System and method for online monitoring of and interaction with chat and instant messaging participants

• US 10,298,700 B2
• Filed: 06/25/2003
• Issued: 05/21/2019
• Est. Priority Date: 06/25/2002
• Status: Active Grant

• Current Assignee: Artimys Technologies LLC
• Original Assignee: Artimys Technologies LLC
• Inventors: Borden, Walter W., Abrams, Alexander, Spiegel, Dana S.
• Primary Examiner(s): Coulter, Kenneth R
• Application Number:
  US10/606,517
• Publication Number:
  US 20040111479A1
• Time in Patent Office: 5,809 Days
• Field of Search: 709224, 709204-207, 709229, 726 22- 27, 715751-759
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: H04L 12/1813 for computer conferences, e.g. chat rooms instant messaging H04L51/04; protocols for multimedia communication H04L65/1101; arrangements for multi-party communication H04L65/403; telephonic conference arrangements H04M3/56; television conference systems H04N7/15
• CPC Class Codes: H04L 43/00 Arrangements for monitoring or testing data switching networks
• CPC Class Codes: H04L 51/04 Real-time or near real-time messaging, e.g. instant messaging [IM]
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 Network security protocols

Abstract

A method and system for on-line monitoring of electronic communications include automatically monitoring text-based communications of one or more chat room or instant messaging participants to determine if a monitoring event has occurred. A monitoring event can be, for example, the combined existence of certain defined factors associated with a particular exchange relating to, for example, the exchange'"'"'s participants, content, recent history, and/or tone. The existence of a monitoring event can result in one or more predetermined actions being taken by the system. In an exemplary embodiment of the present invention, on-line communications are continually monitored and input to a number of pattern recognizing modules, preferably working in parallel. Using known pattern-recognition techniques, each pattern recognizing module can analyze an aspect of such communications by implementing certain algorithms and, as appropriate, set, increase or decrease the values of one or more state variables descriptive of one or more defined attributes of the online communications. Such state variables can reflect, for example, emotional levels, participant turnover frequencies, use of suspicious word patterns, and other metrics regarding the on-line communications. State variables out put form the various pattern recognizing modules can be simultaneously input to a decision module which can launch one or more decision algorithms. The decision module can then output one or more predetermined actions as determined by the decision algorithms.

Claims

1. A method for monitoring an electronic communication, the method comprising:

accessing, by a server, a communication from at least one of a chat room participant or an instant messaging (“

IM”

) participant, wherein the communication is via a client;

analyzing, by the server, a conversational content of the communication using a plurality of artificial intelligence pattern recognition techniques operating in parallel, wherein at least one of the artificial intelligence pattern recognition techniques employs at least one of a natural language parser, a stochastic tagger, a neural network, a knowledge domain specific dictionary, a behavior recognizer, a Bayesian filter, a neural-network filter, or an emotion filter;

deciding, by the server, whether a monitoring event has occurred based upon a result of the analyzing and a set of predetermined rules, wherein the at least one of the artificial intelligence pattern recognition techniques or the set of predetermined rules is dynamically updated; and

taking, by the server, a predetermined action in response to the monitoring event, wherein the accessing, the analyzing, the deciding, and the taking are accomplished automatically in response to the accessing.

2. The method of claim 1, wherein the set of predetermined rules is stored in a database.

3. The method of claim 2, wherein the database includes a SQL database.

4. The method of claim 2, wherein the set of predetermined rules include a rule based on an interaction with at least one of the chat room participant or the IM participant.

5. The method of claim 2, wherein the set of predetermined rules includes a rule based on an interaction between a plurality of non-chat room participants in a chat room.

6. The method of claim 1, wherein an action related to the accessing, the analyzing, and the deciding is not displayed to the chat room participant and the IM participant.

7. The method of claim 1, where the predetermined action includes at least one of informing at least one of the chat room participant or the IM participant of a rule violation, informing at least one of the chat room participant or the IM participant of a potentially dangerous situation, reporting a violation to a parent client or a guardian client of at least one of the chat room participant or the IM participant, reporting at least one of a violation or potential violation to an ISP server, or blocking the communication.

8. The method of claim 7, wherein the reporting is accomplished via at least one of a wireless communication, an electronic mail communication, or a voice communication.

9. The method of claim 8, wherein the wireless communication includes at least one of a cellular telephony or a paging system.

10. The method of claim 1, wherein the monitoring event includes at least one of a violation of a chat rule, an occurrence of a conversational content pattern, or an occurrence of a predetermined conversational behavioral pattern.

11. The method of claim 1, wherein the communication includes a wide area network communication.

12. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs at least one of a natural language parser, a stochastic tagger, a neural network, or a knowledge domain specific dictionary.

13. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques seeks to determine a round-robin behavior of communication.

14. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs at least one of a Bayesian filter, a neural-network filter, or an emotion filter.

15. The method of claim 1, wherein the analyzing is based on parsing the conversational content in accordance with a natural language processing technique and then querying the conversational content in accordance with a natural language query technique.

16. The method of claim 1, wherein the analyzing is in parallel for a plurality of patterns.

17. The method of claim 1, further comprising:

setting, by the server, a value of a state variable descriptive of a defined attribute of the conversational content; and

modifying, by the server, the value based on the analyzing, wherein the deciding is based on the value.

18. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the natural language parser.

19. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the stochastic tagger.

20. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the neural network.

21. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the knowledge domain specific dictionary.

22. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the behavior recognizer.

23. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the Bayesian filter.

24. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the neural-network filter.

25. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques employs the emotion filter.

26. The method of claim 1, wherein the communication is from the chat room participant.

27. The method of claim 1, wherein the communication is from the IM participant.

28. The method of claim 1, wherein the at least one of the artificial intelligence pattern recognition techniques is dynamically updated.

29. The method of claim 1, wherein the set of predetermined rules is dynamically updated.

30. A system for monitoring a network communication, the system comprising:

a system bus;

a central processing unit coupled to the system bus;

a memory coupled to the system bus; and

an I/O interface coupled to the system bus;

wherein the memory contains an executable code to be processed by the central processing unit, wherein the executable code causes the central processing unit to;

access a communication from at least one of a chat room participant or an instant messaging participant, where the communication is via a client remote from the central processing unit;

analyze a conversational content of the communication using a plurality of artificial intelligence pattern recognition techniques operating in parallel, wherein at least one of the artificial intelligence pattern recognition techniques employs at least one of a natural language parser, a stochastic tagger, a neural network, a knowledge domain specific dictionary, a behavior recognizer, a Bayesian filter, a neural-network filter, or an emotion filter;

decide whether a monitoring event has occurred based upon a result of the analyzing and a set of predetermined rules, wherein the at least one of the artificial intelligence pattern recognition techniques or the set of predetermined rules is dynamically updated; and

take a predetermined action in response to the monitoring event.

31. The system of claim 30, wherein the system bus, the central processing unit, the memory, and the I/O interface form at least one of a server computer, a desktop computer, or a laptop computer.

32. The system of claim 30, where the memory includes at least one of a read only memory or a random access memory.

33. The system of claim 30, further comprising:

a secondary storage device coupled to the system bus.

34. The system of claim 33, wherein the secondary storage device includes a direct access storage device.

35. The system of claim 30, wherein the memory includes a rules module, a history module, a user history module and a report module.

36. A computer program product comprising a non-transitory computer readable medium having a computer readable program code embodied therein, the computer readable program code in the computer program product comprising a set of instructions for causing a server to automatically:

access a communication from a participant of at least one of a chat room or an instant message session, wherein the communication is via a client;

analyze a conversational content of the communication using a plurality of artificial intelligence pattern recognition techniques operating in parallel, wherein the artificial intelligence pattern recognition techniques include a first artificial intelligence pattern recognition technique and a second artificial intelligence pattern recognition technique, wherein at least one of the first artificial intelligence pattern recognition technique or the second artificial intelligence pattern recognition technique employs at least one of a natural language parser, a stochastic tagger, a neural network, a knowledge domain specific dictionary, a behavior recognizer, a Bayesian filter, a neural-network filter, or an emotion filter;

decide whether a monitoring event has occurred based upon a result of the analyzing and a set of predetermined rules, wherein at least one of the first artificial intelligence pattern recognition technique, the second artificial intelligence pattern recognition technique, or the set of predetermined rules is dynamically updated based on the analyzing; and

take a predetermined action in response to the monitoring event.

37. The computer program product of claim 36, wherein the conversational content is analyzed via the first artificial intelligence pattern recognition technique and the second artificial intelligence pattern recognition technique in parallel.

38. The computer program product of claim 37, wherein the deciding is based on a first output from the first artificial intelligence pattern recognition technique and a second output from the second artificial intelligence pattern recognition technique.

39. A method comprising:

accessing, by a monitoring server, a real-time message sent from a sender client to a receiver client over a messaging server coupled to the monitoring server, wherein the real-time message comprises a user input conversational communication content;

in response to the accessing, in real-time, automatically;

parsing, by the monitoring server, the user input conversational communication content into a plurality of segments;

analyzing, by the monitoring server, at least one of the segments via a plurality of artificial intelligence pattern recognition techniques operating in parallel;

recognizing, by the monitoring server, a pattern in the at least one of the segments via at least one of the artificial intelligence pattern recognition techniques;

deciding, by the monitoring server, whether a monitoring event has occurred based on the analyzing and the recognizing and a dynamically updateable rule stored in a database coupled to the monitoring server; and

taking, by the monitoring server, a predetermined action in response to the deciding, wherein the predetermined action relates to an intervention with respect to at least one of the sender client or the receiver client.

40. The method of claim 39, wherein the recognizing is based on parsing the real-time message in accordance with a natural language processing technique and then querying the real-time message in accordance with a natural language query technique.

41. The method of claim 39, wherein the pattern is a first pattern, and further comprising:

recognizing, by the monitoring server, a second pattern in the at least one of the segments in parallel to recognizing the first pattern in the at least one of the segments.

42. The method of claim 39, further comprising:

setting, by the monitoring server, a value of a state variable descriptive of a defined attribute of the real-time message; and

modifying, by the monitoring server, the value based on the recognizing, wherein the deciding is based on the value.

43. The method of claim 39, wherein the predetermined action relates to the intervention with respect to the sender client.

44. The method of claim 39, wherein the predetermined action relates to the intervention with respect to the receiver client.

45. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs a natural language parser.

46. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs a stochastic tagger.

47. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs a neural network.

48. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs a knowledge domain specific dictionary.

49. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs a behavior recognizer.

50. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs a Bayesian filter.

51. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs a neural-network filter.

52. The method of claim 39, wherein the at least one of the artificial intelligence pattern recognition techniques employs an emotion filter.

53. A method comprising:

accessing, via a server, a communication from at least one of a chat room participant or an instant messaging participant, wherein the communication includes a conversational content, wherein the communication is received from a client, wherein the client is remote from the server;

performing, via the server, an analysis of the conversational content via a plurality of artificial intelligence pattern recognition techniques operating in parallel, wherein at least one of the artificial intelligence pattern recognition techniques employs at least one of a natural language parser, a stochastic tagger, a neural network, a knowledge domain specific dictionary, a behavior recognizer, a Bayesian filter, a neural- network filter, or an emotion filter;

generating, via the server, a result of the analysis;

deciding, via the server, whether a monitoring event has occurred based on the result and a set of predetermined rules, wherein the at least one of the artificial intelligence pattern recognition techniques or the set of predetermined rules is dynamically updateable; and

taking, via the server, a predetermined action responsive to the monitoring event.

54. The method of claim 53, wherein the communication is from the chat room participant.

55. The method of claim 53, wherein the communication is from the instant messaging participant.

56. The method of claim 53, wherein the server is a plurality of distributed servers.

57. The method of claim 53, wherein the client is a mobile wireless client.

58. The method of claim 53, wherein the server is in communication with at least one of a chat program enabling the chat room participant or an instant messaging program enabling the instant messaging participant.

59. The method of claim 53, wherein the client hosts a browser, wherein the communication is received from the browser.

60. The method of claim 53, wherein at least some of the analysis is performed on a latent basis.

61. The method of claim 53, wherein the conversational content includes a text.

62. The method of claim 53, wherein at least two of the artificial intelligence pattern recognition techniques employ at least one of a natural language parser, a stochastic tagger, a neural network, a knowledge domain specific dictionary, a behavior recognizer, a Bayesian filter, a neural-network filter, or an emotion filter.

63. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs a natural language parser.

64. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs a stochastic tagger.

65. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs a neural network.

66. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs a knowledge domain specific dictionary.

67. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs a behavior recognizer.

68. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs a Bayesian filter.

69. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs a neural-network filter.

70. The method of claim 53, wherein at least one of the artificial intelligence pattern recognition techniques employs an emotion filter.

71. A method comprising:

accessing, by a first server, a real-time message sent from a sender client to a receiver client over a second server coupled to the first server, wherein the real-time message comprises a user input conversational communication content;

based on the real-time message being accessed in real-time;

parsing, by the first server, the user input conversational communication content into a plurality of segments;

performing, by the first server, an analysis of at least one of the segments via a plurality of artificial intelligence pattern recognition techniques operating in parallel;

recognizing, by the first server, a pattern in the at least one of the segments via at least one of the artificial intelligence pattern recognition techniques;

forming, by the first server, a decision whether a monitoring event has occurred based on the analysis, the pattern, and a dynamically updateable rule stored in a database coupled to the first server; and

taking, by the first server, a predetermined action responsive to the decision that the monitoring event has occurred, wherein the predetermined action relates to an intervention with respect to at least one of the sender client or the receiver client.

72. The method of claim 71, wherein the sender client and the receiver client are participating in a chat session including the real-time message.

73. The method of claim 71, wherein the sender client and the receiver client are participating in an instant messaging session including the real-time message.

74. The method of claim 71, wherein at least one of the first server or the second server is a plurality of distributed servers.

75. The method of claim 71, wherein at least one of the sender client or the receiver client is a mobile wireless client.

76. The method of claim 71, wherein the second server is at least one of a chat server or an instant messaging server.

77. The method of claim 71, wherein the sender client hosts a browser, wherein the real-time message is received from the browser.

78. The method of claim 71, wherein at least some of the analysis is performed on a latent basis.

79. The method of claim 71, wherein the real-time message includes a text.

80. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employ at least one of a natural language parser, a stochastic tagger, a neural network, a knowledge domain specific dictionary, a behavior recognizer, a Bayesian filter, a neural-network filter, or an emotion filter.

81. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs a natural language parser.

82. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs a stochastic tagger.

83. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs a neural network.

84. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs a knowledge domain specific dictionary.

85. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs a behavior recognizer.

86. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs a Bayesian filter.

87. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs a neural-network filter.

88. The method of claim 71, wherein at least one of the artificial intelligence pattern recognition techniques employs an emotion filter.