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30
resultados
Última actualización
19/05/2024 [07:27:00]
Resumen de: US2024161387A1
Systems, apparatuses and methods may provide for technology that generates, by a first neural network, an initial set of model weights based on input data and iteratively generates, by a second neural network, an updated set of model weights based on residual data associated with the initial set of model weights and the input data. Additionally, the technology may output a geometric model of the input data based on the updated set of model weights. In one example, the first neural network and the second neural network reduce the dependence of the geometric model on the number of data points in the input data.
Resumen de: US2024161770A1
A system, method and computer product for training a neural network system. The method comprises applying an audio signal to the neural network system, the audio signal including a vocal component and a non-vocal component. The method also comprises comparing an output of the neural network system to a target signal, and adjusting at least one parameter of the neural network system to reduce a result of the comparing, for training the neural network system to estimate one of the vocal component and the non-vocal component. In one example embodiment, the system comprises a U-Net architecture. After training, the system can estimate vocal or instrumental components of an audio signal, depending on which type of component the system is trained to estimate.
Resumen de: WO2024102474A1
A system including a plurality of sensors and a building management system (BMS). The plurality of sensors are configured to receive occupancy data from at least a first room. The BMS includes at least one computer readable storage medium having instructions thereon to employ a gating mechanism to analyze the occupancy data via a recurrent neural network (RNN) to create a first occupancy prediction of the first room during a first upcoming time period. The RNN employs gated recurrent units (GRUs) and each of the GRUs employs at least an update gate and a reset gate. The instructions further cause adjustment of a heating, ventilation, and cooling (HVAC) system for the first upcoming time period based on the first occupancy prediction. The adjustment of the HVAC system includes adjustment of at least one of heating, cooling, and ventilation in the first room.
Resumen de: US2024160768A1
A collection of documents or other files and the like within an enterprise network are labelled according to an enterprise document classification scheme, and then a recognition model such as a neural network or other machine learning model can be used to automatically label other files throughout the enterprise network. In this manner, documents and the like throughout an enterprise can be automatically identified and managed according to features such as confidentiality, sensitivity, security risk, business value, and so forth.
Resumen de: US2024160931A1
One embodiment provides for a computer-readable medium storing instructions that cause one or more processors to perform operations comprising determining a per-layer scale factor to apply to tensor data associated with layers of a neural network model and converting the tensor data to converted tensor data. The tensor data may be converted from a floating point datatype to a second datatype that is an 8-bit datatype. The instructions further cause the one or more processors to generate an output tensor based on the converted tensor data and the per-layer scale factor.
Resumen de: US2024160898A1
The present invention relates to a method of using field-programmable gate array (FPGA) for artificial intelligence (AI) inference software stack acceleration which combines the advantages of flexibility from the AI inference software stack and the programmable hardware acceleration capability of the FPGA, wherein said method comprises the steps of performing quantization on neural network (NN) model, performing layer-by-layer profiling of said NN model using AI inference software stack, identifying compute-intensive layer type of said NN model and implementing acceleration using layer accelerator on said compute-intensive layer type.
Resumen de: US2024163097A1
Disclosed is a neural network enabled interface server and blockchain interface establishing a blockchain network implementing event detection, tracking and management for rule based compliance, with significant implications for anomaly detection, resolution and safety and compliance reporting.
Resumen de: EP4369252A2
In an example, an apparatus comprises a compute engine comprising a high precision component and a low precision component; and logic, at least partially including hardware logic, to receive instructions in the compute engine; select at least one of the high precision component or the low precision component to execute the instructions; and apply a gate to at least one of the high precision component or the low precision component to execute the instructions. Other embodiments are also disclosed and claimed.
Resumen de: US2024152135A1
A system may include a first neural network trained to determine an operating state of a vehicle from data about the vehicle captured in an operating environment of the vehicle, where the first neural network processes information about the vehicle captured by at least one Internet-of things device while the vehicle is operating. A data structure facilitates determining operating parameters configured to influence an operating state of a vehicle. A second neural network operates to: a) process information about a state of the rider occupying the vehicle, b) determine a correlation between the operating state and an effect on the state of the rider, and c) improve at least one of the determined operating parameters of the vehicle based on i) the determined operating state of the vehicle and ii) the correlation between the operating state of the vehicle and the effect on the state of the rider.
Resumen de: US2024152728A1
A method of a communication node may comprise: transmitting required network configurations for applying each of artificial neural network models to a network node; and transmitting a status report of the first model including a model identifier field and a model information field for each of the artificial neural network models to the network node to activate at least one artificial neural network model among the artificial neural network models, wherein each of the required network configurations includes a configuration identifier and network configuration information.
Resumen de: US2024152811A1
A substantial learning curve is required to construct integration processes in an integration platform. This can make it difficult for novice users to construct effective integration processes, and for expert users to construct integration processes quickly and efficiently. Accordingly, embodiments for building and operating a model to predict next steps, during construction of an integration process via a graphical user interface, are disclosed. The model may comprise a Markov chain, prediction tree, or an artificial neural network (e.g., graph neural network, recurrent neural network, etc.) or other machine-learning model that predicts a next step based on a current sequence of steps. In addition, the graphical user interface may display the suggested next steps according to a priority (e.g., defined by confidence values associated with each step).
Resumen de: US2024152755A1
An apparatus comprising: means for providing a first secret and data as inputs to a trained neural network to produce an output by inference; means for sending the output from the trained neural network to a remote server; means for receiving in reply from the server, an encoded label; means for using a second secret to decode the encoded label to obtain a label for the data.
Resumen de: US2024153604A1
There is disclosed an Artificial Intelligence (AI) assisted drug discovery system and a method for generating active and inactive targets for a given drug. The generated targets comprise the complete target landscape for the given drug. The system comprises a processor which is configured to train a multi-label deep learning neural network and a predictive model to generate and validate a prediction score, and further execute the trained multi-label deep learning neural network and the trained predictive model to identify targets associated with the given drug, and generate the active and inactive targets for the given drug molecule in response to the generated prediction score for each target associated with the given drug.
Resumen de: US2024153044A1
Some embodiments provide a neural network inference circuit for executing a neural network that includes multiple nodes that use state data from previous executions of the neural network. The neural network inference circuit includes (i) a set of computation circuits configured to execute the nodes of the neural network and (ii) a set of memories configured to implement a set of one or more registers to store, while executing the neural network for a particular input, state data generated during at least two executions of the network for previous inputs. The state data is for use by the set of computation circuits when executing a set of the nodes of the neural network for the particular input.
Resumen de: EP4365822A1
An image processing apparatus for processing an image by using one or more convolution neural networks, according to an embodiment, may comprise a memory that stores one or more instructions, and at least one processor, by executing the one or more instructions stored in the memory: acquires first feature data by performing a convolution operation between input data, which has been acquired from a first image, and a first kernel; segments, into first groups, a plurality of channels included in the first feature data; acquires pieces of second feature data by performing a convolution operation between pieces of the first feature data respectively corresponding to the first groups and second kernels respectively corresponding to the first groups; acquires pieces of shuffling data by shuffling the pieces of second feature data; acquires output data by performing a convolution operation between data obtained by summing channels included in the pieces of shuffling data and a third kernel; and generates a second image on the basis of the output data.
Resumen de: WO2024090858A1
A neuromorphic device for implementing a neural network may comprise: a memory in which at least one program is stored; an on-chip memory including a crossbar array circuit; and at least one processor which operates the neural network by executing the at least one program, wherein the at least one processor receives an audio signal and inputs the audio signal to the neural network trained on the basis of predetermined training data, to output a speech recognition result, and the neural network comprises: a Fourier transform layer that generates a frequency signal by converting the audio signal in a time band to be in a frequency band; a Mel generation layer that generates a Mel spectrogram from the frequency signal; and an output layer that outputs the speech recognition result on the basis of a speech feature of the Mel spectrogram.
Resumen de: AU2024202152A1
A computer-implemented system and method of analyzing historical business data and business objectives for real estate solution prediction is provided. The method includes receiving a user dataset associated with a business entity by a server computing device. The server may determine a set of forecasted headcounts at different points of future time based on a historical headcount, generate a plurality of scenarios associated with the business entity based on the forecasted headcount and the commercial objective dataset, and construct option trees with respective real estate solutions corresponding to respective scenarios. A neural network system may be configured to perform stress tests against respective scenarios to evaluate respective real estate solutions and determine respective costs and actions associated with respective real estate solutions. The computing device may determine an optimal real estate solution with a minimized cost and a corresponding action that the business entity takes to minimize the costs.
Resumen de: US2024144051A1
This document relates to automated generation of machine learning models, such as neural networks. One example method involves obtaining a first machine learning model having one or more first inference operations. The example method also involves identifying a plurality of second inference operations that are supported by an inference hardware architecture. The example method also involves generating second machine learning models by modifying the first machine learning model to include individual second inference operations that are supported by the inference hardware architecture. The example method also involves selecting a final machine learning model from the second machine learning models based on one or more metrics.
Resumen de: US2024143921A1
Techniques are described for training and/or utilizing sub-agent machine learning models to generate candidate dialog responses. In various implementations, a user-facing dialog agent (202, 302), or another component on its behalf, selects one of the candidate responses which is closest to user defined global priority objectives (318). Global priority objectives can include values (306) for a variety of dialog features such as emotion, confusion, objective-relatedness, personality, verbosity, etc. In various implementations, each machine learning model includes an encoder portion and a decoder portion. Each encoder portion and decoder portion can be a recurrent neural network (RNN) model, such as a RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer.
Resumen de: US2024143940A1
The present invention relates to a context-based QA generation architecture, and an object of the present invention is to generate diverse QA pairs from a single context. To achieve the object, the present invention includes a latent variable generating network including at least one encoder and an artificial neural network (Multi-Layer Perceptron: MLP) and configured to train the artificial neural network using a first context, a first question, and a first answer, and generate a second question latent variable and a second answer latent variable by applying the trained artificial neural network to a second context, an answer generating network configured to generate a second answer by decoding the second answer latent variable, and a question generating network configured to generate a second question based on a second context and the second answer.
Resumen de: US2024142968A1
A vehicle to operate with a rider according to an operating parameter. The vehicle includes a set of physiological monitoring sensors configured to measure a physiological parameter of a rider within the vehicle. The vehicle further includes a neural network trained on data related to a set of rider in-vehicle experiences to determine a state of the rider by processing outputs of the set of physiological monitoring sensors. The vehicle further includes an augmented or virtual reality system configured to present augmented reality content to the rider within the vehicle based, at least in part, on the physiological parameter. The vehicle further includes an optimization system to automatically identify a variation in the operating parameter to improve a measure of the state of the rider and generate a command to vary the operating parameter and the augmented reality content according to the variation.
Resumen de: US2024142971A1
A transportation system includes: a neural network to determine current inferred hormonal state data of an occupant of the vehicle based in part on received sensor data relating to the occupant; and an artificial intelligence-based system trained on a set of outcomes related to occupant in-vehicle experience. The artificial intelligence-based system is configured to: retrieve sensor data of the occupant; identify a difference between the current inferred hormonal state data and a desired hormonal state; determine a variation including one of configuring the vehicle for aggressive driving performance or configuring the vehicle for non-aggressive driving performance to promote the desired hormonal state of the occupant responsive to the current inferred hormonal state; and induce the variation in one or more occupant experience parameters to achieve at least one desired outcome.
Resumen de: US2024142972A1
A system may include a first neural network to detect the state of the rider through expert system-based processing of rider state indicative wearable sensor data of a plurality of wearable physiological condition sensors worn by the rider in the vehicle, the state indicative wearable sensor data indicative of at least one of a first state of the rider and a second state of the rider. A system may include a second neural network to optimize, for at least one of achieving and maintaining a first state of the rider, the operating parameter of the vehicle in response to the detected state of the rider, wherein the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider state, wherein the optimized operational parameter of the vehicle is determined and adjusted to induce the first state in the rider.
Resumen de: US2024142975A1
A method of maintaining a favorable emotional state of a rider of a vehicle. The method includes: receiving emotional state indicative wearable sensor data of the rider in the vehicle; classifying, at a rider emotional state determining neural network, the emotional state indicative wearable sensor data and generating output data indicative of a classification of the rider emotional state as one of a favorable emotional state of the rider or an unfavorable emotional state of the rider; converting the output data from the rider emotional state determining neural network into a vehicle operational state datum; and adjusting a vehicle operating parameter in response to the vehicle operational state datum.
Nº publicación: WO2024091347A1 02/05/2024
Solicitante:
BOOMI LP [US]
BOOMI, LP
Resumen de: WO2024091347A1
A substantial learning curve is required to construct integration processes in an integration platform. This can make it difficult for novice users to construct effective integration processes, and for expert users to construct integration processes quickly and efficiently. Accordingly, embodiments for building and operating a model to predict next steps, during construction of an integration process via a graphical user interface, are disclosed. The model may comprise a Markov chain, prediction tree, or an artificial neural network (e.g., graph neural network, recurrent neural network, etc.) or other machine-learning model that predicts a next step based on a current sequence of steps. In addition, the graphical user interface may display the suggested next steps according to a priority (e.g., defined by confidence values associated with each step).
BOPI
Sede Electrónica
