Yang Chen

US Patent:

20220321541, Oct 6, 2022

Filed:

Mar 31, 2021

Appl. No.:

17/219482

Inventors:

- Redmond WA, US
Yang CHEN - Sunnyvale CA, US
Jiashuo WANG - Mountain View CA, US
Xiaojing CHEN - Santa Clara CA, US
Chencheng WU - Los Altos CA, US
Souvik GHOSH - Saratoga CA, US
Ankit GUPTA - Fremont CA, US
Jing WANG - Los Altos CA, US
John Patrick MOORE - San Francisco CA, US
Henry Heyburn PISTELL - Ridgewood NJ, US
Mira THAMBIREDDY - San Mateo CA, US
Haowen CAO - Sunnyvale CA, US
Keyi YU - Mountain View CA, US

International Classification:

H04L 29/06
G06N 20/00

Abstract:

Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.

US Patent:

20210390385, Dec 16, 2021

Filed:

Jun 12, 2020

Appl. No.:

16/900129

Inventors:

- San Jose CA, US
Chiara Poletti - San Jose CA, US
Sina Modaresi - San Jose CA, US
Yang Chen - San Jose CA, US
Xing Ji - San Jose CA, US

International Classification:

G06N 3/08
G06N 3/04
G06Q 20/30

Abstract:

Techniques are disclosed relating to improving machine learning classification using both labeled and unlabeled data, including electronic transactions. A computing system may train a machine learning module using a first set of transactions (of any classifiable data) with label information that indicates designated classifications for those transactions and a second set of transactions without label information. This can allow for improved classification error rates, particularly when additional labeled data may not be present (e.g., if a transaction was disallowed, it may not be later labeled as fraudulent or not). The training process may include generating first error data based on classification results for the first set of transactions, generating second error data based on reconstruction results for both the first and second sets of transactions, and updating the machine learning module based on the first and second error data.

US Patent:

20210243215, Aug 5, 2021

Filed:

Mar 29, 2021

Appl. No.:

17/215610

Inventors:

- San Jose CA, US
Yang Chen - San Jose CA, US
Hagar Oppenheim - Sunnyvale CA, US
Xing Ji - San Jose CA, US

International Classification:

H04L 29/06
G06Q 20/40
G06F 16/25
G06F 16/22

Abstract:

A risk analysis system configures the decision engine to detect anomalous online activities by analyzing usage patterns associated with one or more user accounts across multiple frequencies. The risk analysis system obtains transaction log data representing transactions associated with one or more accounts, and extracts data from the transaction log data to generate time-series data along a time dimension. The time-series data may represent usage characteristics of one or more user accounts over a period of time. The risk analysis system derives pattern data representing usage patterns across multiple different frequencies based on the time-series data. The risk analysis system then configures the decision engine to detect anomalous account activities based on the derived pattern data.

US Patent:

20200410053, Dec 31, 2020

Filed:

Jun 27, 2019

Appl. No.:

16/455389

Inventors:

- Redmond WA, US
Haifeng Zhao - San Jose CA, US
Yang Chen - Sunnyvale CA, US
Michael A. Menarguez - Dublin CA, US
Liang Zhang - Fremont CA, US

International Classification:

G06F 17/27
G06N 3/08

Abstract:

Computer-implemented techniques for enhanced tagging of natural language queries that are initially segmented and tagged by a named entity recognition system. By doing so, enhanced tagging of a natural language query that represents a deeper understanding of the query is provided. The enhanced tagging improves the operation of search engines that use the enhanced tags by enabling the search engine to identify and return more relevant search results in answers to natural language queries.

US Patent:

20200125325, Apr 23, 2020

Filed:

Nov 5, 2019

Appl. No.:

16/675085

Inventors:

- San Jose CA, US
Yang Chen - San Jose CA, US
Xing Ji - San Jose CA, US

International Classification:

G06F 7/02
G06F 16/33
G06F 16/2455
G06F 16/22
G06F 16/27

Abstract:

A computer system creates a plurality of indexes from a first plurality of records, wherein each index corresponds to an attribute of a plurality of attributes. The computer system detects a record of a second plurality of records, wherein the record includes a value corresponding to each of the plurality of attributes. The computer system determines a first set of values from a first index of the plurality of indexes that corresponds to a first attribute. The computer system determines a plurality of individual similarity scores for the first set of values by utilizing a similarity function. The computer system determines an overall similarity score for each record of at least a portion of the first plurality of records and based on the overall similarity scores, determines a record of the first plurality of records that corresponds to the record of the second plurality of records.

US Patent:

20200007564, Jan 2, 2020

Filed:

Jul 2, 2018

Appl. No.:

16/025451

Inventors:

- San Jose CA, US
Yang Chen - San Jose CA, US
Hagar Oppenheim - Sunnyvale CA, US
Xing Ji - San Jose CA, US

International Classification:

H04L 29/06
G06F 17/30
G06Q 20/40

Abstract:

A risk analysis system configures the decision engine to detect anomalous online activities by analyzing usage patterns associated with one or more user accounts across multiple frequencies. The risk analysis system obtains transaction log data representing transactions associated with one or more accounts, and extracts data from the transaction log data to generate time-series data along a time dimension. The time-series data may represent usage characteristics of one or more user accounts over a period of time. The risk analysis system derives pattern data representing usage patterns across multiple different frequencies based on the time-series data. The risk analysis system then configures the decision engine to detect anomalous account activities based on the derived pattern data.

US Patent:

20180239584, Aug 23, 2018

Filed:

Feb 22, 2017

Appl. No.:

15/439218

Inventors:

- San Jose CA, US
Yang Chen - San Jose CA, US
Xing Ji - San Jose CA, US

International Classification:

G06F 7/02
G06F 17/30

Abstract:

A computer system creates a plurality of indexes from a first plurality of records, wherein each index corresponds to an attribute of a plurality of attributes. The computer system detects a record of a second plurality of records, wherein the record includes a value corresponding to each of the plurality of attributes. The computer system determines a first set of values from a first index of the plurality of indexes that corresponds to a first attribute. The computer system determines a plurality of individual similarity scores for the first set of values by utilizing a similarity function. The computer system determines an overall similarity score for each record of at least a portion of the first plurality of records and based on the overall similarity scores, determines a record of the first plurality of records that corresponds to the record of the second plurality of records.