Patents by Inventor KARTIKEYA VATS
KARTIKEYA VATS 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: 20230420146Abstract: In an approach for automatically identifying one or more updates in a Scientific Drug Label (SL) relevant to a patient and incorporating the one or more updates into a Patient Drug Label (PL), a processor receives a pair of documents, wherein the pair of documents include the SL and the PL. A processor converts a complex medical language of the SL into a simplified patient friendly language. A processor identifies one or more words, one or more phrases, or one or more sentences that have been modified, inserted, or deleted. A processor searches for a location in the PL that closely maps to the one or more words, the one or more phrases, or the one or more sentences to the SL. A processor incorporates the one or more words, the one or more phrases, or the one or more sentences in a mapped location of the PL.Type: ApplicationFiled: June 28, 2022Publication date: December 28, 2023Inventors: Saigeetha Aswathnarayanan Jegannathan, Sridhar Jonnala, V Datta Kamesam Jami, Chinthalapudi Venkata Sai Vishnu Vardhan, Naman Mathur, Shivangi Tak, Kartikeya Vats
-
Patent number: 11849342Abstract: A method and related system determine a localness measure that represents accessibility of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving focus node (FN) information related to an FN of a graph representing the signed network, receiving neighbor node (NN) information related to a plurality of NNs in the signed network, and determining the localness measure based on the NN information and the FN information. The method and system also determine a harmony measure that represents importance of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving source node (SN) information related to an SN of a graph representing the signed network, receiving destination node (DN) information related to a DN in the signed network, and determining the harmony measure based on the SN information and the DN information.Type: GrantFiled: December 7, 2020Date of Patent: December 19, 2023Assignee: International Business Machines CorporationInventors: Mandar Mutalikdesai, Ashish Rao, Kartikeya Vats, Sheetal Srivastava, Sandipto Neogi, Ojasvi Bhalerao
-
Patent number: 11532025Abstract: A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.Type: GrantFiled: August 12, 2019Date of Patent: December 20, 2022Assignee: International Business Machines CorporationInventors: Sujoy Kumar Roy Chowdhury, Tanveer Akhter Khan, Ria Chakraborty, Yogesh Narasimha, Kartikeya Vats, Khyati Baradia
-
Patent number: 11526707Abstract: One or more computer processors creating a plurality of k-hop neighborhood contextual subgraphs utilizing extracted labelled nodes from an input graph; compute an eigenvector centrality score for each node contained in each created subgraph in the plurality of k-hop neighborhood contextual subgraphs; propagate a label for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs leveraging an aggregated mathematical decay function, preserving a topical context of the label; calculate an attributable prestige vector for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs based on the propagated label and the computed eigenvector centrality score associated with each node in each subgraph in the plurality of k-hop neighborhood contextual subgraph; and unsupervised predict a subsequent label for one or more subsequent nodes, subgraphs, or graphs utilizing the calculated attributable prestige vectors for each node in each subgraph.Type: GrantFiled: July 2, 2020Date of Patent: December 13, 2022Assignee: International Business Machines CorporationInventors: Mandar Mutalikdesai, Sheetal Srivastava, Kartikeya Vats, Debasish Kanhar
-
Patent number: 11366967Abstract: Systems and methods are described for generating learning roadmaps from unstructured information. The systems and methods may provide for extracting a plurality of named entities from one or more corpora of information, constructing a graph based on the named entities, inducing a subgraph from the graph based on a target named entity, wherein the subgraph includes a subset of the named entities, ordering the subset of the named entities based on the subgraph, and generating a learning roadmap for the target named entity based on the ordering.Type: GrantFiled: July 24, 2019Date of Patent: June 21, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Mandar Mutalikdesai, Rahul Reddy Nimmakayala, Ashish Rao, Sheetal Srivastava, Kartikeya Vats
-
Publication number: 20220182857Abstract: A method and related system determine a localness measure that represents accessibility of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving focus node (FN) information related to an FN of a graph representing the signed network, receiving neighbor node (NN) information related to a plurality of NNs in the signed network, and determining the localness measure based on the NN information and the FN information. The method and system also determine a harmony measure that represents importance of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving source node (SN) information related to an SN of a graph representing the signed network, receiving destination node (DN) information related to a DN in the signed network, and determining the harmony measure based on the SN information and the DN information.Type: ApplicationFiled: December 7, 2020Publication date: June 9, 2022Inventors: Mandar Mutalikdesai, Ashish Rao, Kartikeya Vats, Sheetal Srivastava, Sandipto Neogi, Ojasvi Bhalerao
-
Patent number: 11328124Abstract: Systems and methods are described for generating learning roadmaps from unstructured information. The systems and methods may provide for extracting a plurality of named entities from one or more corpora of information, constructing a graph based on the named entities, inducing a subgraph from the graph based on a target named entity, wherein the subgraph includes a subset of the named entities, ordering the subset of the named entities based on the subgraph, and generating a learning roadmap for the target named entity based on the ordering.Type: GrantFiled: July 24, 2019Date of Patent: May 10, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Mandar Mutalikdesai, Rahul Reddy Nimmakayala, Ashish Rao, Sheetal Srivastava, Kartikeya Vats
-
Publication number: 20220004826Abstract: One or more computer processors creating a plurality of k-hop neighborhood contextual subgraphs utilizing extracted labelled nodes from an input graph; compute an eigenvector centrality score for each node contained in each created subgraph in the plurality of k-hop neighborhood contextual subgraphs; propagate a label for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs leveraging an aggregated mathematical decay function, preserving a topical context of the label; calculate an attributable prestige vector for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs based on the propagated label and the computed eigenvector centrality score associated with each node in each subgraph in the plurality of k-hop neighborhood contextual subgraph; and unsupervised predict a subsequent label for one or more subsequent nodes, subgraphs, or graphs utilizing the calculated attributable prestige vectors for each node in each subgraph.Type: ApplicationFiled: July 2, 2020Publication date: January 6, 2022Inventors: Mandar Mutalikdesai, Sheetal Srivastava, Kartikeya Vats, Debasish Kanhar
-
Publication number: 20210304043Abstract: An approach is provided for evaluating a recommender system. A user's purchase date of an item and attributes of the item are extracted from a test set. Drop probabilities are assigned to the attributes. Using bootstrapping with aggregation, queries for the user and the item are generated by omitting one or more attributes from each of the queries according to the drop probabilities. Data that became available to the recommender system after the purchase date is identified. Without using the identified data and using data that became available to the recommender system before the purchase date, ranked item recommendation sets for the queries are generated. Similarity at rank K (SIM@K) values for the recommendation sets are calculated. Average SIM@K values are calculated over multiple users specified in the test set. Based on the average SIM@K values, the performance of the recommender system is evaluated.Type: ApplicationFiled: March 26, 2020Publication date: September 30, 2021Inventors: Sujoy Kumar Roy Chowdhury, Ria Chakraborty, Kartikeya Vats, Tanveer Akhter Khan, Khyati Baradia, Yogesh Narasimha
-
Publication number: 20210049665Abstract: A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.Type: ApplicationFiled: August 12, 2019Publication date: February 18, 2021Inventors: Sujoy Kumar Roy Chowdhury, Tanveer Akhter Khan, Ria Chakraborty, Yogesh Narasimha, Kartikeya Vats, Khyati Baradia
-
Publication number: 20210026921Abstract: Systems and methods are described for generating learning roadmaps from unstructured information. The systems and methods may provide for extracting a plurality of named entities from one or more corpora of information, constructing a graph based on the named entities, inducing a subgraph from the graph based on a target named entity, wherein the subgraph includes a subset of the named entities, ordering the subset of the named entities based on the subgraph, and generating a learning roadmap for the target named entity based on the ordering.Type: ApplicationFiled: July 24, 2019Publication date: January 28, 2021Inventors: MANDAR MUTALIKDESAI, RAHUL REDDY NIMMAKAYALA, ASHISH RAO, SHEETAL SRIVASTAVA, KARTIKEYA VATS