Patents Examined by George Giroux
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Patent number: 12136411Abstract: A technique for training a model is disclosed. A training sample including an input sequence of observations and a target sequence of symbols having length different from the input sequence of observations is obtained. The input sequence of observations is fed into the model to obtain a sequence of predictions. The sequence of predictions is shifted by an amount with respect to the input sequence of observations. The model is updated based on a loss using a shifted sequence of predictions and the target sequence of the symbols.Type: GrantFiled: April 3, 2020Date of Patent: November 5, 2024Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Gakuto Kurata, Kartik Audhkhasi
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Patent number: 12112174Abstract: A programmable hardware system for machine learning (ML) includes a core and a streaming engine. The core receives a plurality of commands and a plurality of data from a host to be analyzed and inferred via machine learning. The core transmits a first subset of commands of the plurality of commands that is performance-critical operations and associated data thereof of the plurality of data for efficient processing thereof. The first subset of commands and the associated data are passed through via a function call. The streaming engine is coupled to the core and receives the first subset of commands and the associated data from the core. The streaming engine streams a second subset of commands of the first subset of commands and its associated data to an inference engine by executing a single instruction.Type: GrantFiled: December 19, 2018Date of Patent: October 8, 2024Assignee: Marvell Asia Pte LtdInventors: Avinash Sodani, Ulf Hanebutte, Senad Durakovic, Hamid Reza Ghasemi, Chia-Hsin Chen
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Patent number: 12093817Abstract: A computer-implemented method and a system for configuring and implementing an artificial neural network. The method for configuring includes initializing an artificial neural network; and training, based on a training operation, the artificial neural network so as to form an adaptively deployable artificial neural network defining a plurality of nested artificial neural sub-networks. Each artificial neural sub-network is optimal for a respective resource configuration. The method for implementing includes determining an optimal configuration of the artificial neural network for deployment at an electrical device, and deploying the artificial neural network with the optimal configuration at the electrical device.Type: GrantFiled: August 24, 2020Date of Patent: September 17, 2024Assignee: City University of Hong KongInventors: Tei-wei Kuo, Antoni Bert Chan, Chun Xue, Yufei Cui, Ziquan Liu, Wuguannan Yao, Qiao Li
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Patent number: 12061954Abstract: Disclosed are techniques for implementing an intelligent system with dynamic configurability. These techniques identifying a plurality of flow nodes for a software application and determine a dynamic flow for executions of the intelligent system with the plurality of flow nodes, one or more dynamic conditions, and one or more dynamic actions, without hard coded inter-dependency between two or more flow nodes of the plurality of flow nodes. The intelligent system is transformed into a dynamically configured intelligent system at least by performing a modification pertaining to one or more flow nodes in the dynamic flow, without affecting remaining flow nodes in the dynamic flow.Type: GrantFiled: October 27, 2017Date of Patent: August 13, 2024Assignee: INTUIT INC.Inventor: Matthew L. Sivertson
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Patent number: 12045722Abstract: An apparatus and method for determining architectural plan compliance is illustrated herein. Determining design plan compliance using machine learning includes at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive, by the processor, a design plan, wherein the design plan comprises structure parameters; obtain, by the processor, construction constraint and geographical constraint, identify, by the processor, a compliance threshold as a function of the construction constraints and geographical constraints, determine a divergence element related to compliance of the user design as a function of the compliance threshold and the design plan.Type: GrantFiled: November 14, 2022Date of Patent: July 23, 2024Assignee: Design Group Partners, LLCInventor: John Bews
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Patent number: 12020141Abstract: A deep learning apparatus for an artificial neural network (ANN) having pipeline architecture. The deep learning apparatus for an ANN simultaneously performs output value processing, corrected input data processing, corrected output value processing, weight correction, input bias correction, and output bias correction using pipeline architecture, thereby reducing calculation time for learning and reducing required memory capacity.Type: GrantFiled: August 16, 2018Date of Patent: June 25, 2024Assignee: DEEPX CO., LTD.Inventor: Lokwon Kim
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Patent number: 11977968Abstract: The present application provides an operation device and related products. The operation device is configured to execute operations of a network model, wherein the network model includes a neural network model and/or non-neural network model; the operation device comprises an operation unit, a controller unit and a storage unit, wherein the storage unit includes a data input unit, a storage medium and a scalar data storage unit. The technical solution provided by this application has advantages of a fast calculation speed and energy-saving.Type: GrantFiled: April 17, 2018Date of Patent: May 7, 2024Assignee: SHANGHAI CAMBRICON INFORMATION TECHNOLOGY CO., LTD.Inventors: Tianshi Chen, Yimin Zhuang, Daofu Liu, Xiaobin Chen, Zai Wang, Shaoli Liu
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Patent number: 11966837Abstract: In an approach for compressing a neural network, a processor receives a neural network, wherein the neural network has been trained on a set of training data. A processor receives a compression ratio. A processor compresses the neural network based on the compression ratio using an optimization model to solve for sparse weights. A processor re-trains the compressed neural network with the sparse weights. A processor outputs the re-trained neural network.Type: GrantFiled: March 13, 2019Date of Patent: April 23, 2024Assignee: International Business Machines CorporationInventors: Dzung Phan, Lam Nguyen, Nam H. Nguyen, Jayant R. Kalagnanam
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Patent number: 11966856Abstract: The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. The present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system and train a machine-learning model to predict invalid access-right requests using the collected data set. The collected data set may include a log line that represents each user device, and each log line may be labeled based on an invalidity evaluation. New access-right requests can be processed using the trained machine-learning model to determine whether or not to assign access rights in response to the access-right request.Type: GrantFiled: July 27, 2020Date of Patent: April 23, 2024Assignee: Live Nation Entertainment, Inc.Inventors: Mark Roden, Lee Ha, James Healy
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Patent number: 11961013Abstract: Disclosed is an electronic apparatus. The electronic apparatus may include a memory configured to store one or more training data generation models and an artificial intelligence model, and a processor configured to generate personal training data that reflects a characteristic of a user using the one or more training data generation models, train the artificial intelligence model using the personal learning data as training data, and store the trained artificial intelligence model in the memory.Type: GrantFiled: July 8, 2020Date of Patent: April 16, 2024Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Chiyoun Park, Jaedeok Kim, Youngchul Sohn, Inkwon Choi
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Patent number: 11948070Abstract: A method in a hardware implementation of a Convolutional Neural Network (CNN), includes receiving a first subset of data having at least a portion of weight data and at least a portion of input data for a CNN layer and performing, using at least one convolution engine, a convolution of the first subset of data to generate a first partial result; receiving a second subset of data comprising at least a portion of weight data and at least a portion of input data for the CNN layer and performing, using the at least one convolution engine, a convolution of the second subset of data to generate a second partial result; and combining the first partial result and the second partial result to generate at least a portion of convolved data for a layer of the CNN.Type: GrantFiled: April 10, 2023Date of Patent: April 2, 2024Assignee: Imagination Technologies LimitedInventors: Clifford Gibson, James Imber
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Patent number: 11931950Abstract: Systems and methods for controlling a material extrusion device to extrude a filament of an ink are provided. An extrusion printing control system collects from one or more sensors measurements representing an internal state of material extrusion processing during extrusion of the filament. In addition, the system collects an image of the filament as the filament is extruded. The system applies a classifier to the collected image to generate an image-derived state characterizing the filament. Based on the internal state and the image-derived state, the system estimates a derived state using a model. The system determines control parameters using the model to achieve a desired quality of the filament by minimizing a cost function based on the internal state, the image-derived state, the derived state, and constraints of the material extrusion device. Finally, the system provides the control parameters to a controller of the material extrusion device.Type: GrantFiled: September 5, 2019Date of Patent: March 19, 2024Assignee: LAWRENCE LIVERMORE NATIONAL SECURITY, LLCInventors: Brian Howell, Brian Giera, Maxwell Murialdo, Kyle Sullivan
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Patent number: 11934954Abstract: A pure integer quantization method for a lightweight neural network (LNN) is provided. The method includes the following steps: acquiring a maximum value of each pixel in each of the channels of the feature map of a current layer; dividing a value of each pixel in each of the channels of the feature map by a t-th power of the maximum value, t?[0,1]; multiplying a weight in each of the channels by the maximum value of each pixel in each of the channels of the corresponding feature map; and convolving the processed feature map with the processed weight to acquire the feature map of a next layer. The algorithm is verified on SkyNet and MobileNet respectively, and lossless INT8 quantization on SkyNet and maximum quantization accuracy so far on MobileNetv2 are achieved.Type: GrantFiled: September 22, 2021Date of Patent: March 19, 2024Assignee: SHANGHAITECH UNIVERSITYInventors: Weixiong Jiang, Yajun Ha
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Patent number: 11928582Abstract: Embodiments of the invention provide a system, media, and method for deep learning applications in physical design verification. Generally, the approach includes maintaining a pattern library for use in training machine learning model(s). The pattern library being generated adaptively and supplemented with new patterns after review of new patterns. In some embodiments, multiple types of information may be included in the pattern library, including validation data, and parameter and anchoring data used to generate the patterns. In some embodiments, the machine learning processes are combined with traditional design rule analysis. The patterns being generated and adapted using a lossless process that encodes the information of a corresponding area of a circuit layout.Type: GrantFiled: December 31, 2018Date of Patent: March 12, 2024Assignee: Cadence Design Systems, Inc.Inventors: Piyush Pathak, Haoyu Yang, Frank E. Gennari, Ya-Chieh Lai
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Patent number: 11907837Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting actions from large discrete action sets. One of the methods includes receiving a particular observation representing a particular state of an environment; and selecting an action from a discrete set of actions to be performed by an agent interacting with the environment, comprising: processing the particular observation using an actor policy network to generate an ideal point; determining, from the points that represent actions in the set, the k nearest points to the ideal point; for each nearest point of the k nearest points: processing the nearest point and the particular observation using a Q network to generate a respective Q value for the action represented by the nearest point; and selecting the action to be performed by the agent from the k actions represented by the k nearest points based on the Q values.Type: GrantFiled: December 22, 2020Date of Patent: February 20, 2024Assignee: DeepMind Technologies LimitedInventors: Gabriel Dulac-Arnold, Richard Andrew Evans, Benjamin Kenneth Coppin
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Patent number: 11803764Abstract: A method for building an artificial intelligence (AI) model. The method includes accessing data related to monitored behavior of a user. The data is classified, wherein the classes include an objective data class identifying data relevant to a group of users including the user, and a subjective data class identifying data that is specific to the user. Objective data is accessed and relates to monitored behavior of a plurality of users including the user. The method includes providing as a first set of inputs into a deep learning engine performing AI the objective data and the subjective data of the user, and a plurality of objective data of the plurality of users. The method includes determining a plurality of learned patterns predicting user behavior when responding to the first set of inputs. The method includes building a local AI model of the user including the plurality of learned patterns.Type: GrantFiled: October 3, 2017Date of Patent: October 31, 2023Assignee: Sony Interactive Entertainment Inc.Inventors: Erik Beran, Michael Taylor, Masanori Omote
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Patent number: 11775313Abstract: An accelerator for processing of a convolutional neural network (CNN) includes a compute core having a plurality of compute units. Each compute unit includes a first memory cache configured to store at least one vector in a map trace, a second memory cache configured to store at least one vector in a kernel trace, and a plurality of vector multiply-accumulate units (vMACs) connected to the first and second memory caches. Each vMAC includes a plurality of multiply-accumulate units (MACs). Each MAC includes a multiplier unit configured to multiply a first word that of the at least one vector in the map trace by a second word of the at least one vector in the kernel trace to produce an intermediate product, and an adder unit that adds the intermediate product to a third word to generate a sum of the intermediate product and the third word.Type: GrantFiled: May 25, 2018Date of Patent: October 3, 2023Assignee: Purdue Research FoundationInventors: Eugenio Culurciello, Vinayak Gokhale, Aliasger Zaidy, Andre Chang
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Patent number: 11748610Abstract: Techniques for sequence to sequence (S2S) model building and/or optimization are described. For example, a method of receiving a request to build a sequence to sequence (S2S) model for a use case, wherein the request includes at least a training data set, generating parts of a S2S algorithm based on the at least one use case, determined parameters, and determined hyperparameters, and training a S2S algorithm built from the parts of the S2S algorithm using the training data set to generate the S2S model is detailed.Type: GrantFiled: March 23, 2018Date of Patent: September 5, 2023Assignee: Amazon Technologies, Inc.Inventors: Orchid Majumder, Vineet Khare, Leo Parker Dirac, Saurabh Gupta
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Patent number: 11740905Abstract: In many industrial settings, a process is repeated many times, for instance to transform physical inputs into physical outputs. To detect a situation involving such a process in which errors are likely to occur, information about the process may be collected to determine time-varying feature vectors. Then, a drift value may be determined by comparing feature vectors corresponding with different time periods. When the drift value crosses a designated drift threshold, a predicted outcome value may be determined by applying a prediction model. Sensitivity values may be determined for different features, and elements of the process may then be updated based at least in part on the sensitivity values.Type: GrantFiled: July 25, 2022Date of Patent: August 29, 2023Assignee: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Dushyanth Gokhale
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Patent number: 11741342Abstract: Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy but lacked consideration of computational resource use. Presented herein are embodiments of a Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding. RENA embodiments use a policy network to process the network embeddings to generate new configurations. Example demonstrates of RENA embodiments on image recognition and keyword spotting (KWS) problems are also presented herein. RENA embodiments can find novel architectures that achieve high performance even with tight resource constraints. For the CIFAR10 dataset, the tested embodiment achieved 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size was less than 3M parameters.Type: GrantFiled: March 8, 2019Date of Patent: August 29, 2023Assignee: Baidu USA LLCInventors: Yanqi Zhou, Siavash Ebrahimi, Sercan Arik, Haonan Yu, Hairong Liu, Gregory Diamos