen de: EP4790604A1
Embodiments of this application disclose a neural network training method. A first module in a federated neural network is deployed in each of a plurality of first devices. The first module includes a feature extraction module. A plurality of second modules in the federated neural network and early exit modules connected to the respective second modules are deployed in a second device. It can be learned that, in a federated learning process, the second device may include a plurality of early exit nodes, and each early exit node corresponds to one second module and a corresponding early exit module connected to the second module. In this way, after federated learning, when a target network is deployed in the first device, a structure of the target network may be in a plurality of forms based on the plurality of early exit nodes. In other words, target networks of different structures may be flexibly deployed in different first devices. For example, flexible scheduling may be performed based on resource statuses of different first devices, so that each first device can implement efficient data processing through a target network of an appropriate scale.
Resumen de: US20260228499A1
According to one example of the present disclosure, the neural processing unit may comprise a processing element array configured to perform operations of a neural network model and a post-processing unit configured to process data output from the processing element array. The post-processing unit includes a first computation circuit that extracts a subset of classes for each bounding box by comparing class scores of classes and a second computation circuit configured to extract one or more bounding boxes by comparing a class confidence score of each bounding box with a threshold confidence score.
Resumen de: US20260228302A1
A graphics processing unit (GPU) schedules recurrent matrix multiplication operations at different subsets of CUs of the GPU. The GPU includes a scheduler that receives sets of recurrent matrix multiplication operations, such as multiplication operations associated with a recurrent neural network (RNN). The multiple operations associated with, for example, an RNN layer are fused into a single kernel, which is scheduled by the scheduler such that one work group is assigned per compute unit, thus assigning different ones of the recurrent matrix multiplication operations to different subsets of the CUs of the GPU. In addition, via software synchronization of the different workgroups, the GPU pipelines the assigned matrix multiplication operations so that each subset of CUs provides corresponding multiplication results to a different subset, and so that each subset of CUs executes at least a portion of the multiplication operations concurrently.
Resumen de: US20260228895A1
0000 Techniques for determining a presence of a pathology property in a supra-image are presented. The techniques can include receiving an electronic evaluation supra-image; providing the electronic evaluation supra-image to an electronic neural network that has been trained, using a training corpus of training supra-images and on an electronic computer, to determine the presence of the pathology property in a supra-image, each training supra-image including at least one image, each image corresponding to a plurality of components, wherein each training supra-image of the training corpus is associated with a respective electronic label indicating whether the pathology property is present, where the training corpus is sufficient to train the electronic neural network to determine a presence of the pathology property; receiving from the electronic neural network an output indicative of whether the pathology property is present in the evaluation supra-image; and providing the output.
Resumen de: US20260228308A1
Apparatuses, systems, and techniques estimate parameters to train one or more neural networks based on uniqueuss of training data. In at least one embodiment, a subset of training data is selected and used to estimate parameters to train one or more neural networks, based on, for example, uniqueness of training data.
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.
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.
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.
Resumen de: US20260228305A1
A comprehensive resilience evaluation system for a traditional village is provided. The system employs a deep learning-based neural network model to perform semantic encoding on data for resilience aspects of a traditional village to be evaluated to extract semantic descriptive coding features from the multi-dimensional data for the resilience aspects of the village to be evaluated. Concurrently, the system retrieves village resilience-related data labeled with a first resilience evaluation label from a backend database as reference features. Through conducting a semantic query matching analysis based on multi-source resilience-related data between the village to be evaluated and various villages labeled with the first resilience evaluation label, the system intelligently evaluates whether the village to be evaluated possesses a resilience level corresponding to the first resilience evaluation label. Thus, the accuracy of the comprehensive resilience evaluation for the village to be evaluated can be effectively improved.
Resumen de: US20260228272A1
0000 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for composed image retrieval. In one aspect, a method performed by one or more computers is described. The method includes: receiving a query including: (i) an image depicting a scene, and (ii) a text prompt describing a context of the scene; processing the image, using a visual encoder, to generate a visual embedding of the image; processing the visual embedding of the image, using a mapping neural network, to generate one or more language tokens of the image; generating multiple language tokens of the text prompt; processing the language tokens of the image and text prompt, using a language encoder, to generate a language embedding of the query; and selecting, from a number candidate images, one or more of the candidate images using the language embedding of the query.
Resumen de: US20260228525A1
A processor-implemented method for dynamic class-incremental learning without forgetting includes receiving, by an artificial neural network (ANN), an input. The ANN extracts features of the input to generate a representation of the input. An embedding is generated by the ANN based on the representation and multiple similarity metrics. The ANN generates a new class without retraining the ANN. The new class is generated based on a comparison of the embedding and a set of prior embeddings.
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.
Resumen de: 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.
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.
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.
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.
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.
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.
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.
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.
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.
Nº publicación: US20260220931A1 30/07/2026
Solicitante:
XIAO JIASONG [CN]
ZHANG YIFEI [CN]
RICOH CO LTD [JP]
XIAO Jiasong
Zhang Yifei
Ricoh Company, Ltd.
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.
BOPI
Sede Electrónica
