System and method for determining acceptability of proposed color solution using an artificial intelligence based tolerance model

Abstract

system and method for determining if a proposed color solution, such as paint, pigments, or dye formulations, is acceptable, is provided. The inputs to the system are the color values of a proposed paint or other color formulation and differential color values. The system includes an input device for entering a proposed color solution and an artificial intelligence tolerance model coupled to the input device. The tolerance model produces an output signal for communicating whether the proposed color solution is acceptable. The artificial intelligence model may be embodied in a neural network. More specifically, the tolerance model may be a back propagation neural network.

What is claimed is:  
     
         1 . A computer-based system for determining whether a proposed color solution is acceptable , comprising: 
 an input device for receiving the proposed color solution, the proposed color solution including color values; and    an artificial intelligence tolerance model coupled to the input device for producing an output signal for communicating whether the proposed color solution is acceptable.    
     
     
         2 . A computer-based system, as set forth in  claim 1 , wherein the artificial intelligence tolerance model is a neural network.  
     
     
         3 . A computer based system, as set forth in  claim 2 , wherein the neural network is a back propagation neural network.  
     
     
         4 . A computer-based system, as set forth in  claim 2 , wherein the neural network includes an input layer having a plurality of input nodes for receiving the proposed color solution and an output layer having a plurality of output nodes and one of the plurality of input nodes.  
     
     
         5 . A computer-based system, as set forth in  claim 4 , wherein the neural network includes a hidden layer having a plurality of weighted factors wherein one of the plurality of weighted factors corresponds to one of the plurality of input nodes and a corresponding output node.  
     
     
         6 . A computer-based system, as set forth in  claim 5 , wherein the plurality of weighted factors determine the contribution of the color values to the output signal.  
     
     
         7 . A computer-based system, as set forth in  claim 6 , wherein the plurality of weighted factors are adjusted as a function of the output signal.  
     
     
         8 . A computer-based system, as set forth in  claim 7 , wherein the output signal is an acceptance factor.  
     
     
         9 . A computer-based system, as set forth in  claim 8 , including an acceptance comparator for comparing the acceptance factor from the output layer to an acceptance standard and providing feedback.  
     
     
         10 . A computer-based system, as set forth in  claim 9 , wherein the plurality of weighted factors are adjusted as a function of the feedback received by the input layer from the acceptance comparator.  
     
     
         11 . A computer-based system, as set forth in  claim 1 , including a logic module for transforming the output nodes into a desired format.  
     
     
         12 . A computer-based system, as set forth in  claim 10 , wherein the desired format is a single continuous variable.  
     
     
         13 . A computer-based system, as set forth in  claim 10 , wherein the desired format is a fuzzy variable set.  
     
     
         14 . An artificial intelligence based tolerance model for color solutions, comprising: 
 an input layer having a plurality of input nodes for receiving a proposed color solution, the proposed color solution having color values; and    an output layer having a plurality of output nodes wherein one of the plurality of input nodes corresponds with one of the plurality of output nodes;    wherein the output layer produces an output signal communicating whether the color solution is acceptable.    
     
     
         15 . An artificial intelligence model, as set forth in  claim 14 , wherein the model is a back propagation neural network.  
     
     
         16 . An artificial intelligence model, as set forth in  claim 14 , including a hidden layer having a plurality of weighted factors wherein one of the plurality of weighted factors corresponds to one of the plurality of input nodes and the corresponding one of the plurality of output nodes.  
     
     
         17 . An artificial intelligence model, as set forth in  claim 16 , wherein the plurality of weighted factors determine the contribution of the color values to the output signal.  
     
     
         18 . An artificial intelligence model, as set forth in  claim 17 , wherein the plurality of weighted factors are adjusted according to the output signal.  
     
     
         19 . An artificial intelligence model, as set forth in  claim 18 , wherein the output signal is feedback at the input layer.  
     
     
         20 . An artificial intelligence system, as set forth in  claim 19 , wherein the plurality of weighted factors are adjusted as a function of the feedback received by the input layer.  
     
     
         21 . A computer system for providing a color solution to a customer, comprising: 
 a first module located at a remote location and being adapted to receive a solution request from an operator;    a second module coupled to the first module and being located at a central location, the second module including a composite solution database and a search routine coupled to the composite solution database and being adapted to receive the solution request from the first module, the search routine being adapted to search the composite solution database and determine a proposed color solution as a function of the solution request; and,    an artificial intelligence model for determining the acceptability of the proposed color solution    
     
     
         22 . A computer system, as set forth in  claim 21 , wherein the artificial intelligence model is a neural network.  
     
     
         23 . A computer system, as set forth in  claim 22 , wherein the artificial intelligence model is a back propagation neural network.  
     
     
         24 . A method for determining the acceptability of a proposed color solution using an artificial intelligence model, including the steps of: 
 providing the proposed color solution to the model, the proposed solution having color values; and    producing an output signal indicative of whether the proposed color solution is acceptable.    
     
     
         25 . A method, as set forth in  claim 24 , including the step of determining the contribution of the color values to the output signal.  
     
     
         26 . A method, as set forth in  claim 25 , including the step of using a weighted factor to determine the contribution of the color values to the output signal.  
     
     
         27 . A method, as set forth in  claim 26 , including the step of comparing the output signal to an acceptance standard.  
     
     
         28 . A method, as set forth in  claim 27 , including the step of training the artificial intelligence model for determining acceptability.  
     
     
         29 . A method, as set forth in  claim 28 , wherein the artificial intelligence model is a neural network and the method includes the step of providing feedback to the neural network from the output signal for adjusting the weighted factor.  
     
     
         30 . A method, as set forth in  claim 27 , including the step of transforming the output signal into a desired format.  
     
     
         31 . A method, as set forth in  claim 27 , including the step of transforming the output signal into a single continuous variable.  
     
     
         32 . A method, as set forth in  claim 27 , including the step of transforming the output signal into a fuzzy variable set.  
     
     
         33 . A method for determining the acceptability of a proposed color solution using a computer based model, the model being embodied in a neural network having an input layer and an output layer, including the steps of: 
 providing the proposed color solution to the neural network, the proposed color solution having color values; and    producing an output signal indicative of whether the color solution is acceptable.    
     
     
         34 . A method, as set forth in  claim 32  including the step of using a weighted factor to determine the contribution of the color values to the output signal.  
     
     
         35 . A method, as set forth in  claim 33  including the step of adjusting the weighted factor according to the output signal.  
     
     
         36 . A method, as set forth in  claim 34 , including the step of providing feedback from the output signal to the input layer.  
     
     
         37 . A method, as set forth in  claim 35  including the step of adjusting the weighted factor according to the feedback received by the input layer.  
     
     
         38 . A computer-based method for providing a color solution to a customer over a computer network, including the steps of: 
 receiving a solution request from an operator located at a remote location;    delivering the solution request from the remote location to a central location over the computer network;    searching a composite solution database and determining a proposed color solution as a function of the solution request;    providing an artificial intelligence system for determining the acceptability of the proposed color solution and responsively producing an output signal.    
     
     
         39 . A method for training a neural network having an input layer, a hidden layer, and an output layer, the neural network being adapted to determine the acceptability of a proposed color solution, comprising the steps of: 
 providing a plurality of acceptable color solutions to the input layer, the acceptable color solutions having color values;    using a weighted factor to the color values in the hidden layer to produce an output signal;    providing the output signal to a comparator;    providing an acceptance standard to the comparator to compare the acceptance standard and the output signal for producing an error value;    comparing the error value to an error limit to determine error variation; and    providing error feedback to the neural network corresponding to the error variation, wherein the weighted factor is adjusted according to the error feedback.