Patent ID: 7440928

Claim:
A computer-implemented diagnostic system for determining a likelihood of occurrence of a cause of one or more effects occurring in respect of a subject or process, said one or more effects being input to the system, the system being arranged to receive and/or access training data in the form of discrete values relating to previously-identified relationships between one or more effects and said cause, the system comprising: one or more input nodes arranged to receive respective input signals, the or each input signal being representative of an effect and its relative strength, wherein the number of effects defines the number of dimensional axes of a function representative of said previously-identified relationships; define an input space on the basis of said dimensional axes and select a number of reference points within said input space, a predefined number of said reference points being designated as primary reference points and the rest of said reference points being designated as secondary reference points, said predefined number being dependent on the number of effects associated with said cause in said training data, wherein each reference point has a predetermined weight value assigned thereto, a weight value being representative of a belief value which quantifies the extent of occurrence of said cause given an effect, the weight values assigned to said primary reference points being independent variables (“primary weight”) and the weight values assigned to said secondary reference points being dependent on one or more of said primary weights (“secondary weight”), and wherein, in combination, said primary and secondary weights define a multi-dimensional decision hyper-surface representative of said training data; determine, using said decision hyper-surface, a likelihood of occurrence of said cause given said one or more effects input to the system by: receiving input data representative of one or more effects occurring in respect of said subject or process and the relative strength thereof; mapping said input data onto said decision hyper-surface; and determining from said decision hyper-surface a belief value in said cause given said input data and outputting data representative of said determined likelihood of occurrence of said cause to a recipient mechanism for diagnosing a cause of the occurrence of said one or more effects in respect of said subject or process.