Xian Li

20180218207, Aug 2, 2018

Mar 27, 2018

15/937051

- REDMOND WA, US
Christopher Matthew Degiere - Palo Alto CA, US
Jingjing Huang - Santa Clara CA, US
Aarti Kumar - San Carlos CA, US
Alex Ching Lai - Menlo Park CA, US
Xian Li - San Jose CA, US

G06K 9/00
H04L 29/08
G06F 17/30
G06Q 50/00
G06Q 10/10
G06Q 10/06
G06N 99/00
G06N 7/02
G06K 9/62

In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a first plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted first plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

20180121520, May 3, 2018

Oct 31, 2016

15/339703

- Mountain View CA, US
Aarti Kumar - San Carlos CA, US
Xian Li - Sunnyvale CA, US
Alexander Power - San Francisco CA, US
Derek Ribbons - San Jose CA, US

G06F 17/30
H04L 12/58

Techniques for performing a fuzzy match of data from multiple sources are provided. In one technique, an email address of a sender of an email message is extracted from the email message. The email address is used to retrieve, from a first data source, first entity data about one or more entities, such as users. The first entity data is used to retrieve, from a second data source, second entity data about one or more entities. First data that pertains to the sender and that originates from the first data source is combined with second data that pertains to the sender and that originates from the second data source to generate sender data. The sender data is then presented via an email client that displays the email message.

20180015075, Jan 18, 2018

Jul 14, 2017

15/649786

- Cincinnati OH, US
Xian Li - Lexington KY, US

A61K 31/436
A61K 31/5025
A61K 31/496
A61K 31/506
A61K 9/00

Methods of treating venous malformation (VM) are described. The described methods may include the steps of administering an mTOR inhibitor and an ABL kinase inhibitor to an individual in need thereof. Articles of manufacture comprising a container and a composition comprising the actives used in the disclosed methods are also described.

20170091270, Mar 30, 2017

Oct 30, 2015

14/929109

- Mountain View CA, US
Christopher Matthew Degiere - Palo Alto CA, US
Aarti Kumar - San Carlos CA, US
Alex Ching Lai - Menlo Park CA, US
Xian Li - San Jose CA, US

G06F 17/30
G06N 99/00

In an example embodiment, an organization name is retrieved from an organization name field of a first record. A web search is performed using the organization name, producing web search results. A second plurality of features is extracted for each web search result in the set of web search results. Each of the extracted second plurality of features for each web search result in the set of web search results is input into a supervised machine learning classifier to classify each of the web search results in the set of web search results as either containing an organization web address or not containing an organization web address. In response to a determination by the supervised machine learning classifier that a first web search result contains an organization web address, the organization web address from the first web search result is injected into an organization web address field of the first record.

20170091274, Mar 30, 2017

Oct 30, 2015

14/929104

- Mountain View CA, US
Christopher Matthew Degiere - Palo Alto CA, US
Aarti Kumar - San Carlos CA, US
Alex Ching Lai - Menlo Park CA, US
Xian Li - San Jose CA, US

G06F 17/30
G06N 7/02
G06N 99/00

In an example embodiment, a fuzzy join operation is performed by, for each pair of records, evaluating a first plurality of features for both records in the pair of records by calculating term frequency-inverse term frequency (TF-IDF) for each token of each field relevant to each feature and based on the calculated TF-IDF for each token of each field relevant to each feature, computing a similarity score based on the similarity function by adding a weight assigned to the TF-IDF for any token that appears in both records. Then a graph data structure is created, having a node for each record in the plurality of records and edges between each of the nodes, except, for each record pair having a similarity score that does not transgress a first threshold, causing no edge between the nodes for the record pair to appear in the graph data structure;

20170091543, Mar 30, 2017

Oct 30, 2015

14/929116

- Mountain View CA, US
Christopher Matthew Degiere - Palo Alto CA, US
Jingjing Huang - Santa Clara CA, US
Aarti Kumar - San Carlos CA, US
Alex Ching Lai - Menlo Park CA, US
Xian Li - San Jose CA, US

G06K 9/00
G06N 99/00
G06F 17/30
G06Q 10/10
G06F 17/27
G06K 9/62

In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a second plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted second plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

20160224453, Aug 4, 2016

Feb 25, 2015

14/631743

- Mountain View CA, US
Sheng Zhao - Sunnyvale CA, US
Xian Li - San Jose CA, US
Keith Wai Kit Tsang - Fremont CA, US
Aarti Kumar - San Carlos CA, US
Alex Ching Lai - Menlo Park CA, US

G06F 11/36
G06F 9/44

A machine may be configured to monitor the quality of software systems based on key performance indicators associated with versions of various units of code. For example, the machine accesses a current version of a unit of code that is not marked as evaluated, in a database. The machine generates a current key performance indicator (KPI) value for the current version of the unit of code based on an execution of the current version of the unit of code. The machine identifies a previous KPI value associated with a previous version of the unit of code that is marked as evaluated, in the database. The machine determines that the current KPI value is less than the previous KPI value and generates an alert for presentation in a user interface of a device. The alert may indicate a decreasing quality level associated with the current version of the unit of code.