Data processing: artificial intelligence – Knowledge processing system – Creation or modification
Reexamination Certificate
2001-07-19
2004-06-01
Davis, George B. (Department: 2121)
Data processing: artificial intelligence
Knowledge processing system
Creation or modification
Reexamination Certificate
active
06745172
ABSTRACT:
BACKGROUND
1. Technical Field
The present invention relates generally to expert system processing; and, more particularly, it relates to an expert system adapted data network 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 data network guidance engine. The expert system adapted data network guidance engine includes a data network having a number of nodes. The expert system adapted data network guidance engine employs a number of heuristics to generate data network configurations based on the nodes within the data network. The expert system adapted data network guidance engine transforms one data network configuration into a transformed data network configuration. The expert system adapted data network guidance engine selects a recommended data network solution from among the network configurations and the transformed data network configuration.
In certain embodiments of the invention, the expert system adapted data network guidance engine provides the recommended data network solution to an agent using a graphical user interface. The expert system adapted data network guidance engine provides the recommended data network solution to the agent using the graphical user interface. The expert system adapted data network guidance engine generates a number of initial configurations. The expert system adapted data network guidance engine then applies the heuristics to the initial configuration to generate the data network configurations. The expert system adapted data network guidance engine then selectively applies the heuristics to the initial configuration. The heuristics include any number of heuristics including a distance tree heuristic, a maximal flow heuristic, a mesh of needs heuristic, a hub and spoke heuristic, a partitioned cluster heuristic, a gravity tree heuristic, and a hub mesh heuristic. The expert system adapted data network guidance engine performs any number of iteration, including as few as one, when employing the heuristics to generate the data network configurations based on the nodes within the data network. In addition, the iteration generates at least one data network configuration within the data network configurations. The expert system adapted data network guidance engine employs neural network processing to select a heuristic from the heuristics, the selected heuristic has a higher chance of success than the remaining heuristics. The expert system adapted data network guidance engine employs a mutation heuristic.
Other aspects of the present invention can be found in a data network guidance engine method used within an expert system. The method includes rating a first number of traits that are used to characterize a first data network configuration and rating a second number of traits that are used to characterize a second data network configuration. The method also includes increasing a trait within at least one of the first number of traits and the second number of traits. The increased trait results in the generation of a modified data network configuration; the modified data network configuration is generated from either the first data network configuration and the second data network configuration. The method also includes selecting a recommended data network configuration from among the first data network configuration, the second data network configuration, and the modified data network configuration. Any oth
Erickson Charles R.
Gheith Ahmed
Mancisidor Rod
Norris Rob
Davis George B.
Gray Cary Ware & Freidenrich LLP
Whisperwire, Inc.
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