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.

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.