Abstract: A system and method of semantic to non-semantic routing for locating an expert. An inquiry-type database has a first layer of inquiry types organized from underlying criteria groupings, (humanly understandable descriptors). Additional layers are associated in a one-to-one correspondence with the first layer of inquiry types. Experts, having individualized knowledge, are listed in a skill-set database associated with the inquiry types. The skill-set database entries are linked to the associated inquiry-type by a numerical routing identifier. An expert is selected from the skill-set database entry linked by the numerical routing identifier. In another embodiment, multiple enterprises are mapped to separate layers of inquiry types having a one-to-one correspondence with the underlying groupings. A skill-set database entry is related to the inquiry type through a numerical routing identifier, the identifier being selected from a respective range of identifiers associated with the respective multiple enterprises.

Abstract: The present invention relates to a system and method of locating an expert having individualized knowledge of a seeker's request. A computer based match and route system is used to locate an expert. In the memory component of the system, a seeker profile is created and is embedded, prior to a request for an inquiry, with pre-selected inquiry criteria and values. An interactive problem definition page displays numerous inquiry criteria and values for a seeker to select. An expert having individualized knowledge regarding a seeker's request is located by filtering the pre-selected and seeker selected inquiry criteria and values.

Abstract: A system and method of semantic to non-semantic routing for locating an expert. An inquiry-type database has a first layer of inquiry types organized from underlying criteria groupings, (humanly understandable descriptors). Additional layers are associated in a one-to-one correspondence with the first layer of inquiry types. Experts, having individualized knowledge, are listed in a skill-set database associated with the inquiry types. The skill-set database entries are linked to the associated inquiry-type by a numerical routing identifier. An expert is selected from the skill-set database entry linked by the numerical routing identifier. In another embodiment, multiple enterprises are mapped to separate layers of inquiry types having a one-to-one correspondence with the underlying groupings. A skill-set database entry is related to the inquiry type through a numerical routing identifier, the identifier being selected from a respective range of identifiers associated with the respective multiple enterprises.

Abstract: A system and method of semantic to non-semantic routing for locating an expert. An inquiry-type database has a first layer of inquiry types organized from underlying criteria groupings, (humanly understandable descriptors). Additional layers are associated in a one-to-one correspondence with the first layer of inquiry types. Experts, having individualized knowledge, are listed in a skill-set database associated with the inquiry types. The skill-set database entries are linked to the associated inquiry-type by a numerical routing identifier. An expert is selected from the skill-set database entry linked by the numerical routing identifier. In another embodiment, multiple enterprises are mapped to separate layers of inquiry types having a one-to-one correspondence with the underlying groupings. A skill-set database entry is related to the inquiry type through a numerical routing identifier, the identifier being selected from a respective range of identifiers associated with the respective multiple enterprises.

Abstract: The present invention relates to a system and method of categorizing a user's inquiry and defining the scope of the assistance sought by creating a subject list style. A subject list style is created in a computer based match and route system by selecting a quantity of inquiry criteria and values to present to a user in an interactive manner. The inquiry criteria and values are organized into one or more groups, and an interrelationship between them is defined within each group. The interrelationship may be independent, hierarchical and a combination of independent/hierarchical. Additionally, a parameter is designated that indicates a source of retrieving the inquiry criteria and values. Such a subject list is then presented to a user in an interactive problem definition page.

Abstract: The online tutoring method and system selects a best available tutor and establishes an interactive learning environment for the student. The automated selection process uses a weighting system to incorporate numerous factors from and about the student and tutors. Upon receipt of a request for tutoring from a student, the matching processes is performed and a tutor is selected. Information about the participating tutors, such as proficiency in the subject matter and qualifications with respect to particular age groups, are quantified and associated with competency criteria. Information about the student, such as the type of assistance required and the student's education level are quantified and associated with request criteria that corresponds to the competency criteria. The weighting system combines the quantified information associated with the criteria to select best tutor. The weighting system may further include objective relative weights, input from the student's parent, and/or tutor certifications.