SYSTEM FOR AND METHOD OF PARSING AN ELECTRONIC MAIL

• US 20120303570A1
• Filed: 05/27/2011
• Published: 11/29/2012
• Est. Priority Date: 05/27/2011
• Status: Abandoned Application

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Inventors: Stevens, Ira C. III
• Application Number:
  US13/117,316
• Publication Number:
  US 20120303570A1
• Time in Patent Office: Days
• US Class Current: 706/50
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A system for and method of parsing an electronic mail for scheduling a calendar appointment is presented. The system and method for parsing information from an electronic mail for scheduling a calendar appointment. The method may include receiving an electronic mail comprising observations for scheduling a calendar appointment and decoding, via at least one computer processor, the observations into symbol vectors. The method may also include classifying the symbol vectors based at least in part on a statistical probability and converting the observations based at least in part on a token. The method may further include outputting a dictionary result list based at least in part on the statistical probability, wherein the dictionary result list comprises one or more token name-value pairs.

Claims

1. A method, comprising:

receiving an electronic mail comprising observations for scheduling a calendar appointment;

decoding, via at least one computer processor, the observations into symbol vectors;

classifying the symbol vectors based at least in part on a statistical probability;

converting the observations based at least in part on a token; and

outputting a dictionary result list based at least in part on the statistical probability, wherein the dictionary result list comprises one or more token name-value pairs.

2. The method of claim 1, wherein the symbol vectors are variable symbol vectors having a length based at least in part on a length of the observations.

3. The method of claim 1, wherein decoding the observations into symbol vectors comprises trimming at least one of extra spaces and tab characters from the observations.

4. The method of claim 1, wherein the symbol vectors are classified using a generalization of a mixture statistical probability model with hidden variables which control the mixture of components to be selected for the observations.

5. The method of claim 1, the statistical probability of the symbol vectors is determined based at least in part on previous classified symbol vectors.

6. The method of claim 1, wherein classifying the symbol vectors comprises accessing previously known class corpus.

7. The method of claim 1, wherein the observations is converted from left to right while ignoring unrecognizable texts, numerals, or symbols in the observations.

8. The method of claim 1, wherein converting the observations comprises assuming that the observations comprise at least one left portion and a right portion.

9. The method of claim 8, wherein the at least one left portion comprises optional contextual text followed by the token.

11. The method of claim 8, wherein converting the observation comprises performing a left to right expression search based on the token.

12. The method of claim 1, wherein outputting a dictionary result list comprises removing illogical or irrelevant dictionary results from the dictionary result list.

13. The method of claim 1, further comprising receiving feedback to establish a class corpus.

14. The method of claim 13, wherein the class corpus comprises a set of observations based at least in part on a correlation of probability.

15. A non-transitory computer readable media comprising code to perform the steps of the method of claim 1.

10. The method of claim 10, wherein the right portion comprises optional contextual text followed by the token and an end-of-line delimiter.

16. A system, comprising:

a decoder module comprising at least one computer processor configured to receive an electronic mail comprising observations for scheduling a calendar appointment and decode the observations into symbol vectors;

a classifying module configured to classify the symbol vectors based at least in part on a statistical probability; and

a lexical analyzer module comprising at least one computer processor configured to convert the observations based at least in part on a token and output a dictionary result list based at least in part on the statistical probability, wherein the dictionary result list comprises one or more token name-value pairs.

17. The system of claim 16, further comprising a corpus module configured to establish a plurality of class corpora.

18. The system of claim 16, further comprising a tokens module configured to provide the token to the lexical analyzer module.

19. The system of claim 16, wherein the classifier module uses a hidden Markov model (HMM) using Baum-Welch algorithms to classify the symbol vectors.

20. The system of claim 16, wherein the lexical analyzer module converts the observations using Backus-Naur Form parser logic.