Systems and methods that determine intent of data and respond to the data based on the intent

• US 20050192992A1
• Filed: 06/15/2004
• Published: 09/01/2005
• Est. Priority Date: 03/01/2004
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Ringger, Eric K., Atkinson, Robert G., Gamon, Michael, Pearson, Malcolm E., Reed, David R., Campbell, Richard G., Corston, Simon H.
• Granted Patent: US 7,496,500 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]

Abstract

The present invention relates to systems and methods that determine intent for received data (e.g., email, voice, graphics . . . ) and respond to the data based on the intent. The systems and methods employ various combinations of features based on shallow and deep linguistic analysis (e.g., semantic and syntactic) to yield very high accuracy. The systems and methods analyze and categorize received data to locate data that can include intent. This data can be further refined by extracting features related to the intent. The features can be utilized by a classifier to determine the intent. If the intent warrants a response, the data are further scrutinized and reformulated to generate a description that is indicative of the intent. The reformulation can include representing the features in a logical form, transforming the form and generating a description of the intent that can be presented to a user visually and/or audibly.

Claims

1. A system that facilitates data handling, comprising:

a component that receives data; and

a data manager that determines an intent of the data, reformulates at least a subset of the data based on the intent, and automatically presents the reformulated data to a user.

2. The system of claim 1, the data manager further analyzes the data, groups the data into one or more sets with related characteristics, and extracts features from at least one of the sets of data to form the subset of data.

3. The system of claim 2, the data manager utilizes at least one of metadata, properties, content, context, a keyword, history, heuristics, an inference, a rule, a demarcation, a time of day, a day of week, an associated handling cost, an associated handling benefit, and a source of the data to facilitate analyzing the data.

4. The system of claim 2, the features are extracted based at least in part on one of a syntactic structure, a syntactic property, a linguistic structure, and a linguistic property.

5. The system of claim 4, the linguistic features includes one or more of a word that occurs in a sentence, a word bigram that occurs in the sentence, and a word trigram that occurs in the sentence.

6. The system of claim 1, further comprising a classifier that determines the intent of the data from one or more features extracted from the data.

7. The system of claim 6, the classifier is a machine-learned classifier that is trained via at least one of manually and automatically generated data sets.

8. The system of claim 6, the classifier is one of a support vector machine and a knowledge-engineered component.

9. The system of claim 1, the reformulation of the data includes producing a logical form of the data and generating an action description from the logical form based on the intent.

10. The system of claim 9, logical form comprises at least one normalized deictic element.

11. The system of claim 1, further comprising performing an action based on the intent, the action comprising at least one of automatically responding to a question;

returning requested information;

activating a device;

scheduling a meeting;

recasting a project;

audibly notifying a user;

visually notifying the user;

inserting an item into the user'"'"'s schedule;

contacting the user; and

adding the item to the user'"'"'s to do list.

12. The system of claim 1, the reformulated data represents a user task that is automatically inserted into a user'"'"'s task list.

13. The system of claim 1, the data is an email, a document, an audio file, or an image.

14. The system of claim 1, the intent is represented as one of a probability, a confidence level, a percentage, and a binary value.

15. The system of claim 1, further comprising a threshold component that determines whether to reformulate and present the reformulation to the user based on a comparison of the intent with one of a default, user defined and intelligence generated threshold.

16. A system that automatically responds to messages, comprising:

an analyzer that scrutinizes a received message to distinguish one or more disparate parts of the message;

a parser that separates the parts into respective bins;

an extractor that retrieves sentences from at least one of the bins;

a classifier that determines an intent of the message from the sentences; and

a reformulator that transforms the sentence into a set of data indicative of the intent and presents the set of data to a user.

17. The system of claim 16, the message is an email, a document, audio, and an image

18. The system of claim 16, the bin utilized to obtain the features includes a message body of the received message.

19. The system of claim 16, the classifier generates a probability that the message includes a task.

20. The system of claim 16, the reformulator produces a logical representation of the identified sentence.

21. The system of claim 20, the logical representation is utilized to generate a task description.

22. The system of claim 21, the task description is automatically added to a user'"'"'s task list.

23. The system of claim 16, the set of data indicative of the intent is a word, a phrase, a fragment or a sentence.

24. The system of claim 16, the set of data indicative of the intent includes semantic and syntactic relations between features.

25. The system of claim 16, the classifier is trained with an annotated set of data.

26. The system of claim 16, the reformulator replaces deictic elements with non-deictic elements.

27. A computer-implemented method that facilitates data handling, comprising:

accepting data;

analyzing the data to determine an associated intent;

reformulating at least part of the data to produce a description of the intent; and

presenting the description to a user.

28. The method of claim 27, further comprising providing the analysis to a machine-learned classifier to generate a probability indicating whether to respond to the data.

29. The method of claim 28, the classifier is trained via at least one of manually and automatically generated data sets.

30. The method of claim 28, the classifier is one of a support vector machine and an instruction set that considers extracted features.

31. The method of claim 27, the reformulation comprises producing a logical form of the data, which is transformed to produce the description.

32. The method of claim 27, further comprising extracting features from the data based at least in part on one of a syntactic structure, a syntactic property, a linguistic structure, and a linguistic property.

33. A computer-implemented method that facilitates message handling, comprising:

separating a message into groups of similarly related parts;

extracting salient features from at least one of the groups;

computing an intent from the extracted features;

producing a logic representation of the features based on the intent; and

transforming the representation to generate a description of an action related to responding to the data to provide to a user.

34. The method of claim 33, further comprising categorizing the groups as associated with a header, a message body and a salutation of the message.

35. The system of claim 33, the representation comprises semantic and syntactic relations between constituents in the text of the message.

36. The method of claim 33, reformulating the sentence(s) to produce a word, sentence, phrase, and paragraph indicative of the intent.

37. A data packet transmitted between two or more computer components that facilitates managing messages based intent, comprising:

identifying one or more features of a message;

utilizing the identified features to determine an intent of the message;

reformulating the sentences containing the intent to provide a description of the intent; and

presenting the description to a user.

38. A computer readable medium storing computer executable components to facilitate handling data based on an associated intent, comprising:

a component that receives data;

a component that determines intent of the data;

a component that reformulates at least a subset of the data based on the intent; and

a component the provides the reformulated data to a user in a user interface.

39. A data handling system, comprising:

means for determining an intent of received data;

means for reformulating the data to produce a representation of the intent; and

means for presenting the representation to a user.