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：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：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：US20250124026A1

公开(公告)日：2025-04-17

申请号：US18484661

申请日：2023-10-11

Applicant: Google LLC

Inventor： Xi Cheng ,  Wen Zhang ,  Jiashang Liu ,  Mingge Deng ,  Amir Hormati ,  Omid Fatemieh

IPC: G06F16/2452 ,  G06F16/242 ,  G06F40/40

Abstract: A method includes receiving a text embedding generation query from a user requesting generation of a text embedding for one or more data elements stored at a data warehouse. In response, the method includes selecting, using the text embedding generation query, a text embedding model from a plurality of different text embedding models. The method includes generating, using the selected text embedding model, the text embedding for the one or more data elements and storing the text embeddings at the data warehouse. The method includes receiving a machine learning model training query from the user device requesting training of a machine learning model using the text embeddings. In response to receiving the machine learning model training query, the method includes training the machine learning model using the text embeddings. The method includes providing, to the user device, a notification indicating that training of the machine learning model is complete.

公开(公告)号：US11928017B2

公开(公告)日：2024-03-12

申请号：US17664409

申请日：2022-05-21

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 is an anomalous point data value. The method includes reporting the determined anomalous point data value to the user.

公开(公告)号：US20220382622A1

公开(公告)日：2022-12-01

申请号：US17664409

申请日：2022-05-21

Applicant: Google LLC

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

IPC: G06F11/07 ,  G06K9/62

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 is an anomalous point data value. The method includes reporting the determined anomalous point data value to the user.