US20030195786A1 - Computer-implemented system for human resources management - Google Patents

Computer-implemented system for human resources management Download PDF

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US20030195786A1
US20030195786A1 US10/410,184 US41018403A US2003195786A1 US 20030195786 A1 US20030195786 A1 US 20030195786A1 US 41018403 A US41018403 A US 41018403A US 2003195786 A1 US2003195786 A1 US 2003195786A1
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screening
computer
selection
information
score
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Katrina Dewar
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Epredix Inc
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Epredix Inc
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Assigned to EPREDIX, INC. reassignment EPREDIX, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DEWAR, KATRINA L.
Assigned to WELLS FARGO BANK, N.A., AS ADMINISTRATIVE AGENT reassignment WELLS FARGO BANK, N.A., AS ADMINISTRATIVE AGENT SECURITY AGREEMENT Assignors: EPREDIX, INC.
Assigned to PREVISOR INC. (SUCCESSOR BY MERGER TO EPREDIX, INC.) reassignment PREVISOR INC. (SUCCESSOR BY MERGER TO EPREDIX, INC.) RELEASE OF PATENT COLLATERAL RECORDED AT REEL/FRAME 016490/0907 Assignors: WELLS FARGO BANK, NATIONAL ASSOCIATION
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation; Time management
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0631Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311Scheduling, planning or task assignment for a person or group
    • G06Q10/063112Skill-based matching of a person or a group to a task
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0639Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation; Time management
    • G06Q10/105Human resources
    • G06Q10/1053Employment or hiring
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/02Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201Market modelling; Market analysis; Collecting market data
    • G06Q30/0203Market surveys; Market polls
    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09BEDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B7/00Electrically-operated teaching apparatus or devices working with questions and answers
    • G09B7/02Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student

Definitions

  • FIG. 1 provides a block diagram of an exemplary system in accordance with the present invention.
  • FIG. 2 illustrates a process for testing and evaluating job applicants in accordance with an embodiment of the present invention.
  • FIG. 3 depicts a hiring procedure in accordance with one embodiment of the invention.
  • FIG. 4 is a block diagram of a process employing feedback.
  • FIG. 5 diagrams an online system in accordance with one embodiment of the invention.
  • FIG. 6 shows an example of a web-based presentation for a screening solution.
  • FIG. 7 shows an example of a stack ranked table.
  • FIG. 8 shows an example of a screening solution question presented to an applicant taking a screening solution test over the Internet.
  • FIG. 9 shows an example of a structured interview guide for use in an interview solution.
  • FIG. 10 illustrates procedural steps that may be followed in a web-based applicant system according to an embodiment of the present invention.
  • FIG. 11 illustrates procedural steps that may be followed in a web-based selection solution according to an embodiment of the present invention.
  • FIG. 12 illustrates procedural steps that may be followed by an employer according to an embodiment of the present invention.
  • FIG. 13 illustrates a human capital management life-cycle.
  • a system for testing a job applicant provides a computerized stack ranking of multiple applicants, predictive of the comparative levels of successful job performance.
  • the predictive stack ranking may be used as a dynamic interactive filter with a pool of applicants over the course of the evaluation or employment process.
  • the system may utilize a communications network to communicate between an applicant terminal and a system server.
  • the system may be used for example for screening, selecting, retaining, assigning, or analyzing the job applicant.
  • the job applicant can for example be a new job applicant, an employee seeking to retain a job, an employee seeking a different job in the same organization, or an employee being evaluated for retention, re-assignment, or promotion. Applicants may or may not know they are being evaluated.
  • the system may collect data regarding the employee for use in a feedback loop informing the online hiring process and improving the accuracy of the predictive stack ranking.
  • the data may indicate the employer's rating of the employee's actual job performance.
  • Such a rating can be cross-checked against the answers that the employee gave during the application process.
  • the cross-checking can be used as feedback to refine the questions and evaluation criteria used at each stage of the hiring process.
  • the cross-checking may be analyzed to select from among many questions a small subset having high predictive value. The small subset can then be used in a quick initial screening stage. Or, the small subset can be given greater weight than other questions in a computerized stack ranking of candidates.
  • FIG. 1 provides a block diagram of an exemplary system in accordance with the present invention.
  • a job applicant can use applicant terminal 102 to communicate over network 104 with system server 106 .
  • Applicant terminal 102 may for example be a telephone handset, a personal computer, a workstation, a handheld wireless device such as those marketed under the trademarks PALM or HANDSPRING, or a Wireless Application Protocol enabled device such as a mobile phone.
  • Network 104 may for example be the Internet, the World Wide Web, a wide area network, a local area network, a telephone network, a wireless communication network, a combination thereof, or any other link capable of carrying communications between an applicant terminal and a server.
  • System server 106 employs a testing computer program 108 and has access to a scoring database 110 .
  • System server 106 communicates with applicant terminal 102 in accordance with instructions from testing computer program 108 .
  • System server 106 may communicate with employer server 112 over network 104 or over direct link 114 .
  • System server 106 is shown as a unitary server, but may be a distributed computing platform.
  • An applicant terminal may be remote from, or co-located with, system server 106 and/or employer server 112 .
  • applicant terminal 102 may be located at a job applicant's home
  • applicant terminal 116 may be located at a job fair or employment office
  • applicant terminal 120 may be located at an employer's location.
  • Partner server 121 may be linked to network 104 and system server 106 to facilitate integration of a business partner seeking to participate in the system of FIG. 1.
  • System server 106 may pose questions to a job applicant located at an applicant terminal, receive responses from the job applicant, and score the answers in accordance with scoring database 110 .
  • the scoring may take place in real time, i.e., while the applicant is still online, and may be reported in the form of a comparative stack ranking of multiple applicants.
  • the stack ranking may be delivered from system server 106 , over either network 104 or direct link 114 , to employer server 112 .
  • Scoring of each answer by system server 106 may be instant, i.e., before the next question is answered.
  • adaptive testing techniques may be implemented over network 104 .
  • the answers given by an applicant at applicant terminal 102 to questions propounded early in a test may determine which questions are propounded by system server 106 to the applicant later in the same test.
  • server 106 may immediately terminate the test.
  • the system may test an online applicant for any competency desired, in any sequence.
  • the tested competencies may be abilities, traits, knowledge, skills, etc., that have been proven relevant to and predictive of successful job performance.
  • the following competencies may be tested:
  • system server 106 tests for certain ones of the competencies that have been proven to be predictive of successful performance of the type of job for which the applicant is being considered.
  • the results of the testing are tabulated in a stack ranked table.
  • the stack ranked table may rank a number of applicants against each other and list them in order, from first to last.
  • the table may also present other information for each applicant.
  • the other information may include, by way of example and not limitation:
  • Identifying number e.g. social security number
  • applicant testing 201 includes providing a test to a job applicant and scoring the applicant's answers.
  • the test may be administered online or it may be administered manually off-line.
  • Scores are entered into a system for calculating a stack ranked table.
  • Predictive stack ranking 202 generally includes ranking a job applicant against other job applicants in order from first to last or other comparative ranking.
  • the other job applicants may be current job applicants, past job applicants, or fictional job applicants.
  • FIG. 3 depicts a hiring procedure in accordance with one embodiment of the invention.
  • Announcement 302 may be an online job announcement such as a web page with an “apply now” hyperlink icon.
  • the web page may reside on an employer's website or an employment agency website, for example.
  • an online job announcement may be a recorded announcement on a menu-driven telephone voice processing system.
  • announcement 302 may be an offline job announcement such as a newspaper advertisement.
  • screening test 304 In response to announcement 302 , an interested job applicant requests administration of screening test 304 .
  • Screening test 304 may be remotely administered and scored online, with the scores being automatically provided to predictive stack ranking 306 .
  • screening test 304 may be administered manually with paper and pencil, and then graded by hand or machine, with the scores being provided to predictive stack ranking 306 .
  • the predictive stack ranking may for example be constructed by system server 106 or employer server 112 .
  • Predictive stack ranking 306 totals the graded answers according to particular competencies known to be relevant to successful job performance.
  • Predictive stack ranking 306 may be administered by a computer processor located at system server 106 , for example.
  • Predictive stack ranking 306 may give different weight to different questions, and may at any stage immediately disqualify an applicant providing an unacceptable answer to a “knock-out” question.
  • Predictive stack ranking 306 may rank the applicant in order against other job applicants in a table.
  • Predictive stack ranking 306 may be used to decide which applicants to invite for the next stage, selection test 308 .
  • Selection test 308 is preferably conducted under supervised conditions.
  • selection test 308 may be administered in person.
  • An in-person test may take place at a job fair, an employer's location, a job site, or an employment agency.
  • An in-person test may include verification of the job applicant's identity, such as by examination of a photo identification document produced by a test-taker.
  • Selection test 308 may be administered online or manually.
  • Supervised conditions typically include observation of the test-taker during administration of the test. The answers to selection test 308 are graded and the results are incorporated in predictive stack ranking 306 .
  • Predictive stack ranking 306 may then update a previously created entry for the applicant and rank or re-rank the applicant in order against other job applicants. After this is accomplished, the highest ranking applicants may be invited for interview 310 .
  • Interview 310 may be structured or unstructured, online or in person. If interview 310 is structured, a program leads the interviewer through the interview by suggesting questions one at a time.
  • the program may be a list of questions written on paper or it may be a computer program resident for example in system server 106 .
  • the program suggests questions that are predetermined to be valid, i.e., proven to be associated with successful job performance and legally permitted.
  • the interviewer can input the answers and/or a score for the answers, either after each answer or at the conclusion of the interview. This can be done via employer terminal 124 , for example.
  • Interview 310 results in an interview score being provided to predictive stack ranking 306 .
  • Predictive stack ranking 306 is revised to reflect the interview score.
  • the relative rank of the job applicants is reassessed.
  • FIG. 4 is a block diagram of a process employing feedback.
  • Test design 402 is initially performed using industry-accepted standards.
  • Test administration 404 tests and scores job applicants and/or incumbents.
  • Employee performance evaluation 406 measures actual job performance of the applicant or incumbent after holding the job for a period of time. This information is fed back to test design 402 and/or test administration 404 .
  • Test design 402 may be revised to delete questions which were not predictive of successful job performance. This can be done for example by deleting questions whose answers bore no relation to performance evaluation 406 for a statistically valid sample.
  • Test administration 404 may be revised by adjusting the weight given to certain questions or answers that showed an especially strong correlation to employee performance evaluation 406 . For example, if test administration 404 is associated with predictive stack ranking 306 , feedback from employee performance evaluation 406 may help determine how various job applicants are comparatively ranked against each other.
  • FIG. 5 diagrams an online computer based system 500 in accordance with one embodiment of the invention.
  • Box 502 represents a job vacancy with a requirement for an online screening and selection solution.
  • the vacancy can come to the attention of a potential job applicant in a number of ways.
  • box 504 represents an online application via a hiring company's own website.
  • a company offering a job may post a vacancy announcement on the company's website and invite job seekers to apply by clicking on an icon labeled “apply here” or the like.
  • Box 506 represents a similar posting on an online job board.
  • Box 508 represents candidates given a Uniform Resource Locator (URL) directly by the company. This may occur when the company offering a job identifies a potential candidate.
  • Box 510 represents a media advertisement including a URL for a job. Thus, job seekers observing the advertisement can direct their browsers to the indicated URL.
  • URL Uniform Resource Locator
  • Job seekers may be provided a URL associated with the company or the particular vacancy. Paper-and-pencil measures could also be used at job fairs and entered into the system.
  • a computer terminal may be provided for use of job seekers at job fair 512 , enabling job seekers to participate in the online system.
  • Box 514 represents an executive search via a recruiter network. Job seekers relevant to the search are identified in recruitment firm applicant database 516 . Database 516 can link to a URL associated with the job.
  • the potential applicant is considered at decision 520 .
  • Decision 520 asks whether applicant has completed the required screening solution 524 . If not, the applicant at box 522 is given via e-mail, mail, or in person, a URL for assessment.
  • system 500 may send an e-mail message to a potential applicant, the e-mail message inviting the potential applicant to apply for vacancy 502 by directing a browser to a screening solution URL provided in the e-mail message.
  • the website host can provide a link to a web page identified by the screening solution URL.
  • Decision 520 may be based on a potential applicant's name, e-mail address, and/or other identifying information.
  • Screening solution 524 is administered via the Internet and is hosted at the screening solution URL mentioned above. Screening solution 524 asks screening questions to ascertain if the applicant has the basic qualifications to do the job. These are based on questions typically asked by recruiters but which are statistically validated over time to ensure they are legally defensible and predictive. The questions may include a combination of biodata and personality measures. They may include self-assessments of skill levels appropriate to the job requirements. Screening solution 524 requires applicants to transmit elicited information over the Internet. A possible example of a web-based presentation for screening solution 524 is illustrated in FIG. 6. Screen shot 600 shows a portion of the presentation.
  • screening solution 524 provides applicant feedback 540 and conveys applicant details and screening scores to stack ranked table of applicants 530 .
  • Applicant feedback 540 may provide a message to the online applicant indicating that the screening solution is complete, that the applicant has passed or failed the screening stage, and that the applicant may or may not be contacted in due course.
  • Other information may also be provided to the applicant in the feedback pages, like a realistic job preview, recruiter phone number, scheduling information, etc.
  • system 500 ranks the applicant in comparative order against other applicants in stack ranked table of applicants 530 .
  • a certain number or percentage of applicants in table 530 will be chosen for further consideration. For example, the applicants ranking among the top five of all applicants ranked in table 530 may be chosen for advancement in the system at this juncture. Information identifying the chosen applicants will be included on a “short list” as indicated by box 536 .
  • the short list chosen at box 536 is transmitted to selection solution 538 , at which the advancing applicants are invited to answer selection questions.
  • Selection solution 538 asks additional questions and requires an advancing applicant to input answers.
  • the applicant completes selection solution 538 while sitting at a terminal located at one of the company's locations. The terminal communicates over the Internet with a website set up to administer the selection solution.
  • applicant feedback 540 is provided from the website to the applicant, and applicant details and scores 541 are incorporated in stack ranked table 530 .
  • Feedback 540 may optionally include a sophisticated report on the applicant's strengths and weakness.
  • the applicant may then be directed to an appropriate web page chosen by the hiring company. One page may indicated successful completion and a second page may indicate failure.
  • the appropriate web page may suggest other openings appropriate to the applicant's test responses and may provide hyperlinks the applicant can use to initiate the application process for these other openings.
  • stack ranked table 530 re-ranks the applicants as a result of selection solution 538 , some applicants are invited to participate in interview solution 542 .
  • the top three applicants as ranked by table 530 after selection solution 538 may be invited for an in-person interview. Because the selection solution is preferably in instant communication with stack ranked table 530 , the interview invitation may be extended immediately at the conclusion of the selection solution.
  • Interview solution 542 is preferably a structured interview, with questions provided via the Internet to the interviewer at the company's location.
  • the interviewer reads the provided questions and reports a score over the Internet from the company's location for incorporation in stack ranked table 530 .
  • Benchmark performance anchors may assist the interviewer in grading the applicant's responses.
  • Interview solution 542 can be designed according two exemplary models.
  • an employer is provided with standard interview guides for several job types as well as the competency templates for these types so that the employer can build variations to meet specific needs.
  • an employer can build new interview guides and new competency templates.
  • the employer has access to the full array of work-related competencies and associated questions in a comprehensive question bank.
  • stack ranked table 530 may consider a combination of different biographical, personality, behavioral, and other appropriate information and competencies.
  • table 530 may indicate for each applicant a yes/no recommendation, a percentage likelihood of successful job performance, biographical information not used for evaluative purposes, and so forth.
  • Stack ranked table 530 may be developed by grading the various solution stages with a computer implementing the following algorithm.
  • First search for disqualifying answers to “knock-out” questions.
  • Second give points for answers matching those of the previously hired candidates who achieved a successful performance evaluation.
  • Third deduct points for answers matching those of the previously hired candidates who received an unsuccessful performance rating.
  • Fourth multiply the added or subtracted points by any weighting assigned each question.
  • Fifth sum the points for all questions related to a given competency.
  • Sixth compare the summed points for each competency to norms of either the job-holders in the company or a wider population. Seventh, predict performance of the applicant as a worker in the job, based on the business outcomes identified by the hiring company and the competencies that contribute to those outcomes.
  • a final selection is made based on stack ranked table 530 .
  • the selection is transmitted over the Internet to the company, enabling the company to make an offer to the selected applicant(s). For example, if there is only one opening, an offer may be extended to the applicant ranked highest by stack ranked table 530 . If the applicant accepts the offer, the applicant is employed by the company. If the applicant declines, the next highest ranked applicant in stack ranked table 530 is offered the job. If a plural number of openings exist, that number of applicants may be selected off the top of stack ranked table 530 and offered the job. If one of the applicants declines, the next highest ranked applicant in stack ranked table 530 is offered the job. Data from stack ranked table 530 is forwarded to data warehouse 534 .
  • Data collected at data warehouse 534 are used for research and development and for reporting purposes.
  • functions enabled by storing comprehensive data generated by system 500 may include:
  • system 500 preferably uses instant communications, adaptive testing techniques may be implemented online. An applicant's failure to overcome hurdles in a given solution will deliver a different path through the solution than that of a successful applicant.
  • the degree of advancement of a given applicant through system 500 may result in different charges to the company from a solutions provider. For example, a solutions provider that hosts a website supporting screening solution 524 , selection solution 538 , and interview solution 542 may charge the hiring company the following amounts: one dollar for every applicant completing only the screening solution, five dollars for every applicant advancing only to the end of the selection solution, ten dollars for every applicant rejected after the interview solution, twenty dollars for every applicant offered a job, and fifty dollars for every applicant accepting an offer.
  • any of the various stages may be skipped, re-ordered, combined with other stages, or eliminated.
  • a short telephone interview may be structured early in the process to quickly screen applicants.
  • the questions to be asked at the various stages are selected for a particular type of job being offered in accordance with a proven relationship with desired business outcomes.
  • Business outcomes can for example include: level of sales, customer satisfaction, quality measures such as fault rates, retention and tenure of employment, time keeping, learning ability, progression to more senior roles over time, and supervisor ratings of behavioral success.
  • the particular type of job is defined in conjunction with the U.S. Department of Labor “O*NET” classification system.
  • Some types of jobs might include customer service, technical, professional, or managerial.
  • Various competencies are determined to be associated with desired business outcomes for a given type of job. These competencies are tested for at various solution stages with appropriate questions.
  • the appropriate competencies, questions, scoring, weighting, and ranking factors for a new job can be designed from historical tests for existing jobs, by applying statistical techniques and using the gathering of data on the Internet to ensure rapid validation of the new assessment solution. Confirmatory job analysis is used to determine the appropriateness of solutions for a particular job.
  • FIG. 7 shows an example of a stack ranked table.
  • Computer screen shot 700 illustrates a sample stack ranked table 730 for a customer service job.
  • Various tabs permit viewing of data generated by each solution stage.
  • Tab 702 reveals data 703 from a screening solution
  • tab 704 reveals data 705 from a selection solution
  • tab 706 reveals data 707 from an interview solution
  • tab 708 reveals all results.
  • tab 708 is selected.
  • Section 709 of screen shot 700 shows general information about each applicant, including current rank 710 , a link 712 to application information (not shown), last name 714 , first name 716 , and application date 718 .
  • Screening solution data 703 includes an indication 720 of whether each applicant successfully passed the knockout requirements for the job.
  • Data 703 also includes scores on certain competencies such as educational and work related experience 722 , customer service orientation 724 , and self-confidence 726 .
  • Column 728 indicates whether each applicant is recommended to advance beyond the screening stage.
  • Selection solution data 705 includes scores on certain competencies such as customer focus 732 , conscientiousness 734 , and problem solving 736 .
  • Column 738 indicates whether each applicant is recommended to advance beyond the selection stage.
  • Additional information may include columns for storage of data from other decision-making processes such as drug testing, reference checks, or medical exams.
  • FIG. 8 shows an example of a screening solution question presented to an applicant taking a screening solution test over the Internet.
  • screen shot 800 simulated customer contact record 802 is presented to the applicant.
  • the applicant is asked question 804 , and is required to click on a circle next to one of the answers.
  • Question 804 may test for a competency in working with information, for example.
  • FIG. 9 shows an example of a structured interview guide for use in an interview solution.
  • the interview guide is being presented online on a computer screen to an interviewer conducting an interview with an applicant.
  • Screen shot 900 shows interview item 902 for a sample customer service job.
  • the customer service job opening is for a call center position, and revenue focus has been identified as a relevant and predictive competency.
  • Item 902 elicits from the applicant a situation 904 , the applicant's behavior 906 in the situation, and the outcome 908 reported by the applicant.
  • the interviewer can grade the applicant's responses to item 902 by marking a score 910 from 1 to 10.
  • FIG. 10 illustrates procedural steps that may be followed in a web-based applicant system according to an embodiment of the present invention.
  • FIG. 11 illustrates procedural steps that may be followed in a web-based selection solution according to an embodiment of the present invention. For example, these steps may follow those illustrated in FIG. 10.
  • FIG. 12 illustrates procedural steps that may be followed by an employer according to an embodiment of the present invention.
  • the following tables provide examples of screening solutions and selection solutions designed for different types of jobs.
  • the tables show components (competencies) shown to be relevant to successful performance of each job type. In the tables, some components are considered required, and others are considered optional.
  • Table One may be used for entry level and general skill jobs: TABLE ONE Entry/General Skilled Solutions Solution Component Definition Items Screening 7-10 Minutes Required Educational and Measures potential for success in 15 Work-Related entry-level jobs across industry Experience type and functional area. Scores on Education and Work-Related Experience are derived from candidates' responses to questions regarding developmental influences, self- esteem, work history and work- related values and attitudes. Self-Confidence This component references: be- 7 lief in one's own abilities and skills and a tendency to feel competent in several areas. Optional Decision Making/ Measures potential for success in 8 Flexibility entry level positions.
  • Scores on Decision Making and Flexibility are derived from candidates' responses to questions regarding developmental influences, self- esteem, work history and work- related values and attitudes. Selection 23-35 Minutes Required Conscientiousness This component is designed to 65 predict the likelihood that candidates will follow company policies exactly, work in an organized manner, return from meals and breaks in the allotted time, and keep working, even when coworkers are not working.
  • Retention Measures commitment 44 Predictor impulsiveness, responsibility, and motivation. It predicts the likelihood that a new hire will remain on the job for at least three months.
  • Optional Learning Ability This component measures the 54 tendency to efficiently and (12 effectively use numerical and minute analytical reasoning. This timer) competency is characterized by the ability to learn work-related tasks, processes, and policies.
  • Table Two may be used for customer service jobs: TABLE TWO Customer Service Solution Solution Component Definition Items Screening 8-10 Minutes Required Educational and Measures potential for success in 15 Work-Related customer service jobs. Scores on Experience Education and Work-Related Experience are derived from candidates responses to questions regarding develop- mental influences, self-esteem, work history and work-related values and attitudes. Customer Service Designed to predict the likeli- 20 Orientation hood that candidates will show persistent enthusiasm in customer interaction, apology definitely for inconveniences to customers, be patient with customers, tolerate rude customers calmly, and search for information or products for customers.
  • Optional Self-Confidence This component references: be- 7 lief in one's own abilities and skills and a tendency to feel competent in several areas.
  • Table Three may be used for customer service jobs involving sales: TABLE THREE
  • Customer Service Solution Sales Positions Solution Component Definition Items Screening 9-15 Minutes Required Educational and Measures potential for success in 15 Work-Related customer service jobs. Scores on Experience Education and Work-Related Experience are derived from candidates responses to questions regarding develop- mental influences, self-esteem, work history and work-related values and attitudes. Customer This component is designed to 20 Service predict the likelihood that Orientation candidates will show persistent enthusiasm in customer inter- action, apology whoever for inconveniences to customers, be patient with customers, tolerate rude customers calmly, and search for information or products for customers.
  • Optional Sales Potential Designed to predict the likeli- 23 hood that candidates will suggest or show alternative solutions based on customer needs, direct conversation toward a commitment/order/sale, show confidence even after a hard refusal/rejection, and strive to close a transaction every time.
  • Selection 15-27 Minutes Required Sales Potential Designed to predict the likeli- 60 hood that candidates will suggest or show alternative solutions based on customer needs, direct conversation toward a commitment/order/sale, show confidence even after a hard refusal/rejection, and strive to close a transaction every time.
  • Customer Focus Designed to predict the likeli- 32 hood that candidates will show persistent enthusiasm in customer interaction, apologize knowingly for inconveniences to customers, be patient with customers, tolerate rude customers calmly, and search for information or products for customers.
  • Optional Learning Ability This component measures the 54 tendency to efficiently and ef- (12 fectively use numerical and minute analytical reasoning. This com- timer) petency is characterized by the ability to learn work-related tasks, processes, and policies.
  • Table Four may be used for customer service jobs in a call center: TABLE FOUR Customer Service Solution: Call Center Positions Solution Component Definition Items Screening 9-11 minutes Required Educational and Measures potential for success in 15 Work-Related customer service jobs. Scores on Experience Education and Work-Related Experience are derived from candidates responses to questions regarding develop- mental influences, self-esteem, work history and work-related values and attitudes. Customer Service Designed to predict the likeli- 20 Orientation hood that candidates will show persistent enthusiasm in customer interaction, apology knowingly for inconveniences to customers, be patient with customers, tolerate rude customers calmly, and search for information or products for customers.
  • Optional Self-Confidence This component references: be- 7 lief in one's own abilities and skills and a tendency to feel competent in several areas.
  • This component is designed to 32 predict the likelihood that candidates will show persistent enthusiasm in customer inter- action, apologize hereby for inconveniences to customers, be patient with customers, tolerate rude customers calmly, and search for information or products for customers.
  • Conscientiousness This component is designed to 65 predict the likelihood that candidates will follow company policies exactly, work in an organized manner, return from meals and breaks in the allotted time, and keep working, even when coworkers are not working.
  • Working with This component is designed to 30 Information predict success in customer (15 service call-center jobs by minute assessing a candidate's ability timer) to retrieve information and use it in order to solve problems.
  • Table Five may be used for customer service jobs in a call center involving sales: TABLE FIVE Customer Service Solution: Call Center Sales Positions Solution Component Definition Items Screening 9-15 Minutes Required Educational and Measures potential for success in 15 Work-Related customer service jobs. Scores on Experience Education and Work-Related Experience are derived from candidates' responses to questions regarding develop- mental influences, self-esteem, work history and work-related values and attitudes. Customer Designed to predict the likeli- 20 Service hood that candidates will show Orientation persistent enthusiasm in customer interaction, apology hereby for inconveniences to customers, be patient with customers, tolerate rude customers calmly, and search for information or products for customers.
  • Optional Sales Potential Designed to predict the likeli- 23 hood that candidates will suggest or show alternative solutions based on customer needs, direct conversation toward a commitment/order/sale, show confidence even after a hard refusal/rejection, and strive to close a transaction every time.
  • Selection 30 Minutes Required Sales Focus Designed to predict the likeli- 60 hood that candidates will suggest or show alternative solutions based on customer needs, direct conversation toward a commitment/order/sale, show confidence even after a hard refusal/rejection, and strive to close a transaction every time.
  • Customer Focus Designed to predict the likeli- 32 hood that candidates will show persistent enthusiasm in customer interaction, apologize knowingly for inconveniences to customers, be patient with customers, tolerate rude customers calmly, and search for information or products for customers.
  • Working with This component is designed to 30 Information predict success in customer (15 service call-center jobs by minute assessing a candidate's ability timer) to retrieve information and use it in order to solve problems.
  • Table Six may be used for jobs in sales: TABLE SIX Sales Solutions Solution Component Definition Items Screening 10-14 minutes Required Educational Measures potential for success in 15 and Work- customer service jobs. Scores on Related Education and Work-Related Experience Experience are derived from candidates responses to questions regarding developmental influences, self-esteem, work history and work- related values and attitudes. Sales Potential Designed to predict the likelihood 23 that candidates will suggest or show alternative solutions based on customer needs, direct conversation toward a commitment/order/sale, show confidence even after a hard refusal/rejection, and strive to close a transaction every time.
  • Optional Customer Designed to predict the likelihood 20 Service that candidates will show persistent Orientation enthusiasm in customer interaction, apologize hereby for incon- veniences to customers, be patient with customers, tolerate rude customers calmly, and search for in- formation or products for customers. Selection 10-25-40 Minutes Required Sales Focus Designed to predict the likelihood 60 that candidates will suggest or show alternative solutions based on customer needs, direct conversation toward a commitment/order/sale, show confidence even after a hard refusal/rejection, and strive to close a transaction every time.
  • Table Seven may be used for supervisory jobs: TABLE SEVEN Supervisory Solutions Solution Component Definition Items Screening 10-20 Minutes Required Supervisory Measures potential for supervisory 10 Potential success across industry type and functional area. Scores on Supervisory Potential are derived from candidates' responses to questions regarding academic and social background, and aspirations concerning work. Judgment Measures potential for making good 10 judgments about how to effectively respond to work situations. Scores on Judgment are derived from candidates' responses to questions regarding situations one would likely encounter as a manager/ supervisor. Optional Leadership/ Measures potential for success as a 19 Coaching supervisor. This is done by having Teamwork/ applicants' make judgments about Interpersonal the most effective teamwork and Skills leadership behaviors in specific work situations.
  • Scores are determined by comparing their response profiles to the profiles of supervisors who are known to be successful. Selection 22-37-52 Mins Required Business Measures the candidate's thinking 28 Leadership styles. High scorers are likely to have or learn good planning and organizing skills, be innovative, consider issues from multiple perspectives, and create strategies to build their business. Required Leadership Measures the candidate's desire for 23 Motivation achievement, drive, initiative, energy level, willingness to take charge, and persistence. High scorers are likely to be highly motivated to succeed and to set challenging goals for themselves and others. Self- Measures the candidate's ability to 32 Leadership control emotions, act with integrity, take responsibility for actions, and tolerate stress. High scorers are also likely to have a positive attitude, be optimistic about the future, and demonstrate high levels of professionalism.
  • Interpersonal Measures the candidate's 30 Leadership interpersonal characteristics. High scorers are likely to persuade and influence others, gain commitment, and build effective interpersonal relationships. They also have potential to develop skills in the areas of employee relations, coaching, motivating, and leading a team.
  • Optional Decision Measures the tendency to efficiently 10 Making/ and effectively use numerical and Problem analytical reasoning. This Solving competency is characterized by the ability to solve complex problems, and make reasoned decisions.
  • Table Eight may be used for professional jobs: TABLE EIGHT Professional Solutions Solution Component Definition Items Screening 7 - Minutes Required Dependa- This competency is characterized by: a 40 bility willingness to behave in expected and agree upon ways; following through on assignments and commitments; keep promises; and accept the consequences of one's own actions. Interpersonal This competency is indexed by a Skills tendency to be pleasant, cooperative, and helpful when working with others, as well as flexible in conflict resolution situations. Self-Control This competency is characterized by the ability to: stay calm and collected when confronted with adversity, frustration, or other difficult situations; and avoid defensive reactions or hurt feelings as a result of others' comments. Energy This competency is characterized by a preference to stay busy, active, and avoid inactive events or situations.
  • Table Nine may be used for managerial jobs: TABLE NINE Managerial Solutions Solution Component Definition Items Screening 10-20 Minutes Required Management Measures potential for managerial 10 Potential success across industry type and functional area. Scores on Management Potential are derived from candidates' responses to questions regarding academic and social background, and aspirations concerning work. Judgment Measures potential for making good 10 judgments about how to effectively respond to work situations. Scores on Judgment are derived from candidates' responses to questions regarding situations one would likely encounter as a manager/supervisor. Optional Self- This component references: belief in 10 Confidence one's own abilities and skills and a tendency to feel competent in several areas. Decision Measures potential for success as a Making manager. This is done by having applicants' make judgments about the most effective decisions in specific work situations.
  • Interpersonal Measures the candidate's 41 Leadership interpersonal characteristics. High scorers are likely to persuade and influence others, gain commitment, and build effective interpersonal relationships. They also have potential to develop skills in the areas of employee relations, coaching, motivating, and leading a team.
  • Optional Decision Measures the tendency to efficiently 10 Making/ and effectively use numerical and Problem analytical reasoning. This competency Solving is characterized by the ability to solve complex problems, and make reasoned decisions.
  • Table Ten may be used for technical/professional jobs: TABLE TEN Technical-Professional Solutions Solution Component Definition Items Screening 8 Minutes Required Dependa- This competency is characterized by: a 40 bility willingness to behave in expected and agree upon ways; following through on assignments and commitments; keeping promises; and accepting the consequences of one's own actions. Interpersonal This competency is indexed by a Skills tendency to be pleasant, cooperative, and helpful when working with others, as well as flexible in conflict resolution situations. Self-Control This competency is characterized by the ability to: stay calm and collected when confronted with adversity, frustration, or other difficult situations; and avoid defensive reactions or hurt feelings as a result of others' comments.
  • High scorers are likely to persuade and influence others, gain commitment, and build effective interpersonal relationships. They also have potential to develop skills in the areas of employee relations, coaching, motivating, and leading a team.
  • Decision Measures the tendency to efficiently 10 Making/ and effectively use numerical and Problem analytical reasoning. This competency Solving is characterized by the ability to solve complex problems, and make reasoned decisions.
  • Optional Communi- Measures the tendency to efficiently 10 cation and effectively use verbal reasoning. This competency is characterized by the ability to verbally explain complex information to others.
  • Table Eleven may be used for executive positions: TABLE ELEVEN Executive Solutions Solution Component Definition Items Screening 20 Minutes Required Executive Measures potential for success in 53 Potential high-level organizational positions across industry type and functional area. Scores on Executive Potential are derived from candidates' responses to questions regarding work background, accomplishments, and career aspirations. Selection 35-50 Minutes Required Business Measures the candidate's thinking 32 Leadership styles. High scorers are likely to have or learn good planning and organizing skills, be innovative, consider issues from multiple perspectives, and create strategies to build their business. Leadership Measures the candidate's desire for 35 Motivation achievement, drive, initiative, energy level, willingness to take charge, and persistence. High scorers are likely to be highly motivated to succeed and to set challenging goals for themselves and others.
  • Self- Measures the candidate's ability to 34 Leadership control emotions, act with integrity, take responsibility for actions, and tolerate stress. High scorers are also likely to have a positive attitude, be optimistic about the future, and demonstrate high levels of professionalism. Interpersonal Measures the candidate's 41 Leadership interpersonal characteristics. High scorers are likely to persuade and influence others, gain commitment, and build effective interpersonal relationships. They also have potential to develop skills in the areas of employee relations, coaching, motivating, and leading a team. Decision Measures the tendency to efficiently 10 Making/ and effectively use numerical and Problem analytical reasoning. This competency Solving is characterized by the ability to solve complex problems, and make reasoned decisions. Optional Communi- Measures the tendency to efficiently 10 cation and effectively use verbal reasoning. This competency is characterized by the ability to verbally explain complex information to others.
  • Table Twelve may be used for jobs involving campus recruiting: TABLE TWELVE Campus recruiting Solutions Solution Component Definition Items Screening 12 Minutes Required Supervisory Measures potential for supervisory 26 Potential success across industry type and functional area. Scores on Supervisory Potential are derived from candidates' responses to questions regarding academic and social background, and aspirations concerning work. Judgment Measures potential for making good judgments about how to effectively respond to work situations. Scores on Judgment are derived from candidates' responses to questions regarding situations one would likely encounter as a manager/supervisor. Management Measures potential for managerial Potential success across industry type and functional area. Scores on Management Potential are derived from candidates' responses to questions regarding academic and social background, and aspirations concerning work.
  • Optional Decision Measures the tendency to efficiently 10 Making/ and effectively use numerical and Problem analytical reasoning. This Solving competency is characterized by the ability to solve complex problems, and make reasoned decisions.
  • Optional Communi- Measures the tendency to efficiently 10 cation and effectively use verbal reasoning. This competency is characterized by the ability to verbally explain complex information to others.
  • Table Thirteen may be used for a selection solution for a job involving communication: TABLE THIRTEEN Communication Solution Solution Component Definition Items Selection 37 Minutes Required Listening Measure of the tendency to listen to 73 Orientation and understand others' perspectives, to care for others, to accept and respect the individual differences of people, and to be open both to multiple ideas and to using alternative modes of thinking.
  • Table Fourteen may be used for a selection solution for a job involving financial services jobs referred to series six/seven: TABLE FOURTEEN Series Six/Seven Success Solution Solution Component Definition Items Selection 36 Minutes Required Problem Measures the ability to analyze and 20 Solving evaluate information. Scores on Problem Solving are derived from candidates' responses to mathematical and analytical reasoning items, requiring candidates to respond to facts and figures presented in various formats. Verbal Measures verbal reasoning skills and Reasoning/ critical thinking/reasoning skills. Critical Scores on Verbal Reasoning Ability Thinking are derived from candidates' responses to analogies and involves making inferences from information provided in the form of brief passages
  • Table Fifteen may be used for a selection solution for a job requiring information technology aptitude: TABLE FIFTEEN Information Technology Aptitude Solution Solution Component Definition Items Selection 18 Minutes Required Critical Measure reasoning and critical thinking 58 Thinking skills. Scores on Critical Thinking are derived from candidates' responses to information provided in the form of brief passages. Problem Measure the ability to analyze and Solving evaluate information. Scores on Problem Solving are derived from candidates' responses to mathematical and analytical reasoning items, requiring candidates to respond to facts and figures presented in various scenarios. Communi- Measures the ability to efficiently use cation verbal information. Scores on Communication are derived from candidates' ability to identify synonyms. Spatial Measure the ability to visually Ability manipulate objects. Scores on Spatial Ability are derived from candidates' ability to correctly identify the number of blocks in progressively difficult figures.
  • FIG. 13 illustrates a human capital management life-cycle. Measurement and data 1301 is initially used in the context of recruiting 1302 . For recruiting 1302 , screening, selection, and interview solutions measure applicants' competencies and predict on-the-job performance and thus contribution to business outcomes.
  • data about potential can be weighed against performance data to ensure that high potential employees who are on difficult assignments where they are structurally constrained from succeeding are not underpaid by pure focus on performance.
  • structural constraints may include business environment, poor staff, unreliable equipment, etc.
  • the system can be used to enhance the validity of employee performance evaluation.
  • a company may test current employees in order to design executive training programs addressing each individual's strengths and weaknesses. Or, for employees that took a test and were hired despite weaknesses, the data can be used to structure appropriate training.
  • data on employees may be collected in the process of organization mergers to assist planning for retrenchment or change. Also, by measuring competencies and mapping them between roles, it is possible to assess the potential that an individual may have for a role other than the job they are currently holding, such as for a promotion or a transfer to another area.

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Abstract

A system and method for testing and/or evaluating employees or potential employees is disclosed. A computer arranges a plurality of applicants in a stack ranked table. The table may rank or re-rank applicants against each other, from best to worst, after successive screening, selecting, and/or interviewing stages for a particular job. Performance evaluations of hired workers may be fed back to the computer for adjusting the system and method. Competencies shown to be predictive of successful performance of a given type of job are tested for at various stages in an online testing system.

Description

  • This application claims the benefit of U.S. Provisional Patent Application No. 60/211,044, filed Jun. 12, 2000.[0001]
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 provides a block diagram of an exemplary system in accordance with the present invention. [0002]
  • FIG. 2 illustrates a process for testing and evaluating job applicants in accordance with an embodiment of the present invention. [0003]
  • FIG. 3 depicts a hiring procedure in accordance with one embodiment of the invention. [0004]
  • FIG. 4 is a block diagram of a process employing feedback. [0005]
  • FIG. 5 diagrams an online system in accordance with one embodiment of the invention. [0006]
  • FIG. 6 shows an example of a web-based presentation for a screening solution. [0007]
  • FIG. 7 shows an example of a stack ranked table. [0008]
  • FIG. 8 shows an example of a screening solution question presented to an applicant taking a screening solution test over the Internet. [0009]
  • FIG. 9 shows an example of a structured interview guide for use in an interview solution. [0010]
  • FIG. 10 illustrates procedural steps that may be followed in a web-based applicant system according to an embodiment of the present invention. [0011]
  • FIG. 11 illustrates procedural steps that may be followed in a web-based selection solution according to an embodiment of the present invention. [0012]
  • FIG. 12 illustrates procedural steps that may be followed by an employer according to an embodiment of the present invention. [0013]
  • FIG. 13 illustrates a human capital management life-cycle. [0014]
  • DETAILED DESCRIPTION
  • A system for testing a job applicant provides a computerized stack ranking of multiple applicants, predictive of the comparative levels of successful job performance. The predictive stack ranking may be used as a dynamic interactive filter with a pool of applicants over the course of the evaluation or employment process. The system may utilize a communications network to communicate between an applicant terminal and a system server. [0015]
  • The system may be used for example for screening, selecting, retaining, assigning, or analyzing the job applicant. The job applicant can for example be a new job applicant, an employee seeking to retain a job, an employee seeking a different job in the same organization, or an employee being evaluated for retention, re-assignment, or promotion. Applicants may or may not know they are being evaluated. [0016]
  • Once an applicant becomes an employee, the system may collect data regarding the employee for use in a feedback loop informing the online hiring process and improving the accuracy of the predictive stack ranking. For example, the data may indicate the employer's rating of the employee's actual job performance. Such a rating can be cross-checked against the answers that the employee gave during the application process. The cross-checking can be used as feedback to refine the questions and evaluation criteria used at each stage of the hiring process. For example, the cross-checking may be analyzed to select from among many questions a small subset having high predictive value. The small subset can then be used in a quick initial screening stage. Or, the small subset can be given greater weight than other questions in a computerized stack ranking of candidates. [0017]
  • FIG. 1 provides a block diagram of an exemplary system in accordance with the present invention. A job applicant can use [0018] applicant terminal 102 to communicate over network 104 with system server 106. Applicant terminal 102 may for example be a telephone handset, a personal computer, a workstation, a handheld wireless device such as those marketed under the trademarks PALM or HANDSPRING, or a Wireless Application Protocol enabled device such as a mobile phone. Network 104 may for example be the Internet, the World Wide Web, a wide area network, a local area network, a telephone network, a wireless communication network, a combination thereof, or any other link capable of carrying communications between an applicant terminal and a server.
  • [0019] System server 106 employs a testing computer program 108 and has access to a scoring database 110. System server 106 communicates with applicant terminal 102 in accordance with instructions from testing computer program 108.
  • [0020] System server 106 may communicate with employer server 112 over network 104 or over direct link 114. System server 106 is shown as a unitary server, but may be a distributed computing platform.
  • An applicant terminal may be remote from, or co-located with, [0021] system server 106 and/or employer server 112. For example, applicant terminal 102 may be located at a job applicant's home, applicant terminal 116 may be located at a job fair or employment office, and applicant terminal 120 may be located at an employer's location.
  • [0022] Partner server 121 may be linked to network 104 and system server 106 to facilitate integration of a business partner seeking to participate in the system of FIG. 1.
  • [0023] System server 106 may pose questions to a job applicant located at an applicant terminal, receive responses from the job applicant, and score the answers in accordance with scoring database 110. The scoring may take place in real time, i.e., while the applicant is still online, and may be reported in the form of a comparative stack ranking of multiple applicants. The stack ranking may be delivered from system server 106, over either network 104 or direct link 114, to employer server 112.
  • Scoring of each answer by [0024] system server 106 may be instant, i.e., before the next question is answered. Thus, adaptive testing techniques may be implemented over network 104. For example, the answers given by an applicant at applicant terminal 102 to questions propounded early in a test may determine which questions are propounded by system server 106 to the applicant later in the same test. In addition, if an applicant at terminal 102 provides an unacceptable answer to a disqualifying “knock-out” question, server 106 may immediately terminate the test.
  • These same adaptive testing principles may be applied to a software program used to support a real time interview, either in person or over a communications network. For example, an employer conducting an oral interview in person or over a telephone can enter a candidate's oral answer into [0025] employer terminal 124, which then communicates the answer to system server 106, which in turn suggests via employer terminal 124 the next question for the employer to ask the interviewee.
  • The system may test an online applicant for any competency desired, in any sequence. The tested competencies may be abilities, traits, knowledge, skills, etc., that have been proven relevant to and predictive of successful job performance. By way of example and not limitation, the following competencies may be tested: [0026]
  • 1. dependability [0027]
  • 2. agreeableness [0028]
  • 3. critical thinking [0029]
  • 4. problem solving ability [0030]
  • 5. talkativeness [0031]
  • 6. assertiveness [0032]
  • 7. gregariousness [0033]
  • 8. persuasiveness [0034]
  • 9. achievement [0035]
  • 10. education [0036]
  • 11. experience [0037]
  • 12. customer service orientation [0038]
  • 13. customer focus [0039]
  • 14. conscientiousness [0040]
  • 15. self-confidence [0041]
  • 16. motivation [0042]
  • 17. revenue focus [0043]
  • 18. cognitive ability [0044]
  • 19. leadership [0045]
  • 20. decision making [0046]
  • 21. flexibility [0047]
  • 22. commitment [0048]
  • 23. learning ability [0049]
  • 24. dedication [0050]
  • 25. tenacity [0051]
  • 26. number of jobs held [0052]
  • 27. length of time in job(s) [0053]
  • 28. working with information [0054]
  • 29. supervisory potential [0055]
  • 30. judgment [0056]
  • 31. leadership [0057]
  • 32. coaching skills [0058]
  • 33. teamwork [0059]
  • 34. interpersonal skills [0060]
  • 35. business leadership [0061]
  • 36. leadership motivation [0062]
  • 37. self-leadership [0063]
  • 38. interpersonal leadership [0064]
  • 39. communication skills [0065]
  • 40. management potential [0066]
  • 41. likelihood of retention [0067]
  • 42. self-control [0068]
  • 43. energy [0069]
  • 44. executive potential [0070]
  • 45. listening orientation [0071]
  • 46. language skills (English, etc.) [0072]
  • 47. verbal reasoning [0073]
  • 48. spatial ability [0074]
  • 49. interest [0075]
  • 50. motivation [0076]
  • Typically, [0077] system server 106 tests for certain ones of the competencies that have been proven to be predictive of successful performance of the type of job for which the applicant is being considered. The results of the testing are tabulated in a stack ranked table. The stack ranked table may rank a number of applicants against each other and list them in order, from first to last. The table may also present other information for each applicant. The other information may include, by way of example and not limitation:
  • 1. Name [0078]
  • 2. Identifying number (e.g. social security number). [0079]
  • 3. Score achieved at various stages for various competencies. [0080]
  • 4. Recommendation (or not) to continue the hiring process beyond each stage [0081]
  • 5. Link to application information (e.g. address, resume details) [0082]
  • 6. Contact information (phone number, e-mail address, mailing address, etc.) [0083]
  • 7. Date of application [0084]
  • 8. Success or failure in complying with knockout requirements for the job [0085]
  • 9. Screening solution scores, presented as percentiles [0086]
  • 10. A calculated recommendation to proceed or not to proceed with the applicant [0087]
  • 11. Results (by competency) of the selection solution [0088]
  • 12. Link to allow manual entry of the test answers if not done on computer directly by the applicant [0089]
  • 13. A calculated recommendation to hire or not hire based on a weighted overall score of selection competencies (or other factors the hiring company wishes to use and that are approved as statistically valid and legally defensible) [0090]
  • 14. Additional columns for storage of data from a structured behavioral interview [0091]
  • 15. Additional columns for storage of data from other decision-making processes such as drug testing, reference checks, or medical exams. [0092]
  • A process for testing and evaluating job applicants may be described with reference to FIG. 2. Generally, applicant testing [0093] 201 includes providing a test to a job applicant and scoring the applicant's answers. The test may be administered online or it may be administered manually off-line. Scores are entered into a system for calculating a stack ranked table. Predictive stack ranking 202 generally includes ranking a job applicant against other job applicants in order from first to last or other comparative ranking. The other job applicants may be current job applicants, past job applicants, or fictional job applicants.
  • FIG. 3 depicts a hiring procedure in accordance with one embodiment of the invention. [0094] Announcement 302 may be an online job announcement such as a web page with an “apply now” hyperlink icon. The web page may reside on an employer's website or an employment agency website, for example. Or, an online job announcement may be a recorded announcement on a menu-driven telephone voice processing system. Alternatively, announcement 302 may be an offline job announcement such as a newspaper advertisement.
  • In response to [0095] announcement 302, an interested job applicant requests administration of screening test 304. Screening test 304 may be remotely administered and scored online, with the scores being automatically provided to predictive stack ranking 306. Alternatively, screening test 304 may be administered manually with paper and pencil, and then graded by hand or machine, with the scores being provided to predictive stack ranking 306. The predictive stack ranking may for example be constructed by system server 106 or employer server 112.
  • Predictive stack ranking [0096] 306 totals the graded answers according to particular competencies known to be relevant to successful job performance. Predictive stack ranking 306 may be administered by a computer processor located at system server 106, for example. Predictive stack ranking 306 may give different weight to different questions, and may at any stage immediately disqualify an applicant providing an unacceptable answer to a “knock-out” question. Predictive stack ranking 306 may rank the applicant in order against other job applicants in a table. Predictive stack ranking 306 may be used to decide which applicants to invite for the next stage, selection test 308.
  • [0097] Selection test 308 is preferably conducted under supervised conditions. For example, selection test 308 may be administered in person. An in-person test may take place at a job fair, an employer's location, a job site, or an employment agency. An in-person test may include verification of the job applicant's identity, such as by examination of a photo identification document produced by a test-taker. Selection test 308 may be administered online or manually. Supervised conditions typically include observation of the test-taker during administration of the test. The answers to selection test 308 are graded and the results are incorporated in predictive stack ranking 306.
  • Predictive stack ranking [0098] 306 may then update a previously created entry for the applicant and rank or re-rank the applicant in order against other job applicants. After this is accomplished, the highest ranking applicants may be invited for interview 310.
  • [0099] Interview 310 may be structured or unstructured, online or in person. If interview 310 is structured, a program leads the interviewer through the interview by suggesting questions one at a time. The program may be a list of questions written on paper or it may be a computer program resident for example in system server 106. The program suggests questions that are predetermined to be valid, i.e., proven to be associated with successful job performance and legally permitted. The interviewer can input the answers and/or a score for the answers, either after each answer or at the conclusion of the interview. This can be done via employer terminal 124, for example.
  • [0100] Interview 310 results in an interview score being provided to predictive stack ranking 306. Predictive stack ranking 306 is revised to reflect the interview score. In particular, the relative rank of the job applicants is reassessed.
  • FIG. 4 is a block diagram of a process employing feedback. [0101] Test design 402 is initially performed using industry-accepted standards. Test administration 404 tests and scores job applicants and/or incumbents. Employee performance evaluation 406 measures actual job performance of the applicant or incumbent after holding the job for a period of time. This information is fed back to test design 402 and/or test administration 404. Test design 402 may be revised to delete questions which were not predictive of successful job performance. This can be done for example by deleting questions whose answers bore no relation to performance evaluation 406 for a statistically valid sample. Test administration 404 may be revised by adjusting the weight given to certain questions or answers that showed an especially strong correlation to employee performance evaluation 406. For example, if test administration 404 is associated with predictive stack ranking 306, feedback from employee performance evaluation 406 may help determine how various job applicants are comparatively ranked against each other.
  • FIG. 5 diagrams an online computer based system [0102] 500 in accordance with one embodiment of the invention. Box 502 represents a job vacancy with a requirement for an online screening and selection solution. The vacancy can come to the attention of a potential job applicant in a number of ways.
  • For example, [0103] box 504 represents an online application via a hiring company's own website. A company offering a job may post a vacancy announcement on the company's website and invite job seekers to apply by clicking on an icon labeled “apply here” or the like. Box 506 represents a similar posting on an online job board. Box 508 represents candidates given a Uniform Resource Locator (URL) directly by the company. This may occur when the company offering a job identifies a potential candidate. Box 510 represents a media advertisement including a URL for a job. Thus, job seekers observing the advertisement can direct their browsers to the indicated URL.
  • At job fair [0104] 512, job seekers may be provided a URL associated with the company or the particular vacancy. Paper-and-pencil measures could also be used at job fairs and entered into the system. A computer terminal may be provided for use of job seekers at job fair 512, enabling job seekers to participate in the online system. Box 514 represents an executive search via a recruiter network. Job seekers relevant to the search are identified in recruitment firm applicant database 516. Database 516 can link to a URL associated with the job.
  • Preferably, no matter how a potential applicant becomes aware of or identified for a job opening in system [0105] 500, the potential applicant is considered at decision 520. Decision 520 asks whether applicant has completed the required screening solution 524. If not, the applicant at box 522 is given via e-mail, mail, or in person, a URL for assessment. For example, system 500 may send an e-mail message to a potential applicant, the e-mail message inviting the potential applicant to apply for vacancy 502 by directing a browser to a screening solution URL provided in the e-mail message. Alternatively, when a potential applicant is visiting a website at which decision 520 determines that the required screening solution has not been completed, the website host can provide a link to a web page identified by the screening solution URL. Decision 520 may be based on a potential applicant's name, e-mail address, and/or other identifying information.
  • [0106] Screening solution 524 is administered via the Internet and is hosted at the screening solution URL mentioned above. Screening solution 524 asks screening questions to ascertain if the applicant has the basic qualifications to do the job. These are based on questions typically asked by recruiters but which are statistically validated over time to ensure they are legally defensible and predictive. The questions may include a combination of biodata and personality measures. They may include self-assessments of skill levels appropriate to the job requirements. Screening solution 524 requires applicants to transmit elicited information over the Internet. A possible example of a web-based presentation for screening solution 524 is illustrated in FIG. 6. Screen shot 600 shows a portion of the presentation.
  • Once completed, [0107] screening solution 524 provides applicant feedback 540 and conveys applicant details and screening scores to stack ranked table of applicants 530. Applicant feedback 540 may provide a message to the online applicant indicating that the screening solution is complete, that the applicant has passed or failed the screening stage, and that the applicant may or may not be contacted in due course. Other information may also be provided to the applicant in the feedback pages, like a realistic job preview, recruiter phone number, scheduling information, etc.
  • Once an applicant has completed the screening solution, system [0108] 500 ranks the applicant in comparative order against other applicants in stack ranked table of applicants 530. A certain number or percentage of applicants in table 530 will be chosen for further consideration. For example, the applicants ranking among the top five of all applicants ranked in table 530 may be chosen for advancement in the system at this juncture. Information identifying the chosen applicants will be included on a “short list” as indicated by box 536.
  • The short list chosen at [0109] box 536 is transmitted to selection solution 538, at which the advancing applicants are invited to answer selection questions. Selection solution 538 asks additional questions and requires an advancing applicant to input answers. Preferably, the applicant completes selection solution 538 while sitting at a terminal located at one of the company's locations. The terminal communicates over the Internet with a website set up to administer the selection solution.
  • At the conclusion of [0110] selection solution 538, applicant feedback 540 is provided from the website to the applicant, and applicant details and scores 541 are incorporated in stack ranked table 530. Feedback 540 may optionally include a sophisticated report on the applicant's strengths and weakness. The applicant may then be directed to an appropriate web page chosen by the hiring company. One page may indicated successful completion and a second page may indicate failure. The appropriate web page may suggest other openings appropriate to the applicant's test responses and may provide hyperlinks the applicant can use to initiate the application process for these other openings.
  • Once stack ranked table [0111] 530 re-ranks the applicants as a result of selection solution 538, some applicants are invited to participate in interview solution 542. For example, the top three applicants as ranked by table 530 after selection solution 538 may be invited for an in-person interview. Because the selection solution is preferably in instant communication with stack ranked table 530, the interview invitation may be extended immediately at the conclusion of the selection solution.
  • [0112] Interview solution 542 is preferably a structured interview, with questions provided via the Internet to the interviewer at the company's location. The interviewer reads the provided questions and reports a score over the Internet from the company's location for incorporation in stack ranked table 530. Benchmark performance anchors may assist the interviewer in grading the applicant's responses.
  • [0113] Interview solution 542 can be designed according two exemplary models. In a first model, an employer is provided with standard interview guides for several job types as well as the competency templates for these types so that the employer can build variations to meet specific needs. In a second model, an employer can build new interview guides and new competency templates. In the second model, the employer has access to the full array of work-related competencies and associated questions in a comprehensive question bank.
  • In ranking applicants, stack ranked table [0114] 530 may consider a combination of different biographical, personality, behavioral, and other appropriate information and competencies. In addition to the comparative ranking, table 530 may indicate for each applicant a yes/no recommendation, a percentage likelihood of successful job performance, biographical information not used for evaluative purposes, and so forth.
  • Stack ranked table [0115] 530 may be developed by grading the various solution stages with a computer implementing the following algorithm. First, search for disqualifying answers to “knock-out” questions. Second, give points for answers matching those of the previously hired candidates who achieved a successful performance evaluation. Third, deduct points for answers matching those of the previously hired candidates who received an unsuccessful performance rating. Fourth, multiply the added or subtracted points by any weighting assigned each question. Fifth, sum the points for all questions related to a given competency. Sixth, compare the summed points for each competency to norms of either the job-holders in the company or a wider population. Seventh, predict performance of the applicant as a worker in the job, based on the business outcomes identified by the hiring company and the competencies that contribute to those outcomes.
  • A final selection is made based on stack ranked table [0116] 530. Preferably, the selection is transmitted over the Internet to the company, enabling the company to make an offer to the selected applicant(s). For example, if there is only one opening, an offer may be extended to the applicant ranked highest by stack ranked table 530. If the applicant accepts the offer, the applicant is employed by the company. If the applicant declines, the next highest ranked applicant in stack ranked table 530 is offered the job. If a plural number of openings exist, that number of applicants may be selected off the top of stack ranked table 530 and offered the job. If one of the applicants declines, the next highest ranked applicant in stack ranked table 530 is offered the job. Data from stack ranked table 530 is forwarded to data warehouse 534.
  • The performance of successful applicants is monitored during their employment. At [0117] box 550, performance data for successful applicants are collected at a later date, and sent to data warehouse 534.
  • Data collected at [0118] data warehouse 534 are used for research and development and for reporting purposes. For example, functions enabled by storing comprehensive data generated by system 500 may include:
  • a. Storage of question level responses from applicants for jobs. This can be used for re-checking of applicant information (auditing etc.) and for research to develop new solutions and questions. [0119]
  • b. Reporting on Equal Employment Opportunity Commission requirements. Data on ethnicity etc. can be stored to enable an employer to comply with reporting requirements to government agencies. [0120]
  • c. Source of data for designing new solutions including data on the nature of the job and the competencies that are required by the role (job analysis). This data is collected using online assessments. [0121]
  • d. Source of data for statistical research on correlation between the solutions and their predicted outcomes for applicants, and the actual outcomes for employees who were hired (validation studies). [0122]
  • e. Design of solutions other than recruitment related solutions. [0123]
  • f. Reporting of usage volumes for billing and financing accounting purposes. [0124]
  • Because system [0125] 500 preferably uses instant communications, adaptive testing techniques may be implemented online. An applicant's failure to overcome hurdles in a given solution will deliver a different path through the solution than that of a successful applicant. The degree of advancement of a given applicant through system 500 may result in different charges to the company from a solutions provider. For example, a solutions provider that hosts a website supporting screening solution 524, selection solution 538, and interview solution 542 may charge the hiring company the following amounts: one dollar for every applicant completing only the screening solution, five dollars for every applicant advancing only to the end of the selection solution, ten dollars for every applicant rejected after the interview solution, twenty dollars for every applicant offered a job, and fifty dollars for every applicant accepting an offer.
  • In practice, any of the various stages ([0126] screening solution 524, selection solution 538, and interview solution 542) may be skipped, re-ordered, combined with other stages, or eliminated. Or, a short telephone interview may be structured early in the process to quickly screen applicants.
  • In a preferred embodiment, the questions to be asked at the various stages are selected for a particular type of job being offered in accordance with a proven relationship with desired business outcomes. Business outcomes can for example include: level of sales, customer satisfaction, quality measures such as fault rates, retention and tenure of employment, time keeping, learning ability, progression to more senior roles over time, and supervisor ratings of behavioral success. The particular type of job is defined in conjunction with the U.S. Department of Labor “O*NET” classification system. Some types of jobs might include customer service, technical, professional, or managerial. Various competencies are determined to be associated with desired business outcomes for a given type of job. These competencies are tested for at various solution stages with appropriate questions. [0127]
  • The appropriate competencies, questions, scoring, weighting, and ranking factors for a new job can be designed from historical tests for existing jobs, by applying statistical techniques and using the gathering of data on the Internet to ensure rapid validation of the new assessment solution. Confirmatory job analysis is used to determine the appropriateness of solutions for a particular job. [0128]
  • FIG. 7 shows an example of a stack ranked table. Computer screen shot [0129] 700 illustrates a sample stack ranked table 730 for a customer service job. Various tabs permit viewing of data generated by each solution stage. Tab 702 reveals data 703 from a screening solution, tab 704 reveals data 705 from a selection solution, tab 706 reveals data 707 from an interview solution, and tab 708 reveals all results. In screen shot 700, tab 708 is selected.
  • Section [0130] 709 of screen shot 700 shows general information about each applicant, including current rank 710, a link 712 to application information (not shown), last name 714, first name 716, and application date 718.
  • Screening [0131] solution data 703 includes an indication 720 of whether each applicant successfully passed the knockout requirements for the job. Data 703 also includes scores on certain competencies such as educational and work related experience 722, customer service orientation 724, and self-confidence 726. Column 728 indicates whether each applicant is recommended to advance beyond the screening stage.
  • [0132] Selection solution data 705 includes scores on certain competencies such as customer focus 732, conscientiousness 734, and problem solving 736. Column 738 indicates whether each applicant is recommended to advance beyond the selection stage.
  • Additional information (not shown) may include columns for storage of data from other decision-making processes such as drug testing, reference checks, or medical exams. [0133]
  • FIG. 8 shows an example of a screening solution question presented to an applicant taking a screening solution test over the Internet. In screen shot [0134] 800, simulated customer contact record 802 is presented to the applicant. The applicant is asked question 804, and is required to click on a circle next to one of the answers. Question 804 may test for a competency in working with information, for example.
  • FIG. 9 shows an example of a structured interview guide for use in an interview solution. As illustrated, the interview guide is being presented online on a computer screen to an interviewer conducting an interview with an applicant. Screen shot [0135] 900 shows interview item 902 for a sample customer service job. The customer service job opening is for a call center position, and revenue focus has been identified as a relevant and predictive competency. Item 902 elicits from the applicant a situation 904, the applicant's behavior 906 in the situation, and the outcome 908 reported by the applicant. The interviewer can grade the applicant's responses to item 902 by marking a score 910 from 1 to 10.
  • FIG. 10 illustrates procedural steps that may be followed in a web-based applicant system according to an embodiment of the present invention. [0136]
  • FIG. 11 illustrates procedural steps that may be followed in a web-based selection solution according to an embodiment of the present invention. For example, these steps may follow those illustrated in FIG. 10. [0137]
  • FIG. 12 illustrates procedural steps that may be followed by an employer according to an embodiment of the present invention. [0138]
  • The following tables provide examples of screening solutions and selection solutions designed for different types of jobs. The tables show components (competencies) shown to be relevant to successful performance of each job type. In the tables, some components are considered required, and others are considered optional. [0139]
  • Table One may be used for entry level and general skill jobs: [0140]
    TABLE ONE
    Entry/General Skilled Solutions
    Solution
    Component Definition Items
    Screening 7-10 Minutes
    Required Educational and Measures potential for success in 15
    Work-Related entry-level jobs across industry
    Experience type and functional area. Scores
    on Education and Work-Related
    Experience are derived from
    candidates' responses to
    questions regarding
    developmental influences, self-
    esteem, work history and work-
    related values and attitudes.
    Self-Confidence This component references: be-  7
    lief in one's own abilities and
    skills and a tendency to feel
    competent in several areas.
    Optional Decision Making/ Measures potential for success in  8
    Flexibility entry level positions. Scores on
    Decision Making and Flexibility
    are derived from candidates'
    responses to questions regarding
    developmental influences, self-
    esteem, work history and work-
    related values and attitudes.
    Selection 23-35 Minutes
    Required Conscientiousness This component is designed to 65
    predict the likelihood that
    candidates will follow company
    policies exactly, work in an
    organized manner, return from
    meals and breaks in the allotted
    time, and keep working, even
    when coworkers are not
    working.
    Retention Measures commitment, 44
    Predictor impulsiveness, responsibility,
    and motivation. It predicts the
    likelihood that a new hire will
    remain on the job for at least
    three months.
    Optional Learning Ability This component measures the 54
    tendency to efficiently and (12 
    effectively use numerical and minute
    analytical reasoning. This timer)
    competency is characterized by
    the ability to learn work-related
    tasks, processes, and policies.
  • Table Two may be used for customer service jobs: [0141]
    TABLE TWO
    Customer Service Solution
    Solution
    Component Definition Items
    Screening 8-10 Minutes
    Required Educational and Measures potential for success in 15
    Work-Related customer service jobs. Scores on
    Experience Education and Work-Related
    Experience are derived from
    candidates responses to
    questions regarding develop-
    mental influences, self-esteem,
    work history and work-related
    values and attitudes.
    Customer Service Designed to predict the likeli- 20
    Orientation hood that candidates will show
    persistent enthusiasm in
    customer interaction, apologize
    sincerely for inconveniences
    to customers, be patient with
    customers, tolerate rude
    customers calmly, and search
    for information or products for
    customers.
    Optional Self-Confidence This component references: be-  7
    lief in one's own abilities and
    skills and a tendency to feel
    competent in several areas.
    Selection 17-29-37 Minutes
    Required Customer Focus Designed to predict the likeli- 32
    hood that candidates will show
    persistent enthusiasm in
    customer interaction, apologize
    sincerely for inconveniences
    to customers, be patient with
    customers, tolerate rude
    customers calmly, and search for
    information or products for
    customers.
    Conscientiousness This component is designed to 65
    predict the likelihood that
    candidates will follow company
    policies exactly, work in an or-
    ganized manner, return from
    meals and breaks in the allotted
    time, and keep working, even
    when coworkers are not
    working.
    Optional Learning Ability This component measures the 54
    tendency to efficiently and ef- (12 
    fectively use numerical and minute
    analytical reasoning. This com- timer)
    petency is characterized by the
    ability to learn work-related
    tasks, processes, and policies.
    Optional Retention Measures commitment, im- 44
    Predictor pulsiveness, responsibility, and
    motivation. It predicts the
    likelihood that a new hire will
    remain on the job for at least
    three months.
  • Table Three may be used for customer service jobs involving sales: [0142]
    TABLE THREE
    Customer Service Solution: Sales Positions
    Solution
    Component Definition Items
    Screening 9-15 Minutes
    Required Educational and Measures potential for success in 15
    Work-Related customer service jobs. Scores on
    Experience Education and Work-Related
    Experience are derived from
    candidates responses to
    questions regarding develop-
    mental influences, self-esteem,
    work history and work-related
    values and attitudes.
    Customer This component is designed to 20
    Service predict the likelihood that
    Orientation candidates will show persistent
    enthusiasm in customer inter-
    action, apologize sincerely for
    inconveniences to customers, be
    patient with customers, tolerate
    rude customers calmly, and
    search for information or
    products for customers.
    Optional Sales Potential Designed to predict the likeli- 23
    hood that candidates will suggest
    or show alternative solutions
    based on customer needs, direct
    conversation toward a
    commitment/order/sale, show
    confidence even after a hard
    refusal/rejection, and strive to
    close a transaction every time.
    Selection 15-27 Minutes
    Required Sales Potential Designed to predict the likeli- 60
    hood that candidates will suggest
    or show alternative solutions
    based on customer needs, direct
    conversation toward a
    commitment/order/sale, show
    confidence even after a hard
    refusal/rejection, and strive to
    close a transaction every time.
    Customer Focus Designed to predict the likeli- 32
    hood that candidates will show
    persistent enthusiasm in
    customer interaction, apologize
    sincerely for inconveniences
    to customers, be patient with
    customers, tolerate rude
    customers calmly, and search for
    information or products for
    customers.
    Optional Learning Ability This component measures the 54
    tendency to efficiently and ef- (12 
    fectively use numerical and minute
    analytical reasoning. This com- timer)
    petency is characterized by the
    ability to learn work-related
    tasks, processes, and policies.
  • Table Four may be used for customer service jobs in a call center: [0143]
    TABLE FOUR
    Customer Service Solution: Call Center Positions
    Solution
    Component Definition Items
    Screening 9-11 minutes
    Required Educational and Measures potential for success in 15
    Work-Related customer service jobs. Scores on
    Experience Education and Work-Related
    Experience are derived from
    candidates responses to
    questions regarding develop-
    mental influences, self-esteem,
    work history and work-related
    values and attitudes.
    Customer Service Designed to predict the likeli- 20
    Orientation hood that candidates will show
    persistent enthusiasm in
    customer interaction, apologize
    sincerely for inconveniences
    to customers, be patient with
    customers, tolerate rude
    customers calmly, and search for
    information or products for
    customers.
    Optional Self-Confidence This component references: be-  7
    lief in one's own abilities and
    skills and a tendency to feel
    competent in several areas.
    Selection 16-31-39 Minutes
    Required Customer Focus This component is designed to 32
    predict the likelihood that
    candidates will show persistent
    enthusiasm in customer inter-
    action, apologize sincerely for
    inconveniences to customers, be
    patient with customers, tolerate
    rude customers calmly, and
    search for information or
    products for customers.
    Conscientiousness This component is designed to 65
    predict the likelihood that
    candidates will follow company
    policies exactly, work in an
    organized manner, return from
    meals and breaks in the allotted
    time, and keep working, even
    when coworkers are not
    working.
    Working with This component is designed to 30
    Information predict success in customer (15 
    service call-center jobs by minute
    assessing a candidate's ability timer)
    to retrieve information and use it
    in order to solve problems.
    Optional Retention Measures commitment, impul- 44
    Predictor siveness, responsibility, and
    motivation. It predicts the
    likelihood that a new hire will
    remain on the job for at least
    three months.
  • Table Five may be used for customer service jobs in a call center involving sales: [0144]
    TABLE FIVE
    Customer Service Solution: Call Center Sales Positions
    Solution
    Component Definition Items
    Screening 9-15 Minutes
    Required Educational and Measures potential for success in 15
    Work-Related customer service jobs. Scores on
    Experience Education and Work-Related
    Experience are derived from
    candidates' responses to
    questions regarding develop-
    mental influences, self-esteem,
    work history and work-related
    values and attitudes.
    Customer Designed to predict the likeli- 20
    Service hood that candidates will show
    Orientation persistent enthusiasm in
    customer interaction, apologize
    sincerely for inconveniences
    to customers, be patient with
    customers, tolerate rude
    customers calmly, and search for
    information or products for
    customers.
    Optional Sales Potential Designed to predict the likeli- 23
    hood that candidates will suggest
    or show alternative solutions
    based on customer needs, direct
    conversation toward a
    commitment/order/sale, show
    confidence even after a hard
    refusal/rejection, and strive to
    close a transaction every time.
    Selection 30 Minutes
    Required Sales Focus Designed to predict the likeli- 60
    hood that candidates will suggest
    or show alternative solutions
    based on customer needs, direct
    conversation toward a
    commitment/order/sale, show
    confidence even after a hard
    refusal/rejection, and strive to
    close a transaction every time.
    Customer Focus Designed to predict the likeli- 32
    hood that candidates will show
    persistent enthusiasm in
    customer interaction, apologize
    sincerely for inconveniences
    to customers, be patient with
    customers, tolerate rude
    customers calmly, and search for
    information or products for
    customers.
    Working with This component is designed to 30
    Information predict success in customer (15 
    service call-center jobs by minute
    assessing a candidate's ability timer)
    to retrieve information and use it
    in order to solve problems.
  • Table Six may be used for jobs in sales: [0145]
    TABLE SIX
    Sales Solutions
    Solution
    Component Definition Items
    Screening 10-14 minutes
    Required Educational Measures potential for success in 15
    and Work- customer service jobs. Scores on
    Related Education and Work-Related
    Experience Experience are derived from
    candidates responses to questions
    regarding developmental influences,
    self-esteem, work history and work-
    related values and attitudes.
    Sales Potential Designed to predict the likelihood 23
    that candidates will suggest or show
    alternative solutions based on
    customer needs, direct conversation
    toward a commitment/order/sale,
    show confidence even after a hard
    refusal/rejection, and strive to close a
    transaction every time.
    Optional Customer Designed to predict the likelihood 20
    Service that candidates will show persistent
    Orientation enthusiasm in customer interaction,
    apologize sincerely for incon-
    veniences to customers, be patient
    with customers, tolerate rude
    customers calmly, and search for in-
    formation or products for customers.
    Selection 10-25-40 Minutes
    Required Sales Focus Designed to predict the likelihood 60
    that candidates will suggest or show
    alternative solutions based on
    customer needs, direct conversation
    toward a commitment/order/sale,
    show confidence even after a hard
    refusal/rejection, and strive to close a
    transaction every time.
    Optional Problem Measures the tendency to efficiently 10
    Solving and effectively use numerical and
    analytical reasoning. This com-
    petency is characterized by the ability
    to solve complex problems, and make
    reasoned decisions.
    Optional Communi- Measures the tendency to efficiently 10
    cation and effectively use verbal reasoning.
    This competency is characterized by
    the ability to verbally explain
    complex information to others.
  • Table Seven may be used for supervisory jobs: [0146]
    TABLE SEVEN
    Supervisory Solutions
    Solution
    Component Definition Items
    Screening 10-20 Minutes
    Required Supervisory Measures potential for supervisory 10
    Potential success across industry type and
    functional area. Scores on
    Supervisory Potential are derived
    from candidates' responses to
    questions regarding academic and
    social background, and aspirations
    concerning work.
    Judgment Measures potential for making good 10
    judgments about how to effectively
    respond to work situations. Scores
    on Judgment are derived from
    candidates' responses to questions
    regarding situations one would likely
    encounter as a manager/
    supervisor.
    Optional Leadership/ Measures potential for success as a 19
    Coaching supervisor. This is done by having
    Teamwork/ applicants' make judgments about
    Interpersonal the most effective teamwork and
    Skills leadership behaviors in specific
    work situations. Scores are
    determined by comparing their
    response profiles to the profiles of
    supervisors who are known to be
    successful.
    Selection 22-37-52 Mins
    Required Business Measures the candidate's thinking 28
    Leadership styles. High scorers are likely to
    have or learn good planning and
    organizing skills, be innovative,
    consider issues from multiple
    perspectives, and create strategies
    to build their business.
    Required Leadership Measures the candidate's desire for 23
    Motivation achievement, drive, initiative, energy
    level, willingness to take charge,
    and persistence. High scorers are
    likely to be highly motivated to
    succeed and to set challenging
    goals for themselves and others.
    Self- Measures the candidate's ability to 32
    Leadership control emotions, act with integrity,
    take responsibility for actions, and
    tolerate stress. High scorers are
    also likely to have a positive attitude,
    be optimistic about the future, and
    demonstrate high levels of
    professionalism.
    Interpersonal Measures the candidate's 30
    Leadership interpersonal characteristics. High
    scorers are likely to persuade and
    influence others, gain commitment,
    and build effective interpersonal
    relationships. They also have
    potential to develop skills in the
    areas of employee relations,
    coaching, motivating, and leading a
    team.
    Optional Decision Measures the tendency to efficiently 10
    Making/ and effectively use numerical and
    Problem analytical reasoning. This
    Solving competency is characterized by the
    ability to solve complex problems,
    and make reasoned decisions.
    Optional Communi- Measures the tendency to efficiently 10
    cation and effectively use verbal reasoning.
    This competency is characterized by
    the ability to verbally explain
    complex information to others.
  • Table Eight may be used for professional jobs: [0147]
    TABLE EIGHT
    Professional Solutions
    Solution
    Component Definition Items
    Screening 7 - Minutes
    Required Dependa- This competency is characterized by: a 40
    bility willingness to behave in expected and
    agree upon ways; following through on
    assignments and commitments; keep
    promises; and accept the
    consequences of one's own actions.
    Interpersonal This competency is indexed by a
    Skills tendency to be pleasant, cooperative,
    and helpful when working with others,
    as well as flexible in conflict resolution
    situations.
    Self-Control This competency is characterized by
    the ability to: stay calm and collected
    when confronted with adversity,
    frustration, or other difficult situations;
    and avoid defensive reactions or hurt
    feelings as a result of others'
    comments.
    Energy This competency is characterized by a
    preference to stay busy, active, and
    avoid inactive events or situations.
    Selection 35-50 Minutes
    Required Business Measures the candidate's thinking 32
    Leadership styles. High scorers are likely to have
    or learn good planning and organizing
    skills, be innovative, consider issues
    from multiple perspectives, and create
    strategies to build their business.
    Leadership Measures the candidate's desire for 35
    Motivation achievement, drive, initiative, energy
    level, willingness to take charge, and
    persistence. High scorers are likely to
    be highly motivated to succeed and to
    set challenging goals for themselves
    and others.
    Self- Measures the candidate's ability to 34
    Leadership control emotions, act with integrity,
    take responsibility for actions, and
    tolerate stress. High scorers are also
    likely to have a positive attitude, be
    optimistic about the future, and
    demonstrate high levels of
    professionalism.
    Interpersonal Measures the candidate's 41
    Leadership interpersonal characteristics. High
    scorers are likely to persuade and
    influence others, gain commitment,
    and build effective interpersonal
    relationships. They also have
    potential to develop skills in the areas
    of employee relations, coaching,
    motivating, and leading a team.
    Decision Measures the tendency to efficiently 10
    Making/ and effectively use numerical and
    Problem analytical reasoning. This competency
    Solving is characterized by the ability to solve
    complex problems, and make
    reasoned decisions.
    Optional Communi- Measures the tendency to efficiently 10
    cation and effectively use verbal reasoning.
    This competency is characterized by
    the ability to verbally explain complex
    information to others.
  • Table Nine may be used for managerial jobs: [0148]
    TABLE NINE
    Managerial Solutions
    Solution
    Component Definition Items
    Screening 10-20 Minutes
    Required Management Measures potential for managerial 10
    Potential success across industry type and
    functional area. Scores on Management
    Potential are derived from candidates'
    responses to questions regarding
    academic and social background, and
    aspirations concerning work.
    Judgment Measures potential for making good 10
    judgments about how to effectively
    respond to work situations. Scores on
    Judgment are derived from candidates'
    responses to questions regarding
    situations one would likely encounter as
    a manager/supervisor.
    Optional Self- This component references: belief in 10
    Confidence one's own abilities and skills and a
    tendency to feel competent in several
    areas.
    Decision Measures potential for success as a
    Making manager. This is done by having
    applicants' make judgments about the
    most effective decisions in specific
    work situations. Their potential is de-
    termined by comparing their response
    profiles to the profiles of successful
    managers.
    Selection 20-35-50 Mins
    Required Business Measures the candidate's thinking 32
    Leadership styles. High scorers are likely to have
    or learn good planning and organizing
    skills, be innovative, consider issues
    from multiple perspectives, and create
    strategies to build their business.
    Leadership Measures the candidate's desire for 35
    Motivation achievement, drive, initiative, energy
    level, willingness to take charge, and
    persistence. High scorers are likely to
    be highly motivated to succeed and to
    set challenging goals for themselves
    and others.
    Self- Measures the candidate's ability to 34
    Leadership control emotions, act with integrity,
    take responsibility for actions, and toler-
    ate stress. High scorers are also likely to
    have a positive attitude, be optimistic
    about the future, and demonstrate high
    levels of professionalism.
    Interpersonal Measures the candidate's 41
    Leadership interpersonal characteristics. High
    scorers are likely to persuade and
    influence others, gain commitment,
    and build effective interpersonal
    relationships. They also have potential
    to develop skills in the areas of
    employee relations, coaching,
    motivating, and leading a team.
    Optional Decision Measures the tendency to efficiently 10
    Making/ and effectively use numerical and
    Problem analytical reasoning. This competency
    Solving is characterized by the ability to solve
    complex problems, and make
    reasoned decisions.
    Optional Communi- Measures the tendency to efficiently 10
    cation and effectively use verbal reasoning.
    This competency is characterized by
    the ability to verbally explain complex
    information to others.
  • Table Ten may be used for technical/professional jobs: [0149]
    TABLE TEN
    Technical-Professional Solutions
    Solution
    Component Definition Items
    Screening 8 Minutes
    Required Dependa- This competency is characterized by: a 40
    bility willingness to behave in expected and
    agree upon ways; following through on
    assignments and commitments; keeping
    promises; and accepting the
    consequences of one's own actions.
    Interpersonal This competency is indexed by a
    Skills tendency to be pleasant, cooperative,
    and helpful when working with others,
    as well as flexible in conflict resolution
    situations.
    Self-Control This competency is characterized by the
    ability to: stay calm and collected when
    confronted with adversity, frustration,
    or other difficult situations; and avoid
    defensive reactions or hurt feelings as a
    result of others' comments.
    Energy This competency is characterized by a
    preference to stay busy, active, and
    avoid inactive events or situations.
    Selection 35-50 Minutes
    Required Business Measures the candidate's thinking 32
    Leadership styles. High scorers are likely to have
    or learn good planning and organizing
    skills, be innovative, consider issues
    from multiple perspectives, and create
    strategies to build their business.
    Leadership Measures the candidate's desire for 35
    Motivation achievement, drive, initiative, energy
    level, willingness to take charge, and
    persistence. High scorers are likely to
    be highly motivated to succeed and to
    set challenging goals for themselves
    and others.
    Self- Measures the candidate's ability to 34
    Leadership control emotions, act with integrity,
    take responsibility for actions, and
    tolerate stress. High scorers are also
    likely to have a positive attitude, be
    optimistic about the future, and
    demonstrate high levels of
    professionalism,
    Interpersonal Measures the candidate's 41
    Leadership interpersonal characteristics. High
    scorers are likely to persuade and
    influence others, gain commitment,
    and build effective interpersonal
    relationships. They also have
    potential to develop skills in the areas
    of employee relations, coaching,
    motivating, and leading a team.
    Decision Measures the tendency to efficiently 10
    Making/ and effectively use numerical and
    Problem analytical reasoning. This competency
    Solving is characterized by the ability to solve
    complex problems, and make
    reasoned decisions.
    Optional Communi- Measures the tendency to efficiently 10
    cation and effectively use verbal reasoning.
    This competency is characterized by
    the ability to verbally explain complex
    information to others.
  • Table Eleven may be used for executive positions: [0150]
    TABLE ELEVEN
    Executive Solutions
    Solution
    Component Definition Items
    Screening 20 Minutes
    Required Executive Measures potential for success in 53
    Potential high-level organizational positions
    across industry type and functional
    area. Scores on Executive Potential
    are derived from candidates'
    responses to questions regarding work
    background, accomplishments, and
    career aspirations.
    Selection 35-50 Minutes
    Required Business Measures the candidate's thinking 32
    Leadership styles. High scorers are likely to have
    or learn good planning and organizing
    skills, be innovative, consider issues
    from multiple perspectives, and create
    strategies to build their business.
    Leadership Measures the candidate's desire for 35
    Motivation achievement, drive, initiative, energy
    level, willingness to take charge, and
    persistence. High scorers are likely to
    be highly motivated to succeed and to
    set challenging goals for themselves
    and others.
    Self- Measures the candidate's ability to 34
    Leadership control emotions, act with integrity,
    take responsibility for actions, and
    tolerate stress. High scorers are also
    likely to have a positive attitude, be
    optimistic about the future, and
    demonstrate high levels of
    professionalism.
    Interpersonal Measures the candidate's 41
    Leadership interpersonal characteristics. High
    scorers are likely to persuade and
    influence others, gain commitment,
    and build effective interpersonal
    relationships. They also have
    potential to develop skills in the areas
    of employee relations, coaching,
    motivating, and leading a team.
    Decision Measures the tendency to efficiently 10
    Making/ and effectively use numerical and
    Problem analytical reasoning. This competency
    Solving is characterized by the ability to solve
    complex problems, and make
    reasoned decisions.
    Optional Communi- Measures the tendency to efficiently 10
    cation and effectively use verbal reasoning.
    This competency is characterized by
    the ability to verbally explain complex
    information to others.
  • Table Twelve may be used for jobs involving campus recruiting: [0151]
    TABLE TWELVE
    Campus Recruiting Solutions
    Solution
    Component Definition Items
    Screening 12 Minutes
    Required Supervisory Measures potential for supervisory 26
    Potential success across industry type and
    functional area. Scores on Supervisory
    Potential are derived from candidates'
    responses to questions regarding
    academic and social background, and
    aspirations concerning work.
    Judgment Measures potential for making good
    judgments about how to effectively
    respond to work situations. Scores on
    Judgment are derived from candidates'
    responses to questions regarding
    situations one would likely encounter
    as a manager/supervisor.
    Management Measures potential for managerial
    Potential success across industry type and
    functional area. Scores on
    Management Potential are derived
    from candidates' responses to
    questions regarding academic and
    social background, and aspirations
    concerning work.
    Selection 20-35-50 Mins
    Required Business Measures the candidate's thinking 32
    Leadership styles. High scorers are likely to
    have or learn good planning and
    organizing skills, be innovative,
    consider issues from multiple
    perspectives, and create strategies
    to build their business.
    Leadership Measures the candidate's desire for 35
    Motivation achievement, drive, initiative,
    energy level, willingness to take
    charge, and persistence. High
    scorers are likely to be highly
    motivated to succeed and to set
    challenging goals for themselves
    and others.
    Self- Measures the candidate's ability to 34
    Leadership control emotions, act with integrity,
    take responsibility for actions, and
    tolerate stress. High scorers are
    also likely to have a positive
    attitude, be optimistic about the
    future, and demonstrate high levels
    of professionalism.
    Interpersonal Measures the candidate's 41
    Leadership interpersonal characteristics. High
    scorers are likely to persuade and
    influence others, gain commitment,
    and build effective interpersonal
    relationships. They also have
    potential to develop skills in the
    areas of employee relations,
    coaching, motivating, and leading a
    team.
    Optional Decision Measures the tendency to efficiently 10
    Making/ and effectively use numerical and
    Problem analytical reasoning. This
    Solving competency is characterized by the
    ability to solve complex problems,
    and make reasoned decisions.
    Optional Communi- Measures the tendency to efficiently 10
    cation and effectively use verbal reasoning.
    This competency is characterized by
    the ability to verbally explain complex
    information to others.
  • Table Thirteen may be used for a selection solution for a job involving communication: [0152]
    TABLE THIRTEEN
    Communication Solution
    Solution
    Component Definition Items
    Selection 37 Minutes
    Required Listening Measure of the tendency to listen to 73
    Orientation and understand others' perspectives,
    to care for others, to accept and
    respect the individual differences of
    people, and to be open both to multiple
    ideas and to using alternative modes
    of thinking.
    English Measures usage of verb tense and
    Language sentence construction. Scores on
    Skills English Language Skills are derived
    from candidates responses to
    grammar questions.
    Verbal Measures verbal reasoning skills and
    Reasoning/ critical thinking/reasoning skills.
    Critical Scores on Verbal Reasoning Ability
    Thinking are derived from candidates'
    responses to analogies and questions
    about information provided in brief
    reading passages.
  • Table Fourteen may be used for a selection solution for a job involving financial services jobs referred to series six/seven: [0153]
    TABLE FOURTEEN
    Series Six/Seven Success Solution
    Solution
    Component Definition Items
    Selection 36 Minutes
    Required Problem Measures the ability to analyze and 20
    Solving evaluate information. Scores on
    Problem Solving are derived from
    candidates' responses to mathematical
    and analytical reasoning items,
    requiring candidates to respond to
    facts and figures presented in various
    formats.
    Verbal Measures verbal reasoning skills and
    Reasoning/ critical thinking/reasoning skills.
    Critical Scores on Verbal Reasoning Ability
    Thinking are derived from candidates'
    responses to analogies and involves
    making inferences from information
    provided in the form of brief passages
  • Table Fifteen may be used for a selection solution for a job requiring information technology aptitude: [0154]
    TABLE FIFTEEN
    Information Technology Aptitude Solution
    Solution
    Component Definition Items
    Selection
    18 Minutes
    Required Critical Measure reasoning and critical thinking 58
    Thinking skills. Scores on Critical Thinking are
    derived from candidates' responses to
    information provided in the form of
    brief passages.
    Problem Measure the ability to analyze and
    Solving evaluate information. Scores on
    Problem Solving are derived from
    candidates' responses to mathematical
    and analytical reasoning items,
    requiring candidates to respond to
    facts and figures presented in various
    scenarios.
    Communi- Measures the ability to efficiently use
    cation verbal information. Scores on
    Communication are derived from
    candidates' ability to identify
    synonyms.
    Spatial Measure the ability to visually
    Ability manipulate objects. Scores on Spatial
    Ability are derived from candidates'
    ability to correctly identify the number
    of blocks in progressively difficult
    figures.
  • Although the above disclosure has focused on recruiting applications, the generated data may be used in other human capital applications. FIG. 13 illustrates a human capital management life-cycle. Measurement and data [0155] 1301 is initially used in the context of recruiting 1302. For recruiting 1302, screening, selection, and interview solutions measure applicants' competencies and predict on-the-job performance and thus contribution to business outcomes.
  • For compensation [0156] 1303, data about potential can be weighed against performance data to ensure that high potential employees who are on difficult assignments where they are structurally constrained from succeeding are not underpaid by pure focus on performance. For example, structural constraints may include business environment, poor staff, unreliable equipment, etc.
  • For retention [0157] 1304, business with jobs that have high turnover use the system to ensure that applicants have qualities that contribute to longer tenure in roles.
  • For performance management [0158] 1305, the system can be used to enhance the validity of employee performance evaluation.
  • For training and development [0159] 1306, a company may test current employees in order to design executive training programs addressing each individual's strengths and weaknesses. Or, for employees that took a test and were hired despite weaknesses, the data can be used to structure appropriate training.
  • For succession [0160] 1307, data on employees may be collected in the process of organization mergers to assist planning for retrenchment or change. Also, by measuring competencies and mapping them between roles, it is possible to assess the potential that an individual may have for a role other than the job they are currently holding, such as for a promotion or a transfer to another area.
  • The foregoing description is to be considered as illustrative only. The skilled artisan will recognize many variations and permutations within the spirit of the disclosure. [0161]

Claims (13)

1. A computer system employing a stack ranked table means, said computer system comprising:
identification means for identifying to a computer user a first uniform resource locator;
first computer means for determining whether said computer user has previously completed a screening solution;
second computer means for providing a second uniform resource locator to said computer user if said first computer means determines that said computer user has not previously completed a screening solution;
screening solution means for screening computer users, the screening solution means being implemented on a website identified by said second uniform resource locator and eliciting screening answers from said computer user;
stack ranked table means implemented by a computer for ranking said computer user in order against a plurality of other computer users, said stack ranked table means initially ranking said computer user and said other computer users in order from first to last as soon as said applicant inputs said screening answers;
selection solution means for selecting advancing computer users from said stack ranked table, said selection solution means eliciting selection answers from said computer user and said plurality of other computer users responsive to selection questions, said selecting being accomplished by said selection means evaluating said selection answers and said stack ranked table means re-ranking said computer user and said plurality of other computer users;
interview solution means for providing a structured interview of said advancing computer users, said structured interview comprising interview questions provided via computer, said stack ranked table means receiving interview scores from said interview solution means, and said stack ranked table means re-ranking said advancing computer users upon receiving said interview scores;
performance evaluation recordation means for subsequently recording a performance evaluation of said computer user;
correlation means for correlating said performance evaluation with said screening answers and said selection answers;
system adjustment means for adjusting said computer system in response to said correlating.
2. A computer system for human resources management comprising:
a first computer terminal 102 associated with a first computer user;
a second computer terminal 116 associated with a second computer user;
a third computer terminal 120 associated with a third computer user;
a database 110; and
a first server 106 configured to:
communicate via an Internet 104 with the first computer terminal 102 and collect first identifying information;
host a screening website 524;
display on the screening website 524 screening questions eliciting first screening information from the first computer user;
receive over the Internet 104 from the first computer terminal 102 the first screening information;
evaluate the first screening information and calculate therefrom a first screening score;
associate the first screening score with the first identifying information;
store in a stack ranked table 530 of database 110 the first screening score in association with the first identifying information;
communicate via the Internet 104 with the second computer terminal 116 and collect second identifying information regarding the second computer user from the second computer terminal 116 via the Internet 104;
compare the second identifying information to data stored in the database 110 and determine whether database 110 has previously stored screening data regarding the second computer user;
display on the screening website 524 screening questions eliciting second screening information from the second user;
receive over the Internet 104 from the second computer terminal 116 the second screening information;
evaluate the second screening information and calculate therefrom a second screening score;
associate the second screening score with the second identifying information;
store in the stack ranked table 530 of database 110 the second screening score in association with the second identifying information;
communicate via the Internet 104 with the third computer terminal 120 and collect third identifying information regarding the third computer user from the third computer terminal 120 via the Internet 104;
compare the third identifying information to data stored in the database 110 and determine whether database 110 has previously stored screening data regarding the third computer user;
display on the screening website 524 screening questions eliciting third screening information from the third computer user;
receive over the Internet 104 from the third computer terminal 120 the third screening information;
evaluate the third screening information and calculate therefrom a third screening score;
associate the third screening score with the third identifying information;
store in the stack ranked table 530 of database 110 the third screening score in association with the third identifying information;
rank in the stack ranked table 530 a relative ranking of the first computer user, the second computer user, and third computer user based at least in part on the first screening score, the second screening score, and the third screening score;
provide a selection display 538;
provide access to the selection display 538 by the first computer user and the second computer user but not by the third computer user, based on the relative ranking;
display on the selection display 538 selection questions eliciting first selection information from the first computer user;
receive from a fourth computer terminal 124 the first selection information;
evaluate the first selection information and calculate therefrom a first selection score;
associate the first selection score with the first identifying information;
store in the stack ranked table 530 the first selection score in association with the first identifying information;
display on the selection display 538 selection questions eliciting second selection information from the second computer user;
receive from the fourth computer terminal 124 the second selection information;
evaluate the second selection information and calculate therefrom a second selection score;
associate the second selection score with the second identifying information;
store in the stack ranked table 530 the second selection score in association with the second identifying information;
re-rank in the stack ranked table 530 a relative re-ranking of the first computer user and the second computer user based at least in part on the first selection score and the second selection score;
select a successful employment candidate from among the first computer user and the second computer user based on the re-ranking; and
provide an indication of the identity of the successful employment candidate.
3. The computer system of claim 3 characterized in that the first server 106 is further configured to:
transmit over the Internet 104 to the first computer terminal 102 first feedback 540 responsive to the first screening score;
transmit over the Internet 104 to the second computer terminal 116 second feedback 540 responsive to the second screening score; and
transmit over the Internet 104 to the third computer terminal 120 third feedback 540 responsive to the third screening score.
4. The computer system of claim 3 characterized in that the first server 106 is further configured to direct the successful employment candidate to a success website indicating an employment offer.
5. The computer system of claim 3 characterized in that the first computer terminal 102 comprises a wireless communication device connected to the Internet 104.
6. The computer system of claim 3 characterized in that the fourth computer terminal 124 is located in a controlled facility remote from the first computer terminal 102.
7. The computer system of claim 3 characterized in that the fourth computer terminal 124 comprises the first computer terminal 102 and the second computer terminal 116.
8. The computer system of claim 3 characterized in that the first server 106 comprises a second server 112 in communication with the fourth computer terminal 124.
9. The computer system of claim 3 characterized in that the selection display 538 comprises a website hosted by the first server 106.
10. The computer system of claim 3 characterized in that the first screening information, the second screening information, and the third screening information received over the Internet 104 by the first server 106 indicate relative aptitude in information technology.
11. The computer system of claim 3 characterized in that the first computer terminal 102 is a telephone.
12. A computer-implemented method for managing human resources in a computer system comprising a first computer terminal 102 associated with a first computer user, a second computer terminal 116 associated with a second computer user, a third computer terminal 120 associated with a third computer user, a database 110, and a first server 106, the method comprising:
communicating via an Internet 104 with the first computer terminal 102 and obtaining first identifying information;
hosting a screening website 524;
displaying on the screening website 524 screening questions eliciting first screening information from the first user;
receiving over the Internet 104 from the first computer terminal 102 the first screening information;
evaluating the first screening information and calculating therefrom a first screening score;
associating the first screening score with the first identifying information;
storing in a stack ranked table 530 of database 110 the first screening score in association with the first identifying information;
communicating via the Internet 104 with the second computer terminal 116 and collecting second identifying information regarding the second computer user from the second computer terminal 116 via the Internet 104;
comparing the second identifying information to data stored in the database 110 and determine whether database 110 has previously stored screening data regarding the second computer user;
displaying on the screening website 524 screening questions eliciting second screening information from the second user;
receiving over the Internet 104 from the second computer terminal 116 the second screening information;
evaluating the second screening information and calculating therefrom a second screening score;
associating the second screening score with the second identifying information;
storing in the stack ranked table 530 of database 110 the second screening score in association with the second identifying information;
communicating via the Internet 104 with the third computer terminal 120 and collecting third identifying information regarding the third computer user from the third computer terminal 120 via the Internet 104;
comparing the third identifying information to data stored in the database 110 and determine whether database 110 has previously stored screening data regarding the third computer user;
displaying on the screening website 524 screening questions eliciting third screening information from the third user;
receiving over the Internet 104 from the third computer terminal 120 the third screening information;
evaluating the third screening information and calculate therefrom a third screening score;
associating the third screening score with the third identifying information;
storing in the stack ranked table 530 of database 110 the third screening score in association with the third identifying information;
ranking in the stack ranked table 530 a relative ranking of the first computer user, the second computer user, and third computer user based at least in part on the first screening score, the second screening score, and the third screening score;
providing a selection display 538;
providing access to the selection display 538 by the first computer user and the second computer user but not by the third computer user, based on the relative ranking;
displaying on the selection display 538 selection questions eliciting first selection information from the first user;
receiving from a fourth computer terminal 124 the first selection information;
evaluating the first selection information and calculating therefrom a first selection score;
associating the first selection score with the first identifying information;
storing in the stack ranked table 530 the first selection score in association with the first identifying information;
displaying on the selection display 538 selection questions eliciting second selection information from the second user;
receiving from the fourth computer terminal 124 the second selection information;
evaluating the second selection information and calculating therefrom a second selection score;
associating the second selection score with the second identifying information;
storing in the stack ranked table 530 the second selection score in association with the second identifying information;
re-ranking in the stack ranked table 530 a relative re-ranking of the first computer user and the second computer user based at least in part on the first selection score and the second selection score;
selecting a successful employment candidate from among the first computer user and the second computer user based on the re-ranking; and
providing an indication of the identity of the successful employment candidate.
13. The method of claim 13 further comprising directing the successful employment candidate to a success website indicating an employment offer.
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Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020042786A1 (en) * 2000-08-03 2002-04-11 Unicru, Inc. Development of electronic employee selection systems and methods
US20020055866A1 (en) * 2000-06-12 2002-05-09 Dewar Katrina L. Computer-implemented system for human resources management
US20030037032A1 (en) * 2001-08-17 2003-02-20 Michael Neece Systems and methods for intelligent hiring practices
US20030105642A1 (en) * 2001-11-30 2003-06-05 United Negro College Fund, Inc. Selection of individuals from a pool of candidates in a competition system
US20050080656A1 (en) * 2003-10-10 2005-04-14 Unicru, Inc. Conceptualization of job candidate information
US20060235884A1 (en) * 2005-04-18 2006-10-19 Performance Assessment Network, Inc. System and method for evaluating talent and performance
US20070059671A1 (en) * 2005-09-12 2007-03-15 Mitchell Peter J Career analysis method and system
US20070233547A1 (en) * 2000-04-21 2007-10-04 John Younger Comprehensive employment recruiting communications system with translation facility
US20080249824A1 (en) * 2006-10-18 2008-10-09 Vienna Human Capital Advisors, Llc Method and System for Analysis of Financial Investment in Human Capital Resources
US20090006178A1 (en) * 2007-06-29 2009-01-01 Peopleanswers, Inc. Behavioral Profiles in Sourcing and Recruiting as Part of a Hiring Process
WO2009048757A1 (en) * 2007-10-09 2009-04-16 Pamela Bezona Quick to coach: a performance management tool
US20090138341A1 (en) * 2006-05-19 2009-05-28 Mohan S Raj Web Enabled Method for Managing Life Cycle of Human Capital Related Dynamic Requirement of Organization
US7848947B1 (en) 1999-08-03 2010-12-07 Iex Corporation Performance management system
US20110055098A1 (en) * 2008-04-30 2011-03-03 Stewart Jeffrey A Automated employment information exchange and method for employment compatibility verification
US7991635B2 (en) * 2007-01-17 2011-08-02 Larry Hartmann Management of job candidate interview process using online facility
US20130166358A1 (en) * 2011-12-21 2013-06-27 Saba Software, Inc. Determining a likelihood that employment of an employee will end
US8517742B1 (en) * 2005-05-17 2013-08-27 American Express Travel Related Services Company, Inc. Labor resource testing system and method
US8607888B2 (en) 2007-02-16 2013-12-17 Michael Jay Nusbaum Self-contained automatic fire extinguisher
US8775125B1 (en) 2009-09-10 2014-07-08 Jpmorgan Chase Bank, N.A. System and method for improved processing performance
US8843388B1 (en) * 2009-06-04 2014-09-23 West Corporation Method and system for processing an employment application
US11288607B2 (en) * 2018-07-18 2022-03-29 Merinio Inc. Automated resource management system and method

Families Citing this family (228)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120078804A1 (en) * 2000-08-03 2012-03-29 Kronos Talent Management Inc. Electronic employee selection systems and methods
US7496518B1 (en) * 2000-08-17 2009-02-24 Strategic Outsourcing Corporation System and method for automated screening and qualification of employment candidates
US8156051B1 (en) * 2001-01-09 2012-04-10 Northwest Software, Inc. Employment recruiting system
WO2002065233A2 (en) * 2001-02-09 2002-08-22 Mellon Hr Solutions Llc Individual valuation in a group
US7437309B2 (en) * 2001-02-22 2008-10-14 Corporate Fables, Inc. Talent management system and methods for reviewing and qualifying a workforce utilizing categorized and free-form text data
US20020198765A1 (en) * 2001-02-22 2002-12-26 Magrino Susan A. Human capital management performance capability matching system and methods
US20020143573A1 (en) * 2001-04-03 2002-10-03 Bryce John M. Integrated automated recruiting management system
FI20010838A (en) * 2001-04-24 2002-10-25 Marko Kesti Method and system for process control and optimization
NL1018087C2 (en) * 2001-05-16 2002-09-26 Yourfuture N V Information system and method for bringing together supply and demand on a labor market.
US7778938B2 (en) * 2001-06-05 2010-08-17 Accuhire.Com Corporation System and method for screening of job applicants
NZ522675A (en) * 2001-06-15 2006-01-27 Centranum Ltd Performance management system
JP3949909B2 (en) * 2001-06-27 2007-07-25 富士通株式会社 Purchaser evaluation system, program, and media
US20030009479A1 (en) * 2001-07-03 2003-01-09 Calvetta Phair Employment placement method
US20030050811A1 (en) * 2001-09-10 2003-03-13 Freeman Robert B. System and method for hiring an applicant
GB2412832B (en) * 2001-10-24 2006-04-05 Richard Warrington Computer input progress monitoring system
US20030177052A1 (en) * 2002-03-12 2003-09-18 Smith William W. Human resources management system and method
US20030229510A1 (en) * 2002-05-21 2003-12-11 Jason Kerr Discriminating network recruitment system
US8548929B1 (en) * 2002-06-10 2013-10-01 Citicorp Credit Services, Inc. Methods and systems of employment candidate data management
US20030236699A1 (en) * 2002-06-24 2003-12-25 Anne Krebs System and method of intellectual/immaterial/intangible resource control
US20040018474A1 (en) * 2002-07-25 2004-01-29 D'ippolito Elaine Adult/child system and method for learning life experiences and good habits and activities and knowledge
US20040044538A1 (en) * 2002-08-27 2004-03-04 Mauzy Katherine G. System and method for processing applications for employment
US20040107112A1 (en) * 2002-12-02 2004-06-03 Cotter Milton S. Employment center
US20050240431A1 (en) * 2002-12-02 2005-10-27 Cotter Milton S Employment center
US7610288B2 (en) * 2003-01-07 2009-10-27 At&T Intellectual Property I, L.P. Performance management system and method
US20040138903A1 (en) * 2003-01-13 2004-07-15 Zuniga Sara Suzanne Employment management tool and method
US20040143489A1 (en) * 2003-01-20 2004-07-22 Rush-Presbyterian - St. Luke's Medical Center System and method for facilitating a performance review process
US20040148220A1 (en) * 2003-01-27 2004-07-29 Freeman Robert B. System and method for candidate management
TW200423696A (en) * 2003-02-04 2004-11-01 Ginganet Corp Remote interview system
US20040220852A1 (en) * 2003-04-30 2004-11-04 Posey Ivan Miles System and method for rewarding performance
US20050021623A1 (en) * 2003-05-07 2005-01-27 Ahmed Syed Mike Computer-implemented system for matching parties and subsequent automatic notification to matched parties
US20050131756A1 (en) * 2003-06-19 2005-06-16 Benson Sheila D. Automated and variably weighted applicant and employee screening system
DE10339990B8 (en) * 2003-08-29 2013-01-31 Advanced Micro Devices, Inc. A method of fabricating a metal line having increased resistance to electromigration along an interface of a dielectric barrier layer by implanting material into the metal line
CA2441516A1 (en) 2003-09-18 2005-03-18 Corporate Responsibility System Technologies Ltd. System and method for evaluating regulatory compliance for a company
EP1671266A4 (en) * 2003-09-18 2007-01-10 Corporate Responsibility Syste System and method for evaluating regulatory compliance for a company
US20050240457A1 (en) * 2004-01-30 2005-10-27 Connally Samuel B Systems, methods and computer program products for facilitating evaluation of job applicants by search committees
US20050181339A1 (en) * 2004-02-18 2005-08-18 Hewson Roger D. Developing the twelve cognitive functions of individuals
US20050228709A1 (en) * 2004-04-08 2005-10-13 Hillel Segal Internet-based job placement system for managing proposals for screened and pre-qualified participants
US20060106636A1 (en) * 2004-04-08 2006-05-18 Hillel Segal Internet-based job placement system for creating proposals for screened and pre-qualified participants
US20050246241A1 (en) * 2004-04-30 2005-11-03 Rightnow Technologies, Inc. Method and system for monitoring successful use of application software
US8346593B2 (en) 2004-06-30 2013-01-01 Experian Marketing Solutions, Inc. System, method, and software for prediction of attitudinal and message responsiveness
US8732004B1 (en) 2004-09-22 2014-05-20 Experian Information Solutions, Inc. Automated analysis of data to generate prospect notifications based on trigger events
US20060085736A1 (en) * 2004-10-16 2006-04-20 Au Anthony S A Scientific Formula and System which derives standardized data and faster search processes in a Personnel Recruiting System, that generates more accurate results
US20080183486A1 (en) * 2005-01-21 2008-07-31 Baumgarten Leora B Computer method of collecting, managing and using job applicant data for specific employment opportunities
US20060195335A1 (en) * 2005-01-21 2006-08-31 Christian Lana S System and method for career development
US20060212448A1 (en) * 2005-03-18 2006-09-21 Bogle Phillip L Method and apparatus for ranking candidates
US20110022530A1 (en) * 2005-03-18 2011-01-27 Phillip Lee Bogle Method and apparatus for ranking candidates
US20060212305A1 (en) * 2005-03-18 2006-09-21 Jobster, Inc. Method and apparatus for ranking candidates using connection information provided by candidates
US7472097B1 (en) * 2005-03-23 2008-12-30 Kronos Talent Management Inc. Employee selection via multiple neural networks
US8566144B2 (en) * 2005-03-31 2013-10-22 Amazon Technologies, Inc. Closed loop voting feedback
US20060224404A1 (en) * 2005-04-05 2006-10-05 Carl Keusseyan Web-based system and method for screening job candidates
US20060228689A1 (en) * 2005-04-12 2006-10-12 Rajaram Kishore K Interactive tutorial system and method
US8433713B2 (en) * 2005-05-23 2013-04-30 Monster Worldwide, Inc. Intelligent job matching system and method
US8527510B2 (en) 2005-05-23 2013-09-03 Monster Worldwide, Inc. Intelligent job matching system and method
US8375067B2 (en) 2005-05-23 2013-02-12 Monster Worldwide, Inc. Intelligent job matching system and method including negative filtration
CA2544324A1 (en) * 2005-06-10 2006-12-10 Unicru, Inc. Employee selection via adaptive assessment
US20070016436A1 (en) * 2005-07-12 2007-01-18 Kakar Man M Computer system for resource management
US20070015124A1 (en) * 2005-07-14 2007-01-18 Presidio Sciences, L.P. Automated updating of job analyses
US20070015125A1 (en) * 2005-07-14 2007-01-18 Presidio Sciences, L.P. Automated updating of job assessments
US8719076B2 (en) * 2005-08-11 2014-05-06 Accenture Global Services Limited Finance diagnostic tool
US20070078831A1 (en) * 2005-09-30 2007-04-05 Accenture Global Services Gmbh Enterprise performance management tool
US20070100684A1 (en) * 2005-10-31 2007-05-03 Friedrich Gartner Method of evaluating sales opportunities
US20070116241A1 (en) * 2005-11-10 2007-05-24 Flocken Phil A Support case management system
US8073724B2 (en) * 2005-12-02 2011-12-06 Saudi Arabian Oil Company Systems program product, and methods for organization realignment
US20070160963A1 (en) * 2006-01-10 2007-07-12 International Business Machines Corporation Candidate evaluation tool
US20070224580A1 (en) * 2006-02-23 2007-09-27 Mcfaul William J Method for assessing a mental function activity profile
US7558737B2 (en) * 2006-02-28 2009-07-07 Sap Ag Entity validation framework
JP4894301B2 (en) 2006-03-03 2012-03-14 富士通株式会社 Skill value calculation program and skill value calculation device
US7882040B2 (en) * 2006-05-31 2011-02-01 Gulf Talent Fz-Llc Method for computer server operation
US20080059290A1 (en) * 2006-06-12 2008-03-06 Mcfaul William J Method and system for selecting a candidate for a position
US20080027771A1 (en) * 2006-07-27 2008-01-31 Uti Limited Partnership Selection process
US8036979B1 (en) 2006-10-05 2011-10-11 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US20080109299A1 (en) * 2006-11-06 2008-05-08 Genpact Global Holdings Sicar Sarl Multi-tiered career progression system and method
WO2008060279A1 (en) * 2006-11-15 2008-05-22 Lehman Brothers Inc. Method and system for conducting pre-employment process
US8965868B2 (en) * 2006-11-15 2015-02-24 Barclays Capital Inc. Method and system for conducting pre-employment process
WO2008102255A1 (en) * 2007-02-23 2008-08-28 Gioacchino La Vecchia System and method for routing tasks to a user in a workforce
WO2008134376A1 (en) * 2007-04-24 2008-11-06 Dynamic Connections, Llc Peer ranking
SG148044A1 (en) * 2007-05-08 2008-12-31 Siang Chien Mark Tey Integrated strategic organizational resource and performance alignment management system and method
WO2008147918A2 (en) 2007-05-25 2008-12-04 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US8386481B1 (en) 2007-10-12 2013-02-26 Salesdrive LLC System and method for candidate assessment
US7996521B2 (en) 2007-11-19 2011-08-09 Experian Marketing Solutions, Inc. Service for mapping IP addresses to user segments
US20090164311A1 (en) * 2007-12-19 2009-06-25 Microsoft Corporation Human resource management system
US20090187473A1 (en) * 2008-01-18 2009-07-23 Blaze Jerry M System and method for recruiting online
US10540712B2 (en) 2008-02-08 2020-01-21 The Pnc Financial Services Group, Inc. User interface with controller for selectively redistributing funds between accounts
US20090254401A1 (en) * 2008-04-04 2009-10-08 Iscopia Software Inc. System and method for creating a custom assessment project
US20090276296A1 (en) * 2008-05-01 2009-11-05 Anova Innovations, Llc Business profit resource optimization system and method
US20090276297A1 (en) * 2008-05-05 2009-11-05 Sap Ag Method and system for reviewing and managing employees
US8401938B1 (en) 2008-05-12 2013-03-19 The Pnc Financial Services Group, Inc. Transferring funds between parties' financial accounts
US8219433B2 (en) * 2008-05-12 2012-07-10 Pandya Rajiv D Methods for analyzing job functions and job candidates and for determining their co-suitability
US8751385B1 (en) 2008-05-15 2014-06-10 The Pnc Financial Services Group, Inc. Financial email
US20090299827A1 (en) * 2008-05-30 2009-12-03 Oracle International Corporation Verifying Operator Competence
US20090319344A1 (en) * 2008-06-18 2009-12-24 Tepper Samuel R Assessment of sales force personnel for improvement of sales performance
US10388179B2 (en) 2008-06-18 2019-08-20 Accenture Global Solutions Limited Analytics platform
US20100010880A1 (en) * 2008-07-09 2010-01-14 Learning Sciences International Performance observation, tracking and improvement system and method
WO2010011652A1 (en) * 2008-07-21 2010-01-28 Talent Tree, Inc. System and method for tracking employee performance
US8204809B1 (en) * 2008-08-27 2012-06-19 Accenture Global Services Limited Finance function high performance capability assessment
US8266068B1 (en) * 2008-10-06 2012-09-11 Intuit Inc. Candidate interview assistant
US9147177B2 (en) * 2008-11-07 2015-09-29 Oracle International Corporation Method and system for implementing a scoring mechanism
US10891037B1 (en) * 2009-01-30 2021-01-12 The Pnc Financial Services Group, Inc. User interfaces and system including same
US8965798B1 (en) 2009-01-30 2015-02-24 The Pnc Financial Services Group, Inc. Requesting reimbursement for transactions
US20100262550A1 (en) * 2009-04-08 2010-10-14 Avaya Inc. Inter-corporate collaboration overlay solution for professional social networks
US8639920B2 (en) 2009-05-11 2014-01-28 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US8326771B2 (en) * 2009-06-22 2012-12-04 Accenture Global Services Limited Predicative recruitment reporting and management
US20110166903A1 (en) * 2010-01-05 2011-07-07 Sunmeet Singh Jolly Web based Offshorability Probability Calculator - Job analysis Tool for Individuals made available on Internet
US9047611B2 (en) 2010-01-25 2015-06-02 Yahoo! Inc. System and method for finding relative score and enhancing one or more scores associated with data objects
US20110196802A1 (en) * 2010-02-05 2011-08-11 Nicholas Jeremy Ellis Method and apparatus for hiring using social networks
US8791949B1 (en) 2010-04-06 2014-07-29 The Pnc Financial Services Group, Inc. Investment management marketing tool
US8780115B1 (en) 2010-04-06 2014-07-15 The Pnc Financial Services Group, Inc. Investment management marketing tool
US8473423B2 (en) * 2010-06-09 2013-06-25 Avaya Inc. Contact center expert identification
US8417614B1 (en) 2010-07-02 2013-04-09 The Pnc Financial Services Group, Inc. Investor personality tool
US11475523B1 (en) 2010-07-02 2022-10-18 The Pnc Financial Services Group, Inc. Investor retirement lifestyle planning tool
US11475524B1 (en) 2010-07-02 2022-10-18 The Pnc Financial Services Group, Inc. Investor retirement lifestyle planning tool
US8423444B1 (en) 2010-07-02 2013-04-16 The Pnc Financial Services Group, Inc. Investor personality tool
US9152727B1 (en) 2010-08-23 2015-10-06 Experian Marketing Solutions, Inc. Systems and methods for processing consumer information for targeted marketing applications
US20120077174A1 (en) * 2010-09-29 2012-03-29 Depaul William Competency assessment tool
US20120171648A1 (en) * 2010-12-29 2012-07-05 Paula Price System for increasing the probability of success and identifying required preparation for service in lifestyle occupations
US8352406B2 (en) 2011-02-01 2013-01-08 Bullhorn, Inc. Methods and systems for predicting job seeking behavior
US9602625B2 (en) 2011-02-22 2017-03-21 Theatrolabs, Inc. Mediating a communication in an observation platform
GB2529973B (en) 2011-02-22 2016-04-20 Theatro Labs Inc Observation platform for using structured communications
US10134001B2 (en) * 2011-02-22 2018-11-20 Theatro Labs, Inc. Observation platform using structured communications for gathering and reporting employee performance information
US11599843B2 (en) 2011-02-22 2023-03-07 Theatro Labs, Inc. Configuring , deploying, and operating an application for structured communications for emergency response and tracking
US9407543B2 (en) 2011-02-22 2016-08-02 Theatrolabs, Inc. Observation platform for using structured communications with cloud computing
US11636420B2 (en) * 2011-02-22 2023-04-25 Theatro Labs, Inc. Configuring, deploying, and operating applications for structured communications within observation platforms
US10204524B2 (en) 2011-02-22 2019-02-12 Theatro Labs, Inc. Observation platform for training, monitoring and mining structured communications
WO2012116116A1 (en) * 2011-02-22 2012-08-30 Saul Rosenberg Systems and methods for selecting, ordering, scheduling, administering, storing, interpreting and transmitting a plurality of psychological, neurobehavioral and neurobiological tests
US10375133B2 (en) 2011-02-22 2019-08-06 Theatro Labs, Inc. Content distribution and data aggregation for scalability of observation platforms
US10699313B2 (en) 2011-02-22 2020-06-30 Theatro Labs, Inc. Observation platform for performing structured communications
US11605043B2 (en) 2011-02-22 2023-03-14 Theatro Labs, Inc. Configuring, deploying, and operating an application for buy-online-pickup-in-store (BOPIS) processes, actions and analytics
US8374940B1 (en) 2011-02-28 2013-02-12 The Pnc Financial Services Group, Inc. Wealth allocation analysis tools
US9852470B1 (en) 2011-02-28 2017-12-26 The Pnc Financial Services Group, Inc. Time period analysis tools for wealth management transactions
US8321316B1 (en) 2011-02-28 2012-11-27 The Pnc Financial Services Group, Inc. Income analysis tools for wealth management
US9665908B1 (en) 2011-02-28 2017-05-30 The Pnc Financial Services Group, Inc. Net worth analysis tools
US10733570B1 (en) 2011-04-19 2020-08-04 The Pnc Financial Services Group, Inc. Facilitating employee career development
US8417554B2 (en) * 2011-05-06 2013-04-09 International Business Machines Corporation Tool for manager assistance
US8996359B2 (en) 2011-05-18 2015-03-31 Dw Associates, Llc Taxonomy and application of language analysis and processing
US20130054482A1 (en) * 2011-08-31 2013-02-28 Sap Ag Interview assistant system and method
US8745083B1 (en) 2011-10-31 2014-06-03 James G. Ruiz System and method for matching candidates with personnel needs
US9269353B1 (en) 2011-12-07 2016-02-23 Manu Rehani Methods and systems for measuring semantics in communications
US20130178956A1 (en) * 2012-01-10 2013-07-11 Oracle International Corporation Identifying top strengths for a person
US9020807B2 (en) 2012-01-18 2015-04-28 Dw Associates, Llc Format for displaying text analytics results
US10169812B1 (en) 2012-01-20 2019-01-01 The Pnc Financial Services Group, Inc. Providing financial account information to users
US9667513B1 (en) 2012-01-24 2017-05-30 Dw Associates, Llc Real-time autonomous organization
GB2499827A (en) 2012-03-01 2013-09-04 Do It Solutions Ltd Assessing a person's ability to achieve a pre-determined outcome
US20140149180A1 (en) * 2012-04-19 2014-05-29 Oracle International Corporation Sale prediction engine rules
US20130290210A1 (en) * 2012-04-27 2013-10-31 Furstperson, Inc. System and method for automating pre-employment assessment
US20130297373A1 (en) * 2012-05-02 2013-11-07 Xerox Corporation Detecting personnel event likelihood in a social network
US20130339102A1 (en) * 2012-06-14 2013-12-19 The One Page Company Inc. Proposal evaluation system
WO2014008304A2 (en) 2012-07-02 2014-01-09 Oracle International Corporation Extensibility for sales predictor (spe)
US10346784B1 (en) 2012-07-27 2019-07-09 Google Llc Near-term delivery system performance simulation
WO2014052798A2 (en) * 2012-09-28 2014-04-03 Hireiq Solutions, Inc. System and method of evaluating candidates for a hiring decision
US8973115B2 (en) 2012-10-04 2015-03-03 American Nurses Credentialing Center System and method for assembling and analyzing a candidate application for a credential
US9963954B2 (en) 2012-11-16 2018-05-08 Saudi Arabian Oil Company Caliper steerable tool for lateral sensing and accessing
EP2932147B1 (en) * 2012-12-14 2017-10-18 Wärtsilä Finland Oy Method of filling a fuel tank with liquefied gas and liquefied gas fuel system
US20140172732A1 (en) * 2012-12-14 2014-06-19 Roy Baladi Psychographic based methods and systems for job seeking
US20140180756A1 (en) * 2012-12-21 2014-06-26 Roth Staffing Companies, L.P. Method and System for Modeling Workforce Turnover Propensity
US20140278656A1 (en) * 2013-03-15 2014-09-18 Roth Staffing Companies, L.P. Service Level Model, Algorithm, Systems and Methods
US20140324525A1 (en) * 2013-04-30 2014-10-30 Linkedin Corporation Analyzing career site viewer information
WO2014205504A1 (en) * 2013-06-28 2014-12-31 Big Picture (Ip) Pty Ltd Systems, methods, apparatus and graphical user interfaces for automated candidate rejection and/or feedback
US20150046356A1 (en) * 2013-08-08 2015-02-12 Oracle International Corporation Identification of skills gaps based on previous successful hires
US9734486B2 (en) 2013-08-08 2017-08-15 Sap Se Integrated temporary labor provisioning and monitoring
US9256688B2 (en) * 2013-08-09 2016-02-09 Google Inc. Ranking content items using predicted performance
US10380518B2 (en) 2013-09-30 2019-08-13 Maximus Process tracking and defect detection
EP3058523A4 (en) 2013-10-16 2017-05-24 Lahti, Ken Assessment system
US10102536B1 (en) 2013-11-15 2018-10-16 Experian Information Solutions, Inc. Micro-geographic aggregation system
US8818910B1 (en) * 2013-11-26 2014-08-26 Comrise, Inc. Systems and methods for prioritizing job candidates using a decision-tree forest algorithm
US20150161746A1 (en) * 2013-12-11 2015-06-11 Gerhard H. WENGLORZ Method and apparatus for custom-engineered sourcing
US20190095845A1 (en) * 2014-03-08 2019-03-28 Casey L. Welch Online Computerized Platform/Clearinghose for Providing Rating, Ranking, Recognition, and Comparing of Students
US9378486B2 (en) 2014-03-17 2016-06-28 Hirevue, Inc. Automatic interview question recommendation and analysis
US20160335600A1 (en) * 2014-03-18 2016-11-17 Linkedln Corporation Automated talent nurturing
US9413707B2 (en) 2014-04-11 2016-08-09 ACR Development, Inc. Automated user task management
US8942727B1 (en) 2014-04-11 2015-01-27 ACR Development, Inc. User Location Tracking
US20150302345A1 (en) * 2014-04-16 2015-10-22 International Business Machines Corporation Technology for project scheduling based on team member stress
US9576030B1 (en) 2014-05-07 2017-02-21 Consumerinfo.Com, Inc. Keeping up with the joneses
US9652745B2 (en) 2014-06-20 2017-05-16 Hirevue, Inc. Model-driven evaluator bias detection
US11257117B1 (en) 2014-06-25 2022-02-22 Experian Information Solutions, Inc. Mobile device sighting location analytics and profiling system
WO2016033108A1 (en) * 2014-08-25 2016-03-03 Unitive, Inc. Human resource system providing reduced bias
US20160098653A1 (en) * 2014-10-03 2016-04-07 Soren Hojby Risk Analysis to Improve Operational Workforce Planning
US10528916B1 (en) * 2014-11-06 2020-01-07 Hirevue Inc. Competency-based question selection for digital evaluation platforms
US10445152B1 (en) 2014-12-19 2019-10-15 Experian Information Solutions, Inc. Systems and methods for dynamic report generation based on automatic modeling of complex data structures
US10032385B2 (en) * 2015-03-27 2018-07-24 Hartford Fire Insurance Company System for optimizing employee leadership training program enrollment selection
WO2016161437A1 (en) * 2015-04-03 2016-10-06 Surepeople Llc Data driven assessment apparatus and method
WO2016166598A1 (en) * 2015-04-15 2016-10-20 Dekoekkoek Paul Requirements determination
US10621535B1 (en) 2015-04-24 2020-04-14 Mark Lawrence Method and apparatus to onboard resources
US9626654B2 (en) * 2015-06-30 2017-04-18 Linkedin Corporation Learning a ranking model using interactions of a user with a jobs list
US9280745B1 (en) * 2015-07-08 2016-03-08 Applied Underwriters, Inc. Artificial intelligence expert system for screening
US10592830B2 (en) * 2015-07-14 2020-03-17 Conduent Business Services, Llc Method and system for managing one or more human resource functions in an organization
WO2017027709A1 (en) * 2015-08-11 2017-02-16 Cognoa, Inc. Methods and apparatus to determine developmental progress with artificial intelligence and user input
US9767309B1 (en) 2015-11-23 2017-09-19 Experian Information Solutions, Inc. Access control system for implementing access restrictions of regulated database records while identifying and providing indicators of regulated database records matching validation criteria
US11972336B2 (en) 2015-12-18 2024-04-30 Cognoa, Inc. Machine learning platform and system for data analysis
US10726377B2 (en) * 2015-12-29 2020-07-28 Workfusion, Inc. Task similarity clusters for worker assessment
US10825356B2 (en) 2016-01-05 2020-11-03 SmartSpark, Inc. System and method for determining and providing behavioural modification motivational cues
WO2017182880A1 (en) * 2016-04-21 2017-10-26 Ceb, Inc. Predictive analytics
US20170308811A1 (en) * 2016-04-21 2017-10-26 Vishal Kumar Talent Artificial Intelligence Virtual Agent Bot
US10909469B2 (en) 2016-05-02 2021-02-02 Surepeople Llc Data driven intelligent learning and development apparatus and method
WO2017190165A1 (en) * 2016-05-02 2017-11-09 Red Bull Gmbh Method for testing employability and personal strengths
US11132644B2 (en) 2016-06-29 2021-09-28 At&T Intellectual Property I, L.P. Method and apparatus for managing employment-related decisions
JP6741504B2 (en) * 2016-07-14 2020-08-19 株式会社ユニバーサルエンターテインメント Interview system
US20180039927A1 (en) * 2016-08-05 2018-02-08 General Electric Company Automatic summarization of employee performance
WO2018039377A1 (en) 2016-08-24 2018-03-01 Experian Information Solutions, Inc. Disambiguation and authentication of device users
US20190311329A1 (en) * 2017-01-01 2019-10-10 Darryl Clines Decision Making Add-on System
EP3580700A4 (en) 2017-02-09 2020-11-18 Cognoa, Inc. Platform and system for digital personalized medicine
US11030697B2 (en) 2017-02-10 2021-06-08 Maximus, Inc. Secure document exchange portal system with efficient user access
JP2018173719A (en) * 2017-03-31 2018-11-08 Hrソリューションズ株式会社 Test system, test device, and test method
US11657402B2 (en) 2017-05-16 2023-05-23 Visa International Service Association Dynamic claims submission system
US20190087782A1 (en) * 2017-09-19 2019-03-21 International Business Machines Corporation Cognitive, dynamic assessment advisor or builder
CN109639747B (en) * 2017-10-09 2020-06-26 阿里巴巴集团控股有限公司 Data request processing method, data request processing device, query message processing method, query message processing device and equipment
US11551570B2 (en) 2018-02-15 2023-01-10 Smarthink Srl Systems and methods for assessing and improving student competencies
US10957431B2 (en) 2018-04-20 2021-03-23 International Business Machines Corporation Human resource selection based on readability of unstructured text within an individual case safety report (ICSR) and confidence of the ICSR
JP6596539B1 (en) * 2018-05-16 2019-10-23 国立大学法人佐賀大学 Application / evaluation support system and server
CA3104016A1 (en) * 2018-07-04 2020-01-09 Imi Material Handling Logistics Inc. Automated human resources management and engagement system and method
US11580467B2 (en) 2018-09-04 2023-02-14 Celectiv Llc Integrated system for and method of matching, acquiring, and developing human talent
US11880797B2 (en) * 2019-01-23 2024-01-23 Macorva Inc. Workforce sentiment monitoring and detection systems and methods
US11023530B2 (en) * 2019-02-13 2021-06-01 International Business Machines Corporation Predicting user preferences and requirements for cloud migration
US11238394B2 (en) 2019-03-20 2022-02-01 Microsoft Technology Licensing, Llc Assessment-based qualified candidate delivery
US11232380B2 (en) 2019-03-20 2022-01-25 Microsoft Technology Licensing, Llc Mapping assessment results to levels of experience
US11205144B2 (en) 2019-03-20 2021-12-21 Microsoft Technology Licensing, Llc Assessment-based opportunity exploration
KR102643554B1 (en) 2019-03-22 2024-03-04 코그노아, 인크. Personalized digital treatment methods and devices
TR201904875A1 (en) * 2019-04-01 2020-10-21 Zirve Ikibinbir Uluslararasi Insaat Proje Taahhuet Yazilim Bilisim Danismanlik Ticaret Ltd Sirketi CONSTRUCTION TECHNICAL TRAINING AND MANAGEMENT SYSTEMS AND PERSONNEL SELECTION AND EVALUATION
US20230113574A1 (en) * 2019-07-01 2023-04-13 Strategic Management Decisions, Llc Matching candidates to organization position
US11238410B1 (en) * 2019-12-17 2022-02-01 iCIMS, Inc. Methods and systems for merging outputs of candidate and job-matching artificial intelligence engines executing machine learning-based models
US11682041B1 (en) 2020-01-13 2023-06-20 Experian Marketing Solutions, Llc Systems and methods of a tracking analytics platform
US11743056B2 (en) * 2020-05-26 2023-08-29 Indeed, Inc. Systems and methods for self-contained certificate signing request in delegation scenarios
US11514403B2 (en) * 2020-10-29 2022-11-29 Accenture Global Solutions Limited Utilizing machine learning models for making predictions
US11138007B1 (en) * 2020-12-16 2021-10-05 Mocha Technologies Inc. Pseudo coding platform
CA3142047A1 (en) * 2020-12-22 2022-06-22 Ziigra Inc. Candidate matching system
US11900327B2 (en) * 2021-06-30 2024-02-13 Capital One Services, Llc Evaluation adjustment factoring for bias
US20230177468A1 (en) * 2021-12-07 2023-06-08 B-EQL Software Solutions LLC System and method for hiring users
US20230306352A1 (en) * 2022-01-27 2023-09-28 Next Jump Inc. Interactive electronic evaluation systems and methods
CN115049285A (en) * 2022-06-24 2022-09-13 北京月新时代科技股份有限公司 Digital human resource management method, device, electronic equipment and medium

Citations (34)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5164897A (en) * 1989-06-21 1992-11-17 Techpower, Inc. Automated method for selecting personnel matched to job criteria
US5326270A (en) * 1991-08-29 1994-07-05 Introspect Technologies, Inc. System and method for assessing an individual's task-processing style
US5490097A (en) * 1993-03-22 1996-02-06 Fujitsu Limited System and method for modeling, analyzing and executing work process plans
US5551880A (en) * 1993-01-22 1996-09-03 Bonnstetter; Bill J. Employee success prediction system
US5565316A (en) * 1992-10-09 1996-10-15 Educational Testing Service System and method for computer based testing
US5592375A (en) * 1994-03-11 1997-01-07 Eagleview, Inc. Computer-assisted system for interactively brokering goods or services between buyers and sellers
US5618182A (en) * 1994-09-30 1997-04-08 Thomas; C. Douglass Method and apparatus for improving performance on multiple-choice exams
US5671409A (en) * 1995-02-14 1997-09-23 Fatseas; Ted Computer-aided interactive career search system
US5722418A (en) * 1993-08-30 1998-03-03 Bro; L. William Method for mediating social and behavioral processes in medicine and business through an interactive telecommunications guidance system
US5774883A (en) * 1995-05-25 1998-06-30 Andersen; Lloyd R. Method for selecting a seller's most profitable financing program
US5832497A (en) * 1995-08-10 1998-11-03 Tmp Worldwide Inc. Electronic automated information exchange and management system
US5980096A (en) * 1995-01-17 1999-11-09 Intertech Ventures, Ltd. Computer-based system, methods and graphical interface for information storage, modeling and stimulation of complex systems
US6056556A (en) * 1997-03-05 2000-05-02 Educational Testing Service Computer-based simulation examination of architectural practice
US6115646A (en) * 1997-12-18 2000-09-05 Nortel Networks Limited Dynamic and generic process automation system
US6126448A (en) * 1998-07-06 2000-10-03 Ho; Chi Fai Computer-aided learning methods and apparatus for a job
US6189029B1 (en) * 1996-09-20 2001-02-13 Silicon Graphics, Inc. Web survey tool builder and result compiler
US6259890B1 (en) * 1997-03-27 2001-07-10 Educational Testing Service System and method for computer based test creation
US6266659B1 (en) * 1997-08-07 2001-07-24 Uday P. Nadkarni Skills database management system and method
US6289340B1 (en) * 1999-08-03 2001-09-11 Ixmatch, Inc. Consultant matching system and method for selecting candidates from a candidate pool by adjusting skill values
US6311164B1 (en) * 1997-12-30 2001-10-30 Job Files Corporation Remote job application method and apparatus
US20010042000A1 (en) * 1998-11-09 2001-11-15 William Defoor Method for matching job candidates with employers
US6341267B1 (en) * 1997-07-02 2002-01-22 Enhancement Of Human Potential, Inc. Methods, systems and apparatuses for matching individuals with behavioral requirements and for managing providers of services to evaluate or increase individuals' behavioral capabilities
US6370510B1 (en) * 1997-05-08 2002-04-09 Careerbuilder, Inc. Employment recruiting system and method using a computer network for posting job openings and which provides for automatic periodic searching of the posted job openings
US6385620B1 (en) * 1999-08-16 2002-05-07 Psisearch,Llc System and method for the management of candidate recruiting information
US6484010B1 (en) * 1997-12-19 2002-11-19 Educational Testing Service Tree-based approach to proficiency scaling and diagnostic assessment
US6514079B1 (en) * 2000-03-27 2003-02-04 Rume Interactive Interactive training method for demonstrating and teaching occupational skills
US6524109B1 (en) * 1999-08-02 2003-02-25 Unisys Corporation System and method for performing skill set assessment using a hierarchical minimum skill set definition
US6567784B2 (en) * 1999-06-03 2003-05-20 Ework Exchange, Inc. Method and apparatus for matching projects and workers
US6618734B1 (en) * 2000-07-20 2003-09-09 Spherion Assessment, Inc. Pre-employment screening and assessment interview process
US6640216B1 (en) * 1999-02-04 2003-10-28 Authoria, Inc. Human resource knowledge modeling and delivery system
US6691122B1 (en) * 2000-10-30 2004-02-10 Peopleclick.Com, Inc. Methods, systems, and computer program products for compiling information into information categories using an expert system
US6735570B1 (en) * 1999-08-02 2004-05-11 Unisys Corporation System and method for evaluating a selectable group of people against a selectable set of skills
US6865608B2 (en) * 2000-03-31 2005-03-08 Neomedia Technologies, Inc. Method and system for simplified access to internet content on a wireless device
US6873964B1 (en) * 1998-12-11 2005-03-29 Lockheed Martin Corporation Method and system for recruiting personnel

Family Cites Families (86)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5117353A (en) * 1989-05-05 1992-05-26 Staff-Plus, Inc. System for use in a temporary help business
US5197004A (en) 1989-05-08 1993-03-23 Resumix, Inc. Method and apparatus for automatic categorization of applicants from resumes
US5059127A (en) 1989-10-26 1991-10-22 Educational Testing Service Computerized mastery testing system, a computer administered variable length sequential testing system for making pass/fail decisions
US5170362A (en) 1991-01-15 1992-12-08 Atlantic Richfield Company Redundant system for interactively evaluating the capabilities of multiple test subjects to perform a task utilizing a computerized test system
US5408588A (en) * 1991-06-06 1995-04-18 Ulug; Mehmet E. Artificial neural network method and architecture
US5467428A (en) * 1991-06-06 1995-11-14 Ulug; Mehmet E. Artificial neural network method and architecture adaptive signal filtering
US5371807A (en) * 1992-03-20 1994-12-06 Digital Equipment Corporation Method and apparatus for text classification
US5799282A (en) * 1992-05-19 1998-08-25 Medical Training And Services, International Methods for establishing certifiable informed consent for a medical procedure
US5325862A (en) * 1993-03-26 1994-07-05 The United States Of America As Represented By The Secretary Of The Navy Method and/or system for personal identification and impairment assessment from brain activity patterns
US5608899A (en) * 1993-06-04 1997-03-04 International Business Machines Corporation Method and apparatus for searching a database by interactively modifying a database query
US5619709A (en) * 1993-09-20 1997-04-08 Hnc, Inc. System and method of context vector generation and retrieval
US5461699A (en) * 1993-10-25 1995-10-24 International Business Machines Corporation Forecasting using a neural network and a statistical forecast
US5416694A (en) 1994-02-28 1995-05-16 Hughes Training, Inc. Computer-based data integration and management process for workforce planning and occupational readjustment
JPH08287162A (en) * 1995-02-14 1996-11-01 Toshiba Corp Work flow system
US6061675A (en) * 1995-05-31 2000-05-09 Oracle Corporation Methods and apparatus for classifying terminology utilizing a knowledge catalog
US5887120A (en) * 1995-05-31 1999-03-23 Oracle Corporation Method and apparatus for determining theme for discourse
US5788504A (en) * 1995-10-16 1998-08-04 Brookhaven Science Associates Llc Computerized training management system
US5727128A (en) * 1996-05-08 1998-03-10 Fisher-Rosemount Systems, Inc. System and method for automatically determining a set of variables for use in creating a process model
US6272467B1 (en) * 1996-09-09 2001-08-07 Spark Network Services, Inc. System for data collection and matching compatible profiles
US5978767A (en) * 1996-09-10 1999-11-02 Electronic Data Systems Corporation Method and system for processing career development information
US5845285A (en) * 1997-01-07 1998-12-01 Klein; Laurence C. Computer system and method of data analysis
US6115718A (en) * 1998-04-01 2000-09-05 Xerox Corporation Method and apparatus for predicting document access in a collection of linked documents featuring link proprabilities and spreading activation
US5885097A (en) * 1997-08-28 1999-03-23 Hon Hai Precision Ind. Co., Ltd. Electrical connector with a board locking device
US6049776A (en) * 1997-09-06 2000-04-11 Unisys Corporation Human resource management system for staffing projects
CA2305344A1 (en) 1997-09-29 1999-04-08 Network Recruiting, Inc. On-line recruiting system with improved candidate and position profiling
US6070143A (en) 1997-12-05 2000-05-30 Lucent Technologies Inc. System and method for analyzing work requirements and linking human resource products to jobs
US6094650A (en) * 1997-12-15 2000-07-25 Manning & Napier Information Services Database analysis using a probabilistic ontology
US6144964A (en) * 1998-01-22 2000-11-07 Microsoft Corporation Methods and apparatus for tuning a match between entities having attributes
JP3916749B2 (en) * 1998-03-11 2007-05-23 富士通株式会社 Work mediation apparatus and recording medium
US20010011280A1 (en) * 1998-04-14 2001-08-02 Edward S. Gilbert Computer-based training system
US6405159B2 (en) * 1998-06-03 2002-06-11 Sbc Technology Resources, Inc. Method for categorizing, describing and modeling types of system users
US6591246B1 (en) * 1998-06-16 2003-07-08 The United States Of America As Represented By The Secretary Of The Navy Automated skills program
JP2000113064A (en) 1998-10-09 2000-04-21 Fuji Xerox Co Ltd Optimum acting person selection support system
US6046556A (en) * 1998-10-19 2000-04-04 Hughes Electronics Corporation Motor current sensing circuit
US6263334B1 (en) * 1998-11-11 2001-07-17 Microsoft Corporation Density-based indexing method for efficient execution of high dimensional nearest-neighbor queries on large databases
US6269479B1 (en) 1998-11-30 2001-07-31 Unisys Corporation Method and computer program product for evaluating the performance of an object-oriented application program
US6275812B1 (en) * 1998-12-08 2001-08-14 Lucent Technologies, Inc. Intelligent system for dynamic resource management
US6334133B1 (en) * 1998-12-21 2001-12-25 Frontline Data, Inc. System and method for performing substitute fulfillment
US6513042B1 (en) 1999-02-11 2003-01-28 Test.Com Internet test-making method
US6513027B1 (en) * 1999-03-16 2003-01-28 Oracle Corporation Automated category discovery for a terminological knowledge base
US6721754B1 (en) * 1999-04-28 2004-04-13 Arena Pharmaceuticals, Inc. System and method for database similarity join
US6564197B2 (en) * 1999-05-03 2003-05-13 E.Piphany, Inc. Method and apparatus for scalable probabilistic clustering using decision trees
US6611822B1 (en) * 1999-05-05 2003-08-26 Ac Properties B.V. System method and article of manufacture for creating collaborative application sharing
US6711585B1 (en) * 1999-06-15 2004-03-23 Kanisa Inc. System and method for implementing a knowledge management system
US6598047B1 (en) * 1999-07-26 2003-07-22 David W. Russell Method and system for searching text
US6662194B1 (en) * 1999-07-31 2003-12-09 Raymond Anthony Joao Apparatus and method for providing recruitment information
US7200563B1 (en) * 1999-08-20 2007-04-03 Acl International Inc. Ontology-driven information system
US6311641B1 (en) * 1999-09-13 2001-11-06 Mary Lou Johnson Bird perch, feeder, and bath
US6493723B1 (en) * 1999-09-22 2002-12-10 International Business Machines Corporation Method and system for integrating spatial analysis and data mining analysis to ascertain warranty issues associated with transportation products
US6769066B1 (en) * 1999-10-25 2004-07-27 Visa International Service Association Method and apparatus for training a neural network model for use in computer network intrusion detection
CN1384947A (en) * 1999-12-08 2002-12-11 株式会社技术/视觉 Recorded medium on which program for displaying skill achievement level, display device, and displaying method
AU2726601A (en) * 1999-12-13 2001-06-18 Mary L. Richardson Method and system for employment placement
US6681098B2 (en) * 2000-01-11 2004-01-20 Performance Assessment Network, Inc. Test administration system using the internet
US6865581B1 (en) * 2000-01-27 2005-03-08 Icomp Health Management, Inc. Job analysis system
US6338628B1 (en) 2000-02-15 2002-01-15 Clear Direction, Inc. Personal training and development delivery system
US6944596B1 (en) * 2000-02-23 2005-09-13 Accenture Llp Employee analysis based on results of an education business simulation
WO2001073660A1 (en) * 2000-03-27 2001-10-04 Lawcorps Computer-implemented web database system for staffing of personnel
US7043443B1 (en) * 2000-03-31 2006-05-09 Firestone Lisa M Method and system for matching potential employees and potential employers over a network
US7191138B1 (en) * 2000-04-15 2007-03-13 Mindloft Corporation System for cataloging, inventorying selecting, measuring, valuing and matching intellectual capital and skills with a skill requirement
US6266658B1 (en) 2000-04-20 2001-07-24 Microsoft Corporation Index tuner for given workload
US20010034630A1 (en) * 2000-04-21 2001-10-25 Robert Half International, Inc. Interactive employment system and method
US6728695B1 (en) 2000-05-26 2004-04-27 Burning Glass Technologies, Llc Method and apparatus for making predictions about entities represented in documents
JP2004503877A (en) 2000-06-12 2004-02-05 イープレディックス インコーポレイテッド Computer system for human resources management
US20020019940A1 (en) * 2000-06-16 2002-02-14 Matteson Craig S. Method and apparatus for assigning test and assessment instruments to users
US6463430B1 (en) * 2000-07-10 2002-10-08 Mohomine, Inc. Devices and methods for generating and managing a database
AU2001281017A1 (en) * 2000-08-03 2002-02-18 Unicru, Inc. Electronic employee selection systems and methods
US7356484B2 (en) * 2000-10-03 2008-04-08 Agile Software Corporation Self-learning method and apparatus for rating service providers and predicting future performance
US20020128893A1 (en) * 2000-10-16 2002-09-12 Farenden Rose Mary Web site for recruiting candidates for employment
US20020128892A1 (en) * 2000-10-16 2002-09-12 Farenden Rose Mary Method for recruiting candidates for employment
US20020128894A1 (en) * 2000-10-16 2002-09-12 Rose Mary Farenden System for recruiting candidates for employment
US7437309B2 (en) 2001-02-22 2008-10-14 Corporate Fables, Inc. Talent management system and methods for reviewing and qualifying a workforce utilizing categorized and free-form text data
US6795799B2 (en) * 2001-03-07 2004-09-21 Qualtech Systems, Inc. Remote diagnosis server
US20020173971A1 (en) * 2001-03-28 2002-11-21 Stirpe Paul Alan System, method and application of ontology driven inferencing-based personalization systems
US20030101170A1 (en) * 2001-05-25 2003-05-29 Joseph Edelstein Data query and location through a central ontology model
WO2003003161A2 (en) 2001-06-29 2003-01-09 Humanr System and method for interactive on-line performance assessment and appraisal
US6778979B2 (en) * 2001-08-13 2004-08-17 Xerox Corporation System for automatically generating queries
US20030037032A1 (en) 2001-08-17 2003-02-20 Michael Neece Systems and methods for intelligent hiring practices
US6778995B1 (en) * 2001-08-31 2004-08-17 Attenex Corporation System and method for efficiently generating cluster groupings in a multi-dimensional concept space
US20030120630A1 (en) * 2001-12-20 2003-06-26 Daniel Tunkelang Method and system for similarity search and clustering
US6847966B1 (en) * 2002-04-24 2005-01-25 Engenium Corporation Method and system for optimally searching a document database using a representative semantic space
US7872767B2 (en) * 2003-04-04 2011-01-18 Xerox Corporation Parallel printing system
US7383241B2 (en) * 2003-07-25 2008-06-03 Enkata Technologies, Inc. System and method for estimating performance of a classifier
US7555441B2 (en) 2003-10-10 2009-06-30 Kronos Talent Management Inc. Conceptualization of job candidate information
US20050080657A1 (en) 2003-10-10 2005-04-14 Unicru, Inc. Matching job candidate information
US20060074836A1 (en) * 2004-09-03 2006-04-06 Biowisdom Limited System and method for graphically displaying ontology data
CA2544324A1 (en) 2005-06-10 2006-12-10 Unicru, Inc. Employee selection via adaptive assessment

Patent Citations (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5164897A (en) * 1989-06-21 1992-11-17 Techpower, Inc. Automated method for selecting personnel matched to job criteria
US5326270A (en) * 1991-08-29 1994-07-05 Introspect Technologies, Inc. System and method for assessing an individual's task-processing style
US5565316A (en) * 1992-10-09 1996-10-15 Educational Testing Service System and method for computer based testing
US5551880A (en) * 1993-01-22 1996-09-03 Bonnstetter; Bill J. Employee success prediction system
US5490097A (en) * 1993-03-22 1996-02-06 Fujitsu Limited System and method for modeling, analyzing and executing work process plans
US5722418A (en) * 1993-08-30 1998-03-03 Bro; L. William Method for mediating social and behavioral processes in medicine and business through an interactive telecommunications guidance system
US5592375A (en) * 1994-03-11 1997-01-07 Eagleview, Inc. Computer-assisted system for interactively brokering goods or services between buyers and sellers
US5618182A (en) * 1994-09-30 1997-04-08 Thomas; C. Douglass Method and apparatus for improving performance on multiple-choice exams
US6086382A (en) * 1994-09-30 2000-07-11 Robolaw Corporation Method and apparatus for improving performance on multiple-choice exams
US5885087A (en) * 1994-09-30 1999-03-23 Robolaw Corporation Method and apparatus for improving performance on multiple-choice exams
US5980096A (en) * 1995-01-17 1999-11-09 Intertech Ventures, Ltd. Computer-based system, methods and graphical interface for information storage, modeling and stimulation of complex systems
US5671409A (en) * 1995-02-14 1997-09-23 Fatseas; Ted Computer-aided interactive career search system
US5774883A (en) * 1995-05-25 1998-06-30 Andersen; Lloyd R. Method for selecting a seller's most profitable financing program
US5832497A (en) * 1995-08-10 1998-11-03 Tmp Worldwide Inc. Electronic automated information exchange and management system
US6189029B1 (en) * 1996-09-20 2001-02-13 Silicon Graphics, Inc. Web survey tool builder and result compiler
US6056556A (en) * 1997-03-05 2000-05-02 Educational Testing Service Computer-based simulation examination of architectural practice
US6259890B1 (en) * 1997-03-27 2001-07-10 Educational Testing Service System and method for computer based test creation
US6370510B1 (en) * 1997-05-08 2002-04-09 Careerbuilder, Inc. Employment recruiting system and method using a computer network for posting job openings and which provides for automatic periodic searching of the posted job openings
US6341267B1 (en) * 1997-07-02 2002-01-22 Enhancement Of Human Potential, Inc. Methods, systems and apparatuses for matching individuals with behavioral requirements and for managing providers of services to evaluate or increase individuals' behavioral capabilities
US6266659B1 (en) * 1997-08-07 2001-07-24 Uday P. Nadkarni Skills database management system and method
US6115646A (en) * 1997-12-18 2000-09-05 Nortel Networks Limited Dynamic and generic process automation system
US6484010B1 (en) * 1997-12-19 2002-11-19 Educational Testing Service Tree-based approach to proficiency scaling and diagnostic assessment
US6311164B1 (en) * 1997-12-30 2001-10-30 Job Files Corporation Remote job application method and apparatus
US6213780B1 (en) * 1998-07-06 2001-04-10 Chi Fai Ho Computer-aided learning and counseling methods and apparatus for a job
US6126448A (en) * 1998-07-06 2000-10-03 Ho; Chi Fai Computer-aided learning methods and apparatus for a job
US20010042000A1 (en) * 1998-11-09 2001-11-15 William Defoor Method for matching job candidates with employers
US6873964B1 (en) * 1998-12-11 2005-03-29 Lockheed Martin Corporation Method and system for recruiting personnel
US6640216B1 (en) * 1999-02-04 2003-10-28 Authoria, Inc. Human resource knowledge modeling and delivery system
US6567784B2 (en) * 1999-06-03 2003-05-20 Ework Exchange, Inc. Method and apparatus for matching projects and workers
US6735570B1 (en) * 1999-08-02 2004-05-11 Unisys Corporation System and method for evaluating a selectable group of people against a selectable set of skills
US6524109B1 (en) * 1999-08-02 2003-02-25 Unisys Corporation System and method for performing skill set assessment using a hierarchical minimum skill set definition
US6289340B1 (en) * 1999-08-03 2001-09-11 Ixmatch, Inc. Consultant matching system and method for selecting candidates from a candidate pool by adjusting skill values
US6385620B1 (en) * 1999-08-16 2002-05-07 Psisearch,Llc System and method for the management of candidate recruiting information
US6514079B1 (en) * 2000-03-27 2003-02-04 Rume Interactive Interactive training method for demonstrating and teaching occupational skills
US6865608B2 (en) * 2000-03-31 2005-03-08 Neomedia Technologies, Inc. Method and system for simplified access to internet content on a wireless device
US6618734B1 (en) * 2000-07-20 2003-09-09 Spherion Assessment, Inc. Pre-employment screening and assessment interview process
US6691122B1 (en) * 2000-10-30 2004-02-10 Peopleclick.Com, Inc. Methods, systems, and computer program products for compiling information into information categories using an expert system

Cited By (49)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7848947B1 (en) 1999-08-03 2010-12-07 Iex Corporation Performance management system
US20070233547A1 (en) * 2000-04-21 2007-10-04 John Younger Comprehensive employment recruiting communications system with translation facility
US7873533B2 (en) 2000-04-21 2011-01-18 Accolo, Inc. Comprehensive employment recruiting communications system with translation facility
US7606778B2 (en) 2000-06-12 2009-10-20 Previsor, Inc. Electronic predication system for assessing a suitability of job applicants for an employer
US20020055866A1 (en) * 2000-06-12 2002-05-09 Dewar Katrina L. Computer-implemented system for human resources management
US8086558B2 (en) 2000-06-12 2011-12-27 Previsor, Inc. Computer-implemented system for human resources management
US20110145161A1 (en) * 2000-08-03 2011-06-16 Kronos Talent Management Inc. Electronic employee selection systems and methods
US8265977B2 (en) 2000-08-03 2012-09-11 Kronos Talent Management Inc. Electronic employee selection systems and methods
US7080057B2 (en) 2000-08-03 2006-07-18 Unicru, Inc. Electronic employee selection systems and methods
US8046251B2 (en) 2000-08-03 2011-10-25 Kronos Talent Management Inc. Electronic employee selection systems and methods
US7558767B2 (en) 2000-08-03 2009-07-07 Kronos Talent Management Inc. Development of electronic employee selection systems and methods
US20050114279A1 (en) * 2000-08-03 2005-05-26 Unicru, Inc. Development of electronic employee selection systems and methods
US7310626B2 (en) 2000-08-03 2007-12-18 Kronos Talent Management Inc. Electronic employee selection systems and methods
US20050273350A1 (en) * 2000-08-03 2005-12-08 Unicru, Inc. Electronic employee selection systems and methods
US7562059B2 (en) 2000-08-03 2009-07-14 Kronos Talent Management Inc. Development of electronic employee selection systems and methods
US20100287110A1 (en) * 2000-08-03 2010-11-11 Kronos Talent Management Inc. Electronic employee selection systems and methods
US20100287111A1 (en) * 2000-08-03 2010-11-11 Kronos Talent Management Inc. Electronic employee selection systems and methods
US20020042786A1 (en) * 2000-08-03 2002-04-11 Unicru, Inc. Development of electronic employee selection systems and methods
US20020046199A1 (en) * 2000-08-03 2002-04-18 Unicru, Inc. Electronic employee selection systems and methods
US20030037032A1 (en) * 2001-08-17 2003-02-20 Michael Neece Systems and methods for intelligent hiring practices
US7792685B2 (en) 2001-11-30 2010-09-07 United Negro College Fund, Inc. Selection of individuals from a pool of candidates in a competition system
US8121851B2 (en) 2001-11-30 2012-02-21 United Negro College Fund, Inc. Selection of individuals from a pool of candidates in a competition system
US8560333B2 (en) 2001-11-30 2013-10-15 United Negro College Fund, Inc. Selection of individuals from a pool of candidates in a competition system
US20030105642A1 (en) * 2001-11-30 2003-06-05 United Negro College Fund, Inc. Selection of individuals from a pool of candidates in a competition system
US7321858B2 (en) * 2001-11-30 2008-01-22 United Negro College Fund, Inc. Selection of individuals from a pool of candidates in a competition system
US7555441B2 (en) 2003-10-10 2009-06-30 Kronos Talent Management Inc. Conceptualization of job candidate information
US20050080656A1 (en) * 2003-10-10 2005-04-14 Unicru, Inc. Conceptualization of job candidate information
US20060235884A1 (en) * 2005-04-18 2006-10-19 Performance Assessment Network, Inc. System and method for evaluating talent and performance
US8517742B1 (en) * 2005-05-17 2013-08-27 American Express Travel Related Services Company, Inc. Labor resource testing system and method
US7593860B2 (en) * 2005-09-12 2009-09-22 International Business Machines Corporation Career analysis method and system
US20070059671A1 (en) * 2005-09-12 2007-03-15 Mitchell Peter J Career analysis method and system
US20090138341A1 (en) * 2006-05-19 2009-05-28 Mohan S Raj Web Enabled Method for Managing Life Cycle of Human Capital Related Dynamic Requirement of Organization
US7983945B2 (en) 2006-10-18 2011-07-19 Vienna Human Capital Advisors, Llc Method and system for analysis of financial investment in human capital resources
US20080249824A1 (en) * 2006-10-18 2008-10-09 Vienna Human Capital Advisors, Llc Method and System for Analysis of Financial Investment in Human Capital Resources
US7991635B2 (en) * 2007-01-17 2011-08-02 Larry Hartmann Management of job candidate interview process using online facility
US8607888B2 (en) 2007-02-16 2013-12-17 Michael Jay Nusbaum Self-contained automatic fire extinguisher
WO2008121771A1 (en) * 2007-03-30 2008-10-09 Accolo, Inc. Comprehensive empolyment recruiting communications system with translation facility
US20130246295A1 (en) * 2007-06-29 2013-09-19 Peopleanswers, Inc. Behavioral Profiles in Sourcing and Recruiting as Part of a Hiring Process
US20090006178A1 (en) * 2007-06-29 2009-01-01 Peopleanswers, Inc. Behavioral Profiles in Sourcing and Recruiting as Part of a Hiring Process
US8204778B2 (en) * 2007-06-29 2012-06-19 Peopleanswers, Inc. Behavioral profiles in sourcing and recruiting as part of a hiring process
US8484072B1 (en) * 2007-06-29 2013-07-09 Peopleanswers, Inc. Behavioral profiles in sourcing and recruiting as part of a hiring process
WO2009048757A1 (en) * 2007-10-09 2009-04-16 Pamela Bezona Quick to coach: a performance management tool
US20110055098A1 (en) * 2008-04-30 2011-03-03 Stewart Jeffrey A Automated employment information exchange and method for employment compatibility verification
US8843388B1 (en) * 2009-06-04 2014-09-23 West Corporation Method and system for processing an employment application
US8775125B1 (en) 2009-09-10 2014-07-08 Jpmorgan Chase Bank, N.A. System and method for improved processing performance
US10365986B2 (en) 2009-09-10 2019-07-30 Jpmorgan Chase Bank, N.A. System and method for improved processing performance
US11036609B2 (en) 2009-09-10 2021-06-15 Jpmorgan Chase Bank, N.A. System and method for improved processing performance
US20130166358A1 (en) * 2011-12-21 2013-06-27 Saba Software, Inc. Determining a likelihood that employment of an employee will end
US11288607B2 (en) * 2018-07-18 2022-03-29 Merinio Inc. Automated resource management system and method

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