System and method for aggregation and monitoring of multimedia data stored in a decentralized way

• US 20100076962A1
• Filed: 12/08/2008
• Published: 03/25/2010
• Est. Priority Date: 04/12/2007
• Status: Active Grant

• Current Assignee: Swiss Reinsurance Company
• Original Assignee: Swiss Reinsurance Company
• Inventors: Doyle, Philipp
• Granted Patent: US 8,204,879 B2
• Time in Patent Office: Days
• US Class Current: 707/727
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor

Abstract

System and method for aggregation and monitoring of multimedia data stored in a decentralized way for triggering upcoming class action events, whereas source databases (401,411,421,431) of the network node (40,41,42,43) are accessed by means of a filter module (30), and for at least one rating parameter (320,321,322) in connection with assigned search key words (310,311,312,313) and/or the assigned source databases (401,411,421,431) with respect to an time-based rating and an exposure-based frequency rating a scorecard (330,331,332) is generated with found data sets, and whereas by means of a parameterization module (20), based on the scorecard (330,331,332) for the respective rating parameter (320,321,322) with respect to their exposure-based frequency a variable frequency value (21) is generated at least partially dynamically, which variable frequency value (21) corresponds to network (50) class action frequency variations, with respect to time, and whereas by means of a tracing unit based on a generated assigned distribution of the variable frequency values (21) a predefined exposure threshold is triggered, whereas a trigger signal is generated with respect to time and the variable frequency values (21) and transmitted for activation to a receiving work unit.

Claims

1. Method for aggregation and monitoring of multimedia data stored in a decentralized way triggering upcoming class actions and/or legal actions, one or more linkable search key words (310,311,312,313) being stored in a memory (31), a computing unit (10) accessing, via a network (50), network nodes (40,41,42,43) connected to source databases (401,411,421,431), and data from the source databases (401,411,421,431) being selected based on the search key words (310,311,312,313), characterized

in that stored, assigned to a search key word (310,311,312,313) and/or to a combination of search key words (310,311,312,313), in a memory (32), is at least one rating parameter (320,321,322),

in that the source databases (401,411,421,431) of the network node (40,41,42,43) are accessed by means of a filter module (30) of the computing unit (10), and for each rating parameter (320,321,322) in connection with the assigned search key words (310,311,312,313) and/or the assigned source databases (401,411,421,431) with respect to an time-based rating and an exposure-based frequency rating a scorecard (330,331,332) is generated with found data sets,

in that, by means of a parameterization module (20), based on the scorecard (330,331,332) for the respective rating parameter (320,321,322) with respect to their exposure-based frequency a variable frequency value (21) is generated at least partially dynamically, which variable frequency value (21) corresponds to network (50) class action frequency variations and/or legal action frequency variations, with respect to time, and

in that by means of a tracing unit based on a generated assigned distribution of the variable frequency values (21) a predefined exposure threshold is triggered, whereas a trigger signal is generated with respect to time and the variable frequency values (21) and transmitted for activation to a receiving work unit.

2. Method according to claim 1, characterized in that the scorecard (330,331,332) is stored, in a way accessible to a user, with the found data sets and/or links to found data sets in a content module (60) of the computing unit (10).

3. Method according to one of the claim 1 or 2, characterized in that the variable frequency values (21) are periodically checked by means of the computing unit (10), and, if at least one of the variable frequency values (21) lies outside a definable tolerated range of deviation or a determinable expected value, the corresponding scorecard (330,331,332) with the found data sets and/or links to found data sets are stored in the content module (60) of the computing unit (10) accessible for a user and/or updated.

4. Method according to one of the claims 1 to 3, characterized in that one or more of the rating parameters (320,321,322) are generated by means of a lexicographic evaluation database.

5. Method according to one of the claims 1 to 4, characterized in that one or more of the rating parameters (320,321,322) are generated dynamically by means of the computing unit (10) during the generation of the scorecard (330,331,332).

6. Method according to one of the claims 1 to 5, characterized in that the trigger signal is transmitted as activation signal to a derivative operation system automatically activating or blocking corresponding financial transactions.

7. Method according to one of the claims 1 to 5, characterized in that the trigger signal is transmitted as activation signal to an automated (re)insurance operation system automatically activating or blocking corresponding financial transactions.

8. Method according to one of the claims 1 to 7, characterized in that for generation of the variable frequency values (21) and/or of the data of the content module (60) HTML and/or HDML and/or WML and/or VRML and/or ASD are used.

9. Method according to one of the claims 1 to 8, characterized in that a user profile is created based on user information, user-specific optimized data being generated, by means of a repackaging module (61), based on the found data sets and/or links to the found data sets stored in the content module (60), taking into consideration the data of the user profile, which user-specific optimized data are stored in the content module (60) of the computing unit (10) in a way made available to the user (12).

10. Method according to claim 9, characterized in that different user profiles for different communication devices (111, 112, 113) of the user (12) are stored, assigned to the user.

11. Method according to one of the claims 9 or 10, characterized in that data on user behavior are automatically captured by the computing unit (10), and are stored assigned to the user profile.

12. Method according to one of the claims 1 to 11, characterized in that, by means of a history module (22), the values up to a definable past point in time are stored for each calculated variable frequency values (21).

13. Method according to claim 12, characterized in that the computing unit (10) calculates expected values for a determinable frequency value (21), by means of an extrapolation module (23), based on the data of the history module (22) for a definable future point in time, and stores them in a memory of the computing unit (10).

14. Method according to one of the claims 1 to 13, characterized in that triggering the predefined exposure threshold comprises aggregating a parametric stop index.

15. Method according to claim 14, characterized in that the parametric stop index comprises incrementing occurring class actions.

16. Method according to one of the claims 14 and 15, characterized in that the parametric stop index is assigned to a parametric aggregated stop loss parameter for triggering automated damage intervention systems.

17. System for aggregation and monitoring of multimedia data stored in a decentralized way triggering class actions and/or legal actions, comprising a computing unit (10), a memory (31) for storing one or more linkable search key words (310,311,312,313), and network nodes (40,41,42,43) connected to source databases (401,411,421,431), the source databases (401,411,421,431) being connected bidirectionally over the network (50) to the computing unit (10), characterized in that the computing unit (10) comprises a memory (32) for storing at least one rating parameter (320,321,322), the rating parameter (320,321,322) being assignable to a search key word (310,311,312,313) and/or to a combination of search key words (310,311,312,313),

in that the computing unit (10) comprises a filter module (30) for generating a scorecard (330,331,332) for data sets found in the source databases (401,411,421,431) of the network nodes (40,41,42,43), and for each rating parameter (320,321,322) in connection with the assigned search key words (310,311,312,313) and/or the assigned source databases (401,411,421,431) with respect to an time-based rating and an exposure-based frequency rating a scorecard (330,331,332) is generated with found data sets, and

in that the computing unit (10) comprises a parameterization module (20) for at least partially dynamic generation of a variable frequency value (21) based on the scorecard (330,331,332) for the respective rating parameter (320,321,322), which variable frequency value (21) is at least partially dynamically generatable based on the scorecard (330,331,332) for the respective rating parameter (320,321,322) with respect to their exposure-based frequency, and which variable frequency value (21) corresponds to network (50) class action frequency variations, with respect to time,

in that the computing unit (10) comprises a tracing unit, by means of which tracing unit a predefined exposure threshold is triggerable based on a generated assigned distribution of the variable frequency values (21), whereas the trigger is carried out with respect to time and the variable frequency values (21).

18. System according to claim 17, characterized in that the computing unit comprises a module for dynamic generation of one or more of the rating parameters (320,321,322) during generation of the scorecard (330,331,332).

19. System according to one of the claim 17 or 18, characterized in that the scorecard (330,331,332) is stored with the found data sets and/or links to found data sets in a content module (60) of the computing unit (10) in a way accessible to the user.

20. System according to claim 19, characterized in that, by means of the computing unit (10), the variable frequency values (21) are able to be checked periodically, and, if at least one of the variable frequency values (21) lies outside a definable tolerated range of deviation or a determinable expected value, the corresponding scorecard (330,331,332) with the found data sets and/or links to found data sets is updatable in the content module (60) of the computing unit (10).

21. System according to one of the claims 17 to 20, characterized in that the computing unit (10) comprises a module for generation of the variable frequency values (21) and/or of the data of the content module (60) by means of HTML and/or HDML and/or WML and/or VRML and/or ASD.

22. System according to one of the claims 17 to 21, characterized in that, for each user (12), the computing unit (10) includes a user profile with user information, whereby, based on the found data sets and/or links to the found data sets stored in the content module (60), user-specific optimized data are able to be generated by means of a repackaging module (61), taking into consideration the data of the user profile.

23. System according to claim 22, characterized in that different user profiles for different communication devices (111,112,113) of the user (12) are stored assigned to the user (12).

24. System according to one of the claim 22 or 23, characterized in that data on user behavior are captured automatically by the computing unit (10) and are storable assigned to the user profile.

25. System according to one of the claims 17 to 24, characterized in that the computing unit (10) comprises a history module (22), comprising for each calculated variable frequency values (21) the values up to a definable past point in time, and on which the variable frequency values (21) are accessible by means of the communication devices (111,112,113).

26. System according to claim 25, characterized in that the computing unit (10) comprises an extrapolation module (23), by means of which expected values are calculable up to a future point in time definable by the user (12).

27. System according to one of the claims 17 to 26, characterized in that the predefined exposure threshold comprises an aggregated parametric stop index.

28. System according to claim 27, characterized in that the parametric stop index comprises incremented occurring class action events.

29. System according to one of the claim 27 or 28, characterized in that the parametric stop index is assigned to a parametric aggregated stop loss parameter for triggering automated damage intervention systems.