Patents Examined by Dave Misir
  • Patent number: 11379707
    Abstract: A computer-implemented method that includes receiving, by a processing unit, an instruction that specifies data values for performing a tensor computation. In response to receiving the instruction, the method may include, performing, by the processing unit, the tensor computation by executing a loop nest comprising a plurality of loops, wherein a structure of the loop nest is defined based on one or more of the data values of the instruction. The tensor computation can be at least a portion of a computation of a neural network layer. The data values specified by the instruction may comprise a value that specifies a type of the neural network layer, and the structure of the loop nest can be defined at least in part by the type of the neural network layer.
    Type: Grant
    Filed: November 22, 2017
    Date of Patent: July 5, 2022
    Assignee: Google LLC
    Inventors: Ravi Narayanaswami, Dong Hyuk Woo, Olivier Temam, Harshit Khaitan
  • Patent number: 11367021
    Abstract: A method for standardized model interaction can include: determining a model composition, receiving an input, converting the input into a standard object, converting the standard input object into a model-specific input (MSI) object, executing the model using the MSI object, converting the output from the model-specific output (MSO) object to a standard object, repeating previous steps for each successive model within the model composition, and providing a final model output.
    Type: Grant
    Filed: October 5, 2021
    Date of Patent: June 21, 2022
    Assignee: Grid.ai, Inc.
    Inventors: Luis Capelo, Richard Izzo
  • Patent number: 11367019
    Abstract: A data processing method includes: obtaining first sample data, and determining a target model and a feature set corresponding to the target model; obtaining second sample data, and dividing the second sample data into a development data set and a validation data set based on a predetermined proportion or a predetermined chronological order; respectively determining final sample data of the first sample data and retained sample data of the development data set based on the target model, the feature set corresponding to the target model, the first sample data, the development data set and the validation data set; and merging the final sample data and the retained sample data to obtain a modeling data set corresponding to a first business project. A data processing apparatus, and a computer device for implementing the data processing method are further provided.
    Type: Grant
    Filed: August 17, 2021
    Date of Patent: June 21, 2022
    Assignee: Shanghai IceKredit, Inc.
    Inventors: Lingyun Gu, Minqi Xie, Wan Duan, Yizeng Huang, Tao Zhang, Kai Zhang
  • Patent number: 11361232
    Abstract: The techniques herein include using an input context to determine a suggested action. One or more explanations may also be determined and returned along with the suggested action. The one or more explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and/or other measures such as the ones discussed herein, including certainty. In some embodiments, the explanation data may be used to determine whether to perform a suggested action.
    Type: Grant
    Filed: November 30, 2018
    Date of Patent: June 14, 2022
    Assignee: Diveplane Corporation
    Inventors: Christopher James Hazard, Christopher Fusting, Michael Resnick
  • Patent number: 11361231
    Abstract: The techniques herein include using an input context to determine a suggested action. One or more explanations may also be determined and returned along with the suggested action. The one or more explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and/or other measures such as the ones discussed herein, including certainty. In some embodiments, the explanation data may be used to determine whether to perform a suggested action.
    Type: Grant
    Filed: November 30, 2018
    Date of Patent: June 14, 2022
    Assignee: Diveplane Corporation
    Inventors: Christopher James Hazard, Christopher Fusting, Michael Resnick
  • Patent number: 11354570
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Grant
    Filed: April 6, 2020
    Date of Patent: June 7, 2022
    Assignee: SiMa Technologies, Inc.
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S. J Attia, Spenser Don Gilliland
  • Patent number: 11354596
    Abstract: Machine learning feature engineering systems and methods comprise an event ingestion module that receives event data associated with entities. The ingestion module determines which entities are associated with events of the event data. The ingestion module stores the events, grouped by associated entity, in a related event store. A user defines features associated with the entities via an API and/or a feature studio. A feature computation layer determines values for the features based on the grouped events stored to the related event store. The feature computation layer stores the computed feature values and timestamps to a feature store. When new data is received, the feature computation layer computes one or more of the feature values for different times based on the timestamps. Feature vectors are generated using the computed feature values and output to the user via the API and/or feature studio.
    Type: Grant
    Filed: May 18, 2020
    Date of Patent: June 7, 2022
    Assignee: KASKADA, INC.
    Inventors: Davor Bonaci, Benjamin Chambers, Andrew Concordia, Emily Kruger, Ryan Michael
  • Patent number: 11348017
    Abstract: Embodiments provide efficient, robust, and accurate programmatic prediction of optimized TCAD simulator system settings for future simulation executions to be performed by a TCAD simulation system.
    Type: Grant
    Filed: November 7, 2018
    Date of Patent: May 31, 2022
    Assignee: Synopsys, Inc.
    Inventors: Hiu Yung Wong, Nelson de Almeida Braga, Rimvydas Mickevicius
  • Patent number: 11341430
    Abstract: According to some embodiments, a method performed by a classification scanner comprises receiving an electronic message and determining a classification that applies to the electronic message. The classification is determined based on an express indication from a user. The method further comprises providing a machine learning trainer with the electronic message and an identification of the classification that applies to the electronic message. The machine learning trainer is adapted to determine a machine learning policy that associates attributes of the electronic message with the classification.
    Type: Grant
    Filed: November 19, 2018
    Date of Patent: May 24, 2022
    Assignee: ZixCorp Systems, Inc.
    Inventors: Daniel Joseph Potkalesky, Mark Stephen DeMichele
  • Patent number: 11322256
    Abstract: A method, computer system, and a computer program product for automatic labeling to train a machine learning algorithm is provided. The present invention may include labeling a medical image with at least one finding from a corresponding medical report. The present invention may include determining a localization information from the labeled medical image. The present invention may include training the machine learning algorithm with the determined localization information. The present invention may include detecting at least one candidate in a test medical image. The present invention may include generating a discrepancy list between the at least one detected candidate in the test medical image and at least one human-reported finding in a corresponding test medical report. The present invention may include, in response to determining that the generated discrepancy list is above a threshold, retraining the trained machine learning algorithm until the generated discrepancy list is below the threshold.
    Type: Grant
    Filed: November 30, 2018
    Date of Patent: May 3, 2022
    Assignee: International Business Machines Corporation
    Inventors: Marwan Sati, David Richmond
  • Patent number: 11321607
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Grant
    Filed: April 3, 2020
    Date of Patent: May 3, 2022
    Assignee: SiMa Technologies, Inc.
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S. J Attia, Spenser Don Gilliland
  • Patent number: 11301761
    Abstract: Behavioral prediction for targeted end users is described. In one or more example embodiments, a computer-readable storage medium has multiple instructions that cause one or more processors to perform multiple operations. Targeted selectstream data is obtained from one or more indications of data object requests corresponding to a targeted end user. A targeted directed graph is constructed based on the targeted selectstream data. A targeted graph feature vector is computed based on one or more invariant features associated with the targeted directed graph. A behavioral prediction is produced for the targeted end user by applying a prediction model to the targeted graph feature vector. In one or more example embodiments, the prediction model is generated based on multiple graph feature vectors respectively corresponding to multiple end users. In one or more example embodiments, a tailored opportunity is determined responsive to the behavioral prediction and issued to the targeted end user.
    Type: Grant
    Filed: January 7, 2019
    Date of Patent: April 12, 2022
    Assignee: Adobe Inc.
    Inventors: Balaji Krishnamurthy, Tushar Singla
  • Patent number: 11295226
    Abstract: Aspects of the disclosure provide for mechanisms for providing optimization recommends for quantum computing. A method of the disclosure includes: receiving a first file including a first plurality of quantum instructions for implementing an algorithm; receiving hardware information of a plurality of quantum computer systems, wherein the hardware information comprises information about hardware capacities of the quantum computer systems; and generating, by a processing device, one or more optimization recommendations for implementing the algorithm in view of the first plurality of instructions and the hardware information. In some embodiments, the one or more optimization recommendations include an estimated qubit size required to implement the algorithm in at least one of the plurality of quantum computer systems.
    Type: Grant
    Filed: August 30, 2018
    Date of Patent: April 5, 2022
    Assignee: Red Hat, Inc.
    Inventors: Leigh Griffin, Luigi Zuccarelli
  • Patent number: 11288585
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing and programming quantum hardware for machine learning processes. A Quantum Statistic Machine (QSM) is described, consisting of three distinct classes of strongly interacting degrees of freedom including visible, hidden and control quantum subspaces or subsystems. The QSM is defined with a programmable non-equilibrium ergodic open quantum Markov chain with a unique attracting steady state in the space of density operators. The solution of an information processing task, such as a statistical inference or optimization task, can be encoded into the quantum statistics of an attracting steady state, where quantum inference is performed by minimizing the energy of a real or fictitious quantum Hamiltonian. The couplings of the QSM between the visible and hidden nodes may be trained to solve hard optimization or inference tasks.
    Type: Grant
    Filed: December 22, 2016
    Date of Patent: March 29, 2022
    Assignee: Google LLC
    Inventors: Masoud Mohseni, Hartmut Neven
  • Patent number: 11281997
    Abstract: Some embodiments include a system operable to construct hierarchical training data sets for use with machine-learning for multiple controlled devices. Other embodiments of related systems and methods are also provided.
    Type: Grant
    Filed: December 6, 2018
    Date of Patent: March 22, 2022
    Assignee: SOURCE GLOBAL, PBC
    Inventors: Cody Alden Friesen, Paul Bryan Johnson, Heath Lorzel, Kamil Salloum, Jonathan Edward Goldberg, Grant Harrison Friesen, Jason Douglas Horwitz
  • Patent number: 11270225
    Abstract: A machine learning system continuously receives tag signals indicating membership relations between data objects from a data corpus and tag targets. The machine learning system is asynchronously and iteratively trained with the received tag signals to identify further data objects from the data corpus predicted to have a membership relation with the single tag target. The machine learning system constantly improves its predictive accuracy in short time by the continuous training of a backend machine learning model based on implicit and explicit tag signals gathered from a non-intrusive monitoring of user interactions during a review process of the data corpus.
    Type: Grant
    Filed: August 10, 2018
    Date of Patent: March 8, 2022
    Assignee: CS Disco, Inc.
    Inventor: Alan Lockett
  • Patent number: 11257000
    Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.
    Type: Grant
    Filed: May 9, 2018
    Date of Patent: February 22, 2022
    Assignee: Zoomph, Inc.
    Inventors: Thomas Mathew, John William Seaman, Ali Reza Manouchehri, Jorge Luis Vasquez, Lee Evan Kohn
  • Patent number: 11250341
    Abstract: A system comprising a classical computing subsystem to perform classical operations in a three-dimensional (3D) classical space unit using decomposed stopping points along a consecutive sequence of stopping points of sub-cells, along a vector with a shortest path between two points of the 3D classical space unit. The system includes a quantum computing subsystem to perform quantum operations in a 3D quantum space unit using decomposed stopping points along a consecutive sequence of stopping points of sub-cells, along a vector selected to have a shortest path between two points of the 3D quantum space unit. The system includes a control subsystem to decompose classical subproblems and quantum subproblems into the decomposed points and provide computing instructions and state information to the classical computing subsystem to perform the classical operations to the quantum computing subsystem to perform the quantum operations. A method and computer readable medium are provided.
    Type: Grant
    Filed: September 7, 2018
    Date of Patent: February 15, 2022
    Assignee: LOCKHEED MARTIN CORPORATION
    Inventors: Edward H. Allen, Luke A. Uribarri, Kristen L. Pudenz
  • Patent number: 11250368
    Abstract: A business prediction method includes: obtaining a first business sample set and a second business sample set; performing training based on the first business sample set and the second business sample set to obtain a business prediction model, and predicting received to-be-predicted business information based on the business prediction model to obtain a business prediction result corresponding to the received to-be-predicted business information. A business prediction apparatus is further provided. The business prediction method and the business prediction apparatus take into account data features of some business samples of being rejected in a business validation, while considering business samples of passing the business validation. This restores a business scenario, reduces the waste of costs of the rejected samples, and balances demands for a modeling sample and a rejected sample reasonably when there are insufficient samples of passing the business validation.
    Type: Grant
    Filed: August 13, 2021
    Date of Patent: February 15, 2022
    Assignee: Shanghai IceKredit, Inc.
    Inventors: Lingyun Gu, Minqi Xie, Wan Duan, Zhenyu Wang, Yang Zhang
  • Patent number: 11238955
    Abstract: A computer-implemented method includes generating, by a processor, a set of training data for each phenotype in a database including a set of subjects. The set of training data is generated by dividing genomic information of N subjects selected with or without repetition into windows, computing a distribution of genomic events in the windows for each of N subjects, and extracting, for each window, a tensor that represents the distribution of genomic events for each of N subjects. A set of test data is generated for each phenotype in the database, a distribution of genomic events in windows for each phenotype is computed, and a tensor is extracted for each window that represents a distribution of genomic events for each phenotype. The method includes classifying each phenotype of the test data with a classifier, and assigning a phenotype to a patient.
    Type: Grant
    Filed: February 20, 2018
    Date of Patent: February 1, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Filippo Utro, Aldo Guzman Saenz, Chaya Levovitz, Laxmi Parida