Patents by Inventor Timothy Seegan
Timothy Seegan 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: 11893065Abstract: Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.Type: GrantFiled: June 10, 2020Date of Patent: February 6, 2024Assignee: AON RISK SERVICES, INC. OF MARYLANDInventors: Samuel Cameron Fleming, John E. Bradley, III, Lewis C. Lee, Jared Dirk Sol, Scott Buzan, Timothy Seegan
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Patent number: 11893505Abstract: Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.Type: GrantFiled: June 10, 2020Date of Patent: February 6, 2024Assignee: AON RISK SERVICES, INC. OF MARYLANDInventors: Samuel Cameron Fleming, David Craig Andrews, John E. Bradley, III, Scott Buzan, Jared Dirk Sol, Timothy Seegan, Joseph Henderson Ashmore, Christopher Ali Mirabzadeh
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Publication number: 20230409647Abstract: Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.Type: ApplicationFiled: June 10, 2020Publication date: December 21, 2023Inventors: Samuel Cameron Fleming, John E. Bradley, III, Lewis C. Lee, Jared Dirk Sol, Scott Buzan, Timothy Seegan
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Patent number: 11776291Abstract: Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.Type: GrantFiled: June 10, 2020Date of Patent: October 3, 2023Assignee: AON RISK SERVICES, INC. OF MARYLANDInventors: Samuel Cameron Fleming, David Craig Andrews, John E. Bradley, III, Lewis C. Lee, Jared Dirk Sol, Timothy Seegan, Scott Buzan
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Patent number: 11636272Abstract: Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.Type: GrantFiled: January 28, 2022Date of Patent: April 25, 2023Assignee: Verint Americas Inc.Inventors: Timothy Seegan, Ian Roy Beaver
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Publication number: 20230090090Abstract: Systems and methods for generation and use of intellectual-property (IP) analysis platform architectures are disclosed. A scoring component may be utilized to produce scores for IP assets using user seeded searches in varying areas of interest, such as, for example, target technical fields, targeted publications, targeted products, and/or competitor entity portfolios. The scoring component may be further utilized to produce an interactive graphical element including a spatial representation of the scoring of IP assets. The interactive graphical element may include various functionalities and/or information associated with the of IP assets. The scoring component may utilize data from a coverage component, an opportunity component and/or an exposure component to assess a comprehensive score associated with a group of IP assets of a targeted entity.Type: ApplicationFiled: September 17, 2021Publication date: March 23, 2023Inventors: Rohitasva Dutta, Timothy Seegan, Robert Werfelmann, Joseph Dumoulin
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Publication number: 20230086930Abstract: Systems and methods for generation and use of intellectual-property (IP) analysis platform architectures are disclosed. A scoring component may be utilized to produce scores for IP assets using user seeded searches in varying areas of interest, such as, for example, target technical fields, targeted publications, targeted products, and/or competitor entity portfolios. The scoring component may be further utilized to produce an interactive graphical element including a spatial representation of the scoring of IP assets. The interactive graphical element may include various functionalities and/or information associated with the of IP assets. The scoring component may utilize data from a coverage component, an opportunity component and/or an exposure component to assess a comprehensive score associated with a group of IP assets of a targeted entity.Type: ApplicationFiled: September 17, 2021Publication date: March 23, 2023Inventors: Lewis C. Lee, Samuel Cameron Fleming, Adam Parish, Timothy Seegan, Rohitasva Dutta, Joseph Dumoulin
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Publication number: 20220398857Abstract: Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.Type: ApplicationFiled: June 24, 2022Publication date: December 15, 2022Inventors: Samuel Cameron Fleming, David Craig Andrews, Jared Dirk Sol, Scott Buzan, Timothy Seegan, Christopher Ali Mirabzadeh
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Patent number: 11373424Abstract: Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.Type: GrantFiled: June 10, 2020Date of Patent: June 28, 2022Assignee: AON RISK SERVICES, INC. OF MARYLANDInventors: Samuel Cameron Fleming, David Craig Andrews, Jared Dirk Sol, Scott Buzan, Timothy Seegan, Christopher Ali Mirabzadeh
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Publication number: 20220156467Abstract: Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.Type: ApplicationFiled: January 28, 2022Publication date: May 19, 2022Inventors: Timothy Seegan, Ian Beaver
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Publication number: 20220101462Abstract: Systems and methods for generation and use of intellectual-property (IP) landscaping platform architectures are disclosed. A landscaping component may be utilized to produce refined clusters of IP assets using user seeded searches in varying areas of interest, such as, for example, target technical fields, targeted publications, targeted products, and/or competitor entity portfolios. The landscaping component may be further utilized to produce an interactive graphical element including a spatial representation of the clusters of IP assets. The interactive graphical element may include various functionalities and/or information associated with the clusters of IP assets.Type: ApplicationFiled: September 30, 2020Publication date: March 31, 2022Inventors: Jeffrey Brendan Ryan, Michael John Tobias, Samuel Cameron Fleming, Jared Dirk Sol, Louise Janice, Luvina Bowman, Timothy Seegan
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Patent number: 11270082Abstract: Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.Type: GrantFiled: August 1, 2019Date of Patent: March 8, 2022Assignee: VERINT AMERICAS INC.Inventors: Timothy Seegan, Ian Beaver
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Publication number: 20200057811Abstract: Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.Type: ApplicationFiled: August 1, 2019Publication date: February 20, 2020Inventors: Timothy Seegan, Ian Beaver