Patents Assigned to H2O.ai Inc.
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Patent number: 11922283Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.Type: GrantFiled: April 20, 2018Date of Patent: March 5, 2024Assignee: H2O.ai Inc.Inventors: Mark Chan, Navdeep Gill, Patrick Hall
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Patent number: 11893467Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.Type: GrantFiled: May 20, 2022Date of Patent: February 6, 2024Assignee: H2O.ai Inc.Inventors: Mark Chan, Navdeep Gill, Patrick Hall
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Patent number: 11663061Abstract: A training dataset is used to train an unsupervised machine learning trained model. Corresponding gradient values are determined for a plurality of entries included in the training dataset using the trained unsupervised machine learning model. A first subset of the training dataset is selected based on the determined corresponding gradient values and a first threshold value selected from a set of threshold values. A labeled version of the selected first subset is used to train a first supervised machine learning model to detect one or more anomalies.Type: GrantFiled: January 31, 2019Date of Patent: May 30, 2023Assignee: H2O.ai Inc.Inventor: Ashrith Barthur
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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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Patent number: 11386342Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.Type: GrantFiled: April 20, 2018Date of Patent: July 12, 2022Assignee: H2O.ai Inc.Inventors: Mark Chan, Navdeep Gill, Patrick Hall