Method and system for processing multi-request applications

Abstract

A system receives application data to be used in requests made on behalf of an applicant to a selection of evaluator devices. The system includes a predictive model which predicts actual eligibility criteria for acceptance of a request by the evaluator devices, and is trained with a library of application data including previously evaluated requests and outcomes to the previously evaluated requests. The system compiles the application data into separate requests by synchronizing the application data and identifying a common core of data required by each selected evaluator device and compiling the common core of data along with particular requirements of individual evaluator devices. An applicant can thereby complete a multi-request application which generates requests to a plurality of evaluator devices and which avoids duplication of data storage and data transmission, and reduces effort required by the applicant. Implementations include students making applications for admission to academic institutions

1 .- 19 . (canceled) 
     
     
         20 . A system for automatically generating a universal admission application for an applicant to a plurality of academic institutions, the system comprising a processor operable to:
 receive, from the applicant, a preliminary applicant data and an institution selection identifying the plurality of academic institutions of interest to the applicant;   determine, with an application requirement predictive model, one or more predicted application requirements for each academic institution based at least on a portion of the preliminary applicant data, the one or more predicted application requirements comprising one or more application requirements outside of application requirements stipulated by the plurality of academic institutions;   receive, from the applicant, additional applicant data to satisfy the one or more predicted application requirements;   determine, with an acceptance predictive model trained with prior applicant data and admission results associated with the prior applicant data, an acceptance likelihood for each academic institution to accept the applicant based on the preliminary applicant data and the additional applicant data;   receive, from the applicant, an institution confirmation identifying one or more preferred academic institutions from the plurality of academic institutions after the acceptance likelihood is determined for each academic institution; and   generate an admission application for each preferred academic institution according to the application requirements stipulated by that preferred academic institution and the one or more predicted application requirements determined for that preferred academic institution, the admission application for one preferred academic institution comprising different applicant data from the admission application for another preferred academic institution.   
     
     
         21 . The system of  claim 20 , wherein:
 the preliminary applicant data comprises academic grades; and   the processor is operable to, when generating the admission application for the preferred academic institution, normalize the academic grades according to the application requirements for that preferred academic institution.   
     
     
         22 . The system of  claim 20 , wherein the processor is further operable to:
 determine, using the acceptance predictive model, whether the acceptance likelihood for each academic institution to accept the applicant can be improved with a supplementary applicant data; and   in response to determining that the acceptance likelihood can be improved with the supplementary applicant data, receive the supplementary applicant data from the applicant.   
     
     
         23 . The system of  claim 20 , wherein the processor is further operable to:
 identify one or more likely academic institutions from the plurality of academic institutions, each likely academic institutions being associated with the acceptance likelihood above an acceptance likelihood threshold.   
     
     
         24 . The system of  claim 20 , wherein:
 at least one of the preliminary applicant data and the one or more predicted application requirements comprises a written sample; and   the processor is further operable to apply natural language processing techniques to predict a quality of the written sample for determining the acceptance likelihood for each academic institution with the acceptance predictive model.   
     
     
         25 . The system of  claim 20 , wherein the processor is further operable to determine an expected pendency for each admission application submitted on behalf of the applicant to the one or more preferred academic institutions. 
     
     
         26 . The system of  claim 20 , wherein the processor is further operable to generate a status for each admission application submitted on behalf of the applicant to the one or more preferred academic institutions. 
     
     
         27 . The system of  claim 20 , wherein the processor is further operable to determine an expected commission fee for an agent managing the universal admission application for the applicant. 
     
     
         28 . A method for automatically generating a universal admission application for an applicant to a plurality of academic institutions, the method comprises operating a processor to:
 receive, from the applicant, a preliminary applicant data and an institution selection identifying the plurality of academic institutions of interest to the applicant;   determine, with an application requirement predictive model, one or more predicted application requirements for each academic institution based at least on a portion of the preliminary applicant data, the one or more predicted application requirements comprising one or more application requirements outside of application requirements stipulated by the plurality of academic institutions;   receive, from the applicant, additional applicant data to satisfy the one or more predicted application requirements;   determine, with an acceptance predictive model trained with prior applicant data and admission results associated with the prior applicant data, an acceptance likelihood for each academic institution to accept the applicant based on the preliminary applicant data and the additional applicant data;   receive, from the applicant, an institution confirmation identifying one or more preferred academic institutions from the plurality of academic institutions after the acceptance likelihood is determined for each academic institution; and   generate an admission application for each preferred academic institution according to the application requirements stipulated by that preferred academic institution and the one or more predicted application requirements determined for that preferred academic institution, the admission application for one preferred academic institution comprising different applicant data from the admission application for another preferred academic institution.   
     
     
         29 . The method of  claim 28 , wherein:
 the preliminary applicant data comprises academic grades; and   generating the admission application for each preferred academic institution comprises normalizing the academic grades according to the application requirements for that preferred academic institution.   
     
     
         30 . The method of  claim 28  comprises operating the processor to:
 determine, using the acceptance predictive model, whether the acceptance likelihood for each academic institution to accept the applicant can be improved with a supplementary applicant data; and 
 in response to determining that the acceptance likelihood can be improved with the supplementary applicant data, receive the supplementary applicant data from the applicant. 
 
     
     
         31 . The method of  claim 28  comprises operating the processor to identify one or more likely academic institutions from the plurality of academic institutions, each likely academic institutions being associated with the acceptance likelihood above an acceptance likelihood threshold. 
     
     
         32 . The method of  claim 28 , wherein:
 at least one of the preliminary applicant data and the one or more predicted application requirements comprises a written sample; and   the method further comprises operating the processor to apply natural language processing techniques to predict a quality of the written sample for determining the acceptance likelihood for each academic institution with the acceptance predictive model.   
     
     
         33 . The method of  claim 28  further comprises operating the processor to determine an expected pendency for each admission application submitted on behalf of the applicant to the one or more preferred academic institutions. 
     
     
         34 . The method of  claim 28  further comprises operating the processor to generate a status for each admission application submitted on behalf of the applicant to the one or more preferred academic institutions. 
     
     
         35 . The method of  claim 28  further comprises operating processor to determine an expected commission fee for an agent managing the universal admission application for the applicant.