Data processing: artificial intelligence – Knowledge processing system
Reexamination Certificate
1999-02-04
2003-01-07
Davis, George B. (Department: 2121)
Data processing: artificial intelligence
Knowledge processing system
C706S001000, C706S009000, C706S046000
Reexamination Certificate
active
06505183
ABSTRACT:
BACKGROUND OF THE INVENTION
Human Resource (HR) departments today use many systems for answering employee and manager questions about benefit plans and HR policies, procedures, and practices. These questions include how to: use medical plans, take leaves of absence, and file harassment complaints, as well as other questions regarding paycheck amounts, for example.
The information sources used to address the questions in the typical organization usually reside in various formal and informal repositories. Some sources are static documents, both paper and electronic. These documents may or may not have been generated by the department to answer questions posed by employees. Other information sources include the human resource management systems (HRMS's), which are repositories for accounting information for each employee. Finally, some information lives only in the heads of the experienced HR professionals in the organization. These various and unconnected systems are the basis of most companies' HR information dissemination processes today, but there are problems with the underlying model for information dissemination.
Pre-prepared, static documents, such as policy manuals, summary plan descriptions, and manually maintained web sites, are costly to produce. Further, they tend to become dated quickly, especially during major reorganizations. Also, these systems often only give generalized answers, because many of the detailed answers change depending on the particular employee's situation—employee group, physical location, age, and length of employment, for example.
HRMS's, which are sometimes part of even larger enterprise resource planning systems (ERP's), contain vast quantities of accurate, constantly-updated data. These systems, however, do not contain information on HR policies or on how to submit forms, for example. They tend to be able to answer very specific questions (e.g., what is the net amount on my monthly paycheck), but not general ones (e.g., I'm having a baby, what does the company do for me and what must I do to use these benefits).
Electronic repositories, containing answers to questions previously asked by other employees, are easy to search, but consume resources to prepare and still share the problems of prepared documents. They are also more likely to give wrong answers after changes in policies or organization, since it is hard to find and correct all of the affected answers, and even harder to verify that the changes are correct and complete.
HR professionals themselves can be used to answer questions. This resource, however, is expensive, and finding the right person to answer a question can be a frustrating and slow process for getting the responses. Also, using HR professionals to constantly answer repetitive questions, instead of working on more strategic initiatives, is not the best application of their skills.
Knowledge-bases have grown out of artificial intelligence work of the past three decades. The systems are designed to capture knowledge from subject matter experts, and then deliver that knowledge to non-expert individuals and applications. Many knowledge-base tools have been developed in academia and industry. The resulting knowledge-bases fall into two major categories: application-specific knowledge-bases, and general-purpose knowledge-base tools.
Application-specific knowledge-bases contain pre-configured information, which is ready for immediate use. The producers of these knowledge-bases build systems on various knowledge modeling and delivery technologies, and use subject-matter experts to fill these knowledge-bases with the information needed to address a particular problem set. Any person or organization that has problems covered by the knowledge-base will find the knowledge-base useful. If, however, the pre-configured information in the knowledge-base does not fully cover the problem set of the user, the utility of the knowledge-base is compromised. Also, as the problem set of the user changes over time, the provider of the knowledge-base must update the knowledge-base. Generally, knowledge-bases of this type are commercially successful only when the knowledge they contain is appropriate for multiple users, and the rate of change in the knowledge is slow over time.
General-purpose knowledge-base tools similarly are built on knowledge modeling and delivery technologies, but have no pre-configured information. A person or organization can take the general-purpose tools, learn the knowledge modeling methodologies appropriate to the tools, and then add information, acting as the subject matter expert. All maintenance of the knowledge through time is the responsibility of the user. Knowledge-base systems of this type are commercially successful when there are multiple users willing to populate and maintain their own individual knowledge-bases.
Knowledge-bases have not been widely successful to date because many users: 1) have problems that are unique in enough aspects to make pre-configured knowledge-bases impracticable, and 2) are unwilling to make the effort to learn knowledge modeling methodologies and then build and maintain knowledge using a general-purpose tool.
SUMMARY OF THE INVENTION
HR departments with their need to deliver information about benefit plans, policies, procedures, and practices exemplify problems confronting deployment of knowledge-based systems. While all companies have the underlying information and the information has much in common across companies, the details of the information varies enough from company-to-company such that pre-configured knowledge-bases are rejected by most companies. On the other hand, HR departments do not want to learn knowledge modeling tools and methodologies and to then build all of the complex models themselves, especially with the large amount of information needed to be managed and the high rate of change that is endemic to this information.
The present invention solves these problems by providing a knowledge modeling and delivery system that combines pre-configured, parameterized models of human resource knowledge with organization-specific and employee-specific data. The invention in its current implementation delivers personalized answers to employee and manager questions about benefit plans, payroll, HR policies, procedures, and practices.
The pre-configured, parameterized models can be created and maintained outside the organization with the organizational data being created and maintained by the organization. Then personal data are accessed or read from existing HRMS's. This threefold partitioning yields a system that can be quickly customized to an individual organization, yields detailed and accurate information for individuals within the organization, and can be maintained in a cost-effective manner.
In general, according to one aspect, the invention features a knowledge-based human resource information dissemination system. This system comprises an organization-specific information repository and an employee-specific information repository. An engine or compiler uses a knowledge model to respond to user queries by combining the information from the organization-specific repository and the employee-specific repository. In this way, in a typical implementation, the existing HRMS's can be used to provide detailed, relevant responses to queries while capitalizing on a generalized/formatted system that can be deployed across different organizations using similar knowledge model and engines.
Depending on the implementation, the compiler may use a variety of standard expert system inferencing techniques.
In the preferred embodiment, an information server is used to receive the user queries by a user-operated browser. The query is transferred to the engine and a response is generated, which is sent to the browser. Typically, the information server will use a hyper-text transfer protocol-based system.
The current embodiment uses a knowledge model comprising hierarchically-organized responses to expected queries. The responses have embedded
Farlow Timothy S.
Herbst Steve
Loofbourrow Tod Hayes
Authoria, Inc.
Davis George B.
Hamilton Brook Smith & Reynolds P.C.
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