Patents Examined by Li B. Zhen
  • Patent number: 10387781
    Abstract: An information processing device includes a keyword acquiring unit configured to acquire a plurality of primary keyword and secondary keyword groups; a classifying unit configured to classify each of the plurality of secondary keywords by a plurality of topics; an estimating unit configured to estimate whether or not each primary keyword in the plurality of groups is a related keyword related to any topic having a classified secondary keyword or a mixed keyword unrelated to any of the topics; and an assigning unit configured to preferentially assign a primary keyword estimated to be a related keyword to a topic having a classified secondary keyword in the same group, and assigning a primary keyword estimated to be a mixed keyword to any of all the topics given for classification.
    Type: Grant
    Filed: June 23, 2015
    Date of Patent: August 20, 2019
    Assignee: International Business Machines Corporation
    Inventors: Risa Kawanaka, Issei Yoshida
  • Patent number: 10387787
    Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.
    Type: Grant
    Filed: October 28, 2015
    Date of Patent: August 20, 2019
    Assignee: Intuit Inc.
    Inventors: Joseph Cessna, Massimo Mascaro, Joel R. Minton
  • Patent number: 10387773
    Abstract: Hierarchical branching deep convolutional neural networks (HD-CNNs) improve existing convolutional neural network (CNN) technology. In a HD-CNN, classes that can be easily distinguished are classified in a higher layer coarse category CNN, while the most difficult classifications are done on lower layer fine category CNNs. Multinomial logistic loss and a novel temporal sparsity penalty may be used in HD-CNN training. The use of multinomial logistic loss and a temporal sparsity penalty causes each branching component to deal with distinct subsets of categories.
    Type: Grant
    Filed: December 23, 2014
    Date of Patent: August 20, 2019
    Assignee: eBay Inc.
    Inventors: Zhicheng Yan, Robinson Piramuthu, Vignesh Jagadeesh, Wei Di, Dennis Decoste
  • Patent number: 10387774
    Abstract: Described is a system for converting convolutional neural networks to spiking neural networks. A convolutional neural network (CNN) is adapted to fit a set of requirements of a spiking neural network (SNN), resulting in an adapted CNN. The adapted CNN is trained to obtain a set of learned weights, and the set of learned weights is then applied to a converted SNN having an architecture similar to the adapted CNN. The converted SNN is then implemented on neuromorphic hardware, resulting in reduced power consumption.
    Type: Grant
    Filed: January 30, 2015
    Date of Patent: August 20, 2019
    Assignee: HRL Laboratories, LLC
    Inventors: Yongqiang Cao, Yang Chen, Deepak Khosla
  • Patent number: 10380488
    Abstract: Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.
    Type: Grant
    Filed: September 29, 2014
    Date of Patent: August 13, 2019
    Assignee: APPLE INC.
    Inventors: Binu K. Mathew, Kit-Man Wan, Gaurav Kapoor
  • Patent number: 10380487
    Abstract: An information processing device includes a keyword acquiring unit configured to acquire a plurality of primary keyword and secondary keyword groups; a classifying unit configured to classify each of the plurality of secondary keywords by a plurality of topics; an estimating unit configured to estimate whether or not each primary keyword in the plurality of groups is a related keyword related to any topic having a classified secondary keyword or a mixed keyword unrelated to any of the topics; and an assigning unit configured to preferentially assign a primary keyword estimated to be a related keyword to a topic having a classified secondary keyword in the same group, and assigning a primary keyword estimated to be a mixed keyword to any of all the topics given for classification.
    Type: Grant
    Filed: March 2, 2015
    Date of Patent: August 13, 2019
    Assignee: International Business Machines Corporation
    Inventors: Risa Kawanaka, Issei Yoshida
  • Patent number: 10372711
    Abstract: System(s) and method(s) for predicting effect of database cache on query elapsed response time during an application development stage are disclosed. Query executed on a database is classified and a query type is obtained. Database statistics are collected to further determine one or more cache miss factors with respect to the query type. One or more time components are calculated due to the one or more cache miss factors with respect to the query type. The one or more time components are used to predict the query elapsed response time for varying size of the database.
    Type: Grant
    Filed: December 10, 2014
    Date of Patent: August 6, 2019
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventor: Rekha Singhal
  • Patent number: 10372567
    Abstract: A fault detection and diagnosis (FDD) system designed to automatically and efficiently determine the true fault underlying a set of symptoms, presenting the operator with a clear cause and effect diagram for all faults and symptoms. An Event Cluster is used to group related symptoms and causes, potentially across multiple pieces of equipment, and establish a cause and effect relationship chain.
    Type: Grant
    Filed: February 18, 2015
    Date of Patent: August 6, 2019
    Inventors: Paul Rensing, Keith Corbett, Charles Frankston
  • Patent number: 10373048
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for organizing trained and untrained neural networks. In one aspect, a neural network device includes a collection of node assemblies interconnected by between-assembly links, each node assembly itself comprising a network of nodes interconnected by a plurality of within-assembly links, wherein each of the between-assembly links and the within-assembly links have an associated weight, each weight embodying a strength of connection between the nodes joined by the associated link, the nodes within each assembly being more likely to be connected to other nodes within that assembly than to be connected to nodes within others of the node assemblies.
    Type: Grant
    Filed: August 27, 2015
    Date of Patent: August 6, 2019
    Assignee: Ecole Polytechnique Federale De Lausanne (EPFL)
    Inventors: Henry Markram, Rodrigo de Campos Perin, Thomas K. Berger
  • Patent number: 10366324
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.
    Type: Grant
    Filed: September 1, 2015
    Date of Patent: July 30, 2019
    Assignee: Google LLC
    Inventors: Patrick F. Riley, Marc Berndl
  • Patent number: 10366332
    Abstract: A mechanism is provided in a data processing system for tailoring question answering system output based on user expertise. The mechanism receives an input question from a questioning user and determines a set of features associated with text of the input question. The mechanism determines an expertise level of the questioning user based on the set of features associated with the text of the input question using a trained expertise model. The mechanism generates one or more candidate answers for the input question and tailors output of the one or more candidate answers based on the expertise level of the questioning user.
    Type: Grant
    Filed: August 14, 2014
    Date of Patent: July 30, 2019
    Assignee: International Business Machines Corporation
    Inventors: Nicholas V. Bruno, Donna K. Byron, Julius Goth, III, Dwi Sianto Mansjur
  • Patent number: 10355924
    Abstract: Systems and methods for content selection with first and second recommendation engines are disclosed herein. The system can include a memory include a content library database and a model database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include one or more servers that can include a packet selection system and a presentation system. These one or more servers can: receive response data from the user device; provide received response data to a first recommendation engine; alert a second recommendation engine when a selected next node is a placeholder node; retrieve at least one statistical model relevant to selection of next node content; and select next node content based on an output of the at least one statistical model.
    Type: Grant
    Filed: August 25, 2016
    Date of Patent: July 16, 2019
    Assignee: Pearson Education, Inc.
    Inventors: Angie McAllister, Brian Moriarty, Greg McFall
  • Patent number: 10346744
    Abstract: The field of the disclosure relates generally to a method and system for analyzing behavior of a computer infrastructure and the displaying the behavior of the computer infrastructure in a graphical manner. The system comprises an analytical engine connected to agents running on devices in the computer infrastructure and analyzing continuous data and asynchronous data.
    Type: Grant
    Filed: March 26, 2013
    Date of Patent: July 9, 2019
    Assignee: Elasticsearch B.V.
    Inventor: Stephen Dodson
  • Patent number: 10339466
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a quantum oracle to make inference in complex machine learning models that is capable of solving artificial intelligent problems. Input to the quantum oracle is derived from the training data and the model parameters, which maps at least part of the interactions of interconnected units of the model to the interactions of qubits in the quantum oracle. The output of the quantum oracle is used to determine values used to compute loss function values or loss function gradient values or both during a training process.
    Type: Grant
    Filed: September 11, 2014
    Date of Patent: July 2, 2019
    Assignee: Google LLC
    Inventors: Nan Ding, Masoud Mohseni, Hartmut Neven
  • Patent number: 10338972
    Abstract: A computing resource service provider may store user data in a distributed data storage system. The distributed data storage system may contain one or more storage partitions configured to store based at least in part on prefixes of keys contained in a key-value store, where the size of the keys may vary. The one or more storage partitions may track requests to access data where the requests include a key such that the data may be located by the service provider based at least in part on the key. When a request is received a counter associated with the prefix included in the request may be incremented, the counter may be configured to decay over time. If the counter exceeds a threshold the service provider may split the prefix associated with the counter and generate new partitions responsible for the split prefix.
    Type: Grant
    Filed: May 28, 2014
    Date of Patent: July 2, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Alazel Acheson, Meng Hu, Nauman Zubaid Khan, Mai-Lan Tomsen Bukovec
  • Patent number: 10329900
    Abstract: Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.
    Type: Grant
    Filed: November 10, 2016
    Date of Patent: June 25, 2019
    Assignee: Halliburton Energy Services, Inc.
    Inventors: Dingding Chen, Syed Hamid, Michael C. Dix
  • Patent number: 10331997
    Abstract: A first input is processed via a first configuration of a neural network to produce a first output. The first configuration defines attributes of the neural network, such as connections between neural elements of the neural network. If the neural network requires a context switch to process a second input, a second configuration is applied to the neural network to change the attributes, and the second input is processed via the second configuration of the neural network to produce a second output.
    Type: Grant
    Filed: October 23, 2014
    Date of Patent: June 25, 2019
    Assignee: Seagate Technology LLC
    Inventors: Richard Esten Bohn, Peng Li, David Tetzlaff
  • Patent number: 10332016
    Abstract: The invention concerns a method to compare two data obtained from a sensor or interface, carried out by processing means of a processing unit, the method comprising the computing of a similarity function between two feature vectors of the data to be compared, characterized in that each feature vector of a datum is modelled as the summation of Gaussian variables, said variables comprising: a mean of a class to which the vector belongs, an intrinsic deviation, and an observation noise of the vector, each feature vector being associated with a quality vector comprising information on the observation noise of the feature vector, and in that the similarity function is computed from the feature vectors and associated quality vectors.
    Type: Grant
    Filed: November 3, 2015
    Date of Patent: June 25, 2019
    Assignee: IDEMIA IDENTITY & SECURITY
    Inventors: Julien Bohne, Stephane Gentric
  • Patent number: 10332032
    Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.
    Type: Grant
    Filed: November 1, 2016
    Date of Patent: June 25, 2019
    Assignee: International Business Machines Corporation
    Inventors: Pawan R. Chowdhary, Markus R. Ettl, Roger D. Lederman, Tim Nonner, Ulrich B. Schimpel, Zhengliang Xue, Hongxia Yang
  • Patent number: 10324820
    Abstract: Providing specialization for a static program analysis procedure by executing an automated agent to monitor a code authoring process for a program under examination that includes a plurality of respective lexical scopes. The agent monitors a corresponding amount of coding time, or a corresponding number of edits, for each of the plurality of respective lexical scopes. A mapping associates each of the plurality of respective lexical scopes with a first quantitative measure of the corresponding amount of time, or a second quantitative measure of the corresponding number of edits, that were used to code each of the plurality of respective lexical scopes. The static analysis procedure is specialized by applying a more refined, detailed, precise, or granular analysis to a first lexical scope that is mapped to a greater amount of time or a greater number of edits than a second lexical scope.
    Type: Grant
    Filed: September 21, 2016
    Date of Patent: June 18, 2019
    Assignee: International Business Machines Corporation
    Inventors: Marco Pistoia, Omer Tripp