Patents by Inventor Boxin Jiang
Boxin Jiang 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).
-
Patent number: 11934927Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.Type: GrantFiled: December 22, 2022Date of Patent: March 19, 2024Assignee: Snowflake Inc.Inventors: Orestis Kostakis, Qiming Jiang, Boxin Jiang
-
Publication number: 20240078220Abstract: An example method of tuning a machine learning operation can include receiving a data query comprising a reference to an input data set of a database, generating a plurality of hyperparameter sets based on the input data set, in response to receiving the data query, training a plurality of machine learning models using the plurality of hyperpararneter sets, selecting a first mathine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model, and in response to receiving the data query, returning the output of the first machine learning model.Type: ApplicationFiled: November 9, 2023Publication date: March 7, 2024Inventors: Boxin Jiang, Qiming Jiang
-
Publication number: 20240062098Abstract: The subject technology receives first party training data provided by an end-user of a baseline machine learning model. The subject technology determines a first set of common features based on the first party training data. The subject technology receives, from at least one data source. The subject technology determines a second set of common features based on the set of datasets. The subject technology trains, using the first set of common features and the second set of common features, a second machine learning model, the second machine learning model incorporating additional training data from the external data supplier during training compared to the baseline machine learning model. The subject technology generates a boosted machine learning model based at least in part on the training, the boosted machine learning model comprising the trained second machine learning model.Type: ApplicationFiled: August 23, 2022Publication date: February 22, 2024Inventors: Rachel Frances Blum, Nancy Dou, Matthew J. Glickman, Boxin Jiang, Orestis Kostakis, Justin Langseth, Michael Earle Rainey, Haoran Yu
-
Patent number: 11868326Abstract: An example method of tuning a machine learning operation can include receiving a data query comprising a reference to an input data set of a database, generating a plurality of unique sets of hyperparameters by varying a hyperparameter value of each set of hyperparameters of the plurality of unique sets of hyperparameters based on the input data set, in response to receiving the data query, training a plurality of machine learning models using the input data set of the data query, each of the plurality of machine learning models configured according to a respective one of a plurality of unique sets of hyperparameters, selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model, and returning the output of the first machine learning model in response to the data query.Type: GrantFiled: December 5, 2022Date of Patent: January 9, 2024Assignee: Snowflake Inc.Inventors: Boxin Jiang, Qiming Jiang
-
Publication number: 20230401283Abstract: Using an attributes model of a time series forecasting model, determine a set of features based on time series data, the set of features including periodic components. The time series data may be divided into a set of segments. For each segment of the set of segments, a weight may be assigned using an age of the segment, resulting in a set of weighted segments of time series data. Using a trend detection model of the time series forecasting model, trend data from the set of weighted segments of time series data may be determined. A time series forecast may be generated by combining the set of features and the trend data.Type: ApplicationFiled: February 22, 2023Publication date: December 14, 2023Inventors: Michel Adar, Boxin Jiang, Qiming Jiang, John Reumann, Boyu Wang, Jiaxun Wu
-
Publication number: 20230153455Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for redacting sensitive data within a database. An example method can include receiving a data query referencing unredacted data of a database, wherein the data query that is received comprises a value identifying a type of sensitive data to be redacted from the unredacted data, responsive to the data query, executing, by a processing device, a redaction operation to identify sensitive data that matches the type within the unredacted data of the database, and returning a redacted data set in which the sensitive data that matches the type is replaced or removed to the data query.Type: ApplicationFiled: January 17, 2023Publication date: May 18, 2023Inventors: Boxin Jiang, Qiming Jiang
-
Publication number: 20230136738Abstract: An example method of tuning a machine learning operation can include receiving a data query comprising a reference to an input data set of a database, generating a plurality of unique sets of hyperparameters by varying a hyperparameter value of each set of hyperparameters of the plurality of unique sets of hyperparameters based on the input data set, in response to receiving the data query, training a plurality of machine learning models using the input data set of the data query, each of the plurality of machine learning models configured according to a respective one of a plurality of unique sets of hyperparameters, selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model, and returning the output of the first machine learning model in response to the data query.Type: ApplicationFiled: December 5, 2022Publication date: May 4, 2023Inventors: Boxin Jiang, Qiming Jiang
-
Publication number: 20230132117Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.Type: ApplicationFiled: December 22, 2022Publication date: April 27, 2023Inventors: Orestis Kostakis, Qiming Jiang, Boxin Jiang
-
Patent number: 11609970Abstract: A processing device may analyze a set of time series data using a time series forecasting model comprising an attributes model and a trend detection model. The attributes model may comprise a modified gradient boosting decision tree (GBDT) based algorithm. Analyzing the set of time series data comprises determining a set of features of the set of time series data, the set of features including periodic components as well as arbitrary components. A trend of the set of time series data may be determined using the trend detection model and the set of features and the trend may be combined to generate a time series forecast.Type: GrantFiled: July 29, 2022Date of Patent: March 21, 2023Assignee: Snowflake Inc.Inventors: Michel Adar, Boxin Jiang, Qiming Jiang, John Reumann, Boyu Wang, Jiaxun Wu
-
Patent number: 11580251Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for redacting sensitive data within a database. An example method can include receiving a data query referencing unredacted data of a database, responsive to the data query, executing, by a processing device, a redaction operation to identify sensitive data within the unredacted data of the database, and returning a redacted data set in which the sensitive data is replaced or removed to the data query.Type: GrantFiled: November 5, 2021Date of Patent: February 14, 2023Assignee: Snowflake Inc.Inventors: Boxin Jiang, Qiming Jiang
-
Patent number: 11568320Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.Type: GrantFiled: January 21, 2021Date of Patent: January 31, 2023Assignee: Snowflake Inc.Inventors: Orestis Kostakis, Qiming Jiang, Boxin Jiang
-
Patent number: 11561946Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for executing and tuning a machine learning operation within a database. An example method can include receiving a data query referencing an input data set of a database, executing a plurality of machine learning operations to generate, in view of the input data set, a plurality of output data sets each having a respective accuracy value, wherein each of the plurality of machine learning operations is executed by a processing device according to one of a plurality of unique sets of hyperparameters, selecting a first output data set of the plurality of output data sets in view of the accuracy values, and returning the first output data set in response to the data query.Type: GrantFiled: October 29, 2021Date of Patent: January 24, 2023Assignee: Snowflake Inc.Inventors: Boxin Jiang, Qiming Jiang
-
Publication number: 20220230093Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.Type: ApplicationFiled: January 21, 2021Publication date: July 21, 2022Inventors: Orestis Kostakis, Qiming Jiang, Boxin Jiang