Patents by Inventor Aditya A Kalyanpur

Aditya A Kalyanpur 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: 10614165
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Grant
    Filed: March 20, 2017
    Date of Patent: April 7, 2020
    Assignee: ELEMENTAL COGNITION LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Patent number: 10614725
    Abstract: A method of generating secondary questions in a question-answer system. Missing information is identified from a corpus of data using a computerized device. The missing information comprises any information that improves confidence scores for candidate answers to a question. The computerized device automatically generates a plurality of hypotheses concerning the missing information. The computerized device automatically generates at least one secondary question based on each of the plurality of hypotheses. The hypotheses are ranked based on relative utility to determine an order in which the computerized device outputs the at least one secondary question to external sources to obtain responses.
    Type: Grant
    Filed: September 11, 2012
    Date of Patent: April 7, 2020
    Assignee: International Business Machines Corporation
    Inventors: Branimir K. Boguraev, David W. Buchanan, Jennifer Chu-Carroll, David A. Ferrucci, Aditya A. Kalyanpur, James W. Murdock, IV, Siddharth A. Patwardhan
  • Patent number: 10614166
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Grant
    Filed: March 20, 2017
    Date of Patent: April 7, 2020
    Assignee: ELEMENTAL COGNITION LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Patent number: 10606952
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Grant
    Filed: June 24, 2016
    Date of Patent: March 31, 2020
    Assignee: ELEMENTAL COGNITION LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Patent number: 10599778
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Grant
    Filed: March 20, 2017
    Date of Patent: March 24, 2020
    Assignee: ELEMENTAL COGNITION LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Publication number: 20200034423
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Application
    Filed: March 20, 2017
    Publication date: January 30, 2020
    Applicant: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Publication number: 20200034428
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Application
    Filed: March 20, 2017
    Publication date: January 30, 2020
    Applicant: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Publication number: 20200034420
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Application
    Filed: March 20, 2017
    Publication date: January 30, 2020
    Applicant: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Publication number: 20200034427
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Application
    Filed: March 20, 2017
    Publication date: January 30, 2020
    Applicant: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Publication number: 20200034422
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Application
    Filed: March 20, 2017
    Publication date: January 30, 2020
    Applicant: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Publication number: 20200034421
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Application
    Filed: March 20, 2017
    Publication date: January 30, 2020
    Applicant: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Publication number: 20200034424
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Application
    Filed: March 20, 2017
    Publication date: January 30, 2020
    Applicant: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Patent number: 10496754
    Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
    Type: Grant
    Filed: March 20, 2017
    Date of Patent: December 3, 2019
    Assignee: Elemental Cognition LLC
    Inventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
  • Patent number: 10346751
    Abstract: According to an aspect, a heterogeneous graph in a data store is accessed. The heterogeneous graph includes a plurality of nodes having a plurality of node types. The nodes are connected by edges having a plurality of relation types. One or more intermediary graphs are created based on the heterogeneous graph. The intermediary graphs include intermediary nodes that are the relation types of the edges of the heterogeneous graph and include intermediary links between the intermediary nodes based on shared instances of the nodes between relation types in the heterogeneous graph. The intermediary graphs are traversed to find sets of relations based on intermediary links according to a template. An inference rule is extracted from the heterogeneous graph based on finding sets of relations in the intermediary graphs. The inference rule defines an inferred relation type between at least two of the nodes of the heterogeneous graph.
    Type: Grant
    Filed: September 15, 2014
    Date of Patent: July 9, 2019
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Apoorv Agarwal, Kenneth J. Barker, Jennifer Chu-Carroll, Aditya A. Kalyanpur, Christopher A. Welty, Wlodek W. Zadrozny
  • Patent number: 10242310
    Abstract: A system and method for automatically mapping LATs and candidate answers to multiple taxonomies without a need to merge these taxonomies. The method includes using a syntactic analysis of a corpus to extract all type instances of the LAT. The extracted instances are then mapped to a given taxonomy and clustered in a set of supertypes. Each supertype receives a score based on the coverage of LAT instances in the corpus. The method includes mapping the candidate answer to the same taxonomy to determine if the candidate answer is an instance of a significant supertype. Then the score of a candidate answer is obtained by aggregating or taking a maximum of the score of the matched significant supertypes. This score evaluates the type match between the LAT and candidate answer for a taxonomy. Multiple taxonomies can be used to increase the chance of LAT and candidate answer mapping.
    Type: Grant
    Filed: January 11, 2017
    Date of Patent: March 26, 2019
    Assignee: International Business Machines Corporation
    Inventors: Sugato Bagchi, Mihaela A. Bornea, James J. Fan, Aditya A. Kalyanpur, Christopher Welty
  • Patent number: 10168855
    Abstract: A method for automatic detection of user preferences for alternate user interface model includes operating a digital device with an active user interface model and receiving one or more input signals from a user of the digital device. The method includes comparing the one or more input signals with one or more latent user interface models and determining if one of the latent user interface models has a higher likelihood given the one or more input signals than the active user interface models. The method also includes responsively substituting the latent user interface with the highest likelihood given the one or more input signals for the active user interface model.
    Type: Grant
    Filed: April 21, 2016
    Date of Patent: January 1, 2019
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Aaron K. Baughman, David W. Buchanan, Robert G. Farrell, Aditya A. Kalyanpur
  • Patent number: 10108904
    Abstract: In a method of answering questions, a question is received, a question LAT is determined, and a candidate answer to the question is identified. Preliminary types for the candidate answer are determined using first components to produce the preliminary types. Each of the first components produces a preliminary type using different methods. A first type-score representing a degree of match between the preliminary type and the question LAT is produced. Each preliminary type and each first type-score is evaluated using second components. Each of the second components produces a second score based on a combination of the first type-score and a measure of degree that the preliminary type matches the question LAT. The second components use different methods to produce the second score. A final score representing a degree of confidence that the candidate answer matches the question LAT is calculated based on the second score.
    Type: Grant
    Filed: December 4, 2015
    Date of Patent: October 23, 2018
    Assignee: International Business Machines Corporation
    Inventors: Sugato Bagchi, James J. Fan, David A. Ferrucci, Aditya A. Kalyanpur, James W. Murdock, IV, Christopher A. Welty
  • Patent number: 9946764
    Abstract: According to an aspect, a processing system of a question answering computer system determines a first set of relations between one or more pairs of terms in a question. The processing system also determines a second set of relations between one or more pairs of terms in a candidate passage including a candidate answer to the question. The processing system matches the first set of relations to the second set of relations. A plurality of scores is determined by the processing system based on the matching. The processing system aggregates the scores to produce an answer score indicative of a level of support that the candidate answer correctly answers the question.
    Type: Grant
    Filed: March 6, 2015
    Date of Patent: April 17, 2018
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Michael A. Barborak, James J. Fan, Michael R. Glass, Aditya A. Kalyanpur, Adam P. Lally, James W. Murdock, IV, Benjamin P. Segal
  • Patent number: 9946763
    Abstract: According to an aspect, a processing system of a question answering computer system determines a first set of relations between one or more pairs of terms in a question. The processing system also determines a second set of relations between one or more pairs of terms in a candidate passage including a candidate answer to the question. The processing system matches the first set of relations to the second set of relations. A plurality of scores is determined by the processing system based on the matching. The processing system aggregates the scores to produce an answer score indicative of a level of support that the candidate answer correctly answers the question.
    Type: Grant
    Filed: November 5, 2014
    Date of Patent: April 17, 2018
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Michael A. Barborak, James J. Fan, Michael R. Glass, Aditya A. Kalyanpur, Adam P. Lally, James W. Murdock, IV, Benjamin P. Segal
  • Patent number: 9910844
    Abstract: According to an aspect, a query and a passage are parsed by a language parser to detect noun-centric phrases and verb-centric phrases in the query and the passage. Entities, including at least one untyped entity, are identified based on the noun-centric phrases and relations are identified based on the verb-centric phrases. Entity pairs are created that include an entity identified in the query and an entity identified in the passage, each pair satisfies a matching criteria. Relation pairs are created that include a relation identified in the query and a relation identified in the passage, each pair satisfies a matching criteria. A passage score that indicates the likelihood that an answer to the query is contained in the passage is determined based on the entity pairs, the matching criteria satisfied by each entity pair, the elation pairs, and the matching criteria satisfied by each relation pair.
    Type: Grant
    Filed: March 11, 2015
    Date of Patent: March 6, 2018
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Aditya A. Kalyanpur, James W. Murdock, IV