Patents by Inventor SriSatish Ambati
SriSatish Ambati 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: 20250068399Abstract: An input specifying a schematic of user interface components of an application program is received. A first group of one or more machine learning models is used to automatically identify the user interface components and associated properties specified in the input. Based on the identified user interface components and the associated properties, a second group of one or more machine learning models is used to automatically generate program code implementing the application program including the user interface components.Type: ApplicationFiled: July 31, 2024Publication date: February 27, 2025Inventors: Shivam Bansal, Piraveen Sivakumar, SriSatish Ambati
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Patent number: 12045733Abstract: An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset.Type: GrantFiled: July 7, 2022Date of Patent: July 23, 2024Assignee: H2O.ai Inc.Inventors: SriSatish Ambati, Ashrith Barthur
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Patent number: 12020132Abstract: A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.Type: GrantFiled: August 23, 2022Date of Patent: June 25, 2024Assignee: H2O.ai Inc.Inventors: Arno Candel, Dmitry Larko, Srisatish Ambati, Prithvi Prabhu, Mark Landry, Jonathan C. McKinney
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Publication number: 20230177352Abstract: An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset.Type: ApplicationFiled: July 7, 2022Publication date: June 8, 2023Inventors: SriSatish Ambati, Ashrith Barthur
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Publication number: 20230074025Abstract: A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.Type: ApplicationFiled: August 23, 2022Publication date: March 9, 2023Inventors: Arno Candel, Dmitry Larko, SriSatish Ambati, Prithvi Prabhu, Mark Landry, Jonathan C. McKinney
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Patent number: 11475372Abstract: A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.Type: GrantFiled: February 27, 2019Date of Patent: October 18, 2022Assignee: H2O.ai Inc.Inventors: Arno Candel, Dmitry Larko, SriSatish Ambati, Prithvi Prabhu, Mark Landry, Jonathan C. McKinney
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Patent number: 11416751Abstract: An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset.Type: GrantFiled: March 27, 2018Date of Patent: August 16, 2022Assignee: H2O.ai Inc.Inventors: SriSatish Ambati, Ashrith Barthur
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Publication number: 20190295000Abstract: A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.Type: ApplicationFiled: February 27, 2019Publication date: September 26, 2019Inventors: Arno Candel, Dmitry Larko, SriSatish Ambati, Prithvi Prabhu, Mark Landry, Jonathan C. McKinney
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Publication number: 20180293501Abstract: An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset.Type: ApplicationFiled: March 27, 2018Publication date: October 11, 2018Inventors: SriSatish Ambati, Ashrith Barthur
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Publication number: 20180293462Abstract: Data associated with one or more data sources is transformed into a format associated with a common ontology using one or more transformers. One or more machine learning models are generated based at least in part on the transformed data. The one or more machine learning models and the one or more transformers are provided to a remote device.Type: ApplicationFiled: March 29, 2018Publication date: October 11, 2018Inventors: SriSatish Ambati, Tom Kraljevic, Pasha Stetsenko, Sanjay Joshi