James C Wendt

Author:

James C. Wendt

ISBN #:

0833004018

Author:

James C. Wendt

ISBN #:

0833007157

Author:

James C. Wendt

ISBN #:

0833015893

Author:

James C. Wendt

ISBN #:

0833015346

US Patent:

20180144042, May 24, 2018

Filed:

Nov 23, 2016

Appl. No.:

15/360939

Inventors:

- Mountain View CA, US
Yifeng Lu - Mountain View CA, US
Jing Xie - San Jose CA, US
Jie Yang - Sunnyvale CA, US
Luis Garcia Pueyo - Mountain View CA, US
Jinan Lou - Cupertino CA, US
James Wendt - Los Angeles CA, US

International Classification:

G06F 17/30
G06F 17/24
G06N 99/00

Abstract:

Techniques are described herein for automatically generating data extraction templates for structured documents (e.g., B2C emails, invoices, bills, invitations, etc.), and for assigning classifications to those data extraction templates to streamline data extraction from subsequent structured documents. In various implementations, a data extraction template generated from a cluster of structured documents that share fixed content may be identified. Features of the cluster of structured documents may be applied as input to extraction machine learning model(s) trained to provide location(s) of transient field(s) in structured documents, to determine location(s) of transient field(s) in the cluster of structured documents. An association between the data extraction template and the determined transient field location(s) may be stored. Based on the association, data point(s) may be extracted from a given structured document of a user that shares fixed content with the cluster of structured documents. The extracted data point(s) may be surfaced to the user.

US Patent:

20160314184, Oct 27, 2016

Filed:

Apr 27, 2015

Appl. No.:

14/697342

Inventors:

- Mountain View CA, US
Jie Yang - Sunnyvale CA, US
Amitabh Saikia - Mountain View CA, US
Marc-Allen Cartright - Stanford CA, US
Sujith Ravi - Santa Clara CA, US
Balint Miklos - Zurich, CH
Ivo Krka - Zurich, CH
Vanja Josifovski - Los Gatos CA, US
James Wendt - Los Angeles CA, US
Luis Garcia Pueyo - San Jose CA, US

International Classification:

G06F 17/30

Abstract:

Methods, apparatus, systems, and computer-readable media are provided for classifying, or “labeling,” documents such as emails en masse based on association with a cluster/template. In various implementations, a corpus of documents may be grouped into a plurality of disjoint clusters of documents based on one or more shared content attributes. A classification distribution associated with a first cluster of the plurality of clusters may be determined based on classifications assigned to individual documents of the first cluster. A classification distribution associated with a second cluster of the plurality of clusters may then be determined based at least in part on the classification distribution associated with the first cluster and a relationship between the first and second clusters.