Patents by Inventor David Ferrucci
David Ferrucci 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: 20210020301Abstract: A meta model may be provided as a global structure providing flexible or customizable options for a specific ontology designed by a system operator. A meta model may include generic structures, such as attributes, attribute categories, and attribute properties. A system operator may configure a set of specific attributes, attribute categories, and synthesis rules within the meta model to define a desired ontology, customizing the system to a specific purpose. A system can receive assertions about points of interest known to the system, and store information about attributes of points of interest based on the specified ontology.Type: ApplicationFiled: July 15, 2020Publication date: January 21, 2021Inventors: Ray DALIO, David FERRUCCI, Vincent L. MARSHALL, Steven ABRAMS
-
Patent number: 10823265Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query; conducting a search to identify candidate answers to the input query, and producing a plurality of scores for each of the candidate answers. For each of the candidate answers, one, of a plurality of candidate ranking functions, is selected. This selected ranking function is applied to the each of the candidate answers to determine a ranking for the candidate answer based on the scores for that candidate answer. One or more of the candidate answers is selected, based on the rankings for the candidate answers, as one or more answers to the input query. In an embodiment, the ranking function selection is performed using information about the question. In an embodiment, the ranking function selection is performed using information about each answer.Type: GrantFiled: May 17, 2018Date of Patent: November 3, 2020Assignee: International Business Machines CorporationInventors: Eric W. Brown, David Ferrucci, James W. Murdock, IV
-
Patent number: 10657205Abstract: 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: GrantFiled: March 20, 2017Date of Patent: May 19, 2020Assignee: ELEMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Patent number: 10650099Abstract: 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: GrantFiled: March 20, 2017Date of Patent: May 12, 2020Assignee: ELMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Patent number: 10628523Abstract: 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: GrantFiled: March 20, 2017Date of Patent: April 21, 2020Assignee: ELEMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Patent number: 10621880Abstract: 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: GrantFiled: September 11, 2012Date of Patent: April 14, 2020Assignee: International Business Machines CorporationInventors: Branimir K. Boguraev, David W. Buchanan, Jennifer Chu-Carroll, David A. Ferrucci, Aditya A. Kalyanpur, James W. Murdock, IV, Siddharth A. Patwardhan
-
Patent number: 10621285Abstract: 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: GrantFiled: March 20, 2017Date of Patent: April 14, 2020Assignee: ELEMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Patent number: 10614166Abstract: 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: GrantFiled: March 20, 2017Date of Patent: April 7, 2020Assignee: ELEMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Patent number: 10614725Abstract: 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: GrantFiled: September 11, 2012Date of Patent: April 7, 2020Assignee: International Business Machines CorporationInventors: Branimir K. Boguraev, David W. Buchanan, Jennifer Chu-Carroll, David A. Ferrucci, Aditya A. Kalyanpur, James W. Murdock, IV, Siddharth A. Patwardhan
-
Patent number: 10614165Abstract: 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: GrantFiled: March 20, 2017Date of Patent: April 7, 2020Assignee: ELEMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Patent number: 10606952Abstract: 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: GrantFiled: June 24, 2016Date of Patent: March 31, 2020Assignee: ELEMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Patent number: 10599778Abstract: 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: GrantFiled: March 20, 2017Date of Patent: March 24, 2020Assignee: ELEMENTAL COGNITION LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Publication number: 20200089677Abstract: A method, computer system, and computer program product for decision support is provided. The present invention may include receiving a problem case information and generating a query based on the problem case information. The present invention may also include generating a plurality of answers for the query using the question-answering module. The present invention may also include calculating numerical values for multiple evidence dimensions from evidence sources for each of the answers using the question-answering module and may further include calculating a corresponding confidence value for each of the answers based on the numerical value of each evidence dimension using the question-answering module. The present invention may also include outputting the generated answers, the corresponding confidence values, and the numerical values of each evidence dimension for one or more selected answers using the input/output module.Type: ApplicationFiled: November 25, 2019Publication date: March 19, 2020Inventors: Sugato Bagchi, David A. Ferrucci, Anthony T. Levas, Erik T. Mueller
-
Publication number: 20200034422Abstract: 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: ApplicationFiled: March 20, 2017Publication date: January 30, 2020Applicant: Elemental Cognition LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Publication number: 20200034423Abstract: 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: ApplicationFiled: March 20, 2017Publication date: January 30, 2020Applicant: Elemental Cognition LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Publication number: 20200034421Abstract: 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: ApplicationFiled: March 20, 2017Publication date: January 30, 2020Applicant: Elemental Cognition LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Publication number: 20200034427Abstract: 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: ApplicationFiled: March 20, 2017Publication date: January 30, 2020Applicant: Elemental Cognition LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Publication number: 20200034428Abstract: 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: ApplicationFiled: March 20, 2017Publication date: January 30, 2020Applicant: Elemental Cognition LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Publication number: 20200034424Abstract: 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: ApplicationFiled: March 20, 2017Publication date: January 30, 2020Applicant: Elemental Cognition LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu--Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang
-
Publication number: 20200034420Abstract: 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: ApplicationFiled: March 20, 2017Publication date: January 30, 2020Applicant: Elemental Cognition LLCInventors: David Ferrucci, Mike Barborak, David Buchanan, Greg Burnham, Jennifer Chu-Carroll, Aditya Kalyanpur, Adam Lally, Stefano Pacifico, Chang Wang