Electronic Communications Triage

• US 20120143798A1
• Filed: 12/06/2010
• Published: 06/07/2012
• Est. Priority Date: 12/06/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Kleewein, James, Edelen, James, Pereira, Jorge, Wetmore, Alexander, Winn, John, Sundelin, Tore
• Granted Patent: US 8,744,979 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 21/55 Detecting local intrusion or implementing counter-measures
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: H04L 51/212 using filtering or selective blocking

Abstract

Triaging electronic communications in a computing system environment can mitigate issues related to large volumes of incoming electronic communications. This can include an analysis of user-specific electronic communication data and associated behaviors to predict which communications a user is likely to deem important or unimportant. Client-side application features are exposed based on the evaluation of communication importance to enable the user to process arbitrarily large volumes of incoming communications.

Claims

1. A method for triaging electronic communications in a computing system environment, the method comprising:

training a default model at a computing device to personalize a recipient-specific model for a recipient, wherein the default model is formed from a plurality of weighted factors adjusted against a sample of users having common characteristics with the recipient, and the recipient-specific model is formed from the default model that is modified using the recipient'"'"'s historical behavioral and feedback information;

intercepting an item addressed to the recipient at the computing device;

extracting a plurality of item features associated with the item at the computing device;

retrieving the recipient-specific model, wherein the recipient-specific model comprises the plurality of weighted factors associated to the plurality of extracted item features;

applying an importance classification model to the plurality of extracted item features including forming a combination of the plurality of weighted factors;

generating a predicted item importance based on the combination of the plurality of weighted factors; and

enabling at least one application feature associated with the item for the recipient based on the predicted item importance.

2. The method of claim 1, wherein the common characteristics are selected from a group including:

common vocation; and

common interest.

3. The method of claim 1, further comprising adjusting the plurality of weighted factors based on the recipient'"'"'s historical behavioral and feedback information.

4. The method of claim 1, further comprising continuing training of the default model to personalize the recipient-specific model by acquiring recipient behavior associated with the item.

5. The method of claim 1, further comprising continuing training of the default model to personalize the recipient-specific model by acquiring recipient feedback associated with the item.

6. The method of claim 1, further comprising continuing training of the default model to personalize the recipient-specific model by acquiring recipient customization selected from the group including:

inference correction;

processing rule definition;

threshold definition; and

importance granularity.

7. The method of claim 1, further comprising continuing training of the default model to personalize the recipient-specific model by periodically acquiring recipient behavior associated with the item.

8. The method of claim 1, further comprising the predicted item importance designating relative importance of the item.

9. The method of claim 8, further comprising periodically acquiring recipient behavior associated with the item for a predetermined time period to evaluate correctness of the predicted item importance.

10. The method of claim 9, further comprising adjusting at least one of:

the plurality of weighted factors; and

the predicted item importance based on the acquired recipient behavior.

11. The method of claim 8, further comprising periodically acquiring recipient feedback associated with the item for a predetermined time period to evaluate correctness of the predicted item importance.

12. The method of claim 11, further comprising adjusting at least one of:

the plurality of weighted factors; and

the predicted item importance based on the recipient feedback.

13. The method of claim 1, wherein the item includes a communication selected from a group including:

an e-mail message;

a voicemail message;

a calendar message;

an instant message;

a web-based message, and a social collaboration message.

14. The method of claim 1, wherein the extracted item features includes at least one of a directly observed item characteristic and an inferred item characteristic.

15. The method of claim 1, further comprising enabling the application feature selected from a group including:

an emphasizing feature for highlighting key content of the item;

a display feature for providing a quick view of the item;

a notification feature for providing temporary view of the item and including information related to derived importance of the item;

an auto-prioritize feature for providing an importance sorted view of the item and other items;

an age-out feature for providing an action to the item after a time period;

a synopsis feature for providing synopsis of content of the item; and

a dashboard feature for providing a consolidated view of important communications across different data sources.

16. A computing device, comprising:

a processing unit;

a system memory connected to the processing unit, the system memory including instructions that, when executed by the processing unit, cause the processing unit to implement a training module configured for hierarchical training of a user model for triaging electronic communications in a computing system environment, the training module being configured to;

generate a set of default inferences for a user based on the prototypical user model, wherein a default inference comprises an item attribute, an attribute value, an attribute weight, and an attribute confidence;

acquire user-specific information to personalize the set of default inferences to the user including;

retrieval of user-specific historical behavioral and feedback information, and retrieval of user-specific behavioral and feedback information in response to receipt of an item;

update the set of default inferences with the user-specific information to form a personalized set of inferences for application to an item triage model; and

enable at least one application feature associated with the user for exposing a predicted item importance.

17. The computing device of claim 16, wherein an item comprises an electronic communication, and wherein the item attribute comprises a characteristic of a particular element of the communication, the attribute value comprises a specific instance of the item attribute, the attribute weight comprises a scaled value denoting importance of the attribute value, and the attribute confidence comprises a value designating confidence associated with the attribute weight.

18. The computing device of claim 16, wherein the prototypical model comprises a plurality of weighted factors adjusted against a sample of users having characteristics common with the user selected from a group including:

common vocation; and

common interest.

19. The computing device of claim 16, wherein retrieval of the user-specific behavioral and feedback information in response to receipt of an item comprises periodic data acquisition to continuously adjust the personalized set of inferences.

20. A computer readable storage medium having computer-executable instructions that, when executed by a computing device, cause the computing device to perform steps comprising:

training a default model at a computing device to personalize a recipient-specific model for a recipient, wherein the default model is formed from a plurality of weighted factors adjusted against a sample of users having common characteristics with the recipient, the common characteristics selected from a group including;

common vocation, and common interest, and the recipient-specific model is formed from the default model that is modified using the recipient'"'"'s historical behavioral and feedback information;

intercepting an item addressed to the recipient at the computing device, wherein the item selected from a group including;

an e-mail message, a calendar message, an instant message, a web-based message, and a social collaboration message;

extracting a plurality of item features associated with the item at the computing device, wherein the item features include a characteristic of the item selected from a group including;

an item sender characteristic, an item recipient characteristic, a conversation characteristic, and an attachment characteristic;

retrieving the recipient-specific model, wherein the recipient-specific model comprises the plurality of weighted factors associated to the plurality of extracted item features;

applying an importance classification model to the plurality of extracted item features including forming a combination of the plurality of weighted factors;

generating a predicted item importance based on the combination of the plurality of weighted factors, wherein the predicted item importance designating the item as one of;

important, and unimportant;

enabling at least one application feature associated with the item for the recipient based on the predicted item importance selected from a group including;

an emphasizing feature for highlighting key content of the item; and

display feature for providing a quick view of the item; and

a notification feature for providing temporary view of the item; and

periodically acquiring recipient behavior and feedback associated with the item for a predetermined time period for continuing training of the default model to personalize the recipient-specific model.