Abstract:
A candidate selection system that generates data-driven candidate selections of large populations of submitted résumés using common independent assessment variables and against desired qualities is described. A processor executing the candidate selection program is pre-programmed to include a listing of desired qualities that the ideal candidate for the position should possess. Each submitted résumé is reviewed and data input from the résumés is normalized to produce values representing weighted scores unique to the specific candidate and in terms of the sought-after desired dependant qualities.

Description:
BACKGROUND OF THE INVENTION 
     This application relates generally to hiring processes and, more particularly, to a candidate selection system. 
     Optimally selecting individuals to interview for specific jobs from a large population of résumés is a formidable task to complete for a human resources manager who must attempt to simultaneously balance internal desired skill preferences and considerations against various regulatory and internal hiring criteria while filtering through the large populations of résumés to identify qualifications or skills that may be easily transferable to the specific job. Often these desired qualities include an individual&#39;s analytical ability, self-confidence, initiative, and interpersonal skills. Additionally, each potential job includes several factors which are often unique to the specific job, and must also be considered by the human resources manager. 
     As a result, human resources managers often use labor-intensive screening methods to select candidates to interview for the jobs. Simply identifying ideal candidates for a position may require significant effort. 
     BRIEF SUMMARY OF THE INVENTION 
     In an exemplary embodiment, a processing system under the control of a candidate selection program performs data-driven candidate selections from large populations of submitted résumés while using common independent assessment variables that are normalized against desired qualities. The candidate selection program provides for weighted desired qualities that an ideal candidate should possess. Each submitted résumés is reviewed and a weight factor is entered for each desired quality depending on whether the résuméindicates that the candidate possesses that characteristic. 
     During execution of the candidate selection program, the characteristics of the candidate are input and linked to the various pre-set desired qualities. The data input from the résumés is normalized to produce values which represent weighted scores of the independent candidate characteristics in terms of the sought-after desired dependant qualities. As a result of the normalization process, the candidates may be directly compared to determine which candidates should be more closely reviewed by the human resources manager. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a system block diagram; 
         FIG. 2  is an exemplary embodiment of an information flow diagram of a candidate selection system; 
         FIG. 3  is an independent variable assessment matrix used with the candidate selection system shown in  FIG. 2 ; 
         FIG. 4  is an exemplary graphical representation of candidate strengths displayed using the system shown in  FIG. 2 ; 
         FIG. 5  is an exemplary tabular output displayed using the system shown in  FIG. 2 ; and 
         FIG. 6  is a graphical output of the data shown in  FIG. 5 . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a block diagram of a processing system  10  according to one embodiment of the present invention. Processing system  10  includes a central processing unit (CPU)  12 , a random access memory (RAM)  14 , an output device  16 , for example a monitor, a mass storage device  18 , and an input device  20 , for example a keyboard. Processing system  10  may be a single user system, for example, a microcomputer, or a multi-user system including a server (not shown) and a plurality of devices (not shown) connected to the server. In one embodiment, processing system  10  is accessible via the Internet through many interfaces including through a network, such as a local area network (LAN) or a wide area network (WAN), through dial-in-connections, cable modems and special high-speed ISDN lines. Additionally, system  10  may include multiple input devices  20 , i.e., a keyboard, a mouse, or various automated data input devices, i.e., an optical scanner (not shown). A candidate selection system program  30  is stored in mass storage device  18  and is executed by data processing system  10 . 
       FIG. 2  is an exemplary embodiment of an information flow diagram  40  illustrating process steps executed by processing system  10  under the control of program  30  (shown in  FIG. 2 ).  FIG. 3  is an independent variables assessment matrix  42  used with candidate selection system program  30 . Initially information is gathered  44  for candidate selection system program  30 . More specifically, information pertaining to desired candidate qualities is gathered  44  by assessing a résumés of each candidate submitted. In one embodiment, the information gathered  44  pertains to five desired candidate qualities including analytical ability, self-confidence, initiative, change orientation, and interpersonal skills. The desired candidate qualities are variable and are pre-selected based on hiring criteria of the specific job. 
     Candidate selection program  30  includes independent candidate variables, such as experiences and exhibited abilities, that tend to correlate to sought-after dependant qualities, such as the aforementioned five desired candidate qualities. For example, candidates with significant tutoring exposure may tend to exhibit strong analytical ability, self-confidence, and interpersonal skills, while candidates with extensive education or work experience outside the candidate&#39;s “home” country may be indicative of strong change orientation ability. 
     The information gathered  44  is input  46  into matrix  42 , shown in  FIG. 3 . More specifically, after candidate selection program  30  is accessed, a macro (not shown) automatically guides a user through a series of input selections  48 . The macro prompts a user to enter a one or a zero within a plurality of candidate background categories  50  that represent qualifications of the specific candidate being assessed. More specifically, a user enters a one if a category  50  is applicable to the candidate and a null entry if a category  50  is not applicable to the candidate. 
     In the exemplary embodiment, matrix  42  includes a category  52  representing a grade point average greater than 3.5 out of 4.0, a category  54  representing multiple degrees, majors or minors, a category  56  representing honor society membership, a category  58  representing society office holder or team captain, a category  60  representing military service, a category  62  representing significant travel exposure, and a category  64  representing education outside of “home” country. Additionally, in the exemplary embodiment, matrix  42  also includes a category  66  representing community service participation, a category  68  representing tutor experience, a category  70  representing technical publication including papers, patents, and conferences, a category  72  representing awards including scholarships, academic service awards, and community awards, a category  74  representing exceptional work experience, and a category  76  representing extra-curricular activities. 
     As shown in  FIG. 3 , the macro executed to complete independent variables assessment matrix  42  also prompts a user to input  46  a university  80  attended by the candidate, a degree  82  and major  84  earned by the candidate, and a number  86  assigned to the candidate. A separate matrix  42  is then completed for each candidate being considered for selection. In one embodiment, matrix  42  is displayable in a tabular output format as shown in  FIG. 3 . 
     After all of the individual matrices  42  have been completed for each candidate being considered for selection, information input  46  is normalized  90 . Because date input  46  is normalized  90 , candidate selection system program  30  assesses categories  50  input  46  for each candidate to quantitatively assess  92  each candidate&#39;s background against known the aforementioned desired qualities, and compare the various candidates against each other. 
     Specifically, to normalize  90  the data, each category  50  is totaled  94  to obtain a sum total for all identified independent qualifications input  46  into matrix  42 . Each sum total is then divided  98  by a value representing a total possible per desired candidate quality. The value representing the total possible per desired candidate quality is variable depending upon a weight factor assigned to the desired qualities originally selected and input to the processor. In the exemplary embodiment, each desired quality is assigned a weight factor equal to one. The result represents a weighted score of the independent candidate variables input  46  in terms of the sought-after desired dependant qualities. More specifically, to assess analytical ability, information input  46  in categories  52 ,  54 ,  56 ,  68 ,  70 , and  72  is totaled  94  and divided  98  by the total possible value of six. To assess self-confidence information input  46  in categories  54 ,  58 ,  60 ,  62 ,  64 ,  68 , and  74  is totaled  94  and divided  98  by the total possible value of seven. To assess initiative, information input  46  in categories  52 ,  54 ,  58 ,  66 ,  70 ,  72 , and  76  is totaled and divided by the total possible value of seven. To assess change orientation, information input  46  in categories  60 ,  62 , and  64  is totaled  94  and divided  98  by the total possible value of three. To assess interpersonal skills, information input  46  in categories  58 ,  64 ,  66 , and  68  is totaled  94  and divided by the total possible value of four. 
     The assessments are then displayed  100 . In one embodiment, the assessments are displayed  100  in a tabular output format (not shown in  FIGS. 2 and 3 ). In an alternative embodiment, the assessments are displayed  100  in graphical output format (not shown in  FIGS. 2 and 3 ). The assessments are generated for each candidate, and displayed  100  separately for each candidate. 
     After assessments are generated for each candidate, candidate selection program  30  displays  110  the assessments in a tabular output format (not shown in  FIGS. 2 and 3 ) that includes all of the candidates considered for selection. In one embodiment, the tabular output format includes columns (not shown in  FIGS. 2 and 3 ) that illustrate for each candidate a weighted value in each desired quality, a total value, an average score, and each candidate&#39;s college, their major, and their degree. In another embodiment, a graphical output format is displayed  110  that graphically illustrates either the total score or the average score for each candidate. 
     Candidates to be interviewed are not directly selected as an end result of candidate selection system program  30  being executed. Rather, after candidate selection system program  30  is executed, data is provided to the user to select  120  specific candidates worthy of a more detailed review. As a result, data-driven candidate selections can be made on large populations of submitted résumé using common independent assessment variables and against desired qualities. 
       FIGS. 4 through 6  illustrate exemplary outputs obtained as a result of executing candidate selection system program  30  (shown in  FIG. 1 ) and the process steps illustrated in  FIG. 2  with data processing system  10  (shown in  FIG. 1 ). More specifically,  FIG. 4  is an exemplary graphical output format  130  illustrating assessment values for an individual candidate. More specifically, graphical output format  130  is displayed  100  (shown in  FIG. 2 ) after data entered for a specific candidate has been normalized  90  (shown in  FIG. 2 ). 
     Graphical output format  130  illustrates a total score percentage of the independent variables input  46  (shown in  FIG. 2 ) for each candidate and for each sought-after desired dependant quality. In the exemplary embodiment, the candidate received inputs in four out of six categories  52 ,  54 ,  56 ,  68 ,  70 , and  72  (shown in  FIG. 3 ) assessed for analytical ability, five out of seven categories  54 ,  58 ,  60 ,  62 ,  64 ,  68 , and  74  (shown in  FIG. 3 ) assessed for self-confidence, five out of seven categories  52 ,  54 ,  58 ,  66 ,  70 ,  72 , and  76  (shown in  FIG. 3 ) assessed for initiative, one of three categories  60 ,  62 , and  64  (shown in  FIG. 3 ) assessed for change orientation, and all four categories  58 ,  64 ,  66 , and  68  (shown in  FIG. 3 ) assessed for interpersonal skills. As a result, after normalizing  90  (shown in  FIG. 2 ) the candidate received the total score percentages represented in a summary table  132  and graphical output format  130 . 
       FIG. 5  is an exemplary tabular output format  140  illustrating assessment values for twenty-three candidates. Tabular output format  140  includes columns  142  that illustrate a weighted value in each desired quality for each candidate, a column  144  that illustrates a total value for each candidate, and a column  146  that illustrates an average score for each candidate. Additionally, columns  150 ,  152 , and  154  illustrate respectively, each candidate&#39;s college, their major, and their degree. 
       FIG. 6  is a graphical output format  160  illustrating total score as a percentage for each candidate. More specifically, graphical output format  160  graphically illustrates the total scores for the candidates shown in  FIG. 5 . In an alternative embodiment, graphical output format  160  graphically illustrates other columns  142 ,  144 , and  146  shown in  FIG. 5 . 
     The above-described selection process for candidate selection provides data to a user for identifying specific candidates worthy of a more detailed review. More specifically, as a result of the candidate selection program, large populations of résumés may be assessed using common independent assessment variables and against desired qualities. The process executed within the candidate selection program provides a method of assessing résumés in a manner that is reliable, is easily adaptable to other hiring criteria, and is cost-effective. 
     While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the claims.