Patent Publication Number: US-11393061-B2

Title: System and method for determining an amount of correlation between non-orthogonal vectors characterizing curricula participation

Description:
BACKGROUND 
     Scholarship awarding institutions, such as universities, are often under a large administration burden when it comes to processing and awarding scholarships. In some cases, thousands of applicants can apply for a single scholarship, which makes identifying the best scholarship candidates very arduous. As recognized by the present inventors, passing data for scholarship candidate identification can bog down computer networks across campus during time-intense scholarship review periods. Also, in dynamic technological industries, economic, government, and university priorities can be constantly changing, which also makes selecting scholarship awardees challenging. 
     The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as conventional art at the time of filing, are neither expressly nor impliedly admitted as conventional art against the present disclosure. 
     SUMMARY 
     The implementations described herein are directed toward systems for enhancing on-campus communications through vector correlation quantification for curricula participation. Also described are systems and methods for determining an amount of correlation between non-orthogonal vectors characterizing quantified curricula participation include generating a prioritization matrix for the scholarship including one or more entries associated with ideal characteristics of one or more scholarship applicants; generating an applicant attribute vector characterizing attributes of an applicant extracted from the application; calculating a product for each entry of the prioritization matrix by multiplying corresponding entries of the prioritization matrix and the applicant attribute vector; applying a weighting factor to the product for each entry of the prioritization matrix resulting in weighted products for each entry of the prioritization matrix; accumulating the weighted products into an applicant quality score indicating an amount of correlation between the prioritization matrix and the applicant attribute vector; transmitting an enhanced data packet to another computing device including the applicant attribute vector, the prioritization matrix, or the applicant quality score; and calculating a remaining scholarship budget. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  is a diagram of an example environment for a scholarship management system; 
         FIG. 2  is a flowchart of a potential applicant identification process; 
         FIG. 3  illustrates a screenshot of a scholarship interest inquiry user interface screen; 
         FIG. 4  is a flowchart of an application management process; 
         FIG. 5  illustrates a screenshot of a scholarship application user interface screen; 
         FIG. 6  is a flowchart of a prioritization matrix generation process; 
         FIG. 7  is an exemplary diagram of a data structure of an applicant attribute vector; 
         FIG. 8  is an exemplary diagram of a data structure of a prioritization matrix; 
         FIG. 9  is a graph of an applicant attribute vector and a prioritization matrix in two dimensional space; 
         FIG. 10  is a graphical illustration of an applicant quality score calculation; 
         FIG. 11  is a flowchart of a vector overlap process; 
         FIG. 12  is a flowchart of a scholarship application approval process; 
         FIG. 13  is a flowchart of a scholarship routing process; 
         FIG. 14A  illustrates a screenshot of a scholarship approver user interface screen; 
         FIG. 14B  is an exemplary illustration of a portion of the scholarship management environment; 
         FIG. 15  is a flowchart of a scholarship modification process; 
         FIG. 16  illustrates a screenshot of a scholarship modification user interface screen; 
         FIGS. 17A and 17B  illustrate various aspects of an exemplary architecture implementing a platform for automated management of scholarship applications; and 
         FIGS. 17C and 17D  illustrate an example server interface for connecting user computing devices within a platform for automated management of scholarship applications. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a diagram of an example environment  100  for a scholarship management system  108 . The diagram illustrates a series of interactions between one or more participants and the scholarship management system  108  which identifies potential applicants for one or more offered scholarships, determines priorities for awarding the scholarships to the applicants based on national, economic, and/or university interests, ranks applicants  102  based on an amount of correlation between the applicants  102  and the priorities, and outputs scholarship awards based on the rankings and/or decisions received from one or more scholarship approvers  104 . In some implementations, the scholarship management system  108  can also determine a scholarship award amount for the applicants  102  based on current budget allocations, number of applicants, quality of applicants based on a determined applicant quality score (AQS), and the like. The scholarship management system  108  may be associated with a particular university that awards scholarships or another type of scholarship awarding entity, such as an independently operating research lab, government lab or agency, and the like. 
     In certain embodiments, the scholarship management system  108  expedites processing and approval of one or more designated applicants having a determined AQS that reflects a high correlation between the applicant and the scholarship priorities. The AQS is a numerical representation of how well the applicant meets predetermined scholarship criteria in addition to the scholarship priorities and can be calculated automatically in real-time in response to receiving a submitted application. Providing expedited processing for the one or more designated applicants improves the speed at which the applications are received, processed, and awarded by one or more processing engines of the scholarship management system  108  because processing applications for the most highly rated applicants before other less highly rated applicants reduces a total number of processed applications. The scholarship management system  108  can also provide real-time feedback to the applicants  102  regarding a likelihood that a particular applicant will be awarded the scholarship in light of the calculated AQS, historical data related to previously awarded scholarships, and AQSs that have been calculated for applicants that have already submitted applications. 
     The applicants  102  include a plurality of computing devices and databases distributed across a widely dispersed network that may be distributed across a large, international geographic area. The applicant network can be separate and independent from any network associated with any other participant in the scholarship management environment  100 , such as external entities  106  or approvers  104 . In some implementations, various processing tasks performed by the scholarship application management system  108  can be distributed to computing resources of the submitter network based on processing demands on the computing resources of the scholarship application management system  108 . In some implementations, the applicants  102  can include students affiliated with the university awarding the scholarships, either as students, faculty, researchers, etc. In some implementations, the students affiliated with the university awarding the scholarship are referred to as internal applicants. The applicants  102  can also be external applicants that are not currently affiliated with the university awarding the scholarships. 
     In addition, the data handled and stored by the applicants  102  may be in a different format than the data handled and stored by the other participants of in the scholarship management environment  100 . Likewise, the data handled and stored for internal applicants may be in a different format that the data handled and stored by the external applicants. The applicants  102  provide inputs to the scholarship management system  108  that may include potential applicant data  152  indicating an amount of interest the one or more designated applicants have in applying for a particular scholarship as well as application data  122  that is extracted from an electronically-submitted scholarship application. The data provided to the scholarship management system  108  from the applicants  102  may be independent from the other participants and in a different format than the data provided by the external entities  106  and approvers  104 . 
     The approvers  104  include a plurality of computing devices and databases distributed across a widely dispersed network that may be distributed across a large, international geographic area. The approver network can be separate and independent from any network associated with any other participant in the scholarship management environment  100 , such as the external entities  106  or applicants  102 . In addition, the data handled and stored by the approvers  104  may be in a different format than the data handled and stored by the other participants of in the scholarship management environment  100 . In some implementations, various processing tasks performed by the scholarship application management system  108  can be distributed to computing resources of the approver network based on processing demands on the computing resources of the scholarship application management system  108 . The approvers  104  provide inputs to the scholarship management system  108  that may include prioritization matrix data  156  that may include ideal applicant characteristics that are considered to be important by the approvers  104 . The approvers  104  can also provide inputs to the scholarship management system  108  that included awarded scholarship data  154  indicating which applicants have been approved to receive scholarships as well as budget data  118  indicating a total scholarship budget and/or preferred monetary allocations for each of the awarded scholarships. In some implementations, the approvers  104  include university faculty and leadership that are have a stake in the scholarships awarded to the applicants  102 , such as faculty department heads, deans of colleges, dean of graduate studies, a scholarship and training committee chairman, a scholarship and training committee secretary, a director of personnel, a ministry education member, etc. The data provided to the scholarship management system  108  from the approvers  104  may be independent from the other participants and in a different format than the data provided by the external entities  106  and applicants  102 . In addition, the approvers  104  can receive data outputs from the scholarship management system  108  with respect to how the applicants  102  were ranked with respect to calculated AQS data  157 . 
     The external entities  106  include a plurality of computing devices and databases distributed across a widely dispersed network that may be distributed across a large, international geographic area. The external entity network can be separate and independent from any network associated with any other participant in the scholarship management environment  100 , such as the applicants  102  or approvers  104 . In addition, the data handled and stored by the external entities  106  may be in a different format than the data handled and stored by the other participants of in the scholarship management environment  100 . In some implementations, the external entities  106  may include an independently operating research lab, government lab or agency, a university that is not affiliated with the scholarship management system  108 , or any other entity that has a stake in the scholarships being awarded to the applicants  102  by the scholarship management system  108 . In one example, an one of the external entities  106  is an online Safeer system associated with Saudi Arabia&#39;s Ministry of Education, which acts as a third party application portal for external applicants applying for scholarships awarded by the scholarship management system  108 . The external entities  106  provide inputs to the scholarship management system  108  that may include current student data  128  for external applicants, and external source data  120  that includes data regarding scholarships awarded to students by the external entities  106 . The data provided to the scholarship management system  108  from the external entities  106  may be independent from the other participants and in a different format than the data provided by the applicants  102  or approvers  104 . 
     The applicants  102 , approvers  104 , external entities  106 , and data repository  114  can connect to the scholarship application management system  108  through computing devices  158  (e.g., mobile device  158   a , computer  158   b , or any other type of computing device) via a wired or wireless network (not shown). The network can include one or more networks, such as the Internet and can also communicate via wireless networks such as WI-FI, BLUETOOTH, cellular networks including EDGE, 3G and 4G wireless cellular systems, or any other wireless form of communication that is known. 
     The scholarship management system  108  includes one or more engines or modules that perform processes associated with identifying prospective scholarships applicants, processing incoming applications, collecting data regarding priorities for awarding the scholarships, routing the applications to the approvers  104 , and awarding the scholarships to one or more of the applicants  102 . References to the engines or modules throughout the disclosure are meant to refer to software processes executed by circuitry of one or more processing circuits, which can also be referred to interchangeably as processing circuitry. In one example, a user management engine  130  includes one or more processes associated with providing an interface to interact with one or more users (e.g., individuals employed by or otherwise associated with the approvers  104 , applicants  102 , and/or external entities  106 ) within the scholarship management environment  100 . The processes performed by the engines of the scholarship management system  108  can be executed in real-time in order to provide an immediate response to a system input. In addition, the processes can also be performed automatically in response to a process trigger that can include the reception of data from a data repository, a participant, or another processing engine. For example, the user management engine  130  can control connection and access to the scholarship management system  108  by the approvers  104 , applicants  102 , and external entities  106  via authentication interfaces at one or more external devices  158  of the approvers  104 , applicants  102 , and external entities  106 . 
     The scholarship management system  108  also includes a data mining/collection engine  136  that controls the gathering of survey data from the approvers  104 , applicants  102 , and external entities  106  in real-time. In some implementations, the data mining/collection engine  136  receives data from one or more sources that may have an effect on the generation of a prioritization matrix that is used to determine the AQS for the applicants  102 , which indicates the amount of correlation between qualities of the applicants  102  and the priorities associated with awarding the scholarships. For example, the data mining/collection engine  136  can receive automatically and/or continuously updated data associated with national and international industries that correspond to types of scholarships awarded by the scholarship management system  108 . For example, the data mining and collection engine  136  can receive automatic updates from government economic agencies related to hiring rates, average salaries, and other statistics that indicate a propensity for economic growth with respect to the industries. 
     In addition, the data mining/collection engine  136  can perform web crawling processes to access the updated data from one or more websites associated with government and economic priorities and scholarship priorities associated with the external entities  106  and other types of scholarship awarding institutions that can be used to determine data entries and weighting factors for the prioritization matrix that is used to calculate the AQS for the applicants  102 . In addition, the data mining/collection engine  136  can receive manually entered information from the participants in the scholarship management environment  100 , such as the application data  122  extracted from the electronically-submitted applications as well as scholarship award decisions received from the approvers  104 . The data mining/collection engine  136  also monitors websites and/or servers that receive and manage grades, academic performance parameters, and other performance parameters associated for the students at the university or affiliated with the external entities  106 , which are updated as current student data  128  stored in data repository  114 . For example, the current student data  128  includes academic major, class schedule for each grading period, grades for each class, and other academic or non-academic information. 
     In addition, the scholarship management system  108  includes a data management engine  142  that organizes the data received by the scholarship management system  108  and also controls data handling during execution of the processes associated with determining designated applicants that meet or exceed a set of highest standards for the one or more scholarships, receiving and processing applications from the applicants  102 , ranking the applicants  102  based on the calculated AQS based on a generated prioritization matrix, and routing the applications to the approvers  104  with automatically generated recommendations. In some implementations, the data management engine  142  processes the data received by the data mining/collection engine  136  and loads extracted data to the application data  122 , which can be a database of data files of the scholarship applications received from the applicants  102 . In addition, the data management engine  142  may perform a data validation/normalization process to configure the received applications into a predetermined format compatible with a format of the files of the application data  122  and ensure that the entries of the received applications have been properly completed. 
     The data management engine  142  also controls the interaction of the scholarship management system  108  with at least one data repository  114  associated with the scholarship management environment  100 . For example, the data management engine  142  can output automatically updated data such as current student data  128  and external source data  120 , manually updated data such as the application data  122  to the data repository  114 . In some implementations, the data uploaded to the data repository  114  can be either manually or automatically updated. For example, the budget data  118  that indicates how much money is allocated to each scholarship can be automatically determined by the budget management engine  144  based on budgets from previous scholarship cycles or manually determined based on inputs received from the approvers  104 . The data management engine  142  can also access any of the data from the data repositories  114  for use by the scholarship management system  108 . For example, data generated during the execution of one or more processes by scholarship management system  108  can also be stored in the data repository  114 , which can include the prioritization matrix data  156  or the AQS data  157 . The data management engine  142  controls the flow of data between the data repository  110  and the scholarship management system  108 . 
     The event trigger engine  132  manages the flow of data updates to the scholarship management system  108 . In some implementations, the event trigger data  132  detects updates to the application data  122 , current student data  128 , prioritization matrix data  156 , budget data  118 , external source data  120 , or any other type of data collected or controlled by the scholarship management system  108 . For example, the event trigger engine  132  detects modifications or additions to the files of the application data  122 , which may indicate that an new scholarship application has been received. When an update is detected to the application data  122 , the event trigger engine  132  loads the updated data files to the AQS calculation engine  135  so that the AQS can be calculated for the applicant submitting the application. The event trigger engine  132  operates in real-time to update the AQS calculation engine  135  when updated application data  122  is received from the applicants  102 . The event trigger engine also operates in real-time to update the prioritization matrix data  156  in response to the data mining/collection engine  136  extracting information related to the industries associated with the scholarships, which is part of the external source data  120 . The event trigger engine  132  also operates in real-time to update the current student data  128  in response to the data mining/collection engine  136  extracting updated grades for a completed grading period or updates to the current student data  128  that occur as new students enroll or graduated students matriculate. In addition, the event trigger engine  132  operates automatically when updated data is detected by the data/mining and collection engine  136 . 
     In addition, the event trigger engine  132  is configured to detect updates to the data stored in the data repository  114  from the one or more data sources at multiple update velocities. The update velocity of the data corresponds to a rate or frequency at which the scholarship management system  108  receives data updates from the data sources, such as the current student data  128 , budget data  118 , external source data  120 , or application data  122 . In addition, the velocities with which individual participants provide data updates may also vary. For example, student grades that are updated in the current student data  128  may be provided quarterly to the scholarship management system  108  at time periods that correspond to a conclusion of a grading period while the application data  122  may provide bi-annually to the scholarship management system  108  when that correspond to times of the year when an application window opens to receive scholarships. Also, the event trigger engine  132  can also be configured to detect unscheduled updates to the data stored in the data repository  114 . For example, applications can be received by the scholarship management system  108  at times that do not correspond to a predetermined application window. The event trigger engine  132  can be configured to detect multiple scheduled and unscheduled updates from a multiple data sources in parallel and in real-time. 
     The scholarship management system  108  also includes a potential applicant identification (ID) engine  133  that identifies potential applicants from the current student data  128  stored in the data repository  114 . In some implementations, the current student data  128  includes academic major, class schedule for each grading period, grades for each class, and other academic or non-academic information for students affiliated with the university associated with the scholarship management system  108  as well as external applicants who submitted scholarship applications during previous scholarship cycles through the external entities  106 . In one example, the potential applicant ID engine  133  determines whether a preliminary application window has opened, which corresponds to a predefined period of time before a general application window opens when applications are accepted by the scholarship management system  108 . The preliminary application window may be several days, a week, a month, several months, etc. before the general application window opens. 
     If the preliminary application window has opened, the potential applicant ID engine  133  triggers prioritization matrix engine  138  to generate a preliminary prioritization matrix for the scholarship. In some implementations, the preliminary prioritization matrix is an abbreviated version of the prioritization matrix that is used to calculate the AQS for purposes of determining which of the applicants  102  to award scholarships to during an application management process controlled by application management engine  162 . For example the preliminary prioritization matrix can include one or more entries associated with biographical, demographic, academic, and non-academic characteristics of applicants that indicate whether or not the applicant may be a good candidate for the scholarship and corresponding weighting factors indicating a relative amount of importance for the entries. Details regarding the generation of the prioritization matrix and corresponding weighting factors by the prioritization matrix engine  138  are discussed further herein. 
     The potential applicant ID engine  133  also triggers the AQS calculation engine  135  to calculate a preliminary AQS for each of the students represented by the current student data  128  stored in the data repository  114  based on the preliminary prioritization matrix. In some implementations, the current student data  128  is configured into a preliminary applicant attribute vector that is compared to the preliminary prioritization matrix by performing a vector overlap process. Details regarding the functionality of the AQS calculation engine  135  and the vector overlap process are discussed further herein. In response to receiving the calculated preliminary AQSs from the AQS calculation engine  135 , the potential applicant ID engine  133  saves the preliminary AQSs in the data repository as AQS data  157  and also triggers ranking engine  160  to rank the potential applicants from highest to lowest preliminary AQS. In some implementations, the ranking as well as a calculated preliminary AQS value are used to identify the students that are most qualified for one or more of the scholarships offered by the scholarship management system  108 . 
     The potential applicant ID engine  133  determines whether or not any potential applicants have been identified based on the calculated preliminary AQSs for the students represented by the current student data  128  as well as the preliminary AQS rankings. In one example, if a total number or total percentage of the students has a preliminary AQS that is greater than a preliminary AQS threshold, then the potential applicant ID engine  133  determines that the potential applicants are the students with AQSs that fall within a top predetermined number of AQSs. For example, if 75% of the preliminary AQSs for a particular scholarship out of a total of 100 students are greater than a predetermined threshold, then the potential applicant ID engine  133  determines that the potential applicants are the top ten ranked students with AQSs that are greater than the preliminary AQS threshold. The potential applicant ID engine  133  also stores the preliminary AQS calculations and corresponding rankings in the data repository  114  as potential applicant data  152 . 
     In response to determining the potential applicants, the potential applicant ID engine  133  automatically triggers graphical user interface (GUI) engine  140  to output scholarship interest inquiries to the applicants  102  that are identified as the potential applicants. The scholarship interest inquiry can be output to the applicants  102  via any type of messaging medium, such as email, text message, notification on webpage or electronic device application, etc. In some implementations, the scholarship interest inquiry is output to the potential applicants to gauge a level of interest a particular potential applicant has in applying for the scholarship. For example, the potential applicant can reply to the scholarship interest inquiry by indicating via one or more input fields on the scholarship interest inquiry message or webpage that he or she is “very interested,” “maybe interested,” or “not interested.” The reply to the scholarship interest inquiry is saved as part of the potential applicant data  152  in the data repository  114  with the corresponding potential applicant&#39;s ranking and preliminary AQS. In some implementations, if a potential applicant who has indicated that he or she is “very interested” or “maybe interested” submits an application for the scholarship, that potential applicant&#39;s application is flagged for expedited processing and approval. 
     The scholarship management system  108  also includes an application management engine  162  that processes received applications for scholarships associated with the scholarship management system  108 . The scholarship management engine  162  determines whether the general application window has opened, which corresponds to a predetermined number of days, weeks, or months when applications are received by the scholarship management system  108 . For example, the general application window may occur between November 2016 and February 2017 for a scholarship commencement date of August 2017. 
     If the general application window has opened, the application management engine  162  triggers prioritization matrix engine  138  to generate a prioritization matrix for the scholarship. The preliminary prioritization matrix can include one or more entries associated with biographical, demographic, academic, and non-academic characteristics of applicants that indicate whether or not the applicant may be a good candidate for the scholarship and corresponding weighting factors indicating a relative amount of importance for the entries. Details regarding the generation of the prioritization matrix and corresponding weighting factors are discussed further herein. 
     During the period of time that the general application window is open, the application management engine  162  receives applications for the one or more scholarships submitted by the applicants  102 . The applicants  102  can submit the applications by accessing a scholarship application UI screen output by the GUI engine  140  through a website, server portal, application interface, and the like. In some aspects, one or more of the data entry fields output to the scholarship application UI screen corresponds to the entries of the prioritization matrix. In addition, access to the scholarship application UI screen is controlled by the user management engine  130 . In response to receiving a submitted application, the application management engine  162  validates the submitted data and outputting notifications to the applicants  102  to correct any information in the submitted application that does not conform to predefined criteria for data entry fields of the application and saves the information from the application data entry fields as application data  122 . The application management engine  162  also automatically extracts one or more applicant attributes from the data entry fields of the submitted application and organizes the applicant attributes into an applicant attribute vector, which is also stored in the application data  122  of the data repository  114 . In some implementations, the extracted applicant attributes that make up the applicant attribute vector correspond to the one or more entries of the generated prioritization matrix. For example, the applicant attribute vector can include entries associated with biographical, demographic, academic, and non-academic characteristics of the applicants  102 . 
     The application management engine  162  also triggers the AQS calculation engine  135  to calculate an AQS for each of the applicants  102  based on the prioritization matrix by performing a vector overlap process. Details regarding the functionality of the AQS calculation engine  135  and the vector overlap process are discussed further herein. In response to receiving the calculated AQS for each of the applicants  102  from the AQS calculation engine  135 , the application management engine  162  stores the calculated AQSs as AQS data  157  in the data repository  114  and automatically generates a scholarship prediction, which can be output to the applicants  102  via a UI screen in real time in response to submitting an application to the scholarship management system  108 . 
     In some implementations, the application management engine  162  determines the scholarship prediction for a particular application by comparing the AQS of the applicant  102  to an average AQS for the scholarship that was awarded during a previous scholarship application cycle. For example, the application management engine  162  can assign a level of likelihood or probability that the applicant  102  will be awarded the scholarship by an amount the calculated AQS for the applicant  102  exceeds or falls short of the average AQS for the awarded scholarship during the previous application cycle. The levels of likelihood that the scholarship will be awarded to the applicant  102  may include high, medium, and low likelihoods. In some examples, the level of likelihood is represented by a confidence percentage. The application management engine  162  can also determine the level of likelihood that the scholarship will be awarded to a particular applicant based on how the calculated AQS for the applicant  102  compares to the AQSs of applicants that have already submitted applicants for the same scholarship cycle. In addition to outputting the scholarship prediction to the applicant  102  via a UI, the application management engine  162  also saves the scholarship prediction in the data repository  114  with the corresponding application data  122  for the applicant  102 . 
     The application management engine  162  also determines whether a received application qualifies for expedited approval routing, which can also be referred to as prioritized routing. In some implementations, received applications for potential applicants identified by the potential applicant ID engine  133  who indicated that they were “very interested” or “maybe interested” in applying for the scholarship in response to receiving a scholarship interest inquiry may receive expedited or prioritized routing through a routing chain of the approvers  104 . The application management engine  162  determines whether or not to flag the application for expedited routing by accessing the potential applicant data  152  to determine whether the applicant associated with the received application was identified as a potential applicant and if that potential applicant expressed an interest level of “very interested” or “maybe interested” in applying for the scholarship when replying to the scholarship interest inquiry. Once the application management engine  162  determines whether or not to flag the application for expedited routing, scholarship approval engine  131  is triggered to perform an application routing and approval process, as will be discussed in further detail herein. 
     The scholarship management system  108  also includes the prioritization matrix engine  138 , which automatically generates a prioritization matrix in response to the opening of the application window for a scholarship. The entries of the prioritization matrix provide an indication of what the scholarship approvers  104  may consider to be important applicant attributes when deciding who to award scholarships to and are used to generate the AQS, which is a numerical representation of an amount of correlation between the applicant attribute vector and the prioritization matrix. The prioritization matrix can include one or more entries associated with biographical, demographic, academic, and non-academic characteristics of applicants that indicate whether or not the applicant may be a good candidate for the scholarship and corresponding weighting factors indicating a relative amount of importance for the entries. In some implementations, the prioritization matrix engine  138  also determines weighting factors for each of the entries of the prioritization matrix that provide an indication of a relative importance of each of the entries. For example, for an entry of total grade point average may have a higher weighting factor than entries that indicate age or gender of the applicant  102 . As will be discussed further herein, the weighting factors for each entry of the prioritization matrix are used when calculating the AQS for each of the applicants  102  for the one or more scholarships. 
     In response to determining that the application window has opened, the prioritization matrix engine  138  determines whether or not the scholarship has been awarded in a previous application cycle. If the scholarship was awarded in a previous application cycle, then the prioritization matrix engine  138  accesses the prioritization matrix and corresponding weighting factors from the previous application cycle from prioritization matrix data  156  in the data repository  114 , which is as a basis for generating the prioritization matrix for a current application cycle. If the scholarship was not awarded in the previous application cycle, then the prioritization matrix engine  138  generates a default prioritization matrix. In one example, the data repository stores default prioritization matrices for one or more scholarship categories as part of the prioritization matrix data  156 . The scholarship categories can include degree level (e.g., graduate, undergraduate, etc.) or major associated with the scholarship. For example, the prioritization matrix data  156  can include a default prioritization matrix for graduate biomedical engineering scholarships. In some aspects, the weighting factors for the entries of the default prioritization matrix equal and set to a predetermined value, such as 0.5 in an example where the weighting factors are values between 0 and 1. 
     In addition, the prioritization matrix engine  138  triggers data mining/collection engine  136  to perform web crawling processes to access keyword data and other external source data  120  from one or more websites associated with government and economic priorities and scholarship priorities associated with the external entities  106  and other types of scholarship awarding institutions. For example, the data mining/collection engine  136  gathers information related to how a job market for a particular industry is performing by web crawling through job advertising websites, economic news websites, etc. and detecting a number of keyword “hits” associated with particular industries. The keyword data can include words or phrases such as “petroleum,” “oil,” “biomedical,” “medicine,” “law,” and any other words associated with various industries or research areas. The keyword data along with any other extracted data can be stored in the data repository  114  as the external source data  120 . 
     The prioritization matrix engine  138  uses the keyword data, other external source data  120 , awarded scholarship data  156 , and any other type of data to modify the data entries and weighting factors of the default prioritization matrix or the prioritization matrix that was used in a previous application cycle. For example, if the prioritization matrix engine  138  determines that a scholarship awarded during a previous scholarship application cycle favored a particular gender or hometown, then the prioritization matrix engine  138  may reduce the weighting factors associated with that particular gender or hometown and increase the weighting factors associated with other genders or hometowns in order to increase the diversity of students being awarded scholarships. Also, if a particular scholarship is awarded to a biomedical engineering student, and the extracted keyword data indicates that biomedical engineering that focuses on neurology is more popular than during the previous scholarship application cycle based on the number of keyword hits, then the prioritization matrix engine  138  may increase the weighting factor for an entry of the prioritization matrix associated with a research area focused on neurology. The weighting factors for each of the entries of the prioritization matrix can be determined or modified based on inputs received from the approvers  104  or other staff or administrators of the university or scholarship-awarding entities. 
     The scholarship management system  108  also includes an AQS calculation engine  135  that calculates the AQS, which is a numerical representation of an amount of correlation between the applicant attribute vector and the prioritization matrix and provides an indication of how well the applicants  102  meet certain criteria associated with awarding the scholarship. The AQS calculation engine  135  performs a vector overlap process, which computes the amount of correlation between the applicant attribute vector and the prioritization matrix for a particular scholarship. The output of the vector overlap process is sent to the application management engine  162 , which generates the automatic scholarship prediction as previously discussed. If the vector overlap process is performed in conjunction with a potential applicant identification process, then the output of the vector overlap process is sent to the potential applicant ID engine  133 . Details regarding the vector overlap process are discussed further herein. The AQSs calculated by the AQS calculation engine  135  are stored in the data repository  114  as the AQS data  157 . 
     The scholarship management system  108  also includes a scholarship approval engine  131 , which manages a flow of scholarship applications or scholarship modification requests through the one or more approvers  104  associated with the scholarships. The scholarship approval engine  131  determines an approval chain for a particular scholarship based on approver data  124  stored in the data repository  114 . For example, an electrical engineering scholarship may be routed through an electrical engineering department chairman while a medical school scholarship may be routed through a medical school dean or chairman. 
     In response to determining that the application window has closed, the scholarship approval engine  131  automatically triggers the ranking engine  160  to rank the received applications for a particular scholarship according to the calculated AQS for each applicant. In response to receiving the ranked applicants from the ranking engine  160 , the scholarship approval engine  131  routes the applications through the approval chain. If the application window has not closed, the scholarship approval engine  131  determines whether or not the received application has been flagged for expedited routing based on whether received application was identified as a potential applicant and if that potential applicant expressed an interest level of “very interested” or “maybe interested” in applying for the scholarship when replying to the scholarship interest inquiry. If the application was flagged for expedited routing, then the scholarship approval engine  131  automatically routes the application through the approval chain whether or not the application window has closed. If the application was not flagged for expedited routing, then the received application remains in a queue until the application window closes. 
     The scholarship approval engine  131  controls the flow of applications through the approvers  104  based on the approval chain for a particular scholarship or scholarship modification request. For example, the approvers  104  for a particular scholarship for an internal applicant includes one or more of a department chairman, a college dean, a scholarship committee chairman, or a university chairman. For an external applicant, the approval chain may also include a dean of higher education and a member of ministry of education in addition to the approvers  104  for the internal applicants. 
     In some implementations, the scholarship approval engine  131  triggers the GUI engine  140  to output an approver interface screen to each of the approvers  104  as the applications for the scholarship are routed through the approval chain. The approver interface screen is a dashboard that provides the approvers  104  with various types of data that can include scholarship statistics from previous application cycles, a quick-view list of the applicants  102  for the scholarship with the corresponding AQS, scholarship award statistics for the current application cycle, etc. The approver user interface screen provides the approvers the ability to approve or deny a scholarship award to a particular applicant, input an award amount, and input additional information related to why the approver awarded or did not award the scholarship to the particular applicant. The approver user interface screen can also include the decisions by the previous reviewers in the approval chain so that a final approver can select applicants for scholarship awards based on inputs received from the other approvers  104  in the approval chain. In some implementations, the scholarship award decision made by the final approver in the approval chain is a final award decision. 
     Once the applications have been routed through the approval chain and the award decision has been determined, the scholarship approval engine  131  triggers awarded scholarship management engine  134  to output awarded scholarship information to the applicants  102  who have been awarded scholarships. The awarded scholarship information can be output to the applicants with digital signatures or stamps that indicate the award amount. In addition, in response to receiving the final award decision, the scholarship approval engine  131  triggers a budget management engine  144  to modify a total scholarship budget based on the total amount of money awarded for the scholarship. 
     The scholarship management system  108  also includes the budget management engine  144  that manages a total amount of money budgeted toward the scholarships awarded by the scholarship management system  108 . In some implementations, based on an amount of money awarded in scholarships by the scholarship management system  108  for a scholarship application window, the budget management engine  144  may determine that an additional scholarship application window can be opened and can allocate any remaining money in a scholarship budget to one or more scholarships that can be awarded during the additional scholarship window. The budget management engine  144  accesses historical and current budget data  118  stored in the data repository  114  and updates the budget data  118  based on the awarded scholarships. 
     The scholarship management system  108  also includes an awarded scholarship management engine  134  that controls the award of scholarship to the applicants. In response to receiving the award decision from the scholarship approval engine  131 , the awarded scholarship management engine  134  outputs awarded scholarship information to the applicants  102  as well as stores the awarded scholarship information as awarded scholarship data  154  in the data repository  114 , which can be used in future scholarship application cycles to generate the prioritization matrix, allocate funds, identify potential applications, etc. The awarded scholarship management engine  134  can also interface with financial institutions of the applicants  102  and the university to transfer scholarship funds from the university to the applicants  102  who are awarded the scholarships. 
     In addition to outputting awarded scholarship information to the applicants  102  who are awarded scholarships, the awarded scholarship management engine  134  also manages scholarship modifications in response to receiving a scholarship modification request from an applicant  102 . In some implementations, scholarships have benefits to the applicants  102  other than receiving an education. For example, the awarded scholarships may also allow for applicants who are awarded scholarships, also referred to as awarded applicants, to attend conferences, go on education tours, etc. The awarded applicants can submit a modification request to allocate scholarship funds to attend the conferences or go on the education tours. The awarded applicants can also submit modification requests to extend the scholarship, increase the scholarship award amount, transfer the scholarship to another institution, or terminate the scholarship. In some implementations, the awarded scholarship management engine  134  receive the scholarship modification request from the GUI engine  140  that receives a modification request at a scholarship modification interface screen where the awarded applicants can input a type of modification request as well as a reason for submitting the modification request. 
     In response to receiving the scholarship modification request, the awarded scholarship management engine  134  determines whether the request is within predetermined scholarship terms that provide for automatic approval of the request. For example, if the modification request is a request to attend a conference and the awarded scholarship data  154  for the scholarship indicates that the awarded applicant is allowed to attend one conference under the terms of the scholarship. If the modification request is within the scholarship terms, then the awarded scholarship management engine  134  outputs an approval to the awarded applicant who is making the request and updates the awarded scholarship data  154  in the data repository  114  to indicate that the approval was granted. If the modification request is not within the predetermined scholarship terms, then the awarded scholarship management engine  134  triggers the scholarship approval engine  131  to route the scholarship modification request through the approval chain to process the scholarship modification request. For example, the approval chain for the scholarship modification request is based on the type of modification request being submitted and can include one or more of the department chairman, college dean, scholarship committee chairman, or university chairman. Once the awarded scholarship management engine  134  receives a final modification decision from the scholarship approval engine  131 , the final modification decision is output to the awarded applicant who initiated the request, and the awarded scholarship data  154  stored in the data repository  114  is updated. 
     The scholarship management system  108  also includes a ranking engine  160  that automatically ranks the applicants  102  in real-time according to one or more metrics or categories in response to a ranking request from one of the other processing engines. For example, the potential applicant ID engine  133  triggers the ranking engine  160  to rank the potential applicants from highest to lowest preliminary AQS in order to identify a predetermined number of highest-ranked potential applicants. In addition, when an application window closes, the scholarship approval engine  131  triggers the ranking engine  160  to rank the applicants  102  from highest to lowest AQS. In other implementations, the ranking engine  160  can also rank the applicants  102  according to other metrics such as class rank, GPA, etc. 
     The scholarship management system  108 , in some implementations, also includes a graphical user interface (GUI) engine  140  that controls dissemination and reception of data from the applicants  102 , approvers  104 , and external entities  106  through one or more user interface (UI) screens that are output to the external devices  158 . For example, potential applicants can indicate a level of interest in applying for a particular scholarship at a scholarship interest inquiry UI screen. The GUI engine  140  can also output a scholarship application UI screen to the applicants  102  who are applying for the scholarships managed by the scholarship management system  108 . In addition, the GUI engine  140  can output a scholarship approver UI screen which is a dashboard that provides the approvers  104  with various types of data that can include scholarship statistics from previous application cycles, a quick-view list of the applicants  102  for the scholarship with the corresponding AQS, scholarship award statistics for the current application cycle, etc. Applicants who have been awarded scholarships can submit scholarship modification requests via a scholarship modification UI screen, which is controlled by the GUI engine  140 . 
     The scholarship management system  108 , in some implementations, also includes a real-time notification engine  148  that ensures that data input to scholarship management system  108  is processed in real-time. In addition, the processes executed by the real-time notification engine  148  ensure interactions between the participants and the scholarship management system  108  are processed in real-time. For example, the real-time notification engine  104  outputs alerts and notifications to the approvers  104 , applicants  102 , external entities  106  via the UI screens when an application window has open or closed, when a potential applicant has submitted an application, when a scholarship modification request has been received, etc. 
     In some implementations, data associated with the processes performed by the scholarship management system  108  is stored in one or more data repositories of the scholarship management environment  100  such as the data repository  114 . Data received by the scholarship management system  108  from the one or more data sources can be received and stored in real-time with respect to when the data is received from the data sources. In addition, the data can be stored automatically in response to receiving one or more data files from the data sources. The data stored in the data repository can be auto load data that is updated automatically from one or more sources at predetermined time intervals. For example, auto load data can include the current student data  128  that is automatically updated from institution registrar databases or external source data  120  that is received from the external entities  106  or gathered via web crawling processes. 
     The data stored in the data repository  114  can also include manually entered information that is input via UIs at the external devices  158  and is received by the data mining/collection engine  136  of the scholarship management system  108 . The manually entered information can then be then processed by the data management engine  142  before being disseminated to other processing engines of the scholarship management system  108 . The manually entered data can include the application data  122  extracted from the submitted applications, approver data  124  that indicates the approval chain for the scholarships, and budget data  118  that indicates the budget allocated for the various scholarships. 
     The data repository  114  can also store process execution data that is generated by the scholarship management system  108  when executing the processes associated with identifying potential applicants, processing and awarding scholarship applications, and processing scholarship modification requests. For example, the process execution data can include the potential applicant data  152  that includes an amount of interest a potential applicant has in applying for a particular scholarship based on a response to a scholarship interest inquiry. The process execution data can also include awarded scholarship data  154 , prioritization matrix data  156 , and AQS data  157 . 
       FIG. 2  is an exemplary flowchart of a potential applicant identification process  200 , which is controlled by the potential applicant ID engine  133 . For example, the potential applicant ID engine  133  automatically triggers other processing engines of the scholarship management system  108  in real-time to perform one or more steps of the potential applicant identification process  200  and processes the data received from the other processing engines in accordance with the steps of the process  200 . 
     At step  201 , the potential applicant ID engine  133  determines whether a preliminary application window has opened, which corresponds to a predefined period of time before a general application window opens when applications are accepted by the scholarship management system  108 . The preliminary application window may be several days, a week, a month, several months, etc. before the general application window opens. If the preliminary application window has opened, resulting in a “yes” at step  201 , then step  202  is performed. Otherwise, if the preliminary application window has not opened, resulting in a “no” at step  201 , then the process  200  returns to step  201 . 
     At step  202 , the potential applicant ID engine  133  triggers the prioritization matrix engine  138  to generate a preliminary prioritization matrix for the scholarship. In some implementations, the preliminary prioritization matrix is an abbreviated version of the prioritization matrix that is used to calculate the AQS for purposes of determining which of the applicants  102  to award scholarships to. For example the preliminary prioritization matrix can include one or more entries associated with biographical, demographic, academic, and non-academic characteristics of applicants that indicate whether or not the applicant may be a good candidate for the scholarship and corresponding weighting factors indicating a relative amount of importance for the entries. Details regarding steps of a process for generating the preliminary prioritization matrix are discussed further herein. 
     At step  204 , the potential applicant ID engine  133  also trigger the AQS calculation engine  135  to calculate a preliminary AQS for each of the students represented by the current student data  128  stored in the data repository  114  based on the preliminary prioritization matrix by performing a vector overlap process. The potential applicant ID engine  133  configures the current student data  128  stored in the data repository  114  into a preliminary applicant attribute vector. The preliminary AQS indicates an amount of correlation between the preliminary applicant attribute vector and preliminary prioritization matrix. The AQS calculation engine  135  returns the calculated preliminary AQSs for the students represented by the current student data  128  to the potential applicant ID engine  133 . Details regarding the vector overlap process are discussed further herein. 
     At step  206 , in response to receiving the calculated preliminary AQSs from the AQS calculation engine  135 , the potential applicant ID engine  133  triggers ranking engine  160  to rank the potential applicants from highest to lowest preliminary AQS. In some implementations, the ranking as well as a calculated preliminary AQS value are used to identify the students that are most qualified for one or more of the scholarships offered by the scholarship management system  108 . 
     At step  208 , the potential applicant ID engine  133  determines whether or not any potential applicants have been identified based on the calculated preliminary AQSs for the students represented by the current student data  128  as well as the preliminary AQS rankings. In one example, if a total number or total percentage of the students has a preliminary AQS that is greater than a preliminary AQS threshold, then the potential applicant ID engine  133  determines that the potential applicants are the students with AQSs that fall within a top predetermined number of AQSs. For example, if 75% of the preliminary AQSs for a particular scholarship out of a total of 100 students are greater than a predetermined threshold, then the potential applicant ID engine  133  determines that the potential applicants are the top ten ranked students with AQSs that are greater than the preliminary AQS threshold. The potential applicant ID engine  133  also stores the preliminary AQS calculations and corresponding rankings in the data repository  114  as potential applicant data  152 . If one or more potential applicants are identified, resulting in a “yes” at step  208 , then step  210  is performed. Otherwise, if no potential applicants are identified, resulting in a “no” at step  208 , then the process  200  is terminated. 
     At step  210 , in response to determining the potential applicants, the potential applicant ID engine  133  automatically triggers the GUI engine  140  to output scholarship interest inquiries to the applicants  102  that are identified as the potential applicants. The scholarship interest inquiry can be output to the applicants  102  via any type of messaging medium, such as email, text message, notification on webpage or electronic device application, etc. In some implementations, the scholarship interest inquiry is output to the potential applicants to gauge a level of interest a particular potential applicant has in applying for the scholarship. For example, the potential applicant can reply to the scholarship interest inquiry by indicating via one or more input fields on the scholarship interest inquiry message or webpage that he or she is “very interested,” “maybe interested,” or “not interested.” 
     At step  212 , the potential applicant ID engine  133  determines whether or not the potential applicant has replied to the scholarship interest inquiry. If the potential applicant has replied to the scholarship interest inquiry, resulting in a “yes” at step  212 , then step  216  is performed. Otherwise, if the potential applicant has not replied to the scholarship interest inquiry, resulting in a “no” at step  212 , then step  214  is performed. 
     At step  214 , in response to determining that the potential applicant has not replied to the scholarship interest inquiry, the potential applicant ID engine  133  outputs reminders at predetermined time intervals until the general application window opens when applications are accepted by the scholarship management system  108 . For example, the potential applicant ID engine  133  can output scholarship interest inquiry reminders to the potential applicants who have not replied on a weekly basis until the general application window opens. 
     At step  216 , the reply to the scholarship interest inquiry is received and processed, which includes being saved as part of the potential applicant data  152  in the data repository  114  with the corresponding potential applicant&#39;s ranking and preliminary AQS. In some implementations, if a potential applicant who has indicated that he or she is “very interested” or “maybe interested” submits an application for the scholarship, that potential applicant&#39;s application is flagged for expedited processing and approval. 
       FIG. 3  is an exemplary screenshot of a scholarship interest inquiry user interface (UI)  300 . The scholarship interest inquiry UI  300  may be output by the GUI engine  140  to the potential applicants in one or more formats, such as email, text message, notification on webpage or electronic device application, etc. The scholarship interest inquiry UI  300  includes multiple data fields and input fields that allow the potential applicants to view information related to the scholarship they have been identified as potential applicants for as well as indicate a level of interest in applying for the scholarship. For example, the GUI engine  140  outputs a scholarship title to data field  302  that indicates to the potential applicant which scholarship the potential applicant ID engine  133  has determined that the potential applicant is a good candidate for. The data field  302  also includes an input that the potential applicant can select to view additional details regarding the scholarship, such as monetary award, scholarship term, research opportunities associated with the scholarship, and other details related to the scholarship. In data field  304 , the GUI engine  140  outputs one or more attributes of the current student data  128  for the potential applicant that had a highest amount of contribution to the calculated preliminary AQS. At data field  306 , the GUI engine  140  receives a selection from the potential applicant that indicates whether or not the potential applicant is interested in applying for the scholarship. For example, the data field  306  includes “very interested,” “maybe interested,” and “not interested” selections. The GUI engine  140  passes the received selection to the potential applicant ID engine  133 , and the received selection is saved as part of the potential applicant data  152  in the data repository  114  with the corresponding potential applicant&#39;s ranking and preliminary AQS. 
       FIG. 4  is an exemplary flowchart of an application management process  400 , which is controlled by the application management engine  162 . For example, the application management engine  162  automatically triggers other processing engines of the scholarship management system  108  in real-time to perform one or more steps of the application management process  400  and processes the data received from the other processing engines in accordance with the steps of the process  400 . 
     At step  401 , the scholarship management engine  162  determines whether the general application window has opened, which corresponds to a predetermined number of days, weeks, or months when applications are received by the scholarship management system  108 . For example, the general application window may occur between November 2016 and February 2017 for a scholarship commencement date of August 2017. If the general application window has opened, resulting in a “yes” at step  401 , then step  402  is performed. Otherwise, if the general application window has not opened, resulting in a “no” at step  401 , then the process  400  returns to step  401 . 
     At step  402 , if the general application window has opened, the application management engine  162  triggers prioritization matrix engine  138  to generate a prioritization matrix for the scholarship. The preliminary prioritization matrix can include one or more entries associated with biographical, demographic, academic, and non-academic characteristics of applicants that indicate whether or not the applicant may be a good candidate for the scholarship and corresponding weighting factors indicating a relative amount of importance for the entries. Details regarding steps of a process for generating the prioritization matrix and corresponding weighting factors are discussed further herein. 
     At step  404 , during the period of time that the general application window is open, the application management engine  162  receives applications for the one or more scholarships submitted by the applicants  102 . The applicants  102  can submit the applications by accessing a scholarship application UI screen output by the GUI engine  140  through a website, server portal, application interface, and the like. In some aspects, one or more of the data entry fields output to the scholarship application UI screen corresponds to the entries of the prioritization matrix. In addition, access to the scholarship application UI screen is controlled by the user management engine  130 . In response to receiving a submitted application, the application management engine  162  validates the submitted data and outputting notifications to the applicants  102  to correct any information in the submitted application that does not conform to predefined criteria for data entry fields of the application. 
     At step  405 , the application management engine  162  automatically extracts one or more applicant attributes from the data entry fields of the submitted application and organizes the applicant attributes into an applicant attribute vector. In some implementations, the extracted applicant attributes that make up the applicant attribute vector correspond to the one or more entries of the generated prioritization matrix. For example, the applicant attribute vector can include entries associated with biographical, demographic, academic, and non-academic characteristics of the applicants  102 . Details regarding the applicant attribute vector are discussed further herein. 
     At step  406 , the application management engine  162  triggers the AQS calculation engine  135  to calculate an AQS for each of the applicants  102  based on the prioritization matrix by performing a vector overlap process. Details regarding the functionality of the AQS calculation engine  135  and the vector overlap process are discussed further herein. In response to receiving the calculated AQS for each of the applicants  102  from the AQS calculation engine  135 , the application management engine  162  stores the calculated AQSs as AQS data  157  in the data repository  114 . 
     At step  408 , the application management engine  162  automatically generates a scholarship prediction, which can be output to the applicants  102  via a UI screen in real time in response to submitting an application to the scholarship management system  108 . In some implementations, the application management engine  162  determines the scholarship prediction for a particular application by comparing the AQS of the applicant  102  to an average AQS for the scholarship that was awarded during a previous scholarship application cycle. For example, the application management engine  162  can assign a level of likelihood that the applicant  102  will be awarded the scholarship by an amount the calculated AQS for the applicant  102  exceeds or falls short of the average AQS for the awarded scholarship during the previous application cycle. The levels of likelihood that the scholarship will be awarded to the applicant  102  may include high, medium, and low likelihoods. In some examples, the level of likelihood is represented by a confidence percentage. The application management engine  162  can also determine the level of likelihood that the scholarship will be awarded to a particular applicant based on how the calculated AQS for the applicant  102  compares to the AQSs of applicants that have already submitted applicants for the same scholarship cycle. In addition to outputting the scholarship prediction to the applicant  102  via a UI, the application management engine  162  also saves the scholarship prediction in the data repository  114  with the corresponding application data  122  for the applicant  102 . 
     At step  410 , the application management engine  162  also determines whether a received application qualifies for expedited approval routing. In some implementations, received applications for potential applicants identified by the potential applicant ID engine  133  who indicated that they were “very interested” or “maybe interested” in applying for the scholarship in response to receiving a scholarship interest inquiry may receive prioritized routing through a routing chain of the approvers  104 . The application management engine  162  determines whether or not to flag the application for expedited routing by accessing the potential applicant data  152  to determine whether the applicant associated with the received application was identified as a potential applicant and if that potential applicant expressed an interest level of “very interested” or “maybe interested” in applying for the scholarship when replying to the scholarship interest inquiry. 
     If the application management engine  162  determines that the received application qualifies for expedited routing, resulting in “yes” at step  410 , then step  411  is performed where the application data  122  associated with the expedited application is flagged for expedited routing, and then step  412  is performed. If the application management engine  162  determines that the received application does not qualify for expedited routing, resulting in a “no” at step  410 , then the process  400  proceeds to step  412 . 
     At step  412 , once the application management engine  162  determines whether or not to flag the application for expedited routing, scholarship approval engine  131  is triggered to perform an application routing and approval process, as will be discussed in further detail herein. 
       FIG. 5  is an exemplary screenshot of a scholarship application UI screen  500 . The scholarship application UI  500  may be output by the GUI engine  140  to the applicants  102  accessing the scholarship application UI screen  500  via a webpage, server portal, etc. The scholarship application UI screen  500  includes multiple data fields and input fields that allow the applicants  102  to view information related to the scholarship they are applying for as well as input the application data  122  that is extracted from the electronically-submitted scholarship application by the scholarship management engine  162  in response to a selection of submission button  512 . For example, the GUI engine  140  outputs a scholarship title to data field  502  that indicates which scholarship the applicant  102  is applying for. At data input field  504 , the GUI engine  140  receives biographical information inputs from the applicant  102  that can include name, gender, birth date, applicant/student identification number, ethnicity, affiliated university or institution, etc. At data input field  506 , the GUI engine  140  receives academic information inputs from the applicant  102  that can include semesters until graduation, major, major grade point average (GPA), research focus, class rank within major, overall class rank, etc. At data input field  508 , the GUI engine  140  receives other information inputs from the applicant  102  that can include community service, extracurricular activities, a personal statement or essay, etc. The scholarship application UI screen  500  can also include other types of input data fields than those described herein. In addition, the scholarship application UI screen  500  includes attachment selection  510  that when selected, provides the applicant with another UI screen where the applicant  102  can attach any additional documentation such as transcripts, recommendation letters, etc. The GUI engine  140  passes the received inputs to the application management engine  162 , which are configured into the applicant attribute vector along with any other information not included in the applicant attribute vector, which is saved as the applicant data  122  in the data repository  114 . 
       FIG. 6  is an exemplary flowchart of a prioritization matrix generation process  600 , which is controlled by the prioritization matrix engine  162 . For example, the application management engine  162  automatically triggers other processing engines of the scholarship management system  108  in real-time to perform one or more steps of the prioritization matrix generation process  600  and processes the data received from the other processing engines in accordance with the steps of the process  600 . The prioritization matrix engine  138  automatically generates a prioritization matrix in response to the opening of the application window for a scholarship. The entries of the prioritization matrix provide an indication of what the scholarship approvers  104  may consider to be important applicant attributes when deciding who to award scholarships to and are used to generate the AQS, which is a numerical representation of an amount of correlation between the applicant attribute vector and the prioritization matrix. 
     At step  602 , in response to determining that the application window has opened, the prioritization matrix engine  138  determines whether or not the scholarship has been awarded in a previous application cycle. If the scholarship was awarded in a previous application cycle, resulting in a “yes” at step  602 , then step  606  is performed. Otherwise, if it is determined that the scholarship was not awarded in a previous application cycle, resulting in a “no” at step  602 , then step  604  is performed. 
     At step  606 , if the scholarship was awarded in a previous application cycle, then the prioritization matrix engine  138  accesses the prioritization matrix and corresponding weighting factors from the previous application cycle from prioritization matrix data  156  in the data repository  114 , which is as a basis for generating the prioritization matrix for a current application cycle. 
     At step  604 , if the scholarship was not awarded in the previous application cycle, then the prioritization matrix engine  138  generates a default prioritization matrix. In one example, the data repository stores default prioritization matrices for one or more scholarship categories as part of the prioritization matrix data  156 . The scholarship categories can include degree level (e.g., graduate, undergraduate, etc.) or major associated with the scholarship. For example, the prioritization matrix data  156  can include a default prioritization matrix for graduate biomedical engineering scholarships. In some aspects, the weighting factors for the entries of the default prioritization matrix equal and set to a predetermined value, such as 0.5 in an example where the weighting factors are values between 0 and 1. 
     At step  608 , the prioritization matrix engine  138  triggers data mining/collection engine  136  to perform web crawling processes to access keyword data and other external source data  120  from one or more websites associated with government and economic priorities and scholarship priorities associated with the external entities  106  and other types of scholarship awarding institutions. For example, the data mining/collection engine  136  gathers information related to how a job market for a particular industry is performing by web crawling through job advertising websites, economic news websites, etc. and detecting a number of keyword “hits” associated with particular industries. The keyword data can include words or phrases such as “petroleum,” “oil,” “biomedical,” “medicine,” “law,” and any other words associated with various industries or research areas. The keyword data along with any other extracted data can be stored in the data repository  114  as the external source data  120 . 
     At step  610 , the prioritization matrix engine  138  uses the keyword data, other external source data  120 , awarded scholarship data  156 , and any other type of data to update or modify the data entries and weighting factors of the default prioritization matrix or the prioritization matrix that was used in a previous application cycle. For example, if the prioritization matrix engine  138  determines that a scholarship awarded during a previous scholarship application cycle favored a particular gender or hometown, then the prioritization matrix engine  138  may reduce the weighting factors associated with that particular gender or hometown and increase the weighting factors associated with other genders or hometowns in order to increase the diversity of students being awarded scholarships. Also, if a particular scholarship is awarded to a biomedical engineering student, and the extracted keyword data indicates that biomedical engineering that focuses on neurology is more popular than during the previous scholarship application cycle based on the number of keyword hits, then the prioritization matrix engine  138  may increase the weighting factor for an entry of the prioritization matrix associated with a research area focused on neurology. The weighting factors for each of the entries of the prioritization matrix can be determined or modified based on inputs received from the approvers  104  or other staff or administrators of the university or scholarship-awarding entities. 
       FIG. 7  is a data structure of an applicant attribute vector made that includes the current student data  128  that is used to characterize a particular potential applicant in the potential applicant identification process  200  or an applicant  102  in the application management process  400 . Each of the different components, which will soon be discussed, is used to characterize the constituent components. The applicant attributes may vary depending on the entries of the prioritization matrix for the associated scholarship being applied for. Moreover, the constituent components of the applicant attribute vector are shown in  FIG. 7 , and one example of the components of the corresponding prioritization matrix is shown in  FIG. 8 . 
     With regard to  FIG. 7 , each of the components will now be discussed with respect to typical attributes that can be part of the applicant attribute vector. Attribute  708 , A1, relates to an age of an applicant. The values for attribute A1 range between 0 and 1, and an example breakdown of how the values are mapped is shown in TABLE 1. While the values are shown to range between 0 and 1, this has been done as a matter of convenience to normalize the impact of each attribute. Other ranges of values may be used as well, perhaps even without each attribute having a same range so that some attributes may be weighted more heavily than others. 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Age 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A1, 708 
                 &lt;18 
                 .2 
               
               
                   
                 18-22 
                 .4 
               
               
                   
                 23-28 
                 .6 
               
               
                   
                 28-35 
                 .8 
               
               
                   
                 &gt;35 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  710 , A2, includes gender of the applicant. Example values for A2 are shown in TABLE 2 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Gender 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A2, 710 
                 Male 
                 0 
               
               
                   
                 Female 
                 0.5 
               
               
                   
                 Other 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  712 , A3, includes a hometown the applicant, which can be indicated as a particular country or region of a country. Example values for A3 are shown in TABLE 3 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Hometown/Region 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A3, 712 
                 Northern Region 
                 0 
               
               
                   
                 Southern Region 
                 0.25 
               
               
                   
                 Eastern Region 
                 0.75 
               
               
                   
                 Western Region 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  714 , A4, includes an ethnicity of an applicant. Example values for A4 are shown in TABLE 4 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Ethnicity 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A4, 714 
                 African 
                 0 
               
               
                   
                 Caucasian/European 
                 .25 
               
               
                   
                 Hispanic or Latino 
                 .50 
               
               
                   
                 Asian 
                 .75 
               
               
                   
                 Arab/Middle Eastern 
                 1 
               
               
                   
               
            
           
         
       
     
     Attributes  702 ,  710 ,  712 , and  714  are biographical components of the applicant attribute matrix that provide basic biographical information about the applicant. These components of the applicant attribute vector are referred to as biographical components  702 . 
     Attribute  716 , A5, includes a number of semesters until graduation of the applicant. Example values for A5 are shown in TABLE 5 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                 APPLICANT 
                 # of Semesters to 
                 VALUE 
               
               
                 ATTRIBUTE 
                 Graduation 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A5, 716 
                 &gt;4   
                 0 
               
               
                   
                 3-4 
                 .25 
               
               
                   
                 2 
                 .50 
               
               
                   
                 1 
                 .75 
               
               
                   
                 0 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  718 , A6, includes a major focus of study of the applicant  102 . The major can include a category of majors or specific majors. Examples for A6 are shown in TABLE 7 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 6 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Major 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A6, 718 
                 Arts/Humanities 
                 0 
               
               
                   
                 Sciences 
                 .35 
               
               
                   
                 Biomedical Engineering 
                 .55 
               
               
                   
                 Electrical Engineering 
                 .75 
               
               
                   
                 Other Engineering 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  720 , A7, includes a grade point average (GPA) of the applicant  102  within the applicant&#39;s major focus of study. Examples for A7 are shown in TABLE 7 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 7 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Major GPA (4.0 Scale) 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A7, 720 
                 &lt;2.0  
                 0 
               
               
                   
                 2.0-2.49 
                 .35 
               
               
                   
                 2.5-2.99 
                 .55 
               
               
                   
                 3.0-3.50 
                 .75 
               
               
                   
                 &gt;3.50 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  722 , A8, includes a research focus of the applicant  102  within the applicant&#39;s major focus of study. Examples for A8 within a biomedical engineering major are shown in TABLE 8 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 8 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Research Focus 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A8, 722 
                 Biomaterials and Drug 
                 0 
               
               
                   
                 Delivery 
               
               
                   
                 Biomedical Mechanics 
                 .35 
               
               
                   
                 Imaging and 
                 .55 
               
               
                   
                 Instrumentation 
               
               
                   
                 Micro- and Nano- 
                 .75 
               
               
                   
                 biotechnology 
               
               
                   
                 Molecular, Cellular, and 
                 1 
               
               
                   
                 Tissue Engineering 
               
               
                   
               
            
           
         
       
     
     Attribute  724 , A9, includes a class rank of the applicant  102  within the applicant&#39;s major focus of study which can be indicated as a percentage from a highest rank. Examples for A9 are shown in TABLE 9 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 9 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Class Rank in Major 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A9, 724 
                 Top 5% 
                 0 
               
               
                   
                 Top 10% 
                 .35 
               
               
                   
                 Top 25% 
                 .55 
               
               
                   
                 Top 50% 
                 .75 
               
               
                   
                 Lowest 50% 
                 1 
               
               
                   
               
            
           
         
       
     
     Attributes  714 ,  716 ,  718 ,  720 ,  722 , and  724  are academic components of the applicant attribute matrix that provide information about the applicant&#39;s academic focus and performance. These components of the applicant attribute vector are referred to as academic components  704 . 
     Attribute  726 , A10, includes a community service attribute of the applicant  102 . The community service attribute may provide an indication of an amount of time the applicant  102  has dedicated to community service or a particular type of community service, such as medical, construction, elderly care, youth care, etc. Examples for A10 are shown in TABLE 10 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 10 
               
               
                   
               
               
                 APPLICANT 
                 Community Service 
                 VALUE 
               
               
                 ATTRIBUTE 
                 (Hours) 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A10, 726 
                 0 
                 0 
               
               
                   
                 1-25 
                 .35 
               
               
                   
                 26-75  
                 .55 
               
               
                   
                 76-125 
                 .75 
               
               
                   
                 &gt;125 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  728 , A11, includes an extracurricular (EC) activities attribute of the applicant  102 . The EC activities attribute may provide an indication of a number and or type of EC activities performed by the applicant. Examples for A11 are shown in TABLE 11 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 11 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Extracurricular Activities 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A11, 728 
                 Academic 
                 0 
               
               
                   
                 Activism 
                 .25 
               
               
                   
                 Arts 
                 .50 
               
               
                   
                 Athletic 
                 .75 
               
               
                   
               
            
           
         
       
     
     Attribute  730 , A12, includes an overall grade point average (GPA) of the applicant  102 . Examples for A12 are shown in TABLE 12 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 12 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Overall GPA (4.0 Scale) 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A12, 730 
                 &lt;2.0  
                 0 
               
               
                   
                 2.0-2.49 
                 .35 
               
               
                   
                 2.5-2.99 
                 .55 
               
               
                   
                 3.0-3.50 
                 .75 
               
               
                   
                 &gt;3.50 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  732 , A13, includes an overall class rank of the applicant  102  which can be indicated as a percentage from a highest rank. Examples for A13 are shown in TABLE 13 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 13 
               
               
                   
               
               
                 APPLICANT 
                   
                 VALUE 
               
               
                 ATTRIBUTE 
                 Overall Class Rank 
                 Range 0 to 1 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 A13, 732 
                 Top 5% 
                 0 
               
               
                   
                 Top 10% 
                 .35 
               
               
                   
                 Top 25% 
                 .55 
               
               
                   
                 Top 50% 
                 .75 
               
               
                   
                 Lowest 50% 
                 1 
               
               
                   
               
            
           
         
       
     
     Attribute  734 , A14, includes a well-roundedness (WR) of the applicant  102 . The WR score is calculated by the application management engine  162 , and is a numerical representation of a well-roundedness of the applicant  102 . The WR score is a numerical value in a range from 0 to 1, 0 to 100, or any other numerical range. For example, an applicant with a GPA of 3.5, &gt;125 hours of community service, and participation in an EC activity has a higher WR score than an applicant with a GPA of 3.9 with no EC activities or community service. Examples for A14 are shown in TABLE 14 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 14 
               
               
                   
                   
               
               
                   
                 APPLICANT 
                   
                 VALUE 
               
               
                   
                 ATTRIBUTE 
                 WR Score (0-100 Scale) 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 A14, 734 
                  0-25 
                 0 
               
               
                   
                   
                 26-50 
                 .25 
               
               
                   
                   
                 51-75 
                 .50 
               
               
                   
                   
                  75-100 
                 1.00 
               
               
                   
                   
               
            
           
         
       
     
     Attributes  726 ,  728 ,  730 ,  720 ,  732 , and  734  are cumulative components of the applicant attribute matrix that provide information about the applicant&#39;s overall well-roundedness and performance. These components of the applicant attribute vector are referred to as cumulative components  706 . 
     Attribute(s)  736  are expansion attributes that may compliment the other attributes included in  FIG. 7 . Examples of these expansion attributes include additional applicant attributes such as an additional major or research focus, industry work experience, etc. The expansion attributes can be categorized under the biographical components  702 , academic components  704 , or cumulative components  706 . These too would have exemplary value ranges between 0 and 1. 
       FIG. 8  is a data structure of a prioritization matrix with entries that provide an indication of what the scholarship approvers  104  may consider to be important applicant attributes when deciding who to award scholarships to and are used to generate the AQS. According to some aspects, the components of the prioritization matrix may be of the same type and number as the components of the applicant attribute vector of  FIG. 7 . 
     Attribute  808 , P1, relates to an age of an applicant. The values for attribute P1 range between 0 and 1, and an example breakdown of how the values are mapped into the number of publications in the subject area is shown in TABLE 15. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 15 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Age 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P1, 808 
                 &lt;18 
                 .2 
               
               
                   
                   
                 18-22 
                 .4 
               
               
                   
                   
                 23-28 
                 .6 
               
               
                   
                   
                 28-35 
                 .8 
               
               
                   
                   
                 &gt;35 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  810 , P2, includes gender of the applicant. Example values for P2 are shown in TABLE 16 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 16 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Gender 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P2, 810 
                 Male 
                 0 
               
               
                   
                   
                 Female 
                 0.5 
               
               
                   
                   
                 Other 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  812 , P3, includes a hometown the applicant, which can be indicated as a particular country or region of a country. Example values for P3 are shown in TABLE 17 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 17 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Hometown/Region 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P3, 812 
                 Northern Region 
                 0 
               
               
                   
                   
                 Southern Region 
                 0.25 
               
               
                   
                   
                 Eastern Region 
                 0.75 
               
               
                   
                   
                 Western Region 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  814 , P4, includes an ethnicity of an applicant. Example values for P4 are shown in TABLE 18 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 18 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Ethnicity 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P4, 814 
                 African 
                 0 
               
               
                   
                   
                 Caucasian/European 
                 .25 
               
               
                   
                   
                 Hispanic or Latino 
                 .50 
               
               
                   
                   
                 Asian 
                 .75 
               
               
                   
                   
                 Arab/Middle Eastern 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attributes  802 ,  810 ,  812 , and  814  are biographical components of the applicant attribute matrix that provide basic biographical information about the applicant. These components of the applicant attribute vector are referred to as biographical components  802 . 
     Attribute  816 , P5, includes a number of semesters until graduation of the applicant. Example values for P5 are shown in TABLE 19 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 19 
               
               
                   
                   
               
               
                   
                 MATRIX 
                 # of Semesters to 
                 VALUE 
               
               
                   
                 ENTRY 
                 Graduation 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P5, 816 
                 &gt;4  
                 0 
               
               
                   
                   
                 3-4 
                 .25 
               
               
                   
                   
                 2 
                 .50 
               
               
                   
                   
                 1 
                 .75 
               
               
                   
                   
                 0 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  818 , P6, includes a major focus of study of the applicant  102 . The major can include a category of majors or specific majors. Examples for A6 are shown in TABLE 20 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 20 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Major 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P6, 818 
                 Arts/Humanities 
                 0 
               
               
                   
                   
                 Sciences 
                 .35 
               
               
                   
                   
                 Biomedical Engineering 
                 .55 
               
               
                   
                   
                 Electrical Engineering 
                 .75 
               
               
                   
                   
                 Other Engineering 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  820 , P7, includes a grade point average (GPA) of the applicant  102  within the applicant&#39;s major focus of study. Examples for P7 are shown in TABLE 21 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 21 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Major GPA (4.0 Scale) 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P7, 820 
                 &lt;2.0 
                 0 
               
               
                   
                   
                 2.0-2.49 
                 .35 
               
               
                   
                   
                 2.5-2.99 
                 .55 
               
               
                   
                   
                 3.0-3.50 
                 .75 
               
               
                   
                   
                 &gt;3.50 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  822 , P8, includes a research focus of the applicant  102  within the applicant&#39;s major focus of study. Examples for P8 within a biomedical engineering major are shown in TABLE 22 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 22 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Research Focus 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P8, 822 
                 Biomaterials and Drug 
                 0 
               
               
                   
                   
                 Delivery 
               
               
                   
                   
                 Biomedical Mechanics 
                 .35 
               
               
                   
                   
                 Imaging and 
                 .55 
               
               
                   
                   
                 Instrumentation 
               
               
                   
                   
                 Micro- and Nano- 
                 .75 
               
               
                   
                   
                 biotechnology 
               
               
                   
                   
                 Molecular, Cellular, and 
                 1 
               
               
                   
                   
                 Tissue Engineering 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  824 , P9, includes a class rank of the applicant  102  within the applicant&#39;s major focus of study which can be indicated as a percentage from a highest rank. Examples for P9 are shown in TABLE 23 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 23 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Class Rank in Major 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P9, 824 
                 Top 5% 
                 0 
               
               
                   
                   
                 Top 10% 
                 .35 
               
               
                   
                   
                 Top 25% 
                 .55 
               
               
                   
                   
                 Top 50% 
                 .75 
               
               
                   
                   
                 Lowest 50% 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attributes  814 ,  816 ,  818 ,  820 ,  822 , and  824  are academic components of the applicant attribute matrix that provide information about the applicant&#39;s academic focus and performance. These components of the applicant attribute vector are referred to as academic components  804 . 
     Attribute  826 , P10, includes a community service attribute of the applicant  102 . The community service attribute may provide an indication of an amount of time the applicant  102  has dedicated to community service or a particular type of community service, such as medical, construction, elderly care, youth care, etc. Examples for P10 are shown in TABLE 24 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 24 
               
               
                   
                   
               
               
                   
                 MATRIX 
                 Community Service 
                 VALUE 
               
               
                   
                 ENTRY 
                 (Hours) 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P10, 826 
                 0 
                 0 
               
               
                   
                   
                 1-25 
                 .35 
               
               
                   
                   
                 26-75  
                 .55 
               
               
                   
                   
                 76-125 
                 .75 
               
               
                   
                   
                 &gt;125 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  828 , P11, includes an extracurricular (EC) activities attribute of the applicant  102 . The EC activities attribute may provide an indication of a number and or type of EC activities performed by the applicant. Examples for P11 are shown in TABLE 25 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 25 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Extracurricular Activities 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P11, 828 
                 Academic 
                 0 
               
               
                   
                   
                 Activism 
                 .25 
               
               
                   
                   
                 Arts 
                 .50 
               
               
                   
                   
                 Athletic 
                 .75 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  830 , P12, includes an overall grade point average (GPA) of the applicant  102 . Examples for P12 are shown in TABLE 26 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 26 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Overall GPA (4.0 Scale) 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P12, 830 
                 &lt;2.0 
                 0 
               
               
                   
                   
                 2.0-2.49 
                 .35 
               
               
                   
                   
                 2.5-2.99 
                 .55 
               
               
                   
                   
                 3.0-3.50 
                 .75 
               
               
                   
                   
                 &gt;3.50 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  832 , P13, includes an overall class rank of the applicant  102  which can be indicated as a percentage from a highest rank. Examples for P13 are shown in TABLE 27 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 27 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 Overall Class Rank 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P13, 832 
                 Top 5% 
                 0 
               
               
                   
                   
                 Top 10% 
                 .35 
               
               
                   
                   
                 Top 25% 
                 .55 
               
               
                   
                   
                 Top 50% 
                 .75 
               
               
                   
                   
                 Lowest 50% 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Attribute  834 , P14, includes a well-roundedness (WR) of the applicant  102 . The WR score is a numerical value in a range from 0 to 1, 0 to 100, or any other numerical range. For example, an applicant with a GPA of 3.5, &gt;125 hours of community service, and participation in an EC activity has a higher WR score than an applicant with a GPA of 3.9 with no EC activities or community service. Examples for P14 are shown in TABLE 28 below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 28 
               
               
                   
                   
               
               
                   
                 MATRIX 
                   
                 VALUE 
               
               
                   
                 ENTRY 
                 WR Score (0-100 Scale) 
                 Range 0 to 1 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 P14, 834 
                 0-25 
                 0 
               
               
                   
                   
                 26-50  
                 .25 
               
               
                   
                   
                 51-75  
                 .50 
               
               
                   
                   
                 75-100 
                 1.00 
               
               
                   
                   
               
            
           
         
       
     
     Attributes  826 ,  828 ,  830 ,  820 ,  832 , and  834  are cumulative components of the applicant attribute matrix that provide information about the applicant&#39;s overall well-roundedness and performance. These components of the applicant attribute vector are referred to as cumulative components  806 . 
     Attribute(s)  836  are expansion attributes that may compliment the other attributes included in  FIG. 8 . Examples of these expansion attributes include additional applicant attributes such as an additional major or research focus, industry work experience, etc. The expansion attributes can be categorized under the biographical components  802 , academic components  804 , or cumulative components  806 . These too would have exemplary value ranges between 0 and 1. 
     Next,  FIG. 9  is a graph of an applicant attribute vector and a prioritization matrix in two dimensional space, according to certain embodiments. As seen, the applicant attribute vector, A, may reflect some but not all of the attributes covered by one particular prioritization matrix, P. If this is the case, the magnitude of the projection vector of the applicant attribute vector, A, onto the prioritization matrix, P, may indicate an amount of commonality between the two vectors. Accordingly, the AQS calculated by the AQS calculation engine  135  is a numerical representation of the commonality between the applicant attribute vector, A, and the prioritization matrix, P. 
     Although there are different axes x and y shown, these axes are not necessarily orthogonal, but instead have some attributes in one of the vectors (A or P) that are correlated with other attributes in other vectors. Nevertheless, in order to identify how a set of applicant attribute data maps into the two domains, a mapping process is performed to see how the applicant attribute vector maps onto one or more prioritization matrices. The magnitude of the vectors along each axis is a function of the additive values of the attributes that make up the vector. For example, in the applicant attribute vector, the range of values of a major GPA may be 0.00 to 4.00, for example. The maximum contribution to the magnitude of the A vector is if the applicant has the same major GPAA as the major GPA entry in the corresponding prioritization matrix. This particular attribute (major GPA) can then be weighted based on a weighting table (as will be discussed) to help normalize the amount of contribution that attribute may have relative to other attributes that make up the vector. 
       FIG. 10  is a graphical illustration of how pairs of vectors (P, A) are compared to one another to arrive at a determination of an amount of commonality between the two vectors (e.g., the AQS), according to certain embodiments.  FIG. 10  includes vector A  1000  with exemplary weighted attributes w1 aA1  1002 , w2 aA2  1004 , and wnaAn  1006  (the weights are shown for brevity in the Figure as w), although other weighted attributes would be included in the process. The respective weights are set based on the query that is made based on the application data  122  extracted from the submitted scholarship application. Vector P  1008  includes similar weighted attributes, such as w1pP1. Weighting the attributes adjusts the spatial size associated with that particular attribute, thus affecting the potential size of the correlation graph for that particular vector (e.g., it affects how big the A shape is on the correlation vector, for example). Weighting the correlation calculations adjusts the relative contributions each pair of components (e.g., A1P1) contributes to an overlap area in the correlation graph (e.g., the projection of the A vector onto the P vector). 
     In a first multiplication step, w1aA1  1002  is multiplied with w1pP1  1010 , and the product is multiplied by a correlation weight C1 and the result sent to an accumulator (summation device)  1012 . The correlation weight C1 is a coefficient that adjusts the level of relevance for the matching pair for the query made. The products from the other matching pairs of weighted components from the vectors are multiplied (e.g., w2aA2×w2pP2 . . . wnaAn×wnpPn), adjusted by their respective correlation weight (Cx, x being an index), and summed in the accumulator  1012 . Then the weighted attributes in one vector (vector A  1000  in this example) are shifted left  1014  by one position and then then are multiplied by the corresponding weighted attribute in vector P  1008  and correlation weight C2. For example, in the second step w2aA2  1004  is multiplied by w1pP1  1010  and the product is multiplied by a correlation weight Cx and the result is summed with the other products in the accumulator  1012 . The one exception is that the left most weighted attribute (which in this case is w1aA1) is circular shifted right  1016  so as to take the position of wnaAn  1006 . This process continues until all of the weighted attributes of one vector are multiplied, adjusted by a correlation weight, and summed with all the other attributes of the other vector. 
     With regard to the weights, each attribute of each vector is first weighted such that each attribute is either weighted with a 0 or a value between zero and 1. A zero value means that the subject attribute does not contribute at all. Values closer to 1 are deemed to be associated with attributes that have a higher relevance toward attributes of an ideal candidate for the scholarship. Each attribute of each vector is then combined (multiplied in this example, but could also be added or combined in another mathematical fashion) with each attribute of the other vectors, and a resultant sum is obtained. The weighted vector correlation of the A and P vectors results in the overlap area of the two vectors. 
     While in the above-described embodiment, there a fixed weight is assigned to each attribute for each vector. However, for an even more refined correlation process, a separate weight is applied for each attribute for each multiplication performed. For example, there may be a high correlation between an applicant who has a high well-roundedness score and the quality of the applicant. However, there may be little correlation weight for the ethnicity or hometown of the applicant. 
     Each query will have a relevant subset of weights for each vector (signifying the contribution of each particular attribute to each vector space in the correlation graph (e.g., the size of region A). Furthermore the correlation between the two spaces (e.g., between A and P) is influenced by the weight of the correlation of each pair of vector attributes (e.g., A1, P1) for that particular query. The tables below include the attribute weights and correlation weights for each inquiry. For any weight or coefficient not particularly provided for, its value is set at 0.5, although it may be changed to any value ranging between 0 and 1. 
     Attribute Weight table for a biomedical engineering scholarship. 
     
       
         
           
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
                 TABLE 29 
               
               
                   
               
               
                 Attri- 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 bute 
                 1 
                 2 
                 3 
                 4 
                 5 
                 6 
                 7 
                 8 
                 9 
                 10 
                 11 
                 12 
                 13 
                 14 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 A 
                 .8 
                 1 
                 .6 
                 .7 
                 .7 
                 .8 
                 .6 
                 .8 
                 .8 
                 .9 
                 .7 
                 1 
                 .5 
                 .7 
               
               
                 P 
                 .8 
                 .9 
                 .8 
                 .9 
                 1 
                 .9 
                 .8 
                 .7 
                 .6 
                 .5 
                 .4 
                 .3 
                 .8 
                 .2 
               
               
                   
               
            
           
         
       
     
     A vector Correlation Coefficient Table for P regarding the biomedical engineering scholarship. 
     
       
         
           
               
               
               
               
               
               
               
               
               
               
               
               
             
               
                 TABLE 30 
               
               
                   
               
               
                 Attribute 
                 CCp1 
                 CCp2 
                 CCp3 
                 CCp4 
                 CCp5 
                 CCp6 
                 CCp7 
                 CCp8 
                 CCp9 
                 CCp10 
                 CCp11 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 A1 
                 1 
                 .8 
                 .8 
                 .8 
                 1 
                 .8 
                 .6 
                 .4 
                 .2 
                 .1 
                 .1 
               
               
                 A2 
                 1 
                 .8 
                 .8 
                 .8 
                 1 
                 .8 
                 .6 
                 .4 
                 .2 
                 .1 
                 .1 
               
               
                 A3 
                 1 
                 .8 
                 .8 
                 .8 
                 1 
                 .8 
                 .6 
                 .4 
                 .2 
                 .1 
                 .1 
               
               
                 A4 
                 .8 
                 .8 
                 .6 
                 .8 
                 .8 
                 .6 
                 .4 
                 .2 
                 .1 
                 .1 
                 .1 
               
               
                 A5 
                 .8 
                 .8 
                 .6 
                 .8 
                 .8 
                 .6 
                 .4 
                 .2 
                 .1 
                 .1 
                 .1 
               
               
                 A6 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
               
               
                 A7 
                 .8 
                 .8 
                 .6 
                 .8 
                 .8 
                 .6 
                 .4 
                 .2 
                 .1 
                 .1 
                 .1 
               
               
                 A8 
                 .9 
                 .9 
                 .7 
                 .8 
                 .9 
                 .7 
                 .5 
                 .3 
                 .1 
                 0 
                 .5 
               
               
                 A9 
                 .9 
                 .9 
                 .7 
                 .8 
                 .9 
                 .7 
                 .5 
                 .3 
                 .1 
                 0 
                 .5 
               
               
                 A10 
                 .9 
                 .9 
                 .7 
                 .8 
                 .9 
                 .7 
                 .5 
                 .3 
                 .1 
                 0 
                 .5 
               
               
                 A11 
                 .9 
                 .9 
                 .7 
                 .8 
                 .9 
                 .7 
                 .5 
                 .3 
                 .1 
                 0 
                 .5 
               
               
                 A12 
                 .9 
                 .9 
                 .7 
                 .8 
                 .9 
                 .7 
                 .5 
                 .3 
                 .1 
                 0 
                 .5 
               
               
                 A13 
                 .8 
                 .6 
                 .3 
                 .5 
                 .2 
                 .8 
                 .9 
                 .2 
                 .6 
                 .4 
                 .5 
               
               
                 A14 
                 .7 
                 .8 
                 .9 
                 .5 
                 .6 
                 .7 
                 .7 
                 .6 
                 .4 
                 .3 
                 .5 
               
               
                   
               
            
           
         
       
     
     This process of identifying attributes, weights and correlation coefficients may be applied to other A-P correlation space analyses for different prioritization matrices as well as other database queries. Similar weight and coefficient tables are stored for other types of scholarships managed by the scholarship management system  108 . 
     Next,  FIG. 11  is a flowchart of a vector overlap process  1100  for determining the overlap amounts for the applicant attribute vector and the prioritization matrix for a particular scholarship, according to certain embodiments. The vector overlap process  1100  is controlled by the AQS calculation engine  135 . For example, the AQS calculation engine  135  automatically triggers other processing engines of the scholarship management system  108  in real-time to perform one or more steps of the vector overlap process  1100  and processes the data received from the other processing engines in accordance with the steps of the process  1100 . The AQS calculation engine  135  automatically calculates the AQS for an applicant  102  in response to the generation of an applicant attribute vector by the application management engine  162 . 
     The process begins at step  1101  where the AQS calculation engine  135  stores the prioritization matrix for the scholarship in a first correlation register and the applicant attribute vector for the applicant  102  in a second correlation register. 
     At step  1102  where each of the vector attributes has a weight applied thereto. The weights are stored in a memory table of the prioritization data  156  with the corresponding prioritization matrix. The process then proceeds to step  1104  where pairs of weighted adjacent attributes are multiplied with each other, and then in  1106  the products of all the multiplications are accumulated. Then in step  1108  a query is made regarding whether all the pairs of attributes have been multiplied. If the response to the query is negative, the process proceeds to step  1110  where the attributes in one vector are circular shifted and the process then returns to step  1104 . However, if the response to the query in Step  1108  is affirmative, the process proceeds to  1112 , where the cumulative output is produced and used for graphical analysis. The value that is output is fraction of the total overlap if the max weight is applied to the max value of all attributes for all vectors and the correlation is performed on that maximum condition. Moreover, the output that is produced at step  1112  can be a percentage of the maximum possible overlap space for the applicant attribute vector and the different component prioritization matrices. 
     The output produced at step  1112  corresponds to the AQS is then sent to the application management engine  162 , which generates the automatic scholarship prediction as previously discussed. If the vector overlap process  1100  is performed in conjunction with potential applicant identification process  200 , then the output produced at step  1112  is sent to the potential applicant ID engine  133 . 
       FIG. 12  is an exemplary flowchart of a scholarship application approval process  1200 . The scholarship application approval process  1200  is controlled by the scholarship approval engine  131 , which automatically triggers other processing engines of the scholarship management system  108  in real-time to perform one or more steps of the scholarship application approval process  1200  and processes the data received from the other processing engines in accordance with the steps of the process  1200 . 
     At step  1202 , the scholarship approval engine  131  determines whether the application window has closed. If the application window has closed, resulting in a “yes” at step  1202 , then step  1206  is performed. Otherwise, if the application window has not closed, resulting in a “no” at step  1202 , then step  1204  is performed. 
     At step  1206 , in response to determining that the application window has closed, the scholarship approval engine  131  automatically triggers the ranking engine  160  to rank the received applications for a particular scholarship according to the calculated AQS for each applicant. 
     At step  1204 , if the application window has not closed, the scholarship approval engine  131  determines whether or not the received application has been flagged for expedited routing based on whether received application was identified as a potential applicant and if that potential applicant expressed an interest level of “very interested” or “maybe interested” in applying for the scholarship when replying to the scholarship interest inquiry. If the application was flagged for expedited routing, resulting in a “yes” at step  1204 , then the process continues to step  1208 , and the scholarship approval engine  131  automatically routes the application through the approval chain whether or not the application window has closed. If the application was not flagged for expedited routing, resulting in a “no” at step  1204 , then then the received application remains in a queue until the application window closes, and the process returns to step  1202 . 
     At step  1208 , in response to receiving the ranked applicants from the ranking engine  160  or in response to receiving a scholarship application that is flagged for expedited routing, the scholarship approval engine  131  routes the applications through the approval chain by performing a scholarship routing process. Details regarding the scholarship routing process are discussed further herein. 
     At step  1210 , once the applications have been routed through the approval chain and award decisions have been determined, the scholarship approval engine  131  triggers awarded scholarship management engine  134  to output awarded scholarship information to the applicants  102  who have been awarded scholarships. The awarded scholarship information can be output to the applicants with digital signatures or stamps that indicate the award amount. The awarded scholarship management engine  134  also stores the awarded scholarship information as awarded scholarship data  154  in the data repository  114 , which can be used in future scholarship application cycles to generate the prioritization matrix, allocate funds, identify potential applications, etc. The awarded scholarship management engine  134  can also interface with financial institutions of the applicants  102  and the university to transfer scholarship funds from the university to the applicants  102  who are awarded the scholarships. 
       FIG. 13  is an exemplary flowchart of a scholarship routing process  1300 . The scholarship routing process  1300  is controlled by the scholarship approval engine  131 , which automatically triggers other processing engines of the scholarship management system  108  in real-time to perform one or more steps of the scholarship routing process  1300  and processes the data received from the other processing engines in accordance with the steps of the process  1300 . 
     At step  1302 , the scholarship approval engine  131  determines an approval chain for a particular scholarship based on approver data  124  stored in the data repository  114 . For example, an electrical engineering scholarship may be routed through an electrical engineering department chairman while a medical school scholarship may be routed through a medical school dean or chairman. In some implementations, the approvers  104  for a particular scholarship for an internal applicant includes one or more of a department chairman, a college dean, a scholarship committee chairman, or a university chairman. For an external applicant, the approval chain may also include a dean of higher education and a member of ministry of education in addition to the approvers  104  for the internal applicants. 
     At step  1304 , the scholarship approval engine  131  routes the applications for a particular scholarship to a next approver in the approval chain. In some implementations, the scholarship approval engine  131  triggers the GUI engine  140  to output an approver interface screen to each of the approvers  104  as the applications for the scholarship are routed through the approval chain. The approver interface screen is a dashboard that provides the approvers  104  with various types of data that can include scholarship statistics from previous application cycles, a quick-view list of the applicants  102  for the scholarship with the corresponding AQS, scholarship award statistics for the current application cycle, etc. 
     At step  1306 , the scholarship approval engine  131  receives the decision from the approver  104 . The approver user interface screen provides the approvers the ability to approve or deny a scholarship award to a particular applicant, input an award amount, and input additional information related to why the approver awarded or did not award the scholarship to the particular applicant. The approver user interface screen can also include the decisions by the previous reviewers in the approval chain so that a final approver can select applicants for scholarship awards based on inputs received from the other approvers  104  in the approval chain. In some implementations, the scholarship award decision made by the final approver in the approval chain is a final award decision. 
     At step  1308 , the scholarship approval engine  131  determines whether or not there is another approver in the approval chain. If a current approver is not the final approver, resulting in a “yes” at step  1308 , then the process proceeds to step  1304  to route the applications to the next approver in the approval chain. If the current approver is the final approver, resulting in a “no” at step  1308 , then step  1310  is performed. 
     At step  1310 , in response to receiving the final award decision, the scholarship approval engine  131  triggers a budget management engine  144  to calculate a remaining balance in a scholarship budget and/or modify a total scholarship budget based on the total amount of money awarded for the scholarship. In some implementations, based on an amount of money awarded in scholarships by the scholarship management system  108  for a scholarship application window, the budget management engine  144  may determine that an additional scholarship application window can be opened and can allocate any remaining money in a scholarship budget to one or more scholarships that can be awarded during the additional scholarship window. For example, if the remaining scholarship budget is greater than a predetermined threshold amount of money, the budget management engine  144  may determine that the additional application window can be opened. 
       FIG. 14A  is an exemplary screenshot of a scholarship approver UI screen  1400 . The scholarship approver UI screen  1400  may be output by the GUI engine  140  to the approvers  104  in the approval chain for a scholarship. The scholarship approver UI screen  1400  includes multiple data fields and input fields that allow approvers  104  to view information related to the scholarship and the applicants  102  who have applied for the scholarship. A scholarship identification tab  1402  indicates which scholarship the approver  104  is viewing applications for. For example, the scholarship application tab  1402  can include a major or research area along with a serial number that uniquely identifies the scholarship. In some implementations where the approver  104  is in the approval chain for multiple scholarships, the scholarship approver UI screen can include multiple scholarship identification tabs  1402  associated with each of the scholarships that can be selected by the approver. 
     At data field  1404 , the GUI engine  140  outputs scholarship statistics for previous application cycles that may aid the approvers  104  in making scholarship award decisions. For example, for a selected year and/or application cycle, the previous scholarship statistics can include a number of a particular type of scholarship awarded, an average award amount per scholarship, a highest award amount for the scholarship, and a lowest award amount for the scholarship. 
     At data input field  1406  of the scholarship approver UI screen  1400 , the GUI engine  140  outputs a list of the applicants  102  who have applied for the scholarship along with a student identification number and the corresponding AQS that was calculated for the applicant by the AQS calculation engine  135 . The approver  104  can select an applicant  1408  from the data input field  1406 , and in response, the GUI engine  140  outputs an applicant award decision field  1410  where the approver  104  can view the application submitted by the applicant and indicate a recommendation of whether or not to award the scholarship along with a recommended award amount. 
     At data field  1412 , the GUI engine  140  outputs currently awarded scholarship information that includes statistics related to the scholarships the approver has already awarded or recommended awarding for a particular scholarship or scholarship category. For example, the currently awarded scholarship information can include a number of scholarships awarded, an average award amount per scholarship, highest and lowest award amounts, an amount of money remaining to award, and a number of remaining scholarships to award. 
       FIG. 14B  is an exemplary illustration of a portion of the scholarship management environment that includes computing resources of a scholarship approval engine  1431  and computing resources of an approver network  1404  that presents the scholarship approver UI screens to computing devices  1458  of the approvers  104 . In some implementations, the computing resources of the approver network  1404  can be configured to perform various processes associated with the scholarship application management system  108 , such as the scholarship routing process  1300  ( FIG. 13 ). In one example, in response to detecting network/processing congestion at the scholarship approval engine  1431 , the scholarship approval engine  1431  may transmit an enhanced data packet  1424  to the applicant network  1404 , which can also function as a command to the applicant network  1404  to perform the scholarship routing process  1300 . For example, the enhanced data packet  1424  can include at least one of one or more applicant attribute vectors for the applicants for a particular scholarship, the prioritization matrix for the scholarship, or AQSs of the applicants. By condensing the information associated with the scholarship and applicants into a single data structure that is transmitted as the enhanced data packet, vast amounts of data are condensed, which reduces computer network congestions. In addition, other processing tasks associated with other processing engines of the scholarship application management system  108  can be similarly distributed to the approver network  1404  as well as an applicant network. 
       FIG. 15  is an exemplary flowchart of a scholarship modification process  1500 . The scholarship modification process  1500  is controlled by the awarded scholarship management engine  134 , which automatically triggers other processing engines of the scholarship management system  108  in real-time to perform one or more steps of the scholarship modification process  1500  and processes the data received from the other processing engines in accordance with the steps of the process  1500 . 
     At step  1502 , the awarded scholarship management engine  134  receives a scholarship modification request from an applicant who has been awarded a scholarship. In addition to outputting awarded scholarship information to the applicants  102  who are awarded scholarships, the awarded scholarship management engine  134  also manages scholarship modifications in response to receiving a scholarship modification request from an applicant  102 . In some implementations, scholarships have benefits to the applicants  102  other than receiving an education. For example, the awarded scholarships may also allow for applicants who are awarded scholarships, also referred to as awarded applicants, to attend conferences, go on education tours, etc. The awarded applicants can submit a modification request to allocate scholarship funds to attend the conferences or go on the education tours. The awarded applicants can also submit modification requests to extend the scholarship, increase the scholarship award amount, transfer the scholarship to another institution, or terminate the scholarship. In some implementations, the awarded scholarship management engine  134  receive the scholarship modification request from the GUI engine  140  that receives a modification request at a scholarship modification UI screen where the awarded applicants can input a type of modification request as well as a reason for submitting the modification request. 
     At step  1504 , in response to receiving the scholarship modification request, the awarded scholarship management engine  134  determines whether the request is within predetermined scholarship terms that provide for automatic approval of the request. For example, if the modification request is a request to attend a conference and the awarded scholarship data  154  for the scholarship indicates that the awarded applicant is allowed to attend one conference under the terms of the scholarship. If the modification request is within the scholarship terms, resulting in a “yes” at step  1504 , then step  1508  is performed. Otherwise, if the modification request is not within the scholarship terms, resulting in a “no” at step  1504 , then step  1506  is performed. 
     At step  1508 , if the modification request is within the scholarship terms, then the awarded scholarship management engine  134  outputs an approval to the awarded applicant who is making the request and at updates the awarded scholarship data  154  in the data repository  114  to indicate that the approval was granted at step  1510 . 
     At step  1506 , if the modification request is not within the predetermined scholarship terms, then the awarded scholarship management engine  134  triggers the scholarship approval engine  131  to route the scholarship modification request through the approval chain to process the scholarship modification request by performing the scholarship routing process  1300  described previously. For example, the approval chain for the scholarship modification request is based on the type of modification request being submitted and can include one or more of the department chairman, college dean, scholarship committee chairman, or university chairman. 
     At step  1512 , once the awarded scholarship management engine  134  receives a final modification decision from the scholarship approval engine  131 , the final modification decision is output to the awarded applicant who initiated the request, and the awarded scholarship data  154  stored in the data repository  114  is updated. 
       FIG. 16  is an exemplary screenshot of a scholarship modification UI screen  1600 . The scholarship modification UI screen  1600  may be output by the GUI engine  140  to scholarship awardees in one or more formats, such as email, notification on webpage or electronic device application, etc. The scholarship modification UI screen  1600  includes multiple data fields and input fields that allow the scholarship awardees to input information related to scholarship modification requests. For example, the GUI engine  140  outputs a modification type input field  1602  that allows the scholarship awardees to indicate the type of modification request such as attend a conference, educational tour, extend scholarship, increase award amount, change major, transfer, terminate scholarship, etc. The data input field  1604  allows the scholarship awardee to input a personal statement explaining the reason for the modification request. At data field  1606 , the GUI engine  140  receives a submission selection where the scholarship awardee can submit the request, check a status of a request, or cancel a request. The GUI engine  140  passes the scholarship modification request information input at the UI screen  1600  to the awarded scholarship management engine  134  for approval. 
       FIGS. 17A and 17B  illustrate various aspects of an exemplary architecture implementing a platform  1700  for managing scholarship applications. The high-level architecture includes both hardware and software applications, as well as various data communications channels for communicating data between the various hardware and software components. The platform  1700  may be roughly divided into front-end components  1702  and back-end components  1704 . The front-end components  1702  are primarily disposed within an applicant network  1710  including one or more applicants  1712 . The applicants  1712  may be located, by way of example rather than limitation, in separate geographic locations from each other, including different areas of the same city, different cities, different states, or even different countries. The front-end components  1702  may include a number of workstations  1728 . The workstations  1728 , for example, can be local computers located in the various locations  1712  throughout the network  1710  and executing various applications for ranking the approvers  104  with respect to preferences of the applicants  102 . 
     Web-enabled devices  1714  (e.g., personal computers, tablets, cellular phones, smart phones, web-enabled televisions, etc.) may be communicatively connected to locations  1712  and the system  1740  through a digital network  1730  or a wireless router  1731 , as described below. 
     Referring now to  FIG. 17A , the front-end components  1702 , in some embodiments, include a number of facility servers  1726  disposed at the number of locations  1712  instead of, or in addition to, a number of workstations  1728 . Each of the locations  1712  may include one or more facility servers  1726  that may facilitate communications between the web-enabled devices  1714  and the back-end components  1704  via a digital network  1730 , described below, and between the terminals  1728 ,  1728 A of the locations  1712  via the digital network  1730 , and may store information for a number of applicants/approvers/accounts/etc. associated with each facility. Of course, a local digital network  1784  may also operatively connect each of the workstations  1728  to the facility server  1726 . Unless otherwise indicated, any discussion of the workstations  1728  also refers to the facility servers  1726 , and vice versa. Moreover, environments other than the locations  1712 , such as the kiosks, call centers, and Internet interface terminals may employ the workstations  1728 , the web-enabled devices  1714 , and the servers  1726 . As used herein, the term “location” refers to any of these points of contact (e.g., call centers, kiosks, Internet interface terminals, etc.) in addition to the locations  1712 , etc. described above. 
     The front-end components  1702  communicate with the back-end components  1704  via the digital network  1730 . One or more of the front-end components  1702  may be excluded from communication with the back-end components  1704  by configuration or by limiting access due to security concerns. For example, the web enabled devices  1714  may be excluded from direct access to the back-end components  1704 . In some embodiments, the locations  1712  may communicate with the back-end components via the digital network  1730 . In other embodiments, the locations  1712  and web-enabled devices  1714  may communicate with the back-end components  1704  via the same digital network  1730 , but digital access rights, IP masking, and other network configurations may deny access of the web-enabled devices  1714 . The web-enabled devices may also connect to the network  1730  via the encrypted, wireless router  1731 . 
     The digital network  1730  may be a proprietary network, a secure public Internet, a virtual private network or some other type of network, such as dedicated access lines, plain ordinary telephone lines, satellite links, combinations of these, etc. Where the digital network  1730  includes the Internet, data communication may take place over the digital network  1730  via an Internet communication protocol. In addition to one or more web servers  1790  (described below), the back-end components  1704  may include a central processing system  1740  within a central processing facility. Of course, the locations  1712  may be communicatively connected to different back-end components  1704  having one or more functions or capabilities that are similar to the central processing system  1740 . The central processing system  1740  may include processing circuitry (e.g., one or more computer processors)  1762  adapted and configured to execute various software applications and components of the platform  1700 , in addition to other software applications, such as a medication management system. 
     The central processing system  1740 , in some embodiments, further includes a database  1746  (which may include one or more databases). The database  1746  can be adapted to store data related to the operation of the platform  1700 . The central processing system  1740  may access data stored in the database  1746  when executing various functions and tasks associated with the operation of the platform  1700 . 
     Although the platform  1700  is shown to include a central processing system  1740  in communication with three locations  1712 , and various web-enabled devices  1714  it should be understood that different numbers of processing systems, locations, and devices may be utilized. For example, the digital network  1730  (or other digital networks, not shown) may interconnect the platform  1700  to a number of included central processing systems  1740 , hundreds of locations  1712 , and thousands of web-enabled devices  1714 . According to the disclosed example, this configuration may provide several advantages, such as, for example, enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information. This provides for a primary backup of all the information generated in the wireless data transfer process. Alternatively, some of the locations  1712  may store data locally on the facility server  1726  and/or the workstations  1728 . 
       FIG. 17A  also depicts one possible embodiment of the central processing system  1740 . The central processing system  1740  may have a controller  1755  operatively connected to the database  1746  via a link  1756  connected to an input/output (I/O) circuit  1766 . It should be noted that, while not shown, additional databases may be linked to the controller  1755  in a known manner. 
     The controller  1755  includes a program memory  1760 , the processing circuitry  1762  (may be called a microcontroller or a microprocessor), a random-access memory (RAM)  1764 , and the input/output (I/O) circuit  1766 , all of which are interconnected via an address/data bus  1765 . It should be appreciated that although only one microprocessor  1762  is shown, the controller  1755  may include multiple microprocessors  1762 . Similarly, the memory of the controller  1755  may include multiple RAMs  1764  and multiple program memories  1760 . Although the I/O circuit  1766  is shown as a single block, it should be appreciated that the I/O circuit  1766  may include a number of different types of I/O circuits. The RAM(s)  1764  and the program memories  1760  may be implemented as semiconductor memories, magnetically readable memories, and/or optically readable memories, for example. A link  1735  may operatively connect the controller  1755  to the digital network  1730  through the I/O circuit  1766 . 
       FIG. 17B  depicts one possible embodiment of the front-end components  1702  located in one or more of the locations  1712  from  FIG. 17A . Although the following description addresses the design of the locations  1712 , it should be understood that the design of one or more of the locations  1712  may be different from the design of others of the locations  1712 . Also, each of the locations  1712  may have various different structures and methods of operation. It should also be understood that while the embodiment shown in  FIG. 17B  illustrates some of the components and data connections that may be present in a location  1712 , it does not illustrate all of the data connections that may be present in a location  1712 . For exemplary purposes, one design of a location is described below, but it should be understood that numerous other designs may be utilized. 
     Each of the locations  1712 , as illustrated, has one or more portable computing devices  1733  (e.g., notebook computers, tablet computers, smart phones, personal data assistants, etc.) and/or a facility server  1726 . The digital network  1784  and wireless router  1731  operatively connect the facility server  1726  to the number of portable computing devices  1733  and/or to other web-enabled devices  1714  and workstations  1728 . The digital network  1730  may be a wide area network (WAN), a local area network (LAN), or any other type of digital network readily known to those persons skilled in the art. The digital network  1730  may operatively connect the facility server  1726 , the portable computing devices  1733 , the workstations  1728 , and/or the other web-enabled devices  1714  to the central processing system  1740 . 
     Each portable computing device  1733 , workstation  1728 , applicant device terminal  1728   a , or facility server  1726  includes a controller  1770 , as depicted in  FIG. 17B  in relation to the server  1726 . Similar to the controller  1755  from  FIG. 17A , the controller  1770  includes a program memory  1772 , processing circuitry (e.g., one or more microcontrollers or microprocessors)  1774 , a random-access memory (RAM)  1776 , and an input/output ( 110 ) circuit  1780 , all of which are interconnected via an address/data bus  1778 . In some embodiments, the controller  1770  may also include, or otherwise be communicatively connected to, a database  1782 . The database  1782  (and/or the database  1746  of  FIG. 17A ) includes data such as the types of data stored in the data repository  114  ( FIG. 1 ) described previously. As discussed with reference to the controller  1755 , it should be appreciated that although  FIG. 17B  depicts only one microprocessor  1774 , the controller  1770  may include multiple microprocessors  1774 . Similarly, the memory of the controller  1770  may include multiple RAMs  1776  and multiple program memories  1772 . Although the  FIG. 17B  depicts the I/O circuit  1780  as a single block, the I/O circuit  1780  may include a number of different types of I/O circuits. The controller  1770  may implement the RAM(s)  1776  and the program memories  1772  as semiconductor memories, magnetically readable memories, and/or optically readable memories, for example. 
     Either or both of the program memories  1760  ( FIG. 17A ) and  1772  may also contain machine-readable instructions (i.e., software)  1771 , for execution within the processing circuitry  1762  ( FIG. 17A ) and  1774 , respectively. The software  1771  may perform the various tasks associated with operation of the location or locations, and may be a single module  1771  or a number of modules  1771   a ,  1771   b . While the software  1771  is depicted in  FIGS. 17A and 17B  as including two modules,  1771   a  and  1771   b , the software  1771  may include any number of modules accomplishing tasks related to location operation. 
     In addition to the controller  1770 , the portable computing devices  1733 , the workstations  1728  and the other web-enabled devices  1714  may further include a display and a keyboard as well as a variety of other input/output devices (not shown) such as a scanner, printer, mouse, touch screen, track pad, track ball, isopoint, voice recognition system, digital camera, bar code scanner, RFID reader, etc. An applicant  102  or approver  104  may sign on and occupy each portable computing device  1733 , workstation  1728  or applicant device terminal  1728   a  to assist the employee in performing his or her duties. Employees may sign onto the portable computing device  1733 , workstation  1728  or the applicant device terminal  1728   a  using any available technique, such as entering a user name and password. If an employee signs on to the system using a portable computing device  1733 , the network  1784  communicates this information to the facility server  1726 , so that the controller  1770  may identify which employees are signed onto the platform  1700  and which portable computing device  1733 , workstation  1728  or applicant device terminal  1728   a  the employee is signed onto. 
     Various software applications resident in the front-end components  1702  and the back-end components  1704  implement functions related to location operation, and provide various user interface means to allow users (e.g., brokers) to access the platform  1700 . One or more of the front-end components  1702  and/or the back-end components  1704  may include a user-interface application  1711  for allowing a user to input and view data associated with the platform  1700 , and to interact with the platform described herein. In one embodiment, the user interface application  1711  is a web browser applicant, and the facility server  1726  or the central processing system  1740  implements a server application  1713  for providing data to the user interface application  1711 . However, the user interface application  1711  may be any type of interface, including a proprietary interface, and may communicate with the facility server  1726  or the central processing system  1740  using any type of protocol including, but not limited to, file transfer protocol (FTP), telnet, hypertext-transfer protocol (HTTP), etc. Moreover, some embodiments may include the user interface application  1711  running on one of the web-enabled devices  1714 , while other embodiments may include the application  1711  running on the portable computing device  1733  in a location  1712 . The central processing system  1740  and/or the facility server  1726  may implement any known protocol compatible with the user-interface application  1711  running on the portable computing devices  1733 , the workstations  1728  and the web-enabled devices  1714  and adapted to the purpose of receiving and providing the necessary information during the data transfer process. 
     For purposes of implementing the platform  1700 , the user interacts with location systems (e.g., the central processing system  1740 ) via a number of web pages.  FIG. 17C  depicts a web server  1790  connected via the network  1730  to a number of portable computing devices  1733  and other web-enabled devices through which a user  1792  may initiate and interact with the platform  1700 . The web enabled devices may include, by way of example, a smart-phone  1794   a , a web-enabled cell phone  1794   b , a tablet computer  1733 , a personal digital assistant (PDA)  1794   c , a laptop computer  1794   d , a desktop computer  1794   e , a portable media player (not shown), etc. Of course, any web-enabled device appropriately configured may interact with the platform  1700 . The web-enabled devices  1733  and  1794  need not necessarily communicate with the network  1730  via a wired connection. In some instances, the web enabled devices  1733  and  1794  may communicate with the network  1730  via wireless signals  1796  and, in some instances, may communicate with the network  1730  via an intervening wireless or wired device  1731 , which may be a wireless router, a wireless repeater, a base transceiver station of a mobile telephony provider, etc. Each of the web-enabled devices  1733  and  1794  may interact with the web server  1790  to receive web pages, such as the web page  1798  depicted in  FIG. 17C , for display on a display associated with the web-enabled device  1733  and  1794 . It will be appreciated that although only one web server  1790  is depicted in  FIG. 17C , multiple web servers  1790  may be provided for the purpose of distributing server load, serving different web pages, implementing different portions of the location web interface, etc. 
     Turning now to  FIG. 17D , the web server  1790 , like the facility server  1726 , includes a controller  1706 . Similar to the controllers  1755  and  1770 , the controller  1706  includes a program memory  1708 , processing circuitry (e.g., one or more microcontrollers or microprocessors)  1716 , a random-access memory (RAM)  1718 , and an input/output (I/O) circuit  1720 , all of which are interconnected via an address/data bus  1722 . In some embodiments, the controller  1706  may also include, or otherwise be communicatively connected to, a database  1724  or other data storage mechanism (e.g., one or more hard disk drives, optical storage drives, solid state storage devices, etc.). The database  1724  may include data such as scholarship institution web profiles, product data, web page templates and/or web pages, and other data necessary to interact with the user  1792  through the network  1730 . As discussed with reference to the controllers  1755  and  1770 , it should be appreciated that although  FIG. 17D  depicts only one microprocessor  1716 , the controller  1706  may include multiple microprocessors  1716 . Similarly, the memory of the controller  1706  may include multiple RAMs  1718  and multiple program memories  1708 . Although the  FIG. 17D  depicts the I/O circuit  1720  as a single block, the I/O circuit  1720  may include a number of different types of I/O circuits. The controller  1706  may implement the RAM(s)  1718  and the program memories  1708  as semiconductor memories, magnetically readable memories, and/or optically readable memories, for example. 
     In addition to being connected through the network  1730  to the user devices  1733  and  1794 , as depicted in  FIG. 17C ,  FIG. 17D  illustrates that the web server  1790  may also be connected through the network  1730  to the central processing system  1740  and/or one or more facility servers  1726 . As described below, connection to the central processing system  1740  and/or to the one or more facility servers  1726  facilitates the platform  1700 . 
     The program memory  1708  and/or the RAM  1718  may store various applications for execution by the processing circuitry  1716 . For example, an application  1732  may provide a user interface to the server, which user interface may, for example, allow a network administrator to configure, troubleshoot, or test various aspects of the server&#39;s operation, or otherwise to access information thereon. A server application  1734  operates to populate and transmit web pages to the web-enabled devices  1794 , receive information from the user  1792  transmitted back to the server  1790 , and forward appropriate data to the central processing system  1740  and the facility servers  1726 , as described below Like the software  1771 , the server application  1734  may be a single module  1734  or a number of modules  1734   a ,  1734   b . While the server application  1734  is depicted in  FIG. 17D  as including two modules,  1734   a  and  1734   b , the server application  1734  may include any number of modules accomplishing tasks related to implantation of the web server  1790 . By way of example, the module  1734   a  may populate and transmit the web pages and/or may receive and evaluate inputs from the user  1792  to facilitate in the wireless transfer of data from a first tablet to a second tablet, while the module  1734   b  may communicate with one or more of the back end components to provide the requested data. 
     Typically, a user may launch or instantiate a user interface application (e.g., a web browser or other applicant application) from a web-enabled device, such as the web-enabled devices  1733  and  1794 , to access the web server  1790  cooperating with the system  1740  to implement the platform  1700 . 
     One or more processors can be utilized to implement any functions and/or algorithms described herein, unless explicitly stated otherwise. Additionally, any functions and/or algorithms described herein, unless explicitly stated otherwise, can be performed upon virtual processing circuitry (e.g., one or more virtual processors, for example on one or more physical computing systems such as a computer farm or a cloud drive). 
     Reference has been made to flowchart illustrations and block diagrams of methods, systems and computer program products according to implementations of this disclosure. Aspects thereof are implemented by computer program instructions. These computer program instructions may be provided to processing circuitry of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing circuitry of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The implementations described herein are directed to a scholarship management system  108  that significantly improves the technology of systems and devices that manage the approval of scholarships by a scholarship-awarding institution. Being able to process scholarship applications in real-time, automatically determine an applicant quality score that provides an indication of whether the applicant is a good candidate for the scholarship, and manage the flow of applications through the approvers, the scholarship management system  108  is fully automated and improves the efficiency of how the processing engines and data repositories interact with each other. The automatically triggering of the processing engines during execution of the processes of the scholarship management system  108  increases the amount of processing that is done by the system  108  without any type of human interaction. Also, identifying potential applicants for the scholarships and automatically flagging interested potential applicants for expedited processing allows the best applicants to be awarded scholarships in a more efficient manner. 
     A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of this disclosure. For example, preferable results may be achieved if the steps of the disclosed techniques were performed in a different sequence, if components in the disclosed systems were combined in a different manner, or if the components were replaced or supplemented by other components. The functions, processes and algorithms described herein may be performed in hardware or software executed by hardware, including computer processing circuitry (e.g., processors and/or programmable circuits) configured to execute program code and/or computer instructions to execute the functions, processes and algorithms described herein. Additionally, some implementations may be performed on modules or hardware not identical to those described. Accordingly, other implementations are within the scope that may be claimed.