Patents by Inventor ChitraBharathi Ganapathy
ChitraBharathi Ganapathy 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: 11880435Abstract: A document is received. The document is analyzed to discover text and structures of content included in the document. A result of the analysis is used to determine intermediate text representations of segments of the content included in the document, wherein at least one of the intermediate text representations includes an added text encoding the discovered structure of the corresponding content segment within a structural layout of the document. The intermediate text representations are used as an input to a machine learning model to extract information of interest in the document. One or more structured records of the extracted information of interest are created.Type: GrantFiled: February 4, 2021Date of Patent: January 23, 2024Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, ChitraBharathi Ganapathy, Tao Hong, Rohit Lobo
-
Publication number: 20230153342Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.Type: ApplicationFiled: January 17, 2023Publication date: May 18, 2023Inventors: Baskar Jayaraman, ChitraBharathi Ganapathy, Dinesh Kumar Kishorkumar Surapaneni, Tao Fang, Jun Wang
-
Patent number: 11586659Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.Type: GrantFiled: June 7, 2019Date of Patent: February 21, 2023Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, ChitraBharathi Ganapathy, Dinesh Kumar Kishorkumar Surapaneni, Tao Feng, Jun Wang
-
Publication number: 20220382792Abstract: Systems and methods involving data structures for efficient management of paragraph vectors for textual searching are described. A database may contain records, each associated with an identifier and including a text string and timestamp. A look-up table may contain entries for text strings from the records, each entry associating: a paragraph vector for a respective unique text string, a hash of the respective unique text string, and a set of identifiers of records containing the respective unique text string. A server may receive from a client device an input string, compute a hash of the input string, and determine matching table entries, each containing a hash identical to that of the input string, or a paragraph vector similar to one calculated for the input string. A prioritized list of identifiers from the matching entries may be determined based on timestamps, and the prioritized list may be returned to the client.Type: ApplicationFiled: August 10, 2022Publication date: December 1, 2022Inventors: Baskar Jayaraman, Chitrabharathi Ganapathy, Aniruddha Madhusudan Thakur, Jun Wang
-
Patent number: 11423069Abstract: Systems and methods involving data structures for efficient management of paragraph vectors for textual searching are described. A database may contain records, each associated with an identifier and including a text string and timestamp. A look-up table may contain entries for text strings from the records, each entry associating: a paragraph vector for a respective unique text string, a hash of the respective unique text string, and a set of identifiers of records containing the respective unique text string. A server may receive from a client device an input string, compute a hash of the input string, and determine matching table entries, each containing a hash identical to that of the input string, or a paragraph vector similar to one calculated for the input string. A prioritized list of identifiers from the matching entries may be determined based on timestamps, and the prioritized list may be returned to the client.Type: GrantFiled: September 19, 2018Date of Patent: August 23, 2022Assignee: ServiceNow, inc.Inventors: Baskar Jayaraman, Chitrabharathi Ganapathy, Aniruddha Madhusudan Thakur, Jun Wang
-
Patent number: 11403332Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.Type: GrantFiled: September 30, 2020Date of Patent: August 2, 2022Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, Chitrabharathi Ganapathy, Kannan Govindarajan, Shiva Shankar Ramanna
-
Patent number: 11238230Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space. Word vectors can be used for sentiment analysis, comparison of the topic or content of sentences, paragraphs, or other passages of text or other natural language processing tasks. However, the generation of word vectors can be computationally expensive. Accordingly, when a set of word vectors is needed for a particular corpus of text, a set of word vectors previously generated from a corpus of text that is sufficiently similar to the particular corpus of text, with respect to some criteria, may be re-used for the particular corpus of text. Such similarity could include the two corpora of text containing the same or similar sets of words or containing incident reports or other time-coded sets of text from overlapping or otherwise similar periods of time.Type: GrantFiled: September 19, 2018Date of Patent: February 1, 2022Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Kannan Govindarajan, Aniruddha Madhusudan Thakur, Jun Wang, Chitrabharathi Ganapathy
-
Publication number: 20210266345Abstract: An indication of a message that was identified by a recipient user of the message as being associated with a cybersecurity attack is received. Properties of the message are extracted. The extracted properties of the message are provided as inputs to a machine learning model to determine a likelihood the message is associated with a true cybersecurity attack. The determined likelihood is utilized to handle a security response associated with the message.Type: ApplicationFiled: February 26, 2020Publication date: August 26, 2021Inventors: Xuchang Chen, Patrice Tollenaere, Deepakeswaran Kolingivadi, ChitraBharathi Ganapathy
-
Publication number: 20210256097Abstract: A document is received. The document is analyzed to discover text and structures of content included in the document. A result of the analysis is used to determine intermediate text representations of segments of the content included in the document, wherein at least one of the intermediate text representations includes an added text encoding the discovered structure of the corresponding content segment within a structural layout of the document. The intermediate text representations are used as an input to a machine learning model to extract information of interest in the document. One or more structured records of the extracted information of interest are created.Type: ApplicationFiled: February 4, 2021Publication date: August 19, 2021Inventors: Baskar Jayaraman, ChitraBharathi Ganapathy, Tao Hong, Rohit Lobo
-
Patent number: 10970491Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.Type: GrantFiled: March 4, 2020Date of Patent: April 6, 2021Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, ChitraBharathi Ganapathy, Shiva Shankar Ramanna
-
Publication number: 20210011936Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.Type: ApplicationFiled: September 30, 2020Publication date: January 14, 2021Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, Chitrabharathi Ganapathy, Kannan Govindarajan, Shiva Shankar Ramanna
-
Publication number: 20200349183Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.Type: ApplicationFiled: June 7, 2019Publication date: November 5, 2020Inventors: Baskar Jayaraman, ChitraBharathi Ganapathy, Dinesh Kumar Kishorkumar Surapaneni, Tao Feng, Jun Wang
-
Patent number: 10795923Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.Type: GrantFiled: October 8, 2019Date of Patent: October 6, 2020Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, Chitrabharathi Ganapathy, Kannan Govindarajan, Shiva Shankar Ramanna
-
Publication number: 20200272792Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.Type: ApplicationFiled: March 4, 2020Publication date: August 27, 2020Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, ChitraBharathi Ganapathy, Shiva Shankar Ramanna
-
Publication number: 20200104313Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.Type: ApplicationFiled: October 8, 2019Publication date: April 2, 2020Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, Chitrabharathi Ganapathy, Kannan Govindarajan, Shiva Shankar Ramanna
-
Patent number: 10606955Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.Type: GrantFiled: March 15, 2018Date of Patent: March 31, 2020Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, ChitraBharathi Ganapathy, Shiva Shankar Ramanna
-
Publication number: 20200089781Abstract: Systems and methods involving data structures for efficient management of paragraph vectors for textual searching are described. A database may contain records, each associated with an identifier and including a text string and timestamp. A look-up table may contain entries for text strings from the records, each entry associating: a paragraph vector for a respective unique text string, a hash of the respective unique text string, and a set of identifiers of records containing the respective unique text string. A server may receive from a client device an input string, compute a hash of the input string, and determine matching table entries, each containing a hash identical to that of the input string, or a paragraph vector similar to one calculated for the input string. A prioritized list of identifiers from the matching entries may be determined based on timestamps, and the prioritized list may be returned to the client.Type: ApplicationFiled: September 19, 2018Publication date: March 19, 2020Inventors: Baskar Jayaraman, Chitrabharathi Ganapathy, Aniruddha Madhusudan Thakur, Jun Wang
-
Publication number: 20200089765Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space. Word vectors can be used for sentiment analysis, comparison of the topic or content of sentences, paragraphs, or other passages of text or other natural language processing tasks. However, the generation of word vectors can be computationally expensive. Accordingly, when a set of word vectors is needed for a particular corpus of text, a set of word vectors previously generated from a corpus of text that is sufficiently similar to the particular corpus of text, with respect to some criteria, may be re-used for the particular corpus of text. Such similarity could include the two corpora of text containing the same or similar sets of words or containing incident reports or other time-coded sets of text from overlapping or otherwise similar periods of time.Type: ApplicationFiled: September 19, 2018Publication date: March 19, 2020Inventors: Baskar Jayaraman, Kannan Govindarajan, Aniruddha Madhusudan Thakur, Jun Wang, Chitrabharathi Ganapathy
-
Patent number: 10459962Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.Type: GrantFiled: September 19, 2018Date of Patent: October 29, 2019Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, Chitrabharathi Ganapathy, Kannan Govindarajan, Shiva Shankar Ramanna
-
Publication number: 20190286700Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.Type: ApplicationFiled: March 15, 2018Publication date: September 19, 2019Inventors: Baskar Jayaraman, Aniruddha Madhusudan Thakur, ChitraBharathi Ganapathy, Shiva Shankar Ramanna