Abstract:
An artificial intelligence expert system for screening provides characteristic profiles to candidates to perform a particular task. The profiles have individual screening items within them that are expected to be related to whether or not a person is suitable for the task. The responses from the persons to the items are received by a computer implemented expert system. The expert system applies a combined model to the responses to generate a forecasted performance of the person to the task. The combined model is a linear combination of two or more path dependent regressions performed on data from a set of N training persons with known abilities to do the task. The number of parameters in each path dependent model is limited to a fraction of the number N so that the path dependent models are not over fit to the data. A suitable fraction is ⅕.

Description:
COPYRIGHT AND TRADEMARK NOTICE 
     A portion of the disclosure of this patent document contains material to which a claim for copyright is made. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but reserves all other copyright rights whatsoever. 
     FIELD OF THE INVENTION 
     Embodiments of the present invention relate to artificial intelligence expert systems for screening. 
     BACKGROUND OF THE INVENTION 
     It is difficult to develop artificial intelligence expert systems for screening.  FIG. 1A  is a flow chart  100  of a prior art method for developing an artificial intelligence expert system for screening candidates for employment. It is based on FIG. 5 of U.S. Pat. No. 7,080,057 “Electronic Employee Selection Systems and Methods” (Scarborough). The method comprises collecting pre-hire information  102 , collecting post-hire information  104 , and building a model  106 . The method then refines pre-hire content  108  and repeats the steps.  FIG. 1B  is an illustration of a neural net model  110  generated by this process.  FIG. 1B  is based on FIG. 10 of Scarborough. The neural net comprises input items  112 , weights for said input items  118 , hidden layer nodes  114  and an output  116 . 
     One of the drawbacks of the Scarborough expert system is that it requires large quantities of high quality pre-hire and post-hire data that have to be collected over a long period of time. This is primarily due to the large number of parameters in neural nets that have to be calculated using the data. The weights for each input item, for example, need to be calculated as well as the weights for each neural net node. In example 35 of Scarborough, 2084 complete employment records collected over a year and a half were required to calculate said weights. Even then, the model was still subject to over-training. The Scarborough expert system, therefore, will not work for smaller organizations that might have only 100 persons or less in a given task function. There isn&#39;t enough data from current persons in these small organizations to calculate the parameters in the model without overtraining. There is need, therefore, for an artificial intelligence expert system for screening that can be developed with data from only a small number of current persons in a given task function. 
     SUMMARY OF THE INVENTION 
     The summary of the invention is provided as a guide to understanding the invention. It does not necessarily describe the most generic embodiment of the invention or the broadest range of alternative embodiments. 
       FIG. 2  illustrates an artificial intelligence expert system  200  for screening candidates for a task function. The system comprise a machine learning module  250  for developing a model of expected task performance of a candidate for a task function and a screening module  252  for using the model to screen candidates. The learning module only requires data from a relatively small number of training persons  216  in a given task function in order to develop a model  208  for screening candidates  236  for said task function. The system comprises a computer implemented screening item measuring instrument  202 , a task performance database  204 , a computer implemented modeling engine  206  and a computer implemented screening engine  210 . The measuring instrument, modeling engine and screening engine each comprise a human readable output device for presenting information to a person and a human operable input device for receiving input from a person. Output devices include screens, printers, and speakers. Input devices include keyboards, computer mice, eye scanning equipment, gesture recognition equipment and microphones. These examples are not exhaustive. The elements in said system may be combined into a single system or alternatively divided among a plurality of systems. 
     Machine Learning Module 
     The screening model is developed through the machine learning module  250  by providing one or more characteristic profiles  212  to a set of N training persons  216  in a given task function. N is the number of said training persons. The provision of the characteristic profiles is through the measuring instrument output device. Characteristic profiles comprise screening items that potentially have some bearing on the ability of a person to perform a task function. Characteristic profiles can also comprise non-screening items which are not used in the modeling. Characteristics that the profiles measure include broad aspects of a person such as behavior, personality and reasoning. As used herein, “items” are individual measures of some aspect of a characteristic. An example of a characteristic profile for the behavior of a person is a credit report for said person. An example of an item in a credit report is the number of tradelines a person has. An example of a characteristic profile for the personality of a person is a personality test. Personality tests are described in the Wikipedia article “Personality test” dated 22 Jun. 2015. Said Wikipedia article is incorporated herein by reference. An example of an item from a personality test would be a person&#39;s level of agreement or disagreement with a statement of belief. Other examples of characteristic profiles and their associated items are discussed with reference to  FIGS. 4-6 . 
     After the characteristic profiles are presented to the training persons, the measuring instrument then reads in responses  214  to the items in the characteristic profiles through the measuring instrument input device. In the case of a credit report, the measuring instrument might include the systems in a credit agency that collects transaction data regarding an individual. In the case of a personality profile, the measuring instrument might include the systems giving the profile to an individual and then collecting said individual&#39;s responses. 
     Task performance metric data is also collected  244  from the set of N training persons. If the task function of the persons includes sales, then the performance metric might include the number of completed sales during a given time period, such as monthly. The task performance metric data is then stored in the task performance database  204 . As used herein, a task performance metric is a quantitative measure of how well a person performs a task function. 
     The modeling engine then reads in  246  the responses from the N training persons and reads in  248  the task performance metric data and fits two or more path dependent models to the data. A “path dependent model” is a model whose final form depends upon how it is initiated. A forward stepwise regression is an example of a path dependent model. In a forward stepwise regression, an output variable, such as task performance metric, is first fit to a screening item which has a significant effect on the output variable. For example, the task performance metric might be sales performance and a screening item that has a significant effect might be tradelines. The modeling engine then selects an additional screening item that has an impact on the output variable over and above that of the first item. An example of an additional item might be a personality item from a personality test. The model continues to add items that provide incremental improvements to the model until a preset limit M is reached on the number of parameters in the model. The preset limit might be a fraction 1/E of the number of N training persons. A suitable value for E is 5 or greater. If N is 20, for example, and E is 5, then the number of parameters in the model is limited to N/E=4. If the model is a linear model with each screening item having one multiplier as its parameter and an additional parameter is a constant, then the total number of screening items in the forward stepwise regression is limited to M−1 or 3. This is an exceptionally small number of screening items relative to the prior art which might have 50 or more screening items in a neural net model. The degree of effectiveness of this approach of strictly limiting the number of parameters in a machine learning model will be discussed in more detail with respect to  FIGS. 9 and 10 . 
     In a forward stepwise regression, there may be more than one screening item that can be used to start the process. If a different screening item is selected as the starting item for a second run of the forward stepwise regression, then the forward stepwise regression might select different subsequent screening items or different weights for the screening items as the model is built. The screening items selected by a first run of a path dependent model is termed the first subset of screening items. The screening items selected by the second run of a path dependent model is termed the second subset of screening items, and so on. Thus the forward stepwise regression can produce more than one model using more than one subset of screening items from the same set of data and same total set of available screening items. 
     After multiple path dependent models are produced, the models may be combined  252  into a combined model  208 . The combination may be a linear combination, logarithmic combination or any other suitable combining method. Combining different path dependent models produced by the same data is important when the screening items are relatively coarse. As used herein, a “coarse screening item” is one that has 10 or less discrete quantified values. A screening item from a personality test, for example, might have only 3 possible values over its domain (e.g. “agree”, “disagree”, “not sure”). These can be quantified as values −1, 0, and 1 respectively. This coarseness can produce a large scatter in the model output which cannot be reduced by simply increasing the amount of data used to produce the model (e.g. increase the number of N training persons). One of the advantages of combining different models built with different coarse screening items is that the combination is much more effective at reducing scatter in the output than simply increasing the number of N training persons. 
     In order to select the starting point for each path dependent model, the modeling engine may present  222  a plurality of initial screening items to a modeler  226 . The modeler may then select an initial screening item for each of the path dependent models  224 . The modeler may also select different types of path dependent models, such as a forward stepwise regression, a backward stepwise regression or a bidirectional stepwise regression. Any type of linear or nonlinear path dependent model may be fit to the data provided the number of parameters in each model is limited to M. 
     Screening Module 
     Once the combined model  208  is developed, then the screening engine  210  may read it in  254  and use it to screen candidates  236  for said task function. A candidate is presented  232  with the characteristic profiles used to generate the model through the screening engine output device. The screening engine then receives the candidate&#39;s responses  234  to the items in the characteristic profiles through the screening engine input device. The screening engine then executes the model using the candidate&#39;s responses to generate a projected task performance metric for the candidate. If the projected task performance metric is less than a minimum threshold task performance metric, then the candidate is rejected  238  for the task function. If the projected task performance metric is above the minimum, the candidate is accepted  242  for at least further evaluation for assignment to the task. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1A  is a flow chart  100  of a prior art method for developing an artificial intelligence expert system for screening candidates for a task. 
         FIG. 1B  is an illustration of a neural net model generated by the prior art process of  FIG. 1 . 
         FIG. 2  illustrates an artificial intelligence expert system for screening task candidates for a task function. 
         FIG. 3  is a flow chart of a computer implemented method for generating a combined model for screening candidates for a task function. 
         FIG. 4  shows an example of a behavior profile. 
         FIG. 5  shows two hypothetical items from a personality profile. 
         FIG. 6  shows a set of questions that are a series item from a reasoning profile. 
         FIG. 7A  shows a graph of a task performance metric versus an effective screening item. 
         FIG. 7B  shows a graph of a task performance metric versus an ineffective non-screening item. 
         FIG. 8A  is a graph of forecasted task performance versus observed task performance for a first forward stepwise regression. 
         FIG. 8B  is a graph of forecasted task performance versus observed task performance for a second forward stepwise regression. 
         FIG. 9A  is a graph of forecasted task performance versus observed task performance for a second backward stepwise regression. 
         FIG. 9B  is a graph of forecasted task performance versus observed task performance for a combined model. 
         FIG. 10  shows a bar graph of person retention through the end of a training period. 
         FIG. 11  shows the cumulative task performance versus time for persons assigned to a task. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description describes non-limiting exemplary embodiments. Any individual features may be combined with other features as required by different applications for at least the benefits described herein. 
     As used herein, the term “about” means plus or minus 10% of a given value unless specifically indicated otherwise. 
     As used herein, a “computer-based system”, “computer implemented instrument”, “computer implemented engine” or the like comprises an input device for receiving data, an output device for outputting data in tangible form (e.g. printing or displaying on a computer screen), a permanent memory for storing data as well as computer code, and a microprocessor for executing computer code wherein said computer code resident in said permanent memory will physically cause said microprocessor to read-in data via said input device, process said data within said microprocessor and output said processed data via said output device. 
     Flowchart of Modeling Algorithm 
       FIG. 3  is a flow chart of a computer implemented method  300  for generating a combined model for screening candidates for a given task function. By “task function” it is meant a particular key measurable output of a person in a task. The task function is measurable by a task performance metric. 
     The number of persons in a given organization with the same task function may be relatively small, such as in the range of 10 to 100. Nonetheless, the method described in  FIG. 3  is suitable for developing a combined model for screening future candidates for said task function. 
     The process for creating the combined model begins with selecting a set of N training persons  302  with the same task function and measuring an appropriate task performance metric for each person. The persons may have a distribution of tenure with some engaged in the task for a short time and others engaged in the task for a longer time. The task performance metric data for each person can be weighted according to tenure. Different persons may have different fractions of their time allocated to a given task function. One person may spend 50% of his/her time performing a task function and another may spend 80% of his/her time performing said task function. The task performance metrics for each person, therefore, may be normalized according to the fraction of each person&#39;s time allocated to the task function. 
     A selection is then made  304  of characteristics that might be predictive of task performance. The selection can be made by a modeler based on observations of the persons. The modeler may select the characteristics of behavior, personality, reasoning ability or any other characteristic that might be related to task function performance. The modeler might observe, for example, that successful persons in a given task have the personality trait of “insensitivity to rejection”. The modeler would then select personality as a characteristic to be measured. Similarly, the modeler might observe that many persons selected for a task fail to make it through an initial training program since they find it too confusing. The modeler would then select reasoning ability as a characteristic to be measured. 
     Characteristics can be measured by profiles. Profiles comprise a plurality of items indicative of a characteristic.  FIG. 4  shows an example of a behavior profile  400 . The example is an excerpt from a hypothetical credit report  402 . A credit report comprises numerous items indicative of a person&#39;s credit behavior. These items include number of tradelines  404  and credit utilization  406 . Other behavior profiles may be used, such as driving records. 
       FIG. 5  shows an example of two hypothetical items from a personality profile  500 . A personality profile often comprises written items to which a person is asked to respond. The responses to the different items are interpreted to measure aspects of a person&#39;s personality. Item  502  is a feelings item. As used herein, a feelings item is a statement which a person is asked to indicate the extent to which said statement captures how said person feels. Item  504  is a viewpoint item. As used herein, a viewpoint item asks a person a question to determine to what extent said person agrees or disagrees with a particular point of view. The viewpoint item  504  asks a person to pick a statement they most strongly agree with and another item they most strongly disagree with. 
     The response to a feeling item can be converted into a discrete number by assigning a numerical value to each degree of response. For example, “Strongly agree”=1, “Agree”=2, “Mildly agree”=3, “Mildly Disagree”=4, “Disagree”=5 and “Strongly disagree”=6. These numerical values can be used in statistical correlations. The response to a viewpoint item can be converted into a discrete number by selecting a statement of interest (e.g. “I must have things done immediately”) and assigning a value of 1 if a response indicates that a person most strongly agrees with it, a value of 0 if the person does not indicate either strong agreement or strong disagreement, and a value of −1 if the person indicates they most strongly disagree with it. 
       FIG. 6  shows a set of questions that collectively are a reasoning item  604  from a reasoning profile  600 . The types of questions are pattern recognition  602 . A person is asked to pick the answer that best continues the series. The responses to the questions in the reasoning item can be converted to a discrete number by counting the number of correct answers. Different combinations of pattern recognition questions can be evaluated as potential individual screening items. 
     Referring back to  FIG. 3 , once characteristics predictive of task performance are selected  304 , appropriate characteristic profiles are selected to measure the characteristics  306 . The profiles are presented to the N training persons and responses from the persons are then received  308 . Characteristic profiles prepared by third parties may be received from said third parties as opposed to directly from the training persons. As used herein, however, the items in a characteristic profile prepared by a third party are still considered to be “responses” from a person. 
     The task performance metric data and responses to items in the characteristic profiles are then read in by a modeling engine and a comparison  310  is made between one or more of the individual items in a person&#39;s characteristic profiles and said person&#39;s task performance metric data. The modeling engine then determines which individual items appear to be effective in correlating to task performance. If an item is effective, it is characterized as a “screening item”. If it is not effective, it is characterized as a “non-screening item”. An item may be considered effective if a linear correlation between the task performance metrics of the N training persons with a given task function and the values of a given item in said N training persons&#39; characteristic profiles show an effect of at least 10% of the total range of task performance metric for the N training persons over the domain of the N training persons&#39; item responses. This is illustrated in  FIGS. 7A and 7B . 
       FIG. 7A  shows graph  700  of the task performance metric T p  versus the behavior item “Tradelines (B C1 )”. Data is shown for 35 people in a given organization. A linear regression line  702  is fit to the data. The regression line has a range  704  of about 1 over the domain  708  of the N training persons&#39; tradelines. The range of the task performance metric  706  is about 5. Thus the linear regression shows an effect of about ⅕ or 20% over the N training persons&#39; domain of tradelines. 10% is considered to be the minimum effect necessary for an item to be a screening item. Thus tradelines is considered indicative of task performance and is characterized as a screening item. Higher or lower values for minimum necessary effect, such as 5% or 20% may be used. A modeler may also designate items to be screening items based on the modeler&#39;s judgment. 
       FIG. 7B  shows a similar graph  710  for task performance versus a behavior item “Utilization (B C1 )”. The range  714  of the regression line  712  is only about 0.2 over the domain  718  of the N training persons&#39; utilizations. This is 0.2/5 or 4% of the range  706  of task performance metric. This is less than 10%. Utilization, therefore, is characterized as a non-screening item. Similar linear fits can be done for any other items in the characteristic profiles to identify items that can be designated as screening items and therefore used to build the models. 
     The linear correlations of task performance and screening items may not necessarily be statistically significant. A surprising benefit of the methods described herein is that effective combined models for screening task candidates are developed even when none of the individual items in the characteristic profiles by themselves show a statistically significant correlation to the task performance metric. 
     The non-screening items have also been shown to have surprising utility even though they are not explicitly used in the model. By providing persons with characteristic profiles comprising both screening items and non-screening items, the responses to the screening items are found to be more accurate. While not wishing to be held to the explanation, it is believed that by presenting training persons with screening items embedded in a set of non-screening items, said persons provide more consistent responses to the screening items. Similarly, when candidates for a task function are presented with screening items embedded in a set of non-screening items, their responses are more consistent as well. 
     Referring again back to  FIG. 3 , after effective screening items are identified  310 , a path dependent modeling technique is selected  312 . This may be done automatically or a modeler. The number of the parameters M in the model is limited to a fraction 1/E of the number N of training persons that provided responses to the characteristic profiles (i.e. M&lt;=N/E). It has been found by experiment that a value for E of 5 or greater is suitable. Thus there are at least 5 data points per model parameter. This reduces over fitting of the model to the data. 
     After the modeling technique is selected  312 , a starting point for the model is selected  314 . This may be automatic or by a modeler. The selection may be automated by starting with the most effective screening item for the first run of creating a path dependent model and the second most effective screening item for the second run of creating a path dependent model. 
     The path dependent model is then fit to the task performance metric data and screening item responses  316 . Suitable software for fitting models to the data include IBM® SPSS®, R programming language, and SAS software. The modeling step is then repeated R times  318  for different models and/or different starting points. A suitable value for R is 3 or more. 
     After the individual models are generated they are combined  322 . The combination may be a simple averaging or a weighted linear combination based on minimizing the errors between the combined model output and the task performance metric data. 
     Once the combined model is developed, it can be used as a screening tool for candidates for the task function. The output of the model is considered a forecast of a given candidate&#39;s future task performance. If a candidate&#39;s forecasted task performance is above a minimum threshold, the candidate is accepted for at least additional evaluation and possible assignment to the task. If the candidate&#39;s forecasted task performance is below said minimum threshold, then the candidate is rejected for the task. 
     Modeling Example 
     The above systems and methods were utilized by a midsized organization to develop a combined screening model for sales person candidates. Task performance metric data was collected for about 25 training persons (i.e. N˜25) already in the organization. The N training persons were also presented with a personality profile comprising personality and reasoning items. Responses from the N training persons were collected. Credit reports for the N training persons were also obtained. Screening items and non-screening items within said characteristic profiles were identified using linear correlations as illustrated in  FIGS. 7A and 7B . Data was then read into a computer implemented modeling system and a first forward stepwise regression was done with a modeler selecting a first screening parameter as a starting point. The results are illustrated in  FIG. 8A . 
       FIG. 8A  is a graph of forecasted task performance versus observed task performance for the N training persons. The observed task performance ranged from 2 to 7 units. There were no persons below about 2 units since that was considered a performance cutoff  804 . Any persons with less than 2 units of task performance were removed from the task. 
     The spread  802  in the observed task performance about a diagonal line  806  in the graph  800  gives an indication of the goodness of fit between the output of the model (forecasted task performance) and the observed task performance of the N training persons. The spread is about 2 to 4 units. Thus if this model alone were used, a candidate scoring a 3 units of forecasted task performance would be expected to have an observed task performance in the range of 2 to 4 units. This spread is relatively wide. A narrow spread would give a more useful model. 
     In this example, the equation for the first forward stepwise regression (FR1) was:
 
Task performance=−0.04 B   C1 +0.35 P   F2 +0.55 R   M1 +1.2
 
where:
         Task performance is the forecasted task performance;   B C1  is a screening item from a behavior profile;   P F2  is a screening item from a personality profile; and   R M1  is a screening item from a reasoning profile.
 
B C1  was the first screening item selected by the modeler to initiate the first forward stepwise regression. It was the most effective screening item for forecasting task performance. The other items in the model and their associated weights were determined by the forward stepwise regression program. Together B C1 , P F2 , and R M1  form a first subset of screening items.
       

       FIG. 8B  is a graph  810  of forecasted task performance from a second forward stepwise regression (FR2) versus the same observed task performance as in  FIG. 8A . The spread  812  is about the same as for the first forward stepwise regression. The starting point for the second forward stepwise regression, however, was a second screening item, B C3 , selected by a modeler. This screening item had a coarse discretization of about 5 levels as evidenced by the output gaps  814  in the forecasted task performance. The modeling program selected a same screening item P F2  from the N persons&#39; personality profiles for the second forward stepwise regression but replaced the reasoning screening item R M1  from the first regression with another personality screening item P V2 . Together P F2 , R M1 , and P V2  form a second subset of screening items. The first subset of screening items and the second subset of screening items are different by at least one screening item. Thus there is at least some independence between the models. 
     For the third run of the modeling program, a backward stepwise regression (BR1) was run.  FIG. 9A  is a graph  900  of the results from the backward stepwise regression. The spread  902  is comparable to the two other forward stepwise regressions. Two of the personality screening items P F1  and P V1  were different than the personality screening items in the other two regressions. Thus each model had a subset of screening items which was different by at least one screening item than the other subsets of screening items for the other models. 
       FIG. 9B  shows a graph  910  of the results when the three path dependent models were combined using a linear combination. A regression was done on the weights for each model in the linear combination to minimize the spread  912  between forecasted and observed task performance. The linear combination gives a more robust forecasted task performance that is less sensitive to small changes in the responses to the screening items. A candidate that has a forecasted task performance of a 3 for example, can be expected to have an observed task performance in the range of 2.5 to 3.5. This makes the combined model a more useful screening tool. 
     Candidate Screening Example 
     The above referenced organization used the combined model to screen new candidates for the task functions. The minimum threshold for a candidate&#39;s forecasted task performance metric was 2 units. About 32 of the candidates that met the minimum threshold were ultimately assigned to the task over the course of about a year. Each candidate went through an 8 week training period and then joined a pool of about 38 other persons who had been assigned to the task before the screening was implemented. 
       FIG. 10  shows a bar graph  1000  of person retention through the end of the training period. Persons assigned to the task before the screening was implemented had had a training completion rate of about 68%. Candidates assigned to the task after the screening was implemented had a training completion rate of about 80%. 
       FIG. 11  shows the cumulative task performance  1100  versus time for the first year of persons being assigned to the task function. By cumulative task performance, it is meant the total number of times the task was completed successfully up until a given time. The solid line  1104  is for persons who had been screened with the combined model before assignment. The dashed line  1106  is for persons who had not been screened with the combined model before assignment to the task. There were no tasks completed during the initial training period  1102 . Task completions then started off slowly and increased more rapidly as persons got more experience. The total cumulative task completions for the persons assigned after screening were 60% higher at the end of their first year than for persons assigned before screening. 
     Alternative Embodiments 
     The methods and systems described herein have been with respect to screening candidates for a task. The same methods and systems can be applied to any situation where persons need to be screened for a particular task as long as there are 10 or more individuals available to build the combined model. 
     The modeling step can be iterated as data is obtained for additional training persons performing the task function and/or additional performance data is obtained for existing training persons. 
     CONCLUSION 
     While the disclosure has been described with reference to one or more different exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the disclosure. In addition, many modifications may be made to adapt to a particular situation without departing from the essential scope or teachings thereof. Therefore, it is intended that the disclosure not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out the invention.