U.S. patent application number 09/874699 was filed with the patent office on 2002-12-05 for system and method for determining acceptability of proposed color solution using an artificial intelligence based tolerance model.
Invention is credited to McClanahan, Craig J., Soss, James.
Application Number | 20020184168 09/874699 |
Document ID | / |
Family ID | 25364364 |
Filed Date | 2002-12-05 |
United States Patent
Application |
20020184168 |
Kind Code |
A1 |
McClanahan, Craig J. ; et
al. |
December 5, 2002 |
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.
Inventors: |
McClanahan, Craig J.;
(Bowling Green, OH) ; Soss, James; (Toledo,
OH) |
Correspondence
Address: |
BASF CORPORATION
ANNE GERRY SABOURIN
26701 TELEGRAPH ROAD
SOUTHFIELD
MI
48034-2442
US
|
Family ID: |
25364364 |
Appl. No.: |
09/874699 |
Filed: |
June 5, 2001 |
Current U.S.
Class: |
706/16 |
Current CPC
Class: |
G01J 3/463 20130101;
G01J 3/462 20130101; G06N 3/02 20130101; G01J 3/46 20130101; G01J
2003/466 20130101 |
Class at
Publication: |
706/16 |
International
Class: |
G06E 001/00; G06E
003/00; G06G 007/00; G06F 015/18 |
Claims
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.
Description
FIELD OF THE INVENTION
[0001] The present invention relates generally to color matching,
and more particularly, to a method and system for assessing the
acceptability of a color match using artificial intelligence.
BACKGROUND OF THE INVENTION
[0002] Products today are offered to consumers in a wide variety of
colors. Consumer products may be colored by means of colorants or
dye or painted. Color matching is required in a variety of areas,
including textiles, plastics, various synthetic materials,
prosthetics, dental applications, and paint applications, due to
the many variations in color, and due to the wide variations in
shades and hues of any given color and color variations in an
article. The actual color produced in a given article may vary due
to a number of factors. For example, textile colors vary according
to fiber composition. Colorants for plastic vary according to the
plastic composition. Painted articles vary in color depending on
any number of factors, such as paint composition, variations in the
paint application process, including application method, film
thickness, drying technique and number of layers. An important
application for color matching is in the area of automotive color
matching. Frequent uses for color matching in automotive paint
occur in matching the same color from different batches or matching
similar colors from different manufacturers. Additionally, there is
a requirement for color matching refinish paint to an OEM (original
equipment manufacture) color when a vehicle body panels are damaged
and require repainting.
[0003] A paint manufacturer supplies one or more paint formulations
for the original paint color to refinish paint shops. By supplying
a plurality of formulations or variants for a particular color, the
paint manufacturer accounts for those factors that affect the
actual color. Matching of dyes or colorants for other applications
is also done through formulations for a particular color.
Typically, the formulations for a particular color are distributed
on paper, microfiche, and/or compact disks (CD). A color tool,
composed of swatches of the variants for each color may also be
produced and delivered to each customer. The customer must select a
formulation most closely matching the existing color of the
article. This is typically done visually, i.e., by comparing
swatches of paint or color to the part or in the case of paint,
spraying a test piece with each formulation.
[0004] Different formulations are derived from actual data gathered
by inspectors at various locations, e.g., the textile, plastic or
automobile manufacturer or vehicle distribution point. The
inspectors take color measurement readings from articles of a
particular color. These readings are used to develop color
solutions, i.e., different formulations for the same color.
[0005] There are several disadvantages to the present method of
color matching. Conventional color laboratories that use human
analysis to determine color matching require significant numbers of
people, equipment and materials for identifying pigments and
locating a close match from a database. In some cases, an existing
formula may provide a close match. In other cases, the formula must
be adjusted, mixed, applied and compared to a standard. These steps
are repeated until a suitably close match is found. In other cases,
no match is found and a formula must be developed from scratch.
Correction of the formula requires a highly skilled technician
proficient in the interaction of light with several different
pigments.
[0006] Moreover, traditional computer software that assists a
technician has several disadvantages. Traditional computer software
has not proven to be very effective on colors containing "effect
pigments." This software is typically based on a physical model of
the interaction between illuminating light and the colorant or
coating. These models involve complex physics and do not account
for all aspects of the phenomena. A traditional approach is to use
a model based on the work of Kubleka-Munk or modifications thereof.
The model is difficult to employ with data obtained from
multi-angle color measuring devices. One particular difficulty is
handling specular reflection that occurs near the gloss angle.
Another deficiency of the Kubleka-Munk based models is that only
binary or ternary pigment mixtures are used to obtain the constants
of the model. Thus, the model may not properly account for the
complexities of the multiple interactions prevalent in most paint
or colorant recipes.
[0007] The present invention is directed to solving or more of the
problems identified above.
SUMMARY OF THE INVENTION AND ADVANTAGES
[0008] Acceptable tolerances vary depending on the color.
Tolerances are expressed in differential color values, e.g.,
.DELTA.L*, .DELTA.C*, .DELTA.H*. The differential values will vary
as a function of the color. Historically, these values have been
determined manually, i.e., by visual evaluation. The tolerances for
that formulation are determined as a function of all of the color
measurement values that have been deemed acceptable (usually by
visible methods).
[0009] In one aspect of the present invention, a system for
determining the acceptability of a proposed color solution using an
artificial intelligence tolerance model, is provided. The model is
embodied in a neural network and, in particular, a feed-forward
back propagation neural network. The color standard is expressed as
color values (L*,C*,h*). The neural network is trained using the
color values for each formulation of each color and the
differential color values from all acceptable measurements.
[0010] When a proposed color solution has been chosen by a search
routine, the color values of the solution from a composite solution
database and color measurement data taken from the subject part
form the input to the neural network. The output of the neural
network is whether or not the color solution is acceptable. The
neural network can also be used in other color difference measuring
systems to express acceptability of the measured color
difference.
[0011] The neural network includes an input layer having nodes for
receiving input data related to color values of the standard and
differences between the color values of the standard and the color
solution. Weighted connections connect to the nodes of the input
layer and have coefficients for weighting the input data. An output
layer having nodes is either directly or indirectly connected to
the weighted connections. The output layer generates output data
that is related to the acceptability of the color match. The data
of the input layer and the data from the output layer are
interrelated through the neural network's nonlinear
relationship.
[0012] Neural networks have several advantages over conventional
logic-based expert systems or computational schemes. Neural
networks are adaptive and provide parallel computing. Further,
because neural responses are non-linear, a neural network is a
non-linear device, which is critical when applied to nonlinear
problems. Moreover, systems incorporating neural networks are fault
tolerant because the information is distributed throughout the
network. Thus, system performance is not catastrophically impaired
if a processor experiences a fault.
[0013] Another aspect of the present invention provides a system
and a method for providing color solutions using an artificial
intelligence tolerance model to a customer over a computer network.
The system includes a first module located at a remote location.
The first module receives a solution request from an operator. A
second module is coupled to the first module via a computer
network. The second module is located at a central location and
includes a composite solution database, an artificial intelligence
tolerance model and a search routine coupled to the composite
solution database. The second module is adapted to receive the
solution request from the first module. The search routine is
adapted to search the composite solution database for a color code
and determine a paint color solution from a plurality of color
solutions as a function of the solution request. The artificial
intelligence tolerance model is adapted to determine if the color
solution chosen by the search routine based on the color values of
the solution input into the first module is acceptable.
[0014] The method includes the steps of receiving a solution
request and color values from an operator located at a remote
location, delivering the solution request and color values from the
remote location to a central location over the computer network,
and searching a composite solution database for a color solution
and determining a whether the color solution as a function of the
solution request is acceptable.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Other advantages of the present invention will be readily
appreciated as the same becomes better understood by reference to
the following detailed description when considered in connection
with the accompanying drawings wherein:
[0016] FIG. 1 is a block diagram of a system for determining the
acceptability of a proposed color solution having an artificial
intelligence model, according to an embodiment of the present
invention;
[0017] FIG. 2 is a diagram depicting a neural network for use in
the artificial intelligence model of FIG. 1, according to an
embodiment of the present invention;
[0018] FIG. 3 is a block diagram depicting the training of the
color tolerance neural network of FIG. 2, according to an
embodiment of the present invention;
[0019] FIG. 4 is a block diagram of a color management and solution
distribution system, according to an embodiment of the present
invention;
[0020] FIG. 5 is a flow diagram of a color management and solution
distribution method, according to an embodiment of the present
invention; and
[0021] FIG. 6 is a block diagram of a color management and solution
distribution method, according to another embodiment of the present
invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0022] Referring to the Figs., wherein like numerals indicate like
or corresponding parts throughout the several views, a system 100
and method 600 for determining if a proposed color solution, such
as paint, pigments, or dye formulations, is acceptable, is
provided.
[0023] For example, the proposed color solution may be a paint
formulation to be used in the repair of an automobile body panel.
The inputs to the system are the color values (see below) of a
proposed paint formulation and differential color values. The
differential color values represent the differences between the
color values of the proposed paint formulation and the actual color
values of the part to be repaired.
[0024] With specific reference to FIG. 1, the system 100 includes
an input device 102 for entering a proposed color solution.
Preferably, the system 100 is embodied in a computer program run on
a general purpose computer (not shown). The input device 102 may be
embodied in a user interface for inputting the proposed color
solution, such as a keyboard. Furthermore, the input device 102 may
be embodied in an element of a computer system so as to receive the
proposed color solution as input from another element of the
computer system, such as a computer database, an electronic mail
file or other suitable element of the computer system (see
below).
[0025] The system 100 of the present invention further includes an
artificial intelligence tolerance model 104 coupled to the input
device 102. The tolerance model 104 produces an output signal 106
for communicating whether the proposed color solution is
acceptable. The artificial intelligence model 104 may be embodied
in a neural network. More specifically, the tolerance model 104 may
be a back propagation neural network or any other suitable neural
network. The output signal 106 may be embodied in an acceptable/not
acceptable format, an acceptance factor format or any other
suitable format.
[0026] The proposed color solution includes color measurement data
in the form of color values. Color measurement data is an
indication of the actual color of an object. Preferably, the color
measurement data may be determined using a multi-angle or spherical
geometry color measuring device, a spectrophotometer, digital
camera or other suitable device.
[0027] Color values refer to color attributes used to quantify
color. The color values may include color space values, reflectance
values or other suitable color attributes. One example of color
space values are defined by L*a*b*, where L* represents luminous
intensity, a* represents a red/green appearance, b* represents a
yellow/blue appearance. Another example of color space values are
defined by L*, C*, h, where L* represents lightness, C* represents
chroma, and h represents hue. The color values (L*, a*, and b* or
L*, C*, and h) at various angles are obtained using a color
measurement device.
[0028] Referring to FIG. 2, an artificial neural network is
generally shown at 200. Artificial neural networks 200 are
computing systems that model vertebrate brain structure and
processes. Artificial neural network techniques are a member of a
group of methods which fall under the umbrella of artificial
intelligence. Artificial intelligence is commonly associated with
logic rule-based expert systems where the rule hierarchies used are
reasoned from human knowledge. In contrast, artificial neural
networks 200 are self-trained based on experience acquired through
data compilation and computation. Thus, artificial intelligence
utilizing neural networks 200 is particularly useful in conjunction
with complex systems or phenomena where the analysis is
complicated, and deriving a model from human knowledge for use in a
conventional expert system is a daunting task.
[0029] Although neural networks differ in geometry, activation
function and training mechanics, they are typically organized into
at least three layers. The first layer is an input layer 220 having
one or more input nodes 224, 226, 228. The second layer is an
output layer 260 having one or more output nodes 264, 266, 268.
Each output node 264, 266, 268 corresponds with an input node 224,
226, 228. Between the inner and outer layers, there are one or more
hidden layers 240, each having one or more hidden nodes 244, 246,
248 corresponding to an input node and output node pair 224,264,
226, 266, 228, 268. Each input variable is associated with an input
node 224, 226, 228 and each output variable is associated with an
output node 264, 266, 268. Within the neural network 200, data
flows in only one direction, such that each node 224, 226, 228,
244, 246, 266, 268 only sends a signal to one or more nodes and
receives no feedback.
[0030] The enabling power of a neural network 200 is its
connectivity, or the connections between the various nodes 224,
226, 228, 244, 246, 266, 268. (A configuration technique modeled
after the structure of the human brain.) Moreover, because the
network is structured, or connected, in such a way as to provide
parallel processing (where each node 224, 226, 228, 244, 246, 266,
268 has connections with other nodes 224, 226, 228, 244, 246, 266,
268), it is extremely efficient at acquiring and storing
experiential knowledge and, then recalling and using that
knowledge. More specifically, a node 224, 226, 228, 244, 246, 266,
268 receives input values, processes them and provides an output.
The processing step includes summing the inputs, adding a bias
value and submitting this total input to an activation function
which limits the magnitude of the output. The connections between
the various nodes 224, 226, 228, 244, 246, 266, 268 are weighted.
An output sent from one node 224, 226, 228, 244, 246, 266, 268 to
another is multiplied by the weighting factor associated between
those two particular nodes 224, 226, 228, 244, 246, 266, 268. The
weighting factor represents the knowledge of the system. The system
continues to accumulate knowledge and adjust the weighting factor
in accordance with training and the further acquisition of
knowledge by the network 200. Consequently, the output of the
network 200 agrees with the experience of the network 200.
[0031] With particular reference to FIG. 1, the output of the
tolerance model 104 may be communicated to a logic module 102 for
transforming the output signal 106 into a desired format. The
desired format of the output signal 106 may take the form of a
single continuous variable, a fuzzy variable set or any other
suitable format.
[0032] A single continuous variable is a variable that may assume
any value between two endpoints. An example being the set of real
numbers between 0 and 1.
[0033] A fuzzy variable set is the basis for a mathematical system
of fuzzy logic. "Fuzzy" refers to the uncertainty inherent in
nearly all data. Fuzzy logic may be used in artificial intelligence
models, specifically neural networks, because there is a fuzziness
in the output of the neural network. Fuzzy logic is based on fuzzy
variables. Inputs to a neural network may be provided for the
fuzziness associated with each network parameter. An output
parameter depicting the fuzziness of the result could also be
incorporated into the neural network. The output parameter could
range in value from 0 to 1, with a 1 indicating no uncertainty in
the result. For example, when gauging color match quality, there
may be uncertainty in the measurement of the color values and in
the descriptive value of the goodness of the match. A fuzzy
variable set as an output signal from the neural network indicates
the level of uncertainty and the quality level of the result. Thus,
the quality and confidence of a color match may be expressed as
0.9, 0.8, where the quality is rated as very good at 0.9 and the
confidence, or level of certainty, is quite high at 0.8.
[0034] With particular reference to FIG. 4, the neural network 104
of the subject invention is trained using the color values for each
formulation of each color and all acceptability results. There are
two different types of training (learning) for a neural network
104. In supervised training (or external training), the network 104
is taught to match its output to external targets using data having
input and output pairs. In supervised training, the weighting
factors are typically modified using a back-propagation method of
learning where the output error is propagated back through the
network 104. In unsupervised training (or internal training), the
input objects are mapped to an output space according to an
internal criteria.
[0035] Referring to FIG. 3, in the preferred embodiment of the
subject invention neural network 104 is a back propagation neural
network 104. The training of the back propagation neural network
104 will now be discussed. In a first process block 402 color
values are provided to an artificial intelligence cluster model. In
a second process block the artificial intelligence cluster model
determines if the color solution is acceptable. In a third process
block 306, an output signal is produced (see above).
[0036] In a fourth process block 308, acceptance ratings are input
and transformed into a desired format (fifth process block
310).
[0037] In a sixth process block 312, the transformed acceptance
ratings are input and compared to the output signal 106 of the
neural network 104. In a first decision block 314, if the output
signal 106 is within accepted tolerance limits, no further action
is taken. However, where the output signal 106 is outside the
accepted tolerance limit, the plurality of weighted factors are
adjusted based on the acceptance factor output at the output signal
106 in a seventh process block 316.
[0038] With reference to FIG. 4, another embodiment of the present
invention provides a computer system 400 for managing and providing
color solutions, such as paint, pigments or dye formulations. The
system 400 includes a first module 402 located at a remote location
404, such as a customer site. Preferably, the first module 402 is
implemented on a computer (not shown), such as a personal computer
or wireless computing device. The first module 402 is adapted to be
operated by a user or operator 406, i.e., the customer. The
operator 406 inputs a solution request to the first module 402. The
solution request includes a paint or color identifier (or color
code) which identifies the color of a sample or painted substrate
408, and color measurements from a color measurement device
410.
[0039] The color measurement device 410 is used to provide color
measurements, i.e., an indication of the actual color of the sample
408. Preferably, the color measurement device 410 is a
spectrophotometer such as is available from X-Rite, Incorporated of
Grandville, Minn. as model no. MA58. Alternatively, the color
measurement device 410 may be a spherical geometry color measuring
device, a digital camera or other suitable device.
[0040] The first module 402 is coupled to a second computer based
module 412 located at a central location 414, such as the paint,
dye or colorant manufacturer's facility. The first and second
computer based modules 402, 412 are coupled across a computer
network 416. In the preferred embodiment, the computer network 416
is the internet.
[0041] The second module 412 receives the solution request from the
operator 406 via the first module 402 and the computer network 416.
The second module 412 includes a composite solution database 418, a
search engine or routine 420, and an artificial intelligence
tolerance model 422. The search routine 420 is adapted to search
the composite solution database 418 and determine a paint color
solution as a function of the solution request. The artificial
intelligence tolerance model 422 is adapted to determine if the
color solution, chosen by the search routine 420 based on the color
values of the solution input into the first module 402, is
acceptable.
[0042] With reference to FIG. 5, a computer based method 500 for
providing color solutions to a customer will now be explained. In a
first control block 502, color values and, the solution request
from the operator 406 located at the remote location 404 is
received. In a second control block 504, the solution request and
color values are delivered over the computer network 416 from the
remote location 404 to the central location 404. In a third control
block 506, the composite solution database 418 is searched for a
color solution and the acceptability of the color solution is
determined.
[0043] With particular reference to FIG. 6, a system 600 for
managing and providing color solutions using derived color
tolerances is provided. The system 600 includes three databases:
the composite solution database 418, a color measurement database
602, and a customer and solution usage database 604.
[0044] A customer interface 606 is implemented on the first module
402 located at the remote location 604. The customer interface 606
allows the operator 406 to log on to the system, communicate with
the system 400,600, e.g., to request color solutions, to
communicate color values and color measurement data, and to receive
color solutions from the system 400,600. The customer interface 606
is graphical in nature, and, preferably, is accessed through a
generic world wide web (WWW) browser, such as Microsoft.TM.
Internet Explorer, available from Microsoft of Redmond,
Washington.
[0045] The customer interface 606 may be implemented in hyper text
markup language (HTML), the JAVA language, and may include
JavaScript. The system 600 also includes several processes: a
solution creation process 608, a quality control process 610, a
formula conversion process 612, a variant determination process
614, and a derived tolerance process 616.
[0046] Referring to FIGS. I and 2, the artificial intelligence
tolerance model 100 of the subject invention is embodied in a
neural network 104. The tolerance model neural network 104 includes
input data from the input device 102 in the form of a proposed
color solution having color values. When a proposed color solution
has been chosen by the search routine 420, the color values of the
solution from the composite solution database 418 form the input to
the tolerance model neural network 406. The neural network 200
determines whether the proposed color solution is within the
learned color tolerances and, thus, deemed acceptable.
[0047] Specifically, the subject invention neural network 200
includes an input layer 220 having a plurality of input nodes 224,
226, 228 for receiving a color solution having color values. The
subject invention neural network 200 further includes an output
layer 260 having a plurality of output nodes 264, 266, 268 for
providing an acceptance factor of the color solution wherein one of
the plurality of input nodes 224, 226, 228 corresponds with one of
the plurality of output nodes 264, 266, 268. The subject invention
neural network 200 further includes a hidden layer 240 having a
plurality of weighted factor nodes 244, 246, 248 wherein one of the
plurality of weighted factor nodes 244, 246, 248 corresponds to one
of the plurality of input nodes 224, 226, 228 and the corresponding
one of the plurality of output nodes 264, 266, 268. The plurality
of weighted factors non-linearly determine the contribution of the
color values to the acceptance factor.
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