JP2001043277A - Method and system for collating employment post and at least one applicant while using quality and quantity evaluation parameter - Google Patents

Abstract

PROBLEM TO BE SOLVED: To cancel the waste of an employer for an oral test with an applicant unsuitable for the organization culture of a company by collating the applicant with the employment post of an enterprise based on the assessment and evaluation of an applicant parameter and an employment parameter. SOLUTION: Information from the employer related to the employment post and information from applicants are collected and stored in a data base(DB) 500. The information from the employer is expressed as a block 100 and contains the employment parameter such as salary or geographical location. On the other hand, the information from the applicant is expressed as a block 300 and contains the applicant parameter such as the effort target of work or working style. Next, a collation process expressed as a block 700 is performed to the specified post, the applicant information in the DB 500 is searched and applicants more than one matched to the employment parameter are searched. The result of the collation process is shown as a list expressing the degree of similarity between the employment parameter and the applicant and the post-collation task of manual work expressed as a block 900 is performed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

TECHNICAL FIELD This invention relates generally to the recruitment of volunteers. More particularly, the present invention relates to a computer system and method for matching employment units (jobs) to candidates using qualitative and quantitative evaluation parameters.

[0002]

BACKGROUND OF THE INVENTION Traditionally, companies have used two means to recruit and recruit employees. The two methods are a recruiting company (a job placement and recruiting company) and a job advertisement. Recruiting companies offer a common and effective way to hire employees. This is because the recruiting company can obtain more interest from and contact with the recruiting company as compared to a case where a company recruits candidates (candidates) by itself. Further, by using a recruiting company, the company itself does not need to spend much time and resources on evaluating each candidate.

[0003] Generally, a recruiting company is established by collecting resumes from applicants. The number of potential candidates is infinite, especially in the area of intermediate to advanced management. However, with conventional methods (including networks and advertising), recruiting companies can only obtain a limited number of applicants based solely on quantified factors.

[0004] When a recruiting company receives a resume from a candidate, the resume is entered or scanned into a database. However, since the recruiting company does not have an appropriate method for updating the applicant's personal information, most of the recruiting company's database becomes obsolete in a short period of time and becomes virtually unusable. As a result, recruiting companies must constantly recruit new applicants.

[0005] To initiate a search for applicants, recruiting company personnel search the database using one or more "keywords" and select applicants based on quantitative factors such as employment or educational background. Create a group for. However, this method has a limitation that only resumes that include the same search word or phrase as entered can be retrieved. For example, if the employer is "C ++" and "Qu
When searching for a candidate with "ark" experience, a resume describing that the candidate has "programming" and "desktop publishing" experience cannot be retrieved. In addition, once a group of selected applicants has been created, the recruiter evaluates qualitative factors, such as whether the applicants fit into the company's organizational culture, using subjective criteria.

[0006] If the recruiter determines that the candidate is qualified, the recruiter will meet the applicant and, if deemed appropriate, arrange for an interview with the employer. Generally, recruiters negotiate hiring conditions. In a typical recruiting company, it may take six months for a candidate's search to be complete. Most of that time is spent searching for and evaluating applicants. If no one is in that position during that period, it can be a big hit to the company.

Placing a recruitment advertisement in a newspaper is also one of the recruiting means. Generally, employers place advertisements in newspapers and provide addresses or telephone numbers so that applicants can contact the company. This would be cheaper than using a recruiting company. However, employers must spend a significant amount of time and resources in organizing and interviewing applicants' resumes, and many of them are unqualified or equally important. Or is not compatible with the corporate culture. In addition, the target human resources are limited to the readers of a particular publication.

[0008] Over the past few years, CareerMosa
ic. com, Monster. com, JobTra
k. com, HotJobs. Internet companies such as com have used the World Wide Web to provide new ways to find and recruit employees. Generally, these Internet professional sites are operated by providing an online searchable database of resumes and population requirements. These resumes and population requirements are submitted and updated at will. Many of these sites are very similar to electronic job ads. In other words, the employer pays a fee to list the population, and the applicant searches for the population free of charge. By simply connecting to the Internet, you can quickly find applicants' resumes and stay up to date,
Traditional databases at recruiting companies are losing value. At present, how to access data (search for human resources) and evaluate it has become an issue.

[0009] Like traditional recruiting companies, these internet sites can usually only be searched using the keyword method, but if a vast number of resumes must be evaluated. In some cases, little effect can be obtained. Furthermore, even if a single candidate is screened based on quantitative factors, these internet companies will not be able to ensure that the candidate is an appropriate person for the employment position (job). It does not provide a means of assessing strategic factors. In this case, as with the use of job advertisements, recruiters must spend time and resources organizing their resumes and evaluating candidates. FIG. 1 is a functional diagram of a conventional typical Internet business site system. Generally, such a system comprises a display of a website (display), a computer server connected to the Internet, and a database.

[0010]

Accordingly, a wide area such as the Internet for providing applicants and recruiting companies with access to recruitment services around the world, with the ability to provide and update information at any time.
There is a need for systems and methods that utilize networks. There is also a need for a recruiting service that can search a database using multiple parameters to better match applicants and particular employment positions based on quantitative factors. There is also a need for a system and method that can evaluate candidates based on qualitative factors. Furthermore, based on quantitative and qualitative parameters,
There is also a need for automated systems and methods for screening and matching eligible applicants to employment positions.

[0011]

SUMMARY OF THE INVENTION The present invention provides a system and method for matching one or more applicants to a corporate employment position based on the assessment and evaluation of qualitative and quantitative parameters. This improves the problem of recruiting applicants. Quantitative parameters include items of information such as salary, geographic location, and degree requirements related to employment positions. Similarly, the quantitative parameters include the occupation category of the applicant and the employer. The qualitative parameters include work challenges (effort goals),
Includes work style, role style, leadership style, motivation (motivation), business environment experience, etc.

[0012] In one embodiment, a computer system includes a processor and a database accessible to the processor. The processor retrieves the employment parameters from the database; retrieves the candidate parameters from the database; compares the candidate parameters with the employment parameters; and calculates the correspondence between the candidate parameters and the employment parameters. It is programmed to calculate degrees.

In one embodiment, a method and system employing a qualitative assessment tool to match applicants to employment positions, taking into account qualitative characteristics that are important to the employer, is used. In some embodiments, such a computer-implemented method and / or system is provided. Also, in some embodiments, such computer-implemented methods and / or systems use the Internet.

In one embodiment of the present invention, various parameters are collected and stored in a database.
For example, a record is set for each employment position. Each record includes a set of employment position parameters that define the required or desired characteristics for that employment position.
The employer can define these employment position parameters based on the desired or required characteristics and background. Similarly, applicants enter information and parameters that are stored in the database. The parameters associated with the candidate are organized in a candidate record. The database can include many candidate records associated with many applicants, and many employment position records associated with many employment positions. The reconciliation process compares a given employment position record with one or more parameters by comparing all or some parameters associated with a given employment position with all or some parameters associated with each candidate record. Match against applicant record. In this way,
Match applicants to employment positions.

[0015] In some embodiments, the employment position record and the candidate record include both "quantitative" and "qualitative" parameters. In some embodiments of the invention, one or more candidate records are matched to a given employment position based on a comparison of relevant quantitative and / or qualitative parameters.

[0016]

DETAILED DESCRIPTION OF THE INVENTION The present invention combines the best features of traditional recruiting companies and Internet profession sites to form a new system and method for searching and evaluating applicant data. The present invention takes advantage of the speed and connectivity of the Internet, which allows anyone around the world to access and allows applicants to update their information at any time. . The recruiter can also perform a database search based on multiple parameters using a series of complex algorithms to find qualified applicants and match the applicant's profile to the company's needs.
In addition, the present invention provides an evaluation means for evaluating qualitative factors, such as whether or not a candidate conforms to a particular organizational culture of the company. One of the most important factors in successfully placing applicants in-house is to ensure that the company's organizational culture is compatible with the personal style and personality of the individual, and the present invention allows for the transfer of labor. (Reorganization) can be dramatically reduced. The present invention also provides an essential recruiting means for today's information society.
Further, the present invention can significantly reduce the time spent searching for qualified candidates, thus dramatically reducing the total time spent searching for candidates, for example, 50-60%.

[0017] The present invention provides a system and method for automatically selecting candidates based on quantitative and qualitative characteristics, thereby enabling employers to conduct interviews with candidates who do not fit into the company's organizational culture. The user's resources are not wasted.

FIG. 2 is a functional schematic diagram of an embodiment of the present invention. In this embodiment, a system 10 for matching a candidate to a valid employment position (job) is shown. Information from the employer and information from volunteers regarding valid employment positions is collected and stored in a database, such as database 500. Collected information from the employer regarding the employment position is represented by block 100. The information gathered during the employer input phase can include employment position parameters such as required and / or desirable education, work experience, salary, place of employment, and industry experience. In addition, the specific skills required, the qualitative assessment of the employer, and the employer's weight for each parameter are entered.
Some embodiments for collecting employment position parameters are described in detail below in connection with FIGS.

Similarly, information from a large number of volunteers is collected and stored in a database, such as database 500. The database 500 includes one or more databases. For example, one database may be used to store applicant data, another database may be used to store employee data, and so on. The information collected from the candidate is represented by block 300. Information gathered during the applicant entry phase includes the candidate's educational background, work experience, previous experience, current employer, current position,
Candidate parameters such as a desired position, a desired geographic location (work location), and qualitative evaluation parameters can be included. Some embodiments for collecting candidate parameters are described in detail below in connection with FIGS.

Next, a matching process is used for a particular employment position. As a result, the information of the applicant stored in the database 500 is searched, and one or more applicants having information matching the employment parameters of the specific employment position are found. The search and match process is represented by block 700. In the reconciliation process, the employment parameters entered by the employer for the possible employment positions are compared with the applicant information. In some embodiments,
The degree of similarity between the information provided by a particular applicant and the employment parameters is calculated. Next, the system 10 provides information regarding the results of the matching process.
The information is provided, for example, by a list showing the number of applicants and the degree of similarity between each applicant and the employment parameters associated with that particular employment position. The degree of similarity between the employment parameters and the parameters associated with each candidate is reviewed, for example, by the recruiter or research consultant who performed the particular matching process. Depending on the results of the search, the modified search may be performed one or more times.

In order to further determine whether the found candidate (candidate) fits a specific employment position, a further task after collation may be added. This post-match processing is represented by block 900. This post-matching processing includes manual interviews and / or work performed by the employer and / or research consultant, such as telephone interviews, video conferencing, personal interviews, negotiations, and ultimately the placement of eligible applicants in employment positions. Includes post-match tasks.

Some embodiments of the present invention provide an Internet-based recruitment system. In such an embodiment, the entity that manages the recruiting server (entity) configures the system. This allows the employer to enter online the employment position parameters associated with the employment position that he or she wants to supplement. A recruiting server may be comprised of one or more interconnected servers. For example, one server can be used for the operation of a website, and another server can perform the processing described here. recruit·
The entity that operates the server is, for example, a recruiting company or a research company. Similarly,
The server can also be configured to allow applicants to enter information about themselves online. The server can also be configured such that a separate website is established for each employer and / or volunteer. Similarly, the server may provide a separate website for each recruiting employment position. Thus, it is possible for a given employer to have several websites on the server. Access to these individual websites may also be secured so that unauthorized persons cannot access the websites. Security can be provided, for example, such that individual websites cannot be accessed without entering a password or authorization code.

FIG. 3 illustrates the process of setting up individual employment position websites so that the employer can enter information relating to the newly added employment position into the system. In the embodiment shown in FIG. 3, the server is managed by a recruiting company. In this embodiment, as represented by block 108, a research consultant enters a recruiting website. Thereafter, the research consultant selects an existing customer (employer) from the menu of existing customers or enters a new employer (block 110). Each employer can have an identification number associated with it. In the case of a new employer, the system generates such an employer identification code or number when a new employer is specified, or a research consultant specifies it. Next, a contract is selected or created. That is, as shown in block 112, the possible consultation positions are entered by the research consultant. Also, each employment position can have an associated contract code or number generated by the system or specified by a research consultant, for example.
Thus, a particular employment position website may have both a customer code and its associated contract code. Alternatively, a particular employment position website may be identified by a single code, such as a contract code, without requiring a customer code. At block 114, the research consultant enters the name of each user on the employer's side who is allowed to enter information regarding the selected contract or employment position. In embodiments where individual websites are protected with a password, the research consultant
The password (s) to be used can be specified. At block 116, the research consultant provides the employer with the user identification code (s) and the associated password to enter the particular employment position website.

FIG. 4 shows an embodiment in which employment position parameters created in the manner described above in connection with FIG. 3 are entered via individual employment position websites. The registered employer logs on to the recruiting server by entering, for example, a company name on the employer's website (block 118). At the time the employer website asks for the password (s) to gain access to the recruitment server,
First enter the appropriate password. The employer then enters the desired contract number, ie, the number of the desired employment position to be replenished. The employer then enters various employment position parameters that are required and / or desirable for the employment position (block 120). These employment parameters include the applicant's educational background, qualifications, occupational history, classification of the current employer, etc., as well as the employer's open employment position (open position), employment position location, and qualitative evaluation parameters of the employer. Can be included. The employment parameters are stored in a database, such as database 500.

The term "qualitative assessment" as used herein includes assessment of organizational culture, assessment of job profiles, and conjoint analysis. The term "qualitative evaluation" also refers to the degree of a candidate's suitability for a company's organizational culture. Such evaluations include personality type matching between the candidate and the company. One such assessment is the Myers Briggspers personality type with 16 different personality types.
online type) system. In one embodiment, the qualitative assessment tool of the present invention allows the applicant to set parameters such as job challenge (work effort goal), work style (style), role style, leadership style, motivation (motivation), and business environment experience. Be evaluated. Similarly, the employer's organization and management style should be based on company and position endeavors, company management style,
He is assessed on parameters such as leadership style, company motivation, fast-growing business culture, and rapidly changing business environment. The qualitative assessment tool of the present invention evaluates applicants and employment positions and matches applicants and employment positions where the company culture matches the applicant's personality traits. This qualitative assessment tool measures the decision-making style, thinking style, cognitive goals, goals, emotional styles, occupational consciousness, and motives of applicants and employers. Thus, in one embodiment, a qualitative assessment tool seeks to match professional goals and responsibilities to potential applicants with the expectations and organization of a particular company.

In one embodiment, the qualitative evaluation is based on Michael J. et al. Driver, Kennis R. Brusseau, Philip L. Hansaker (Michael J. Driver, Kenneth R. Br
ousseau & Phillip L. Hunsaker)
Decision Maker: Five Styles of Decision Making for Managers and Business Success (The Dynamic Decision Make)
r: Five decision Styles for Executive and Business
Success), Self Discovery Press, 199
It uses questions derived from the 8th year (Self Discovery Press 1998). In another embodiment, the qualitative assessment uses binding analysis questions derived from tools such as ACQNET adaptive binding analysis. ACQNET
Adaptive binding analysis is provided by dataDirect, a subsidiary of Kingsley Research, Inc. of New York, NY.
Is a tool jointly provided by Sawtooth Software, Inc. of Shikim, Washington.

FIGS. 5-11 are examples of employer screens that may be necessary and / or desirable, such as a description of the employer's position, the job scope of the position, the required industry experience, and the desired industry experience. It is used when specifying the quantitative employment parameter to be used. The employer selects the parameters that are deemed important for that possible position and assigns an importance to each parameter as shown in FIG.

FIGS. 12-27 are examples of employer screens, which are used by the employer to specify qualitative evaluation parameters as required and / or desirable. These parameters include job challenges (effort goals), operations (work)
Style, role style, leadership style,
Motivation (motivation), business environment experience, etc. are included. The employer selects the qualitative parameters that it considers important for its possible position and assigns an importance to each parameter, as shown in FIGS. Figures 14-27 are examples of employer screens, which are used to obtain qualitative information from employers, so that the system can perform an organizational culture assessment for employers and employment positions. become.

FIG. 28 is a flow diagram relating to ensuring that the employment position parameters are fully specified and preparing the employment position record for the matching process. In the embodiment shown in FIG.
The research consultant enters the recruiting website and selects the appropriate employer and contract number corresponding to the position to be recruited, as shown in. As shown in block 124, the research consultant determines whether the employment position record has been completely filled out. If not, the employer is contacted to complete the employment questionnaire, as shown at block 126. If the employment question has been completed, the research consultant confirms the answer to the employment question completed by the employer, as shown at block 128.

At block 132, the research consultant determines if there is an issue of employer variability, that is, differences in what various users consider to be important parameters for a particular employment position. For example, a HR manager indicated communication as an important parameter (eg, giving this parameter 7/7), while a manager above that position indicated that communication skills were less important (eg, If this parameter is given 1/7), the research consultant will discuss with their respective managers, understand why the importance to this parameter is different for each user, and based on the results of the discussion, Rewrite the entry to give it importance. Thus, if a variation problem is found, the problem is resolved by adjusting input parameters, such as qualitative assessment parameters for the tissue, as shown in block 130.

If there are no variability problems or if those problems are resolved, the system 10 proceeds to block 136
Create a detailed preliminary report of the job profile generated for the employment position, as shown in. This report is reviewed by the employer to assess whether the profile is inconsistent or problematic, i.e., that the attributes that the employer requires of the applicant are accurately represented in the job profile (block 140). That is, it is ascertained that the candidate profile includes what the appropriate managers within the company consider as desirable or necessary candidate characteristics. Applicants' characteristics that may be desirable or necessary include, for example, whether they have sufficient work experience, whether their work experience is in a sufficiently large or top-tier company, or if their work experience is of an appropriate position within the company. Or even appropriate education, qualifications, salaries, occupational motivations, fitness for the company's organizational culture, and decision-making style. If the report has inconsistencies or problems, blocks 130, 136,
Steps 140 and 142 are repeated until all of those problems are solved. If so, the employment position record is made available to the matching process, as shown in block 144.

FIG. 29 illustrates a flow diagram associated with collecting candidate parameter information, according to one embodiment of the present invention.
In the embodiment shown in FIG. 29, as shown at block 204, a candidate enters a recruiting website to register with a recruiting company online. There you will find a series of registration screen questions, including the applicant's name, email address, current home address, current telephone number, education, work experience, previous experience, and current employer. ,
Information such as the current position, desired position, and desired work location is obtained from applicants. In some embodiments, the questionnaire also includes a qualitative assessment of the candidate who answers online. Similarly, in some embodiments, the qualitative assessment includes a binding analysis tool available online to the candidate. The results of these evaluations and / or analyzes are stored in the database 500 as candidate parameters along with other registration information.

FIGS. 30-39 and FIGS. 40-63 are examples of applicant screens, which are used to obtain information on quantitative and qualitative parameters from applicants. The quantitative parameters shown in FIGS. 30-39 include the applicant's name, address, phone number, desired position,
Includes current levels, education and qualifications, work experience, geographic preferences, salaries, and more. The qualitative parameters include
Applicants' skills, business environment experience, and answers to qualitative assessment tools are included, as shown at ~ 63. 42 to 54 are examples of applicant screens,
It is used to obtain qualitative information from the applicant to enable the system to perform an assessment of the applicant's organizational culture. FIGS. 55-63 are examples of candidate screens, which are used to obtain qualitative information from the applicant to enable the system to perform the candidate's joint analysis.

As shown in FIG. 29, when information is input into the database 500, the submitted applicant information is organized as applicant records. When a candidate record is created, the system periodically determines whether the record is complete. That is, it is determined whether the applicant has entered all items or at least the minimum required information (for example, see FIGS. 64 to 65). For example, after a candidate leaves the recruiting website, the system may determine whether the candidate has provided all required information, for example, by answering all questions asked in the questionnaire. You can judge. For example, in the candidate information entry process represented by block 204, the user may be allowed to answer all or only some of the questions during any access. In this way, information can be stored in the database as the candidate is provided, and the applicant does not need to complete the entire input process during a single access. as a result,
Certain applicants may access the recruitment website to begin entering information, exit the website, and then return to the website one or more times to complete the entry process. it can.

In some embodiments, when a candidate first enters information into the system 10, the system may be designated to create a separate website for that individual candidate. . Such individual applicant websites are provided with security features such as passwords associated therewith. When an individual candidate website is created, the associated website identification code (s) and password can be assigned to the website and provided to the candidate. For example, the identification code can include the applicant's name. Thus, after leaving the recruiting website, applicants can return to their personal website by entering their name and an appropriate password at the recruiting website.

If the candidate has not entered all the required input information, the candidate is instructed to complete the registration process, as shown at block 210 (see FIG. 74). Methods for prompting a candidate include sending a notice to the candidate's personal applicant website or sending an email to the candidate. Once the candidate has completed the registration process, all information is stored in the database 500, as shown in block 212, and the system 1
0 can be used for processing. For example, as described below, a complete candidate record may be available for the matching process. In some embodiments, the system may include an incomplete candidate record,
It can be enabled for limited processing by the system 10. For example, if a candidate answers all questions related to the candidate's quantitative parameters, the system 10 can determine whether the candidate has at least the required degree and employment history for an employed position. .
If those quantitative parameters are met, the employer or research consultant will determine if it is beneficial to contact the candidate and obtain more information on the candidate's qualitative parameters. can do.

When the input (capture) process is completed,
The answer to the question is analyzed, and the result of the analysis is stored in the database 500. For example, qualitative candidate parameters are determined and stored in a database based on the candidate's response to the question presented to the qualitative assessment tool. This qualitative evaluation tool is described in FIGS.
3 and FIGS. 69-73 below. In some embodiments, qualitative candidate parameters are made available to candidates, as represented by the candidate feedback block 213 of FIG. For example, the results may be emailed to the applicant, or the results may be made available online on the applicant's personal applicant website and used in subsequent searches by the applicant. In one embodiment, this result is communicated to the candidate within 24 hours.

FIGS. 64 and 65 show flow diagrams associated with determining whether a candidate record is complete and determining various qualitative candidate parameters. In the embodiment shown in FIGS. 64 and 65, as shown at block 220, the system 10 determines whether the candidate's qualitative assessment questionnaire has been completed. If not, at block 211 the candidate is instructed to complete the questionnaire. In one embodiment, the applicant is asked to complete the qualitative assessment questionnaire in the manner described above (ie, posting a notice to the candidate on the recruiting website, sending a notification by email, etc.). Instructions are given. In another embodiment, an appropriate incomplete flag is set. In either embodiment, the system then proceeds to block 226.
Otherwise, if the assessment question has been completed, the system 10 determines the qualitative assessment of the candidate from the answer to the question, as shown at block 222.

One way to determine a candidate's qualitative assessment, including an assessment of organizational culture, is described in Michael J. et al. Driver, Kennis R. Brusseau, Philip L. This is described in more detail in Hanseiker's "Dynamic Decision Maker: Five Judgment Styles for Managers and Business Success," Self Discovery Press, 1998. Several companies provide qualitative assessment tools, including Hogan Assessment System, Inc., Tulsa, Okla. And TTI Performance Systems, Inc., Scottsdale, AZ. Ltd.), Waldrup Butler, Brookline, Mass.
Associates (Waldroop Butler Associates) and Winslow Research Institute, San Mateo, CA
(The Winslow Behavioral Assessment
System (The Winslow Behavioral Assessment Syste
m)) is an example. The following factors have been found to be appropriate as factors in selecting an appropriate qualitative assessment tool from the many available.

Generation of consistent scores both over time and between data; no need for additional input from others (eg, without input from others evaluating the candidate); Scores based on input from the individual are evaluated;-A focus on evaluation tools, with a primary focus on selecting and matching candidates for the job, is preferred-Testing in a foreign language A tool that provides numerical results over a tool whose overall feedback is textual; prefers the time required to complete the test; tests Those that require less time are preferred: the area where the test is perceived is validated by the person taking the test (facial validity).

For example, the system 10 may include various qualitative applicant parameters, such as a candidate's problem-solving abilities, decision-making abilities, task execution abilities, interpersonal abilities, leadership abilities, sense of purpose, work style, and cultural adaptation. Is determined on a scale showing the characteristics of the applicant for the parameters of In one embodiment, the system 10 graphically depicts the applicant's ability (proficiency) for each candidate qualitative evaluation parameter on a scale and indicates where the candidate is located on the scale for each parameter. .
FIGS. 69-73 are examples of candidate and / or employer qualitative feedback screens showing the candidate's proficiency at several evaluated parameters.

For example, a candidate's proficiency in problem-solving ability is evaluated from a "very behavior-oriented" to a "moderately behavior-oriented and analytical", or a "very analytical". Similarly, a candidate's proficiency in job performance (mission performance) is rated from "very stubborn (persistent)" to "moderately stubborn and flexible" and "very flexible." You. In addition, the proficiency of the applicant with respect to the human relations ability is evaluated from "very leadership" to "medium leadership and cooperative" and "very cooperative". Other parameters are evaluated by the system 10 as well.

Thereafter, the applicant's profile, comprised of the applicant parameters, is stored in the database 500 indicated by block 224 in FIG. Block 2
At 25, the system 10, for example,
Using the qualitative evaluation information calculated in step 2 to update the applicant's home page on the recruiting website and provide feedback to the applicant.

At block 226 of FIG.
Determines if the candidate's binding analysis questionnaire has been completed. If not, at block 211, the candidate is instructed to complete the questionnaire. This instruction may be provided, for example, by posting a notice to the applicant on the recruiting website, notifying the applicant by email, or returning to block 220 with the appropriate incomplete flag set. Otherwise, if the binding analysis questionnaire has been completed, system 10 performs a candidate binding analysis based on the answer to the questionnaire, as shown at block 228. The binding analysis forces the candidate to choose between two different options. For example, a candidate is first asked to select either option A or B, then asked to select either option B or C,
Hereinafter, the same operation is performed. 55 to 63 show examples of the questions of the binding analysis. The results of this analysis can help determine whether a candidate has the same professional motivation factor as a particular employer or a particular job by assessing the candidate's behavioral characteristics. One of the binding analysis tools available on the market is ACQNET Adaptive Binding Analysis, which DataDirect, a subsidiary of Kingsley Research, Inc. of New York, NY, has joined forces with Sawtooth Software, Inc. of Shikim, Washington. It is a tool provided jointly. Binding analysis has been featured in several publications. For example, Susan M. Sheridan, Thomas R. Cratockwell, John R. Burgundy (Susan M. Sheridan, Thomas R. Kratochwill, &
John R. Bergan, Conjoint Behavioral Consultation, Procedure Manual (Applied
Clinical Psychology (Conjoint Behavioral Cons)
ultation: A Procedural Manual (Applied Clinical Ps
ychology) ", Jordan J. Louviere (Jordan J.
Louviere), "Metric conjoint analysis (quantitative application in social sciences, No. 67)" (Analyzing Decision Making: Me
tric Conjoint Analysis (Quantitative Applications
in the Social Sciences, No.67)) "(1998),
"Conjoint Analysis: A Guide to Designing and Understanding Conjoint Research / 044 (Conjoint Analysis: A Gu
ide for Designing and Interpreting Conjoint Studie
s / 044) "(1992), David B.S. Montgomery's "Customers in the Industrial Market /
Conjoint Calibration of the C (International Calibration of the C)
ustomer / Competitor Interface in Industrial Markets
(Report No85 112)) ”(1985),“ SAS Technical Report R-109, Example of Conjoint Analysis Ex ”
amples) (SAS is a registered trademark) "(1993); Richard P. Bagozzi, “Advanced Methods of Marketing Research (Blac)
kwell Publishing), Joe Curry, "Understanding Conjoint Analysis in 15 Minutes (Quarks Marketing Research Review) (Un
derstanding Conjoint Analysis in 15 Minutes (Quir
k's Marketing Research Review)) ”, which are incorporated herein by reference. For examples of binding analysis, see Appendix A and Appendix B
Please refer to.

Thereafter, the results of the binding analysis of the applicant are shown in FIG.
Database 500 as shown in block 230 of FIG.
Is stored in At block 234, the system 10
For example, providing feedback to the candidate by updating the candidate's home page on the recruiting website using the binding analysis information calculated at block 228.

In the embodiment in which the incomplete flag is set, upon completion of the processing of FIGS. 64 and 65, an appropriate signal will be to notify the candidate on the candidate's home page on the recruiting website, It is sent to the applicant using a method such as sending a notification by e-mail. If the incomplete flag is not set, a complete record flag is set indicating that the candidate's record can be used for a full match.

FIG. 74 is a flow diagram relating to identifying incomplete applicant records, notifying applicants of incomplete records, and completing those records. In the embodiment shown in FIG. 74, the system 10 generates a report on the incomplete candidate profile, as shown at block 236. Then block 238
One reminder email is created for all applicants with incomplete profiles, as shown at. Additionally or alternatively, the system 10 creates a separate reminder email for each candidate with an incomplete profile, as shown at block 240. Additionally or alternatively, the system 10 may provide a suggestion to each candidate with an incomplete profile to return to the website and complete their registration profile, as shown at block 242. Create on top. For example, one promotional offer is an online subscription to a new service. The candidate can re-enter the recruiting website to complete his incomplete profile, as shown at block 244. Thereafter, when the candidate completes the candidate questionnaire screen, the system 10 accepts an answer to the candidate questionnaire, as shown at block 248. In some embodiments, candidate responses are stored in database 500 as each screen is completed.

FIG. 75 is a flow chart showing an example of a method employing a database including records of employment positions and applicants in order to select a desired number of applicants suitable for the employment position. In the embodiment shown in FIG. 75,
As shown at block 302, a research consultant enters a recruitment website and enters an employer identification number and a desired contract number. Next, the research consultant selects a matching function from a menu on the website, as indicated by block 304. Thereafter, block 306
As shown at, the research consultant checks the importance (weight) of the employment parameters predetermined by the employer. These priorities are assigned based on their importance to the employment position being replenished. Employment parameters include educational background, required or desired qualifications, required or desired degree, experience (job description, position, and / or years of experience), recruitment (recruiting position), salary, employer Location, desired company size, desired company, desired company category, required or desired industry, employer qualitative assessment (work profile and cultural adaptability), professional effort goals, and responsibilities (skills adaptation) parameters Is included.

Thereafter, as indicated by block 307, the system 10 prompts the employer to select a subset of the above search parameters as needed. In this way, a research consultant can focus on a search that emphasizes particular parameters. afterwards,
System 10 performs the matching function at block 308. This will be described later in detail with reference to FIGS. Next, as indicated by block 310:
The research consultant will then check the results of the matching function (eg, top 5
Check the list of 0 applicants). The research consultant may review, print, or mark a suitable candidate, as shown at block 312. FIGS. 66-68 and 69-72 are examples of screens showing how one candidate is compared to the employment position parameter. This report has been reviewed by the employer and the characteristics of the candidate are
Or, it is evaluated whether it matches the desired characteristics.

At block 314, the research consultant determines whether a sufficient number of eligible candidates have been found by the matching function of block 308. If found, the matched candidate and its corresponding parameters are recorded, printed, or marked, as indicated by block 316. If a sufficient number of eligible applicants have not been found, the research consultant determines which parameters to change to obtain more applicants, as indicated by block 318. Block 3
As shown at 20, the number of applicants retrieved from the database 500 is adjusted, for example, from 50 to 500. Additionally or alternatively, the research consultant may redefine restrictions on employment positions and perform a new database search, as indicated by block 322. Additionally or alternatively, the research consultant may re-search the database 500, as shown at block 324, focusing on the candidate's desired position, the applicant's actual work experience, or both. Additionally or alternatively, the research consultant may reconsider the weighting of the employer's initial hiring parameters and / or as shown at blocks 326 and 306.
Or can be changed. Thereafter, the search is re-executed (block 308). Blocks 308, 310, 312, 314, until a sufficient number of eligible applicants are found.
318, 320, 322, 324, and / or 32
A series of six loops can be repeated.

The matching function of block 308 of FIG. 75 is described in detail in FIG. In the embodiment shown in FIG. 76, at block 330, the system 10 compares the responsibilities in employment with the responsibilities of each candidate in the database 500 (see FIG. 77). At block 332, the system 10 compares the effort goals in employment (defined by the employer) with the effort goals indicated by each candidate in the database 500 (see FIG. 78). Next, as shown at block 334, the targeted and desired industries of the employer are compared to the industries of each candidate in the database with experience (see FIGS. 79 and 80).

As shown in block 336, system 1
0 then compares the contents of the particular company that wants to hire the applicant (specified by the employer) with the contents of the company in which each candidate works in the database 500 (see FIG. 81). At block 338, the system 10 compares the required or desired company size with the size of the company in which each candidate works in the database 500 (see FIG. 82). Next, as shown in block 340, system 1
0 is required or desired company classification and database 500
The comparison is made with the classification of the company in which each applicant works (see FIG. 283). At blocks 342 and 343, the system 10 compares the required or desired work experience with the work experience of each candidate in the database 500 (see FIG. 84).

Thereafter, as indicated by block 344, the system 10 establishes the required or desired degree and database 5
Compare with each applicant's degree within 00 (FIGS. 85 and 8)
6). Next, if there is a required or desired professional qualification, as shown at block 346, the system 10 compares the required or desired professional qualification with the professional qualification of each candidate in the database 500 (FIG. 87). As indicated by block 348, system 10 includes:
The qualitative evaluation parameter of the employer is compared with the qualitative evaluation result of each applicant in the database 500 (see FIG. 88).

Thereafter, at block 350, the system 10 applies the importance of the employment parameter and
As shown in the score table of No. 1, the degree of agreement between the applicant information and the employment parameter for each applicant is calculated. In one embodiment, the system 10 calculates a degree of agreement between each employment parameter and its corresponding candidate parameter. Parameter comparison values are calculated for each parameter. In one embodiment, the degree of coincidence of each parameter is represented by a numerical value between 0 and 100, with 100 indicating a perfect match. After that, system 1
0 calculates a candidate verification value for each applicant based on the parameter comparison value. In one embodiment, the parameter comparison values for each parameter are added and adjusted according to the parameter weight specified by the employer. For example, if the employer specifies the importance of 10 parameters equally (ie, 10 points for each parameter), a candidate with 80 points for a particular parameter will have an adjusted score of 8 points. Will be. Next, the sum of all adjusted scores for the ten parameters is added, and the resulting sum is the candidate's score (collation value). The degree of coincidence (collation value) of each applicant is stored in the database 500.

It is not necessary to complete all matching steps for each candidate. Rather, the candidate record can be removed when a mismatch is found.
For example, if your employment position is a marketing job at a pharmaceutical company and some applicants want an associate at a private law firm as their employment position, then those candidates will have the desired employment position. Are excluded after the matching of the parameters for Similarly, if the requirements for an employment position include a college degree and some applicants do not have a college degree, those applicants will be excluded Is done.

FIG. 77 illustrates one embodiment of the responsibility matching process of block 330 of FIG. 76 in more detail. Here, the skill parameter is a fast growing business,
It refers to the skills of an employee, such as working in a rapidly changing environment. In the embodiment shown in FIG. 77, the system 10 first obtains requirements regarding the employer's skills, as indicated by block 362. next,
As shown in block 364, the system 10 retrieves the candidate's skill information from the database 500. At block 365, the system 10 determines whether the employment position sought by the applicant and the employment position the employer is seeking to replenish are the same. If the positions sought by the applicant do not match, system 10 proceeds to block 370.
Otherwise, the system proceeds to block 366, where it calculates the degree of match between the skill required by the employer and the candidate's skill information. In one embodiment, the system 1
0 determines the degree of agreement by calculating the least squares value between the skill requirement of the employer and the skill information of the candidate. By using this curve fitting method, the correspondence between the employer's parameters and the applicant's parameters can be evaluated well. As will be appreciated by those skilled in the art, 2
Other known techniques for determining correspondence between two parameters may be used. At block 368, the system 10
Calculates a score for skill matching. Thereafter, the system 10 proceeds to the next function block 332 and calculates a match for the effort goal (challenge). If the candidate's employment position is different from the employment position the employer is seeking to replenish, the system 10 proceeds to block 370, as shown in block 370.
Determine if the candidate's skills are in the same job category as the employer has specified. If not, system 10 proceeds to effort goal matching (block 332). Otherwise, if the candidate's skill is in the job area specified by the employer, the system sets a default score corresponding to the candidate's skill job area, as shown in block 371. Next, as shown in block 372, the system stores the applicable match scores corresponding to the skill match and / or the job area match. Thereafter, the system 10 proceeds to the next function block 332 and calculates a match for the effort goal.

FIG. 78 illustrates one embodiment of the effort goal matching process of block 332 of FIG. 76 in more detail. In the embodiment shown in FIG.
As shown in FIG. 4, system 10 first obtains an employment effort target specified by the employer. Then block 37
As shown at 6, the system 10 obtains the candidate effort goals specified by the candidate. These goals include:
Working in a fast changing work environment, taking the initiative in major new systems, being first or second in the industry, working in a fast growing company, engaging in major new systems, market share Focusing on rate growth can be included. Thereafter, the system 10
The employer's effort goals are compared to the effort goals indicated by each candidate in the database 500, as shown at block 378. Next, at block 380, at least three matching candidates are identified. Next, at block 382, the two matching candidates are identified.
Next, as shown in block 384, the system 10
Identify the applicants who matched. At block 386, the system 10 calculates a corresponding score for each candidate for the effort goal match. Thereafter, as shown at block 388, those scores are stored in database 5
00 is stored.

FIGS. 79 and 80 are block 3 of FIG.
34 illustrates one embodiment of the 34 industry sector matching process in more detail. In the embodiment shown in FIG. 79, as shown at block 390, the system 10 searches the database 500 for the industry category required by the employer, ie, the industry category (specified by the employer) in which the applicant is to be acquired. . In one embodiment, the United States Government's Standard Industry Classification (SIC) is used to determine if the employer's required industrial experience matches the applicant's industrial experience.
Is used. The SIC codes are classified such that each category has a two-digit code, and each subordinate subcategory has a three to five digit code depending on the specificity of the subcategory. An example of the SIC code related to engineering is shown below.

87 Engineering and Business Administration Operations 871 Engineering and Construction Operations 8711 Engineering Operations 8712 Construction Operations 8713 Investigation Operations 872 Accounting, Auditing, and Bookkeeping.

As shown in block 392, system 1
0 is the SIC for each applicant from the database 500
Search for code. Next, as shown in block 394, the system 10 checks the employer's SIC code against each candidate's SIC code. If the SIC codes match, as shown in block 400, the system 10 calculates a score in response to the match. Otherwise, if the SIC codes do not match, as shown in block 396, the system 10 proceeds to the SIC of the employer's required classification.
The last one digit is removed from the code, and the resulting SIC code is checked against each applicant's SIC code to determine if they match. Due to the classification structure of the SIC code, the last one digit of the SIC code required by the employer is removed, and the SIC code consisting of the remaining digits and the code of each applicant are compared again, so that a wider range can be obtained. It can be determined whether the industrial classifications match. This is the operation performed in block 396. However, for example, the North American Industry Classif
It will be appreciated that other industry classification systems may be used, such as the Communication System (NAICS) and the like.
NAICS uses a 6-digit classification code, unlike a 5-digit SIC code. NAICS code is SIC
Similar to the code, the first two numbers represent major economic sectors.

In the embodiment described above, if the SIC classifications match, system 10 proceeds to block 400. Otherwise, if the SIC classifications do not match, the system 10
Determines if the employer's SIC code generated in block 396 is less than two digits, as shown in block 398. If not, blocks 396 and 3
98 is repeated. If not, the classifications do not match and system 10 proceeds to block 400. The system 10 then calculates a score for the industry classification (s) match. If there is only a partial match of the industry classification (eg, a two digit SIC code match), a small score is given. Thereafter, as shown in block 402, the given total score is stored in database 500.

In the embodiment shown in FIG.
At 4, the system 10 obtains from the database 500 the SIC industry classification that the employer considers "desirable."
This corresponds to the "need" SI described above in connection with FIG.
It is different from C industry classification. Thereafter, as shown in block 406, the system 10 searches the database 500 for the SIC industry classification of each applicant. Next, block 408
As shown, the system 10 matches the SIC industry classification that the employer desires with the SIC code of each candidate. If the SIC codes match, the system 10 calculates a score corresponding to the match, as shown in block 416. Otherwise, if the SIC codes do not match,
As shown in block 410, the system 10 removes the last digit from the SIC code of the industry category desired by the employer and matches the remaining SIC code to the SIC code of each applicant for a match. Judge whether to do. S
If the IC codes match, system 10 proceeds to block 4
Proceed to 16. Otherwise, i.e., if the SIC codes do not match, the system 10 determines whether the employer's SIC code generated at block 410 is less than two digits, as shown at block 412. If not,
Blocks 410 and 412 are repeated. Otherwise, there is no classification match and system 10 proceeds to block 4
Proceed to 14. Block 414 checks to see if system 10 has reached the candidate's last SIC code. That is, if the applicant has worked in multiple industries, the system 10 compares each classification code of the applicant for each industry. If the system has not reached the end of the candidate's SIC list, the candidate's next SIC code is retrieved (block 406) and the process is repeated. Otherwise, the system 10 proceeds to block 416. The system 10 then calculates a score for the industry classification (s) match. Thereafter, the score is stored in the database 500, as shown in block 418.

FIG. 81 illustrates one embodiment of the company matching process of block 336 of FIG. 76 in more detail. In the embodiment shown in FIG. 81, the system 10 first
In block 420, the database 500 is searched for the employer's preferred company (company preference), that is, data on which company the employer wants to recruit human resources from. Thereafter, as shown in block 422, the system 10 searches the database 500 for the company in which each candidate is currently working or has worked previously. At block 424, the system 10 stores the company identification number for each applicant's current or previous company in the database 50.
Check within 0. Company identification number is Fortune 500
(Fortune 500), America's Most Admired Companies
ies), The Global 500, etc. Next, as shown in block 426, the system 10 determines whether the employer's desired company matches each applicant's company. If the companies match, the system 10 calculates a score based on the result, as indicated by block 432. Otherwise, if the companies do not match, the system 10 determines whether each candidate has reached the end of the list of companies that have worked, as shown at block 428. If the system has not reached the end of the candidate's company list, the next listed company is retrieved (block 422) and processing continues with the previous employer of the candidate. Otherwise, system 1
0 proceeds to block 430 to determine whether the end of the list of employer preferred companies has been reached. If the system 10 has not reached the end of the employer's list, at block 420 the next listed preferred company is retrieved and processing continues with the employer's next preferred company. . Otherwise, the system 10 returns to block 43
Proceed to 2 to calculate the score for the company (s) verification. The score is stored in the database 500, as shown in block 434.

FIG. 82 illustrates one embodiment of the company size matching process of block 338 of FIG. 76 in more detail. At block 600 of the embodiment shown in FIG. 82, the system 10 searches the database 500 for a company size that the employer desires. In other words, it searches for data on how large the employer wants to recruit human resources who worked for a company. Next, as shown in block 602, the system searches for experience data regarding the candidate's company size. In one embodiment, the position of the applicant in each company is retrieved along with the size of each company the applicant was in front of (for example, if the applicant was a manager in a company with a scale adjustment reason). Multiplies the company size correction value by a multiplier). The system 10 then calculates the size of the company where each candidate is currently working, as a percentage of the size of the company that the employer desires, as shown in block 604, and applies the corresponding number of matching scores. At block 606, the system determines if the candidate has any other work experience. If the candidate has no other career, the system proceeds to block 614. If the applicant has other occupational histories, the system calculates and responds to the size of the company that each applicant was prior to, as shown in block 608, as a percentage of the company size desired by the employer. Apply the number of matching scores. At block 610, the system again determines whether the candidate has any other work experience.
If the candidate has no other work experience, the system will block 61
Proceed to 4. If the applicant has other occupations, the system calculates the size of the company that each applicant was two before, as shown in block 612, as a percentage of the company size that the employer desires, and responds. Apply the number of matching scores to be made. Thereafter, at block 614, the system provides each candidate with a score corresponding to the largest current or past company of the candidate. This score is entered in the database 5 as shown in block 616.
00 is stored. Next, the system 10 proceeds to the next function block 340 and performs a company classification match.

FIG. 83 illustrates one embodiment of the company classification matching process of block 340 of FIG. 76 in more detail. At block 436 in the embodiment shown in FIG. 83, the system 10 searches the database 500 for the employer's preferred company classification, i.e., data as to which type of company the employer wants to hire. Examples of preferred company classifications include Fortune 100, Fortune 500, Fortune 1000, Global 50
0, America's Fastest Growing Companies, America's Most Admire Companies, etc. Thereafter, the system 10 proceeds to block 438
As shown in FIG. 5, the company in which each applicant works (or worked) is searched from the database 500. At block 440, the system 10 looks up the company identification number for each applicant's company in the database 500. Next, the system 10 matches the company classification desired by the employer with the company classification of each applicant, as shown at block 442. If the company classifications match, the system 10 calculates a score according to the match, as indicated by block 448. Otherwise, if the company classifications do not match, the system 10 determines whether the end of the list of companies in which the candidate has worked has been reached, as shown at block 444. If the system has not reached the end of the candidate's list, the next company in the list is searched (block 438) and processing continues with the company that the candidate was in before. Otherwise, the system 10 proceeds to block 446 and checks whether the employer has reached the end of the list of preferred company categories. If the system 10 has not reached the end of the list of employers, the next preferred company classification is captured (block 436) and processing continues with the employer's next preferred company classification. Otherwise, the system 10 proceeds to block 448 and proceeds to company classification (s).
Calculate the score for the match. This score is given in block 4
As shown at 50, it is stored in the database 500.

FIG. 84 illustrates one embodiment of the history matching process of blocks 342 and 343 of FIG. 76 in more detail. Block 45 of the embodiment shown in FIG.
At 2, the system 10 searches the database 500 for a career requested by the employer. Then the system 10
Returns the database 500 as shown in block 454.
Search for the number of years of experience of each applicant by job position and job field, and the total number of years of experience of each applicant. Block 4
As shown at 56, the system 10 can translate the job experience of the applicant into job-specific experience required by the employer (eg, CFO).
Calculate as a percentage of For example, a candidate may have 60% of the years of work experience required by the employer for that employment position.
And so on. Block 458
In, the system 10 calculates a score according to the degree of coincidence determined in the block 456. After that, system 1
A zero value, as shown in block 459, divides each candidate's experience into a percentage of the job-specific experience (eg, accounting) required by the employer. Thereafter, at block 460, the system 10 determines each applicant's experience as a percentage of the total years of experience required by the employer. next,
At block 461, the system 10 calculates a combined score corresponding to the degree of match between each candidate's job experience and each candidate's total years of experience. Thereafter, each candidate's score is stored in the database 500, as indicated by block 462. Next, the system 10 proceeds to the next function block 344, where the degree is verified.

FIGS. 85 and 86 are block 3 of FIG.
FIG. 4 illustrates one embodiment of the 44 degree matching process in more detail. At block 463 of the embodiment shown in FIG. 85, the system 10 searches the database 500 for the degree requested by the employer. Then the system 10
Retrieves each applicant's educational data (including degree) from the database, as shown in block 464. At block 466, the system 10 determines whether the employer's required degree matches the degree of each candidate.
If the degrees match, the system 10 calculates a score in response to the match, as shown in block 472. Otherwise, ie, if the degrees do not match, the system 10
A determination is made as to whether the end of each candidate's degree list has been reached, as shown at block 468. If the system has not reached the end of the list of applicants, the candidate's next degree is retrieved (block 464) and processing continues using that degree. Otherwise, the system 10 proceeds to block 470 to determine whether the end of the list of degrees requested by the employer has been reached. If the system 10 has not reached the end of the list of employers, the next required degree is retrieved (block 463) and processing continues with the employer's next required degree. Otherwise,
The system 10 proceeds to block 472 and calculates a score for the degree (s) match. This score is stored in the database 500, as shown in block 474.

At block 476 of the embodiment shown in FIG. 86, the system 10 determines whether the employer, such as a degree, degree type, and / or major, differs from the required degree described above in connection with FIG. The desired educational background data is retrieved from the database 500. The term "degree" refers to whether the degree is a bachelor's degree, a bachelor's degree, or an associate's degree, and the "degree type"
Whether a candidate's degree is a college undergraduate or graduate degree, and "major" means the major of the candidate in accounting, finance, sales, marketing, engineering, and so on. Block 4
At 78, the system 10 retrieves each applicant's educational data (including degree (s), degree type (s), major (s)) from the database 500.

At block 480, the system 10
Determines whether the degree desired by the employer matches the degree of each applicant. If the degrees match, the system 10 proceeds to block 486 and determines whether the end of the candidate's educational data has been reached. If the system 10 has not reached the end of the candidate's data, the candidate's next degree is retrieved (block 478) and processing continues using that degree. Otherwise, if the degrees do not match, the system 10 determines whether the desired degree type (eg, master's degree) matches the candidate's degree type, as shown at block 482. If the degree types match, the system 10 proceeds to block 486 and determines whether the end of the candidate's educational data has been reached. If the system 10 has not reached the end of the candidate's data, the candidate's next degree and / or degree type is retrieved (block 47).
8), processing continues using the degree and / or degree type. Otherwise, the system 10 determines whether the major desired by the employer matches the major of the candidate, as shown at block 484.

Regardless of whether the majors match, the system 10 proceeds to block 486 and determines whether the end of the candidate's educational data has been reached. If the system 10 has not reached the end of the applicant's data, the applicant's next degree, degree type, and / or major is retrieved (block 478) and the degree, degree type, and / or major is retrieved. Use to continue processing. Otherwise,
The system 10 proceeds to block 488 and determines whether the end of the list of employer desired educational data has been reached.
If the system 10 has not reached the end of the list of employers, it searches for the next desired degree, degree type, and / or major (block 476) and retrieves the degree, degree type, and / or major. Use to continue processing. Otherwise, the system 10 proceeds to block 490 and calculates a score in response to matching the educational data. The score is stored in the database 5 as shown in block 492.
00 is stored.

FIG. 87 illustrates one embodiment of the qualification process of block 346 of FIG. 76 in more detail. In block 620 of the embodiment shown in FIG. 87,
The system 10 searches the database 500 for professional qualifications as required or desired by the employer. Thereafter, the system retrieves, at block 622, the candidate's experience data including the professional qualification, if any.
Next, at block 624, the system determines whether the applicant is entitled to the employer's desire. If not, the system proceeds to block 628. Otherwise, that is, if the desired qualifications match, the system proceeds to block 626 and provides the total score possible for the match. At block 628, the system determines if the candidate is otherwise qualified. If the system has not reached the end of the candidate's qualification, the next qualification for that candidate is retrieved (block 6).
22), processing continues using the qualification. Blocks 622, 624, 626, and 628 are repeated until all qualifications for the candidate have been evaluated. Next, the score is stored in the database 500, as shown in block 630. Thereafter, the system 10 proceeds to the next function block 348, where the evaluation is verified.

FIG. 88 illustrates one embodiment of the qualitative matching process of block 348 of FIG. 76 in more detail. In block 632 of the embodiment shown in FIG. 88,
The system 10 searches the database 500 for a qualitative evaluation result for the employer. Thereafter, the system 10
At block 634, qualitative evaluation results for the candidate are retrieved. Next, at block 636, the system determines the degree of correspondence between the qualitative evaluation results of the employer and the candidate. In one embodiment, system 10 calculates the least squares value between the results of the employer and the applicant,
Determine the degree of similarity. This is one of many known curve fitting techniques available. In another embodiment, the Decision Dynamics Group of Thousand Oaks, California.
Group) is used. This embodiment is described in detail below in connection with FIGS. In addition, the system 10 stores the average degree of match percentage in the database 500, as shown in block 644. Thereafter, the system 10 proceeds to the next function block 350, the scorecard.

In a matching program developed by the Decision Dynamics Group, at block 650,
The system 10 searches for answers to the applicant's questions (representative questions for the applicant are shown in FIGS. 42-63). The candidate answers each question, for example, by selecting a qualitative parameter value between 1 and 7. Next, the system 10 retrieves, at block 652, the answer to the employer's questionnaire (typical questions for the employer are shown in FIGS. 14-27). The employer answers each question, for example, by selecting a qualitative parameter value between 1 and 7. Next, as shown in block 658, the system matches the candidate's answer with the employer's answer. The matching block 658 is shown in more detail in FIG. The applicant's qualitative parameters are then matched to their corresponding employer's qualitative parameters, and the degree of agreement between them is calculated as a percentage (block 660). For example, if the candidate assigns a value of 6 to the first parameter and the employer also assigns a value of 6 to the first parameter, the match will be 100%. Thereafter, a percentage match (match result) for each parameter group is determined (block 662). For example, when the qualitative parameters are divided into three groups, an average value of the degree of matching for each group is calculated (for example, FIG.
6, "Overall Style (Overall Style) Conformance",
The three qualitative parameters of "compatibility with duty style (role style)" and "fitness with practical style (work style)"
See an example of an employer screen showing the percentage match results for a group). For example, there are three parameters per group, and the agreement between the employer parameter and the applicant parameter is 100%, 80%, and 60%, respectively.
In this case, the average coincidence of the group is 80%. Next, the percentage matching results for all qualitative parameters are summed and averaged (block 664). The average agreement between all qualitative parameters is determined by summing the matching results, expressed as a percentage, between the respective employer and volunteer parameters and dividing by the total number of parameters.

FIGS. 89 to 91 are block 350 of FIG.
3 is a table representing one embodiment of the scoring process of FIG. In one embodiment, the system 10 first applies the parameter weights assigned by the employer and then calculates a total match score for each candidate. Referring to the scoring table of FIG. 89, block 330
Is determined, in one embodiment, by calculating the least squares value between the employer's duty requirement and the applicant's duty information. First, the employer assigns 100 points to 10 required responsibilities (see, for example, FIG. 13). Similarly, the candidate assigns 100 points to ten responsibilities he has experience (see, for example, FIG. 40). The calculation proceeds by subtracting the points assigned by the employer from the points assigned by the applicant and squaring the difference for each duty. Thereafter, the sum of the squared values is added to give a grand total. Based on where this aggregate value is located on the matching reference scale, the system 10 gives an appropriate score. For example, if the aggregate value is 350, the candidate has a duty "related" to the employer's request. Thus, the system gives 70 points for that "related" match.

In one embodiment, the effort goal matching of block 332 is performed by determining whether the candidate has indicated any of the effort goals that the employer has listed as important. In one embodiment, the employer selects, for example, three of the fifteen effort goals as important effort goals for the employment position (eg, FIG. 12).
See). Thereafter, if the candidate selects the same three effort goals (see, for example, FIG. 41), the system will award 100 points. Similarly, if a candidate selects two of the same effort goals, the system will score 80 points, and if the candidate selects one of the same effort goals, the system will score 60.
Give points.

In one embodiment, the required industry match in block 334 is determined by comparing the industry classification indicated by the applicant with the required industry classification indicated by the employer. For example, the system provides a list of industries from which the candidate can select (see, for example, FIG. 31). Each industry has a corresponding two to five digit SIC code. Thus, the SIC code corresponding to the industry indicated by the applicant is compared to the required industry SIC code indicated by the employer (see, for example, FIG. 6). If five digits match, 100 points will be awarded. Similarly, if four digits match, 80 points are given, if three digits match, 60 points are given, and if two digits match, 40 points are given. Otherwise, no score is given.

The desired industry match in block 334 is determined in a similar manner. However, in order to distinguish between the required industry match and the desired industry match, the highest score given is 80 points. Therefore, if 5 digits match, 80 points, and if 4 digits match, 60 points.
Points are awarded 40 points if the three digits match, and 20 points if the two digits match. Otherwise, no score is given. If the candidate has both the required and desired industrial experience that the employer seeks, the higher of the two scores will be awarded.

A determination as to whether the candidate is working for a preferred company specified by the employer (block 336) results in either a match or no match. That is, whether the applicant is working or not working for the company that makes it desirable. Therefore, the system will give 100
Points are awarded, otherwise no points are awarded.

Similarly, a determination as to whether a candidate is working for a company of the desired category specified by the employer (block 340) may result in a match or no match. Either. That is, whether the applicant works or does not work for a company of the class that he / she wants. Therefore, if they match, 100 points are given, and otherwise, no points are given.

Referring to FIG. 90, in one embodiment, the company size reconciliation of block 338 is based on the candidate's annual sales at the current employee and the desired annual sales range specified by the employee. Are determined by comparing. For example, if the employer has annual sales of 101 million
If the applicant wants a candidate from a company with $ 300 million, and the candidate works, for example, at a manufacturing company with annual sales of $ 150 million, the system will give 100 points. However, if a candidate works, for example, at a manufacturing company with annual sales of $ 25 million, the system scores 6 points.
Give 0 points. In another embodiment, the number of employees at a candidate's company (eg, a law firm or accounting firm) is compared to a desired number of employees range specified by the employer. For example, an employer wants a candidate from a law firm with 75-99 lawyers,
If the applicant works for a law firm with 80 lawyers, the system will give 100 points. However, if a candidate works for a law firm with 10 lawyers, the system will give 40 points. In yet another embodiment, the total assets at the applicant's company (eg, a bank or brokerage company) are compared to a desired asset range specified by the employer. For example, if the employer is 251
If the applicant wants a candidate from a brokerage company with assets of $ 500-500 billion and the candidate works for a brokerage company with assets of $ 270 billion, the system will give 100 points. However, if a candidate works for a brokerage firm with $ 15 billion in assets, the system will give 60 points.

The matching of the experience (work experience) in block 342 is determined, in one embodiment, by comparing the applicant's years of experience (measured in months) with the years of experience required by the employer. . If the candidate's years of experience are less than 50% of the required years, no points will be awarded. However, if the candidate's years of experience are at least 50% of the required years, the system will give a score between 50 and 100 points. This is a linear function. That is, if the applicant's years of experience is 52% of the required years, four points are awarded. Similarly, if the candidate's years of experience are 99% of the required years, 98 points will be awarded.

Experience by job type of applicant (block 342)
Is determined by comparing the years of experience (measured in months) of the candidate's job (eg, accounting) with the number of years of experience required by the employer for that job. this is,
Calculated as a linear function. That is, if the applicant's years of experience by job is 52% of the required years of experience by job, 52 points are awarded. Similarly, if the candidate's years of work experience is 99% of the required years of work experience, 99 points will be awarded.

An average is calculated from the total points given to the applicant's years of job experience and years of experience for each job.
That is, in the above example, if the points given to the applicant's years of work experience and the years of experience for each job are 4 points and 52 points, the system gives a total of 28 points as the applicant's experience. Become.

The position experience (block 343) of the applicant is:
It is calculated by comparing the applicant's years of experience in that position (eg, CFO) in months (measured in months) and the employer's required years of experience in that position. It is calculated as a linear function. The candidate's current job and the past three jobs are evaluated, in which case the sum of the scores of each previous job is discounted by an appropriate multiplier. That is, if the candidate's current job is 52% of the required position experience, 52 points will be awarded. Similarly, the job prior to the candidate is four times the required position experience.
When it becomes 0%, it is 80% of 40 points, that is, 32 points.
Points are given. If the candidate's previous job is 75% of the required position experience, 60 points for 75 points
%, Ie, 45 points. If the job before the candidate is 30% of the required position experience, 40% of the 30 points, or 12 points will be awarded. All of the points awarded for the candidate's current job and the past three jobs are summed, giving a maximum of 100 points.

Referring to FIG. 91, the degree match in block 344 is determined in one embodiment by comparing the candidate's degree with the employer's requested degree. In this case, the degrees either match or do not match. Thus, the system gives 100 points for a match, otherwise no score.

The desired degree match at block 344 is calculated by comparing the candidate's degree with the employer's desired degree. For example, if the desired degree is a doctorate in finance and the candidate has a doctorate in finance, 80 points will be awarded (between the required degree and the desired degree). (Not 100 points to give a difference). If the candidate has a Ph.D. in accounting, this equivalent level (ie, a Ph.D.)
Are given 60 points. If the candidate has a master's degree in business administration, 40 points will be awarded for this lower level degree (ie, master's degree). If the candidate has a bachelor's degree in Finance, a score of 0 will be awarded.

The matching of the qualifications at block 346 is determined, in one embodiment, by comparing the qualifications of the applicant with the qualifications required by the employer. In this case, the qualification (for example,
CPA (Certified Public Accountant) either matches or does not match. Therefore, the system returns 1 for a match.
00 is given, otherwise no score is given.

Similarly, the qualifications that the employer desires (block 346) either match or do not match the qualifications of the applicant. Thus, the system
100 points are given for a match, otherwise no score is given.

Thereafter, blocks 330, 332, 334, 336, 338, 34 shown in FIGS.
The weight of the quantitative parameter is applied to the sum of the ten scores from 0, 342, 343, 344 and 346, so that each candidate has a score between 0 and 100.
For example, when the employer inputs the information shown in FIG.
If equal importance (weight) is assigned to all of the parameters (that is, 10 points each), 8 points are given to a candidate who has obtained 80 points in the effort target collation.
All adjusted scores for the ten parameters are summed, resulting in (y) being calculated. However, if the employer assigns equal importance to the four parameters (ie, 25 points each for four of the ten parameters), the candidate who obtained 80 points in the effort goal match Will be awarded 20 points. The adjusted scores for the four parameters assigned importance are summed, resulting in (y) being calculated. The subtotal of the candidate's quantitative parameters is the sum of the scores for each quantitative parameter (x) and the sum of the adjusted scores (y)
, The result is (z).

The qualitative reconciliation of block 348 is determined by comparing the candidate's answer and the employer's answer for each parameter group. In the illustrated embodiment, three qualitative parameter groups (a),
(B) and (c). The percentage matching results for each group are summed and averaged, resulting in (d).

The final score of a candidate is determined by taking the percentage (d) of the qualitative match (multiplying by 100), adding the result to the subtotal (z) of the quantitative parameter and dividing by two. (A candidate's final score is a number between 1 and 100).

In one embodiment, the qualitative evaluation result (d) is 70% of the candidate's score and the subtotal of the ten quantitative parameters is 30% of the candidate's score. However, the research consultant can adjust these default settings to change the weights or any quantitative parameters that led to the results of the qualitative evaluation, thereby prioritizing the candidate found by the system. Can be attached.

In another embodiment, by advertising in publications such as the Wall Street Journal,
You can also recruit applicants for unique employment positions. In the system 10 of this embodiment, an advertisement is placed in a publication and announces the position of the employer and the particular candidate of interest. The advertisement instructs the candidate to access the recruitment website, and the advertisement includes a code that can be entered at the site. This code permits the candidate to access a modified registration screen specifically aimed at obtaining information relating to the recruitment of the advertised employment position. The employment parameters are checked against the candidate parameters, as described above with respect to FIGS. The candidate is provided with feedback as described above in connection with FIG.

Although the present invention has been described with reference to one or more embodiments, those skilled in the art will appreciate that they do not depart from the spirit and scope of the invention as set forth in the appended claims.
It will be appreciated that many changes can be made.

Appendix A The following example of a conjoint analysis assumes that a questionnaire was provided to each respondent.
The following information is data corresponding to the questions listed in the questionnaire, and applicants will respond to them. Utilities (utility) are also included before they are scaled (evaluated) by prospective answers. There is one record for each respondent.

Shown below is a sample question (TUTOR (training) question) for vehicle parameters.
It is an example of a record of a respondent of one respondent to.

[0097]

[Table 1]

Explanation of the first line: The first line contains the respondent number (1
04), the number of pairs displayed in the “pairs” section of the interview (12), the number of attributes in the survey (5), and the number of attribute levels in the survey (23).

[0099] A serial number is assigned to the attribute level. Numbers are assigned from each level of the first attribute, then to the level of the second attribute, and so on. For example, the following table shows the TU
Shows how numbers are assigned to attribute levels in a TOR questionnaire.

[0100]

[Table 2]

Second and Next Lines, Importance Rating (Rating): The following follows one line for each attribute. Each row has 12 columns. In these twelve columns, the maximum number of levels (9) are displayed, and the displayed two attribute levels and the answer of the respondent to the importance question are displayed.

The numerical values in the first and second columns in each row represent the attribute levels shown in the “importance evaluation” section. The numbers in the third column are the respondents' answers to the importance questions. The remaining nine columns have numerical values for each level, and the numerical values indicate the order of preference (priority) for each level in the number of levels in which the attribute exists. These numerical values represent the order of preference in descending order, with “1” representing the lowest preference level. “−1” indicates that the respondent rated “unacceptable”. “0” is shown in other columns.

When the example record of this appendix having five attributes is used, the above-mentioned lines are the second to sixth lines.

[0104]

[Table 3]

The second row indicates a second attribute (driving mode). The first number 7 (front wheel drive) and the next number 6 (rear wheel drive) represent the attribute levels displayed in the "importance rating" section. The third numerical value 4 is the answer of the respondent to the importance question. The following numbers 1, 3 and 2 have the lowest preference rank for the first level (rear wheel drive) (1), the highest preference rank for the second level (front wheel drive) (3), Third level (all-wheel drive)
Is intermediate (2). Since this second attribute has only three levels, the remaining columns in this row are zero.

Next line, paired comparison: The following follows one line for each pair that was displayed in the “pairs” section of the interview. Each row has 11 columns. These eleven columns allow the maximum number of levels in the "pair" to be displayed (five for each two concepts), one column for respondent answers. These numbers represent the indicated levels. A number with a minus sign indicates that the level appears on the left side of the screen, and a number without the minus sign indicates that the level appears on the right side of the screen. The last number on the line is the respondent's answer.

When the example record of this appendix having 12 pairs is used, the above-mentioned lines are from the seventh line to the eighteenth line.

[0108]

[Table 4]

After these operations, the first row represents the first pair. The first numerical value -1 indicates that the two-door sedan with the level number 1 is displayed on the left side of the screen. Second numerical value-
Reference numeral 7 indicates that the front wheel drive of level number 7 is displayed on the left side of the screen. These levels were "paired" with the next two numbers displayed on the right side of the screen, 2 (4-door sedan) and 8 (all-wheel drive).

The number 7 at the end of the row indicates the respondent's answer to this question. Next line, Utility before proofreading: The next line shows, for this respondent, the usefulness of each attribute in each level. Each row has up to 10 utilities.

In the case where a record example having 23 attribute levels in this appendix is used, the above-mentioned lines are from the 19th line to the 21st line.

[0112]

[Table 5]

[0113] These values are displayed for each level. The value “−9.999” indicates a level that was unacceptable to the respondent. When setting up the simulation, -9.9 to represent the unacceptable level
You can choose a number other than 99.

Next line, previous utility: The next line is
For this respondent, the approximate utility for each level of each attribute is shown in turn. These utilities are based solely on information obtained from previous interviews in the "pairs" section. Each row has up to 10 utilities.

When this example record of appendix is used, the above-mentioned lines are from the 22nd line to the 24th line.

[0116]

[Table 6]

[0117] These values are displayed for each level. The next line, Contribution to the utility from the "pair" section: The next line, in turn, represents the "partial" utility for each level of each attribute for this respondent. These are the components of the utility from the "pairs" section. Each row has up to 10 utilities.

In the case of using the record example of the appendix, the above-mentioned lines are from the 25th line to the 27th line.

[0119]

[Table 7]

[0120] These values are displayed for each level. Next line, number of proofreading concepts and number of attributes: The next line contains two numbers that report to the respondents the number of proofreading concepts and the number of attributes in each concept. is there.

In the record example in this appendix, this is the 28th line. 45 The first number 4 indicates that 4 concepts were shown. The second numerical value 5 indicates that each concept had five attributes.

Next line, calibration concept: Next, one line is shown for each calibration concept. There is a column for the number of attributes in each concept, and the numbers represent the attribute levels included in the concept. The next two columns show the utilities calculated for the concept. The first number is the total utility for the concept using only the "previous" estimates obtained from the first part of the questionnaire, the second number is the utility component estimated from the "vs" question It is a total utility using only. The last column contains the respondent's likely purchase value for that estimate.

In the record example of the appendix, the above-mentioned lines are from the 29th line to the 32nd line.

[0124]

[Table 8]

The previous line, line 28, shows that there are four concepts, so there are four lines (lines 29 to 32), one for each concept. Also, the 28th
The row also shows that each concept had 5 attributes,
Thus, each of the four rows uses the first five columns to indicate the attribute level involved in the concept. For example, the first five numbers in line 29 are levels of 1 (two-door sedan), 6 (rear-wheel drive), 9 (red), 17 (European convertible), and 23 ($ 20,000). Is indicated. Next numerical value -3.11
1 shows the summation utility of the concepts obtained from the "first" part of the questionnaire, using only the previous evaluations. The next number, 30.834, shows the total utility using only the utility components estimated from the "pair" query. The last numerical value 12 represents a purchase expectation value of the respondent for the concept.

Last row, calibration factor: The last row contains the calibration factor from logit regression, which predicts purchase prospects and combines with previous components in evaluating the “optimal” utility. Used to weight pairs and components.

In the appendix record example, the above-mentioned line is the 33rd line. -0.572 0.337 0.982 0.935 In the last row, -0.572 is the intercept from the logit regression, 0.377 is the weight given to the previous utility, 0.982 is The weight given to the paired component, 0.935, is the squared composite correlation in predicting a probable answer from the two components of the utility.

Appendix B The following describes the operation of the binding analysis tool used in one embodiment of the present invention, with reference to Table 1 below. Once the applicant survey is completed, each attribute (parameter)
Floating point values (eg, -100
To +100) is stored. In one embodiment, there are a total of 40 values for each respondent. In another embodiment, at least four more values (regression weights) are added to the attribute value for each respondent. The following is an example where an employer has a job description that includes information about all binding attributes. This is called the "representation" step.

Various applicants come from various places
Therefore, the system needs to determine what the specification represents for each candidate. -For example, for salary, the job offers $ 60,000,
If the candidate's current salary is $ 50,000, this is 20
% Salary increase. This is 4.0 for this attribute
It represents the level.

For some attributes, it is difficult for each respondent to determine the level of details.
For example, how can you know how "interesting / stimulating" a particular job is for a given applicant? For some attributes, a subjective decision step is provided, where a human assigns a level to a particular candidate attribute. Alternatively, an algorithm for assigning the correct attribute level can be provided.

For each applicant, the output of the "representation" step is a vector of eleven numbers, each of which represents a level for one of the eleven attributes. The final step is to calculate the total utility for each applicant's statement. In one embodiment, the system uses the following numerical values as input for each candidate.

11 levels representing this specification, 40 utility values, and 4 regression values for a total of 55 values.

For each respondent, the total utility program looks at the utility for each attribute and the aggregate utility between attributes. If the level score is not an integer (eg, 4.5), linear interpolation is used. The regression weights are then used to generate a "probability of accepting" a possible job offer. This output will be a single number for each respondent that represents their likelihood of accepting their work.

Examples of binding attributes include expanded responsibilities, wealth creation, annual income, industry / company, geographic location, life / work balance, participation in key events, interesting / stimulating jobs, occupational occupations. Includes, but is not limited to, opportunities / acquisitions, compatibility with corporate culture, and the ability to influence outcomes.

Table 1 [ACA_ATTR1] Attr_Name = Expansion of Responsibility Attr_Rank = 1 Ask Mostlikelies = 0 Ask_Unacceptables-0 LEVEL1ELTEX1 Effort = LEXTEL2EL1VEL2EL1VEL2EL1VEL2EL1VEL2EL1VEL2EL1VEL1VEL2V1ELE LEVEL2VALUE = 2.000000 LEVEL3TEXT1 = Expanded LEVEL3TEXT2 = Responsibilities & Effort Goals LEVEL3VALUE = 3.000000 LEVEL4TEXT1 = Key Newly Added Duties LEVEL4TEXT2 = Responsibility & Effort Goals LEVEL.000.

[ACA_ATTR2] Attr_Name = Creation of wealth Attr_Rank = 1 Ask_Mostlikeries = 0 Ask_Unacceptables = 0 LEVEL1TEXT1 = No stock selection option LEVEL1TEL2VEL1TEL2VEL1T2VEL1T2VEL1T2VEL1V2T1V2T1V2T1T2T1V2T1T2V1T2T1T2T1T2T1T2T1T2T1T2T1T2T1T2T1T2T1T2T1T2 as as as as as as follows: $ 100,000 LEVEL2VALUE = 2.000000 LEVEL3TEXT1 = Option to buy stock and expected value LEVEL3TEXT2 = $ 250,000 in 3 years LEVEL3VALUE = 3.000000 LEVEL4TEXT1 = Option to buy stock and expected value EXTEL4 = 3LEVEL2 $ 500,000 per year LEVEL4VALUE = 4.0000 0.

[ACA_ATTR3] Attr_Name = Annual Revenue (Salary + Special Bonus) Attr_Rank = 1 Ask_Mostliqueries = 0 Ask_Unacceptables = 0 LEVEL1TEXT = Annual Revenue: -10% LEVEL1ELVEL2VEL.VEL1VEL.2 000003 LEVEL3TEXT1 = annual income: + 10% LEVEL3VALUE = 3.000000 LEVEL4TEXT1 = annual income: +25 LEVEL4VALUE = 4.000000 LEVEL5TEXT1 = annual income: + 50% LEVEL5VALUE = 5.000000.

[0138] [ACA_ATTR4] Attr_Name = industry / company Attr_Rank = 1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEXT1 = undesirable company or LEVEL1TEXT2 = industry LEVELlVALUE = 1.000000 LEVEL2TEXT1 = same type of company and LEVEL2TEXT2 = industry LEVEL2VALUE = 2.000000 LEVEL3TEXT1 LEVEL3TEXT2 = Company or Industry LEVEL3VALUE = 3.000000 LEVEL4TEXT1 = Very Desirable Industry LEVEL4TEXT2 = or Company LEVEL4VALUE = 4.000000.

[ACA_ATTR5] Attr_Name = geographic location Attr_Rank = 1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEXT1 = ELVEL1TEL2VEL1VELTEL2VEL1V2T1VEL1V2T1VEL1V2T1VEL1V2T1VEL1V2V LEVEL3TEXT2 = move to location LEVEL3VALUE = 3.000000.

[ACA_ATTR6] Attr_Name = Life / Work Balance Attr_Rank = 1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEXT1 = 70 hours / Avg. Hours / week LEVEL3VALUE = 3.000000 LEVEL4TEXT1 = Average 40 hours / week LEVEL4VALUE = 4.000000.

[ACA_ATTR7] Attr_Name = Participation in Major Event Attr_Rank-1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEXT1 = Experience of Major Event / No Experience: Major Event, etc. Experience /
Actual experience: LEVEL2TEXT2 = IPO, conversion, etc. LEVEL2VALUE = 2.000000 LEVEL3TEXT1 = Experience many major events / Experience: LEVEL3TEXT2 = IPO, conversion LBVEL3VALUE = 3.000000.

[ACA_ATTR8] Attr_Name = interesting / exciting work Attr_Rank = 1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEXT1 = Elevation of current level stimulus = ELVEL1TEXT2 = EL / VEL1V1T2 = EVOLT1V LEVEL2TEXT2 = more work LEVEL2VALUE = 2.000000 LEVEL3TEXT1 = very stimulating / interesting LEVEL3TEXT2 = more work LEVEL3VALUE = 3.000000.

(ACA_ATTR9) Attr_Name = Occupational Opportunity / Acquisition Attr_Rank = 1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEX1 Occupational Opportunity, Acquisition LEVEL1ELVEL1TEX2 LEVEL2VALUE = 2.000000 LEVEL3TEXT1 = Occupational Opportunity / Acquisition LEVEL3TEXT2 = Potentially good LEVEL3VALUE = 3.000000.

[ACA_ATTR10] Attr_Name = Compatibility with Corporate Culture Attr_Rank = 1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEXT1ELTEX2 = Compatibility with Corporate Culture LEVEL1TEXT2 = Excel1TEXT2 LEVEL2VALUE = 2.000000 LEVEL3TEXT1 = Compatibility with corporate culture LEVEL3TEXT2 = Somewhat better LEVEL3VALUE = −3.000000 LEVEL4TEXT1 = Compatibility with corporate culture LEVEL4TEXT2 = 4000VEL.

[ACA_ATTR11] Attr_Name = ability to affect the results Attr_Rank = 1 Ask_Mostlikelies = 0 Ask_Unacceptables = 0 LEVEL1TEXT1 = Similar Ability to Affect the Results LEVEL1TEX1VEL1TEX1V1T1V Some good ability to affect LEVEL2TEXT2 = and feel rated LEVEL2VALUE = 2.0000000 LEVEL3TEXT1 = A fairly good ability to affect results LEVEL3TEXT2 = and feel rated LEVEL3VALUE = 3.000000.

[Brief description of the drawings]

FIG. 1 is a diagram illustrating a typical prior art Internet recruitment process.

FIG. 2 is a diagram showing an overview of an embodiment of the present invention.

FIG. 3 is a flowchart illustrating a process for setting an employer and / or employment position.

FIG. 4 is a flowchart illustrating a process for specifying employment position parameters.

FIG. 5 is an example of a screen used to specify quantitative employment parameters required and / or desired by an employer.

FIG. 6 is an example of a screen used to specify quantitative employment parameters required and / or desired by an employer.

FIG. 7 is an example of a screen used to specify quantitative employment parameters required and / or desired by an employer.

FIG. 8 is an example of a screen used to specify quantitative employment parameters required and / or desired by an employer.

FIG. 9 is an example of a screen used to specify quantitative employment parameters required and / or desired by an employer.

FIG. 10 is an example of a screen used to specify quantitative employment parameters required and / or desired by an employer.

FIG. 11 is an example of a screen used to specify quantitative employment parameters required and / or desired by an employer.

FIG. 12 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 13 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 14 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 15 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 16 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 17 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 18 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 19 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 20 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 21 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 22 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 23 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 24 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 25 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 26 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 27 is an example of a screen used to specify qualitative employment parameters required and / or desired by an employer.

FIG. 28 is a flowchart related to ensuring that employment position parameters are fully specified and preparing employment position records for the matching process.

FIG. 29 is a flowchart related to collecting candidate parameter information according to one embodiment of the present invention.

FIG. 30 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 31 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 32 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 33 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 34 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 35 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 36 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 37 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 38 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 39 is an example of a screen used to obtain quantitative applicant parameters.

FIG. 40 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 41 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 42 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 43 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 44 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 45 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 46 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 47 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 48 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 49 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 50 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 51 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 52 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 53 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 54 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 55 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 56 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 57 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 58 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 59 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 60 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 61 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 62 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 63 is an example of a screen for obtaining qualitative applicant parameters.

FIG. 64 is a flowchart related to determining whether a candidate record has been completed.

FIG. 65 shows a continuation of the flowchart in FIG. 64;

FIG. 66 is an example of a qualitative evaluation feedback screen for an employer.

FIG. 67 is an example of a qualitative evaluation feedback screen of an employer.

FIG. 68 is an example of an employer's qualitative evaluation feedback screen.

FIG. 69 is an example of a qualitative evaluation feedback screen of a candidate.

FIG. 70 is an example of a qualitative evaluation feedback screen of a candidate.

FIG. 71 is an example of a qualitative evaluation feedback screen of a candidate.

FIG. 72 is an example of a qualitative evaluation feedback screen of a candidate.

FIG. 73 is an example of a qualitative evaluation feedback screen of a candidate.

FIG. 74 shows the identification of an incomplete candidate record;
FIG. 4 is a flowchart relating to the notification of a candidate with an incomplete record and the completion of such a record.

FIG. 75 is a flowchart illustrating an example of a method employing a database including records of employment positions and applicants to select an appropriate number of eligible applicants for the position.

FIG. 76 is a flowchart illustrating a collation function according to an embodiment of the present invention.

FIG. 77 is a flowchart of an example of a duty collation subroutine in FIG. 76;

FIG. 78 is a flowchart showing an example of an effort target collation subroutine in FIG. 76.

FIG. 79 is a flowchart showing an example of the industry collation subroutine in FIG. 76.

FIG. 80 is a flowchart showing an example of the industry collation subroutine in FIG. 76.

FIG. 81 is a flowchart illustrating an example of a company matching subroutine in FIG. 76;

FIG. 82 is a flowchart showing an example of a company size collation subroutine in FIG. 76;

FIG. 83 is a flowchart showing an example of a company classification collation subroutine in FIG. 76;

FIG. 84 is a flowchart showing an example of the experience matching subroutine in FIG. 76.

FIG. 85 is a flowchart showing an example of a degree matching subroutine in FIG. 76.

FIG. 86 is a flowchart showing an example of a degree matching subroutine in FIG. 76.

FIG. 87 is a flowchart showing an example of a qualification collation subroutine in FIG. 76.

FIG. 88 is a flowchart showing an example of a qualitative evaluation matching subroutine in FIG. 76;

FIG. 89 is a table showing an example of a scoring subroutine in FIG. 76;

FIG. 90 shows a continuation of the table in FIG. 89;

FIG. 91 shows a continuation of the table in FIG. 90;

FIG. 92 is a flowchart of an example of a matching subroutine in FIG. 76;

FIG. 93 is a flowchart of an example of a collation subroutine in FIG. 76;

 ────────────────────────────────────────────────── ─── Continuing on the front page (72) Inventor Man Jit Thing, Brooklyn Drive, Los Angeles, California 90077, USA 100077 (72) Inventor Yaron Hankin, 91326, Northridge, Brasilia Drive, California, USA 18801F Terms (reference) 5B049 AA06 BB68 EE02 GG04 GG07 GG09 5B075 QM08 UU40

Claims (21)

[Claims] 1. A computer system for collating one or more applicants with an employer's employment position, comprising: a processor; and a database accessible by the processor, wherein the processor comprises: Searching for a parameter, searching for a candidate parameter from the database, comparing the candidate parameter with the employment parameter, and calculating a degree of agreement between the candidate parameter and the employment parameter. Computer system. 2. The computer system of claim 1, further programmed to assign a score based on a degree of match. 3. The computer system of claim 2, further programmed to accumulate a score total corresponding to a degree of match for the candidate. 4. The computer system according to claim 1, further comprising: providing the candidate with a candidate question including a candidate question and a qualitative evaluation question; and answering the candidate question. A computer system programmed to determine candidate parameters from answers to the candidate questions and to store the candidate parameters in the database. 5. The computer system of claim 1, further comprising: providing an employer with an employment question including an employment position question and a qualitative evaluation question; receiving an answer to the employment question; A computer system programmed to determine employment parameters from answers to the employment questions, and to store the employment parameters in the database. 6. The computer system of claim 1, wherein the employment parameters include educational background, experience, open positions, salary, employer location, and employer qualitative evaluation parameters. Computer system. 7. The computer system of claim 6, wherein the candidate parameter is a current employer,
A computer system that includes educational background, employment history, desired position, desired salary, location preferences, and qualitative evaluation parameters of the candidate. 8. The computer system of claim 7, further comprising: providing the candidate with a qualitative evaluation parameter of the candidate.
Computer system. 9. The computer system of claim 7, further programmed to compare the set of candidate parameters with the set of employment parameters, the comparison being performed by each candidate. Comparing the parameters of the employment history of each candidate with the educational background of the employment position, comparing the parameters of the employment history of each applicant with the work experience of the employment position, Comparing the parameters of each applicant's desired salary and the salary of the employment position; comparing the parameters of each applicant's location preference with the location of the employment position; and A computer system that is performed by comparing the parameters of a qualitative assessment and an employer's qualitative assessment. 10. A method of collating one or more applicants with an employment position of an employer by processing information obtained electronically, the application including a candidate question and a qualitative evaluation question. Providing a candidate questionnaire to a candidate; electronically obtaining an answer to the candidate questionnaire; determining a candidate parameter from the answer to the candidate questionnaire; and Storing in a database an employment question including an employment position question and a qualitative evaluation question to the employer; electronically obtaining an answer to the employment question; and Determining an employment parameter from the answer to, storing the employment parameter in a database, Retrieving the applicant parameter and the employment parameter from a source, comparing the applicant parameter with the employment parameter, calculating a degree of agreement between the applicant parameter and the employment parameter. Performing the steps of: 11. The method of claim 10, further comprising assigning a score based on the degree of match. 12. The method of claim 11, further comprising the step of accumulating a total score for the candidate corresponding to a degree of match. 13. The method of claim 12, further comprising selecting a candidate based on at least a portion of the total score. 14. A computer server for processing data from an Internet web site to match one or more applicants with an employer's employment position, the processor comprising: a processor; A database, wherein the processor provides the candidate with a candidate questionnaire including a candidate question and a qualitative evaluation question, receives an answer to the candidate questionnaire, and the answer to the candidate questionnaire. Determining candidate parameters from the database, storing the candidate parameters in the database, providing employment questions including employment position questions and qualitative evaluation questions to the employer, receiving an answer to the employment questions, Determine the employment parameters from the answers to the employment questions and record the employment parameters in the database. Searching the database for the applicant parameter and the employment parameter, comparing the applicant parameter with the employment parameter, and calculating a degree of agreement between the applicant parameter and the employment parameter. Computer server, programmed in 15. The computer server of claim 14, further programmed to assign a score based on a degree of match.
server. 16. The computer server of claim 15, further programmed to accumulate a candidate's total score corresponding to the degree of agreement with the candidate. . 17. The computer server of claim 14, wherein the candidate parameters include current employer, educational background, employment history, desired position, desired salary, location preference, and applicant's Computer server containing qualitative evaluation parameters. 18. The computer server of claim 14, wherein the employment parameters include educational background, experience, open positions, salary, employer location, and employer qualitative evaluation parameters. Computer server. 19. A computer system for collating one or more applicants with an employer's employment position, comprising: a processor; and a database accessible to the processor, the processor comprising: Providing employment questions to the employer, including qualitative assessment questions; receiving answers to the employment questions; from the answers to the employment questions, educational background, experience,
Determining employment parameters, including open positions, salaries, employer locations, and qualitative evaluation parameters of the employer, storing the employment parameters in the database, and applying for applicant and qualitative evaluation questions Provide the applicant with the applicant's questionnaire, receive the answer to the applicant's questionnaire, and, based on the answer to the applicant's questionnaire, the current employer, educational background, employment history, desired position, desired salary, and location preference. Determining a candidate parameter including a qualitative evaluation parameter of the candidate, storing the candidate parameter in the database, providing the qualitative evaluation parameter of the candidate to the candidate, Comparing the parameters of the applicant's educational background with the educational background of the employment position; and comparing the applicant's employment history with the employment position. Comparing the parameters of the candidate's desired position with the position of the position being recruited; comparing the parameters of the candidate's desired salary with the salary of the employed position; Comparing the parameters of the preference of the place of the employer and the place of the employment position, and comparing the parameters of the qualitative evaluation of the applicant and the qualitative evaluation of the employer. A computer system programmed to calculate the degree of agreement between the employment parameter and the employment parameter, assign a score based on the degree of agreement, and accumulate points for applicants corresponding to the degree of agreement. . 20. By posting an advertisement in a publication,
A computer system for matching one or more applicants with a particular employment position of an employer, comprising: a processor; and a database accessible to the processor, the processor receiving a code from the advertisement. Receiving responses to applicant questions including applicant questions and qualitative evaluation questions related to the employment position posted in the advertisement, and from responses from each applicant to the applicant questions,
A computer programmed to determine candidate parameters for each candidate, compare the candidate parameters with the employment parameters, and calculate a degree of agreement between the candidate parameters and the employment parameters; ·system. 21. A computer-readable storage medium including computer-executable code for providing an employer with an employment question including an employment position question and a qualitative evaluation question, receiving an answer to the employment question. A computer-readable storage medium comprising computer-executable code for determining a hiring parameter from the answer to the hiring questionnaire and instructing a computer to operate to store the hiring parameter in a database.