Patents Examined by Ahsif A. Sheikh
  • Patent number: 11222263
    Abstract: A lightened neural network method and apparatus. The neural network apparatus includes a processor configured to generate a neural network with a plurality of layers including plural nodes by applying lightened weighted connections between neighboring nodes in neighboring layers of the neural network to interpret input data applied to the neural network, wherein lightened weighted connections of at least one of the plurality of layers includes weighted connections that have values equal to zero for respective non-zero values whose absolute values are less than an absolute value of a non-zero value. The lightened weighted connections also include weighted connections that have values whose absolute values are no greater than an absolute value of another non-zero value, the lightened weighted connections being lightened weighted connections of trained final weighted connections of a trained neural network whose absolute maximum values are greater than the absolute value of the other non-zero value.
    Type: Grant
    Filed: June 22, 2017
    Date of Patent: January 11, 2022
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Changyong Son, Jinwoo Son, Byungin Yoo, Chang Kyu Choi, Jae-Joon Han
  • Patent number: 11200511
    Abstract: At a machine learning service, an indication of a training data set for a model is obtained. One or more training iterations of the model are conducted using an adaptive input sampling strategy. In a particular iteration, index values for a set of training observations are selected based on a set of sampling weights, parameters of the model are updated based on results using training observations identified by the index values, and sampling weights are modified. A result obtained from a trained version of the machine learning model is provided.
    Type: Grant
    Filed: November 17, 2017
    Date of Patent: December 14, 2021
    Assignee: Amazon Technologies, Inc.
    Inventor: Benjamin Alexei London
  • Patent number: 11176469
    Abstract: A first training participant performs an iterative process until a predetermined condition is satisfied, where the iterative process includes: obtaining, using secret sharing matrix addition and based on the current sub-model of each training participant and a corresponding feature sample subset of each training participant, a current prediction value of the regression model for a feature sample set, where the corresponding feature sample subset of each training participant is obtained by performing vertical segmentation on the feature sample set; determining a prediction difference between the current prediction value and a label corresponding to the current prediction value; sending the prediction difference to each second training participant; and updating a current sub-model of the first training participant based on the current sub-model of the first training participant and a product of a corresponding feature sample subset of the first training participant and the prediction difference.
    Type: Grant
    Filed: April 29, 2021
    Date of Patent: November 16, 2021
    Assignee: Advanced New Technologies Co., Ltd.
    Inventors: Chaochao Chen, Liang Li, Jun Zhou
  • Patent number: 11170660
    Abstract: Embodiments can provide a computer implemented method for harvesting training data for a training set for use by a system capable of answering questions, the system comprising a processor and a memory comprising instructions executed by the processor, the method comprising receiving, from a user, an input question; processing the input question and returning, to the user, a result set comprising one or more ranked hypotheses and one or more ranked evidence passages corresponding to the one or more ranked hypotheses; receiving, from the user, an indication that one of the one or more ranked hypotheses is to be designated a watched hypothesis; adding the input question and the watched hypothesis to a to-be-vetted question/answer (QA) pair set comprising one or more to-be-vetted QA pairs; vetting each of the one or more to-be-vetted QA pairs in the to-be-vetted QA pair set through a first-pass automatic vetting procedure; if a vetted QA pair passes the first-pass automatic vetting procedure, adding the vetted QA
    Type: Grant
    Filed: September 29, 2017
    Date of Patent: November 9, 2021
    Assignee: International Business Machines Corporation
    Inventors: Charles E. Beller, William G. Dubyak, Palani Sakthi, Kristen M. Summers
  • Patent number: 11138524
    Abstract: Cascaded, boosted predictive models trained using distinct sets of exogenous and endogenous features are configured to predict component of performance ratings of entities. From the distinct predicted components, the second entity's rating factor can be determined. A second entity's rating factor represents the specific contribution a second entity makes to his average performance rating, as distinct from the rating that an arbitrary or hypothetical second entity would obtain.
    Type: Grant
    Filed: December 9, 2016
    Date of Patent: October 5, 2021
    Assignee: Uber Technologies, Inc.
    Inventors: David Purdy, Li Chen, Theodore Russell Sumers
  • Patent number: 11138498
    Abstract: Disclosed herein is a system, which comprises a plurality of processing units. Each of the processing units comprises a first oscillator, a second oscillator, and a counter. Each of the processing units is configured to receive a first input and a second input and to send an output as a function of the first input and the second input. The function has a plurality of parameters. Each of the processing units is configured to receive and send values of the parameters. The system can be used together with a microprocessor to perform parallel computing.
    Type: Grant
    Filed: April 24, 2020
    Date of Patent: October 5, 2021
    Assignee: SHENZHEN GENORIVISION TECHNOLOGY CO., LTD.
    Inventor: Peiyan Cao
  • Patent number: 11132605
    Abstract: Cardinal sine function used as an activation function for a hierarchical classifier. Application of a sine function, or a cardinal sine function, for hierarchical classification of a subject within subject matter domains and sub-domains. Hierarchical classification or multi-level classification is improved through use of the cardinal sine function or even standard sine function. Some embodiments of the present invention focus on the usage of cardinal sine function as activation function and how to apply this cardinal sine function for hierarchical classification of a subject. Some embodiments include a technique by which hierarchical classification or multi-level classification can benefit from application of a cardinal sine function.
    Type: Grant
    Filed: November 20, 2017
    Date of Patent: September 28, 2021
    Assignee: International Business Machines Corporation
    Inventor: Abhishek Dasgupta
  • Patent number: 11113609
    Abstract: Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include identifying two tree persons in a genealogical database and extracting a plurality of features from both tree persons to generate two vectors. Embodiments also include calculating a plurality of metrics between the two vectors to generate a metric function. Embodiments further include generating feature weights using a recursive process based on training data input by external users, and generating a score by calculating a weighted sum of the metric function being weighted by the feature weights. The generated score may then be compared to a threshold value.
    Type: Grant
    Filed: April 5, 2017
    Date of Patent: September 7, 2021
    Assignee: ANCESTRY.COM OPERATIONS INC.
    Inventors: Atanu Roy, Jianlong Qi, Peng Jiang, Aaron Ling, Rey Furner, Lei Wu, Eugene Greenwood, Ian Stiles
  • Patent number: 11087235
    Abstract: A system rapidly produces training cases for machine based learning by automatically creating training cases from a database of historical data. The system determines a plurality of attributes relevant to each of the training cases. The system identifies a first attribute of the plurality of attributes as an issue, and a second attribute of the plurality attributes as a response to the issue. The system identifies a plurality of cohort members from the database of historical data, where each cohort member comprises cohort member attributes that match a subset of the plurality of attributes. The system analyzes the cohort member attributes of each of the plurality of cohort members to identify the most frequent responses to the issue. The system creates the training cases where each training case comprises the issue and the most frequent responses. The system then trains a machine based learning system using the training cases.
    Type: Grant
    Filed: August 2, 2016
    Date of Patent: August 10, 2021
    Assignee: International Business Machines Corporation
    Inventors: Richard J. Stevens, Fernando J. Suarez Saiz
  • Patent number: 11087229
    Abstract: A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.
    Type: Grant
    Filed: February 3, 2017
    Date of Patent: August 10, 2021
    Assignee: Adxcel Inc.
    Inventors: Yuri Khidekel, Dmitry Aryshev
  • Patent number: 11068774
    Abstract: Provided is a spiking neural network system for dynamical control of flexible, stable, and hybrid memory storage. An information storage method may include converting input information to a temporal pattern in a form of a spike; and storing the information that is converted to the temporal pattern in a spiking neural network. The storing may comprise storing information by applying, to the spiking neural network, a spike-timing-dependent plasticity (STDP) learning rate that is an unsupervised learning rule.
    Type: Grant
    Filed: September 29, 2017
    Date of Patent: July 20, 2021
    Assignee: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
    Inventors: Se-Bum Paik, Youngjin Park
  • Patent number: 11062225
    Abstract: Certain embodiments involve generating personalized recommendations for users by inferring a propensity of each individual user to accept a recommendation. For example, a system generates a personalized user model based on a historical transition matrix that provides state transition probabilities from a general population of users. The probabilities are adjusted based on the propensity for a user to accept a recommendation. The system determines a recommended action for the user to transition between predefined states based on the user model. Once the user has performed an activity that transitions from a current state, the system adjusts a probability distribution for an estimate of the propensity based on whether the activity is the recommended action.
    Type: Grant
    Filed: December 9, 2016
    Date of Patent: July 13, 2021
    Assignee: ADOBE INC.
    Inventors: Nikolaos Vlassis, Georgios Theocharous
  • Patent number: 11045828
    Abstract: The disclosure relates to a device that saves water, energy, and money and may record the savings with a software analytics dashboard. The device allows cold water to flow out when the shower is first turned on and slows or shuts water flow once the water is heated and the shower is unoccupied. After the presence of the user is detected, the shower flow may resume.
    Type: Grant
    Filed: October 18, 2019
    Date of Patent: June 29, 2021
    Assignee: ABSTRACT ENGINEERING, INC.
    Inventors: Greg Floyd, Ian Howard, Rahul Verma, Cameron Meziere, Emily Hood, Michael Mayes
  • Patent number: 11010342
    Abstract: A system and method of obtaining and utilizing an activity signature that is representative of a specific category of network activities based on directory service (DS) log data. The activity signature may be determining by a learning process, including segmenting and pruning a training dataset into a plurality of event segments and matching them with activities based on DS log data of known activities. Once obtained, the activity signature can advantageously be utilized to analyze any DS log data and activities in actual deployment. Using activity signatures to analyze DS event log can reveal roles of event-collection machines, aggregate information dispersed across their component events to reveal actors involved in particular AD activities, augment visibility of DS by enabling various vantage points to better infer activities at other domain machines, and reveal macro activities so that logged information becomes easily interpretable to human analysts.
    Type: Grant
    Filed: April 3, 2017
    Date of Patent: May 18, 2021
    Assignee: Splunk Inc.
    Inventors: Stanislav Miskovic, Satheesh Kumar Joseph Durairaj, George Apostolopulous, Dimitrios Terzis
  • Patent number: 11004011
    Abstract: A digital medium environment includes an action processing application that performs actions including personalized recommendation. A learning algorithm operates on a sample-by-sample basis (e.g., each instance a user visits a web page) and recommends an optimistic action, such as an action found by maximizing an expected reward, or a base action, such as an action from a baseline policy with known expected reward, subject to a safety constraint. The safety constraint requires that the expected performance of playing optimistic actions is at least as good as a predetermined percentage of the known performance of playing base actions. Thus, the learning algorithm is conservative during exploratory early stages of learning, and does not play unsafe actions. Furthermore, since the learning algorithm is online and can learn with each sample, it converges quickly and is able to track time varying parameters better than learning algorithms that learn on a block basis.
    Type: Grant
    Filed: February 3, 2017
    Date of Patent: May 11, 2021
    Assignee: Adobe Inc.
    Inventors: Mohammad Ghavamzadeh, Abbas Kazerouni
  • Patent number: 10997515
    Abstract: A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.
    Type: Grant
    Filed: February 3, 2017
    Date of Patent: May 4, 2021
    Assignee: Adxcel Inc.
    Inventors: Yuri Khidekel, Dmitry Aryshev
  • Patent number: 10972332
    Abstract: The present disclosure is directed toward systems and methods for identifying contributing factors associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify sub-factors associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors that caused the particular anomaly and how those factors correlate with each other.
    Type: Grant
    Filed: August 31, 2015
    Date of Patent: April 6, 2021
    Assignee: ADOBE INC.
    Inventors: John Bates, James Meyer, William Brandon George
  • Patent number: 10970719
    Abstract: Techniques for identifying fraudulent transactions are described. In one example method, an operation sequence and time difference information associated with a transaction are identified by a server. A probability that the transaction is a fraudulent transaction is predicted based on a result provided by a deep learning network, where the deep learning network is trained to predict fraudulent transactions based on operation sequences and time differences associated with a plurality of transaction samples, and where the deep learning network provides the result in response to input including the operation sequence and the time difference information associated with the transaction.
    Type: Grant
    Filed: March 15, 2019
    Date of Patent: April 6, 2021
    Assignee: Advanced New Technologies Co., Ltd.
    Inventor: Longfei Li
  • Patent number: 10949736
    Abstract: Systems, apparatus and methods are described including operations for a flexible neural network accelerator.
    Type: Grant
    Filed: November 3, 2016
    Date of Patent: March 16, 2021
    Assignee: Intel Corporation
    Inventors: Michael E Deisher, Ohad Falik
  • Patent number: 10949762
    Abstract: The present disclosure provides a method and a system for optimizing Hidden Markov Model based land change prediction. Firstly, remotely sensed data is pre-processed and classified into a plurality of land use land cover classes (LULC). Then socio-economic driver variables data for a pre-defined interval of time are provided from a database. A Hidden Markov Model (HMM) is defined with LULC as hidden states and socio-economic driver variables data as observations and trained for generating a MINI state transition probability matrix. Again the defined MINI is trained by taking input data from scenario based temporal variables to generate another set of HMM state transition probability matrix. The generated MINI state transition probability matrix is then integrated with a spatio-temporal model to obtain an integrated model for predicting LULC changes to generate at least one prediction image.
    Type: Grant
    Filed: September 21, 2016
    Date of Patent: March 16, 2021
    Assignee: Tata Consultancy Services Limited
    Inventors: Shamsuddin Nasiruddin Ladha, Piyush Yadav, Shailesh Shankar Deshpande