Zidong Yang

US Patent:

20200311518, Oct 1, 2020

Filed:

Jun 15, 2020

Appl. No.:

16/901985

Inventors:

- San Francisco CA, US
Nghi Huu Nguyen - Union City CA, US
Jacob Leverich - San Francisco CA, US
Zidong Yang - Millbrae CA, US

International Classification:

G06N 3/04
G06F 16/26
G06F 16/25

Abstract:

Systems and methods include obtaining a set of events, each event in the set of events comprising a time-stamped portion of raw machine data, the raw machine data produced by one or more components within an information technology or security environment and reflects activity within the information technology or security environment. Thereafter, a first neural network is used to automatically identify variable text to extract as a field from the set of events. An indication of the variable text is provided as a field extraction recommendation, for example, to a user device for presentation to a user.

US Patent:

20200090027, Mar 19, 2020

Filed:

Nov 22, 2019

Appl. No.:

16/692144

Inventors:

- San Francisco CA, US
Zidong Yang - San Francisco CA, US
Sinduja Sreshta - San Francisco CA, US

International Classification:

G06N 3/04
G06F 17/27
G06Q 10/06
G06F 16/2453

Abstract:

Described herein is a technology that facilitates the production of and the use of automated datagens for event-based systems. A datagen (i.e., data-generator or data generation system) is a component, module, or subsystem of computer systems that searches, monitors, and analyzes machine data. Existing datagens are not capable of detecting an anomaly in machine data. An anomaly is a variance in the input data stream that exceeds some acceptable amount of deviation from the norm (i.e., standard, expectation, etc.). An embodiment of datagen, in accordance with the technology described herein, detects anomalies in the input machine data.

US Patent:

20180089561, Mar 29, 2018

Filed:

Jan 31, 2017

Appl. No.:

15/420754

Inventors:

- San Francisco CA, US
Nghi Huu Nguyen - Union City CA, US
Jacob Leverich - San Francisco CA, US
Zidong Yang - Millbrae CA, US

International Classification:

G06N 3/08
G06F 17/30

Abstract:

Systems and methods include obtaining a set of events, each event in the set of events comprising a time-stamped portion of raw machine data, the raw machine data produced by one or more components within an information technology or security environment and reflects activity within the information technology or security environment. Thereafter, a first neural network is used to automatically identify variable text to extract as a field from the set of events. An indication of the variable text is provided as a field extraction recommendation, for example, to a user device for presentation to a user.

US Patent:

20180032861, Feb 1, 2018

Filed:

Jul 29, 2016

Appl. No.:

15/224489

Inventors:

- San Francisco CA, US
Zidong Yang - San Francisco CA, US
Sinduja Sreshta - San Francisco CA, US

International Classification:

G06N 3/08

Abstract:

Described herein is a technology that facilitates the production of and the use of automated datagens for event-based. A datagen (i.e., data-generator or data generation system) is a component, module, or subsystem of computer systems that searches, monitors, and analyzes machine data. A datagen produces events that are further processed in various ways for subsequent use (such as searching, monitoring, and analysis).

US Patent:

20180032862, Feb 1, 2018

Filed:

Jul 29, 2016

Appl. No.:

15/224493

Inventors:

- San Francisco CA, US
Zidong Yang - San Francisco CA, US
Sinduja Sreshta - San Francisco CA, US

International Classification:

G06N 3/08
G06N 5/02

Abstract:

Described herein is a technology that facilitates the production of and the use of automated datagens for event-based systems. A datagen (i.e., data-generator or data generation system) is a component, module, or subsystem of computer systems that searches, monitors, and analyzes machine data. Existing datagens are not capable of detecting an anomaly in machine data. An anomaly is a variance in the input data stream that exceeds some acceptable amount of deviation from the norm (i.e., standard, expectation, etc.). An embodiment of datagen, in accordance with the technology described herein, detects anomalies in the input machine data.

US Patent:

20170220672, Aug 3, 2017

Filed:

Jan 29, 2016

Appl. No.:

15/010732

Inventors:

- San Francisco CA, US
Nghi Huu Nguyen - Union City CA, US
Zidong Yang - Millbrae CA, US

International Classification:

G06F 17/30
G06N 7/00

Abstract:

Embodiments of the present invention are directed to facilitating enhancement of time series prediction. In accordance with aspects of the present disclosure, a set of time series data is determined to have at least one missing data value. Based on the missing data value(s), a predicted missing value is generated for each of the at least one missing data values. The predicted missing value for a missing data value is generated, for example, based on a weighted average of a time series data value preceding the missing data value and a time series data value following the missing data value. The set of time series data and the predicted missing values for each of the at least one missing data values can then be used to determine periodicity associated with the set of time series data.

US Patent:

20170220938, Aug 3, 2017

Filed:

Apr 29, 2016

Appl. No.:

15/143335

Inventors:

- San Francisco CA, US
Nghi Huu Nguyen - Union City CA, US
Zidong Yang - Millbrae CA, US

International Classification:

G06N 5/04
H04L 29/06

Abstract:

Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.