Patents Examined by Lut Wong
  • Patent number: 10540610
    Abstract: Methods, apparatus, and computer-readable media are provided for analyzing a cluster of communications, such as B2C emails, to generate a template for the cluster that defines transient segments and fixed segments of the cluster of communications. More particularly, methods, apparatus, and computer-readable media are provided for generating and/or applying a trained structured machine learning model for a generated template that can be used to determine, for one or more transient segments of subsequent communications, a corresponding probability that a given semantic label is the correct semantic label for extracted content of the transient segment(s).
    Type: Grant
    Filed: April 27, 2016
    Date of Patent: January 21, 2020
    Assignee: GOOGLE LLC
    Inventors: Jie Yang, Amr Ahmed, Luis Garcia Pueyo, Mike Bendersky, Amitabh Saikia, Marc-Allen Cartright, Marc Alexander Najork, MyLinh Yang, Hui Tan, Weinan Zhang, Vanja Josifovski, Alexander J. Smola
  • Patent number: 10534779
    Abstract: The current disclosure generally relates to database management systems (DBMSs) and may be generally directed to methods and systems of using artificial intelligence (i.e. machine learning and/or anticipation functionalities, etc.) to learn a user's use of a DBMS, store this “knowledge” in a knowledgebase, and anticipate the user's future operating intentions. The current disclosure may also be generally directed to associative methods and systems of constructing DBMS commands. The current disclosure may also be generally directed to methods and systems of using a simplified DBMS command language (SDCL) for associative DBMS command construction. The current disclosure may also be generally directed to artificially intelligent methods and systems for associative DBMS command construction. The current disclosure may also be generally directed to methods and systems for associative DBMS command construction through voice input.
    Type: Grant
    Filed: May 2, 2016
    Date of Patent: January 14, 2020
    Inventor: Jasmin Cosic
  • Patent number: 10528570
    Abstract: The current disclosure generally relates to database management systems (DBMSs) and may be generally directed to methods and systems of using artificial intelligence (i.e. machine learning and/or anticipation functionalities, etc.) to learn a user's use of a DBMS, store this “knowledge” in a knowledgebase, and anticipate the user's future operating intentions. The current disclosure may also be generally directed to associative methods and systems of constructing DBMS commands. The current disclosure may also be generally directed to methods and systems of using a simplified DBMS command language (SDCL) for associative DBMS command construction. The current disclosure may also be generally directed to artificially intelligent methods and systems for associative DBMS command construction. The current disclosure may also be generally directed to methods and systems for associative DBMS command construction through voice input.
    Type: Grant
    Filed: May 3, 2016
    Date of Patent: January 7, 2020
    Inventor: Jasmin Cosic
  • Patent number: 10504024
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.
    Type: Grant
    Filed: June 28, 2016
    Date of Patent: December 10, 2019
    Assignee: Google LLC
    Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
  • Patent number: 10430690
    Abstract: A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix. A label is selected for each observation. A mean observation vector and a covariance matrix are computed from the unlabeled observations selected to have each respective label. A number of eigenvalues that have a smallest value is selected from each covariance matrix and used to define a null space for each respective label. A distance value is computed for a distance vector computed to the mean observation vector and projected into the null space associated with the label selected for each respective observation. A diversity rank is determined for each respective observation based on minimum computed distance values. A predefined number of observations having highest values for the diversity rank are included in labeled observations and removed from the unlabeled observations.
    Type: Grant
    Filed: May 1, 2019
    Date of Patent: October 1, 2019
    Assignee: SAS INSTITUTE INC.
    Inventor: Xu Chen
  • Patent number: 10425376
    Abstract: A cloud learning system for smart windows is provided. The system includes at least one server configured to couple via a network to a plurality of window systems, each of the plurality of window systems having at least one control system and a plurality of windows with electrochromic windows and sensors, wherein the at least one server includes at least one physical server or at least one virtual server implemented using physical computing resources. The at least one server is configured to gather first information from the plurality of window systems, and configured to gather second information from sources on the network and external to the plurality of window systems. The at least one server is configured to form at least one rule or control algorithm usable by a window system, based on the first information and the second information, and configured to download the at least one rule or control algorithm to at least one of the plurality of window systems.
    Type: Grant
    Filed: August 7, 2015
    Date of Patent: September 24, 2019
    Assignee: Kinestral Technologies, Inc.
    Inventors: Paul Nagel, Wally Barnum, Stephen Coffin, Brandon Nichols, Ashish Nagar, Kamil Bojanczik, Jonathan Ziebarth
  • Patent number: 10423886
    Abstract: A method for electronic logging of carrier data is described. The method includes monitoring a vehicle motion status for a predetermined period and assigning a logical state to at least one duty status variable. A plurality of travel conditions capable of changing a transit period, such as weather, traffic, and construction are monitored, aggregated, and used to determine a predicted likelihood of changing the transit period. The likelihood of occurrence of a compliance rule violation is determined, based on the aggregated values, the predicted likelihood of changing the transit period, and a group of compliance rules. An indicator of the likelihood of occurrence of a compliance rule violation may be generated, and presented to any number of electronic devices. Remedial actions to avoid or mitigate the compliance rule violation may be suggested.
    Type: Grant
    Filed: December 29, 2017
    Date of Patent: September 24, 2019
    Assignee: Forward Thinking Systems, LLC
    Inventors: David Isler, Stuart Lowenstein
  • Patent number: 10410128
    Abstract: Methods, devices, and servers for friend recommendation are provided. A user association set of a target user is obtained. Original data of each associated user in the user association set is obtained. The original data include location relationship data, associated friend data, time relationship data, or combinations thereof, between each associated user and the target user. The original data of each associated user is screened to obtain feature data to form a feature collection for each associated user. A pre-configured N-Tree prediction model is used to process the feature collection for a prediction calculation to obtain an association-predicting value for each associated user. According to the association-predicting value of each associated user, a friend user for the target user from the user association set is determined and recommended to the target user.
    Type: Grant
    Filed: December 12, 2014
    Date of Patent: September 10, 2019
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
    Inventors: Wenlong Zhang, Feng Jiao, Bin Wang, Lei Zeng, Xiaohui Chen
  • Patent number: 10402749
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizable machine learning models. In some implementations, data is received, including (i) example data sets and (ii) data specifying one or more criteria to be assessed. A set of multiple models is trained, where each model in the set of models is trained using a training data set comprising a different subset of the example data sets. Output of the models is obtained for various example data sets, and a combination of n-grams is selected based on the outputs. The example data sets are used to train a classifier to evaluate input data with respect to the specified one or more criteria based on whether the input data includes the n-grams in the selected combination of n-grams.
    Type: Grant
    Filed: August 25, 2015
    Date of Patent: September 3, 2019
    Assignee: SHL US LLC
    Inventors: Arya Ryan Aminzadeh, Aman Cherian Alexander
  • Patent number: 10379502
    Abstract: An unsupervised machine learning model can make prediction on time series data. Variance of time-varying parameters for independent variables of the model may be restricted for continuous consecutive time intervals to minimize overfitting. The model may be used in a control system to control other devices or systems. If predictions for the control system are for a higher granularity time interval than the current mode, the time-varying parameters of the model are modified for the higher granularity time interval.
    Type: Grant
    Filed: June 1, 2016
    Date of Patent: August 13, 2019
    Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Sanjay Sharma, Nilesh Kumar Gupta, Samik Adhikary, Pinaki Asish Ghosh
  • Patent number: 10372704
    Abstract: Mathematical technologies for recommending content to a user based on a user's preferences are disclosed. Embodiments of these technologies can generate a probabilistic representation of a data set, and then adjust the probabilistic representation to reflect a user-specific weighting scheme. The user preference-adjusted representation of the data set can be used to recommend content to the user.
    Type: Grant
    Filed: September 1, 2015
    Date of Patent: August 6, 2019
    Assignee: SRI International
    Inventors: John Byrnes, Dayne Freitag, Robert Sasseen, Melinda Gervasio
  • Patent number: 10366333
    Abstract: Embodiments associate a relevant semantic data type (e.g., date) with incoming raw data (e.g., a column of digits) which lacks metadata. Assignment of semantic data type is inferred from a plurality of features. A first step determines a first feature comprising success rate in converting the raw data into various semantic data types. Then, alignment between observed/reference distributions of other features (e.g., data first digit, data length) is determined per-semantic data type. Total scores for each semantic data type are calculated from the combined features, and used as a basis for ranking the semantic data types. The total scores may reflect a weighting of the various features. In a second step, top-ranked semantic data types may be further differentiated from one another by applying additional features. User feedback regarding accuracy of semantic data type assignment, may be incorporated into training data used to modify the feature reference distributions.
    Type: Grant
    Filed: June 17, 2016
    Date of Patent: July 30, 2019
    Assignee: SAP SE
    Inventors: Burak Yoldemir, Alex MacAulay
  • Patent number: 10353901
    Abstract: The current disclosure generally relates to database management systems (DBMSs) and may be generally directed to methods and systems of using artificial intelligence (i.e. machine learning and/or anticipation functionalities, etc.) to learn a user's use of a DBMS, store this “knowledge” in a knowledgebase, and anticipate the user's future operating intentions. The current disclosure may also be generally directed to associative methods and systems of constructing DBMS commands. The current disclosure may also be generally directed to methods and systems of using a simplified DBMS command language (SDCL) for associative DBMS command construction. The current disclosure may also be generally directed to artificially intelligent methods and systems for associative DBMS command construction. The current disclosure may also be generally directed to methods and systems for associative DBMS command construction through voice input.
    Type: Grant
    Filed: April 30, 2016
    Date of Patent: July 16, 2019
    Inventor: Jasmin Cosic
  • Patent number: 10354204
    Abstract: A computing device automatically classifies an observation vector. A label set defines permissible values for a target variable. Supervised data includes a labeled subset that has one of the permissible values. A converged classification matrix is computed based on the supervised data and an unlabeled subset using a prior class distribution matrix that includes a row for each observation vector. Each column is associated with a single permissible value of the label set. A cell value in each column is a likelihood that each associated permissible value of the label set occurs based on prior class distribution information. The value of the target variable is selected using the converged classification matrix. A weighted classification label distribution matrix is computed from the converged classification matrix. The value of the target variable for each observation vector of the plurality of observation vectors is output to a labeled dataset.
    Type: Grant
    Filed: October 17, 2018
    Date of Patent: July 16, 2019
    Assignee: SAS Institute Inc.
    Inventors: Xu Chen, Saratendu Sethi
  • Patent number: 10339447
    Abstract: A method for selecting a reduced number of model neurons in a neural network includes generating a first sparse set of non-zero decoding vectors. Each of the decoding vector is associated with a synapse between a first neuron layer and a second neuron layer. The method further includes implementing the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors.
    Type: Grant
    Filed: July 31, 2014
    Date of Patent: July 2, 2019
    Assignee: QUALCOMM Incorporated
    Inventors: Sachin Subhash Talathi, David Jonathan Julian, Venkata Sreekanta Reddy Annapureddy
  • Patent number: 10339440
    Abstract: In some aspects, the present disclosure relates to neural language modeling. In one embodiment, a computer-implemented neural network includes a plurality of neural nodes, where each of the neural nodes has a plurality of input weights corresponding to a vector of real numbers. The neural network also includes an input neural node corresponding to a linguistic unit selected from an ordered list of a plurality of linguistic units, and an embedding layer with a plurality of embedding node partitions. Each embedding node partition includes one or more neural nodes. Each of the embedding node partitions corresponds to a position in the ordered list relative to a focus term, is configured to receive an input from an input node, and is configured to generate an output.
    Type: Grant
    Filed: February 18, 2016
    Date of Patent: July 2, 2019
    Assignee: Digital Reasoning Systems, Inc.
    Inventors: Andrew Trask, David Gilmore, Matthew Russell
  • Patent number: 10325216
    Abstract: A system and method can be used to facilitate a strategy for decision making in a fantasy sports league. The method can be used in conjunction with a web or mobile based application. By entering data into the various matrices associated with the application, a customized data set can be created. This data set can then be used with graphical overlays to facilitate future decision making based on created scores attributable to each individual athlete. The end result being a streamlined process that gives one an advantage over others in the fantasy sports league.
    Type: Grant
    Filed: August 11, 2014
    Date of Patent: June 18, 2019
    Inventor: Ernest Schulten
  • Patent number: 10324983
    Abstract: Recurrent neural networks (RNNs) can be visualized. For example, a processor can receive vectors indicating values of nodes in a gate of a RNN. The values can result from processing data at the gate during a sequence of time steps. The processor can group the nodes into clusters by applying a clustering method to the values of the nodes. The processor can generate a first graphical element visually indicating how the respective values of the nodes in a cluster changed during the sequence of time steps. The processor can also determine a reference value based on multiple values for multiple nodes in the cluster, and generate a second graphical element visually representing how the respective values of the nodes in the cluster each relate to the reference value. The processor can cause a display to output a graphical user interface having the first graphical element and the second graphical element.
    Type: Grant
    Filed: September 21, 2018
    Date of Patent: June 18, 2019
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis
  • Patent number: 10325224
    Abstract: Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.
    Type: Grant
    Filed: July 7, 2017
    Date of Patent: June 18, 2019
    Assignee: Palantir Technologies Inc.
    Inventors: Daniel Erenrich, Matthew Elkherj
  • Patent number: 10304001
    Abstract: A target estimator that properly conditions measurement variates in the case of a series of sensor measurements collected against a target, a system model that captures visible and hidden stochastic information including but not limited to target state, target identity, and sensor measurements and that marginalizes measurement failure and a dynamic mixed quadrature expression facilitating real-time implementation of the estimator are presented.
    Type: Grant
    Filed: August 4, 2015
    Date of Patent: May 28, 2019
    Assignee: Raytheon Company
    Inventors: Timothy Campbell, David S. Douglas, Ryan Quiller