Patents Examined by Luis Sitiriche
  • Patent number: 10289954
    Abstract: A system can generate a heavy load pre-warning or an overload pre-warning for distribution transformers. Operation of the system can include selecting data records received from a plurality of data sources; converting the data records in the plurality of different data formats; filtering the data records in the database by using a predetermined threshold and matching each of the filtered data records with one of a plurality of distribution transformers; transforming the matched data records to a plurality of predefined predictor variables; selecting a subset of the plurality of predefined predictor variables; training, testing and tuning a model and forecasting at least one of heavy load or overload for each of the plurality of distribution transformers in a predetermined region.
    Type: Grant
    Filed: January 6, 2015
    Date of Patent: May 14, 2019
    Assignee: ACCENTURE GLOBAL SERVICES LIMITED
    Inventors: Ming Li, Qin Zhou, Zhihui Yang, Ming Ye, Long He, Guang Lin
  • Patent number: 10289964
    Abstract: Various embodiments train a prediction model for predicting a label to be allocated to a prediction target explanatory variable set. In one embodiment, one or more sets of training data are acquired. Each of the one or more sets of training data includes at least one set of explanatory variables and a label allocated to the at least one explanatory variable set. A plurality of explanatory variable subsets is extracted from the at least one set of explanatory variables. A prediction model is trained utilizing the training data. The plurality of explanatory variable subsets is reflected on a label predicted by the prediction model to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.
    Type: Grant
    Filed: December 12, 2017
    Date of Patent: May 14, 2019
    Assignee: International Business Machines Corporation
    Inventors: Takayuki Katsuki, Yuma Shinohara
  • Patent number: 10282679
    Abstract: Various embodiments train a prediction model for predicting a label to be allocated to a prediction target explanatory variable set. In one embodiment, one or more sets of training data are acquired. Each of the one or more sets of training data includes at least one set of explanatory variables and a label allocated to the at least one explanatory variable set. A plurality of explanatory variable subsets is extracted from the at least one set of explanatory variables. A prediction model is trained utilizing the training data. The plurality of explanatory variable subsets is reflected on a label predicted by the prediction model to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.
    Type: Grant
    Filed: December 12, 2017
    Date of Patent: May 7, 2019
    Assignee: International Business Machines Corporation
    Inventors: Takayuki Katsuki, Yuma Shinohara
  • Patent number: 10210456
    Abstract: Various technologies described herein pertain to estimating predictive accuracy gain of a potential feature added to a set of features, wherein an existing predictor is trained on the set of features. Outputs of the existing predictor for instances in a dataset can be retrieved from a data store. Moreover, a predictive accuracy gain estimate of a potential feature added to the set of features can be measured as a function of the outputs of the existing predictor for the instances in the dataset. The predictive accuracy gain estimate can be measured without training an updated predictor on the set of features augmented by the potential feature.
    Type: Grant
    Filed: December 3, 2014
    Date of Patent: February 19, 2019
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Mikhail Bilenko, Hoyt Adam Koepke
  • Patent number: 10176428
    Abstract: The various aspects configure a mobile computing device to efficiently identify, classify, model, prevent, and/or correct the conditions and/or behaviors occurring on the mobile computing device that are related to one or more peripheral devices connected to the mobile computing device and that often degrade the performance and/or power utilization levels of the mobile computing device over time. In the various aspects, the mobile computing device may obtain a classifier model that includes, tests, and/or evaluates various conditions, features, behaviors and corrective actions on the mobile computing device that are related to one or more peripheral devices connected to the mobile computing device. The mobile computing device may utilize the classifier model to quickly identify and correct undesirable behaviors occurring on the mobile computing device that are related to the one or more connected peripheral devices.
    Type: Grant
    Filed: March 13, 2014
    Date of Patent: January 8, 2019
    Assignee: QUALCOMM Incorporated
    Inventors: Vinay Sridhara, Rajarshi Gupta
  • Patent number: 10146851
    Abstract: Methods are provided for clustering events. Data is received at an extraction engine from managed infrastructure. Events are converted into alerts and the alerts mapped to a matrix M. One or more common steps are determined from the events and clusters of events are produced relating to the alerts and or events.
    Type: Grant
    Filed: January 26, 2015
    Date of Patent: December 4, 2018
    Assignee: Moogsoft, Inc.
    Inventors: Philip Tee, Robert Duncan Harper, Charles Mike Silvey
  • Patent number: 10140576
    Abstract: A computer-implemented system and method for detecting anomalies using sample-based rule identification is provided. Data for data is maintained analytics in a database. A set of anomaly rules is defined. A rare pattern in the data is statistically identified. The identified rare pattern is labeled as at least one of anomaly and non-anomaly based on verification by a domain expert. The set of anomaly rules is adjusted based on the labeled anomaly. Other anomalies in the data are detected and classified by applying the adjusted set of anomaly rules to the data.
    Type: Grant
    Filed: August 10, 2014
    Date of Patent: November 27, 2018
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Hoda Eldardiry, Sricharan Kallur Palli Kumar, Daniel H. Greene, Robert Price
  • Patent number: 10140582
    Abstract: A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern.
    Type: Grant
    Filed: June 14, 2015
    Date of Patent: November 27, 2018
    Assignee: APTIMA, INC.
    Inventors: E. Webb Stacy, Alexandra Geyer
  • Patent number: 10127496
    Abstract: Systems and methods are provided for estimating arrival time associated with a ride order. An exemplary method may comprise: inputting transportation information to a trained machine learning model. The transportation information may comprise an origin and a destination associated with the ride order, and the trained machine learning model may comprise a wide network, a deep neural network, and a recurrent neural network all coupled to a multilayer perceptron network. The method may further comprise, based on the trained machine learning model, obtaining an estimated time for arriving at the destination via a route connecting the origin and the destination.
    Type: Grant
    Filed: March 16, 2018
    Date of Patent: November 13, 2018
    Assignee: BEIJING DIDI INFINITY TECHNOLOGY AND DEVELOPMENT
    Inventors: Kun Fu, Zheng Wang
  • Patent number: 10082778
    Abstract: Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system and operate in a partially decoupled manner with respect to each other, such as by each decision module operating to synchronize its local solutions and proposed control actions with those of one or more other decision modules, in order to determine a consensus with those other decision modules. Such inter-module synchronizations may occur repeatedly to determine one or more control actions for each decision module at a particular time, as well as to be repeated over multiple times for ongoing control.
    Type: Grant
    Filed: June 22, 2015
    Date of Patent: September 25, 2018
    Assignee: Veritone Alpha, Inc.
    Inventors: Wolf Kohn, Michael Luis Sandoval, Vishnu Vettrivel, Jonathan Cross, Jason Knox, David Talby, Mike Lazarus
  • Patent number: 10055684
    Abstract: A method for implementing a reservoir simulator is described. The method comprises developing training data by performing a calculation on an initial set of input data relating to reservoir conditions to obtain corresponding output data; training an artificial neural network (“ANN”) to perform the calculation using the training data; and using the trained ANN to perform the calculation on a second set of input data to obtain corresponding output data for use by the reservoir simulator in performing simulations.
    Type: Grant
    Filed: January 31, 2011
    Date of Patent: August 21, 2018
    Assignee: LANDMARK GRAPHICS CORPORATION
    Inventors: Graham Fleming, Terry Wong
  • Patent number: 9990591
    Abstract: Invoking an agent during a dialog between a user and an automated assistant. Some implementations are directed to receiving, during a human-to-automated assistant dialog, natural language input of the user that indicates a desire to engage an agent, but that fails to indicate a particular agent to be engaged. Those implementations are further directed to selecting a particular agent from a plurality of available agents, and transmitting an invocation request to the selected particular agent. In some implementations an agent selection model can be utilized in selecting the particular agent, such as a machine learning model. The machine learning model can be trained to enable generation of output that indicates, for each of a plurality of available agents (and optionally intent(s) for those agents), a probability that the available agent (and optionally intent) will generate appropriate responsive content.
    Type: Grant
    Filed: April 18, 2017
    Date of Patent: June 5, 2018
    Assignee: GOOGLE LLC
    Inventors: Ilya Gennadyevich Gelfenbeyn, Artem Goncharuk, Pavel Sirotin
  • Patent number: 9974226
    Abstract: A method begins by agriculture equipment collecting current on-site gathered agriculture data regarding an agriculture region and sending at least a representation of the current on-site gathered agriculture data to a host device. The method continues with the host device processing one or more of the at least a representation of the current on-site gathered agriculture data, current off-site gathered agriculture data, historical on-site gathered agriculture data, historical off-site gathered agriculture data, and historical analysis of agriculture predictions regarding the agriculture region to produce a current agriculture prediction for the agriculture region. The method continues with the host device generating an agriculture prescription regarding at least a portion of the agriculture region based on the current agriculture prediction and sending the agriculture prescription to one or more of the agriculture equipment.
    Type: Grant
    Filed: April 20, 2015
    Date of Patent: May 22, 2018
    Assignee: The Climate Corporation
    Inventors: Craig Eugene Rupp, A. Corbett S. Kull, Steve Richard Pitstick, Patrick Lee Dumstorff
  • Patent number: 9971974
    Abstract: Provided are methods and systems for knowledge discovery utilizing knowledge profiles.
    Type: Grant
    Filed: August 25, 2016
    Date of Patent: May 15, 2018
    Assignee: Elsevier, Inc.
    Inventors: Edwin Adriaansen, Bob J. A. Schijvenaars
  • Patent number: 9959504
    Abstract: Certain relationships representing material insights are identified from among a set of discovered relationships. Cognitive discovery of relationships in a knowledge base, or corpus, are ranked according to one or more metrics indicative of material insights, including recentness and degree of alignment.
    Type: Grant
    Filed: December 2, 2015
    Date of Patent: May 1, 2018
    Assignee: International Business Machines Corporation
    Inventors: John B. Gordon, John P. Hogan, Sanjay F. Kottaram
  • Patent number: 9959940
    Abstract: A computer implemented system and method provides a volume of activation (VOA) estimation model that receives as input two or more electric field values of a same or different data type at respective two or more positions of a neural element and determines based on such input an activation status of the neural element. A computer implemented system and method provides a machine learning system that automatically generates a computationally inexpensive VOA estimation model based on output of a computationally expensive system.
    Type: Grant
    Filed: March 10, 2017
    Date of Patent: May 1, 2018
    Assignee: Boston Scientific Neuromodulation Corporation
    Inventors: Michael A. Moffitt, G. Karl Steinke
  • Patent number: 9953271
    Abstract: Technologies are generally described for systems, devices and methods relating to determining weights in a machine learning environment. In some examples, a training distribution of training data may be identified, information about a test distribution of test data, and a coordinate of the training data and the test data may be identified. Differences between the test distribution and the training distribution may be determined, for the coordinate. A weight importance parameter may be identified, for the coordinate. A processor may calculate weights based on the differences, and based on the weight importance parameter. The weights may be adapted to cause the training distribution to conform to the test distribution at a degree of conformance. The degree of conformance may be based on the weight importance parameter.
    Type: Grant
    Filed: August 5, 2014
    Date of Patent: April 24, 2018
    Assignee: CALIFORNIA INSTITUTE OF TECHNOLOGY
    Inventors: Yaser Said Abu-Mostafa, Carlos Roberto Gonzalez
  • Patent number: 9946970
    Abstract: Embodiments described herein are directed to methods and systems for performing neural network computations on encrypted data. Encrypted data is received from a user. The encrypted data is encrypted with an encryption scheme that allows for computations on the ciphertext to generate encrypted results data. Neural network computations are performed on the encrypted data, using approximations of neural network functions to generate encrypted neural network results data from encrypted data. The approximations of neural network functions can approximate activation functions, where the activation functions are approximated using polynomial expressions. The encrypted neural network results data are communicated to the user associated with the encrypted data such that the user decrypts the encrypted data based on the encryption scheme. The functionality of the neural network system can be provided using a cloud computing platform that supports restricted access to particular neural networks.
    Type: Grant
    Filed: November 7, 2014
    Date of Patent: April 17, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ran Gilad-Bachrach, Thomas William Finley, Mikhail Bilenko, Pengtao Xie
  • Patent number: 9934338
    Abstract: Building models and predicting operational outcomes of a drilling operation. At least some of the illustrative embodiments are methods including: gathering sensor data regarding offset wells and context data regarding the offset wells, and placing the sensor data and context data into a data store; creating a reduced data set by identifying a correlation between data in the data store and an operational outcome in a drilling operation; creating a model based on the reduced data set; and predicting the operational outcome based on the model.
    Type: Grant
    Filed: June 7, 2013
    Date of Patent: April 3, 2018
    Assignee: LANDMARK GRAPHICS CORPORATION
    Inventors: Olivier Germain, Keshava P. Rangarajan, Amit K. Singh, Hermanus Teunissen, Ram N. Adari
  • Patent number: 9928468
    Abstract: Various embodiments train a prediction model for predicting a label to be allocated to a prediction target explanatory variable set. In one embodiment, one or more sets of training data are acquired. Each of the one or more sets of training data includes at least one set of explanatory variables and a label allocated to the at least one explanatory variable set. A plurality of explanatory variable subsets is extracted from the at least one set of explanatory variables. A prediction model is trained utilizing the training data. The plurality of explanatory variable subsets is reflected on a label predicted by the prediction model to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.
    Type: Grant
    Filed: January 31, 2017
    Date of Patent: March 27, 2018
    Assignee: International Business Machines Corporation
    Inventors: Takayuki Katsuki, Yuma Shinohara