System and method for recursive database file management

Abstract

A system for recursive data base file management is disclosed that includes an assessment tool that is configured to receive a plurality of responses from a plurality of users, a scoring system configured to receive the plurality of responses and to generate a score for each of a plurality of categories for each of the users, a content data file selection system configured to select one or more content data files as a function of the scores for each of the plurality of categories, a score predictor system configured to receive the score for each of the plurality of categories and the selected content data files and to generate a predicted score for each of the plurality of categories for each of the users after reviewing the content data files, and a prediction feedback correction system configured to receive updated scores and to modify the score predictor.

Description

SYSTEM AND METHOD FOR RECURSIVE DATABASE FILE MANAGEMENTRELATED APPLICATIONSThis application claims benefit of and priority to U . S . provisional patent application no . 63 / 530 , 172 , filed August 1 , 2023 , which his hereby incorporated by reference for all purposes as if set forth herein in its entirety .TECHNICAL FIELD

[0001] The present disclosure relates generally to database file management , and more speci fically to a system and method for recursive database file management that uses recursive processes to improve database file management .BACKGROUND OF THE INVENTION

[0002] Data for performing and monitoring complex matters , such as brain health assessment , is often subj ective and may lack objective metrics . As a result , it can be dif ficult to determine whether a person could benefit from treatment , such as access to and review of training and educational materials , or if they receive treatment , whether they benefitted from the treatment .SUMMARY OF THE INVENTION

[0003] A system for recursive data base file management is disclosed that includes an assessment tool that is configured to receive a plurality of responses from a plurality of users , a scoring system configured to receive the plurality of responses and to generate a score for each of a plurality of categories for each of the users , a content data file selection system configured to select one or more content data files as a function of the scores for each of the plurality of categories ,a score predictor system configured to receive the score for each of the plurality of categories and the selected content data files and to generate a predicted score for each of the plurality of categories for each of the users after reviewing the content data files , and a prediction feedback correction system configured to receive updated scores and to modify the score predictor .

[0004] Other systems , methods , features , and advantages of the present disclosure will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description . It is intended that all such additional systems , methods , features , and advantages be included within this description, be within the scope of the present disclosure, and be protected by the accompanying claims .BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0005] Aspects of the disclosure can be better understood with reference to the following drawings . The components in the drawings may be to scale, but emphasis is placed upon clearly illustrating the principles of the present disclosure . Moreover, in the drawings , like reference numerals designate corresponding parts throughout the several views , and in which :

[0006] FIGURE 1 is a diagram of a system for precision brain health assessment , in accordance with an example embodiment of the present disclosure;

[0007] FIGURE 2 is a diagram of a system for a brain health assessment tool , in accordance with an example embodiment of the present disclosure;

[0008] FIGURE 3 is a diagram of an algorithm for precision brain health assessment , in accordance with an example embodiment of the present disclosure;

[0009] FIGURE 4 is a diagram of an algorithm for populationbased factor assessment , in accordance with an example embodiment of the present disclosure; and

[0010] FIGURE 5 is a diagram of a system for modified brain health assessment , in accordance with an example embodiment of the present disclosure .DETAILED DESCRIPTION OF THE INVENTION

[0011] In the description that follows , like parts are marked throughout the speci fication and drawings with the same reference numerals . The drawing figures may be to scale and certain components can be shown in generalized or s chematic form and identified by commercial designations in the interest of clarity and conciseness .

[0012] This application claims benefit of and priority to U . S . provisional patent application no . 63 / 530 , 172 , filed August 1 , 2023 , which his hereby incorporated by reference for all purposes as if set forth herein in its entirety .

[0013] FIGURE 1 is a diagram of a system 100 for precision brain health assessment , in accordance with an example embodiment of the present disclosure . System 100 can be implement in hardware or a suitable combination of hardware and software .

[0014] Assessment tool 102 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to perform an initial assessment based on an assumption of no content review and subsequent tests or assessments ( generally tests , herein) based on reviewed content . In one example embodiment , a user can generate a profile and can be presented with a series of tests , where the tests are selected as a function of user experience level and reviewed content . In this example embodiment , a user can receive a test that is configured for someone who has not reviewed content , to evaluate whether content needs to be provided and reviewed . I f the results of the test indicate that content review should be performed, a subsequent test can include assessment tools forevaluating whether the content was success fully understood and implemented by the user . By way of example and not by limitation, a user can be presented with a question in an initial evaluation, such as to select a course of action when crossing a street . I f the user selects a correct answer to look both ways , then the user can be scored as requiring no additional content . I f the user instead selects a different answer, such as to run across the street as fast as possible , the user can be flagged for further content review, such as content that explains how drivers might not be paying attention or expect the user to run across the street . Content as used herein is generally understood to include audiovisual content , reading material , counseling or other suitable materials that can help the user to understand better ways to deal with problems . The user can then be tested after viewing the content to determine whether the content was understood . While this simple example is for a single hypothesi zed user response , assessment tool 102 can be configured to coordinate multiple di fferent user responses that are correlated to scores that are further correlated to appropriate content and follow-on testing, as discussed further herein . For example, an assessment can include a plurality of questions that are related and coordinated to provide an evaluation of multiple factors .

[0015] Scoring system 104 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to generate a score based on an assessment , where the score identifies areas where content should be selected for review . In one example embodiment , scoring system 104 can receive a plurality of responses to questions and can evaluate the responses to select content for a user . In this example embodiment , the content selection can be based on predetermined profiles that have been developed based on case studies of other users , can be optimized for a speci fic user based on a user-specific analysis of the score results , or can be selected in other suitable manners .

[0016] Score predictor system 106 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to recursively predict an improvement in a score if selected content has been viewed and understood . In one example embodiment , content can be evaluated from results of population testing for effectiveness , and an improvement in a score for demographic groups of the population starting from a predetermined initial score can be statistically determined . Score predictor system 106 can generate predicted score increases for a user as a function of the user' s initial score and selected content , a user' s subsequent score and additional selected content , or in other suitable manners .

[0017] Prediction feedback correction system 108 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to identify di fferences between a predicted score and the actual score a user receives after they have viewed content . Prediction feedback correction system 108 can evaluate additional content for viewing if previously viewed content resulted in the predicted score increase , can suggest viewing previously presented content again, can indicate that selection of new content should be performed i f the score increase was less than expected, and can perform other suitable functions .

[0018] Content selection system 110 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to select content for a user based on scores , past performance and other suitable criteria . In one example embodiment , content selection system 110 can be implemented as a database that having a plurality of associations with factors ,behavioral components , demographic data fields and other suitable data fields , and can select content that satis fies multiple categories of training as a function of the factors , behavioral components , demographic data fields and other suitable data fields , as discussed further herein .

[0019] FIGURE 2 is a diagram of a system 200 for a brain health assessment tool , in accordance with an example embodiment of the present disclosure . System 200 can be implement in hardware or a suitable combination of hardware and software .

[0020] Assessment tool 102 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to perform an initial assessment as a function of no prior formal training and subsequent assessments as a function of subsequent training, such as based on reviewed content , counseling, experience or other activities .

[0021] Cognitive factor assessment system 202 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to perform assessment of predetermined behavioral components , such as strategic learning, innovation, ability to understand and apply simple rules ( such as proverbs ) , visual selective learning and other cognitive factors . Cognitive factors can be assessed as a function of content selections that have been viewed, counseling, experience or other activities , such as by presenting coordinated questions that have been developed to assess whether the content has been understood . For example, strategic learning, innovation, ability to understand and apply simple rules ( such as an abstraction assessment ) , visual selective learning and other cognitive factor assessments can be based on guidance from recorded content or counseling, and subsequent experiences by the user of applying the guidance to the user' s daily events that involve strategic learning, innovation, ability tounderstand and apply simple rules ( such as an abstraction assessment ) , visual selective learning and other cognitive factors .

[0022] In one example embodiment , the abstraction assessment is a cognitive assessment tool that measures abstract thinking . For example , a user can be asked to explain the meaning of a proverb, where responses are scored based on their level of abstraction . A higher score can be assessed for non-literal , figurative interpretations that reflect a deeper understanding of the proverb ' s meaning, while literal interpretations can receive lower scores . This tool can help to gauge the user' s cognitive capabilities and is also useful in predetermined psychological evaluations .

[0023] Wellbeing factor assessment 204 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to perform assessment of predetermined behavioral components , such as depression, resilience , satisfaction, happiness and other wellbeing factors . Wellbeing factors can be assessed as a function of content selections that have been viewed, counseling, experience or other activities , such as by presenting coordinated questions that have been developed to assess whether the content has been understood . For example , depression, resilience , satisfaction and happiness assessments can be based on guidance from recorded content or counseling, and subsequent experiences by the user of applying the guidance to the user' s daily events that involve depression, resilience, satisfaction and happiness .

[0024] Social interactions factor assessment 206 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to perform assessment of predetermined behavioral components such as social supports , engagements , compassion and other social interaction factors .Social interaction factors can be assessed as a function of content selections that have been viewed, counseling, experience or other activities , such as by presenting coordinated questions that have been developed to assess whether the content has been understood . For example , social supports , engagements and compassion assessments can be based on guidance from recorded content or counseling, and subsequent experiences by the user of applying the guidance to the user' s daily events that involve social supports , engagements and compassion .

[0025] Daily life factor assessment 208 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to perform assessment of predetermined behavioral components such as outlook, activities , fitness , sleep and other daily li fe factors . Daily life factors can be assessed as a function of content selections that have been viewed, counseling, experience or other activities , such as by presenting coordinated questions that have been developed to assess whether the content has been understood . For example , activities , fitness and sleep assessments can be based on guidance from recorded content or counseling, and subsequent experiences by the user of applying the guidance to the user' s daily activities , fitness routines and sleep habits .

[0026] Factor assessment system 210 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to determine whether to drop, reassign or add new factors or factor assessments . In one example embodiment , scores for a population of individuals can be analyzed to determine whether groupings of factors provide an accurate estimate of brain health for that population or demographic groups within the population . For example, assessment of brain health for a demographic group of active military in a population of tested users can be dif ferent from assessment of brain healthfor a demographic group of veterans within that population, assessments of brain health for a demographic group of young persons within that population can be dif ferent from assessments of brain health for a demographic group of older persons within that population, and so forth . Likewise, di fferent populations may have di fferent demographic groups or different responses from demographic groups . Factor assessment system 210 allows factors to be modified as a function of specific population or individual characteristics .

[0027] Scoring system 104 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to perform scoring of individuals and tracking scores of populations and demographic groups within the population . In one example embodiment , scoring can be a function of an individual , a demographic group of a population or of a population, such as where scores can be assessed based on individual characteristics ( e . g . personal history) , demographic groups (e . g . young people, people who drive trucks ) , population characteristics (e . g . people from all demographic groups who live in a city or state ) and so forth . For example , scores for social interaction for a demographic group of young persons can take into account activities that are specific for young persons ( such as attendance at school ) , scores for populations can take into account activities that are specific for those populations ( such as skiing for populations near mountains ) , scores for populations can take into account activities performed by that population ( such as amount of driving for truck drivers or amount of bicycle riding for bicycle riders ) and so forth .

[0028] FIGURE 3 is a diagram of an algorithm 300 for precision brain health assessment , in accordance with an example embodiment of the present disclosure . Algorithm 300 can be implemented in hardware or a suitable combination of hardware and software .

[0029] Algo rithm 300 begins at 302, where a baseline assessment of a user is performed. The algorithm then proceeds to 304.

[0030] At 304, a test or assessment is provided and a score is generated. The algorithm then proceeds to 306.

[0031] At 306, content, counseling or other information is selected for a user based on the test or assessment results. The algorithm then proceeds to 308.

[0032] At 308, the user is monitored, such as to ensure that they are viewing content, attending counseling or so forth. The algorithm then proceeds to 310.

[0033] At 310, a new score is predicted that the user should attain if content is reviewed and understood, counseling is competed or so forth. The algorithm then proceeds to 312.

[0034] At 312, it is determined whether the content been reviewed, counseling has been completed or other associated activities have concluded. If not, a prompt is generated for the user to complete the activities and the algorithm returns to 312. If content / counseling / activities have been completed, the algorithm proceeds to 314 where the user is tested and a new score is generated. The algorithm then proceeds to 316.

[0035] At 316, the new score is compared to the predicted score. The algorithm then proceeds to 318.

[0036] At 318, it is determined whether the new score different from the predicted score by a predetermined tolerance. If the new score is different from predicted and is worse than predicted, the algorithm proceeds to 322 where the reason for the difference is determined and new content is selected. In one example embodiment, the user may have misrepresented review of content, participation in counseling or other activities. In another example embodiment, the content, counseling or activities may need to be improved. Artificial intelligence, deep learning or other tools can be used to evaluate the behavior of the individual in relation to behaviors of populations, suchas to determine whether the individual is a member of a previously-unidenti fied population . For example, individuals with certain untreated disorders such as depression or post- traumatic stress disorder may be identified based on their responses to assessments i f they correspond to responses of populations with those disorders , and may require more extensive counseling or other assistance to make progress . After the reason or suspected reason for the discrepancy has been identified and corrective measures have been implemented, the algorithm returns to 306 where new content / counseling / activities are selected . Otherwise, if it is determined at 320 that the score is as good or better than predicted, the algorithm returns to 306, where new content / counseling / activities are selected .

[0037] In operation, algorithm 300 is used to provide precision brain health assessment . While algorithm 300 is shown as a flow chart , a person of skill in the art will recogni ze that it can also or alternatively be implemented using obj ect- oriented programming, as a state diagram, as a ladder diagram or in other suitable manners .

[0038] FIGURE 4 is a diagram of an algorithm 400 for population-based factor assessment , in accordance with an example embodiment of the present disclosure . Algorithm 400 can be implemented in hardware or a suitable combination of hardware and software .

[0039] Algo rithm 400 begins at 402 , where population scores are received . The algorithm then proceeds to 404 .

[0040] At 404 , score groupings for the factors that make up the score are evaluated for population consistency . In one example embodiment , it can be determined whether the scores for a factor ( such as strategic learning, innovation, the ability to understand and apply simple rules ( such as proverbs ) or visual selective learning) is a meaningful indicator for an index ( such as cognitive assessment ) . Other suitable assessments , factors , indices or components can also or alternatively be utili zed . Forexample , a test of improvement in functional magnetic resonance imaging ( fMRI ) activity, hemodynamic response function (HRF) response or other suitable physical measurements can be used to assess whether an increase in a score for an assessment correlates to a measurable parameter . Statistical analysis can also or alternatively be performed, such as to identi fy groups of factors other than the original groups are better indicators of brain health, to add or drop factors or for other suitable purposes . The algorithm then proceeds to 406 .

[0041] At 406, it is determined whether there are outliers , such as tests that do not appear to be relevant / determinative for the factor or i f other changes in the test profile are needed . I f it is determined that no change in the test profile is needed, the algorithm proceeds to 422 , otherwise the algorithm proceeds to 408 .

[0042] At 408 , outliers are evaluated to determine whether there are statistically-relevant groupings of outliers , such as outlier scores that are greater than mean, less than mean, groups of scores that are greater than the mean and others that are less than the mean, and so forth . The algorithm then proceeds to 410 .

[0043] At 410 , it is determined whether a test should be dropped from a factor, whether a new factor or category is needed or if other changes are needed . I f it is determined that a change is needed, the algorithm proceeds to 412 , otherwise the algorithm proceeds to 414 .

[0044] At 412 the test is dropped from the factor and the previous factor scores are updated to reflect dropped test , to allow them to be compared to future scores . The algorithm then proceeds to 404 .

[0045] At 414 , it is determined whether to reassign a test from one factor to another or to create a new factor, such as where results from a test correlate more closely to scores for other factors . For example , a high innovation score may bedetermined for an individual , a population or other groups to be relevant for a daily life factor but not cognition, or other suitable determinations can also or alternatively be made . I f it is determined that a reassignment is needed, the algorithm proceeds to 416, otherwise the algorithm proceeds to 418 .

[0046] At 416 , the assessment is reassigned to a dif ferent factor, a new factor is created or other suitable modifications are made to the scoring process , and the previous factor scores are updated to reflect the reassigned test or tests . The algorithm then proceeds to 418 .

[0047] At 418 , it is determined whether to add a newly developed test to a factor . I f so , the algorithm proceeds to 420 , otherwise the algorithm proceeds to 422 .

[0048] At 420 , the new test is added and the date of new test is tracked for use with application of prior scores . The algorithm then proceeds to 422 .

[0049] At 422 , the updated factors are released for use with scoring .

[0050] In operation, algorithm 400 is used to provide population-based brain health factor assessment . While algorithm 400 is shown as a flow chart , a person of skill in the art will recogni ze that it can also or alternatively be implemented using obj ect-oriented programming, as a state diagram, as a ladder diagram or in other suitable manners .

[0051] FIGURE 5 is a diagram of a system 500 for modified brain health assessment , in accordance with an example embodiment of the present disclosure . System 500 can be implemented in hardware or a suitable combination of hardware and software .

[0052] System 500 includes cognitive factor assessment system 202 , wellbeing factor assessment 204 , social interactions factor assessment 206 and daily life factor assessment 208 , which generate population outputs when statistically signi ficant populations of individuals have been tested .

[0053] Population score evaluation 502 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to evaluate the population scores from cognitive factor assessment system 202 , wellbeing factor assessment 204 , social interactions factor assessment 206 and daily li fe factor assessment 208 and to re-allocate the tests into new categories , where suitable and when the re-allocation more accurately measures brain health .

[0054] Clarity factor assessment 504 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to re-allocate the tests from cognitive factor assessment system 202 , wellbeing factor assessment 204 , social interactions factor assessment 206 and daily life factor assessment 208 to clarity factor assessment 504 . In one example embodiment , it can be determined that a clarity factor provides a better indication of brain health, where the clarity factor includes the following test components : strategic learning tests , proverb tests and visual selective learning tests from cognitive factor assessment system 202 ; compassion tests from social interactions factor assessment 206; and sleep tests from daily life factor assessment 208 . The new clarity factor assessment can be created when it is determined that the reallocation of the tests from the di fferent factors more accurately reflects brain health .

[0055] Emotional balance factor assessment 506 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to reallocate the tests from the cognitive factor assessment system 202 , wellbeing factor assessment 204 , social interactions factor assessment 206 and daily life factor assessment 208 to an emotional balance factor . In one example embodiment , it can be determined that emotionalbalance factor assessment 506 provides a better indication of brain health, where the emotional balance factor includes the following components : depression, anxiety and stress tests from wellbeing factor assessment system 204 ; and sleep tests from daily li fe factor assessment 208 . The new emotional balance factor can be created when it is determined that the reallocation of the tests from dif ferent factors more accurately reflects brain health .

[0056] Connectedness factor assessment 508 can be implemented as one or more algorithms that are stored in a data memory device and that are loaded into a working memory of a processor to cause the processor to reallocate the tests from the cognitive factor assessment system 202 , wellbeing factor assessment 204 , social interactions factor assessment 206 and daily life factor assessment 208 to connectedness factor assessment 508 . In one example embodiment , it can be determined that connectedness factor assessment 508 provides a better indication of brain health, where connectedness factor assessment 508 includes the following components : resilience tests , satisfaction tests and happiness tests from wellbeing factor assessment 204 ; social support tests , engagement tests and compassion tests from social interactions factor assessment 206; and activities tests from daily life factor assessment 208 . Connectedness factor assessment 508 can be created when it is determined that the reallocation of the tests from dif ferent factors more accurately reflects brain health .

[0057] In operation, system 500 reallocates tests from cognitive factor assessment system 202 , wellbeing factor assessment 204 , social interactions factor assessment 206 and daily life factor assessment 208 to new clarity factor assessment 504 , emotional balance factor assessment 506 and connectedness factor assessment 508 . It is noted that the reallocation results in the omission of the innovation test from cognitive factor assessment system 202 , and the outlook andfitness tests from daily life factor assessment 208, to create a new brain health index for a population of interest. Likewise, other suitable allocations of tests can also or alternatively be used, as discussed and described further herein.

[0058] As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising, " when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, phrases such as "between X and Y" and "between about X and Y" should be interpreted to include X and Y. As used herein, phrases such as "between about X and Y" mean "between about X and about Y . " As used herein, phrases such as "from about X to Y" mean "from about X to about Y

[0059] As used herein, "hardware" can include a combination of discrete components, an integrated circuit, an applicationspecific integrated circuit, a field programmable gate array, or other suitable hardware. As used herein, "software" can include one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code or other suitable software structures operating in two or more software applications, on one or more processors (where a processor includes one or more microcomputers or other suitable data processing units, memory devices, input-output devices, displays, data input devices such as a keyboard or a mouse, peripherals such as printers and speakers, associated drivers, control cards, power sources, network devices, docking station devices, or other suitable devices operating undercontrol of software systems in con junction with the processor or other devices ) , or other suitable software structures . In one exemplary embodiment , software can include one or more lines of code or other suitable software structures operating in a general purpose software application, such as an operating system, and one or more lines of code or other suitable software structures operating in a specific purpose software application . As used herein, the term "couple" and its cognate terms , such as "couples" and "coupled, " can include a physical connection ( such as a copper conductor) , a virtual connection ( such as through randomly assigned memory locations of a data memory device) , a logical connection ( such as through logical gates of a semiconducting device) , other suitable connections , or a suitable combination of such connections . The term "data" can refer to a suitable structure for using, conveying or storing data, such as a data field, a data buffer, a data message having the data value and sender / receiver address data, a control message having the data value and one or more operators that cause the receiving system or component to perform a function using the data, or other suitable hardware or software components for the electronic processing of data .

[0060] In general , a software system is a system that operates on a processor to perform predetermined functions in response to predetermined data fields . A software system is typically created as an algorithmic source code by a human programmer, and the source code algorithm is then compiled into a machine language algorithm with the source code algorithm functions , and linked to the specific input / output devices , dynamic link libraries and other specific hardware and software components of a processor, which converts the processor from a general purpose processor into a specific purpose processor . This well-known process for implementing an algorithm using a processor should require no explanation for one of even rudimentary skill in the art . For example , a system can bedefined by the function it performs and the data fields that it performs the function on . As used herein, a NAME system, where NAME is typically the name of the general function that is performed by the system, refers to a software system that is configured to operate on a processor and to perform the disclosed function on the disclosed data fields . A system can receive one or more data inputs , such as data fields , user-entered data, control data in response to a user prompt or other suitable data, and can determine an action to take based on an algorithm, such as to proceed to a next algorithmic step if data is received, to repeat a prompt if data is not received, to perform a mathematical operation on two data fields , to sort or display data fields or to perform other suitable well-known algorithmic functions . Unless a specific algorithm is disclosed, then any suitable algorithm that would be known to one of skill in the art for performing the function using the associated data fields is contemplated as falling within the scope of the disclosure . For example , a message system that generates a message that includes a sender address field, a recipient address field and a message field would encompass software operating on a processor that can obtain the sender address field, recipient address field and message field from a suitable system or device of the processor, such as a buffer device or buf fer system, can assemble the sender address field, recipient address field and message field into a suitable electronic message format ( such as an electronic mail message, a TCP / IP message or any other suitable message format that has a sender address field, a recipient address field and message field) , and can transmit the electronic message using electronic messaging systems and devices of the processor over a communications medium, such as a network . One of ordinary skill in the art would be able to provide the specific coding for a specific application based on the foregoing disclosure , which is intended to set forth exemplary embodiments of the present disclosure, and not toprovide a tutorial for someone having less than ordinary skill in the art , such as someone who is unfamiliar with programming or processors in a suitable programming language . A specific algorithm for performing a function can be provided in a flow chart form or in other suitable formats , where the data fields and associated functions can be set forth in an exemplary order of operations , where the order can be rearranged as suitable and is not intended to be limiting unless explicitly stated to be limiting .

[0061] It should be emphasi zed that the above-described embodiments are merely examples of possible implementations . Many variations and modifications may be made to the abovedescribed embodiments without departing from the principles of the present disclosure . All such modi fications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims .

Claims

What is claimed is :1 . A system for recursive data base file management , comprising : an assessment tool operating on a processor and configured to receive a plurality of responses from a plurality of users ; a scoring system operating on the processor and configured to receive the plurality of responses and to generate a score for each of a plurality of categories for each of the users ; a content data file selection system operating on the processor and configured to select one or more content data files as a function of the scores for each of the plurality of categories ; a score predictor system operating on the processor and configured to receive the score for each of the plurality of categories and the selected content data files and to generate a predicted score for each of the plurality of categories for each of the users after reviewing the content data files ; and a prediction feedback correction system operating on the processor and configured to receive updated scores from the scoring system after the users have reviewed the content and provided a second plurality of responses to the assessment tool and to modify the score predictor and content data file associations in a database as a function of one or more di fferences between the predicted scores and the updated scores .2 . The system of claim 1 wherein each of the plurality of users are associated with one of a plurality of demographics and the content data files are associated with only one of the plurality of demographics .3 . The system of claim 1 wherein each of the plurality of users are associated with one of a plurality of demographics and the content data file associations are changed from only oneof the plurality of demographics to two or more of the plurality of demographics .4 . The system of claim 1 wherein each of the plurality of users are associated with one of a plurality of demographics and the content data file associations are changed from two or more of the plurality of demographics to one of the plurality of demographics .5 . The system of claim 1 wherein the assessment tool generates a score for each of a plurality of factors for each user .6 . The system of claim 1 wherein the assessment tool generates a score for each of a plurality of predetermined behavioral components .7 . The system of claim 1 wherein the assessment tool generates a score for each of a plurality of predetermined behavioral components of each of a plurality of factors .8 . The system of claim 1 wherein the prediction feedback correction system deletes a factor from the assessment tool as the function of the one or more di fferences between the predicted scores and the updated scores .9 . The system of claim 1 wherein the prediction feedback correction system deletes a factor from the assessment tool as the function of the one or more di fferences between the predicted scores and the updated scores for a factor .10 . The system of claim 1 wherein the prediction feedback correction system deletes a factor from the assessment tool as the function of the one or more di fferences between the predictedscores and the updated scores for a plurality of behavioral components of a factor .11 . A method for recursive data base file management , comprising : receiving a plurality of responses from a plurality of users at a processor; generating a score using the processor for each of a plurality of categories for each of the users as a function of the plurality of responses ; selecting one or more content data files using the processor as a function of the scores for each of the plurality of categories ; generating a predicted score using a prediction algorithm of the processor for each of the plurality of categories for each of the users after reviewing the content data files after receiving the score for each of the plurality of categories and the selected content data files ; receiving updated scores at the processor after the users have reviewed the content and provided a second plurality of responses ; and modi fying the prediction algorithm and content data file associations in a database as a function of one or more di fferences between the predicted scores and the updated scores .12 . The method of claim 11 wherein each of the plurality of users are associated with one of a plurality of demographics and the content data files are associated with only one of the plurality of demographics .13 . The method of claim 11 wherein each of the plurality of users are associated with one of a plurality of demographics and the content data file associations are changed from only one of the plurality of demographics to two or more of the pluralityof demographics .14 . The method of claim 11 wherein each of the plurality of users are associated with one of a plurality of demographics and the content data file associations are changed from two or more of the plurality of demographics to one of the plurality of demographics .15 . The method of claim 11 further comprising generating the score for each of a plurality of factors for each user using the processor .16 . The method of claim 11 further comprising generating the score for each of a plurality of predetermined behavioral components using the processor .17 . The method of claim 11 further comprising generating the score for each of a plurality of predetermined behavioral components of each of a plurality of factors using the processor .18 . The method of claim 11 further comprising deleting a factor from the assessment tool as the function of the one or more differences between the predicted scores and the updated scores using the processor .19 . The method of claim 11 further comprising deleting a factor from the assessment tool as the function of the one or more differences between the predicted scores and the updated scores for a factor using the processor .

Images