公开(公告)号：US20250036670A1

公开(公告)日：2025-01-30

申请号：US18736618

申请日：2024-06-07

Applicant: Google LLC

Inventor： Xi Cheng ,  Amir Hossein Hormati ,  Bo Yang ,  Mingge Deng ,  Qiang Hao

IPC: G06F16/33

Abstract: Aspects of the disclosure are directed to integrating one or more large language models (LLMs) into a cloud database platform, such as a data warehouse. Users of the cloud database platform can provide queries to instruct one or more LLMs to perform generative natural language processing tasks by manipulating or generating text directly in the cloud database platform with a table valued function. Users can provide input to register or generate one or more LLMs of the cloud database platform for performing the natural language processing tasks. Integrating LLMs into the cloud database platform can improve processing capabilities of the LLMs and save computing resources, as specialized LLMs or application-specific API may no longer be necessary.

公开(公告)号：US11842291B2

公开(公告)日：2023-12-12

申请号：US18062271

申请日：2022-12-06

Applicant: Google LLC

Inventor： Mingge Deng ,  Amir H. Hormati ,  Xi Cheng

IPC: G06F17/00 ,  G06F7/00 ,  G06N5/04 ,  G06N20/00 ,  G06F7/14 ,  G06F16/29

CPC classification number: G06N5/04 ,  G06F7/14 ,  G06F16/29 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

公开(公告)号：US20230094479A1

公开(公告)日：2023-03-30

申请号：US17449660

申请日：2021-09-30

Applicant: Google LLC

Inventor： Xi Cheng ,  Lisa Yin ,  Mingge Deng ,  Amir Hormati ,  Umar Ali Syed ,  Jiashang Liu

IPC: G06N7/00 ,  G06K9/62

Abstract: A method includes receiving a model analysis request from a user. The model analysis requests requesting the data processing hardware to provide one or more statistics of a model trained on a dataset. The method also includes obtaining the trained model. The trained model includes a plurality of weights. Each weight is assigned to a feature of the trained model. The model also includes determining, using the dataset and the plurality of weights, the one or more statistics of the trained model based on a linear regression of the trained model. The method includes reporting the one or more statistics of the trained model to the user.

公开(公告)号：US20230274180A1

公开(公告)日：2023-08-31

申请号：US17652863

申请日：2022-02-28

Applicant: Google LLC

Inventor： Xi Cheng ,  Jiashang Liu ,  Lisa Yin ,  Amir Hossein Hormati ,  Mingge Deng ,  Weijie Shen ,  Kashif Yousuf

IPC: G06N20/00 ,  G06F16/248

CPC classification number: G06N20/00 ,  G06F16/248

Abstract: A method for forecasting time-series data, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving a time series forecasting query from a user requesting a time series forecast forecasting future data based on a set of current time-series data. The operations include obtaining, from the set of current time-series data, a set of training data. The operations include training, using a first portion of the set of training data, a first sub-model of a forecasting model and training, using a second portion of the set of training data, a second sub-model of the forecasting model. The second portion is different than the first portion. The operations include forecasting, using the forecasting model, the future data based on the set of current time-series data and returning, to the user, the forecasted future data for the time series forecast.

公开(公告)号：US20230153311A1

公开(公告)日：2023-05-18

申请号：US18053738

申请日：2022-11-08

Applicant: Google LLC

Inventor： Xi Cheng ,  Zichuan Ye ,  Peng Lin ,  Jiashang Liu ,  Amir Hormati ,  Mingge Deng

IPC: G06F16/2458 ,  G06F16/215 ,  G06F16/25

CPC classification number: G06F16/2462 ,  G06F16/215 ,  G06F16/256

Abstract: A method for anomaly detection includes receiving an anomaly detection query from a user. The anomaly detection query requests data processing hardware determine one or more anomalies in a dataset including a plurality of examples. Each example in the plurality of examples is associated with one or more features. The method includes training a model using the dataset. The trained model is configured to use a local outlier factor (LOF) algorithm. For each respective example of the plurality of examples in the dataset, the method includes determining, using the trained model, a respective local deviation score based on the one or more features. The method includes determining that the respective local deviation score satisfies a deviation score threshold and, based on the location deviation score satisfying the threshold, determining that the respective example is anomalous. The method includes reporting the respective anomalous example to the user.

公开(公告)号：US11544596B2

公开(公告)日：2023-01-03

申请号：US16843371

申请日：2020-04-08

Applicant: Google LLC

Inventor： Mingge Deng ,  Amir H. Hormati ,  Xi Cheng

IPC: G06F17/00 ,  G06N5/04 ,  G06N20/00 ,  G06F7/14 ,  G06F16/29

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

公开(公告)号：US20220405623A1

公开(公告)日：2022-12-22

申请号：US17354392

申请日：2021-06-22

Applicant: Google LLC

Inventor： Xi Cheng ,  Lisa Yin ,  Jiashang Liu ,  Amir H. Hormati ,  Mingge Deng ,  Christopher Avery Meyers

IPC: G06N5/04 ,  G06K9/62 ,  G06F16/245 ,  G06N20/00

Abstract: The disclosure is directed to a query-driven machine learning platform for generating feature attributions and other data for interpreting the relationship between inputs and outputs of a machine learning model. The platform can receive query statements for selecting data, training a machine learning model, and generating model explanation data for the model. The platform can distribute processing for generating the model explanation data to scale in response to requests to process selected data, including multiple records with a variety of different feature values. The interface between a user device and the machine learning platform can streamline deployment of different model explainability approaches across a variety of different machine learning models.

公开(公告)号：US20250013937A1

公开(公告)日：2025-01-09

申请号：US18739429

申请日：2024-06-11

Applicant: Google LLC

Inventor： Honglin Zheng ,  Haoming Chen ,  Jun Ya Zhang ,  Xi Cheng ,  Weijie Shen ,  Jiashang Liu ,  Mingge Deng ,  Amir Hossein Hormati

IPC: G06Q10/04 ,  G06Q10/1057

Abstract: Aspects of the disclosure are directed methods, systems, and computer readable media for in-database holiday effect modeling for time series forecasting. The modeling can be accurate, explainable, customizable, and scalable. Machine learning models can receive a first dataset for time series data and a second dataset for configurable holiday data. The models can detect and model effects of each configurable holiday on one or more forecasts, effectively accumulating effects of overlapping holidays, to manage different levels of holiday modeling. Holiday data can be customizable, including an ability to modify existing holidays and/or add new holidays, through one or more interfaces that can display default holiday information, combined holiday information based on both default and customizable holidays, effects of each holiday on forecasts, and accumulated effects of multiple holidays on forecasts.

公开(公告)号：US20240193035A1

公开(公告)日：2024-06-13

申请号：US18438717

申请日：2024-02-12

Applicant: Google LLC

Inventor： Zichuan Ye ,  Jiashang Liu ,  Forest Elliott ,  Amir Hormati ,  Xi Cheng ,  Mingge Deng

IPC: G06F11/07

CPC classification number: G06F11/0793 ,  G06F11/0709 ,  G06F11/079

Abstract: A method includes receiving a point data anomaly detection query from a user. The query requests the data processing hardware to determine a quantity of anomalous point data values in a set of point data values. The method includes training a model using the set of point data values. For at least one respective point data value in the set of point data values, the method includes determining, using the trained model, a variance value for the respective point data value and determining that the variance value satisfies a threshold value. Based on the variance value satisfying the threshold value, the method includes determining that the respective point data value includes an anomalous point data value. The method includes reporting the determined anomalous point data value to the user.

公开(公告)号：US11693867B2

公开(公告)日：2023-07-04

申请号：US16986861

申请日：2020-08-06

Applicant: Google LLC

Inventor： Xi Cheng ,  Amir H. Hormati ,  Lisa Yin ,  Umar Syed

IPC: G06F16/2458 ,  G06F16/22

CPC classification number: G06F16/2477 ,  G06F16/221 ,  G06F16/2282

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.