Automated Media Analysis And Document Management System

• US 20080183710A1
• Filed: 01/29/2007
• Published: 07/31/2008
• Est. Priority Date: 01/29/2007
• Status: Active Grant

• Current Assignee: Agility PR Solutions Canada Ltd.
• Original Assignee: Nikip Technology Limited
• Inventors: Serjeantson, Brett, Williamson, Paul
• Granted Patent: US 7,860,872 B2
• Time in Patent Office: Days
• US Class Current: 707/8
• CPC Class Codes: G06F 16/30 of unstructured textual data document management systems G06F16/93
• CPC Class Codes: G06F 2216/01 Automatic library building
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: Y10S 707/917 Text

Abstract

A web-based media analysis system, consisting of automated media analysis and document management tools, which processes news articles by parsing the news contents or documents and assigning, relating, and extracting information from the news contents for media analysis and relationally storing them in at least one database. The system further comprises a toning engine for toning articles accurately, based on words, attributes and categories of the article, and optionally based on the author of the article, if applicable.

Claims

1. A web-based media analysis system for analyzing at least one media content, comprising:

(a) an uploader for uploading said at least one media content from at least one content provider over a communication network;

(b) a parser for converting each of said at least one media content into serialized data, filtering out unessential data from the serialized data according to a predefined list of unessential data and extracting attributes, categories, quotes, and attributes of said quotes from said at least one media content using regular expressions and predefined parsing rules, and relationally storing said serialized and filtered data and said extracted attributes, said categories said quotes, and said attributes of said quotes into a plurality of tables in at least one database; and

(c) an analysis module for retrieving data from said plurality of said tables in said at least one database and generating a report in response to a request from a client browser.

2. The web-based media analysis system recited in claim 1, wherein said parser further comprises a pre-parser for converting and reformatting said media content into parsable single articles for the parser according to a plurality of pre-parsing and conversion rules.

3. The web-based media analysis system recited in claim 1, wherein the uploader is arranged to set up web session with a client browser of said at least one content provider for uploading said at least one media content.

4. The web-based media analysis system recited in claim 1, wherein the uploader is arranged to initiate web-service with at least one server of said at least one content provider for uploading said at least one media content.

5. The web-based media analysis system recited in claim 1, wherein the analysis module further comprises a toning engine for toning said at least one media content by carrying out the process comprising the steps of:

(a) parsing each of said at least one media content and splitting into serialized meaningful minimum sections;

(b) retrieving, from said at least one database, tone level probabilities for all of said serialized meaningful minimum sections that cause said each of said at least one media content to be toned at, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said serialized meaningful minimum sections;

(c) retrieving, from said at least one database, tone level probabilities for said attributes of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes;

(d) retrieving, from said at least one database, tone level probabilities for said categories of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories; and

(e) establishing the most probable tone level of said each of said at least one media content by weighing and ranking said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said each of said serialized meaningful minimum sections, said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes, and said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories.

6. The web-based media analysis system recited in claim 5, wherein said meaningful minimum sections are words.

7. The web-based media analysis system recited in claim 5, wherein said toning engine further weighs tones of authors of said at least one media content for establishing said most probable tone level of said each of said at least one media content, wherein said tones of said authors of said at least one media content are established by retrieving, from said at least one database, tone level probabilities for the author of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said authors.

8. The web-based media analysis system recited in claim 7, wherein said authors are individuals or organizations.

9. The web-based media analysis system recited in claim 5, wherein said tone level probabilities for all of said serialized meaningful minimum sections, said tone level probabilities for said attributes of said each of said at least one media content, and said tone level probabilities for said categories of said each of said at least one media content are determined through training said toning engine by feeding a plurality of sample media contents with suggested tones thereof and updating said at least one database.

10. The web-based media analysis system recited in claim 9, wherein said training comprises the steps of:

(a) splitting each of said plurality of said sample media contents into meaningful minimum sections while filtering out any unessential sections and extracting attributes and categories of said each of said plurality of said sample media contents using regular expressions;

(b) tracking number of appearances of each of said meaningful minimum sections in said each of said plurality of said sample media contents and associating said number of appearances and said suggested tone corresponding to said each of said plurality of said sample media contents for deriving said tone level probabilities of said each of said meaningful minimum sections cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said meaningful minimum sections;

(c) deriving said tone level probabilities of said attributes of said each of said plurality of said sample media contents cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said attributes; and

(d) deriving said tone level probabilities of said categories of said each of said plurality of said sample media contents cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said categories.

11. The web-based media analysis system recited in claim 10, wherein said meaningful minimum sections are words.

12. The web-based media analysis system recited in claim 1 further comprises a filter builder for interfacing with said client browser to customize said regular expressions and said predefined parsing rules.

13. The web-based media analysis system recited in claim 1 further comprises a communications module accessible from said client browser for searching said at least one database pursuant to a request for at least one search query, collecting and organizing search result, and pushing said search result to a plurality of recipients through said communication network.

14. The web-based media analysis system recited in claim 13, wherein said recipients are client browsers.

15. The web-based media analysis system recited in claim 1, wherein said parser delimits said quotes by quote delimiting characters or quote delimiting phrases.

16. The web-based media analysis system recited in claim 15, wherein said quote delimiting characters are the start quotation mark and the end quotation mark.

17. The web-based media analysis system recited in claim 1, wherein said at least one media content is in a word processing filer scanned image file, streaming file, XML, RSS or any combination thereof.

18. A method for managing and analyzing at least one media content comprising the steps of:

(a) uploading said at least one media content from at least one content provider;

(b) converting each of said at least one media content into serialized data, filtering out unessential data from the serialized data according to a predefined list of unessential data, and extracting attributes, categories, quotes, and attributes of said quotes of said each of the said at least one media content using regular expressions;

(c) relationally storing said serialized and filtered data and said extracted attributes, said categories, said quotes, and attributes of said quotes into a plurality of tables in at least one database; and

(d) retrieving data from said plurality of tables in said at least one database in response to a request from a client browser and generating a report.

19. The method for managing and analyzing media contents recited in claim 18, prior to said step ii, further comprises the sub-steps of converting and reformatting said at least one media content into parsable single articles in accordance with pre-parsing and conversion rules.

20. The method for managing and analyzing said at least one media content recited in claim 18, wherein said step i further comprises the sub-step of setting up a web session with a client browser of said at least one content provider for uploading said at least one media content.

21. The method for managing and analyzing said at least one media content recited in claim 18, wherein said step i further comprises the sub-step of initiating web-service with said at least one content provider server for uploading said at least one media content.

22. The method for managing and analyzing said at least one media content recited in claim 18, wherein said step iv) further comprises the sub-step of toning said at least one media content prior to said generating said report by carrying out the process comprising the steps of:

(a) parsing each of said at least one media content and splitting into serialized meaningful minimum sections;

(b) retrieving, from said at least one database, tone level probabilities for all of said serialized meaningful minimum sections said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said serialized meaningful minimum sections;

(c) retrieving, from said at least one database, tone level probabilities for said attributes of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes;

(d) retrieving, from said at least one database, tone level probabilities for said categories of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories; and

(e) establishing the most probable tone level of said each of at least one media content by weighing and ranking said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said each of the said serialized meaningful minimum sections, said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes, and said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories.

23. The method for managing and analyzing said at least one media content recited in claim 22, wherein said meaningful minimum sections are words.

24. The method for managing and analyzing media contents recited in claim 22, wherein said toning engine further weighs tones of authors of said at least one media content for establishing said most probable tone level of said each of said at least one media content, wherein said tones of said authors of said at least one media content are established by retrieving, from said at least one database, tone level probabilities for the author of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said authors.

25. The method for managing and analyzing said at least one media content recited in claim 24, wherein said authors are individuals or organizations.

26. The method for managing and analyzing said at least one media content recited in claim 22, wherein said tone level probabilities for all of said serialized meaningful minimum sections, said tone level probabilities for said attributes of said each of said at least one media content, and said tone level probabilities for said categories of said each of said at least one media content are determined through a training process by feeding a plurality of sample media contents with suggested tones thereof and updating said at least one database, wherein said training process comprises the steps of:

(a) splitting each of said plurality of said sample media contents into meaningful minimum sections while stripping out any unessential sections and extracting attributes and categories of said each of said plurality of said sample media contents using regular expressions;

(b) tracking number of appearances of each of said meaningful minimum sections in said each of said plurality of said sample media contents and associating said number of appearances and said suggested tone corresponding to said each of said plurality of said sample media contents for deriving said tone level probabilities of said each of said meaningful minimum sections cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said meaningful minimum sections;

(c) deriving said tone level probabilities of said attributes of said each of said plurality of said sample media contents cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said attributes; and

(d) deriving said tone level probabilities of said categories of said each of said plurality of said sample media contents cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said categories.

27. The method for managing and analyzing said at least one media content recited in claim 26, wherein said meaningful minimum sections are words.

28. The method for managing and analyzing said at least one media content recited in claim 18 further comprises the step of interfacing with said client browser to customize said regular expressions and said predefined parsing rules.

29. The method for managing and analyzing said at least one media content recited in claim 18 further comprises the steps of searching said at least one database pursuant to a request for at least one search query from said client browser, collecting and organizing search result, and pushing said search result to a plurality of recipients through said communication network.

30. The method for managing and analyzing said at least one media content recited in claim 29, wherein said recipients are defined by at least one administrator through said client browser.

31. The method for managing and analyzing said at least one media content recited in claim 18, wherein said parser delimits said quotes by quote delimiting characters or quote delimiting phrases.

32. The method for managing and analyzing said at least one media content recited in claim 31, wherein said quote delimiting characters are the start quotation mark and the end quotation mark.

33. The method for managing and analyzing said at least one media content recited in claim 18, wherein said at least one media content is in a word processing file, scanned image file, streaming file, XML, RSS or any combination thereof.

34. A computer readable medium storing executable computer program instructions which, when executed at a server, cause the server to perform a process for analyzing at least one media content, the process comprising the steps of:

(a) uploading said at least one media content from at least one content provider over a communication network;

(b) converting each of said at least one media content into serialized data, filtering out unessential data from the serialized data according to a predefined list of unessential data, and extracting attributes, categories, quotes, and attributes of said quotes of said each of said at least one media content using regular expressions;

(c) relationally storing said serialized and filtered data and said extracted attributes, categories and quotes into a plurality of tables in at least one database; and

(d) retrieving data from said plurality of said tables in said at least one database and generating a report in response to request from a user.

35. The computer readable medium recited in claim 34, wherein the process further comprises the sub steps of converting and reformatting said at least one media content into parsable single articles in accordance with pre-parsing and conversion rules prior to said step ii.

36. The computer readable medium recited in claim 34, wherein said step i further comprises the sub-step of setting up a web session with a client browser of said at least one content provider for uploading said at least one media content.

37. The computer readable medium recited in claim 34, wherein said step i further comprises the sub-step of initiating a web-service with said at least one content provider server for uploading said at least one media content.

38. The computer readable medium recited in claim 34, wherein said step iv) further comprises the sub-step of toning said at least one media content prior to said generating said report by carrying out the process steps of:

(a) parsing each of said at least one media content and splitting into serialized meaningful minimum sections;

(b) retrieving, from said at least one database, tone level probabilities for all of said serialized meaningful minimum sections, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said serialized meaningful minimum sections;

(c) retrieving, from said at least one database, tone level probabilities for said attributes of said media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes;

(d) retrieving, from said at least one database, tone level probabilities for said categories of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories; and

(e) establishing the most probable tone level of said each of said at least one media content by weighing and ranking said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said each of the said serialized meaningful minimum sections, said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes, and said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories.

39. The computer readable medium recited in claim 38, wherein said meaningful minimum sections are words.

40. The computer readable medium recited in claim 39, wherein said toning engine further weighs tones of authors of said at least one media content for establishing said most probable tone level of said each of said at least one media content, wherein said tones of said authors of said at least one media content are established by retrieving, from said at least one database, tone level probabilities for the author of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said authors.

41. The computer readable medium recited in claim 40, wherein said authors are individuals or organizations.

42. The computer readable medium recited in claim 38, wherein said tone level probabilities for all of said serialized meaningful minimum sections, said tone level probabilities for said attributes of said each of said at least one media content, and said tone level probabilities for said categories of said each of said at least one media content are determined through a training process by feeding a plurality of sample media contents with suggested tones thereof and updating said at least one database, wherein said training process comprises the steps of:

(a) splitting each of said plurality of said sample media contents into meaningful minimum sections while stripping out any unessential sections and extracting attributes and categories of said each of said plurality of said sample media contents using regular expressions;

(b) tracking number of appearances of each of said meaningful minimum sections in said each of said plurality of said sample media contents and associating said number of appearances and said suggested tone corresponding to said each of said plurality of said sample media contents for deriving said tone level probabilities of said each of said meaningful minimum sections, and updating said at least one database with said tone level probabilities of said meaningful minimum sections;

(c) deriving said tone level probabilities of said attributes of said each of said plurality of said sample media contents cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said attributes; and

(d) deriving said tone level probabilities of said categories of said each of said plurality of said sample media contents cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said categories.

43. The computer readable medium recited in claim 42, wherein said meaningful minimum sections are words.

44. The computer readable medium recited in claim 34, wherein the process further comprises the step of interfacing with said client browser to customize said regular expressions and said predefined parsing rules.

45. The computer readable medium recited in claim 34, wherein the process further comprises the steps of searching said at least one database pursuant to a request for at least one search query from said client browser, collecting and organizing search result, and pushing said search result to a plurality of recipients through said communication network.

46. The computer readable medium recited in claim 45, wherein said recipients are defined by at least one administrator through said client browser.

47. The computer readable medium recited in claim 34, wherein said parser delimits said quotes by quote delimiting characters or quote delimiting phrases.

48. The computer readable medium recited in claim 47, wherein said quote delimiting characters are the start quotation mark and the end quotation mark.

49. A method of toning at least one media contents comprising the steps of:

(a) parsing each of said media content for splitting and serializing into meaningful minimum sections;

(b) retrieving, from at least one database, tone level probabilities for all of said serialized meaningful minimum sections, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said serialized meaningful minimum sections;

(c) retrieving, from said at least one database, tone level probabilities for said attributes of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes;

(d) retrieving, from said at least one database, tone level probabilities for said categories of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories, and

(e) establishing the most probable tone level of said each of said at least one media content by weighing and ranking said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said each of the said serialized meaningful minimum sections, said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes, and said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories.

50. The method of toning said at least one media content recited in claim 49, wherein said meaningful minimum sections are words.

51. The method of toning said at least one media content recited in claim 49, wherein said toning engine further weighs tones of authors of said at least one media content for establishing said most probable tone level of said each of said at least one media content, wherein said tones of said authors of said at least one media content are established by looking up from said at least one database probabilities of tone levels for the author of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said authors.

52. The method of toning said at least one media content recited in claim 51, wherein said authors are individuals or organizations.

53. The method of toning said at least one media content recited in claim 49, wherein said tone level probabilities for all of said serialized meaningful minimum sections, said tone level probabilities for said attributes of said each of said at least one media content, and said tone level probabilities for said categories of said each of said at least one media content are determined through a training process by feeding a plurality of sample media contents with suggested tones thereof and updating said at least one database, wherein said training process comprises the steps of:

(a) splitting each of said plurality of said sample media contents into meaningful minimum sections while stripping out any unessential sections and extracting attributes and categories of said each of said plurality of said sample media contents using regular expressions;

(b) tracking number of appearances of each of said meaningful minimum sections in said each of said plurality of said sample media contents and associating said number of appearances and said suggested tone corresponding to said each of said plurality of said sample media contents for deriving probabilities of said each of said meaningful minimum sections cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said meaningful minimum sections;

(c) deriving said tone level probabilities of said attributes of said each of said plurality of said sample media contents, and updating said at least one database with said tone level probabilities of said attributes; and

(d) deriving said tone level probabilities of said categories of said each of said plurality of said sample media contents, and updating said at least one database with said tone level probabilities of said categories.

54. The method of toning said at least one media content recited in claim 53, wherein said meaningful minimum sections are words.