Patents Examined by Kakali Chaki
  • Patent number: 10210458
    Abstract: A social networking system identifies users to receive a recommendation to establish a connection to an object maintained by the social networking system. The social networking system determines one or more classifiers identifying attributes of users to receive the recommendation based on attributes of users connected to the object and additional users connected to those users. The attributes of an additional user may be weighted by a factor that provides a measure of the overlap between the attributes of the additional user and a user connected to the object.
    Type: Grant
    Filed: November 19, 2013
    Date of Patent: February 19, 2019
    Assignee: Facebook, Inc.
    Inventor: Deepayan Chakrabarti
  • Patent number: 10204301
    Abstract: One embodiment of the invention provides a system for mapping a neural network onto a neurosynaptic substrate. The system comprises a reordering unit for reordering at least one dimension of an adjacency matrix representation of the neural network. The system further comprises a mapping unit for selecting a mapping method suitable for mapping at least one portion of the matrix representation onto the substrate, and mapping the at least one portion of the matrix representation onto the substrate utilizing the mapping method selected. The system further comprises a refinement unit for receiving user input regarding at least one criterion relating to accuracy or resource utilization of the substrate. The system further comprises an evaluating unit for evaluating each mapped portion against each criterion. Each mapped portion that fails to satisfy a criterion may be remapped to allow trades offs between accuracy and resource utilization of the substrate.
    Type: Grant
    Filed: March 18, 2015
    Date of Patent: February 12, 2019
    Assignee: International Business Machines Corporation
    Inventors: Arnon Amir, Rathinakumar Appuswamy, Pallab Datta, Myron D. Flickner, Paul A. Merolla, Dharmendra S. Modha, Benjamin G. Shaw
  • Patent number: 10204707
    Abstract: A method for assessing a neuropsychiatric condition (such as, but not limited to, a risk that a subject may attempt to commit suicide or repeat an attempt to commit suicide, a risk that terminally ill patient is not being care-for or treated according to the patient's true wishes, a risk that a subject may perform or repeat a criminal act and/or a harmful act, a risk of the subject having a psychiatric illness, and/or a risk of a subject feigning a psychiatric illness) may include a plurality of steps. A step may include receiving biomarker data associated from an analysis of the subject's biological sample and a step of receiving thought-marker data obtained pertaining to one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings. A step may include generating a biomarker score associated with the neuropsychiatric condition from the biomarker data.
    Type: Grant
    Filed: April 27, 2010
    Date of Patent: February 12, 2019
    Assignee: Children's Hospital Medical Center
    Inventors: John Pestian, Tracy A. Glauser, Bruce Aronow
  • Patent number: 10197979
    Abstract: Methods and systems are described for determining occupancy with user provided information. According to at least one embodiment, a method for determining occupancy with user provided information includes using at least one sensor to detect occupancy in a building over time, determining a predictive schedule based on the occupancy detected with the at least one sensor, and requesting information relevant to the predictive schedule from a user.
    Type: Grant
    Filed: May 30, 2014
    Date of Patent: February 5, 2019
    Assignee: Vivint, Inc.
    Inventors: Jeremy B. Warren, Brandon Bunker, Jefferson Lyman, Jungtaik Hwang
  • Patent number: 10198400
    Abstract: A system for selecting an optimal data set from a plurality of candidate data sets based at least in part on a client data packet received through a web interface may include a web server that receives first data packets from a client device, and a cloud computing platform that receives the first data packets from the web server and validates the first data packets using information imported from a third-party computer system. The platform may also receive second data packets from a second third-party computer system, select candidate data sets from a collection of available data sets based on information that is descriptive of the user, and select at least one optimal data set from the candidate data sets based on a user priority. The optimal data set may include adjustable parameters with values that are set through the client device.
    Type: Grant
    Filed: July 10, 2017
    Date of Patent: February 5, 2019
    Inventors: Jay D. Farner, Regis Hadiaris, Jenna Bush, Dan Chrobak, Jason Tomlinson
  • Patent number: 10187405
    Abstract: A governance apparatus and a communication method for communicating within the governance apparatus. The governance apparatus includes a Government. The Government includes Councils such that a macro grid including an artificial intelligence and the Government is configured to respond to an alert pertaining to an event through use of the artificial intelligence and the Government. The governance apparatus also includes an enhanced Transmission Control Protocol/Internet Protocol (TCP/IP) communication stack of layers including a Governance Layer and an Intelligence Layer. The Intelligence Layer includes intelligence software configured to process data pertaining to the event, data pertaining to the alert, and data pertaining to the Government.
    Type: Grant
    Filed: June 13, 2016
    Date of Patent: January 22, 2019
    Assignee: International Business Machines Corporation
    Inventor: Ian E. Oakenfull
  • Patent number: 10182766
    Abstract: A system for guiding and evaluating physical positioning, orientation and motion of the human body, comprising: a cloud computing-based subsystem including an artificial neural network and spatial position analyzer said cloud computing-based subsystem adapted for data storage, management and analysis; at least one motion sensing device wearable on the human body, said at least one motion sensing device adapted to detect changes in at least one of spatial position, orientation, and rate of motion; a mobile subsystem running an application program (app) that controls said at least one motion sensing device, said mobile subsystem adapted to capture activity data quantifying said changes in at least one of spatial position, orientation, and rate of motion, said mobile subsystem further adapted to transfer said activity data to said cloud computing-based subsystem, wherein said cloud computing-based subsystem processes, stores, and analyzes said activity data.
    Type: Grant
    Filed: January 29, 2016
    Date of Patent: January 22, 2019
    Assignee: University of Central Oklahoma
    Inventors: Jicheng Fu, Maurice Haff
  • Patent number: 10169720
    Abstract: Systems and methods are provided for performing data mining and statistical learning techniques on a big data set. More specifically, systems and methods are provided for linear regression using safe screening techniques. Techniques may include receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop an improved prediction hierarchy. It may include pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment. For each time series, the system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the prediction hierarchy at which the each individual time series comprises an need output amount greater than a threshold amount.
    Type: Grant
    Filed: December 16, 2016
    Date of Patent: January 1, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Yung-Hsin Chien, Pu Wang, Yue Li
  • Patent number: 10170117
    Abstract: A method and system for teaching an object of a deictic reference to a machine. A processor of the machine teaches the object of the deictic reference to the machine which results in the machine learning the object. The teaching includes: the processor finds an item in a region indicated by a physical pointing gesture, by the user, that points to the object; the processor shines a laser light on the item and in response, the processor receives a negative spoken indication from the user that the item shined on by the laser light is not the object; in response to the negative spoken indication from the user, the processor interacts with the user in an iterative procedure wherein the machine learns the object in a final iteration of the procedure. The processor stores the learned object in a storage repository.
    Type: Grant
    Filed: March 24, 2016
    Date of Patent: January 1, 2019
    Assignee: International Business Machines Corporation
    Inventors: Liam D. Comerford, Mahesh Viswanathan
  • Patent number: 10163058
    Abstract: A device, method and system for automatically inferring a mobile user's current context includes applying a user activity knowledge base to real-time inputs and stored user-specific information to determine a current situation. Automated reasoning is used to infer a user-specific context of the current situation. Automated candidate actions may be generated and performed in accordance with the current situation and user-specific context.
    Type: Grant
    Filed: August 14, 2012
    Date of Patent: December 25, 2018
    Assignee: SRI International
    Inventors: Kenneth C. Nitz, Patrick D. Lincoln, Karen L. Myers, Hung H. Bui, Rukman Senanayake, Grit Denker, William S. Mark, Norman D. Winarsky, Steven S. Weiner
  • Patent number: 10152677
    Abstract: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream.
    Type: Grant
    Filed: June 18, 2015
    Date of Patent: December 11, 2018
    Assignee: International Business Machines Corporation
    Inventors: Alain E. Biem, Dattaram Bijavara Aswathanarayana Rao, Bharath K. Devaraju
  • Patent number: 10147046
    Abstract: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream.
    Type: Grant
    Filed: October 30, 2014
    Date of Patent: December 4, 2018
    Assignee: International Business Machines Corporation
    Inventors: Alain E. Biem, Dattaram Bijavara Aswathanarayana Rao, Bharath K. Devaraju
  • Patent number: 10127494
    Abstract: A circuit for performing neural network computations for a neural network is described. The circuit includes plurality of neural network layers each including a crossbar arrays. The plurality of crossbar arrays are formed in a common substrate in a stacked configuration. Each crossbar array includes a set of crosspoint devices. A respective electrical property of each of the crosspoint devices is adjustable to represent a weight value that is stored for each respective crosspoint device. A processing unit is configured to adjust the respective electrical properties of each of the crosspoint devices by pre-loading each of the crosspoint devices with a tuning signal. A value of the turning signal for each crosspoint device is a function of the weight value represented by each respective crosspoint device.
    Type: Grant
    Filed: August 2, 2017
    Date of Patent: November 13, 2018
    Assignee: Google LLC
    Inventors: Pierre-luc Cantin, Olivier Temam
  • Patent number: 10127495
    Abstract: Systems and methods for reducing the size of deep neural networks are disclosed. In an embodiment, a server computer stores a plurality of training datasets, each of which comprise a plurality of training input matrices and a plurality of corresponding outputs. The server computer initiates training of a deep neural network using the plurality of training input matrices, a weight matrix, and the plurality of corresponding outputs. While the training of the deep neural network is being performed, the server computer identifies one or more weight values of the weight matrix for removal. The server computer removes the one or more weight values from the weight matrix to generate a reduced weight matrix. The server computer then stores the reduced weight matrix with the deep neural network.
    Type: Grant
    Filed: April 14, 2017
    Date of Patent: November 13, 2018
    Inventors: Rohan Bopardikar, Sunil Bopardikar
  • Patent number: 10102478
    Abstract: Each computer of a peer-to-peer (P2P) network performs an iterative computer-based modeling task defined by a set of training data including at least some training data that are not accessible to the other computers of the P2P network, and by a set of parameters including a shared parameter. The modeling task optimizes an objective function comparing a model parameterized by the set of parameters with the training data. Each iteration includes: performing an iterative gradient step update of parameter values stored at the computer based on the objective function; receiving parameter values of the shared parameter from other computers of the P2P network; adjusting the parameter value of the shared parameter stored at the computer by averaging the received parameter values; and sending the parameter value of the shared parameter stored at the computer to other computers of the P2P network.
    Type: Grant
    Filed: June 26, 2015
    Date of Patent: October 16, 2018
    Assignee: Conduent Business Services, Inc.
    Inventors: Guillaume Bouchard, Julien Perez, James Brinton Henderson
  • Patent number: 10102254
    Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for providing confidence rankings based on temporal semantics. Responsive to receiving an input question, a set of candidate answers is identified from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain. A confidence score is associated with each of the candidate answers and each confidence score associated with each candidate answer is refined based on a set of temporal characteristics identified in the input question. A set of temporally refined candidate answers is then provided to the user.
    Type: Grant
    Filed: February 11, 2016
    Date of Patent: October 16, 2018
    Assignee: International Business Machines Corporation
    Inventors: John P. Bufe, III, Donna K. Byron, Alexander Pikovsky, Timothy P. Winkler
  • Patent number: 10095981
    Abstract: Methods, systems, and apparatus for solving optimization tasks. In one aspect, a method includes receiving input data comprising (i) data specifying an optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives; processing the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, wherein at least one initial solution is obtained from a first quantum computing resource; and processing the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives.
    Type: Grant
    Filed: March 22, 2017
    Date of Patent: October 9, 2018
    Assignee: Accenture Global Solutions Limited
    Inventors: Daniel Garrison, Andrew E. Fano, Jurgen Albert Weichenberger
  • Patent number: 10095660
    Abstract: Various embodiments are generally directed to techniques for producing statistically correct and efficient combinations of multiple simulated posterior samples from MCMC and related Bayesian sampling schemes are described. One or more chains from a Bayesian posterior distribution of values may be generated. It may be determine whether the one or more chains have reached stationarity through parallel processing on a plurality of processing nodes. Based upon the determination, each of the one or more chains that have reached stationarity through parallel processing on the plurality of processing nodes may be sorted. The one or more sorted chains may be resampled through parallel processing on the plurality of processing nodes. The one or more resampled chains may be combined. Other embodiments are described and claimed.
    Type: Grant
    Filed: March 13, 2014
    Date of Patent: October 9, 2018
    Assignee: SAS Institute Inc.
    Inventors: Christian Macaro, Jan Chvosta, Mark Roland Little
  • Patent number: 10088815
    Abstract: A wire electric discharge machine according to the present invention includes a machine learning device which performs machine learning for adjustment of a machining condition of the wire electric discharge machine, the machine learning device includes a state observation unit which acquires data related to a machining state of a workpiece, a reward calculation unit which calculates a reward based on data related to a machining state, a machining condition adjustment learning unit which determines an adjustment amount of a machining condition based on a machine learning result and data related to a machining state, and a machining condition adjustment unit which adjusts a machining condition based on the determined adjustment amount of a machining condition, and the machining condition adjustment learning unit performs machine learning for adjustment of a machining condition based on the determined adjustment amount of a machining condition, data related to a machining state and acquired by the state observat
    Type: Grant
    Filed: August 26, 2016
    Date of Patent: October 2, 2018
    Assignee: FANUC CORPORATION
    Inventors: Mitsuharu Onodera, Kaoru Hiraga
  • Patent number: 10089581
    Abstract: A computer implemented data driven classification and data quality checking system is provided. The system has an interface application enabled to receive data and has an associative memory software. The system has a data driven associative memory model configured to categorize one or more fields of received data and to analyze the received data. The system has a data quality rating metric associated with the received data. The system has a machine learning data quality checker for the received data, and is configured to add the received data to a pool of neighboring data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold. The machine learning data quality checker is configured to generate and communicate an alert of a potential error in the received data, if the data quality rating metric is less than the data quality rating metric threshold.
    Type: Grant
    Filed: June 30, 2015
    Date of Patent: October 2, 2018
    Assignee: The Boeing Company
    Inventors: Jaime A. Flores, Brian Warn, Danielle C. Young, Patrick N. Harris