Patents Examined by Li B. Zhen
  • Patent number: 11521120
    Abstract: An inspection apparatus of the present disclosure includes: a machine learning device that performs machine learning on a basis of state data acquired from an inspection target and label data indicating an inspection result related to the inspection target to generate a learning model; a learning model evaluation index calculation unit that calculates a learning model evaluation index related to the learning model generated by the machine learning device as an evaluation index to be used to evaluate the learning model; an inspection index acquisition unit that acquires an inspection index to be used in the inspection; and a learning model selection unit that displays the learning model evaluation index and the inspection index so as to be comparable with each other regarding the learning model generated by the machine learning device, receives selection of the learning model by an operator, and outputs a result of the selection.
    Type: Grant
    Filed: September 11, 2019
    Date of Patent: December 6, 2022
    Inventors: Keisuke Watanabe, Yasuhiro Shibasaki
  • Patent number: 11521106
    Abstract: This disclosure relates to learning with transformed data such as determining multiple training samples from multiple data samples. Each of the multiple data samples comprises one or more feature values and a label that classifies that data sample. A processor determines each of the multiple training samples by randomly selecting a subset of the multiple data samples, and combining the feature values of the data samples of the subset based on the label of each of the data samples of the subset. Since the training samples are combinations of randomly chosen data samples, the training samples can be provided to third parties without disclosing the actual training data. This is an advantage over existing methods in cases where the data is confidential and should therefore not be shared with a learner of a classifier, for example.
    Type: Grant
    Filed: October 23, 2015
    Date of Patent: December 6, 2022
    Assignee: National ICT Australia Limited
    Inventors: Richard Nock, Giorgio Patrini, Tiberio Caetano
  • Patent number: 11514465
    Abstract: Methods and apparatus to perform multi-level hierarchical demographic classification are disclosed.
    Type: Grant
    Filed: March 2, 2017
    Date of Patent: November 29, 2022
    Assignee: The Nielsen Company (US), LLC
    Inventors: Jiabo Li, Devin T. Jones, Kevin Charles Lyons
  • Patent number: 11507797
    Abstract: An information processing apparatus having an input device for receiving data, an operation unit for constituting a convolutional neural network for processing data, a storage area for storing data to be used by the operation unit and an output device for outputting a result of the processing. The convolutional neural network is provided with a first intermediate layer for performing a first processing including a first inner product operation and a second intermediate layer for performing a second processing including a second inner product operation, and is configured so that the bit width of first filter data for the first inner product operation and the bit width of second filter data for the second inner product operation are different from each other.
    Type: Grant
    Filed: January 26, 2018
    Date of Patent: November 22, 2022
    Assignee: Hitachi, Ltd.
    Inventors: Toru Motoya, Goichi Ono, Hidehiro Toyoda
  • Patent number: 11501197
    Abstract: Methods and systems for quantum computing based sample analysis include computing cross-correlations of two images using a quantum processing system, and computing less noisy image based of two or more images using a quantum processing system. Specifically, the disclosure includes methods and systems for utilizing a quantum computing system to compute and store cross correlation values for two sets of data, which was previously believed to be physically impossible. Additionally, the disclosure also includes methods and systems for utilizing a quantum computing system to generate less noisy data sets using a quantum expectation maximization maximum likelihood (EMML). Specifically, the disclosed systems and methods allow for the generation of less noisy data sets by utilizing the special traits of quantum computers, the systems and methods disclosed herein represent a drastic improvement in efficiency over current systems and methods that rely on traditional computing systems.
    Type: Grant
    Filed: August 15, 2019
    Date of Patent: November 15, 2022
    Assignee: FEI Company
    Inventors: Valentina Caprara Vivoli, Yuchen Deng, Erik Michiel Franken
  • Patent number: 11494667
    Abstract: Example aspects of the present disclosure are directed to systems and methods that enable improved adversarial training of machine-learned models. An adversarial training system can generate improved adversarial training examples by optimizing or otherwise tuning one or hyperparameters that guide the process of generating of the adversarial examples. The adversarial training system can determine, solicit, or otherwise obtain a realism score for an adversarial example generated by the system. The realism score can indicate whether the adversarial example appears realistic. The adversarial training system can adjust or otherwise tune the hyperparameters to produce improved adversarial examples (e.g., adversarial examples that are still high-quality and effective while also appearing more realistic). Through creation and use of such improved adversarial examples, a machine-learned model can be trained to be more robust against (e.g.
    Type: Grant
    Filed: January 18, 2018
    Date of Patent: November 8, 2022
    Assignee: GOOGLE LLC
    Inventors: Victor Carbune, Thomas Deselaers
  • Patent number: 11475290
    Abstract: The present disclosure provides systems and methods that use machine learning to improve whole-structure relevance of hierarchical informational displays. In particular, the present disclosure provides systems and methods that employ a supervised, discriminative machine learning approach to jointly optimize the ranking of items and their display attributes. One example system includes a machine-learned display selection model that has been trained to jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display. For example, the machine-learned display selection model can optimize a nested submodular objective function to jointly select the items and attributes.
    Type: Grant
    Filed: December 30, 2016
    Date of Patent: October 18, 2022
    Assignee: GOOGLE LLC
    Inventors: Jeffrey Jon Dalton, Karthik Raman, Tobias Schnabel, Evgeniy Gabrilovich
  • Patent number: 11475269
    Abstract: Systems and methods of implementing a more efficient and less resource-intensive CNN are disclosed herein. In particular, applications of CNN in the analog domain using Sampled Analog Technology (SAT) methods are disclosed. Using a CNN design with SAT results in lower power usage and faster operation as compared to a CNN design with digital logic and memory. The lower power usage of a CNN design with SAT can allow for sensor devices that also detect features at very low power for isolated operation.
    Type: Grant
    Filed: December 14, 2016
    Date of Patent: October 18, 2022
    Assignee: Analog Devices, Inc.
    Inventors: Eric G. Nestler, Mitra M. Osqui, Jeffrey G. Bernstein
  • Patent number: 11443200
    Abstract: Described are a system, method, and computer program product for optimizing a predictive condition classification model and automatically enacting reactive measures based thereon. The method includes receiving event data representative of a plurality of events. The method also includes receiving the predictive condition classification model configured to categorize each event as satisfying a condition or not. The predictive condition classification model is configured to order the plurality of events by likelihood of satisfying the condition. The method includes generating a performance evaluation dataset and plotting data configured to cause a visual display to represent at least two model performance metrics of the performance evaluation dataset on a same output plot. The method includes automatically rejecting a top percent of the plurality of events for suspected satisfaction of the condition, determined at least partially from a customized rejection algorithm or a preset rejection algorithm.
    Type: Grant
    Filed: March 6, 2018
    Date of Patent: September 13, 2022
    Assignee: Visa International Service Association
    Inventors: Hung-Tzaw Hu, Benjamin Scott Boding, Ge Wen, Haochuan Zhou
  • Patent number: 11443226
    Abstract: A computer-implemented method applies labels to unlabeled public data for use by a global model. One or more processors train one or more local machine learning models with local private data to create one or more trained models. Processor(s) generate a label for each of the local private data using the one or more trained models, where each label describes the local private data, and then apply the label to unlabeled public data to create labeled public data. One or more processors then input the labeled public data into a global model that uses the public data.
    Type: Grant
    Filed: May 17, 2017
    Date of Patent: September 13, 2022
    Assignee: International Business Machines Corporation
    Inventors: Stephen M. Chu, Min Gong, Guo Qiang Hu, Dong Sheng Li, Liang Wu, Jun Chi Yan
  • Patent number: 11443233
    Abstract: A classification apparatus includes: an encoding module that includes an element classification part that extracts a feature of input data and outputs classification information based on an element classification model stored in a first storage unit; an integration module that includes an element estimation part that receives the classification information and converts the classification information to a collation vector based on an element estimation model stored in a second storage unit; and a determination module that includes a determination part that determines a group to which the collation vector belongs by collating the collation vector with a representative vector of an individual group stored as a semantic model in a third storage unit and outputs a group ID of the group as a classification result.
    Type: Grant
    Filed: February 20, 2018
    Date of Patent: September 13, 2022
    Inventors: Kosuke Nishihara, Norihiko Taya
  • Patent number: 11442748
    Abstract: Systems and methods for ordering software applications in a computing environment. The methods involve: presenting user-selectable icons for launching a plurality of software applications in a graphical user interface in accordance with a first order; performing a machine-learning algorithm to determine a weighting value for each software application of the plurality of software applications based on information specifying at least one aspect of a software launch request and at least one aspect of a first user's current circumstance; determining a second order in which the user-selectable icons should be presented in the graphical user interface based on the weighting values determined for the software applications; and dynamically modifying the graphical user interface to present the user-selectable icons in the second order which is different from the first order.
    Type: Grant
    Filed: April 26, 2017
    Date of Patent: September 13, 2022
    Assignee: CITRIX SYSTEMS, INC.
    Inventors: Edward J. Swindell, Duncan Gabriel, Henry J. Ashman
  • Patent number: 11436469
    Abstract: Described herein is a conversation engine that can be used in a system such as a personal digital assistant or search engine that combines a dynamic knowledge graph built during execution of a request and one or more static knowledge graphs holding long term knowledge. The conversation engine comprises a state tracker that holds the dynamic knowledge graph representing the current state of the conversation, a policy engine that selects entities in the dynamic knowledge graph and executes actions provided by those entities to move the state of the conversation toward completion, and a knowledge graph search engine to search the static knowledge graph(s). The conversation is completed by building the dynamic knowledge graph over multiple rounds and chaining together operations that build toward completion of the conversation. Completion of the conversation results in completion of a request by a user.
    Type: Grant
    Filed: July 31, 2017
    Date of Patent: September 6, 2022
    Inventors: Marius Alexandru Marin, Paul Anthony Crook, Vipul Agarwal, Imed Zitouni
  • Patent number: 11429833
    Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining communication data streams, extracting data relevant to a point of view of a user, and generating a point of view record in a knowledge base that may be utilized by another user communicating with the user.
    Type: Grant
    Filed: June 19, 2017
    Date of Patent: August 30, 2022
    Assignee: Kyndryl, Inc.
    Inventors: James E. Bostick, Danny Y. Chen, Sarbajit K. Rakshit, Keith R. Walker
  • Patent number: 11429895
    Abstract: Herein are techniques for exploring hyperparameters of a machine learning model (MLM) and to train a regressor to predict a time needed to train the MLM based on a hyperparameter configuration and a dataset. In an embodiment that is deployed in production inferencing mode, for each landmark configuration, each containing values for hyperparameters of a MLM, a computer configures the MLM based on the landmark configuration and measures time spent training the MLM on a dataset. An already trained regressor predicts time needed to train the MLM based on a proposed configuration of the MLM, dataset meta-feature values, and training durations and hyperparameter values of landmark configurations of the MLM. When instead in training mode, a regressor in training ingests a training corpus of MLM performance history to learn, by reinforcement, to predict a training time for the MLM for new datasets and/or new hyperparameter configurations.
    Type: Grant
    Filed: April 15, 2019
    Date of Patent: August 30, 2022
    Assignee: Oracle International Corporation
    Inventors: Anatoly Yakovlev, Venkatanathan Varadarajan, Sandeep Agrawal, Hesam Fathi Moghadam, Sam Idicula, Nipun Agarwal
  • Patent number: 11422546
    Abstract: A method includes fusing multi-modal sensor data from a plurality of sensors having different modalities. At least one region of interest is detected in the multi-modal sensor data. One or more patches of interest are detected in the multi-modal sensor data based on detecting the at least one region of interest. A model that uses a deep convolutional neural network is applied to the one or more patches of interest. Post-processing of a result of applying the model is performed to produce a post-processing result for the one or more patches of interest. A perception indication of the post-processing result is output.
    Type: Grant
    Filed: December 18, 2015
    Date of Patent: August 23, 2022
    Inventors: Michael J. Giering, Kishore K. Reddy, Vivek Venugopalan, Soumik Sarkar
  • Patent number: 11416745
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for adversarial training of a neural network. One of the methods includes obtaining a plurality of training inputs; and training the neural network on each of the training inputs, comprising, for each of the training inputs: processing the training input using the neural network to determine a neural network output for the training input; applying a perturbation to the training input to generate an adversarial perturbation of the training input; processing the adversarial perturbation of the training input using the neural network to determine a neural network output for the adversarial perturbation; and adjusting the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to optimize an adversarial objective function.
    Type: Grant
    Filed: November 22, 2019
    Date of Patent: August 16, 2022
    Assignee: Google LLC
    Inventors: Christian Szegedy, Ian Goodfellow
  • Patent number: 11409347
    Abstract: The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S101, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set; S102, determining a deep learning model for predicting power load; S103, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval; S104, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval.
    Type: Grant
    Filed: February 25, 2019
    Date of Patent: August 9, 2022
    Assignee: Hefei University of Technology
    Inventors: Kaile Zhou, Zhifeng Guo, Shanlin Yang, Pengtao Li, Lulu Wen, Xinhui Lu
  • Patent number: 11386349
    Abstract: In one embodiment, a system is configured to identify, based on predetermined criteria, a first set of users of an online system who belong to a population segment. The system may monitor activities performed by the first set of users on the online system over a predetermined period of time and store the monitored activities as time-series data. A feature set associated with the first set of users may be generated by transforming the time-series data into a frequency domain. The system may train a machine-learning model using the feature set and other feature sets to determine whether activities associated with a given set of users exhibit diurnal behavior pattern. Using the trained machine-learning model, the system may determine whether activities performed by a second set of users on the online system exhibit diurnal behavior pattern.
    Type: Grant
    Filed: May 16, 2017
    Date of Patent: July 12, 2022
    Assignee: Meta Platforms, Inc.
    Inventors: Nedyalko Prisadnikov, Hüseyin Kerem Cevahir
  • Patent number: 11381468
    Abstract: A distributed system may implement identifying correlated workloads for resource allocation. Resource data for resources hosted at resource hosts in a distributed system may be analyzed to determine behavioral similarities. Historical behavior data or resource configuration data, for instance, may be compared between resources. Behaviors between resources may be identified as correlated according to the determined behavioral similarities. An allocation of one or more resource hosts in the distributed system may be made for a resource based on the behaviors identified as correlated. For instance, resources may be migrated from a current resource host to another resource host, new resources may be placed at a resource host, or resources may be reconfigured into different resources. Machine learning techniques may be implemented to refine techniques for identifying correlated behaviors.
    Type: Grant
    Filed: March 16, 2015
    Date of Patent: July 5, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: James Michael Thompson, Marc Stephen Olson, Marc John Brooker