Patents Examined by Marshall L Werner
  • Patent number: 10841401
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for predicting a future context of a computing device. In some implementations, a context daemon can use historical context information to predict future events and/or context changes. For example, the context daemon can analyze historical context information to predict user sleep patterns, user exercise patterns, and/or other user activity. In some implementations, a context client can register a callback for a predicted future context. For example, the context client can request to be notified ten minutes in advance of a predicted event and/or context change. The context daemon can use the prediction to notify a context client in advance of the predicted event.
    Type: Grant
    Filed: May 17, 2016
    Date of Patent: November 17, 2020
    Assignee: Apple Inc.
    Inventors: Song Li, Gaurav Kapoor, Alexander Barraclough Brown, Varaprasad Lingutla, Daniel Ben Pollack, David M. Chan
  • Patent number: 10839452
    Abstract: Aspects of this disclosure include technologies to detect unpackaged, unlabeled, or mislabeled products based on product images. Leveraging from improved machine learning techniques, the disclosed technical solution can reduce a full product space for product search to a partial product space. Accordingly, a limited number of product candidates in the partial product space may be visually presented for user evaluation. Sometimes, a product candidate is to be comparatively presented with a live image of the product via a uniquely designed graphic user interface, which further improves the confidence of the user and the accuracy of the underlying transaction.
    Type: Grant
    Filed: June 27, 2019
    Date of Patent: November 17, 2020
    Inventors: Sheng Guo, Haozhi Zhang, Weilin Huang, Tiangang Zhang, Yu Gao, Le Yin, Matthew Robert Scott
  • Patent number: 10832129
    Abstract: A method for transferring acoustic knowledge of a trained acoustic model (AM) to a neural network (NN) includes reading, into memory, the NN and the AM, the AM being trained with target domain data, and a set of training data including a set of phoneme data, the set of training data being data obtained from a domain different from a target domain for the target domain data, inputting training data from the set of training data into the AM, calculating one or more posterior probabilities of context-dependent states corresponding to phonemes in a phoneme class of a phoneme to which each frame in the training data belongs, and generating a posterior probability vector from the one or more posterior probabilities, as a soft label for the NN, and inputting the training data into the NN and updating the NN, using the soft label.
    Type: Grant
    Filed: October 7, 2016
    Date of Patent: November 10, 2020
    Assignee: International Business Machines Corporation
    Inventors: Takashi Fukuda, Masayuki A. Suzuki, Ryuki Tachibana
  • Patent number: 10825178
    Abstract: Provided are a computerized image interpretation method and a device for analyzing a medical image. The image interpretation method may include receiving, at a processor, a medical image, and receiving report information including a healthcare worker's judgement result of the medical image. The method may also include generating, at the processor, result information representing correspondence between first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, by applying the first lesion information and the second lesion information to a third analysis model. The method may further include outputting, at the processor, the result information.
    Type: Grant
    Filed: December 9, 2019
    Date of Patent: November 3, 2020
    Assignee: Lunit Inc.
    Inventors: Nayoung Jeong, Ki Hwan Kim, Minhong Jang
  • Patent number: 10817794
    Abstract: Methods, systems, and devices are disclosed for determining causality among data. In one aspect, a method includes receiving a first data and a second data associated with a first event and a second event, respectively; and determining a data set including quantitative causality dependence values between the first and the second data.
    Type: Grant
    Filed: June 2, 2016
    Date of Patent: October 27, 2020
    Inventors: Ishanu Chattopadhyay, Hod Lipson
  • Patent number: 10803143
    Abstract: A computer-implemented method for deriving biopsy results in a non-invasive manner includes acquiring a plurality of training data items. Each training data item comprises non-invasive patient data and one or more biopsy derived scores associated with an individual. The method further includes extracting a plurality of features from the non-invasive patient data based on the one or more biopsy derived scores and training a predictive model to generate a predicted biopsy score based on the plurality of features and the one or more biopsy derived scores.
    Type: Grant
    Filed: July 29, 2016
    Date of Patent: October 13, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Ali Kamen, Noha El-Zehiry, David Liu, Dorin Comaniciu, Atilla Peter Kiraly
  • Patent number: 10803397
    Abstract: Various techniques employed by an application performance management service to generate an application behavior learning based capacity forecast model are disclosed. In some embodiments, such a capacity forecast model is at least in part generated by clustering collected transaction data into one or more usage patterns, analyzing collected usage pattern data, and solving a mathematical model generated from the usage pattern data to determine a sensitivity of a resource to each type of transaction associated with an application.
    Type: Grant
    Filed: June 13, 2014
    Date of Patent: October 13, 2020
    Assignee: Appnomic Systems Private Limited
    Inventors: Padmanabhan Desikachari, Sumanth Narasappa
  • Patent number: 10762439
    Abstract: Embedding representation for a document is generated based on clustering words in the document. Representative clusters are selected and a weighted sum of the embeddings of the words in the selected clusters is determined as a document embedding. Documents are labeled based on document embeddings. A machine learning algorithm is trained using the documents. The machine learning algorithm predicts a label of a given document based on the given document's document embedding.
    Type: Grant
    Filed: July 26, 2016
    Date of Patent: September 1, 2020
    Assignee: International Business Machines Corporation
    Inventors: Feng Cao, Boliang Chen, Zheng Yu
  • Patent number: 10713559
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for environment simulation. In one aspect, a system comprises a recurrent neural network configured to, at each of a plurality of time steps, receive a preceding action for a preceding time step, update a preceding initial hidden state of the recurrent neural network from the preceding time step using the preceding action, update a preceding cell state of the recurrent neural network from the preceding time step using at least the initial hidden state for the time step, and determine a final hidden state for the time step using the cell state for the time step. The system further comprises a decoder neural network configured to receive the final hidden state for the time step and process the final hidden state to generate a predicted observation characterizing a predicted state of the environment at the time step.
    Type: Grant
    Filed: May 3, 2019
    Date of Patent: July 14, 2020
    Assignee: DeepMind Technologies Limited
    Inventors: Daniel Pieter Wierstra, Shakir Mohamed, Silvia Chiappa, Sebastien Henri Andre Racaniere
  • Patent number: 10558923
    Abstract: In an example, one or more member profiles and corresponding Boolean attributes indicating, for each of the one or more member profiles, whether the corresponding member of a social networking service interacted with a request for confidential data, are obtained. A first set of one or more features are extracted from the one or more member profiles. The first set of one or more features and corresponding Boolean attributes are fed into a machine learning algorithm to train a confidential data response propensity prediction model to output a predicted propensity to interact with a request for confidential data for a candidate member profile. A second set of one or more features are extracted from the candidate member profile. The extracted second set of one or more features are fed to the confidential data response propensity prediction model, outputting the predicted propensity to interact with a request for confidential data.
    Type: Grant
    Filed: December 7, 2016
    Date of Patent: February 11, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Krishnaram Kenthapadi, Stuart MacDonald Ambler, Edoardo M. Airoldi
  • Patent number: 10552753
    Abstract: Techniques for inferring the identity (e.g., member profile attributes) of members of an online social network service are described. According to various embodiments, a member profile attribute missing from a member profile page associated with a particular member of an online social network service is identified. Member profile data and behavioral log data associated with a plurality of members of the online social network service is then accessed. Thereafter, a prediction modeling process is performed, based on a prediction model and feature data including the member profile data and the behavioral log data, to generate a confidence score associated with the particular member and the missing member profile attribute, the confidence score indicating a likelihood that the missing member profile attribute corresponds to a candidate value.
    Type: Grant
    Filed: December 10, 2015
    Date of Patent: February 4, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Zhigang Hua, Kin Fai Kan, Peter N. Skomoroch, Gloria Lau, Saveliy Uryasev
  • Patent number: 10546247
    Abstract: An approach is provided in which an information handling system trains multiple classifiers using a set of training samples. The information handling system selects a leader classifier from the multiple classifiers that generates the most amount of correct decisions corresponding to the set of training samples. Next, the information handling system identifies an endorser classifier from the multiple classifiers that generates the highest proportion of correct decisions among the endorser classifier's decisions matching the leader classifier's decisions, and combines the leader classifier and the endorser classifier into a combined classifier stage. In turn, the information handling system utilizes the combined classifier stage to process inquiries and generate results.
    Type: Grant
    Filed: December 1, 2015
    Date of Patent: January 28, 2020
    Assignee: International Business Machines Corporation
    Inventors: Tin Kam Ho, Luis A. Lastras-Montano, Vinith Misra
  • Patent number: 10515317
    Abstract: In an example embodiment, a machine learning algorithm is used to train an engagement score model to calculate an engagement score for a particular member indicating a probability that the particular member would increase engagement with the social networking service if provided with statistical information about confidential data submitted by other members. Member usage information is obtained corresponding to a first member of a social networking service. Then a plurality of features are extracted from the member usage information corresponding to the first member. This plurality of features is inputted into the engagement model to obtain an engagement score for the first member. It is then determined whether or not to provide statistical information to the first member about confidential data submitted by other members based on the engagement score for the first member.
    Type: Grant
    Filed: July 29, 2016
    Date of Patent: December 24, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Krishnaram Kenthapadi, Ahsan Chudhary, Ryan Wade Sandler, Anthony Duane Duerr
  • Patent number: 10366344
    Abstract: A computer-implemented method for selecting features for classification may include (1) generating a matrix X, a column vector Y, and a matrix Z from a training dataset that includes a plurality of samples with a plurality of features, (2) generating an augmented matrix from the matrix X, the column vector Y, and the matrix Z, (3) identifying one or more most-relevant features from the plurality of features by iteratively applying a sweep operation to the augmented matrix, and (4) training a classification model using the most-relevant features from the plurality of features rather than all of the plurality of features. Various other methods, systems, and computer-readable media may have similar features.
    Type: Grant
    Filed: March 31, 2016
    Date of Patent: July 30, 2019
    Assignee: Symantec Corporation
    Inventors: Nikolaos Vasiloglou, Jugal Parikh, Andrew Gardner