Patents by Inventor Ganesh Venkataraman

Ganesh Venkataraman 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: 10474725
    Abstract: Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method accesses, by a social networking server, a plurality of job applications, with each job application being submitted by a member for a job in a company, the member and the job having a respective industry from a plurality of industries. Semantic analysis of the job applications is performed by a machine-learning program to identify similarity coefficients among the plurality of industries. A job search query is received from a first member, the job search query including a query industry, and the job search query is expanded with industries that are similar to the query industry. The social networking server executes the expanded job search query to generate a plurality of job results. Presentation is provided on a display of one or more of the top job results.
    Type: Grant
    Filed: December 15, 2016
    Date of Patent: November 12, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Aman Grover, Dhruv Arya, Ganesh Venkataraman, Kimberly McManus, Liang Zhang
  • Patent number: 10426049
    Abstract: An electrical enclosure includes a plurality of walls. The electrical enclosure also includes at least one compartment defined, at least in part, by the plurality of walls, and at least one ventilation opening formed in one of the plurality of walls. The at least one ventilation opening provides a flow path from the at least one compartment. At least one closure is provided on the one of the plurality of walls at the at least one ventilation opening. The at least one closure is responsive to a pressure wave to selectively close the at least one ventilation opening and re-open the at least one ventilation opening after the pressure wave has passed from the electrical enclosure.
    Type: Grant
    Filed: March 30, 2015
    Date of Patent: September 24, 2019
    Assignee: ABB Schweiz AG
    Inventors: Erik Ryan Khzouz, Kevin Joseph Audibert, Michael Paul Lafond, Ganesh Venkataraman
  • 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: 20190197012
    Abstract: Methods and systems are disclosed herein for accurately training a machine learning model with a reduced training data set. A large number of data records may be parsed. Each record may be reduced to a set of symbols representing the composition of each record. A user may assign a classification to each symbol within each record. Records with identical arrangements and classifications of symbols may be grouped together, and a representative sample of data records from each group may be fed into the model as the reduced training data set.
    Type: Application
    Filed: December 26, 2017
    Publication date: June 27, 2019
    Inventors: Sankar Ardhanari, Sai Rahul Reddy Pulikunta, Sashikumar Venkataraman, Abubakkar Siddiq, Ganesh Ramamoorthy
  • 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
  • Patent number: 10296849
    Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size. Items are distributed based on similarities identified with matrix factorization. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.
    Type: Grant
    Filed: February 15, 2017
    Date of Patent: May 21, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Yi Li, Victor Chen, Krishnaram Kenthapadi, Ganesh Venkataraman
  • Patent number: 10262299
    Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size, items are distributed based on similarities identified with matrix factorization. A matrix used in the matrix factorization is customized based on the recommendation type. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.
    Type: Grant
    Filed: February 15, 2017
    Date of Patent: April 16, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Yi Li, Victor Chen, Krishnaram Kenthapadi, Ganesh Venkataraman
  • 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: 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
  • Publication number: 20180336241
    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: May 19, 2017
    Publication date: November 22, 2018
    Inventors: Yongwoo Noh, Dhruv Arya, Ganesh Venkataraman, Aman Grover
  • Patent number: 10055457
    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: Grant
    Filed: August 30, 2016
    Date of Patent: August 21, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Benjamin Hoan Le, Dhruv Arya, Ganesh Venkataraman, Shakti Dhirendraji Sinha
  • Publication number: 20180232700
    Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size, items are distributed based on similarities identified with matrix factorization. A matrix used in the matrix factorization is customized based on the recommendation type. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.
    Type: Application
    Filed: February 15, 2017
    Publication date: August 16, 2018
    Inventors: Yi Li, Victor Chen, Krishnaram Kenthapadi, Ganesh Venkataraman
  • Publication number: 20180232661
    Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size. Items are distributed based on similarities identified with matrix factorization. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.
    Type: Application
    Filed: February 15, 2017
    Publication date: August 16, 2018
    Inventors: Yi Li, Victor Chen, Krishnaram Kenthapadi, Ganesh Venkataraman
  • Publication number: 20180232375
    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 he used to retrieve results instead of applying the computationally expensive ranking algorithm.
    Type: Application
    Filed: February 13, 2017
    Publication date: August 16, 2018
    Inventors: Ganesh Venkataraman, Dhruv Arya, Aman Grover, Liang Zhang
  • Patent number: 10042939
    Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which provide for a personalized expertise searching. When a user of the social networking service enters a search query, the system determines if the user is searching for members who possess a particular skill. If the user is searching for members who possess a particular skill, the search results are post-processed by personalizing the search results using one or more machine-learning models which utilize one or more observed features about the user that enters the query, the skills of the members of the social networking service, and the query itself. In some examples, the system may utilize multiple machine-learning models in multiple passes to fine tune the relevance of the search results and to ensure that the post-processing returns search results in a timely manner.
    Type: Grant
    Filed: October 31, 2014
    Date of Patent: August 7, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Shakti Dhirendraji Sinha, Viet-Ha Thuc, Ganesh Venkataraman, Mario Sergio Rodriguez
  • Publication number: 20180173802
    Abstract: Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method identifies job titles of members in a social network and performs, utilizing a machine-learning program, semantic analysis of the job titles to identify similarity coefficients among the job titles. The machine-learning program utilizes social network data to identify the similarity coefficients. Further, the method includes an operation for receiving a job search query, from a first member, including a query job title, and for expanding the job search query with job titles that are similar to the query job title. The method further includes operations for executing the expanded job search query to generate a plurality of job results, and for causing presentation on a display of one or more of the top job results.
    Type: Application
    Filed: December 15, 2016
    Publication date: June 21, 2018
    Inventors: Aman Grover, Dhruv Arya, Ganesh Venkataraman, Kimberly McManus, Liang Zhang
  • Publication number: 20180173803
    Abstract: Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method accesses, by a social networking server, a plurality of job applications, with each job application being submitted by a member for a job in a company, the member and the job having a respective industry from a plurality of industries. Semantic analysis of the job applications is performed by a machine-learning program to identify similarity coefficients among the plurality of industries. A job search query is received from a first member, the job search query including a query industry, and the job search query is expanded with industries that are similar to the query industry. The social networking server executes the expanded job search query to generate a plurality of job results. Presentation is provided on a display of one or more of the top job results.
    Type: Application
    Filed: December 15, 2016
    Publication date: June 21, 2018
    Inventors: Aman Grover, Dhruv Arya, Ganesh Venkataraman, Kimberly McManus, Liang Zhang
  • Publication number: 20180113943
    Abstract: This disclosure relates to systems and methods for searching names using name clusters. A method includes receiving names, generating a plurality of phonetic cluster identifiers, forming a plurality of name clusters by grouping the names having an equivalent cluster id, removing names from the respective name clusters that differ from a root name by more than either a particular spelling of a phonetic sound or a specific member's reformulation according to a reformulation dictionary, and suggesting one or more names by generating a phonetic cluster id for the received name using the database of phonetic associations and returning names found in the name cluster that matches the phonetic cluster id.
    Type: Application
    Filed: October 20, 2016
    Publication date: April 26, 2018
    Inventors: Lin Guo, Abhimanyu Lad, Ganesh Venkataraman