Prediction Method and Device For Evaluating and Forecasting Stochastic Events

• US 20080147702A1
• Filed: 03/16/2005
• Published: 06/19/2008
• Est. Priority Date: 03/16/2004
• Status: Abandoned Application

• Current Assignee: Bernhard-Muhling-Weineck Gbr
• Original Assignee: Bernhard-Muhling-Weineck Gbr
• Inventors: Bernhard, Michael
• Application Number:
  US10/592,731
• Publication Number:
  US 20080147702A1
• Time in Patent Office: Days
• US Class Current: 707/102
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

The invention relates to a prediction method and device for evaluating and forecasting stochastic events. The problem with prediction methods and devices of this type is that they have a potentially static structure and cannot be adapted to modified data records or modified marginal conditions of the stochastic events. As the invention uses a feedback of the evaluation results, a novel prediction method and a dynamic prediction device can be provided. The method and device are in addition characterized in that they can process the input parameter records and input conditions in real time, while allowing a modified variable allocation to be included via an additional set-up input.

Claims

1-18. -18. (canceled)

19. Prediction method for dynamically evaluating and forecasting stochastic events, in which an event data set is applied to a request input (11) of a processing unit (5) as a request (35), in the form of a defined, but not necessarily standardized, n-tuple, and each event data set is answered with a binary event value, 0 or 1, at a response output (12) of the processing unit (5), whereby then the event data set is rejected or passed to a subsequent evaluation unit (6), as a function of this event value, the evaluation result of which unit is fed back to a return input (10) of the processing unit (5), whereby the parameters of the event data sets can be defined by means of a set-up input (15) of the processing unit (5), whereby additional parameters can be entered into and defined in the event data set to be processed, “

on the fly,”

or parameters can be eliminated, by way of the set-up input (15).

20. Prediction method according to claim 19, wherein the process unit (5) has an additional cut-off input (14), at which the ratio of the binary event values relative to one another is set.

21. Prediction method according to claim 19, wherein the processing unit (5) and the subsequent evaluation unit (6) are switched in the manner of a simple, self-adapting regulation circuit, whereby cycling and control of the prediction method as a whole are carried out by the processing unit (5).

22. Prediction method according to claim 19, wherein at the subsequent evaluation unit (6), a characteristic vector, in each instance, is handed over to two separate inputs (16, 17), whereby the one characteristic vector, in each instance, comprises a target parameter value, and the other characteristic vector, in each instance, is not occupied with regard to the target parameter, and for each paid of characteristic vectors handed over to the evaluation unit (6), a target parameter value is output, after the evaluation process has been run through, whereby this target parameter value is fed back to an additional score input (23) of the processing unit (5).

23. Prediction method according to claim 19, wherein the event data sets are applied to the request input (11) of the processing unit (5) in the form of an n-tuple, whereby n is changeable.

24. Prediction method according to claim 19, wherein the evaluation result fed back to the return input (10) of the processing unit (5) is a numerical value.

25. Prediction method according to claim 19, wherein the evaluation process applied in the evaluation unit (6) has an incremental learning mechanism for improving the evaluation result, in which first optimization of the evaluation process by means of a defined number of predetermined training event data sets takes place, which are applied sequentially, whereby subsequently, further optimization of the evaluation process is provided, in such a manner that a time-related evaluation of the evaluation results takes place, in such a manner that older evaluation results flow into the self-adaptation of the evaluation process with weaker priority than more recent evaluation results.

26. Prediction method according to claim 19, wherein the prediction method is divided, depending on the learning progress, into at least three method runs that can be differentiated, whereby in a first method run, the event data sets to be evaluated are written into a request cache (24) of the processing unit (5), and fundamentally evaluated with the event value 1, and the evaluation results returned to the return input (11) are stored and their quality is evaluated, whereby when a defined threshold value of the quality is reached, a switch takes place to a second method run, in which now the self-adapting evaluation process that takes place in the evaluation unit (6) is interposed, and it now depends on this evaluation whether 1 or 0 is output as the event value at the response output (12), whereby in the further proceedings, only the event data sets in connection with which the event value 1 was output at the response output (12) are stored in the request cache (24), and finally, when a further threshold value of the threshold parameter counter (31) is reached, a third method run is started, in the course of which the work is carried out with a changed parameter data set, within the evaluation unit (6).

27. Prediction method according to claim 19, wherein the changes in the parameter set are detected and displayed on a display device, preferably in the form of a change curve.

28. Prediction method according to claim 19, wherein a sequential training data stream is passed to the prediction method, by way of an endless loop, until the prediction method has reached a predetermined quality and/or stability, and the results are filed in a score card.

29. Prediction device for dynamically evaluating and predicting stochastic events, comprising a processing unit (5) and an evaluation unit (6), for implementing an evaluation process, which are connected with one another in the form of a simple, self-adapting regulation circuit, whereby the processing unit (5) has a request input (11) to which an event data set in the form of an n-tuple is applied, in each instance, and a response output (12) for outputting a digital event value, 0 or 1, in response to the event data set, in each instance, is provided, whereby either feed-back of the evaluation result of the evaluation unit (6) to an additional score input (23) of the processing unit (5) is provided as a function of the event value, with the interposition of the evaluation unit (6), or no further processing of the event data set is provided, and the processing unit (5) has an additional set-up input (15), by way of which the type and number of the variables of the event data set can be entered and/or changed “

on the fly.”

30. Prediction device according to claim 29, wherein the processing unit (5) has an additional cut-off input (14) at which the ratio of the digital event values relative to one another can be set.

31. Prediction device according to claim 29, wherein a request cache (24) for intermediate storage of the event data sets as well as a counter for storing the number of the event data sets answered with the event value 1 is assigned to the processing unit (5).

32. Prediction device according to claim 29, wherein the evaluation device (6) that follows the processing unit (5) has two separate inputs (16, 17), to which two characteristic vectors are applied, in each instance, whereby one of the characteristic vectors, in each instance, has a target variable, and in the case of the other characteristic vector, in each instance, the target value is not occupied.

33. Prediction device according to claim 29, wherein the processing unit (5) and the evaluation unit (6) are disposed in a common computer system, whereby this computer system is connected with a display unit (1) and this computer system stands in data connection with a customer database (3), whereby the event data set comprises the purchase decision of the customers in connection with possible offers and/or other parameters.

34. Prediction device according to claim 29, wherein the prediction device (4) is connected with a telephone system (2), and the customer data set from the customer database (3) is played for the prediction device (4) as a function of the telephone number of the caller, in each instance, and subsequently, a prediction of the purchase decision is output by way of the display device (1), by means of one or more event data sets that represent possible offers to the customer, in each instance.