MESSAGING DIGEST

• US 20150178385A1
• Filed: 11/14/2014
• Published: 06/25/2015
• Est. Priority Date: 12/24/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bauchot, Frederic, Clement, Jean-Yves, Murard, Isabelle
• Granted Patent: US 9,904,728 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: H04L 51/18 Commands or executable codes
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

A method and associated system for processing messages. An action to be subsequently performed on the message is identified. In response to the action being identified, one or more word objects are extracted from the message. A first combination of one or more concepts is derived. from the one or more word objects. Each concept of the one or more concepts is associated with at least one object of the one or more word objects. Each word object is an instance of the associated concept. A first interest value associated with the first combination is determined. In response to a determination that the first interest value is at least a specified first threshold value, a concept interest dataset that includes combinations of concepts is updated. The updated concept interest dataset includes the first combination and a cumulative interest value that encompasses the first interest value.

Claims

1. A method for processing messages, said method comprising:

identifying, by a processor of a computer system, an action to be subsequently performed on the message;

in response to said identifying the action, said processor extracting one or more word objects from the message;

said processor deriving a first combination of one or more concepts from the one or more word objects, wherein each concept of the one or more concepts is associated with at least one word object of the one or more word objects, and wherein each word object of the at least one word object is an instance of the associated concept;

said processor determining a first interest value associated with the first combination;

said processor determining that the first interest value is at least a specified first threshold value; and

in response to said determining that the first interest value is at least the specified first threshold value, said processor updating a concept interest dataset, wherein the concept interest dataset prior to being updated comprises a plurality of combinations of at least one concept, wherein each concept of the at least one concept has been derived from a previous processing of messages, and wherein the updated concept interest dataset comprises the first combination and a cumulative interest value that encompasses the first interest value.

2. The method of claim 1, said method further comprising:

said processor extracting a group of word objects from one or more messages;

said processor deriving a second combination of concepts from the extracted group of word objects;

said processor using the updated concept interest dataset to determine a second interest value associated with the second combination of concepts;

said processor determining that the second interest value is at least a specified second threshold value; and

in response to said determining that the second interest value is at least the specified second threshold value, said processor generating a message filter that comprises logical operators in conjunction with the second combination of concepts; and

said processor using the message filter to filter a set of messages to determine a group of messages from the set of messages such that the group of messages satisfy the message filter.

3. The method of claim 1, wherein the plurality of combinations does not include the first combination, and wherein the cumulative interest value consists of the first interest value.

4. The method of claim 1, wherein the plurality of combinations includes the first combination, and wherein the cumulative interest value consists of the first interest value added to a value of the first combination that existed in the concept interest dataset prior to said updating.

5. The method of claim 1, wherein a concept dictionary dataset comprises concepts and associated word objects, wherein said deriving the first combination comprises:

said processor finding in the concept dictionary dataset an occurrence of each word object of the one or more word objects; and

said processor locating in the concept dictionary dataset the concept associated with each word object whose occurrence was found in the concept dictionary dataset, wherein the first combination consists of the located concepts associated with each word object whose occurrence was found in the concept dictionary dataset.

6. The method of claim 1, wherein the first interest value is either a positive or negative number in dependence on the action.

7. The method of claim 1, wherein N denotes a total number of concepts of the one or more concepts in the first combination, and wherein N is at least 2.

8. The method of claim 7, wherein the first interest value is independent of N.

9. The method of claim 7, wherein the first interest value is linearly proportional to N.

10. The method of claim 7, wherein the first interest value is non-linearly dependent on N.

11. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code containing instructions which, upon being executed by a processor of a computer system, implement a method for processing messages, said method comprising:

said processor identifying an action to be subsequently performed on the message;

in response to said identifying the action, said processor extracting one or more word objects from the message;

said processor deriving a first combination of one or more concepts from the one or more word objects, wherein each concept of the one or more concepts is associated with at least one word object of the one or more word objects, and wherein each word object of the at least one word object is an instance of the associated concept;

said processor determining a first interest value associated with the first combination;

said processor determining that the first interest value is at least a specified first threshold value; and

in response to said determining that the first interest value is at least the specified first threshold value, said processor updating a concept interest dataset, wherein the concept interest dataset prior to being updated comprises a plurality of combinations of at least one concept, wherein each concept of the at least one concept has been derived from a previous processing of messages, and wherein the updated concept interest dataset comprises the first combination and a cumulative interest value that encompasses the first interest value.

12. The computer program product of claim 11, said method further comprising:

said processor extracting a group of word objects from one or more messages;

said processor deriving a second combination of concepts from the extracted group of word objects;

said processor using the updated concept interest dataset to determine a second interest value associated with the second combination of concepts;

said processor determining that the second interest value is at least a specified second threshold value; and

in response to said determining that the second interest value is at least the specified second threshold value, said processor generating a message filter that comprises logical operators in conjunction with the second combination of concepts; and

said processor using the message filter to filter a set of messages to determine a group.

13. The computer program product of claim 11, wherein the plurality of combinations does not include the first combination, and wherein the cumulative interest value consists of the first interest value.

14. The computer program product of claim 11, wherein the plurality of combinations includes the first combination, and wherein the cumulative interest value consists of the first interest value added to a value of the first combination that existed in the concept interest dataset prior to said updating.

15. The computer program product of claim 11, wherein a concept dictionary dataset comprises concepts and associated word objects, wherein each concept in the concept dictionary dataset is a word or phrase, wherein said deriving the first combination comprises:

said processor finding in the concept dictionary dataset an occurrence of each word object of the one or more word objects; and

said processor locating in the concept dictionary dataset the concept associated with each word object whose occurrence was found in the concept dictionary dataset, wherein the first combination consists of the located concepts associated with each word object whose occurrence was found in the concept dictionary dataset.

16. The computer program product of claim 11, wherein the first interest value is either a positive or negative number in dependence on the action.

17. A computer system comprising a processor, a memory coupled to the processor, and a computer readable storage device coupled to the processor, said storage device containing program code which, upon being executed by the processor via the memory, implements a method for processing messages, said method comprising:

said processor identifying an action to be subsequently performed on the message;

in response to said identifying the action, said processor extracting one or more word objects from the message;

said processor deriving a first combination of one or more concepts from the one or more word objects, wherein each concept of the one or more concepts is associated with at least one word object of the one or more word objects, and wherein each word object of the at least one word object is an instance of the associated concept;

said processor determining a first interest value associated with the first combination;

said processor determining that the first interest value is at least a specified first threshold value; and

in response to said determining that the first interest value is at least the specified first threshold value, said processor updating a concept interest dataset, wherein the concept interest dataset prior to being updated comprises a plurality of combinations of at least one concept, wherein each concept of the at least one concept has been derived from a previous processing of messages, and wherein the updated concept interest dataset comprises the first combination and a cumulative interest value that encompasses the first interest value.

18. The computer system of claim 17, said method further comprising:

said processor extracting a group of word objects from one or more messages;

said processor deriving a second combination of concepts from the extracted group of word objects;

said processor using the updated concept interest dataset to determine a second interest value associated with the second combination of concepts;

said processor determining that the second interest value is at least a specified second threshold value; and

in response to said determining that the second interest value is at least the specified second threshold value, said processor generating a message filter that comprises logical operators in conjunction with the second combination of concepts; and

said processor using the message filter to filter a set of messages to determine a group.

19. The computer system of claim 17, wherein the plurality of combinations does not include the first combination, and wherein the cumulative interest value consists of the first interest value.

20. The computer system of claim 17, wherein the plurality of combinations includes the first combination, and wherein the cumulative interest value consists of the first interest value added to a value of the first combination that existed in the concept interest dataset prior to said updating.

21. The computer system of claim 17, wherein a concept dictionary dataset comprises concepts and associated word objects, wherein each concept in the concept dictionary dataset is a word or phrase, wherein said deriving the first combination comprises:

said processor finding in the concept dictionary dataset an occurrence of each word object of the one or more word objects; and

said processor locating in the concept dictionary dataset the concept associated with each word object whose occurrence was found in the concept dictionary dataset, wherein the first combination consists of the located concepts associated with each word object whose occurrence was found in the concept dictionary dataset.

22. The computer system of claim 17, wherein the first interest value is either a positive or negative number in dependence on the action.

23. A message filtering method, said method comprising:

providing, by a processor of a computer system, one or more messages;

said processor extracting word objects from the one or more messages;

said processor deriving a combination of concepts from the extracted word objects;

said processor determining a first interest value associated with the combination of concepts;

said processor determining that the first interest value is at least a specified threshold value;

in response to said determining that the first interest value is at least the specified threshold value, said processor generating a message filter that comprises logical operators in conjunction with the combination of concepts; and

said processor using the message filter to filter a set of messages to determine a group of messages from the set of messages such that the group of messages satisfy the message filter.

24. The method of claim 23, wherein a concept dictionary dataset comprises concepts and associated word objects, wherein said deriving the combination of concepts comprises:

said processor finding in the concept dictionary dataset an occurrence of each word object of the extracted word objects; and

said processor locating in the concept dictionary dataset the concept associated with each word object whose occurrence was found in the concept dictionary dataset, wherein the combination of concepts consists of the located concepts associated with each word object whose occurrence was found in the concept dictionary dataset.

25. The method of claim 23, wherein a concept interest dataset comprises concepts and associated interest values, wherein said determining the first interest value comprises:

said processor finding the combination of concepts in the concept interest dataset; and

said processor locating in the concept interest dataset the interest value associated with combination of concepts, wherein the first interest value consists of the interest value associated with the combination of concepts in the concept interest dataset.