1-18. (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, \u201con the fly,\u201d 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 andor 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 andor changed \u201con the fly.\u201d
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 andor 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.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A shape measuring apparatus comprising:
a probe having a stylus tip at the tip;
a mover that moves the stylus tip on a surface of a workpiece to be measured;
an information acquirer, implemented by a processor, that acquires design information of the workpiece;
a path setter, implemented by a processor, that sets a path along which the stylus tip is moved, based on the design information;
a path component calculator, implemented by a processor, that calculates a path velocity vector which is a velocity component vector of the probe along the path;
a push direction component calculator, implemented by a processor, that detects a deflection of the probe toward the workpiece, and calculates a push correction vector which is a velocity component vector to be used for correcting the deflection to a prescribed reference deflection;
a locus correction component calculator, implemented by a processor, that detects an amount and a direction of locus deviation of the probe from the path, and calculates a locus correction vector which is a velocity component vector to be used for returning the probe position to the path based on a current position of the probe and the path;
a velocity synthesizer, implemented by a processor, that calculates a velocity synthesis vector by combining the path velocity vector, the push correction vector, and the locus correction vector; and
a drive controller, implemented by a processor, that moves the probe according to the velocity synthesis vector,
wherein the push direction component calculator calculates the push correction vector using a normal direction of the workpiece at a position where the stylus tip is in contact with the surface of the workpiece as a push direction.
2. The shape measuring apparatus according to claim 1, wherein:
the velocity synthesizer corrects the path velocity vector by multiplying the path velocity vector by a path direction gain, and calculates a velocity synthesis vector based on a corrected path velocity vector, the push correction vector, and the locus correction vector; and
the path direction gain is set smaller when the difference between the deflection and the reference deflection is larger than a prescribed first threshold value and the locus deviation amount is larger than a prescribed second threshold value than when at least one of a condition that the difference is smaller than the first threshold value and a condition that the locus deviation amount is smaller than the second threshold value is satisfied.
3. The shape measuring apparatus according to claim 1, wherein:
the velocity synthesizer corrects the push correction vector by multiplying a push direction correction gain, and calculates a velocity synthesis vector based on the path velocity vector, a corrected push correction vector, and the locus correction vector; and
the push direction correction gain is set smaller when the difference between the deflection and the reference deflection is smaller than or equal to a prescribed first threshold value than when the difference is larger than the first threshold value.
4. The shape measuring apparatus according to claim 1, wherein:
the velocity synthesizer corrects the locus correction vector by multiplying the locus correction vector by a locus correction gain, and calculates a velocity synthesis vector based on the path velocity vector, the push correction vector, and a corrected locus correction vector; and
the locus correction gain is set smaller when the locus deviation amount is smaller than or equal to a prescribed second threshold value than when the locus deviation amount is larger than the second threshold value.