Patents by Inventor Naren M. Chittar
Naren M. Chittar 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).
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Patent number: 11210304Abstract: As part of providing the services to users, an online system stores multiple records that are accessible by users of the online system. When a user provides a search query, the online system extracts morphological and dictionary features from the query. The online system provides the extracted features to a machine learning model as an input. The machine learning model outputs a score for each potential entity type that indicates a likelihood that the search query is for a record associated with the entity type. The output from the machine learning model is used by the online system to select one or more entity types that the user is likely searching for. The online system searches the stored records based on the search query but limits the searching to records associated with at least one of the selected entity types.Type: GrantFiled: March 11, 2020Date of Patent: December 28, 2021Assignee: salesforce.com, inc.Inventors: Naren M. Chittar, Jayesh Govindarajan, Edgar Gerardo Velasco, Anuprit Kale, Francisco Borges, Guillaume Kempf, Marc Brette
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Patent number: 11061955Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.Type: GrantFiled: December 27, 2018Date of Patent: July 13, 2021Assignee: salesforce.com, inc.Inventors: Zachary Alexander, Naren M. Chittar, Alampallam R. Ramachandran, Anuprit Kale, Tiffany McKenzie, Sitaram Asur, Jacob Nathaniel Huffman
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Patent number: 11061954Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.Type: GrantFiled: September 21, 2018Date of Patent: July 13, 2021Assignee: salesforce.com, inc.Inventors: Zachary Alexander, Naren M. Chittar, Alampallam R. Ramachandran, Anuprit Kale, Tiffany Deiandra McKenzie, Sitaram Asur, Jacob Nathaniel Huffman
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Publication number: 20200233874Abstract: As part of providing the services to users, an online system stores multiple records that are accessible by users of the online system. When a user provides a search query, the online system extracts morphological and dictionary features from the query. The online system provides the extracted features to a machine learning model as an input. The machine learning model outputs a score for each potential entity type that indicates a likelihood that the search query is for a record associated with the entity type. The output from the machine learning model is used by the online system to select one or more entity types that the user is likely searching for. The online system searches the stored records based on the search query but limits the searching to records associated with at least one of the selected entity types.Type: ApplicationFiled: March 11, 2020Publication date: July 23, 2020Inventors: Naren M. Chittar, Jayesh Govindarajan, Edgar Gerardo Velasco, Anuprit Kale, Francisco Borges, Guillaume Kempf, Marc Brette
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Patent number: 10628431Abstract: As part of providing the services to users, an online system stores multiple records that are accessible by users of the online system. When a user provides a search query, the online system extracts morphological and dictionary features from the query. The online system provides the extracted features to a machine learning model as an input. The machine learning model outputs a score for each potential entity type that indicates a likelihood that the search query is for a record associated with the entity type. The output from the machine learning model is used by the online system to select one or more entity types that the user is likely searching for. The online system searches the stored records based on the search query but limits the searching to records associated with at least one of the selected entity types.Type: GrantFiled: April 6, 2017Date of Patent: April 21, 2020Assignee: salesforce.com, inc.Inventors: Naren M. Chittar, Jayesh Govindarajan, Edgar Gerardo Velasco, Anuprit Kale, Francisco Borges, Guillaume Kempf, Marc Brette
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Patent number: 10614061Abstract: An online system stores objects that may be accessed by users. The online system also stores indexes of terms related to different entity types of objects. When a user provides a search query, the online system compares the search terms with terms stored in the indexes. Based on the comparisons, the online system determines term features for entity types associated with an index. The online system provides the term features as inputs to a machine learning model. The machine learning model outputs a score for each entity type indicating a likelihood that the search query is for an object associated with the entity type. The machine learning model output is used by the online system to select one or more entity types that the user is likely searching for. The online system offers objects of the likely entity types to the user as results of the search query.Type: GrantFiled: June 28, 2017Date of Patent: April 7, 2020Assignee: salesforce.com, inc.Inventors: Guillaume Kempf, Marc Brette, Naren M. Chittar, Anuprit Kale, Yasaman Mohsenin, Pranshu Sharma
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Publication number: 20200097563Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.Type: ApplicationFiled: September 21, 2018Publication date: March 26, 2020Inventors: Zachary Alexander, Naren M. Chittar, Alampallam R. Ramachandran, Anuprit Kale, Tiffany Deiandra McKenzie, Sitaram Asur, Jacob Nathaniel Huffman
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Publication number: 20200097496Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.Type: ApplicationFiled: December 27, 2018Publication date: March 26, 2020Inventors: Zachary Alexander, Naren M. Chittar, Alampallam R. Ramachandran, Anuprit Kale, Tiffany McKenzie, Sitaram Asur, Jacob Nathaniel Huffman
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Publication number: 20190005089Abstract: An online system stores objects that may be accessed by users. The online system also stores indexes of terms related to different entity types of objects. When a user provides a search query, the online system compares the search terms with terms stored in the indexes. Based on the comparisons, the online system determines term features for entity types associated with an index. The online system provides the term features as inputs to a machine learning model. The machine learning model outputs a score for each entity type indicating a likelihood that the search query is for an object associated with the entity type. The machine learning model output is used by the online system to select one or more entity types that the user is likely searching for. The online system offers objects of the likely entity types to the user as results of the search query.Type: ApplicationFiled: June 28, 2017Publication date: January 3, 2019Inventors: Guillaume Kempf, Marc Brette, Naren M. Chittar, Anuprit Kale, Yasaman Mohsenin, Pranshu Sharma
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Publication number: 20180293241Abstract: As part of providing the services to users, an online system stores multiple records that are accessible by users of the online system. When a user provides a search query, the online system extracts morphological and dictionary features from the query. The online system provides the extracted features to a machine learning model as an input. The machine learning model outputs a score for each potential entity type that indicates a likelihood that the search query is for a record associated with the entity type. The output from the machine learning model is used by the online system to select one or more entity types that the user is likely searching for. The online system searches the stored records based on the search query but limits the searching to records associated with at least one of the selected entity types.Type: ApplicationFiled: April 6, 2017Publication date: October 11, 2018Inventors: Naren M. Chittar, Jayesh Govindarajan, Edgar Gerardo Velasco, Anuprit Kale, Francisco Borges, Guillaume Kempf, Marc Brette