Patents Examined by Li B. Zhen
  • Patent number: 11880746
    Abstract: Media and method for a user interface for training an artificial intelligence system. Many artificial intelligence systems require large volumes of labeled training data before they can accurately classify previously unseen data items. However, for some problem domains, no pre-labeled training data set may be available. Manually labeling training data sets by a subject-matter expert is a laborious process. An interface to enable such a subject-matter expert to accurately, consistently, and quickly label training data sets is disclosed herein. By allowing the subject-matter expert to easily navigate between training data items and select the applicable labels, operation of the computer is improved.
    Type: Grant
    Filed: April 26, 2017
    Date of Patent: January 23, 2024
    Assignee: HRB Innovations, Inc.
    Inventors: Daniel Cahoon, Mansoor Syed, Robert T. Wescott
  • Patent number: 11880390
    Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: collecting location data of users and identifying candidates for an impromptu interaction amongst the users based on converging locations of the candidates. A topic of the impromptu interaction is determined by common work interests amongst the candidates. Notification of the impromptu interaction is sent to the candidates to inform the topic and the other candidate, also with resources relevant to the topic.
    Type: Grant
    Filed: May 16, 2017
    Date of Patent: January 23, 2024
    Assignee: International Business Machines Corporation
    Inventors: James E. Bostick, John M. Ganci, Jr., Martin G. Keen, Sarbajit K. Rakshit
  • Patent number: 11875260
    Abstract: The architectural complexity of a neural network is reduced by selectively pruning channels. A cost metric for a convolution layer is determined. The cost metric indicates a resource cost per channel for the channels of the layer. Training the neural network includes, for channels of the layer, updating a channel-scaling coefficient based on the cost metric. The channel-scaling coefficient linearly scales the output of the channel. A constant channel is identified based on the channel-scaling coefficients. The neural network is updated by pruning the constant channel. Model weights are updated via a stochastic gradient descent of a training loss function evaluated on training data. The channel-scaling coefficients are updated via an iterative-thresholding algorithm that penalizes a batch normalization loss function based on the cost metric for the layer and a norm of the channel-scaling coefficients.
    Type: Grant
    Filed: February 13, 2018
    Date of Patent: January 16, 2024
    Assignee: Adobe Inc.
    Inventors: Xin Lu, Zhe Lin, Jianbo Ye
  • Patent number: 11875273
    Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for machine learning (ML) classification of digital content for mobile communication devices, such as implemented in connection with one or more computing and/or communication networks and/or protocols.
    Type: Grant
    Filed: March 29, 2017
    Date of Patent: January 16, 2024
    Assignee: Yahoo Ad Tech LLC
    Inventors: Marc Bron, Mounia Lalmas, Huw Evans, Mahlon Chute, Miriam Redi, Fabrizio Silvestri
  • Patent number: 11836578
    Abstract: A device receives historical data associated with multiple cloud computing environments, trains one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model. The device receives particular data, associated with a cloud computing environment, that includes data identifying usage of resources associated with the cloud computing environment, and processes the particular data, with the trained machine learning models, to generate anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment. The device processes the one or more anomaly scores, with the trained model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment, and performs one or more actions based on the final anomaly score.
    Type: Grant
    Filed: August 26, 2019
    Date of Patent: December 5, 2023
    Assignee: Accenture Global Solutions Limited
    Inventors: Kun Qiu, Vijay Desai, Laser Seymour Kaplan, Durga Kalyan Ganjapu, Daniel Marcus Lombardo
  • Patent number: 11829855
    Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.
    Type: Grant
    Filed: May 25, 2022
    Date of Patent: November 28, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Mayank Shrivastava, Hui Zhou, Pushpraj Shukla, Emre Hamit Kok, Sonal Prakash Mane, Dimitrios Brisimitzis
  • Patent number: 11822616
    Abstract: Disclosed are a method and an apparatus for performing an operation of a convolutional layer in a convolutional neural network.
    Type: Grant
    Filed: November 28, 2018
    Date of Patent: November 21, 2023
    Assignee: Nanjing Horizon Robotics Technology Co., Ltd.
    Inventors: Delin Li, Kun Ling, Liang Chen, Jianjun Li
  • Patent number: 11803745
    Abstract: A method for estimating firefighting data includes: obtaining firefighting condition data of a site, wherein the firefighting condition data comprises information on firefighting equipment, information on flammable articles; and estimating firefighting input data and firefighting damage data based on the firefighting condition data using a simulation analysis model, wherein the simulation analysis model is created based on firefighting condition data, firefighting input data and firefighting damage data of different sites.
    Type: Grant
    Filed: April 9, 2020
    Date of Patent: October 31, 2023
    Assignee: Fulian Precision Electronics (Tianjin) Co., LTD.
    Inventor: Shih-Cheng Wang
  • Patent number: 11797868
    Abstract: At least some embodiments are directed to an insights inference system that produces multiple insights associated with an entity. The insights inference system generates a decision tree machine learning model, assigning a first insight to a parent node of a decision tree machine learning model and assigning at least one second insight to child nodes of the decision tree machine learning model. Each child node is associated with a sequence number and a rank number. The sequence number and the rank number are indicative of a significance associated with the at least one second insight. The insight inference system responds to queries by traversing the decision tree machine learning model to compute at least one response insight based on the sequence number and the rank number associated with each child node and outputs the at least one response insight to a client terminal.
    Type: Grant
    Filed: December 5, 2019
    Date of Patent: October 24, 2023
    Assignee: American Express Travel Related Services Company, Inc.
    Inventors: Varun Agarwal, Krishnaprasad Narayanan, Rahul Ghosh, Swetha P. Srinivasan, Anshul Jain, Bobby Chetal, Ashni Jauhary
  • Patent number: 11790242
    Abstract: Techniques are described for generating and applying mini-machine learning variants of machine learning algorithms to save computational resources in tuning and selection of machine learning algorithms. In an embodiment, at least one of the hyper-parameter values for a reference variant is modified to a new hyper-parameter value thereby generating a new variant of machine learning algorithm from the reference variant of machine learning algorithm. A performance score is determined for the new variant of machine learning algorithm using a training dataset, the performance score representing the accuracy of the new machine learning model for the training dataset. By performing training of the new variant of machine learning algorithm with the training data set, a cost metric of the new variant of machine learning algorithm is measured by measuring usage the used computing resources for the training.
    Type: Grant
    Filed: October 19, 2018
    Date of Patent: October 17, 2023
    Assignee: Oracle International Corporation
    Inventors: Sandeep Agrawal, Venkatanathan Varadarajan, Sam Idicula, Nipun Agarwal
  • Patent number: 11783164
    Abstract: The technology disclosed provides a so-called “joint many-task neural network model” to solve a variety of increasingly complex natural language processing (NLP) tasks using growing depth of layers in a single end-to-end model. The model is successively trained by considering linguistic hierarchies, directly connecting word representations to all model layers, explicitly using predictions in lower tasks, and applying a so-called “successive regularization” technique to prevent catastrophic forgetting. Three examples of lower level model layers are part-of-speech (POS) tagging layer, chunking layer, and dependency parsing layer. Two examples of higher level model layers are semantic relatedness layer and textual entailment layer. The model achieves the state-of-the-art results on chunking, dependency parsing, semantic relatedness and textual entailment.
    Type: Grant
    Filed: October 26, 2020
    Date of Patent: October 10, 2023
    Assignee: Salesforce.com, Inc.
    Inventors: Kazuma Hashimoto, Caiming Xiong, Richard Socher
  • Patent number: 11775876
    Abstract: A method comprising, by a processing unit and a memory: obtaining a training set of data; dividing sets of data into a plurality of groups, wherein all sets of data for which feature values meet at least one similarity criterion, are in the same group, storing in a reduced training set of data, for each group, at least one aggregated set of data, wherein, for a plurality of the groups, a number of aggregated sets of data is less than a number of the sets of data of the group, wherein the reduced training set of data is suitable to be used in a classification algorithm for determining a relationship between the at least one label and the features of the electronic items, thereby reducing computation complexity when processing the reduced training set of data, compared to processing the training set of data.
    Type: Grant
    Filed: August 20, 2019
    Date of Patent: October 3, 2023
    Assignee: Optimal Plus Ltd.
    Inventor: Katsuhiro Shimazu
  • Patent number: 11755879
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing and storing inputs for use in a neural network. One of the methods includes receiving input data for storage in a memory system comprising a first set of memory blocks, the memory blocks having an associated order; passing the input data to a highest ordered memory block; for each memory block for which there is a lower ordered memory block: applying a filter function to data currently stored by the memory block to generate filtered data and passing the filtered data to a lower ordered memory block; and for each memory block: combining the data currently stored in the memory block with the data passed to the memory block to generate updated data, and storing the updated data in the memory block.
    Type: Grant
    Filed: February 11, 2019
    Date of Patent: September 12, 2023
    Assignee: DeepMind Technologies Limited
    Inventors: Razvan Pascanu, William Clinton Dabney, Thomas Stepleton
  • Patent number: 11755953
    Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user; performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation producing an inferred function based upon the data; and, generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation.
    Type: Grant
    Filed: December 31, 2020
    Date of Patent: September 12, 2023
    Assignee: Tecnotree Technologies, Inc.
    Inventors: Ayan Acharya, Matthew Sanchez
  • Patent number: 11748641
    Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving information regarding a temporal sequence of events; performing a temporal topic machine learning operation on the temporal sequence of events; generating a cognitive profile based upon the information generated by performing the temporal topic machine learning operation; and, generating a cognitive insight based upon the cognitive profile generated using the temporal topic machine learning operation.
    Type: Grant
    Filed: May 25, 2021
    Date of Patent: September 5, 2023
    Assignee: Tecnotree Technologies, Inc.
    Inventors: Ayan Acharya, Matthew Sanchez, Omar Eid
  • Patent number: 11727252
    Abstract: The present disclosure relates to a neuromorphic neuron apparatus comprising an output generation block and at least one adaptation block. The apparatus has a current adaptation state variable corresponding to previously generated one or more signals. The output generation block is configured to use an activation function for generating a current output value based on the current adaptation state variable. The adaptation block is configured to repeatedly: compute an adaptation value of its current adaptation state variable using the current output value and a correction function; use the adaption value to update the current adaptation state variable to obtain an updated adaptation state variable, the updated adaptation state variable becoming the current adaptation state variable; receive a current signal; and cause the output generation block to generate a current output value based on the current adaptation state variable and input value that obtained from the received signal.
    Type: Grant
    Filed: August 30, 2019
    Date of Patent: August 15, 2023
    Assignee: International Business Machines Corporation
    Inventors: Stanislaw Andrzej Wozniak, Angeliki Pantazi
  • Patent number: 11727246
    Abstract: Embodiments provide systems and methods which facilitate optimization of a convolutional neural network (CNN). One embodiment provides for a non-transitory machine-readable medium storing instructions that cause one or more processors to perform operations comprising processing a trained convolutional neural network (CNN) to generate a processed CNN, the trained CNN having weights in a floating-point format. Processing the trained CNN includes quantizing the weights in the floating-point format to generate weights in an integer format. Quantizing the weights includes generating a quantization table to enable non-uniform quantization of the weights and quantizing the weights from the floating-point format to the integer format using the quantization table. The operations additionally comprise performing an inference operation utilizing the processed CNN with the integer format weights.
    Type: Grant
    Filed: February 22, 2019
    Date of Patent: August 15, 2023
    Assignee: Intel Corporation
    Inventors: Liwei Ma, Elmoustapha Ould-Ahmed-Vall, Barath Lakshmanan, Ben J. Ashbaugh, Jingyi Jin, Jeremy Bottleson, Mike B. Macpherson, Kevin Nealis, Dhawal Srivastava, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman, Altug Koker, Abhishek R. Appu
  • Patent number: 11694072
    Abstract: A method and system are disclosed for training a model that implements a machine-learning algorithm. The technique utilizes latent descriptor vectors to change a multiple-valued output problem into a single-valued output problem and includes the steps of receiving a set of training data, processing, by a model, the set of training data to generate a set of output vectors, and adjusting a set of model parameters and component values for at least one latent descriptor vector in the plurality of latent descriptor vectors based on the set of output vectors. The set of training data includes a plurality of input vectors and a plurality of desired output vectors, and each input vector in the plurality of input vectors is associated with a particular latent descriptor vector in a plurality of latent descriptor vectors. Each latent descriptor vector comprises a plurality of scalar values that are initialized prior to training the model.
    Type: Grant
    Filed: November 29, 2017
    Date of Patent: July 4, 2023
    Assignee: NVIDIA Corporation
    Inventors: Tero Tapani Karras, Timo Oskari Aila, Samuli Matias Laine
  • Patent number: 11681942
    Abstract: One or more embodiments of a content naming system provide machine-learned name suggestions to a user for naming content items. Specifically, an online content management system can train a machine-learning model to identify a naming pattern from previously stored content items corresponding to a user account of the user. The online content management system uses the machine-learning model to determine a plurality of name suggestions for naming a content item associated with the user account. One or more embodiments provide graphical elements corresponding to the name suggestions within a graphical user interface. The user can select one or more graphical elements to add the corresponding name suggestion(s) to the name of the content item.
    Type: Grant
    Filed: October 27, 2016
    Date of Patent: June 20, 2023
    Assignee: Dropbox, Inc.
    Inventor: Neeraj Kumar
  • Patent number: 11669758
    Abstract: For machine learning data reduction and model optimization,a method randomly assigns each data feature of a training data set to a plurality of solution groups. Each solution group has no more than a solution group number k of data features and each data feature is assigned to a plurality of solution groups. The method identifies each solution group as a high-quality solution group or a low-quality solution group. The method further calculates data feature scores for each data feature comprising a high bin number and a low bin number. The method determines level data for each data feature from the data feature scores using a fuzzy inference system. The method identifies an optimized data feature set based on the level data. The method further trains a production model using only the optimized data feature set. The method predicts a result using the production model.
    Type: Grant
    Filed: November 12, 2019
    Date of Patent: June 6, 2023
    Assignee: Rockwell Automation Technologies, Inc.
    Inventors: Francisco Maturana, Phillip LaCasse