Patents by Inventor Richard B. Segal
Richard B. Segal 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: 11436487Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: GrantFiled: October 2, 2019Date of Patent: September 6, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Publication number: 20220092659Abstract: A method, system, and computer program product for representational learning of product formulas are provided. The method accesses a set of product formulas. Each product formula includes a set of ingredient tuples. A directed graph is generated from the set of product formulas. The directed graph including a node for each ingredient of the sets of ingredient tuples of the set of formulas. The method generates a weighted graph from the directed graph. The weighted graph has a weight assigned to each edge in the directed graph. The method generates an embedding model based on the directed graph. A set of embeddings is determined for the weighted graph where each node is represented with low-dimensional numerical vectors.Type: ApplicationFiled: September 24, 2020Publication date: March 24, 2022Inventors: Petar Ristoski, Richard T. Goodwin, Jing Fu, Richard B. Segal, Robin Lougee, Kimberly C. Lang, CHRISTIAN HARRIS, Tenzin Yeshi
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Patent number: 11200504Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.Type: GrantFiled: December 28, 2015Date of Patent: December 14, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yi-Min Chee, Ashish Jagmohan, Pamela N. Luna, Krishna C. Ratakonda, Richard B. Segal, Piyawadee Sukaviriya
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Patent number: 11195112Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.Type: GrantFiled: January 27, 2016Date of Patent: December 7, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yi-Min Chee, Ashish Jagmohan, Pamela N. Luna, Krishna C. Ratakonda, Richard B. Segal, Piyawadee Sukaviriya
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Patent number: 10657189Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: GrantFiled: August 18, 2016Date of Patent: May 19, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Patent number: 10642919Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: GrantFiled: August 18, 2016Date of Patent: May 5, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Patent number: 10579940Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: GrantFiled: August 18, 2016Date of Patent: March 3, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Publication number: 20200034741Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: ApplicationFiled: October 2, 2019Publication date: January 30, 2020Inventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Publication number: 20190197564Abstract: Embodiments of the present invention disclose a method, computer program product, and system for generating a special map of a plurality of products based on relationships between products. A query is received, from a user, via a user device, wherein the query includes an associated target product. A set of data associated with a plurality of products is received. An earth mover's distance value is calculating for at least one pair of products of the plurality of products. The earth mover's distance is communicated value to a user. A weight is received value based on a user input selection. The earth mover's distance value is modified based on the received weight. A flow vector is determining based on the modified earth mover's distance value of the at least one pair of products and each product of the at least one pair of products is mapped to a vector graph.Type: ApplicationFiled: December 22, 2017Publication date: June 27, 2019Inventors: Flavio du Pin Calmon, Aditya Vempaty, Ashish Jagmohan, Richard T. Goodwin, Richard B. Segal
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Publication number: 20190164039Abstract: A compositional artifact may be identified, and a set of logical coordinates within a composition model may be determined for the compositional artifact. The set of logical coordinates may be determined based on the components of the compositional artifact. Tolerance parameters may be used in conjunction with the set of logical coordinates to calculate a logical distance, and other artifacts in the composition model whose logical coordinates fall within the logical distance may be displayed to a user.Type: ApplicationFiled: November 30, 2017Publication date: May 30, 2019Inventors: Aditya Vempaty, Richard B. Segal, Ashish Jagmohan, Richard T. Goodwin, Flavio du Pin Calmon
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Publication number: 20180052857Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: ApplicationFiled: August 18, 2016Publication date: February 22, 2018Inventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Publication number: 20180052924Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: ApplicationFiled: August 18, 2016Publication date: February 22, 2018Inventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Publication number: 20180052849Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.Type: ApplicationFiled: August 18, 2016Publication date: February 22, 2018Inventors: Ashish Jagmohan, Elham Khabiri, Richard B. Segal, Roman Vaculin
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Patent number: 9760592Abstract: A service engagement map may be generated based on data collected associated with the service transition and delivery processes. The service engagement map may be refined iteratively by discovering additional data associated with the service transition and delivery processes and updating the service engagement map according to the additional data. Engagement metrics may be computed based on the service engagement map and presented. The service engagement map may also be presented visually.Type: GrantFiled: February 20, 2014Date of Patent: September 12, 2017Assignee: International Business Machines CorporationInventors: Pu Huang, Kaan K. Katircioglu, Ta-Hsin Li, Ying Li, Axel Martens, Richard B. Segal
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Publication number: 20170116518Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.Type: ApplicationFiled: December 28, 2015Publication date: April 27, 2017Inventors: Yi-Min Chee, Ashish Jagmohan, Pamela N. Luna, Krishna C. Ratakonda, Richard B. Segal, Piyawadee Sukaviriya
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Publication number: 20170116538Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.Type: ApplicationFiled: January 27, 2016Publication date: April 27, 2017Inventors: Yi-Min Chee, Ashish Jagmohan, Pamela N. Luna, Krishna C. Ratakonda, Richard B. Segal, Piyawadee Sukaviriya
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Patent number: 9355388Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.Type: GrantFiled: August 14, 2013Date of Patent: May 31, 2016Assignee: International Business Machines CorporationInventors: T. K. Balachandran, Pu Huang, Kaan K. Katircioglu, Ta-Hsin Li, Ying Li, Axel Martens, Rakesh Mohan, Krishna C. Ratakonda, Richard B. Segal, Lisa A. Smith
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Patent number: 9336516Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.Type: GrantFiled: September 11, 2013Date of Patent: May 10, 2016Assignee: International Business Machines CorporationInventors: T. K. Balachandran, Pu Huang, Kaan K. Katircioglu, Ta-Hsin Li, Ying Li, Axel Martens, Rakesh Mohan, Krishna C. Ratakonda, Richard B. Segal, Lisa A. Smith
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Publication number: 20150236934Abstract: A service engagement map may be generated based on data collected associated with the service transition and delivery processes. The service engagement map may be refined iteratively by discovering additional data associated with the service transition and delivery processes and updating the service engagement map according to the additional data. Engagement metrics may be computed based on the service engagement map and presented. The service engagement map may also be presented visually.Type: ApplicationFiled: February 20, 2014Publication date: August 20, 2015Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Pu Huang, Kaan K. Katircioglu, Ta-Hsin Li, Ying Li, Axel Martens, Richard B. Segal
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Patent number: 9047423Abstract: A method, system and computer program product for choosing actions in a state of a planning problem. The system simulates one or more sequences of actions, state transitions and rewards starting from the current state of the planning problem. During the simulation of performing a given action in a given state, a data record is maintained of observed contextual state information, and observed cumulative reward resulting from the action. The system performs a regression fit on the data records, enabling estimation of expected reward as a function of contextual state. The estimations of expected rewards are used to guide the choice of actions during the simulations. Upon completion of all simulations, the top-level action which obtained highest mean reward during the simulations is recommended to be executed in the current state of the planning problem.Type: GrantFiled: January 12, 2012Date of Patent: June 2, 2015Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Gerald J. Tesauro, Alina Beygelzimer, Richard B. Segal, Mark N. Wegman