Patents Assigned to ZESTFINANCE, INC.
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Patent number: 11972338Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.Type: GrantFiled: May 2, 2023Date of Patent: April 30, 2024Assignee: ZestFinance, Inc.Inventors: David Sheehan, Siavash Yasini, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik
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Patent number: 11960981Abstract: Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.Type: GrantFiled: March 8, 2019Date of Patent: April 16, 2024Assignee: ZESTFINANCE, INC.Inventors: Douglas C. Merrill, Michael Edward Ruberry, Ozan Sayin, Bojan Tunguz, Lin Song, Esfandiar Alizadeh, Melanie Eunique DeBruin, Yachen Yan, Derek Wilcox, John Candido, Benjamin Anthony Solecki, Jiahuan He, Jerome Louis Budzik, Armen Avedis Donigian, Eran Dvir, Sean Javad Kamkar, Vishwaesh Rajiv, Evan George Kriminger
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Patent number: 11941650Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.Type: GrantFiled: August 1, 2018Date of Patent: March 26, 2024Assignee: ZestFinance, Inc.Inventors: Douglas C. Merrill, Michael Edward Ruberry, Ozan Sayin, Bojan Tunguz, Lin Song, Esfandiar Alizadeh, Melanie Eunique DeBruin, Yachen Yan, Derek Wilcox, John Candido, Benjamin Anthony Solecki, Jiahuan He, Jerome Louis Budzik, Sean Javad Kamkar
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Patent number: 11893466Abstract: Systems and methods for training models to improve fairness.Type: GrantFiled: January 12, 2021Date of Patent: February 6, 2024Assignee: ZESTFINANCE, INC.Inventors: Sean Javad Kamkar, Michael Egan Van Veen, Feng Li, Mark Frederick Eberstein, Jose Efrain Valentin, Jerome Louis Budzik, John Wickens Lamb Merrill
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Patent number: 11847574Abstract: Systems and methods for generating and processing modeling workflows.Type: GrantFiled: April 25, 2019Date of Patent: December 19, 2023Assignee: ZESTFINANCE, INC.Inventors: Douglas C. Merrill, Armen Avedis Donigian, Eran Dvir, Sean Javad Kamkar, Evan George Kriminger, Vishwaesh Rajiv, Michael Edward Ruberry, Ozan Sayin, Yachen Yan, Derek Wilcox, John Candido, Benjamin Anthony Solecki, Jiahuan He, Jerome Louis Budzik, John J. Beahan, Jr., John Wickens Lamb Merrill, Esfandiar Alizadeh, Liubo Li, Carlos Alberta Huertas Villegas, Feng Li, Randolph Paul Sinnott, Jr.
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Patent number: 11816541Abstract: Systems and methods for understanding diverse machine learning models.Type: GrantFiled: November 19, 2019Date of Patent: November 14, 2023Assignee: ZestFinance, Inc.Inventors: John Wickens Lamb Merrill, Geoffrey Michael Ward, Sean Javad Kamkar, John Joseph Beahan, Jr., Mark Frederick Eberstein, Jose Efrain Valentin, Jerome Louis Budzik
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Patent number: 11720962Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.Type: GrantFiled: November 24, 2021Date of Patent: August 8, 2023Assignee: ZESTFINANCE, INC.Inventors: Sean Javad Kamkar, Geoffrey Michael Ward, Jerome Louis Budzik
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Patent number: 11720527Abstract: This invention relates generally to data assembly and analytics, as can be used in the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for creating objects which can be used in multiple implementations to generate scores.Type: GrantFiled: April 6, 2021Date of Patent: August 8, 2023Assignee: ZestFinance, Inc.Inventors: John W. L. Merrill, John J. Beahan
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Publication number: 20220343197Abstract: Systems and methods for generating explanation information for a result of an application system. Explanation configuration is generated based on received user input. Responsive to an explanation generation event, a plurality of modified input variable value sets are generated for a first applicant by using the explanation configuration. For each modified input variable value set: a request is provided to a first application system for generation of a result for the modified input variable value set, and a result is received for the modified input variable value set. At least one input variable value is selected based on a comparison between a first result of a first input variable value set of the first applicant and results for the modified input variable value set. Explanation information is generated for the first result by using human-readable description information for each selected input variable value, in accordance with the explanation configuration.Type: ApplicationFiled: July 12, 2022Publication date: October 27, 2022Applicant: ZestFinance, Inc.Inventors: Douglas C. Merrill, Michael Edward Ruberry, Ozan Sayin, Bojan Tunguz, Lin Song, Esfandiar Alizadeh, Melanie Eunique DeBruin, Yachen Yan, Derek Wilcox, John Candido, Benjamin Anthony Solecki, Jiahuan He, Jerome Louis Budzik
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Publication number: 20220164877Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.Type: ApplicationFiled: November 24, 2021Publication date: May 26, 2022Applicant: ZestFinance, Inc.Inventors: Sean Javad Kamkar, Geoffrey Michael Ward, Jerome Louis Budzik
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Patent number: 11301484Abstract: Systems and methods for converting a data item provided by an external data provider system into a data type specified by a data processing system for a data field of the data item. A data processing system stores a coercion rule for each data field of a first data set provided by the data provider system. Each stored coercion rule identifies at least one data type for the corresponding data field. Responsive to a second data set provided by the data provider system, the data processing system coerces each data item of the second data set into at least one data type specified by the stored coercion rule for the data field of the data item to generate at least one converted data item of the second data set. The data processing system generates information from at least one converted data item, and provides the information to a consuming system.Type: GrantFiled: March 5, 2019Date of Patent: April 12, 2022Assignee: ZestFinance, Inc.Inventors: John W. L. Merrill, John J. Beahan
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Publication number: 20220035841Abstract: Systems and methods for a multi-tenant parser generation platform. A human-readable document of a data provider system is accessed. The document includes a data dictionary table for opaque data of the data provider system that has a first type. Data dictionary information of the data dictionary table is extracted, and a schema file is generated from the extracted information. The schema file defines a parsing process for parsing an opaque data record of the first. The first schema file specifies each field of the opaque data record of the first type. Parsing instructions are generated based on the schema file. The parsing instructions are for parsing an opaque data record of the first type provided by the data provider system into a set of data fields. The parsing instructions are provided to an entity system external to the platform. The first entity system is associated with a first platform account.Type: ApplicationFiled: July 30, 2021Publication date: February 3, 2022Applicant: ZestFinance, Inc.Inventors: John Joseph Beahan, JR., Sean Javad Kamkar, Chris Rasario, Kalvin Huang, Amol Patil
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Publication number: 20220027986Abstract: Systems and methods for augmenting data by performing reject inference are disclosed. In one embodiment, the disclosed process trains an auto-encoder based on a subset of known labeled rows (e.g., non-default loan applicants). The process then infers labels for unlabeled rows using the auto-encoder (e.g., label some rows as non-default and some as default). The process then trains a machine learning model based on the known labeled rows and the inferred labeled rows. Applicant data is then processed by this new machine learning model to determine if a loan applicant is likely to default. If the loan applicant is not likely to default, the loan applicant is funded. For example, the loan applicant may be mailed a physical working credit card. However, if the loan applicant is likely to default, the loan applicant is rejected. For example, the loan applicant may be mailed a physical adverse action letter.Type: ApplicationFiled: July 26, 2021Publication date: January 27, 2022Applicant: ZestFinance, Inc.Inventors: Peyman Hesami, Sean Kamkar, Jerome Budzik
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Publication number: 20220004923Abstract: Systems and methods for model explanation are disclosed. In one embodiment, the disclosed process determines a score based on a scoring function and a plurality of values associated with a plurality of features of a denied credit applicant. (e.g., credit score of 550, no loans repaid, etc.). The process then determines a score of an approved credit applicant. (e.g., credit score of 750, 3 loans repaid, etc.). A next differential credit assignment associated with the current denied/approved pair is then calculated. If a convergence stopping criteria, (e.g., current accuracy>99% based on a statistical t-distribution) is not satisfied, the process repeats for a different approved credit applicant. When the convergence stopping criteria is satisfied, explanation information is generated. For example, the explanation information may include an adverse action reason code, fairness metric, disparate impact metric, human readable text, feature importance metric, credit value, and/or an importance rank.Type: ApplicationFiled: July 1, 2021Publication date: January 6, 2022Applicant: ZestFinance, Inc.Inventors: Sean Kamkar, Geoffrey Ward
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Patent number: 11106705Abstract: Systems and methods for a multi-tenant parser generation platform. A human-readable document of a data provider system is accessed. The document includes a data dictionary table for opaque data of the data provider system that has a first type. Data dictionary information of the data dictionary table is extracted, and a schema file is generated from the extracted information. The schema file defines a parsing process for parsing an opaque data record of the first. The first schema file specifies each field of the opaque data record of the first type. Parsing instructions are generated based on the schema file. The parsing instructions are for parsing an opaque data record of the first type provided by the data provider system into a set of data fields. The parsing instructions are provided to an entity system external to the platform. The first entity system is associated with a first platform account.Type: GrantFiled: April 20, 2017Date of Patent: August 31, 2021Assignee: ZestFinance, Inc.Inventors: John Joseph Beahan, Jr., Sean Javad Kamkar, Chris Rasario, Kalvin Huang, Amol Patil
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Patent number: 11010339Abstract: This invention relates generally to data assembly and analytics, as can be used in the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for creating objects which can be used in multiple implementations to generate scores.Type: GrantFiled: October 11, 2018Date of Patent: May 18, 2021Assignee: ZestFinance, Inc.Inventors: John W. L. Merrill, John J. Beahan
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Patent number: 10977729Abstract: Systems and methods for training models to improve fairness.Type: GrantFiled: March 18, 2020Date of Patent: April 13, 2021Assignee: ZestFinance, Inc.Inventors: Sean Javad Kamkar, Michael Egan Van Veen, Feng Li, Mark Frederick Eberstein, Jose Efrain Valentin, Jerome Louis Budzik, John Wickens Lamb Merrill
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Patent number: 10261959Abstract: Systems and methods for converting a data item provided by an external data provider system into a data type specified by a data processing system for a data field of the data item. A data processing system stores a coercion rule for each data field of a first data set provided by the data provider system. Each stored coercion rule identifies at least one data type for the corresponding data field. Responsive to a second data set provided by the data provider system, the data processing system coerces each data item of the second data set into at least one data type specified by the stored coercion rule for the data field of the data item to generate at least one converted data item of the second data set. The data processing system generates information from at least one converted data item, and provides the information to a consuming system.Type: GrantFiled: June 28, 2017Date of Patent: April 16, 2019Assignee: ZestFinance, Inc.Inventors: John W. L. Merrill, John J. Beahan
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Patent number: 10127240Abstract: This invention relates generally to data assembly and analytics, as can be used in the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for creating objects which can be used in multiple implementations to generate scores.Type: GrantFiled: October 19, 2015Date of Patent: November 13, 2018Assignee: ZestFinance, Inc.Inventors: John W. L. Merrill, John J. Beahan, Jr.
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Patent number: 9720953Abstract: Systems and methods for converting a data item provided by an external data provider system into a data type specified by a data processing system for a data field of the data item. A data processing system stores a coercion rule for each data field of a first data set provided by the data provider system. Each stored coercion rule identifies at least one data type for the corresponding data field. Responsive to a second data set provided by the data provider system, the data processing system coerces each data item of the second data set into at least one data type specified by the stored coercion rule for the data field of the data item to generate at least one converted data item of the second data set. The data processing system generates information from at least one converted data item, and provides the information to a consuming system.Type: GrantFiled: June 20, 2016Date of Patent: August 1, 2017Assignee: ZestFinance, Inc.Inventors: John W. L. Merrill, John J. Beahan, Jr.