Automatic online form filling using semantic inference

• US 20080120257A1
• Filed: 02/05/2007
• Published: 05/22/2008
• Est. Priority Date: 11/20/2006
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Goyal, Amit, Mukherjee, Shouvick, Kamat, Gajendra Nishad
• Application Number:
  US11/702,848
• Publication Number:
  US 20080120257A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/957 Browsing optimisation, e.g. caching or content distillation
• CPC Class Codes: G06F 40/174 Form filling; Merging

Abstract

A machine learning based automated online form-filling technique provides for automatically completing user input controls based on previously stored information. An associative parser is used to identify and associate characteristics related to form controls with the corresponding form controls. The characteristics of the user input controls are input into a machine learning based semantic inference engine that was trained for the purpose of identifying the type of information that is supposed to be input into various user input controls. The semantic inference engine operates to label the controls in a manner that describes the meaning of the control, i.e., the type of information that should be automatically input into the corresponding controls. Consequently, the user input controls can be automatically filled in with previously stored user profile information associated with the corresponding labels.

Claims

1. A method comprising performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more processors, cause the one or more processors to perform certain steps including:

determining one or more characteristics associated with a user input control that is in a web document;

computing a data identifier for said user input control by inputting said one or more characteristics into a machine learning mechanism that has been previously trained based on a training set; and

based on said data identifier, automatically providing input to said user input control based on previously stored information associated with said data identifier;

wherein the machine-executed operation is at least one of (a) sending said instructions over transmission media, (b) receiving said instructions over transmission media, (c) storing said instructions onto a machine-readable storage medium, and (d) executing the instructions.

2. The method of claim 1, wherein determining comprises determining a characteristic of said user input control based on what would be the spatial location of an element in said web document relative to said user input control when said web document is graphically rendered.

3. The method of claim 2, wherein said element is an HTML label element.

4. The method of claim 2, wherein determining comprises first determining whether said element is to the left of said user input control and, if said element is not to the left of said user input control, then determining whether said element is above said user input control.

5. The method of claim 1, wherein determining comprises using a table-based parser to identify label elements associated with said controls.

6. The method of claim 5, wherein determining comprises determining whether a caption and/or format element associated with said user input control is to the right of or below said user input control.

7. The method of claim 1, wherein computing comprises inputting said one or more characteristics into a machine learning mechanism based on conditional random fields.

8. The method of claim 1, wherein said one or more characteristics of said user input control includes (a) a unique identifier for said user input control and (b) an associated element in said web document.

9. The method of claim 8, wherein said unique identifier is an HTML “

id”

corresponding to said user input control and said associated element is an HTML “

label”

element corresponding to said user input control.

10. The method of claim 1, wherein said one or more characteristics of said user input control includes (a) a unique identifier for said user input control, (b) a label element associated with said user input control in said web document, and (c) a control type corresponding to said user input control.

11. The method of claim 1, wherein said one or more characteristics of said user input control includes (a) a unique identifier for said user input control, (b) a label element associated with said user input control in said web document, (c) a control type corresponding to said user input control, and (d) possible values for said user input control based on options associated with a menu type of user input control in said web document.

12. The method of claim 1, wherein said one or more characteristics of said user input control includes (a) a set of words, and (b) a control type corresponding to said user input control.

13. The method of claim 1, wherein said determining is performed by a client-side application and said computing is performed by a server-side application.

14. The method of claim 13, wherein said instructions are instructions which, when executed by one or more processors, cause the one or more processors to perform certain steps including:

transmitting said one or more characteristics from said client application to said server application using Asynchronous JavaScript® and

XML (AJAX).

15. The method of claim 1, wherein said web document is constructed in a non-English language.

16. The method of claim 1, wherein said instructions are instructions which, when executed by one or more processors, cause the one or more processors to perform certain steps including:

instructing said machine learning mechanism about a mistake that said machine learning mechanism made in computing a data identifier for a user input control to further train said machine learning mechanism.