State of the art was the European patent application EP 99105884.3 (see application data sheet). This patent application used already non-linear systems of equations and conditional probabilities with one single item in the condition. It was necessary, however, to perfect these theoretical methods and make them practicable. Many improvements and innovative modifications were needed. The following list identifies the innovations that had to be provided: The accurate indication of all systems of equations concerning 2, 3 and 4 hypotheses. Those equations can be entered in exactly the presented form into the calculation program. The introduction of coefficients aik and bik that can be applied without changes for any areas of use. The delivery of a scheme that enables the mechanized production of the aik and bik. Introducing schematic tables with identical follow events in one row (e.g. Table 3). Using appreciation factors AF(i) if the hypotheses Ki′ have the same a-priori probability. Uncomplicated approach to the causes of the causative events Ki and to the inhibitors. New factors fij for creating a simplification in order to allow an immediate consideration of symptoms which, although expected, did not occur. Continuous updating of probabilities used. No self-developed iterative methods of solution are used, but commercially available calculation programs. A complete and workable example of the automated analysis of electrocardiograms is presented which may serve also as a design template. The entire operation (using the calculation program selected at this point) is done with just one mouse click.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented universal method that can be used in numerous areas of knowledge, for example, earthquake research, geological prospecting, criminal forensics, aircraft accident investigation, on-board diagnostics in road cars and aircraft, monitoring of sea-based electricity generators, and in medicine, in the latter case amongst other things for the evaluation of electrocardiograms; the method is applicable in all tasks where several hypotheses stand for selection and the most likely candidate will be determined by the symptoms (observed or missing although expected), the surrounding hypotheses, and—according to need—the inhibitors; the invention is characterized by an algebraic method which works on four competing diagnoses Ki′, i:=1, . . . , 4 and calculates for each one of them the appreciation factor AF(i) and the a-posteriori probability xi, with the highest upgrading factor determining the correct diagnosis; every Ki′ is affiliated with a set of follow events (Fij, j:=1, . . . , 6) with the properties that each event from {Fij} is the follow event of at least two diagnoses, that the elements in the set {Ki′, i:=1, . . . , 4} are stochastically independent and stochastically self-reliant and that {Ki′, i:=1, . . . , 4} contains either all causes of the Fij or is supplemented by additional Ki in negated form; all together enables the formation of the following equations: x1:=p(K1|F11 . . . F16K2′K3′K4′), x2:=p(K2|F21 . . . F26K1′K3′K4′), x3:=p(K3|F31 . . . F36K2′K1′K4′), x4:=p(K4|F41 . . . F46K2′K3′K1′), which get a transformation into x i := 1 1 + Z i N i · p  ( K _ i ) p  ( K i ) , i:=1, . . . ,4, with Z1:=p(F11 . . . F16| K1′K2′K4′) and N1:=p(F11 . . . F16|K1K2′K3′K4′), Z2:=p(F21 . . . F26| K2K1′K3′K4′) and N2:=p(F21. . . F26|K2K1′K3′K4′), Z3:=p(F31 . . . F36| K3K2′K1′K4′) and N3:=p(F31 . . . F36|K3K2′K1′K4′), Z4=p(F41 . . . F46| K4K2′K3′K1′) and N4:=p(F41 . . . F46|K4K2′K3′K1′), whereby the Zi and Ni are subjected to a linear interpolation, so that for e.g. Z1 goes on in p  ( F 11   …   F 16  K _ 1  K 2 ′  K 3 ′  K 4 ′ ) = p  ( F 11   …   F 16  K _ 1  K 2  K 3  K 4 ) · x 2 · x 3 · x 4 + ( F 11   …   F 16  K _ 1  K 2  K 3  K _ 4 ) · x 2 · x 3 · x _ + p  ( F 11   …   F 16  K _ 1  K 2  K _ 3  K 4 ) · x 2 · x _ 3 · x 4 + p  ( F 11   …   F 16  K _ 1  K 2  K _ 3  K _ 4 ) · x 2 · x _ 3 · x _ 4 + p  ( F 11   …   F 16  K _ 1  K _ 2  K 3  K 4 ) · x _ 2 · x 3 · x 4 + p  ( F 11   …   F 16  K _ 1  K _ 2  K 3  K _ 4 ) · x _ 2 · x 3 · x _ 4 + p  ( F 11   …   F 16  K _ 1  K _ 2  K _ 3  K 4 ) · x _ 2 · x _ 3 · x 4 + p  ( F 11   …   F 16  K _ 1  K _ 2  K _ 3  K _ 4 ) · x _ 2 · x _ 3 · x _ 4 , and wherein for the conditional probabilities present in such interpolations, the designations aik and bik are chosen, k:=0, . . . , 7, in detail aik for the interpolations of Zi and bik for the interpolations of Ni, in a manner that, for example, the first factor in the equation above will be replaced by a10 with a10:=p(F11 . . . F16| K1K2K3K4); using ci:=p(Ki), a system of equations in the four unknowns xi x 1 = 1 1 + a 10  x 2  x 3  x 4 + a 11  x 2  x 3  x _ 4 + a 12  x 2  x _ 3  x 4 + a 13  x 2  x _ 3  x _ 4 + a 14  x _ 2  x 3  x 4 + a 15  x _ 2  x 3  x _ 4 + a 16  x _ 2  x _ 3  x 4 + a 17  x _ 2  x _ 3  x _ 4 b 10  x 2  x 3  x 4 + b 11  x 2  x 3  x _ 4 + b 12  x 2  x _ 3  x 4 + b 13  x 2  x _ 3  x _ 4 + b 14  x _ 2  x 3  x 4 + b 15  x _ 2  x 3  x _ 4 + b 16  x _ 2  x _ 3  x 4 + b 17  x _ 2  x _ 3  x _ 4  ( c _ 1 c 1 ) x 2 = 1 1 + a 20  x 1  x 3  x 4 + a 21  x 1  x 3  x _ 4 + a 22  x 1  x _ 3  x 4 + a 23  x 1  x _ 3  x _ 4 + a 24  x _ 1  x 3  x 4 + a 25  x _ 1  x 3  x _ 4 + a 26  x _ 1  x _ 3  x 4 + a 27  x _ 1  x _ 3  x _ 4 b 20  x 1  x 3  x 4 + b 21  x 1  x 3  x _ 4 + b 22  x 1  x _ 3  x 4 + b 23  x 1  x _ 3  x _ 4 + b 24  x _ 1  x 3  x 4 + b 25  x _ 1  x 3  x _ 4 + b 26  x _ 1  x _ 3  x 4 + b 27  x _ 1  x _ 3  x _ 4  ( c _ 2 c 2 ) x 3 = 1 1 + a 30  x 2  x 1  x 4 + a 31  x 2  x 1  x _ 4 + a 32  x 2  x _ 1  x 4 + a 33  x 2  x _ 1  x _ 4 + a 34  x _ 2  x 1  x 4 + a 35  x _ 2  x 1  x _ 4 + a 36  x _ 2  x _ 1  x 4 + a 37  x _ 2  x _ 1  x _ 4 b 30  x 2  x 1  x 4 + b 31  x 2  x 1  x _ 4 + b 32  x 2  x _ 1  x 4 + b 33  x 2  x _ 1  x _ 4 + b 34  x _ 2  x 1  x 4 + b 35  x _ 2  x 1  x _ 4 + b 36  x _ 2  x _ 1  x 4 + b 37  x _ 2  x _ 1  x _ 4  ( c _ 3 c 3 ) x 4 = 1 1 + a 40  x 2  x 3  x 1 + a 41  x 2  x 3  x _ 1 + a 42  x 2  x _ 3  x 1 + a 43  x 2  x _ 3  x _ 1 + a 44  x _ 2  x 3  x 1 + a 45  x _ 2  x 3  x _ 1 + a 46  x _ 2  x _ 3  x 1 + a 47  x _ 2  x _ 3  x _ 1 b 40  x 2  x 3  x 1 + b 41  x 2  x 3  x _ 1 + b 42  x 2  x _ 3  x 1 + b 43  x 2  x _ 3  x _ 1 + b 44  x _ 2  x 3  x 1 + b 45  x _ 2  x 3  x _ 1 + b 46  x _ 2  x _ 3  x 1 + b 47  x _ 2  x _ 3  x _ 1  ( c _ 4 c 4 ) results, wherein for all Ki′ equiprobability with p(Ki):=0.25 is first assumed; in order to get the aik and bik—by using the conditional stochastic independence of the F-elements—a factorization with respect to the F-elements is carried out, for example as a10:=p(F11| K1K2K3K4)· . . . ·p(F16| K1K2K3K4); a further factorization of the emerging conditional probabilities is performed—taking into account the stochastic self-reliance of the Ki—for example p(F11| K1K2K3K4)=[1−p( F11|K2˜)·p( F11|K3˜)·p( F11|K4˜)], wherein the tilde symbol denotes a product of events (synonymous: compound of events, logic product) which apart from the Ki entered before the tilde contains all competing diagnoses in negated form, and wherein the statement p(Fij|Ki˜)=0 is true, if Fij is no follow event of Ki; in order to carry out the calculation we introduce factors fij with fij:=1, if Fij is present as a symptom, and fij:=0, if Fij is not present as a symptom, so that in the example chosen we get for a10 the form a 10 := [ f 11 · p  ( F 11  K _ 1  K 2  K 3  K 4 ) + ( 1 - f 11 ) · ( 1 - p  ( F 11  K _ 1  K 2  K 3  K 4 ) ] ·   ⋮   · [ f 16 · p  ( F 16  K _ 1  K 2  K 3  K 4 ) + ( 1 - f 16 ) · ( 1 - p  ( F 16  K _ 1  K 2  K 3  K 4 ) ] ; with this method a system of four nonlinear equations with the four unknowns xi is obtained, which will be solved by a commercial calculation program providing the numerical values of the xi and consequently the numerical values of the AF  ( i ) := x i p  ( K i ) . 2. A method as in claim 1, with the difference that now only three competing diagnoses Ki′, i: =1, . . . , 3 are considered with all other diagnoses being considered in negated form, with the resulting difference that the evaluation equations x1:=p(K1|F11 . . . F16K2′K3′ K4), x2:=p(K2|F21 . . . F26K1′K3′ K4), x3:=p(K3|F31 . . . F36K2′K1′ K4), now apply, which after a transformation merge into x i := 1 1 + Z i N i · p  ( K _ i ) p  ( K i ) , i:=1, . . . ,3, with Z1:=p(F11 . . . F16| K1K2′K3′ K4) and N1:=p(F11 . . . F16|K1K2′K3′ K4), Z2:=p(F21 . . . F26| K2K1′K3′ K4) and N2:=p(F21 . . . F26|K2K1′K3′ K4), Z3:=p(F31 . . . F36| K3K2′K1′ K4) and N3:=p(F31 . . . F36|K3K2′K1′ K4), wherein the further procedure is as in claim 1, with the difference that the equations x 1 = 1 1 + a 10  x 2  x 3 + a 11  x 2  x _ 3 ++  a 12  x _ 2  x 3 + a 13  x _ 2  x _ 3 b 10  x 2  x 3 + b 11  x 2  x _ 3 ++  b 12  x _ 2  x 3 + b 13  x _ 2  x _ 3  ( c _ 1 c 1 ) ,  x 2 = 1 1 + a 20  x 1  x 3 + a 21  x 1  x _ 3 + a 22  x _ 1  x 3 + a 23  x _ 1  x _ 3 b 20  x 1  x 3 + b 21  x 1  x _ 3 + b 22  x _ 1  x 3 + b 23  x _ 1  x _ 3  ( c _ 2 c 2 ) ,  x 3 = 1 1 + a 30  x 2  x 1 + a 31  x 2  x _ 1 + a 32  x _ 2  x 1 + a 33  x _ 2  x _ 1 b 30  x 2  x 1 + b 31  x 2  x _ 1 + b 32  x _ 2  x 1 + b 33  x _ 2  x _ 1  ( c _ 3 c 3 ) , result, whereby for all Ki′ an equiprobability with p(Ki): =0.33, i: =1, . . . ,3 is assumed, and whereby for each xi in turn aik and bik, k:=0, . . . , 3 are elaborated, depending on the expressions to be interpolated, so that using this method a system of three equations is obtained with the three unknowns xi. 3. A method as in claims 1 and 2, with the difference that now only two competing diagnoses Ki′, i: =1, . . . , 2 are considered, and all other diagnoses are considered in negated form, with the consequential difference that the evaluation equations x1:=p(K1|F11 . . . F16K2′ K3 K4) x2:=p(K2|F21 . . . F26K1′ K3 K4) now apply, which after transformation merge into x i := 1 1 + Z i N i · p  ( K _ i ) p  ( K i ) , i:=1, . . . ,2, with Z1:=p(F11 . . . F16| K1K2′ K3 K4) and N1:=p(F11 . . . F16|K1K2′ K3 K4), Z2:=p(F21 . . . F26| K2K1′ K3 K4) and N2:=p(F21 . . . F26|K2K1′ K3 K4), wherein the procedure is followed as in claims 1 and 2, with the difference that the equations x 1 = 1 1 + a 10  x 2 + a 11  x _ 2 b 10  x 2 + b 11  x _ 2  ( c _ 1 c 1 ) ,  x 2 = 1 1 + a 20  x 1 + a 21  x _ 1 b 20  x 1 + b 21  x _ 1  ( c _ 2 c 2 ) , result, whereby initially for all Ki′ an equiprobability p(Ki)=0.5, i: =1, . . . , 2 is assumed, and whereby for each xi in turn aik and bik, k:=0, . . . ,1 are elaborated, depending on the expressions to be interpolated, so that with this method a system of two equations is obtained with the two unknowns xi. 4. A method as in claims 1, 2 and 3, with the difference that now the aik and bik are not set when the linear interpolation of the Zi and Ni has taken place, but rather that they arise directly from the Zi and Ni following a schematic procedure which is to be used especially where there are five or more apostrophized diagnoses; the scheme will be illustrated by using p(F11 . . . F16| K1K2′K3′K4′) as an example, wherein as a first step for an arbitrary coefficient aik, for example a14, the second digit standing in the index (here we have k=4) is written in binary (100), and wherein in a second step, the binary number is projected right-aligned onto the apostrophized elements, as in the example whereby the apostrophes are then omitted, and the digits “1” of the binary numbers indicate the negations to be carried out which leads to a14:=(F11 . . . F16| K1 K2K3K4); in a third step it follows a factorization with respect to the F-elements p(F11 . . . F16| K1 K2K3K4)=p(F11| K1 K2K3K4)· . . . ·p(F16| K1 K2K3K4) and a factorization with respect to the K-elements p(F11 . . . F16| K1 K2K3K4):=[1−p( F11|K3˜)·p( F11|K4˜)]· . . . ·[1−p( F16|K3˜)˜p( F16|K4˜)]; the fourth step deals with the product of unknowns which belongs to a14 whereby the individual elements of the product have the same negations and indices as those obtained in Step 2, i.e. the projection is continued directly to determining x2·x3·x4 as the product of unknowns associated with the coefficient a14 thereby obtaining as result a14 x2x3x4=[1−p( F11|K3 . . . )·p( F11|K4 . . . )]· . . . ·[1−p( F16|K3 . . . )·p( F16|K4 . . . )]· x2x3x4. 5. A method as in claims 1 to 4, wherein five or more competing diagnoses Ki′ and any number of other diagnoses in negated form are considered, and wherein for each additional apostrophized diagnosis the members in {aik} and {bik} are each doubled, for example, there is k:=0, . . . , 15 for five and k:=0, . . . , 31 for six apostrophized diagnoses, so that systems of five or more equations with five or more unknowns xi arise. 6. A method as in claims 1 to 5 with the addition that for any particular Ki′-grouping, and in order to achieve an orderly and clear procedure, a tabular arrangement of the following layout is used, wherein all symptoms to be considered are recorded in the first column, and wherein one column is created for each diagnosis, and wherein all follow events arising from a diagnosis are entered in the column associated with that diagnosis together with their p(Fij|Ki˜) numerical values, and wherein it is laid out in such a way that identical follow events, i.e. events with a different Fij indexing, but the same symptom affiliation, stand in a single row. 7. A method as in claims 1 to 6 with the difference that for any particular Ki′ the number of associated follow events is not strictly set to j:=6, but in which the number of follow events is freely selectable and unlimited. 8. A method as in claims 1 to 7 with the difference that for the diagnoses Ki the a-priori probabilities p(Ki) are not assumed to be equal, and that for ci:=p(Ki) the actual “true” a-priori probability—generally determined stochastically—is used, whereby on the basis of the calculated final result it must be decided whether the highest AF(i) or the highest xi indicates the correct diagnosis, so that in the case of a non-agreement an option can be taken by changing ci:=p(Ki) to ci:=p(Ki|Ui1 Ui2 . . . ) for any Ki′ and arbitrarily chosen {Ui1, Ui2 . . . } wherein the latter are the causes of the causative events Ki′, and so that in the case of a continuing non-agreement the highest xi will determine the correct diagnosis. 9. A method as in claims 1 to 8, with the difference that the a-priori probabilities p(Ki) are not used if the causes of the causative events Ki′ can be considered, and that with additional consideration of any number of freely selectable causes, e.g. the arbitrarily chosen causes Ui1 to Ui4 of any Ki′, an improvement in reliability is achieved simply by replacing the previously used  c i := p  ( K i )  with  c i := p  ( K i  U i   1 )  or  c i := p  ( K i  U i   1  U i   2 ) = 1 - p  ( K _ i  U i   1  U _ i   2 ) · p  ( K _ i  U _ i   1  U i   2 ) p  ( K _ i  U _ i   1  U _ i   2 )  or c i := p  ( K i  U i   1  U i   2  U i   3  ) = 1 - p  ( K _ i  U i   1  U _ i   2  U _ i   3 ) · p  ( K _ i  U _ i   1  U i   2  U _ i   3 ) · p  ( K _ i  U i   1  U _ i   2  U i   3 ) p  ( K _ i  U i   1  U _ i   2  U _ i   3 ) 2  or c i := p  ( K i  U i   1  U i   2  U i   3  U i   4 ) = 1 - p  ( K _ i  U i   1  U _ i   2  U _ i   3  U _ i   4 ) · p  ( K _ i  U _ i   1  U i   2  U _ i   3  U _ i   4 ) · p  ( K _ i  U _ i   1  U _ i   2  U i   3  U _ i   4 ) · p  ( K _ i  U _ i   1  U _ i   2  U _ i   3  U i   4 ) p  ( K _ i  U _ i   1  U _ i   2  U _ i   3  U _ i   4 ) 3 , whereby Ui1 to Ui4 must be stochastically independent, whereby any K′-element, e.g. K1′, separates the causes of K1′ from the follow events of K1′, and whereby in the final outcome the highest xi determines the correct diagnosis. 10. A method as in claim 9 with the difference that with any Ki′ the arbitrarily chosen causes Ui1 to Ui4 are linked with their respectively associated inhibitors I, i.e. that any Ui1 forms a logic product with its inhibitory events IUi1→Ki, which inhibit the causal pathway Ui1→Ki with a probability 0<p<1, and that such a logic “event & inhibitors product”, e.g. (Ui1IUi1→Ki), occurs in place of the non-negated Ui1, e.g. in c i := p  ( K i  U i   1  U i   2 ) = 1 - p  ( K _ i  U i   1  I U i   1 → K i  U _ i   2 ) · p  ( K _ i  U _ i   1  U i   2 ) p  ( K _ i  U _ i   1  U _ i   2 ) , so that in this way an improvement in the reliability and selectivity is achieved simply by expanding the non-negated Ui in the expressions for determining the ci, with the requirement that the events from the union of the U-elements and the I-elements are stochastically independent. 11. A method as in claims 1 to 10, with the difference that now the Ki′ are linked with their respectively associated inhibitors J, i.e. that for an arbitrarily selected probability, e.g. for p(F16|K3˜), the element K3 forms a logic product with its inhibitory events JK3→F16 that inhibit the causal pathway K3→F16 with a probability 0<p<1, and that such a logic product, e.g. (K3JK3→F16#1JK3→F16#2), replaces the non-negated event K3 which leads to p(F16|K3JK3→F16#1JK3→F16#2), whereby #1 and #2 merely represent a serial numbering where there is more than one inhibitor, and that in such a way an improvement in reliability is achieved simply by expanding the condition within the probabilities of the form p(Fij|Ki˜), with the requirement that the events from the union of the K-elements and the J-elements are stochastically independent. 94. A method as in claim 1, with the difference that in the case of two or more inhibitors of a causal pathway, e.g. K3→F16, a factorization may be used with respect to the inhibitors, which is carried out using a simple template, e.g. p  ( F 16  K 3  J K 3 → F 16  #1  J K 3 → F 16  #2  J K 3 → F 16  #3 ∼ ) := p  ( F 16  K 3  J K 3 → F 16  #1 ∼ ) · p  ( F 16  K 3  J K 3 → F 16  #2 ∼ ) · p  ( F 16  K 3  J K 3 → F 16  #3 ∼ ) [ p  ( F 16  K 3 ∼ ) ] s - 1 , where “s” is the number of inhibitors of the causal pathway K3→F16, having regard to the requirements that no hidden causes of F16 exist, and that the inhibitors of the causal pathway K3→F16 are stochastically self-reliant causes of the event ( K3→K16). 13. A method as in claims 1 to 12, with the difference that now the expressions of the form p(Fij|K1 . . . Kt)—where “t” is a natural integer and the Ki may occur also negated—get a factorization with respect to the Ki in a different kind of way considering hidden causes, stemming from the possibility that diagnoses outside of the fixed set of Ki′-elements may exist, coming into consideration as causing a single Fij, so that e.g. the expressions p(F11|K1 K2 K3 K4), p(F11|K1 K2 K3 K4), p(F11|K1 K2 K3 K4) can be factorized into p  ( F 11  K 1  K 2  K 3  K 4 ) = 1 - p  ( F _ 11  K 1  K _ 2  K _ 3  K _ 4 ) · p  ( F _ 11  K _ 1  K 2  K _ 3  K _ 4 ) · p  ( F _ 11  K _ 1  K _ 2  K 3  K _ 4 ) · p  ( F _ 11  K _ 1  K _ 2  K _ 3  K 4 ) ( p  ( F _ 11  K _ 1  K _ 2  K _ 3  K _ 4 ) ) 3 ,  p  ( F 11  K 1  K 2  K 3  K _ 4 ) = 1 - p  ( F _ 11  K 1  K _ 2  K _ 3  K _ 4 ) · p  ( F _ 11  K _ 1  K 2  K _ 3  K _ 4 ) · p  ( F _ 11  K _ 1  K _ 2  K 3  K _ 4 ) · p  ( F _ 11  K _ 1  K _ 2  K 3  K _ 4 ) ( p  ( F _ 11  K _ 1  K _ 2  K _ 3  K _ 4 ) ) 2 ,  p  ( F 11  K 1  K 2  K _ 3  K _ 4 ) = 1 - p  ( F _ 11  K 1  K _ 2  K _ 3  K _ 4 ) · p  ( F _ 11  K _ 1  K 2  K _ 3  K _ 4 ) ( p  ( F _ 11  K _ 1  K _ 2  K _ 3  K _ 4 ) ) , whereby in contrast to claims 1 to 12 p(F11| K1 K2 K3 K4) is not zero. 95. A method as in claims 1 to 13, with the difference that now the numerical values of the probabilities p(Fi0j|Ki0˜) do not remain unchanged, but that after a diagnosis Ki0 which is found to be a correct diagnosis, the probability p(Fi0j|Ki0˜) is subject to a revision so that a new value is entered in the procedure, in such a way that e.g. p(F16|K1˜)=68/100=0.6800 in the case that K1 is confirmed to be present and F16 is confirmed to be present—goes on in p(F16|K1˜)=69/101=0.6832 and in the case that K1 is confirmed to be present and F16 is not present—goes on in p(F16|Ki˜)=68/101=0.6733; also subject to a revision are the p(Ki) in such a way that e.g. p(Ki)=150/1000=0.1500 in the case that K1 is confirmed to be present—receives the new value p(Ki)=151/1001=0.1508 and in the case K1 is confirmed to be not present—it receives the new value p(Ki)=150/1001=0.1499, all to be done on the fly or after collection over any period.