Patents by Inventor Tianshi Gao

Tianshi Gao has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20200118004
    Abstract: The present disclosure provides a processing device for performing generative adversarial network and a method for machine creation applying the processing device. The processing device includes a memory configured to receive input data including a random noise and reference data, and store a discriminator neural network parameter and a generator neural network parameter, and the processing device further includes a computation device configured to transmit the random noise input data into a generator neural network and perform operation to obtain a noise generation result, and input both of the noise generation result and the reference data into a discriminator neural network and perform operation to obtain a discrimination result, and further configured to update the discriminator neural network parameter and the generator neural network parameter according to the discrimination result.
    Type: Application
    Filed: November 25, 2019
    Publication date: April 16, 2020
    Inventors: Tianshi CHEN, Shuai HU, Yifan HAO, Yufeng GAO
  • Patent number: 10602207
    Abstract: An online system receives content items from a third party content provider. For each content item, the online system inputs an image into a neural network and extracts a feature vector from a hidden layer of the neural network. The online system compresses each feature vector by assigning a label to each feature value representing whether the feature value was above a threshold value. The online system identifies a set of content items that the user has interacted with and determines a user feature vector by aggregating feature vectors of the set of content items. For a new set of content items, the online system compares the compressed feature vectors of the content item with the user feature vector. The online system selects one or more of the new content items based on the comparison and sends the selected content items to the user.
    Type: Grant
    Filed: August 3, 2018
    Date of Patent: March 24, 2020
    Assignee: Facebook, Inc.
    Inventors: Tianshi Gao, Xiangyu Wang, Ou Jin, Yifei Huang, Vignesh Ramanathan
  • Publication number: 20200089623
    Abstract: The disclosure provides an information processing device and method. The information processing device includes a storage module a storage module configured to acquire information data, wherein the information data including at least one key feature and the storage module pre-storing true confidence corresponding to the key feature; an operational circuit configured to determine predicted confidence corresponding to the key feature according to the information data and judge whether the predicted confidence of the key feature exceeds a preset threshold value range of the true confidence corresponding to the key feature or not; a controlling circuit configured to control the storage module to modify the key feature or send out a modification signal to the outside when the predicted confidence exceeds the preset threshold value of the true confidence. The information processing device of the disclosure can automatically correct and modify handwriting, text, image or video actions instead of artificial method.
    Type: Application
    Filed: November 25, 2019
    Publication date: March 19, 2020
    Inventors: Tianshi Chen, Shuai Hu, Yifan Hao, Yufeng Gao
  • Publication number: 20200045354
    Abstract: An online system receives content items from a third party content provider. For each content item, the online system inputs an image into a neural network and extracts a feature vector from a hidden layer of the neural network. The online system compresses each feature vector by assigning a label to each feature value representing whether the feature value was above a threshold value. The online system identifies a set of content items that the user has interacted with and determines a user feature vector by aggregating feature vectors of the set of content items. For a new set of content items, the online system compares the compressed feature vectors of the content item with the user feature vector. The online system selects one or more of the new content items based on the comparison and sends the selected content items to the user.
    Type: Application
    Filed: August 3, 2018
    Publication date: February 6, 2020
    Inventors: Tianshi Gao, Xiangyu Wang, Ou Jin, Yifei Huang, Vignesh Ramanathan
  • Patent number: 10511886
    Abstract: When an online system receives a request to present content items to a user, a content selection system included in the online system selects content items for presentation to the user. A feedback control mechanism communicates with each computing device of the content selection system to determine the latency period and the CPU utilization of each computing device. The feedback control mechanism also determines a target latency period and a target CPU utilization in which content items are selected. By comparing the latency period of each computing device to the target latency period and the CPU utilization to the target CPU utilization, an amount of information to be evaluated by each computing device is determined based on the comparisons.
    Type: Grant
    Filed: July 3, 2018
    Date of Patent: December 17, 2019
    Assignee: Facebook, Inc.
    Inventors: Vibhor Rastogi, Mircea Grecu, Puneet Sharma, Tianshi Gao
  • Publication number: 20190171766
    Abstract: To present one or more content to users of an online system, the online system identifies a content evaluation pipeline including an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item. The content evaluation pipeline selects a decreasing number of content items, from each stage of the order, according to the order of the stages in the order. The online system identifies a set of candidate modifications to one or more operational parameters of the content evaluation pipeline. For each candidate modification, the online system determines a compute time value and a content selection value. For a given amount of compute time, the online system optimizes the one or more operational parameters based on the determined content time value and the determined content selection value to increase the content selection value of the content evaluation pipeline.
    Type: Application
    Filed: December 1, 2017
    Publication date: June 6, 2019
    Inventors: Tianshi Gao, Pengjun Pei, Bingqing Wang
  • Patent number: 10296534
    Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.
    Type: Grant
    Filed: June 3, 2015
    Date of Patent: May 21, 2019
    Assignee: Dolby Laboratories Licensing Corporation
    Inventors: Tianshi Gao, Regunathan Radhakrishnan, Wenyu Jiang, Claus Bauer
  • Publication number: 20190073581
    Abstract: A preprocessing module of a neural network has a first input and second input. The module generates multiple, different first latent vector representations of its first input, and multiple, different second latent vector representations of its second input. The module then models pairwise interactions between every unique pairwise combination of the first and second latent vector representations. The module then produces an intermediate output by combining the results of the modeled pairwise interactions.
    Type: Application
    Filed: September 1, 2017
    Publication date: March 7, 2019
    Inventors: Xianjie Chen, Wenlin Chen, Liang Xiong, Tianshi Gao
  • Publication number: 20190073586
    Abstract: In one embodiment, a method includes a preprocessing stage of a neural network model, where the preprocessing stage includes first and second preprocessing modules. Each of the two modules has first input that may receive a dense input and a second input that may receive a sparse input. Each module generates latent vector representations of their respective first and second inputs, and combine the latent vectors with the original first input to define an intermediate output. The intermediate output of the first module is fed into the first input of the second module.
    Type: Application
    Filed: September 1, 2017
    Publication date: March 7, 2019
    Inventors: Xianjie Chen, Wenlin Chen, Liang Xiong, Tianshi Gao
  • Publication number: 20190065978
    Abstract: A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).
    Type: Application
    Filed: August 30, 2017
    Publication date: February 28, 2019
    Inventors: Christian Alexander Martine, Robert Oliver Burns Zeldin, Dinkar Jain, Jurgen Anne Francois Marie Van Gael, Anand Sumatilal Bhalgat, Tianshi Gao
  • Patent number: 10147041
    Abstract: Some embodiments include a method of generating a compatibility score for a grouping of objects based on correlations between attributes of the objects. An example grouping is a pair of user and ad. The method may be implemented using a multi-threaded pipeline architecture that utilizes a learning model to compute the compatibility score. The learning model determines correlations between a first object's attributes (e.g., user's liked pages, user demographics, user's apps installed, pixels visited, etc.) and a second object's attributes (e.g., expressed or implied). Example expressed attributes can be targeting keywords; example implied attributes can be object IDs associated with the ad.
    Type: Grant
    Filed: July 14, 2015
    Date of Patent: December 4, 2018
    Assignee: Facebook, Inc.
    Inventors: Tianshi Gao, Shyamsundar Rajaram, Stuart Michael Bowers, Mircea Grecu
  • Publication number: 20180336490
    Abstract: To select the content to be presented to the user, a first latent vector is determined for a content item based on a first object associated with the content item. A second latent vector is determined for the content item based on a second object associated with the content item. A content item vector is then determined based on the first and second latent vectors. Furthermore, a user vector is determined based on interactions of the user with the first set of content objects and the second set of content objects. A score indicative of the likelihood of the user interacting with the content item is determined based on the content item vector and the user vector.
    Type: Application
    Filed: May 18, 2017
    Publication date: November 22, 2018
    Inventors: Tianshi Gao, Ahmad Abdulmageed Mohammed Abdulkader, Yifei Huang, Ou Jin, Liang Xiong
  • Publication number: 20170017886
    Abstract: Some embodiments include a method of generating a compatibility score for a grouping of objects based on correlations between attributes of the objects. An example grouping is a pair of user and ad. The method may be implemented using a multi-threaded pipeline architecture that utilizes a learning model to compute the compatibility score. The learning model determines correlations between a first object's attributes (e.g., user's liked pages, user demographics, user's apps installed, pixels visited, etc.) and a second object's attributes (e.g., expressed or implied). Example expressed attributes can be targeting keywords; example implied attributes can be object IDs associated with the ad.
    Type: Application
    Filed: July 14, 2015
    Publication date: January 19, 2017
    Inventors: Tianshi Gao, Shyamsundar Rajaram, Stuart Michael Bowers
  • Publication number: 20160275554
    Abstract: An online system tracks stores information identifying content provided by third party systems and accessed by online system users as well as interactions with advertisements performed by online system users. When the online system identifies an opportunity to present an advertisement to a viewing user, the online system identifies content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. A score is computed for various advertisements based at least in part on correlations between content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. The online system selects candidate advertisements to evaluate for presentation to the viewing user based on the scores.
    Type: Application
    Filed: March 18, 2015
    Publication date: September 22, 2016
    Inventors: Feng Yan, Shyamsundar Rajaram, Hao Zhang, Lu Zheng, Tianshi Gao, David Michael Viner
  • Publication number: 20160247204
    Abstract: An online system maintains topic vectors associated with various content items, where a vector associated with a content item indicates a topic vector of a content item. Words in a content item and context traits describing presentation of the words in the content item are used by the online system to determine a topic vector associated with the content item. When a subject content item for display, the online system determines the topic vector associated with the subject content item and identifies topic vectors associated with other content items nearest to the topic vector associated with the subject content item in a vector space through application of one or more clustering algorithms to the topic vectors. Content items associated with the identified topic vectors are indicated as similar to the subject content item by the online system.
    Type: Application
    Filed: February 20, 2015
    Publication date: August 25, 2016
    Inventors: Seyed Mohsen Amiri, Daniel Tam, Tianshi Gao
  • Publication number: 20150269149
    Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.
    Type: Application
    Filed: June 3, 2015
    Publication date: September 24, 2015
    Inventors: Tianshi Gao, Regunthan Radhakrishnan, Wenyu Jiang, Claus Bauer
  • Patent number: 9075897
    Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.
    Type: Grant
    Filed: May 5, 2010
    Date of Patent: July 7, 2015
    Assignee: DOLBY LABORATORIES LICENSING CORPORATION
    Inventors: Tianshi Gao, Regunathan Radhakrishnan, Wenyu Jiang, Claus Bauer
  • Patent number: 8635211
    Abstract: Fingerprint-based content identification is described. Bin-counting detects linear trends based on data points that are generated from a set of query fingerprints, which are extracted from a query content item and a matching set of corresponding reference content items that are extracted from a reference content item. RANSAC may detect trends based on confidence scores that are computed for multiple candidate content items. The trends detected for the candidate content items are compared against a threshold in order of decreasing confidence score until a trend that satisfies the threshold is found. A ranking-based mechanism for fingerprint-based content identification may use a bin-counting or RANSAC-based trend detection mechanism to identify duplicate or multiple reference content items that all match the same query content item.
    Type: Grant
    Filed: June 10, 2010
    Date of Patent: January 21, 2014
    Assignee: Dolby Laboratories Licensing Corporation
    Inventors: Wenyu Jiang, Tianshi Gao
  • Publication number: 20120078894
    Abstract: Fingerprint-based content identification is described. Bin-counting detects linear trends based on data points that are generated from a set of query fingerprints, which are extracted from a query content item and a matching set of corresponding reference content items that are extracted from a reference content item. RANSAC may detect trends based on confidence scores that are computed for multiple candidate content items. The trends detected for the candidate content items are compared against a threshold in order of decreasing confidence score until a trend that satisfies the threshold is found. A ranking-based mechanism for fingerprint-based content identification may use a bin-counting or RANSAC-based trend detection mechanism to identify duplicate or multiple reference content items that all match the same query content item.
    Type: Application
    Filed: June 10, 2010
    Publication date: March 29, 2012
    Applicant: Dolby Laboratories Licensing Corporation
    Inventors: Wenyu Jiang, Tianshi Gao
  • Publication number: 20120054194
    Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.
    Type: Application
    Filed: May 5, 2010
    Publication date: March 1, 2012
    Applicant: DOLBY LABORATORIES LICENSING CORPORATION
    Inventors: Tianshi Gao, Regunathan Radhakrishnan, Wenyu Jiang, Claus Bauer