ADAPTIVE TEACHING SYSTEM FOR GENERATING GAMIFIED TRAINING CONTENT AND INTEGRATING MACHINE LEARNING

Abstract

An improved system and method for generating gamified training content using multimodal human-machine interfaces controlled by an adaptive backend training system. Such a system can include a gamification engine working in conjunction with a machine learning engine for adaptively updating or restructuring database elements by analyzing existing data and applying external training data. Training content can thus be progressively optimized for various users, groups, and training contexts as the system adapts.

Claims

1. An adaptive teaching system, comprising: a content database for storing content data including free format text data; a gamification engine coupled to the content database, the gamification engine for producing a gamified text; a machine learning engine coupled to the gamification engine for executing machine learning program code; at least one human-machine interface coupled to the gamification engine, the at least one human-machine interface for presenting the gamified text interface and gathering text data; a diagnostic database coupled to the gamification engine for storing diagnostic data; and the machine learning engine executing the machine learning program code for: querying the diagnostic database to obtain the diagnostic data; comparing the text data to the diagnostic data; and generating at least one diagnostic result based on the comparison.

2. The adaptive teaching system of claim 1, further comprising an intelligence database coupled to the gamification engine for storing intelligence data.

3. The adaptive teaching system of claim 2, wherein the intelligence data includes at least one semantic element associated with the free format text data.

4. The adaptive teaching system of claim 1, wherein the gamified text interface includes a first navigational path through at least one gamified representation of the free format text data.

5. The adaptive teaching system of claim 4, wherein the gamification engine is further configured for creating, based on the at least one diagnostic result, an updated gamified text interface including a second navigational path through at least one gamified representation of the free format text data.

6. The adaptive teaching system of claim 1, wherein the diagnostic result is provided to the gamification engine as feedback and used to generate an additional gamified text interface

7. The adaptive teaching system of claim 1, wherein the gamification engine associates the diagnostic result with a user and in response generates an additional gamified text interface for presenting a treatment to the user via the human-machine interface.

8. The adaptive teaching system of claim 1, wherein the machine learning program code is further configured for: calling at least one machine learning algorithm for analyzing diagnostic patterns; evaluating the diagnostic database via the at least one machine learning algorithm; generating at least one modification suggestion in response to evaluating; and modifying the diagnostic database based on said modification suggestion.

9. The adaptive teaching system of claim 1, further comprising an event history database for storing event history data based on previous interactions with gamified text interfaces via human-machine interfaces.

10. The adaptive teaching system of claim 8, wherein in evaluating the diagnostic database, the machine learning program code identifies at least one diagnostic parameter associated with second free format text data stored in the event history database.

11. The adaptive teaching system of claim 8, wherein in evaluating the diagnostic database, the machine learning program code identifies at least one diagnostic parameter associated with training data, the training data stored an external database accessed via a remote API.

12. The adaptive teaching system of claim 8, wherein the gamification engine further executes program code for: comparing the modified diagnostic database to the intelligence database; and modifying the at least one semantic parameter in the intelligence database based on the comparison.

13. The adaptive teaching system of claim 1, wherein the comparing further includes comparing the text data to the event history data.

14. The adaptive teaching system of claim 1, wherein the gamification engine is configured to iteratively create updated gamified text interfaces having respective additional navigational paths through respective additional gamified representations of the free format text data in response to progressively receiving new diagnostic results based on new text data.

15. The adaptive teaching system of claim 5, wherein creating the updated gamified text interface is further based on information received from either the event history database or the navigational database.

16. The adaptive teaching system of claim 5, wherein the updated gamified text interface includes a treatment instructing a user to take a remedial action.

17. The adaptive teaching system of claim 16, wherein the treatment is further provided in response to the gamification engine calculating that the diagnostic result matches a user to a sufficient degree of accuracy.

18. The adaptive teaching system of claim 17, wherein in calculating the degree of accuracy, the gamification engine identifies at least one super diagnostic associated with the diagnostic result.

19. The adaptive teaching system of claim 18, wherein the super diagnostic is identified at least in part based on fuzzy logic.

20. The adaptive teaching system of claim 5, wherein the updated gamified text interface includes a query instructing a user to provide additional text data via the human-machine interface.

21. The adaptive teaching system of claim 1, wherein the at least one gamified representation of the free format text data includes at least one gami-animation element.

22. The adaptive teaching system of claim 1, wherein the human-machine interface includes at least one of a virtual reality interface, augmented reality interface, robot or robot-like device, holographic projector, wearable device, kinetic device, brain-computer interface, tactile interface, olfactory interface, or taste interface.

23. The adaptive teaching system of claim 1, wherein in producing the gamified text interface, the gamification engine associates the free format text data with at least one of a question, treatment, lesson, chapter, or program.

24. The adaptive teaching system of claim 1, wherein in producing the gamified text interface, the gamification engine associates the free format text data with at least one of a user or a condition.

25. The adaptive teaching system of claim 1, wherein the event history database includes at least one link or pointer referencing the intelligence database for associating the event history data with the free format text data.

26. The adaptive teaching system of claim 1, wherein the intelligence database includes at least one link or pointer referencing the diagnostic database for associating the free format text data with the diagnostic data.

27. The adaptive teaching system of claim 1, wherein at least one of the machine learning engine is further configured for modifying the intelligence database based on the diagnostic data.

28. The adaptive teaching system of claim 1, wherein the gamification engine is further configured for providing a therapeutic response only when a diagnostic result is determined with a sufficient degree of accuracy.

29. The adaptive teaching system of claim 1, wherein the gamification engine further determines the degree of accuracy based on at least one super diagnostic associated with the diagnostic result.

30. The adaptive teaching system of claim 28, wherein the determination of the degree of accuracy is further determined at least in part based on fuzzy logic.

31. An adaptive teaching system, comprising: a content database for storing content data including free format text data; a gamification engine coupled to the content database, the gamification engine for producing a gamified text interface; at least one human-machine interface coupled to the gamification engine, the human-machine interface for presenting the gamified text interface and gathering text data; an event history database coupled to the gamification engine for storing event history data; a diagnostic database coupled to the gamification engine for storing diagnostic data; an intelligence database coupled to the gamification engine for storing intelligence data; a machine learning engine coupled to the gamification engine, wherein the machine learning engine executes machine learning program code for: calling a machine learning algorithm; comparing the diagnostic data to at least one of the event history data, the text data, or the intelligence data via the machine learning algorithm; determining, based on the comparison, at least one diagnostic association between the diagnostic data and the at least one of the event history data, the text data, the content data, the intelligence data, or external data from a remote database; and modifying the diagnostic database based on the diagnostic association.

32. The adaptive teaching system of claim 31, wherein the at least one gamified representation of the free format text data includes at least one gami-animation element.

33. The adaptive teaching system of claim 31, wherein the human-machine interface includes at least one of a virtual reality interface, augmented reality interface, robot or robot-like device, holographic projector, wearable device, kinetic device, brain-computer interface, tactile interface, olfactory interface, or taste interface.

34. The adaptive teaching system of claim 31, wherein in producing the gamified text interface, the gamification engine associates the free format text data with at least one of a question, treatment, lesson, chapter, or program.

35. The adaptive teaching system of claim 31, wherein in producing the gamified text interface, the gamification engine associates the free format text data with at least one of a user or a condition.

36. The adaptive teaching system of claim 31, wherein the event history database includes at least one link or pointer referencing the intelligence database for associating the event history data with the free format text data.

37. The adaptive teaching system of claim 31, wherein the intelligence database includes at least one link or pointer referencing the diagnostic database for associating the free format text data with the diagnostic data.

38. The adaptive teaching system of claim 31, wherein at least one of the machine learning engine is further configured for modifying the intelligence database based on the diagnostic data.

39. The adaptive teaching system of claim 31, wherein the gamification engine is further configured for providing a therapeutic response only when a diagnostic result is determined with a sufficient degree of accuracy.

40. The adaptive teaching system of claim 31, wherein the gamification engine further determines the degree of accuracy based on at least one super diagnostic associated with the diagnostic result.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0107] FIGS. 1A-1C are diagrams depicting various embodiments of the gamified training system discussed herein.

[0108] FIG. 2 is a diagram depicting a logical representation of an embodiment of a gamified training system.

[0109] FIGS. 3A-3B are diagrams depicting interface screens associated with an embodiment of a gamified training system.

[0110] FIG. 4 is a diagram illustrating a learning process in accordance with aspects of an intelligent training system herein.

[0111] FIG. 5 is a diagram illustrating a learning process in accordance with aspects of an intelligent training system.

[0112] FIG. 6 is a flow chart representing a diagnostic process in accordance with aspects of an intelligent or adaptive training system.

[0113] FIGS. 7A-7B are diagrams depicting interface screens associated with an embodiment of a gamified training system.

[0114] FIGS. 8A-8C are diagrams depicting interface screens associated with an embodiment of a gamified training system.

[0115] FIG. 9 is a diagram showing an example interface screen associated with an embodiment of a gamified training system.

[0116] FIG. 10 is a diagram illustrating an embodiment of an event history database.

[0117] FIG. 11 is a diagram illustrating an embodiment of a content database.

[0118] FIG. 12 is a diagram illustrating an embodiment of a navigational database.

[0119] FIG. 13 is a diagram illustrating an embodiment of a diagnostic database.

[0120] FIG. 14 is a diagram, depicting aspects of gamification as they pertain to embodiments of a gamified training system.

[0121] FIG. 15 is a flow chart showing an exemplary diagnostic process in accordance with aspects and embodiments of the present invention.

[0122] FIG. 16 is a diagram depicting a study associated with therapeutic techniques as it pertains to embodiments of an intelligent training system.

[0123] FIG. 17 is a logical diagram showing an exemplary process associated with super diagnostics in accordance with aspects and embodiments of the present invention.

[0124] While the present invention may be embodied in many different forms, a number of illustrative embodiments are described next with reference to the above-described figures, with the understanding that the present disclosure is to be considered as providing examples of the principles of the invention and such examples are not intended to limit the invention to preferred embodiments described herein and/or illustrated herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0125] In general, the word engine or module as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, C or C++. A software module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices may be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and may be originally stored in a compressed or installable format that requires installation, decompression or decryption prior to execution). Such software code may be stored, partially or fully, on a memory device of the executing computing device, for execution by the computing device. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays or processors. The modules or computing device functionality described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage.

[0126] Each engine and module of the present invention may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with one or more computer system(s) causes or programs computer system to be a special-purpose machine. According to one embodiment, the techniques herein are performed by one or more computer system(s) in response to processor(s) executing one or more sequences of one or more instructions contained in main memory. Such instructions may be read into main memory from another storage medium, such as storage device. Execution of the sequences of instructions contained in main memory causes processor(s) to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0127] As will be apparent to the skilled person, various engines and testing software discussed herein can be implemented on a number or arrangements of hardware devices and their associated operating systemsfor example clusters, servers, computers, tablets, smartphones, etc. running any sufficiently powerful version of Windows, macOS, Linux, iOS, Android, etc., capable of executing processes described herein.

[0128] It is to be appreciated that examples of the methods and apparatuses discussed herein are not limited in application to the details of construction and the arrangement of components set forth in the following description or illustrated in the accompanying drawings. The methods and apparatuses are capable of implementation in other examples and of being practiced or of being carried out in various ways. Examples of specific implementations are provided herein for illustrative purposes only and are not intended to be limiting. Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use herein of including, comprising, having, containing, involving, and variations thereof is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. References to or may be construed as inclusive so that any terms described using or may indicate any of a single, more than one, and all of the described terms. Any references to front and back, left and right, top and bottom, upper and lower, and vertical and horizontal are intended for convenience of description, not to limit the present systems and methods or their components to any one positional or spatial orientation.

[0129] FIGS. 1A-1C, are block diagrams illustrating an intelligent training system (system, claimed system) in accordance with aspects, embodiments, and implementations of the present invention.

[0130] Referring to FIG. 1A, system 100 may include one or more human-machine interfaces 108 for interactive with a user 101. The system is preferably coupled with digital network 116, such as, but not limited to the Internet, LAN or WAN, to allow other users to remotely connect through other devices. System 100 may also include navigational database 102, event history database 104, machine learning engine 106, gamification engine 109, diagnostic database 111, intelligence database 112, content database 114 and egress network 118. These components are in electronic communication with each other as illustrated and configured to perform the various functions and processes described herein.

[0131] Content database 114 is for storing content data including free format text data. The gamification engine 109 is coupled to the content database and is for producing a gamified test interface including a first navigational path through at least one gamified representation of the free format text data. The at least one human-machine interface 108 coupled to the gamification engine 109 and is for presenting the gamified test interface and gathering test data. The intelligence database 112 is coupled to the gamification engine 109 for storing intelligence data including at least one semantic element associated with the free format text data. The diagnostic database 111 is coupled to the gamification engine 109 and is for storing diagnostic data.

[0132] According to certain embodiments, the gamification engine 109 further executes program code for querying the diagnostic database 111 to obtain the diagnostic data, comparing the test data to the diagnostic data, and generating at least one diagnostic result based on the comparison.

[0133] FIG. 1B presents another embodiment of the claimed system 100 wherein gamification engine 109, event history database 104, machine learning engine 106, diagnostic database 111, content database 114 and intelligence database 112 are communicatively coupled via linking module 120. As is described herein, the linking module 120 can be configured to communicatively couple or facilitate data communication between each of the engines 106, 109 and the databases 104, 106, 111, 112, 114.

[0134] FIG. 1C presents yet another embodiment of the claimed system 100 wherein an interactions engine 122 is incorporated and communicatively coupled to gamification engine 109, event history database 104, machine learning engine 106, diagnostic database 111, content database 114 and intelligence database 112. The interactions engine 122 can be configured to execute instructions for controlling data interactions between each of engines 109, 106; databases 104, 106, 109, 111, 112; or any of other engines, modules, or databases discussed in this document. For example, the interactions engine 122 could control the gamification engine 109 to delay a process for generating subsequent training content until the machine learning engine 106 or the intelligence database 112 has finished an interaction with the diagnostic database 111. Once the interactions engine 122 determines that the interaction is complete, it can allow the gamification engine 109 to proceed, thus potentially enabling the gamification engine 109 to draw upon newly updated information in the diagnostic database 111 and thereby improving the diagnostic quality of the subsequent training content.

[0135] The system and processes of the present invention are preferably implemented with artificial intelligence technology having machine learning. Exemplary technology is descrived in U.S. Pat. No. 5,701,400, the entire disclosure of which is incorporated by reference.

[0136] FIG. 2 is a logical block diagram according to an embodiment of the present invention, wherein adaptive teaching lesson 236 is presented to a student 237. The adaptive teaching lesson 236 comprises information 239-242 from gamification engine 238, information 231 and 232 from content database 234, information 226 and 227 from navigational database 228, information 222 from intelligence database 221 and information 225 from diagnostic database 224. Further shown in this embodiment is linking module 220 communicatively coupling adaptive teaching lesson 236 with content database 234, navigational database 228, intelligence database 221 and diagnostic database 224.

[0137] FIGS. 3A and 3B are exemplary interface screens 300 of a gamified interface associated with an embodiment of the claimed system 100, wherein a student (not shown) is presented a first interface 301 comprising boxes 303-306, each containing the word hello in a different language. In response to input from a user, a second interface 302 may then presented with question 308 and answer choices 309-314 pertaining to boxes 303-306. This is an example of basic navigation according to an aspect of the present invention.

[0138] FIG. 4 is a flow diagram illustrating an example implementation 400 of a learning process 413 (e.g. training content) in accordance with the present learning system (e.g. system 100), Implementation 401 comprises components 402-404, which facilitate learning process 413 via gamification engine 408 and human machine interface 406. Learning process 413 includes techniques 409-411 which are realized, at least in part, by gamification engine 408 and human machine interface 406. Although techniques 409-411 are specifically illustrated, the techniques implemented by the gamification engine 408 can alternately or additionally include any of the educational techniques and strategies discussed herein.

[0139] FIG. 5 is a diagram depicting one aspect 500 of an embodiment of the claimed training system 100 including a depiction of a human machine interface 501 having one or more interface modes (sub-interfaces) 502-505. Each interface mode 502-506 can comprise a device, such as an Internet of Things (IoT) device, smart device, or other human-machine interface examples discussed herein. Human machine interface 501 is communicatively coupled to gamification engine 508, content database 509, and diagnostic database 510. Further, via, at least in part, gamification engine 508, and using data received from human machine interface (HMI) 501 interface modes 502-506, one or more results 511-513 can be realized and subsequently stored in or otherwise associated with a content database 509 and/or diagnostic database 510.

[0140] Signals obtained in the data from human machine interface 501 sub-components may include an attentiveness index, a cognition index, heart rate, breathing rhythm data, corporal movements measurements, and other variables of mental and physical activity and temporary condition, so that different combinations of these variables allow the invention, after reading and processing their values, to improve the configuration of adaptive learning exercises for a particular individual at any particular time. Environmental effects and further alteration of their condition by mechanisms controlled by the invention, simulation of desired emotions and a fully controllable interaction are additional output the gamification engine 508 can provide for such improved learning experience. In the course of learning events such as user actions or screen displays, and through processing of the additional data coming from 501, the content database 509 and diagnostic database 510 store these output variables from the gamification engine 508.

[0141] FIG. 6 is a flow chart representing a process 600 according to an embodiment of the present invention. It should be understood that the process 600 and the various steps 601-618, and variations thereof, is preferably implemented by embodiments of system 100 and its respectivce components, modules, engines (e.g. 106, 109, 122), and databases (e.g. databases 104, 111, 112, 114) described herein with reference to FIGS. 1A-C, and elsewhere, as applicable.

[0142] The process 600 begins with step 601 where the student is evaluated. The evaluation may performed using a human-machine interface as described herein using a navigation that may be preprogrammed or adaptive. The results of evaluation 601 are then received in step 602, including system tags for use in step 604 when the intelligence database is queried. Proceeding onto step 606, the content database is queried, followed by step 607 where the event database is queried. In response, in step 608 a corresponding exercise is selected and in step 609 the gamification engine is called to present a gamified test. In step 610 the testing data is received followed by step 612 where the diagnostic database is queried. Following step 612, the process can optionally proceed to step 615 where feedback is presented, or to step 614 to query one or more additional databases if there is insufficient information from which to proceed to step 615. Alternatively, rather than proceeding from step 612 to step 614 or 615, the system can query the navigational database as shown in step 616, present the next adaptive teaching session in step 618 and loop back to either step 604, 606 or 607.

[0143] FIGS. 7A and 7B show examples of user interface screens 705, 707, as they might be seen by a student (not shown) interacting with an HMI 108 pursuant to an embodiment of a training system 100. Screen 705 includes row 701 comprising general identification information with respect to the subject and exercise. Rows 702 and 703 include information relating to the exercise being given. Row 704 provides a hypothetical scenario or problem used in the questions and answers row 706 where a specific question and various answer options are presented to the student. Row 708 provides additional instructions or context if necessary, and row 710 includes the students answer or answer combinations. Screen 707 is an example that might be viewed by an administrator or the like in order to help understand a student's answers and diagnosis, and includes rows 712 and 714 which display the student's answers and information necessary to understand how those answers correspond to the diagnosis.

[0144] FIGS. 8A-8C show additional examples of user interface screens 801-803, as they might be seen by a student (not shown) interacting with an HMI 108 pursuant to an embodiment of a training system 100. More specifically, screen image 801 displays instructions 805 that explains the adaptive teaching session that a given student is about to take. Screen image 801 further comprises test counter 807 which keeps track of the duration of time spent on the adaptive teaching session, as well as user prompt 806, which can be selected to begin the adaptive teaching session. Screen image 802 shows test counter 807, test question 810, potential answers 811 and user prompt 812, which can be selected to submit the selected answer from potential answer set 811. Screen image 803 shows test counter 807, question answer results 814 and user prompt 813, which can be selected to move onto the next test question in the adaptive teaching session.

[0145] FIG. 9 shows an additional example of a user interface screen 905, as might be seen by a student (not shown) interacting with an HMI 108 pursuant to an embodiment of a training system 100. Screen image 905 represents a test summary screen that a student or administrator might be presented upon the completion of the adaptive learning session. Screen image 905 comprises overall points scored 901, informational box 912 with user prompt 903, questions answered 908-909, user's answers 911-912 and answer results 904-905, which indicate whether a response was correct or incorrect.

[0146] FIG. 10 is a logic diagram showing one embodiment of an event history database 1001, 104, which may comprise one or more pieces of information 1002-1018. The information stored in event history database 1001 may, more particularly, include ID information 1002, company or school name 1004, subject information 1005, subject preferences and/or tendencies 1006, subject abilities 1009, knowledge level and course 1008, points earned 1010, history of interactions 1012, company or school profile 1016 and/or per-user profile 1014. The event history database 1001 may also store additional types of event history data 1018 corresponding to any number or combination of the event history data, parameter, and element examples disclosed herein.

[0147] FIG. 11 is a logic diagram showing one embodiment a content database 2001, 114, which may comprise one or more pieces of information 2002-2018. The information stored in content database 2001 may, more particularly, include free-form text 2002, multimedia objects 2004, exercise questions 2006, full course content 2007, list of alternative answers 2008, identification of correct answers 2010, execution parameters 2012, interaction parameters 2016 and gamified exercises 2013. The content database 2001 may also store additional types of content data 2018 corresponding to any number or combination of the content data, parameter, and element examples disclosed herein.

[0148] FIG. 12 is a logic diagram showing one embodiment of a navigational database 3001, 102, which may comprise one or more pieces of information 3002-3006. The information stored in navigational database 3001 may, more particularly, include linking to sequence of exercises 3002 and current status of on-going courses 3003. The navigational database 3001 may also store additional types of navigational data 3006 corresponding to any number or combination of the navigational data, parameter, and element examples disclosed herein.

[0149] FIG. 13 is a logic diagram showing one embodiment of a diagnostic database 4001, 111, which may comprise one or more pieces of information 4002-4009. The information stored in diagnostic database 4001 may, more particularly, include objective indications of conditions and subconscious behavior 4002, diagnostic conditions 4004, personality/behavior traits 4005, links to other databases 4006, links to content database and event history database 4009, and feedback for subjects 4008. The diagnostic database 4001 may also store additional types of diagnostic data 4010 corresponding to any number or combination of the diagnostic data, parameter, and element examples disclosed herein.

[0150] FIG. 14 is a logic diagram depicting a set 5000 of aspects pertaining to gamification as implemented, for example, in a training system 100. Specifically shown is a sliding scale of gamification 5001, having various aspects 5002-04 associated with various sections 5006, 5007, 5008, each including certain gamification properties/characteristics. Each segment and its respective gamification properties/characteristics are further associated with various training approaches (e.g. gamified training content presented via an HMI 108). One aspect 5004 is associated with properties having little to no gamification 5004, another aspect 5002 is associated with properties having full gamification 5002, and a third aspect 5003 positioned in between is associated with partial gamification 5003. The non-overlapping portion of section 5007 comprises properties 5018-5026 associated with little to no gamification 5004, the non-overlapping section of circle 5006 comprises properties 5010-5016 associated with with full gamification 5002, and overlapping section 5008 includes properties 5028-5036 associated with partial gamification 5003.

[0151] Sample characteristics of a non-gamified system such as 5004 may include conscious of answer 5018, tedious 5019, tiring 5020, repetitive 5021, disengaging 5022, static presentation 5024, administrator controlled 5025 and input determined by administrator 5026.

[0152] Exemplary elements of fully gamified 5002 may include fantastical 5010, input selected by user 5011, distracting 5012, objectiveless 5014, user controlled 5015, and full immersion 5016.

[0153] Exemplary elements of partially gamified 5003 may include meta-cognition 5028, engaging 5029, subconscious 5030, dynamic presentation 5032, gradual incorporation of graphical elements 5034, and hybrid controlled 5036.

[0154] FIG. 15 is a flow diagram of a process 6000 employed by the machine learning engine 106, 6005 of the inventive system, according to embodiments of the present invention. Specifically, the flow of machine learning engine 6005 is shown with respect to its updating capability starting with step 6002 where an outside source of expert data/tests 6001 is queried. The intelligence engine is then queried in step 6003 followed by the execution of the expert tests/data on the intelligence engine in step 6004. The results are then received in step 6006 followed by the tagging of content based on the results in step 6008. In step 6009 outside data source 6001 is queried again and then in step 6010 the execution of the expert tests/data on the diagnostic database. In step 6011 the results of step 6010 are received and then the content is tagged based on the results in step 6012 and the process completes at step 6014. The flow can optionally move from step 6012 to step 6015 where one or more additional databases are queried at which point the process completes at step 6014. A machine learning engine 106 or other module of a training system 100 may iteratively perform successive reptitions of process 6000 pursuant to an iterative operation, for example an iterative operation associated with gamification, diagnostics, content, intelligence, (e.g. a machine learning algorithm; database restructuring/management; and the like).

[0155] FIG. 16 is a diagram depicting an exemplary study 7001 of recovery rates 7004a-f associated with respective therapeutic techniques 7002a-f used on patients diagnosed with one or more conditions 7006 (e.g. a depression diagnosis). Computerized cognitive behavior therapy 7002a was associated with a 58.4% recovery rate 7004a. Interpersonal psychotherapy 7002b was associated with a 53.9% recovery rate 7004b. Brief psychodynamic psychotherapy 7002c was associated with a 47.0% recovery rate 7004c. Counseling 7002d was associated with a 45.2% recovery rate 7004d. Behavioral action 7002e was associated with a 44.8% recovery rate 7004e. And cognitive behavior therapy 7002f was associated with a 44.1% recovery rate 7004f.

[0156] The example 7001 in FIG. 16 illustrates how different therapeutic techniques 7002 can have different degrees of treatment effectiveness (e.g. recovery rates) 7004 for a given condition or set of conditions 7006. It is to be understood that in the context of this invention, diagnostic data stored in a diagnostic database (e.g. 111, 4001) can include or otherwise be associated with conditions 7006, treatment effectiveness parameters 7004, and techniques 7002. Conditions 7006, treatment effectiveness parameters 7004, and therapeutic techniques 7006, can each further correspond to any of the diagnostic information 4002-4009, or diagnostic parameters and related diagnostic parameters discussed herein. Thus, knowledge that certain treatment techniques are more effective for certain conditions can generally be applied to the intelligence of the system and used to optimize the selection of gamified training content.

[0157] FIG. 17 is a logical diagram depicting an exemplary set of structural and functional aspects 8001 associated with a machine learning engine 106 or intelligence database 112 in an embodiment of an intelligent training system 100. The various aspects 8002-8018 describe various structural elements and/or associated functionality for identifying and storing diagnostics or super diagnostics based on logical and expert tests (e.g. machine learning algorithms or other database operations described herein), as can be implemented in various aspects of a system such as the adaptive training system 100 depicted in FIGS. 1A-1C.

[0158] A first group of aspects corresponds to content and history database aspects 8006, and a second group of aspects corresponds to intelligence database aspects 8018. Within the first group 8006, a content database 8002 may contain numerical data, or free form text database, or a plurality of semantic text elements which may be organized in a semantic, interconnected network structure. A history database 8004 containing a record of events taking place during usage of the invention, with link pointers of other means to link to associated screens, position in navigational paths, and a time-stamp for all or selected events.

[0159] Within the second group 8018, logical tests 8010 or analysis rules, represent true-or-false logical expressions running on selected content in the content database, and may incorporate pattern matching or classifier systems, numerical testing through formula expressions, or other types of testing. These tests are capable of running on selected contents from the content and history databases. When their result is true, logical tests generate new diagnostic results. Any logical test may contain a unique identifier code, an associated pre-defined diagnostic such as a message and, if the test turns true and a new diagnostic is generated, information about link elements such as bidirectional links to any associated data content. There can also be links to functional program code updating navigation paths, or screens, or acting on devices of any kind, for actions are to be performed if the diagnostic turns true. Diagnostic results 8012 (diagnostic statements) may be stored in a diagnostic database. Expert tests 8014 are true-or-false logical expressions and may be applied to selected content from the diagnostics database. If true, these generate new super diagnostics 8016, to be stored in a super diagnostics database.

[0160] One or more of the databases, engines, or modules discussed herein, may further store and/or execute definitions stating on which contents in the content and history databases 8002 will logical tests or analysis rules 8010 be run, and on which specific contents in the diagnostic database 8012 will expert tests 8014 run. Fuzzy logic values (or formula expressions) may be associated to any particular test 8010 or 8014, so that a fuzzy logic value is stored, associated with its resulting diagnostic 8012. Then, other Fuzzy logic formulas to be applied on fuzzy logic values associated with diagnostics 8012 may be used for the generation of super diagnostics 8016.

[0161] In one possible implementation, fuzzy logic uses probabilistic values as variables or parameters, and diagnostics 8012 and super diagnostics 8016 would then have associated probabilities or relevance index values applicable in different measures to different users that may consult the diagnostic and super diagnostics databases. Particular contents of the diagnostic database 8012 are bidirectionally linked 8008 or associated with particular elements of said data databases through the use of link pointers or other means. For example, when a logical test runs 8010 on particular content, if the logical test 8010 turns true and generates a new diagnostic 8012, this diagnostic 8012 may be bi-directionally linked to the particular content 8002 so tested. In a similar manner, particular contents of the super diagnostic 8016 database may be bi-directionally linked 8008 or associated with particular contents of the diagnostic 8012 databases. Diagnostics 8012 and super diagnostics 8016, logical true-or-false tests 8010 and expert tests 8014, may be stored in an integrated intelligence database 8018.

[0162] Any logical tests 8010 or any expert test 8014 may make use, within its expression, of any pattern matching or classification algorithm(s) known in the machine learning and artificial intelligence fields. This complete implementation executes as a content information compiler, by creating an intelligence database 8018 parallel to content 8006, like a different dimension and interpretation of the same data, interconnecting selected elements by bidirectional links 8008 allowing for certain types of Organic Knowledge collaboration. And, multiple runs of expert tests generating new levels of super diagnostics may work as an equivalent to multiple levels in a Gestalt-Multiplex-Layering (GML) system.

[0163] Having described above several aspects of at least one implementation, it is to be appreciated various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure and are intended to be within the scope of the description. Accordingly, the foregoing description and drawings are by way of example only, and the scope of the disclosure should be determined from proper construction of the appended claims, and their equivalents.