Alex Lai

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.

20170091692, Mar 30, 2017

Oct 30, 2015

14/929128

- Mountain View CA, US
Ke Wang - Cupertino CA, US
Alex Ching Lai - Menlo Park CA, US
Aarti Kumar - San Carlos CA, US
Keith Wai Kit Tsang - Fremont CA, US
Rekha Thakur - Santa Clara CA, US
Song Lin - Santa Clara CA, US
Christopher Matthew Degiere - Palo Alto CA, US

G06Q 10/06
G06Q 50/00
H04L 29/08

In an example embodiment, a member profile corresponding to a member of a social networking service is obtained. Usage information for the member is then obtained, and one or more member metrics are calculated based on the member profile and usage information for the corresponding member. A plurality of features are extracted from the member profile and the one or more member metrics. The plurality of features is inserted into an organization name confidence score model to obtain a confidence score for an organization name in the member profile.

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.

20160065429, Mar 3, 2016

Aug 29, 2014

14/473988

- Mountain View CA, US
Songtao Guo - Cupertino CA, US
Baoshi Yan - Belmont CA, US
Alex Ching Lai - Menlo Park CA, US

H04L 12/26
G06Q 50/00
H04L 29/08

Techniques for determining a location of an entity are described. A determination module using member profile data may determine the location of the entity. Member profile data can include information about a member's employer and the employer's location. The determination module accesses member profiles from a social network. Additionally, the determination module may create a subgroup of entity-related member profiles from the accessed member profiles. The entity-related member profiles can be associated with a specific entity. Furthermore, the determination module may determine a suggested location for the specific entity based on location information derived from the related member profiles. Moreover, the determination module may calculate a connection density for the specific entity based on connections associated with the entity-related member profiles. Subsequently, the determination module may determine that the suggested location is a physical location when the calculated connection density is above a pre-determined threshold value.

20160065628, Mar 3, 2016

Aug 29, 2014

14/474009

- Mountain View CA, US
Baoshi Yan - Belmont CA, US
Alex Ching Lai - Menlo Park CA, US
Ke Wang - Cupertino CA, US

H04L 29/06
G06Q 30/02
G06Q 50/00
G06F 17/30

Techniques for determining a location of an entity are described. A determination module using member profile data may determine the location of the entity. Member profile data can include information about a member's employer and the employer's location. The module accesses member profiles from a social network. Additionally, the module may create a subgroup of entity-related member profiles from the accessed member profiles. The entity-related member profiles can be associated with a specific entity. Furthermore, the module may determine a suggested location for the specific entity based on location information derived from the related member profiles. Moreover, the module may calculate a connection density for the specific entity based on connections associated with the entity-related member profiles. Subsequently, the module may determine that the suggested location is a physical location when the calculated connection density is above a pre-determined threshold value.

20150379113, Dec 31, 2015

Mar 30, 2015

14/672948

- Mountain View CA, US
Songtao Guo - Cupertino CA, US
Baoshi Yan - Belmont CA, US
Alex Ching Lai - Menlo Park CA, US

G06F 17/30
H04L 29/06
H04L 29/08

Methods, systems and computer program products for identifying a relationship between sub-units of an entity are described. The sub-units are segmented into one or more candidate related groups based on one or more general attributes and a pair of the sub-units of the one or more candidate related sub-units is selected. The pair of sub-units is analyzed to determine a relationship between the sub-units and the relationship between the sub-units is identified based on the determined relationship.