Expert system adapted dedicated internet access guidance engine Number:7,031,951 from the United States Patent and Trademark Office (PTO) owispatent An expert system adapted dedicated Internet access guidance engine. The invention allows an agent to interact with a customer and to provide selection and recommendation of data network products and/or services for the customer. The invention allows for the use of agents of varying skill levels, including relatively low skill level, without suffering deleterious performance. The dedicated Internet access guidance engine is operable to perform selection and rating of Internet access products and/or services to provide a solution that meets the needs of a customer. The dedicated Internet access guidance engine is operable to select recommended solutions from among a number of potential solutions that may include compatible solutions. The dedicated Internet access guidance engine is one of the underlying engines within the expert system that allows the agent to provide real time interaction with a customer and to provide a real time recommended solution to that customer.<H dedicated, guidance, Expert, system, a, 7031951.html, Patent, pto, 7031951

Title: Expert system adapted dedicated internet access guidance engine

Abstract: An expert system adapted dedicated Internet access guidance engine. The invention allows an agent to interact with a customer and to provide selection and recommendation of data network products and/or services for the customer. The invention allows for the use of agents of varying skill levels, including relatively low skill level, without suffering deleterious performance. The dedicated Internet access guidance engine is operable to perform selection and rating of Internet access products and/or services to provide a solution that meets the needs of a customer. The dedicated Internet access guidance engine is operable to select recommended solutions from among a number of potential solutions that may include compatible solutions. The dedicated Internet access guidance engine is one of the underlying engines within the expert system that allows the agent to provide real time interaction with a customer and to provide a real time recommended solution to that customer.

Patent Number: 7,031,951 Issued on 04/18/2006 to Mancisidor, et al.

Inventors: Mancisidor; Rod (Round Rock, TX); Erickson; Charles R. (Cedar Park, TX); Gilpin; Gordon (Austin, TX)
Assignee: Convergys Information Management Group, Inc. (Cincinnati, OH)

Appl. No.: 909241

Filed: July 19, 2001

Current U.S. Class: 706/60

Current International Class: G06F 17/00 (20060101)

Field of Search: 706/60,925,8 705/7

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Primary Examiner: Knight; Anthony
Assistant Examiner: Holmes; Michael B.
Attorney, Agent or Firm: Frost Brown Todd LLC

Parent Case Text

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority pursuant to 35 U.S.C. Sec. 119(e) to U.S. Provisional Application Ser. No. 60/219,783, filed Jul. 19, 2000, now abandoned, which is hereby incorporated herein by reference in its entirety. The present application also claims priority pursuant to 35 U.S.C. Sec. 120 to U.S. Regular application Ser. No. 09/764,662, filed Jan. 18, 2001, pending, which is hereby incorporated by reference in its entirety.

The following U.S. patent applications are hereby incorporated herein by reference in their entirety and made part of the present U.S. patent application for all purposes: 1) U.S. patent application Ser. No. 09/909,250, entitled "EXPERT SYSTEM SUPPORTED INTERACTIVE PRODUCT SELECTION AND RECOMMENDATION," filed on even date herewith. 2) U.S. patent application Ser. No. 09/909,240, entitled "EXPERT SYSTEM ADAPTED DATA NETWORK GUIDANCE ENGINE," filed on even date herewith.

Claims

What is claimed is:

1. A method for recommending a product to a customer, the method comprising: a) presenting a set of problem questions to a customer, the set of problem questions including one or more questions presented to determine one or more product needs of the customer; b) receiving a set of problem answers from the customer, the set of problem answers including one or more answers to the set of problem questions, the one or more answers corresponding to the one or more product needs of the customer; c) determining a problem domain based at least in part on the set of problem answers; d) presenting a set of needs questions to the customer, the set of needs questions including one or more questions corresponding to the determined problem domain; e) receiving a set of needs answers from the customer, the set of needs answers including one or more answers to the set of needs questions, each needs answer corresponding to at least one of the one or more product needs of the customer; f) transforming at least a part of the set of needs answers into one or more values to represent a set of product traits, the one or more values comprising at least one of a fuzzy value or a crisp value, at least a portion of the one or more values corresponding to at least one of the one or more product needs of the customer; g) processing at least one of the one or more values to produce one or more product recommendations, wherein the one or more product recommendations correspond to the determined problem domain, wherein at least a portion of the step of processing is performed on a computer processor using fuzzy logic; and h) providing at least one of the one or more product recommendations to the customer.

2. The method of claim 1, wherein at least one of the set of problem questions or the set of needs questions is presented to the customer in part by a human agent.

3. The method of claim 1, wherein the step of providing at least one of the one or more product recommendations to the customer is performed in part by a human agent.

4. The method of claim 1, further comprising: providing one or more explanations to the customer, at least one of the one or more explanations corresponding to at least one of the one or more products being recommended, the at least one of the one or more explanations comprising at least one reason as to why a corresponding product is being recommended.

5. The method of claim 1, wherein the one or more product recommendations includes one or more products that are designated as not recommended.

6. The method of claim 5, further comprising: providing one or more explanations to the customer, at least one of the one or more explanations corresponding to the one or more products that are designated as not recommended, the at least one of the one or more explanations comprising at least one reason as to why a corresponding product is being not recommended.

7. The method of claim 1, wherein the step of processing further comprises rating each of the products being recommended according to at least a portion of the needs answers.

8. The method of claim 7, wherein a certain number of product traits correspond to the one or more product needs of the customer, wherein the step of rating comprises determining the certain number of product traits corresponding to the one or more product needs of the customer.

9. The method of claim 8, wherein a plurality of product recommendations are produced, the step of processing further comprising providing a ranking for each product of the produced product recommendations, wherein the ranking is conducted in accordance with the determined certain number of product traits corresponding to the one or more product needs of the customer.

10. The method of claim 7, wherein each rating is selected from the group consisting of recommended, compatible, and not recommended.

11. The method of claim 1, wherein the one or more needs of the customer comprise telecommunications needs relating to at least one of cost, ability to host a website, reliability, or bandwidth.

12. The method of claim 11, wherein the needs relating to bandwidth include needs relating to at least one of e-mail, web browsing, website hosting, file transfer, or voice over internet protocol applications.

13. The method of claim 11, wherein the needs relating to reliability are characterized by values including at least one of a maximum value indicative of internet use by the customer or a maximum value indicative of a data type transmitted via the internet by the customer.

14. The method of claim 1, wherein at least one of the values is a crisp value comprising at least one of an integer value or a Boolean value.

15. The method of claim 1, wherein the step of transforming further comprises transforming at least one crisp value to a transformed fuzzy value to represent at least one of the product traits with the transformed fuzzy value.

16. The method of claim 1, wherein each trait of the product traits is defined by z fuzzy sets and s fuzzy sets.

17. The method of claim 1, wherein each customer has a physical location, wherein the step of processing includes processing information corresponding to the physical location of the customer in addition to the at least one of the one or more values.

18. The method of claim 1, wherein the step of processing further comprises accessing one or more product catalogs to compare at least a portion of the values to information stored in the one or more product catalogs.

19. The method of claim 18, wherein each product catalog comprises a database comprising information relating to a plurality of products.

20. The method of claim 19, wherein the information comprises available products, supported products, and compatible products.

21. The method of claim 19, wherein each product is provided by a product provider, wherein the catalog is configured to permit each product provider to provide updates to the information corresponding to at least one product provided by the product provider.

22. The method of claim 1, further comprising adding customer information to a customer profile, the customer profile being stored in a computer readable medium.

23. The method of claim 22, wherein the customer information comprises at least one of the one or more product needs of the customer, financial information about the customer, and products being used by the customer.

24. The method of claim 1, wherein the step of processing further comprises accessing a customer profile associated with the customer, the customer profile comprising information relating to the customer, wherein the customer profile is stored in a computer readable medium, wherein the one or more product recommendations are produced at least in part in accordance with the information in the customer profile.

25. The method of claim 1, further comprising attributing a weight to at least one of the one or more values.

26. The method of claim 25, wherein the weight represents a weight accorded by the customer to the product need corresponding to the weighted value.

27. A method for recommending a product to a customer, the method comprising: a) presenting a GUI to a human agent, the GUI including one or more fields for entry by the agent of information relating to a customer's product needs; b) receiving input from the agent via the GUI, the input comprising information relating to a customer's product needs; c) processing the received input through fuzzy logic to produce one or more product recommendations corresponding to the customer's product needs, the step of processing comprising transforming at least a part of the information relating to a customer's product needs into at least one of a fuzzy value or a crisp value; and d) displaying an output to the agent via the GUI, the output comprising the one or more product recommendations.

28. The method of claim 27, wherein the output further comprises one or more explanations, each explanation corresponding to at least one of the one or more product recommendations.

29. The method of claim 27, wherein the customer's product needs and the one or more product recommendations relate to telecommunications.

30. The method of claim 27, further comprising attributing a weight to at least one of the at least one of a fuzzy value or a crisp value.

31. A method for recommending a product to a customer, the method comprising: a) presenting a set of questions to a customer via a human agent, the set of questions comprising one or more questions relating to the customer's product needs; b) receiving a set of answers from the customer via the agent in response to the set of questions, the answers comprising one or more answers relating to the customer's product needs; c) entering information corresponding to the set of answers into a computer system via a GUI, the information being entered by the agent; d) receiving output from the computer system, the output being displayed to the agent via the GUI, the output being produced by the computer system, wherein the output is produced by the computer system via a process comprising: i) transforming at least a part of the set of answers into one or more values to represent a set of product traits, the one or more values comprising at least one of a fuzzy value or a crisp value, at least a portion of the one or more values corresponding to at least one of the one or more product needs of the customer, and ii) processing at least one of the one or more values to produce one or more product recommendations, wherein at least a portion of the step of processing is performed on a computer processor using fuzzy logic within the computer system, wherein the output comprises one or more product recommendations corresponding to the customer's needs; and e) presenting at least a portion of the output to the customer via the agent.

32. The method of claim 29, wherein the output further comprises one or more explanations, each explanation corresponding to at least one of the one or more product recommendations.

33. The method of claim 29, wherein the customer's product needs and the one or more product recommendations relate to telecommunications.

Description

BACKGROUND

1. Technical Field

The present invention relates generally to expert system processing; and, more particularly, it relates to an expert system adapted dedicated Internet access guidance engine.

2. Related Art

Within conventional systems and methods that are operable to perform product selection and recommendation, a key limiting factor is the skill level of the agent or user who performs the customer or client interaction is selling and marketing those products. Within the scope of products, services are also envisioned as well within the context of the generic label "product." Regardless of the industry, the skill level of the agent in communicating the available products to a customer and, as is oftentimes the case, educating the customer and helping him to quantify his needs, are limiting factors that govern the degree of effectiveness that a particular salesperson can achieve. Within technology areas that are more amorphous and complex, the limitations of salespeople is even further compromised.

There have been a number of technology areas where the availability of adequately skilled salespersons has been limiting to the full tapping of the market. As one example, the Internet has rapidly become an indispensable system that many businesses require simply to conduct their day to day affairs. During Internet access blackouts within such industries, when the access to the Internet has been temporarily interrupted, it is evident the degree to which businesses have grown dependent on the availability and reliability of such services. Yet, as is often the case, there are relatively few individuals within the company who are able to seek adequate Internet services to meet the needs of their business. While there is this sometimes great limitation of sufficiently skilled individuals within customer-businesses that seek such services, when there are not sufficiently skilled and trained salespeople, or agents, to sell those services, the problem becomes ever more complex. The likelihood of a sufficient solution that will adequately satisfy the needs of the customer is relatively low.

The conventional solutions have been geared towards training the salespeople in such situations to a level at which they can adequately communicate and understand the technology that they are seeking to sell. This approach, while being effective for a particular individual once that individual is sufficiently trained, is catastrophic for an industry that has a high degree of turnover. In the example used above to illustrate the deficiency of this traditional approach, the turnover rate of individuals, once they have acquired a high skill level, is oftentimes astronomical. There is such a high need for specifically trained individuals in these key technology areas that the lateral opportunities are sometimes to good to pass up. Moreover, the problem is further complicated by the fact that companies in the business of selling such goods and services, namely providers, are loathe to invest a high degree of money and effort to train up their work base in light of the radical degree of turnover within the industry.

Another limitation within conventional approaches is the turn around time simply to provide a prospective customer with a list of available and operable options that may meet his needs. In some industries, given the high degree of technical complexity, there may be a turn around time of several weeks before a customer even gets an estimate or recommendation of products that may serve his needs. This latency is oftentimes extremely costly in terms of getting work up and running.

The frustration of customers who seek such products and services, when forced to deal with the all too often poorly qualified salespeople within their given industry, leads to an interaction that is less than effective. Oftentimes, both parties leave an interaction with a high degree of frustration. The conventional solution of simply trying to train up the sales force to a sufficiently high level so as to accomplish this desired effectiveness has simply been deficiency in light of many of the intrinsic limitations of the high technology industries. Moreover, even within relatively low technology level industries, there is oftentimes a great deal of "expert knowledge" that must or should be acquired before making an effective salesperson. Within these industries as well, the limitations of the skill level, experience, and expertise of the agent (salesperson) is often the limiting factor in the overall effectiveness of the solution.

Further limitations and disadvantages of conventional and traditional systems will become apparent to one of skill in the art through comparison of such systems with the present invention as set forth in the remainder of the present application with reference to the drawings.

SUMMARY OF THE INVENTION

Various aspects of the present invention can be found in an expert system adapted dedicated Internet access guidance engine. The expert system adapted dedicated Internet access guidance engine includes a dedicated Internet access guidance engine, a number of available Internet access products, customer need input concerning a number of dedicated Internet access guidance engine questions such that the customer needs are provided to the dedicated Internet access guidance engine. The dedicated Internet access guidance engine uses the customer need input to rate at least one available Internet access product of the number of available Internet access products, and the dedicated Internet access guidance engine selects a recommended Internet access solution, that is operable to meet a customer need, from among the number of available Internet access products and the rated at least one available Internet access product.

In certain embodiments of the invention, the dedicated Internet access guidance engine provides the recommended Internet access solution to an agent using a graphical user interface. The dedicated Internet access guidance engine may also provide a single Internet access solution (or several compatible Internet access solutions) and a single not recommended Internet access solution (or several not recommended Internet access solutions) to the agent using the graphical user interface. The rating of the at least one available Internet access product may involve determining a number of traits corresponding to the customer need such that the determination made using the customer need input. The number of traits includes a variety of traits that may include a cost, an ability to host a web site, a reliability, and a bandwidth. As an example of the granularity in which the various traits may be parsed, the bandwidth trait may further include information of various bandwidth needs that are required for various applications such as email, web browsing, web site hosting, file transfer, and voice over Internet protocol applications. The reliability may be determined by taking a maximum of a value indicative of a criticality of Internet use and a value indicative of a data type transmitted via the Internet. The customer need input may take several forms including both integer input form and Boolean input form. If desired, the ability to host a web site is treated as one of the Boolean inputs.

Other aspects of the present invention can be found in a dedicated Internet access guidance engine method used within an expert system. The method involves providing a number of dedicated Internet access guidance engine questions to a customer, receiving answers to those questions and providing those answers to dedicated Internet access guidance engine. The method also involves performing dedicated Internet access guidance engine processing to calculate a number of crisp values to represent a number of traits corresponding to a number of customer needs. The method may also involve transforming at least one crisp value of the number of crisp values to a fuzzy value to represent at least one trait within the number of traits with the fuzzy value; it is noted that some embodiments employ no crisp values at all, and they use only fuzzy values. The method also involves rating at least one available Internet access product within a number of available Internet access products, using the dedicated Internet access guidance engine, based on the fuzzy value. Again, in some embodiments, at least one crisp value within the number of crisp values is also used here; however, some embodiments employ only fuzzy values and no crisp values. Ultimately, the method involves recommending an Internet access solution, that is operable to meet the customer needs, from among the available Internet access products and the rated at least one available Internet access product.

In certain embodiments of the invention, the expert system also includes a data network guidance engine. The method may also involve providing the recommended Internet access solution to an agent using a graphical user interface. In addition, the method may also include providing a compatible Internet access solution and a not recommended Internet access solution to the agent using the graphical user interface. The method may also involve providing an explanation to the agent, using the graphical user interface, that explains why the recommended Internet access solution was recommended. The number of traits may include a cost, an ability to host a web site, a reliability, and a bandwidth. The ability to host a web site may be treated as a Boolean value. The crisp values may include both integer values and Boolean values.

Other aspects of the present invention can be found in a dedicated Internet access guidance engine method used within an expert system. The method involves performing real time interaction between a customer and an agent and providing a number of dedicated Internet access guidance engine questions to the customer. The method also involves receiving answers, corresponding to the dedicated Internet access guidance engine questions and inputting the answers into a dedicated Internet access guidance engine via a graphical user interface. The method also involves performing dedicated Internet access guidance engine processing to calculate a number of crisp values to represent a number of traits corresponding to a number of customer needs. The crisp values include a cost, an ability to host a web site, a reliability, and a bandwidth. The method also involves transforming at least one crisp value of the crisp values to a fuzzy value to represent at least one trait with the fuzzy value. The method also involves rating at least one available Internet access product within a number of available Internet access products, using the dedicated Internet access guidance engine, based on the fuzzy value and at least one crisp value within the plurality of crisp values, and recommending an Internet access solution, that is operable to meet the customer needs, from among the available Internet access products and the rated at least one available Internet access product. In addition, the rating of the at least one available Internet access product within a available Internet access products includes disqualifying the at least one available Internet access product when the at least one available Internet access product is inoperable to support web site hosting and when the ability to host a web site crisp value comprises a true value.

In certain embodiments of the invention, the rating of the at least one available Internet access product involves employing dynamic calculation. The ability to host a web site may include a Boolean customer need. If desired, a fuzzy value trait associated with bandwidth is generated from the at least one crisp value.

Other aspects, advantages and novel features of the present invention will become apparent from the following detailed description of the invention when considered in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained when the following detailed description of various exemplary embodiments is considered in conjunction with the following drawings.

FIG. 1 is a system diagram illustrating an embodiment of an interactive product selection/recommendation system built in accordance with the present invention.

FIG. 2 is a system diagram illustrating an embodiment of an agent to expert system interface that is built in accordance with the present invention.

FIG. 3 is a functional block diagram illustrating an embodiment of an interactive product selection/recommendation method that is performed in accordance with the present invention.

FIG. 4 is a system diagram illustrating another embodiment of an interactive product selection/recommendation system built in accordance with the present invention.

FIG. 5 is a block diagram illustrating an embodiment of some of the inputs that are usable within an expert system that is built in accordance with the present invention.

FIG. 6 is a block diagram illustrating an embodiment of program interaction with an expert system that is built in accordance with the present invention.

FIG. 7 is a block diagram illustrating another embodiment of program interaction with an expert system that is built in accordance with the present invention.

FIG. 8 is a block diagram illustrating an embodiment of real time catalog integration that is achieved in accordance using certain aspects of the present invention.

FIG. 9 is a system diagram illustrating an embodiment of customer base profiling that is achieved in accordance using certain aspects of the present invention.

FIG. 10 is a system diagram illustrating an embodiment of expert system processing that is performed in accordance with the present invention.

FIG. 11 is a system diagram illustrating an embodiment of interaction between a domain expert and an expert system in accordance with certain aspects of the present invention.

FIG. 12 is a block diagram illustrating an embodiment of definitions of problem domains and traits that are usable within an expert system that is built in accordance with certain aspects of the present invention.

FIG. 13 is a block diagram illustrating an embodiment of dedicated Internet access guidance engine operation that is performed in accordance with certain aspects of the present invention.

FIG. 14 is a system diagram illustrating an embodiment of some inputs used by a dedicated Internet access guidance engine that is built in accordance with certain aspects of the present invention.

FIG. 15 is a functional block diagram illustrating an embodiment of dedicated Internet access guidance engine processing that is performed in accordance with certain aspects of the present invention.

FIG. 16 is a block diagram illustrating an embodiment of dedicated Internet access guidance engine rating functionality that is performed in accordance with certain aspects of the present invention.

FIG. 17 is a block diagram illustrating an embodiment of dedicated Internet access guidance engine rating combination that is performed in accordance with certain aspects of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Within all of the various embodiments of the invention described below, the use of terminology of product or products is intended to include both services and products. The generic use of the term "products" or "product" is in no way to limit the operability of various aspects of the invention, as it is equally applicable within both industries selling and providing services, as well as those selling and providing products or combinations of services and/or products. For brevity, the term product is sometimes used instead of goods and/or services.

The invention is operable to allow and assist an agent to select certain solutions to meet the needs of a customer. In one embodiment, the invention is operable to help an agent select telecommunication technologies and products based on customer needs. In light of the comments made above regarding products and services, telecommunication technologies is also included within the scope and spirit of the terminology of "products and services" or simply, "products." The invention employs functionality offered within fuzzy logic expert systems. From certain perspectives, various aspects of the invention are based on fuzzy logic expert systems.

To assist an agent to select an amenable product for a customer, the problem domain must first be determined. The problem domain is the general area where the customer needs a solution. For example, Internet access would be one problem domain. Another one would be local data services. There are numerous problem domains where the functionality of the invention may assist an agent to find a customer solution. Once the problem domain is determined, the agent may then be presented with a set of questions relating to the needs of the customer with respect to that problem domain. The customer's answers are then transformed into values for each element of a set of product traits. In addition, the relative importance of each of the traits, from a customer's perspective, may also be considered.

A trait is a characteristic of a product of relevance to the user. These traits include any number of parameters and may include measurements such as cost, bandwidth, reliability, availability, etc. Each trait that is measurable in a continuum is represented as a fuzzy variable. Other traits are not measurable in a continuum, but are rather better represented as crisp values. One such example of a crisp value is the need for static IP addresses or the location of the site. These are represented with non-fuzzy values (crisp values). An expert system built in accordance with the invention then takes the collection of these values that represents the needs of the customer (one per trait, called needs) and attempts to match them against the corresponding collection of values provided by each product (called specs). Based on this, the expert system is operable to obtain a collection of ratings: one rating for each combination of product and trait. The expert system is also operable to summarize the ratings to give each product a summary rating. The summary rating of each product are used to sort them and classify them with respect to the level of recommendation assigned to each product.

In determining success of the operation of an expert system built in accordance with the invention, one consideration is that a customer belong to a set of "satisfied customers" once they deploy a solution suggested by an agent. In traditional set theory, an element either belongs to a set or does not. So, if customer satisfaction were treated in such a manner, then customers would either be "satisfied" or "dissatisfied." However, customers are rarely 100% satisfied or 100% dissatisfied. Their satisfaction usually falls somewhere in the middle.

The invention is operable using Fuzzy Sets. In Fuzzy Set Theory, an element does not have to belong to a set 0 or 100%. Instead, an element may belong to the set to a given degree. This type of model provides a lower semantic gap when working with sets that don't have precise mathematical definitions (as many don't). In fuzzy sets, each set has a corresponding membership function that maps all elements from the Universe of Discourse to a real number in the range [0, 1]. If the membership function maps an element to 1, then it completely belongs to the corresponding set. If it maps an element to 0, then it does not belong to the set at all. Other values provide a more fuzzy definition of whether the element belongs to the set. In the example of customer satisfaction, the membership function determines the level of satisfaction of a customer.

In many problems, the Universe of Discourse is a range of real numbers. As some examples: bandwidth, cost and reliability traits are all measurable and can be represented by real numbers. So, as an example, a fuzzy set may be created to represent the level of satisfaction of a customer who uses ISDN using the following membership functions as the spec of the ISDN product: P(x)=1 for x<=48 P(x)=2-x/48 for 48<x and x<96 P(x)=0 for x>=96

Where x is measured in kbps. ISDN has a bandwidth of 64 kbps and in this case, the domain expert who came up with this membership function estimates that customers who need a bandwidth of 48 kbps or less will be completely satisfied with ISDN. Customers that need a bandwidth between 48 kbps and 96 kbps will have a linear reduction in their level of satisfaction. Finally customers who need 96 kbps or more will be completely dissatisfied with this solution. Presumably, we will be able to obtain this type of information from domain experts. This particular fuzzy set is meant only as an example.

On the other hand, the bandwidth requirements for a customer can rarely be estimated precisely. So, a fuzzy set can also be used to represent the bandwidth need of a customer: N(x)=0 for x<=50 N(x)=x/10-5 for x>50 and x<=60 N(x)=-x/10+7 for 60<x and x<=70 N(x)=0 for x>70

This set represents a customer whose bandwidth need is estimated to be between 50 and 70 kbps. The set peaks at 60 kbps because it is the most likely bandwidth requirement. One assumption may be made: if the fuzzy set that represents a customer need is a subset of what is offered by a product, then it may be assumed that the customer will be perfectly satisfied. It may also be determined if there is no overlap, then the customer will be completely dissatisfied. If there is partial overlap there will be partial satisfaction. Fuzzy logic provides rules to determine the level of satisfaction.

There are at least three practical considerations that affect how fuzzy sets are represented. First, since fuzzy sets deal with concepts that are difficult to pin down, it is rare to come up with complex membership functions. Second, in order to simplify calculations, fuzzy sets are commonly defined with a set of linear functions. Third, although it is relatively easy to come up with equations for line segments, it quickly becomes bothersome. So, a common notation for fuzzy sets is shown as follows:

FUZZY-SET::=[POINTS] POINTS::=POINT|POINT POINTS POINT::=Y/X Y::=<a number in the interval 0.0 to 1.0 inclusive> X::=<any number>

The first y.sub.0/x.sub.0 point delimits a horizontal line from (-infinity, y.sub.0) to (x.sub.0, y.sub.0). The last y.sub.n/x.sub.n point delimits a horizontal line from (x.sub.n, y.sub.n) to (infinity, y.sub.n). All other consecutive points (x.sub.i, y.sub.i)(x.sub.i+1, Y.sub.i+1) delimit straight lines.

As examples, the 2 membership functions defined above could be written as: ISDN-BANDWIDTH-SATISFACTION=[1/48 0/96] CUSTOMER-BANDWIDTH-NEEDS=[0/50 1/60 0/70]

Fuzzy sets defined with 2 coordinates are called Z Fuzzy Sets when y.sub.0>Y.sub.1. They are called S-Fuzzy Sets when y.sub.0<y.sub.1. This is in reference to the shape of the membership function. There are also triangular, rectangular and trapezoidal fuzzy sets, where the name is assigned based on the shape of the membership function.

In an expert system built in accordance with the invention, the use of Z and S Fuzzy Sets to define each trait of each product may be employed. For example, for traits where higher numbers are preferable, such as bandwidth and reliability (measured as MTBF or mean time between failures), a Z Fuzzy Set is used. For traits where lower numbers are preferred, such as cost or MTTR (Mean Time To Repair), an S Fuzzy Set is used. To represent customer needs we generally use triangular fuzzy sets, although other shapes are allowed and the expert system will work with any arbitrary membership function for any fuzzy set. When the needs of the customer are known precisely or where there are no means to "fuzzify" the needs of the customer, it is valid to use a crisp value to represent its needs. A crisp value is just a special case of a fuzzy value where the membership function evaluates to one on the crisp value and to zero elsewhere.

A common notation in Fuzzy Set Theory is that for a given Fuzzy Set A its membership function is also called A and defined as A:UOD.fwdarw.[0, 1] Where UOD is the Universe of Discourse. Using this notation we define the following set operations: C=A.andgate.B iff C(x)=min(A(x), B(x)).A-inverted.x in UOD C=A.orgate.B iff C(x)=max(A(x), B(x)).A-inverted.x in UOD C= iff C(x)=1 -A(x).A-inverted.x in UOD

The height of a set A is h(A) and is defined as the maximum value produced by its membership function.

When the spec of a product P is matched against the need N (where N is a crisp value), for a given trait, the fuzzy logic expert system produces P(N) as the matching measure.

When the spec of a product P is matched against the need N (where N is a fuzzy value), for a given trait, the fuzzy logic expert system proceeds as follows: Good=h(P.andgate.N) Bad=h({overscore (P)}.andgate.N) Rating=Good/(Good+Bad)

Rating is the matching measure. The rating formula described above is not in the literature. The customary way of obtaining the level of matching of 2 fuzzy sets is: h(P.andgate.N)

This is, however, overly optimistic.

The concept of a fuzzy variable is relevant and important as well,