Patents by Inventor Jeffrey William Pasternack

Jeffrey William Pasternack 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: 20200293585
    Abstract: Methods, systems, and computer programs are presented for managing counters with automatic obsolescence of outdated values. One method includes operations for initializing a counter at a first time, and receiving, at a second time, a request to add a value to the counter. Further, a time period corresponding to the second time is calculated, as well as a promotion factor based on the time period. The method further includes calculating an incremental value based on the received value and the promotion factor, and adding the incremental value to the counter. Additionally, a prediction or estimate is based on a current value of the counter, such as identifying areas of interest for a user on an online service, identifying languages spoken by the user, strength of connections between users, determining if the user is actively searching for a new job, and so forth.
    Type: Application
    Filed: March 11, 2019
    Publication date: September 17, 2020
    Inventor: Jeffrey William Pasternack
  • Publication number: 20200293562
    Abstract: Applications are presented for selecting response suggestions in a Graphical User Interface (GUI). Responses are identified for a message received in the GUI. Each response has a score and occupies a width of pixels in the GUI, which provides an available width of pixels for presenting the responses. Further, the method includes an operation for identifying sets of the responses, each set having one or more of the suggested responses. For each set, a utility of the set is calculated based on the score of the responses in the set, the width of pixels of the responses in the set, and the available width of pixels. Further, the set with the greatest utility is selected and then the responses of the set are presented in the GUI. Same principles may be applied to organizing screen layouts for applications that scroll vertically, such as user feeds or search results.
    Type: Application
    Filed: March 14, 2019
    Publication date: September 17, 2020
    Inventor: Jeffrey William Pasternack
  • Patent number: 10740690
    Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.
    Type: Grant
    Filed: March 24, 2017
    Date of Patent: August 11, 2020
    Assignee: Facebook, Inc.
    Inventors: Jeffrey William Pasternack, David Vickrey, Justin MacLean Coughlin, Prasoon Mishra, Austen Norment McDonald, Max Christian Eulenstein, Jianfu Chen, Kritarth Anand, Polina Kuznetsova
  • Patent number: 10721190
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for a sequence to sequence to classification model for generating recommended messages. A messaging system generates, using a sequence to sequence encoder, an embedding vector from a message being transmitted as part of a communication session, the sequence to sequence encoder having been trained based on historical message data that includes messages transmitted between users of the messaging system. The messaging system determines, based on the embedding vector, a set of candidate responses for replying to the first message, the set of candidate responses being a subset of a set of available responses. The messaging system selects, from the set of candidate responses, a set of recommended responses to the first message, and causes the set of recommended responses to be presented by a client device of a recipient user of the first message.
    Type: Grant
    Filed: July 31, 2018
    Date of Patent: July 21, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Bing Zhao, Jeffrey William Pasternack, Nandeesh Channabasappa Rajashekar, Nimesh Madhavan Chakravarthi, Chung Yu Wang, Arpit Dhariwal
  • Patent number: 10680978
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for generating recommended responses based on historical data. A messaging system receives a message as part of a communication session between a first client device and a second client device. The message originated from the first client device. The messaging system determines, using the message as input in a statistical model, a set of candidate responses for replying to the message. The statistical model was generated based on historical message data transmitted as part of previous communication sessions between a plurality of client devices. The set of candidate responses is a subset of a set of available candidate responses. The messaging system determines, based on a set of candidate selection rules, a subset of the candidate responses yielding a set of recommended responses to the message, and causes the set of recommended responses to be presented on the second client device.
    Type: Grant
    Filed: January 31, 2018
    Date of Patent: June 9, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Adam Leon, Nandeesh Channabasappa Rajashekar, Nimesh Chakravarthi, Jeffrey William Pasternack, Birjodh Tiwana, Arpit Dhariwal, Bing Zhao
  • Publication number: 20200134013
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for a language proficiency inference system used to determine a user's proficiency in one or more languages. The language proficiency inference system determines both text-based probability scores and profile-based probability scores indicating a probability that a user speaks a language or set of languages. The text-based probability score is based on text associated with the first user, whereas the profile-based probability score is based profile data of the user. The language proficiency inference system determines aggregated probability scores based on the corresponding text-based and profile-based probability scores. For example, the aggregated probability score is the sum of the text and profile-based probability scores. The language proficiency inference system uses the aggregated scores to determine the languages in which the user is proficient.
    Type: Application
    Filed: October 31, 2018
    Publication date: April 30, 2020
    Inventor: Jeffrey William Pasternack
  • Publication number: 20200044990
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for a sequence to sequence to classification model for generating recommended messages. A messaging system generates, using a sequence to sequence encoder, an embedding vector from a message being transmitted as part of a communication session, the sequence to sequence encoder having been trained based on historical message data that includes messages transmitted between users of the messaging system. The messaging system determines, based on the embedding vector, a set of candidate responses for replying to the first message, the set of candidate responses being a subset of a set of available responses. The messaging system selects, from the set of candidate responses, a set of recommended responses to the first message, and causes the set of recommended responses to be presented by a client device of a recipient user of the first message.
    Type: Application
    Filed: July 31, 2018
    Publication date: February 6, 2020
    Inventors: Bing Zhao, Jeffrey William Pasternack, Nandeesh Channabasappa Rajashekar, Nimesh Madhavan Chakravarthi, Chung Yu Wang, Arpit Dhariwal
  • Publication number: 20200004825
    Abstract: Techniques for generating diverse smart replies using a synonym hierarchy are disclosed herein. A computer system may detect that a first set of one or more messages having first content has been transmitted from a first computing device of a first user to a second computing device of a second user, determine a plurality of candidate replies based on the first content of the first set of one or more messages, and then select a plurality of smart replies from the plurality of candidate replies using a hierarchical graph data structure and at least one diversity rule. The selecting of the plurality of smart replies comprises omitting at least one of the plurality of candidate replies from selection based on the at least one diversity rule, which limits a number of the plurality of smart replies that have a common parent node.
    Type: Application
    Filed: June 27, 2018
    Publication date: January 2, 2020
    Inventors: Jeffrey William Pasternack, Arpit Dhariwal, Bing Zhao, Nimesh Madhavan Chakravarthi, Nandeesh Channabasappa Rajashekar
  • Publication number: 20200004826
    Abstract: Techniques for generating smart replies are disclosed herein. A computer system may generate candidate replies to a message from a first device of a first user to a second device of a second user based on content of the message using a first model, and determine synonym replies based on the candidate replies using a hierarchical graph data structure, with the synonym replies including the candidate replies in addition to synonyms of the candidate replies, the hierarchical graph data structure comprising a tree of concepts ranging from root nodes to leaf nodes of synonym replies. The computer system may generate smart replies using a second model based on the synonym replies and corresponding user selection data for each one of the plurality of synonym replies, with the user selection data indicating a number of times the second user has selected the corresponding synonym reply for replying to messages.
    Type: Application
    Filed: June 27, 2018
    Publication date: January 2, 2020
    Inventor: Jeffrey William Pasternack
  • Publication number: 20200007475
    Abstract: Techniques for generating smart replies involving image files are disclosed herein. In some example embodiments, a computer system detects that a first message comprising a first image file has been transmitted from a first computing device of a first user to a second computing device of a second user, and generates a first plurality of smart replies based on a first embedding vector of the first image file, where the first embedding vector of the first image file is based on at least one of first textual metadata of the first image file, first image data of the first image file, and a first set of query text used by a first set of users in a first set of searches that resulted in the first image file being included in a first set of transmitted messages.
    Type: Application
    Filed: June 27, 2018
    Publication date: January 2, 2020
    Inventors: Jeffrey William Pasternack, Christopher Szeto, Arpit Dhariwal
  • Publication number: 20190372923
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for using a language classification system for generating response messages. A messaging system receives a message transmitted from a first user to a second user, and determines, based on a set of language counter values determined for the second user, a probability value that the second user will respond to the message in a first language and a probability value that the second user will respond to the message in a second language. The language counter values are determined using a text classification model and indicate a number of times that the second user has used the languages in previous messages. Based on the probability values, the messaging system determines that the second user will respond to the message in the first language and causes a set of recommended responses in the first language be presented on the second client device.
    Type: Application
    Filed: May 31, 2018
    Publication date: December 5, 2019
    Inventor: Jeffrey William Pasternack
  • Publication number: 20190354586
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for an improved text classification model. A text classification system determines a first embedding for a first set of characters in a text, and a second embedding for a second set of characters in the text. The text classification system applies a first coefficient to the first embedding and a second coefficient to the second embedding, yielding a first weighted embedding and a second weighted embedding. The first coefficient is different than the second coefficient. The text classification system determines a weighted average embedding for the text based on the first weighted embedding, the second weighted embedding, the first coefficient and the second coefficient. The text classification system identifies at least a first classification label and a second classification label for the text based on the weighted average embedding for the text.
    Type: Application
    Filed: May 16, 2018
    Publication date: November 21, 2019
    Inventor: Jeffrey William Pasternack
  • Patent number: 10459950
    Abstract: In one embodiment, a method includes deriving input topics based on a content item, generating a matrix of scores for the input topics according to a first set of cross-indexed topics, where each of the scores indicates a degree of similarity between a corresponding one of the input topics and a corresponding one of the first cross-indexed topics, calculating a total score for each of the first cross-indexed topics based on the scores for the first cross-indexed topic across all of the input topics, and selecting one or more of the first cross-indexed topics based on the total scores of the first cross-indexed topics. Deriving the input topics may include using a topic tagger to identify the topics based on the content item. The first set of cross-indexed topics may be generated from a database of topics, such as an online encyclopedia.
    Type: Grant
    Filed: December 28, 2015
    Date of Patent: October 29, 2019
    Assignee: Facebook, Inc.
    Inventors: Jeffrey William Pasternack, Giridhar Rajaram
  • Patent number: 10353963
    Abstract: A social networking system receives messages from users that include links to webpages that designate keywords of the webpage. The social networking system identifies webpages linked by users to generate computer models that predict whether a webpage or message should be associated with particular keywords. The social networking system generates computer models that are trained on example webpages and related keywords linked by users in messages. Prior to generating computer models, the social networking system applies one or more filters to exclude webpages and keywords from consideration. The filters may exclude webpages that have low-reliability, are associated with an excessive number of keywords, or keywords that appear on an insufficient number of domains. After training the computer models, messages composed by users may be analyzed and a keyword predicted for the message, which may be suggested to the user to categorize the message.
    Type: Grant
    Filed: December 19, 2014
    Date of Patent: July 16, 2019
    Assignee: Facebook, Inc.
    Inventors: David Vickrey, Jeffrey William Pasternack
  • Publication number: 20190197434
    Abstract: Systems, devices, media, and methods are presented for dynamically sampling training elements while training a plurality of machine learning models. The systems and methods access a plurality of training elements and generate an element buffer including a specified number of positions. The systems and methods generate a random value for each position for the element buffer and map a training element of the plurality of training elements to each position within the element buffer in a first order. The systems and methods generate a second order for the training elements and allocate the training elements to a plurality of machine learning models according to the second order.
    Type: Application
    Filed: February 28, 2018
    Publication date: June 27, 2019
    Inventor: Jeffrey William Pasternack
  • Publication number: 20190124019
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for generating recommended responses based on historical data. A messaging system receives a message as part of a communication session between a first client device and a second client device. The message originated from the first client device. The messaging system determines, using the message as input in a statistical model, a set of candidate responses for replying to the message. The statistical model was generated based on historical message data transmitted as part of previous communication sessions between a plurality of client devices. The set of candidate responses is a subset of a set of available candidate responses. The messaging system determines, based on a set of candidate selection rules, a subset of the candidate responses yielding a set of recommended responses to the message, and causes the set of recommended responses to be presented on the second client device.
    Type: Application
    Filed: January 31, 2018
    Publication date: April 25, 2019
    Inventors: Adam Leon, Nandeesh Channabasappa Rajashekar, Nimesh Chakravarthi, Jeffrey William Pasternack, Birjodh Tiwana, Arpit Dhariwal, Bing Zhao
  • Publication number: 20180276561
    Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.
    Type: Application
    Filed: March 24, 2017
    Publication date: September 27, 2018
    Inventors: Jeffrey William Pasternack, David Vickrey, Justin MacLean Coughlin, Prasoon Mishra, Austen Norment McDonald, Max Christian Eulenstein, Jianfu Chen, Kritarth Anand, Polina Kuznetsova
  • Patent number: 9959503
    Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.
    Type: Grant
    Filed: December 31, 2014
    Date of Patent: May 1, 2018
    Assignee: Facebook, Inc.
    Inventors: David Vickrey, Jeffrey William Pasternack
  • Patent number: 9830313
    Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.
    Type: Grant
    Filed: April 17, 2017
    Date of Patent: November 28, 2017
    Assignee: Facebook, Inc.
    Inventor: Jeffrey William Pasternack
  • Publication number: 20170220556
    Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.
    Type: Application
    Filed: April 17, 2017
    Publication date: August 3, 2017
    Inventor: Jeffrey William Pasternack