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68
results.
Updated on
18/04/2026 [07:59:00]
Results 25 to 50 of 68
Absstract of: US20260094399A1
0000 Apparatuses, systems, and techniques to generate bounding box information. In at least one embodiment, for example, bounding box information is generated based, at least in part, on a plurality of candidate bounding box information.
Absstract of: US20260094429A1
Techniques related to poly-scale kernel-wise convolutional neural network layers are discussed. A poly-scale kernel-wise convolutional neural network layer is applied to an input volume to generate an output volume and include filters each having a number of filter kernels with the same sample rate and differing dilation rates optionally in a repeating pattern of dilation rate groups within each of filters with the pattern of dilation rate groups offset between the filters the poly-scale kernel-wise convolutional neural network layer.
Absstract of: WO2026071683A1
According to an embodiment of the present disclosure, disclosed is a method for predicting the price of cryptocurrency on the basis of an artificial neural network. The method may comprise the steps of: acquiring monitoring reference information from a user terminal; generating a chart image according to the monitoring reference information; generating a pattern prediction result corresponding to the chart image on the basis of an artificial neural network-based pattern prediction model; and transmitting, to the user terminal, notification information generated on the basis of the pattern prediction result.
Absstract of: WO2026070418A1
In this information processing method using a neural network for a structure represented as a set of nodes arranged in space, a computer executes processing including: receiving input of a state of each node; calculating, on the basis of states between the nodes, a frame representing a coordinate axis for each node; and extracting, using the frame, information having predetermined symmetry of the structure from each node.
Absstract of: WO2026069497A1
This information processing device performs predetermined processing using a neural network model and comprises an inference unit that performs the predetermined processing using the model. The model includes a positional encoding unit that calculates relative positional information of each token in a token string using a wavelet function, and an attention mechanism that calculates a latent representation of the token string using the positional information.
Absstract of: WO2026070140A1
The present disclosure relates to vehicle control based on a neural network. In particular, the present disclosure relates to determining confidence in the output of a neural network by comparing a firing pattern observed during operation of a vehicle with a reference firing pattern obtained by observing firing of neurons during a training phase.
Absstract of: EP4718327A1
0001 An electronic device for executing a neural network model including a non-linear operation and an operation method thereof are provided. The operation method of the electronic device includes obtaining data to be inferred and obtaining an inference result of the data output from the neural network model as the data is input to the neural network model including a plurality of nodes, wherein, in an inference process, a first weight applied when a value of a first node among the plurality of nodes is transmitted to a second node may be updated based on a value of a first reference node, which is any one of the plurality of nodes.
Absstract of: EP4718326A2
0001 The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.
Absstract of: EP1000000A1
The invention relates to an apparatus (1) for manufacturing green bricks from clay for the brick manufacturing industry, comprising a circulating conveyor (3) carrying mould containers combined to mould container parts (4), a reservoir (5) for clay arranged above the mould containers, means for carrying clay out of the reservoir (5) into the mould containers, means (9) for pressing and trimming clay in the mould containers, means (11) for supplying and placing take-off plates for the green bricks (13) and means for discharging green bricks released from the mould containers, characterized in that the apparatus further comprises means (22) for moving the mould container parts (4) filled with green bricks such that a protruding edge is formed on at least one side of the green bricks.
Absstract of: US20260087304A1
0000 Systems and method of classification are provided. Upon receiving an input, a feature set is defined from the input. A semantic cluster to be associated with the input is defined based on the feature set, the semantic cluster being one of a plurality of semantic clusters each defining a subset of outputs of a neural network based on semantic similarity of the subset. The feature set is applied to a subgraph corresponding to the semantic cluster, the subgraph being one of a plurality of subgraphs each defining a portion of the neural network. A classification for the input is then be determined based on an output of the subgraph.
Absstract of: US20260086524A1
Embodiments of the present disclosure relate to generating controller logic. Indication of a controller logic generation request associated with an asset identifier may be received. A prompt template set associated with a controller logic generation workflow may be identified based on the asset identifier. The prompt template of the prompt template set may comprise one or more instruction sets. The prompt template set may be input into a large language model comprising one or more transformer neural networks and configured to generate a controller logic configuration file for the asset identifier based on the prompt template set and intent classification associated with each prompt template. The controller logic configuration file may be received from the large language model. Performance of one or more prediction-based actions may be initiated based on the controller logic configuration file.
Absstract of: US20260085661A1
0000 A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.
Absstract of: US20260087787A1
A method of condensing a training dataset and an image processing device are provided. The method of includes generating a cluster set by clustering a training dataset; generating an initial condensed high-resolution (HR) dataset by selecting, for each cluster included in the cluster set, one or more images from among a respective cluster in the training dataset; obtaining a first loss of a first neural network model based on the training dataset and obtaining a second loss of a second neural network model based on the initial condensed HR dataset. The method further includes generating a condensed HR dataset by updating, based on the first loss and the second loss, pixels in each of the one or more images included in each cluster of the initial condensed HR dataset; and executing an operation instruction to transmit the condensed HR dataset to an image processing device.
Absstract of: WO2026061185A1
The present application discloses a model training method and apparatus, a construction safety evaluation method and apparatus, and a device. The model training method comprises: acquiring sample data; on the basis of association information between training samples and true value labels, determining label missing ratios of the training samples in different dimensions and first weight values of the training samples in different dimensions; determining second weight values of the training samples on the basis of the label missing ratios; and inputting the sample data, the first weight values, and the second weight values into a preset neural network model for training until a loss value of a target loss function of the preset neural network model meets a model convergence condition, so as to obtain a target prediction model. In this way, by improving a weighted loss function, when sample data having a partially missing label is kept, a loss value of a missing label of a sample is calculated with reference to a weight, thereby solving the problem of reduced prediction accuracy caused by missing samples, improving the accuracy of a model, and simultaneously and accurately predicting prediction values of multiple dimensions.
Absstract of: US20260086522A1
0000 Disclosed in the present disclosure is a method and system for controlling and distributing wave energy in offshore aquaculture. The method includes: obtaining an aquaculture cycle of each aquaculture sub-zone of an offshore aquaculture zone, sorting remaining aquaculture cycles of the aquaculture sub-zones from small to large, and obtaining a plurality of work cycles according to sorting results; obtaining a predicted wave energy yield of a next work cycle through a preset neural network model; obtaining an importance coefficient value sorting result of each aquaculture zone through a preset recursive feature elimination (RFE) model; and adjusting operation cycles and operation power of first-type aquaculture apparatuses, second-type aquaculture apparatuses, and third-type aquaculture apparatuses in sequence according to an apparatus type of each aquaculture apparatus, the aquaculture zone where each aquaculture apparatus is located, the predicted wave energy yield, and the importance coefficient value sorting results.
Absstract of: US20260085602A1
Computer implemented methods and systems for testing one or more operational changes in a drill rig includes initiating the one or more operational changes and using, in part, image data of a mechanical mud separation machines (“MMSM”) to detect the impact of the one or more changes. The image data may be processed by a Deep Neural Network to identify objects in the object flow, operational parameters of the MMSM, and wellbore environmental conditions. Additional image data may be selected for additional processing based on the results of the analysis. The results of the test may be used to update the drilling operation or a drilling model.
Absstract of: US20260086013A1
A particulate matter detection device takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides larger particles to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a CMOS image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse and triplicate holograms of the same particle are recorded at a single frame revealing different perspectives of each particle. A deep neural network classifies the particles based on the acquired holographic images. The device was tested using different types of pollen and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ˜700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods.
Absstract of: US20260086912A1
The present disclosure relates to methods and systems for providing inferences using machine learning systems. The methods and systems receive a load forecast for processing requests by a machine learning model and split the machine learning model into a plurality machine learning model portions based on the load forecast. The methods and systems determine a batch size for the requests for the machine learning model portions. The methods and systems use one or more available resources to execute the plurality of machine learning model portions to process the requests and generate inferences for the requests.
Absstract of: US20260086636A1
0000 Aspects of the present disclosure relate to systems and methods for controlling a function of a computing system using gaze detection. In examples, one or more images of a user are received and gaze information may be determined from the received one or more images. Non-gaze information may be received when the gaze information is determined to satisfy a condition. Accordingly, a function may be enabled based on the received non-gaze information. In examples, the gaze information may be determined by extracting a plurality of features from the received one or more images, providing the plurality of features to a neural network, and determining, utilizing the neural network, a location at a display device at which a gaze of the user is directed.
Absstract of: US20260087646A1
0000 An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of a first object and a second object. The apparatus further includes a memory storage unit to store the raw data. In addition, the apparatus includes a neural network engine to receive the raw data. The neural network engine is to generate a segmentation map and a boundary map. The apparatus also includes a post-processing engine to identify the first object and the second object based on the segmentation map and the boundary map.
Absstract of: US20260089329A1
A computer-implemented method for lossy image or video compression, transmission and decoding, the method including the steps of (i) receiving an input image at a first computer system; (ii) encoding the input image using a first trained neural network, using the first computer system, to produce a latent representation; (iii) quantizing the latent representation using the first computer system to produce a quantized latent; (iv) entropy encoding the quantized latent into a bitstream, using the first computer system; (v) transmitting the bitstream to a second computer system; (vi) the second computer system entropy decoding the bitstream to produce the quantized latent; (vii) the second computer system using a second trained neural network to produce an output image from the quantized latent, wherein the output image is an approximation of the input image.
Absstract of: US20260088021A1
Apparatuses, systems, and techniques to facilitate understanding of media content using neural networks to adjust playback speed and volume based on environmental and other factors. In at least one embodiment, playback of media content is slowed down or sped up if audio associated with said media content is difficult to understand based on background noise, accent, difficulty of material, as well as other factors that decrease understandability of media content.
Absstract of: US20260087822A1
The present invention relates to a method for monitoring a harbor performed by a computing device, the method for monitoring the harbor according to an aspect of the present invention comprising: obtaining a harbor image having a first view attribute; generating a segmentation image having the first view attribute and corresponding to the harbor image by performing an image segmentation using an artificial neural network trained to output information, from an input image, related to an object included in the input image; generating a transformed segmentation image having a second view attribute from the segmentation image having the first view attribute based on a first view transformation information used to transform an image having the first view attribute into an image having the second view attribute different from the first view attribute; and calculating berthing guide information of the ship based on the transformed segmentation image.
Absstract of: US20260087344A1
0000 A method using a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.
Nº publicación: US20260088023A1 26/03/2026
Applicant:
GOOGLE LLC [US]
Google LLC
Absstract of: US20260088023A1
A method for training hotword detection includes receiving a training input audio sequence including a sequence of input frames that define a hotword that initiates a wake-up process on a device. The method also includes feeding the training input audio sequence into an encoder and a decoder of a memorized neural network. Each of the encoder and the decoder of the memorized neural network include sequentially-stacked single value decomposition filter (SVDF) layers. The method further includes generating a logit at each of the encoder and the decoder based on the training input audio sequence. For each of the encoder and the decoder, the method includes smoothing each respective logit generated from the training input audio sequence, determining a max pooling loss from a probability distribution based on each respective logit, and optimizing the encoder and the decoder based on all max pooling losses associated with the training input audio sequence.
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