Patents by Inventor Seiji James Yamamoto
Seiji James Yamamoto 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: 11670415Abstract: Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.Type: GrantFiled: December 18, 2020Date of Patent: June 6, 2023Assignee: INCLUDED HEALTH, INC.Inventors: Seiji James Yamamoto, Ranjit Chacko
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Publication number: 20210104316Abstract: Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.Type: ApplicationFiled: December 18, 2020Publication date: April 8, 2021Inventors: Seiji James YAMAMOTO, Ranjit CHACKO
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Publication number: 20210056438Abstract: Computer-implemented systems and methods are disclosed for data driven expertise mapping. The systems and methods provide for obtaining data sets from data sources, wherein the data sets include services related data, analyzing the data sets, wherein the analysis generates information representative of the services related data, and generating training sets related to the data sets, wherein the training sets are based on known values. The systems and methods further provide for generating models, wherein the models are based on determining services provided by service providers using a combination of the services related data, the analysis of the data sets and the training sets, and provide a mapping of at least one service to service providers. The systems and methods additionally include evaluating the models based on known values and storing an indication for providing to a graphical user interface based on more models.Type: ApplicationFiled: November 6, 2020Publication date: February 25, 2021Applicant: Grand Rounds, Inc.Inventors: Nathaniel Sayer Freese, Ricardo Nuno Silva Moura Pinho, Matthew Steven Pancia, Seiji James Yamamoto
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Patent number: 10872692Abstract: Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.Type: GrantFiled: August 31, 2018Date of Patent: December 22, 2020Assignee: GRAND ROUNDS, INC.Inventors: Seiji James Yamamoto, Ranjit Chacko
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Patent number: 10832170Abstract: Computer-implemented systems and methods are disclosed for data driven expertise mapping. The systems and methods provide for obtaining data sets from data sources, wherein the data sets include services related data, analyzing the data sets, wherein the analysis generates information representative of the services related data, and generating training sets related to the data sets, wherein the training sets are based on known values. The systems and methods further provide for generating models, wherein the models are based on determining services provided by service providers using a combination of the services related data, the analysis of the data sets and the training sets, and provide a mapping of at least one service to service providers. The systems and methods additionally include evaluating the models based on known values and storing an indication for providing to a graphical user interface based on more models.Type: GrantFiled: June 26, 2017Date of Patent: November 10, 2020Assignee: GRAND ROUNDS, INC.Inventors: Nathaniel Sayer Freese, Ricardo Nuno Silva Moura Pinho, Matthew Steven Pancia, Seiji James Yamamoto
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Patent number: 10430716Abstract: Computer-implemented systems and methods are disclosed for automatically generating predictive models using data driven featurization. The systems and methods provide for obtaining data associated with a target event, annotating the data to identify a target event and establishing one or more limits on the data, censoring the data based on the annotations, determining features of the censored data, and analyzing the features to determine a predictive model. In some embodiments, the systems and methods further provide for converting the features into a binary representation and analyzing the binary representation to produce the predictive model.Type: GrantFiled: February 10, 2016Date of Patent: October 1, 2019Assignee: GROUND ROUNDS, INC.Inventor: Seiji James Yamamoto
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Publication number: 20190164196Abstract: The disclosed computer-implemented method may include (1) sampling links from an online system, (2) receiving, from a human labeler for each of the links, a label indicating whether the human labeler considers a landing page of the link to be a low-quality webpage, (3) deriving features from a landing page of each of the links, (4) using the label and the features of each of the links to train a model configured to predict a likelihood that a link is to a low-quality webpage, (5) identifying content items that are candidates for a content feed of a user of the online system, (6) applying the model to a link of each of the content items to determine a ranking of the content items, and (7) displaying the content items in the content feed of the user based on the ranking. Various other methods, systems, and computer-readable media are also disclosed.Type: ApplicationFiled: November 29, 2017Publication date: May 30, 2019Inventors: Sijian Tang, Shengbo Guo, Jiayi Wen, Gregory Matthew Marra, James Li, Seiji James Yamamoto, Grace Louise Jackson, Kristin S. Hendrix, Benxiong Wu, Jiun-Ren Lin, Sara Lee Su, Panagiotis Papadimitriou, Michael Charles Bailey, Cristian Orellana, Emanuel Alexandre Strauss
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Publication number: 20180374575Abstract: Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.Type: ApplicationFiled: August 31, 2018Publication date: December 27, 2018Applicant: Grand Rounds, Inc.Inventors: Seiji James YAMAMOTO, Ranjit CHACKO
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Patent number: 10068666Abstract: Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.Type: GrantFiled: June 1, 2016Date of Patent: September 4, 2018Assignee: GRAND ROUNDS, INC.Inventors: Seiji James Yamamoto, Ranjit Chacko
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Publication number: 20170351819Abstract: Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.Type: ApplicationFiled: June 1, 2016Publication date: December 7, 2017Applicant: Grand Rounds, Inc.Inventors: Seiji James YAMAMOTO, Ranjit CHACKO
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Publication number: 20170308814Abstract: Computer-implemented systems and methods are disclosed for data driven expertise mapping. The systems and methods provide for obtaining data sets from data sources, wherein the data sets include services related data, analyzing the data sets, wherein the analysis generates information representative of the services related data, and generating training sets related to the data sets, wherein the training sets are based on known values. The systems and methods further provide for generating models, wherein the models are based on determining services provided by service providers using a combination of the services related data, the analysis of the data sets and the training sets, and provide a mapping of at least one service to service providers. The systems and methods additionally include evaluating the models based on known values and storing an indication for providing to a graphical user interface based on more models.Type: ApplicationFiled: June 26, 2017Publication date: October 26, 2017Applicant: Grand Rounds, Inc.Inventors: Nathaniel Sayer Freese, Ricardo Nuno Silva Moura Pinho, Matthew Steven Pancia, Seiji James Yamamoto
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Publication number: 20170286843Abstract: Computer-implemented systems and methods are disclosed for automatically generating predictive models using data driven featurization. The systems and methods provide for obtaining data associated with a target event, annotating the data to identify a target event and establishing one or more limits on the data, censoring the data based on the annotations, determining features of the censored data, and analyzing the features to determine a predictive model. In some embodiments, the systems and methods further provide for converting the features into a binary representation and analyzing the binary representation to produce the predictive model.Type: ApplicationFiled: June 16, 2017Publication date: October 5, 2017Inventor: Seiji James Yamamoto
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Publication number: 20170228651Abstract: Computer-implemented systems and methods are disclosed for automatically generating predictive models using data driven featurization. The systems and methods provide for obtaining data associated with a target event, annotating the data to identify a target event and establishing one or more limits on the data, censoring the data based on the annotations, determining features of the censored data, and analyzing the features to determine a predictive model. In some embodiments, the systems and methods further provide for converting the features into a binary representation and analyzing the binary representation to produce the predictive model.Type: ApplicationFiled: February 10, 2016Publication date: August 10, 2017Inventor: Seiji James Yamamoto
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Patent number: 9691026Abstract: Computer-implemented systems and methods are disclosed for data driven expertise mapping. The systems and methods provide for obtaining data sets from data sources, wherein the data sets include services related data, analyzing the data sets, wherein the analysis generates information representative of the services related data, and generating training sets related to the data sets, wherein the training sets are based on known values. The systems and methods further provide for generating models, wherein the models are based on determining services provided by service providers using a combination of the services related data, the analysis of the data sets and the training sets, and provide a mapping of at least one service to service providers. The systems and methods additionally include evaluating the models based on known values and storing an indication for providing to a graphical user interface based on more models.Type: GrantFiled: March 21, 2016Date of Patent: June 27, 2017Assignee: GRAND ROUNDS, INC.Inventors: Nathaniel Sayer Freese, Ricardo Nuno Silva Moura Pinho, Matthew Steven Pancia, Seiji James Yamamoto