Patents by Inventor Thomas C. Copeman
Thomas C. Copeman 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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Publication number: 20240078586Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: May 4, 2023Publication date: March 7, 2024Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. HUESKE, Thomas C. COPEMAN, Evan Favermann EISERT, Jana B. EGGERS, Raymond J. PLANTE, Michael D. HOULE
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Patent number: 11651412Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: GrantFiled: October 15, 2019Date of Patent: May 16, 2023Assignee: Nara Logics, Inc.Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman, Evan Favermann Eisert, Jana B. Eggers, Raymond J. Plante, Michael D. Houle
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Publication number: 20220207575Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: August 6, 2021Publication date: June 30, 2022Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. HUESKE, Thomas C. COPEMAN
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Patent number: 11151617Abstract: A recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: GrantFiled: April 15, 2015Date of Patent: October 19, 2021Assignee: Nara Logics, Inc.Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman
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Publication number: 20200184538Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: October 15, 2019Publication date: June 11, 2020Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. HUESKE, Thomas C. COPEMAN, Evan Favermann EISERT, Jana B. EGGERS, Raymond J. PLANTE, Michael D. HOULE
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Patent number: 10467677Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: GrantFiled: April 15, 2015Date of Patent: November 5, 2019Assignee: Nara Logics, Inc.Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman, Evan Favermann Eisert, Jana B. Eggers, Raymond J. Plante, Michael D. Houle
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Patent number: 10423880Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: GrantFiled: November 2, 2015Date of Patent: September 24, 2019Assignee: NARA LOGICS, INC.Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman
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Publication number: 20190286998Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: April 15, 2015Publication date: September 19, 2019Applicant: NARA LOGICS, INC.Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman, Evan Favermann Eisert, Jana B. Eggers, Raymond J. Plante, Michael D. Houle
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Publication number: 20170140262Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: January 30, 2017Publication date: May 18, 2017Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. HUESKE, Thomas C. COPEMAN
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Patent number: 9449336Abstract: In certain implementations, a system may receive attribute data corresponding to attributes of a plurality of users and to one or more venues for which the plurality of users has an affinity. A user personality matrix may be calculated for one or more of the plurality of users based on interrelational nodal link strengths between the one or more users and the venues. The user personality matrices may be merged to calculate a combined personality matrix representing a unified taste profile for the one or more users. A candidate list of venues having the highest link strength with the combined personality matrix may be determined. One or more recommended venues from the candidate list of venues that have the strongest links to the combined personality matrix may be determined, and recommendation data corresponding to the recommended venues may be output.Type: GrantFiled: March 13, 2015Date of Patent: September 20, 2016Assignee: NARA LOGICS, INC.Inventors: Nathan R. Wilson, Luyao Li, Emily A. Hueske, Eleanor C. Kenyon, Thomas C. Copeman
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Publication number: 20160055417Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: November 2, 2015Publication date: February 25, 2016Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. HUESKE, Thomas C. COPEMAN
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Patent number: 9208443Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: GrantFiled: November 10, 2014Date of Patent: December 8, 2015Assignee: NARA LOGICS, INC.Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman
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Publication number: 20150220835Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: April 15, 2015Publication date: August 6, 2015Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. Hueske, Thomas C. Copeman
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Publication number: 20150220836Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: April 15, 2015Publication date: August 6, 2015Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. Hueske, Thomas C. Copeman
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Publication number: 20150186951Abstract: In certain implementations, a system may receive attribute data corresponding to attributes of a plurality of users and to one or more venues for which the plurality of users has an affinity. A user personality matrix may be calculated for one or more of the plurality of users based on interrelational nodal link strengths between the one or more users and the venues. The user personality matrices may be merged to calculate a combined personality matrix representing a unified taste profile for the one or more users. A candidate list of venues having the highest link strength with the combined personality matrix may be determined. One or more recommended venues from the candidate list of venues that have the strongest links to the combined personality matrix may be determined, and recommendation data corresponding to the recommended venues may be output.Type: ApplicationFiled: March 13, 2015Publication date: July 2, 2015Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Luyao Li, Emily A. Hueske, Eleanor C. Kenyon, Thomas C. Copeman
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Patent number: 9009088Abstract: In certain implementations, a system may receive attribute data corresponding to attributes of a plurality of users and to one or more venues for which the plurality of users has an affinity. A user personality matrix may be calculated for one or more of the plurality of users based on interrelational nodal link strengths between the one or more users and the venues. The user personality matrices may be merged to calculate a combined personality matrix representing a unified taste profile for the one or more users. A candidate list of venues having the highest link strength with the combined personality matrix may be determined. One or more recommended venues from the candidate list of venues that have the strongest links to the combined personality matrix may be determined, and recommendation data corresponding to the recommended venues may be output.Type: GrantFiled: April 17, 2014Date of Patent: April 14, 2015Assignee: Nara Logics, Inc.Inventors: Nathan R. Wilson, Luyao Li, Emily A. Hueske, Eleanor C. Kenyon, Thomas C. Copeman
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Publication number: 20150066830Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: ApplicationFiled: November 10, 2014Publication date: March 5, 2015Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Emily A. Hueske, Thomas C. Copeman
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Patent number: 8909583Abstract: In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.Type: GrantFiled: May 1, 2014Date of Patent: December 9, 2014Assignee: Nara Logics, Inc.Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman
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Publication number: 20140280226Abstract: In certain implementations, a system may receive attribute data corresponding to attributes of a plurality of users and to one or more venues for which the plurality of users has an affinity. A user personality matrix may be calculated for one or more of the plurality of users based on interrelational nodal link strengths between the one or more users and the venues. The user personality matrices may be merged to calculate a combined personality matrix representing a unified taste profile for the one or more users. A candidate list of venues having the highest link strength with the combined personality matrix may be determined. One or more recommended venues from the candidate list of venues that have the strongest links to the combined personality matrix may be determined, and recommendation data corresponding to the recommended venues may be output.Type: ApplicationFiled: April 17, 2014Publication date: September 18, 2014Applicant: NARA LOGICS, INC.Inventors: Nathan R. WILSON, Luyao Li, Emily A. Hueske, Eleanor C. Kenyon, Thomas C. Copeman
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Publication number: 20140279196Abstract: In certain implementations, data is spatially segmented into a variety of grids having particular keyed location data. Items of interest located within the boundaries of each grid are identified and stored in association with the grid location information. Data with respect to venue attributes is encoded and stored in association with corresponding grid location data. The system will identify a grid location based on a recommendation request or based on the user location and will generate a list of items of interest in that location and neighboring locations. This information is filtered based on the particularities of the user request to form a final filter set. User attribute weights are then applied to the final filter set to determine an overall score for each item of interest. Items of interest are then recommended to the user based on their overall score.Type: ApplicationFiled: March 15, 2013Publication date: September 18, 2014Applicant: Nara Logics, Inc.Inventors: Nathan R. WILSON, Michael D. Houle, Joakim A. Sternberg, Thomas C. Copeman