Patents by Inventor Dhruv Arya

Dhruv Arya 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).

  • Patent number: 10380553
    Abstract: In an example, a plurality of member profiles in a social networking service are obtained, each member profile identifying a member and listing one or more skills the corresponding member has explicitly added to the member profile, the one or more skills indicating a proficiency by the member in the corresponding skill. A members-skills matrix is formed, wherein each cell in the matrix is assigned a value based on whether the corresponding member has the corresponding skill. The dot product of the members matrix and the skills matrix is then computed and used to identify one or more latent skills of a first member of the social networking service. Then a first digitally stored member profile is augmented with the one or more latent skills by combining the one or more latent skills with explicit skills for purposes of one or more searches that utilize member skills as an input variable.
    Type: Grant
    Filed: April 14, 2017
    Date of Patent: August 13, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jia Li, Dhruv Arya, Shakti Dhirendraji Sinha, Viet Thuc Ha, Deepak Agarwal
  • Patent number: 10380127
    Abstract: A trained search system can be configured to retrieve a candidate subset of results, where the trained search system uses data extracted from a machine learning scheme. The machine learning scheme can be trained to identify results that are ranked by a computationally expensive algorithm, such as a ranking algorithm. When a query is received, the trained search system can be used to retrieve results instead of applying the computationally expensive ranking algorithm.
    Type: Grant
    Filed: February 13, 2017
    Date of Patent: August 13, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ganesh Venkataraman, Dhruv Arya, Aman Grover, Liang Zhang
  • Publication number: 20190236063
    Abstract: Various embodiments described herein provide for systems and methods for using a machine-learning model to rank job search results based on the similarity of the job title of each job search result and a job search query that produces the job search results. According to some embodiments, the machine-learning model comprises a word-embedding machine-learning model that maps a word to a vector.
    Type: Application
    Filed: April 12, 2019
    Publication date: August 1, 2019
    Inventors: Yongwoo Noh, Dhruv Arya, Ganesh Venkataraman, Aman Grover
  • Publication number: 20190197480
    Abstract: This disclosure relates to systems and methods for recommending relevant positions. A method includes receiving a request from a member for available employment positions posted at a social networking service, determining a cohort for the member, retrieving a query that is associated with the cohort for the member, executing the query at a database of employment positions, receiving results of the query, and causing the results of the query to be displayed, using an electronic user interface, to the member.
    Type: Application
    Filed: December 21, 2017
    Publication date: June 27, 2019
    Inventors: Huichao Xue, Dhruv Arya, Nadia Fawaz, Liang Zhang
  • Publication number: 20190171727
    Abstract: In some embodiments, the disclosed subject matter involves techniques for generating personalized query suggestions for a user in a specific subject or application domain that are ranked using confidence levels and contextual scoring. Partial query strings may be parsed for literal matching and be processed for spell checks, acronym expansion, and other expansion and rewriting of the partial query to a known possible query suggestion. Possible query suggestions are weighted using global feature metrics and personalized metrics. Various weighting, confidence levels and merging based on scoring may be used to rank the suggestions. A machine learning model may be used to assist in assigning scores based on metrics on interaction in the search domain. Other embodiments are described and claimed.
    Type: Application
    Filed: March 15, 2018
    Publication date: June 6, 2019
    Inventors: Swanand Wakankar, Dhruv Arya, Saurabh Kataria, Ada Cheuk Ying Yu
  • Publication number: 20190171728
    Abstract: In some embodiments, the disclosed subject matter involves techniques for generating type-ahead query suggestions for a user in a specific subject or application domain that are ranked using confidence levels and contextual scoring. Partial query strings may be parsed for literal matching and be processed for spell checks, acronym expansion, and other expansion and rewriting of the partial query to a known possible query suggestion. Possible query suggestions are weighted using global feature metrics. Various weighting, confidence levels and merging based on scoring may be used to rank the suggestions. A machine learning model may be used to assist in assigning scores based on metrics on interaction in the search domain. Other embodiments are described and claimed.
    Type: Application
    Filed: March 15, 2018
    Publication date: June 6, 2019
    Inventors: Swanand Wakankar, Dhruv Arya, Saurabh Kataria, Ada Cheuk Ying Yu
  • Publication number: 20190171764
    Abstract: This disclosure relates to systems and methods for recommending relevant positions. A method includes receiving, from a member of an online networking service, a query for one or more available employment positions; executing the query, at a database of employment positions, to retrieve the one or more available employment positions; filtering results of the query according to one or more facets; generating an electronic user interface to display the filtered results; and allowing the member to adjust the facets using the electronic user interface.
    Type: Application
    Filed: December 1, 2017
    Publication date: June 6, 2019
    Inventors: Dhruv Arya, Kevin Kao, Huichao Xue
  • Patent number: 10303681
    Abstract: Various embodiments described herein provide for systems and methods for using a machine-learning model to rank job search results based on the similarity of the job title of each job search result and a job search query that produces the job search results. According to some embodiments, the machine-learning model comprises a word-embedding machine-learning model that maps a word to a vector.
    Type: Grant
    Filed: May 19, 2017
    Date of Patent: May 28, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Yongwoo Noh, Dhruv Arya, Ganesh Venkataraman, Aman Grover
  • Publication number: 20190129995
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system identifies, based on search parameters received from a client device, a target company identified in the search parameters. The search parameters include a search term comprising at least two keywords, a first one of the at least two keyword identifying the target company, and the second one of the at least two keywords identifying an employment position. The search system identifies a second company based on a set of peer scores indicating a probability of employees transitioning between companies. The peer scores are calculated based on historical movement data indicating employee transitions between companies. The search system generates an expanded search term comprising a new keyword identifying the second company and the second keyword identifying the employment position.
    Type: Application
    Filed: February 28, 2018
    Publication date: May 2, 2019
    Inventors: Saurabh Kataria, Yiqun Liu, Ada Cheuk Ying Yu, Dhruv Arya
  • Publication number: 20190129998
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system executes a search query based on a search term and the geographic indicator. In response to determining that a number of the search results is less than a threshold number, the search system determines, based on historical search logs from other users in the first geographic region, a likelihood value indicating a likelihood that the other users in the first geographic region expanded the geographic region of their search queries. The search system compares the likelihood value to a threshold likelihood value, and determines, based on the comparison, that the likelihood value meets or exceeds the threshold likelihood value. The search system then executes an expanded search based on the search term and an expanded geographic indicator that encompasses the first geographic region.
    Type: Application
    Filed: February 28, 2018
    Publication date: May 2, 2019
    Inventors: Saurabh Kataria, Ada Cheuk Ying Yu, Dhruv Arya, Swanand Wakankar
  • Publication number: 20190130023
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system determines a set of candidate alternate search terms based on historical search logs that include records of previously submitted search terms, corresponding search results that were presented to users, and corresponding search results that were selected by the users. The set of candidate alternate search terms is selected from titles of the corresponding search results that were selected by the users. The search system ranks the set of candidate alternate search terms based on determined probabilities that each of the alternate candidate search terms will be selected if presented to a user, and selects a first candidate alternate search term from the set of candidate alternate search terms based on the ranking. The search system generates an expanded search term based on the first candidate alternate search term.
    Type: Application
    Filed: February 28, 2018
    Publication date: May 2, 2019
    Inventors: Saurabh Kataria, Lin Guo, Ada Cheuk Ying Yu, Dhruv Arya
  • Publication number: 20190068730
    Abstract: In an example embodiment, one or more query terms are obtained. Then, for each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term, with the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities. A confidence score is then calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term, and the query term is tagged with the entity identification corresponding to the standardized entity that most closely matches the query term and the calculated confidence score.
    Type: Application
    Filed: October 29, 2018
    Publication date: February 28, 2019
    Inventors: Dhruv Arya, Abhimanyu Lad, Shakti Dhirendraji Sinha, Satya Pradeep Kanduri
  • Publication number: 20190034792
    Abstract: In an example embodiment, a machine learning algorithm is used to train a. deep semantic similarity neural network to output a semantic similarity score between a candidate job search query and a candidate job search result. This semantic similarity score can then be used in a ranking phase to rank job search results in response to a first job search query.
    Type: Application
    Filed: July 25, 2017
    Publication date: January 31, 2019
    Inventors: Saurabh Kataria, Dhruv Arya, Ganesh Venkataraman
  • Publication number: 20190034793
    Abstract: In an example embodiment, a machine learning algorithm is used to train a query-based deep semantic similarity neural network to output a query context vector in a vector space that includes both query context vectors and document context vectors. Both the query context vectors and document context vectors are clustered using a clustering algorithm. When an input search query is obtained, the input search query is also passed into the query-based deep semantic similarity neural network and its output document context vector assigned to a first cluster based on the clustering algorithm. Documents within the first cluster are then retrieved in response to the input search query.
    Type: Application
    Filed: July 25, 2017
    Publication date: January 31, 2019
    Inventors: Saurabh Kataria, Dhruv Arya, Ganesh Venkataraman
  • Publication number: 20190034882
    Abstract: In an example, a first hash function is performed on job posting features extracted from a job posting to obtain hashed job posting features. The hashed job posting features are stored in a forward-index corresponding to the job posting in the database. When a job search query is received from a first member of a social networking service, job search query features are extracted from the job search query and a second hash function is performed on the job search query features. The hashed job posting features and the hashed job search query features are fed to a job posting result ranking model trained via a machine learning algorithm to compare the hashed job posting features to the hashed job search query features to generate an application likelihood score indicating a likelihood that the first member will apply for a job corresponding to the job posting.
    Type: Application
    Filed: July 25, 2017
    Publication date: January 31, 2019
    Inventors: Ankan Saha, Dhruv Arya, Shahdad Irajpour
  • Publication number: 20190018884
    Abstract: Method and system to generate multiple entity aware typeahead suggestions is provided. The system is configured to use multiple Finite State Transducers (FSTs) to examine an input string submitted by a user via a search box, and to generate one or more typeahead suggestions based on the results of the examination. Different FSTs are constructed with respect to strings identified as associated with different entity types. At least one of the typeahead suggestions includes a portion associated with one entity type and a portion associated with a different entity type.
    Type: Application
    Filed: July 12, 2017
    Publication date: January 17, 2019
    Inventors: Swanand Wakankar, Dhruv Arya, Saurabh Kataria
  • Publication number: 20190018885
    Abstract: Method and system to generate index aware typeahead suggestions is provided. The system is configured to generate one or more typeahead suggestions that are index aware, by taking into account the number of valid search results that match a query that corresponds to a typeahead suggestion. The system detects an input string in the search box, generates a candidate typeahead suggestion string, interrogates an index of the electronic publications with the candidate typeahead suggestion string to generate a recall value that represents a number of electronic publications that include the candidate typeahead suggestion string, and includes the candidate typeahead suggestion string in a list of typeahead suggestions based on the recall value. The list of typeahead suggestions is communicated to a client system.
    Type: Application
    Filed: July 12, 2017
    Publication date: January 17, 2019
    Inventors: Swanand Wakankar, Dhruv Arya, Saurabh Kataria
  • Publication number: 20190019157
    Abstract: In an example embodiment, a generalized linear mixed effect model is trained using sample job posting results resulting from sample queries from sample members having sample member data. The generalized linear mixed effect model has coefficients based on a global ranking model as well as coefficients based on features from job posting results. The generalized linear mixed effect model may be trained to output application likelihood scores for each of a plurality of candidate job posting results produced by a query from a first member. The application likelihood scores may then be used to sort the candidate job posting results.
    Type: Application
    Filed: July 13, 2017
    Publication date: January 17, 2019
    Inventors: Ankan Saha, Dhruv Arya
  • Publication number: 20180349440
    Abstract: In an example embodiment, one or more query terms are obtained. For each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term. A confidence score is calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term. In response to a determination that the confidence score transgresses a threshold, the query term is associated with an entity identification corresponding to the standardized entity that most closely matches the query term. One or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification are obtained. The one or more query rewriting rules are executed to rewrite the first query such that the rewritten query, when performed on a data source, returns fewer search results than the first query would have.
    Type: Application
    Filed: August 8, 2018
    Publication date: December 6, 2018
    Inventors: Benjamin Hoan Le, Dhruv Arya, Ganesh Venkataraman, Shakti Dhirendraji Sinha
  • Patent number: 10148777
    Abstract: In an example embodiment, one or more query terms are obtained. Then, for each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term, with the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities. A confidence score is then calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term, and the query term is tagged with the entity identification corresponding to the standardized entity that most closely matches the query term and the calculated confidence score.
    Type: Grant
    Filed: May 23, 2016
    Date of Patent: December 4, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Dhruv Arya, Abhimanyu Lad, Shakti Dhirendraji Sinha, Satya Pradeep Kanduri