Abstract:
A Dyslexia screening and management system and process for individual user, community and group in general are described. An electronic media based tests for reading, writing, drawing, spelling and listening skills, family drawing, and letter writing test, which uses text, audio, video, and gaze movement to detect a set of symptoms of having dyslexia is described. Multi-modal, language-independent screening test modules have been developed, which gives indications of further dyslexia diagnosis tool. The multimedia retrieval framework is presented to accelerate and ease the process of testing dyslexia at the global level, and to identify and auto assess potential dyslexic patterns and to accumulate huge collection of multimedia test data for in-depth clinical dyslexia pattern analysis.

Description:
FIELD OF TECHNOLOGY 
     The present invention relates generally to a system and process for language-independent dyslexia screening test results, analyzing the big data using a Dyslexia intelligent analytics engine and managing dyslexia within individual, group and community. 
     BACKGROUND 
     Dyslexia is a cognitive disability that hinders one in normal reading, writing and drawing texts and objects (Rello, I et. al. 2015). Although children with dyslexia tend to show gifted intelligence, the learning disability poses a great challenge to adapt to the normal learning styles in the school (Gagi, O et. al. 2012). Hence, dyslexic children need to be identified by the parents and the school administration so that special assisting technologies can be provided to them (Eshshan, H. M., et. al. (2012). Although various test mechanisms have been proposed in the past in finding the presence of dyslexia among children in preschool and junior schools (Bartolome, N. A. et. al. 2012, Costa, M et. al. 2013, Nur, S. S. et. al. 2014), most of them cannot be applied to a mass level or support one single modality of tests. Thanks to the recent advancements in multimedia technologies, tablet PCs, high speed Internet communication even at rural areas and big data analytics, to name a few, a new dimension of finding symptoms of dyslexia is now possible. There is need to create a solution to implement this test that is ubiquitous but also fits the individual needs. 
     SUMMARY 
     The present invention relates generally to a system and process for language-independent dyslexia screening test results, for individuals and they can be grouped as community and country results for screening for Dyslexia and predicting health outcomes for the society. 
     In one embodiment, a mobile device enabled system and process for Dyslexia screening and management is disclosed. In another embodiment, a Dyslexia system comprising of computer hardware, server, processor and software for a user to test their skills in writing, drawing and talking along with their eye tracking is captured and analyzed for doctors to treat. In one embodiment, a Dyslexia analytics system that is intelligent which includes processes and systems to collect, capture, classify, store, and manage dyslexia data, and dynamically analyze, process and report personalized metrics using such data are disclosed. 
     In one embodiment, when a user takes these tests the results are captured and specific equations such Equation 1 to 4 are implemented to calculate the results and results are used by the auto grading module to produce results automatically. The results may also be verified manually by producing results using doctor analyzer software to corroborate. In one embodiment, the system provides visibility and transparency about performance management to individual, school and group participants and may be used by healthcare and policy maker participants to guide them towards better health care outcomes for the community and country. 
     In one embodiment, intelligent Dyslexia analytics system provides the analytic engine to do real-time and forensic analysis of the data captured from individual users for dyslexia. The data is analyzed for individual, group, school and community and common metrics are measured. In another embodiment, expert system is introduced for the Dyslexia analytics system to be further analyzed by experts such as doctors and policy makers for checking the higher level value for health care, impact and investments. 
     In one embodiment, several test modules are used in multiple languages for the user to take their test. The test increase in complexity, are age dependent, school curriculum depended and may be tailored to the user level. 
     In one embodiment, a video is captured for registering the eye tracking movement for Dyslexic patients as that is one of the symptoms that is described by the physicians for diagnosis. In another embodiment, a user uses a test module housed on a server or a processor of a computer device used for logging in to take for Dyslexia, wherein the test module is at least one of a reading, writing, drawing, spelling and listening skills, family drawing, and letter writing test. In another embodiment, a backend server captures the interaction of the user for a session for a given test module by capturing the interaction and video captured during the test period. In yet another embodiment, Dyslexia analytics system that is intelligent is housed on a backend server or a processor to auto grade a test result using specific equation and augments the video data captured during the session for a reading, writing, drawing, spelling and listening skills, family drawing, and letter writing test; and a doctor to review the test result using a doctor analyzer screen and rendering their opinion to the user by filtering and flagging each test module that shows symptoms of dyslexia and a treatment plan for the user. The Dyslexia analytics system is called intelligent Dyslexia system because the system and process automatically captures, stores, analysis and presents the results as a comparative graph to the physician as a result for a given user. No manual intervention is done and all the calculations are done at the backend servers or processors and presented on a user interface. 
     In one embodiment, the test module is 20-minute long and has 4 different tests: reading test, writing test, clock drawing test, and cognitive test through drawing family members, all with an eye tracking and audio capturing capabilities. 
     The systems and process disclosed herein may be implemented in any means for achieving various aspects, and may be executed in a form of a machine-readable medium embodying a set of instructions that, when executed by a machine, cause the machine to perform any of the operations disclosed herein. Other features will be apparent from the accompanying drawings and from the detailed description that follows. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The embodiments of this invention are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which: 
         FIG. 1  is a block diagram of an example of a dyslexia screening and management system, according to one or more embodiments. 
         FIG. 2  is a block diagram illustrating the different modules within the management system to accomplish the gathering and analyzing of test results. 
         FIG. 3  illustrates the performance metric impacts of the dyslexia testing intelligent system. 
         FIG. 4  provides insights into various feedback types. 
         FIG. 5  illustrates the multimedia data flow that occurs as part of the dyslexia screening system operation. 
         FIG. 6  illustrates the intelligent dyslexia analytics architecture. 
         FIG. 7  illustrates the intelligent dyslexia analytics solution and components of the backend intelligence. It also illustrates the dependency on the cloud based expert system. 
         FIG. 8  shows the client side components for the intelligent dyslexia analytics system. 
         FIG. 9  illustrates the intelligent dyslexia analytics system components. 
         FIG. 10  shows the intelligent dyslexia analytics system server components. 
         FIG. 11  illustrates the cloud based expert system components and the dependencies to the backend intelligence. The figure shows the expert client interface and the interfaces to the redundant knowledgebase. 
         FIG. 12  illustrates the control and the data flow to the user interface for user and doctors. 
         FIG. 13  shows the high level message sequence diagram illustrating a user using the system. 
         FIG. 14  illustrates one example display generated by the user interface allowing a user or an expert such as doctor to view the results. The results are displayed at various granularities. 
         FIG. 15  shows an embodiment of example inputs into the dyslexia system application, and tests for the user. 
         FIGS. 16A and 16B  shows an embodiment of example of the clock drawing test. 
         FIGS. 17A and 17B  shows an embodiment of example of the writing test. 
         FIGS. 18A and 18B  illustrate an example of the reading test. 
         FIG. 19  illustrates the pupil movement tracking. 
         FIGS. 20A and 20B  shows an example of the family member drawing test. 
         FIGS. 21A, 21B, 21C, 21D and 21E  shows an example of test data analysis by the cloud based intelligent analytics engine and presented as summary to be viewed by a human experts based on audio, video, text, drawing, and pupil movement data analysis. 
         FIGS. 22A and 22B  shows a use case diagram of the end to end dyslexia test system from a user (student, test admin, stakeholders, and dyslexia therapist) stand point. 
         FIGS. 23A and 23B  illustrate the user interface for an examiner registration and login. 
         FIGS. 24A, 24B and 24C  illustrate the user interface for a student or user registration and login. 
         FIGS. 25A, 25B, 25C, 25D, 25E and 25F  illustrate the dyslexia test module stages for users above age 10. 
         FIGS. 26A, 26B, 26C and 26D  illustrate the dyslexia test module for younger kids. 
         FIGS. 27A and 27B  illustrate the dyslexia test module user interface for doctors and experts to register and login. 
         FIGS. 28A and 28B  illustrate the test result dashboard for an expert and doctor for examination of analysis and results. 
         FIG. 29  illustrates the user interface view for an expert or a doctor for clock drawing test result. The interface shows automatic grading by considering possible dyslexic pattern observed from the video and stylus pen movements. 
         FIG. 30  illustrates the user interface view for an expert or a doctor for spelling test result. The interface shows automatic grading by considering possible dyslexic pattern observed from the video and stylus pen movements. 
         FIGS. 31A, 31B and 31C  illustrate the user interface view for an expert or a doctor for viewing the reading test result. The result shows normal and recorded results for comparison. The interface shows automatic grading by considering possible dyslexic pattern observed from the video and pupil movement trails superimposed on the actual text. It also allows manual grading of the reading test. 
         FIG. 32  illustrates the user interface view for an expert or a doctor for viewing the family member drawing test result. The interface shows automatic grading by considering possible dyslexic pattern observed from the video and stylus pen movements. 
         FIG. 33  illustrates the user interface view for an expert or a doctor for viewing the listening test result. 
         FIG. 34  illustrates the user interface view for an expert or a doctor for viewing the letter writing test result. 
         FIG. 35  illustrates the system organization of the proposed methodology. 
         FIG. 36  illustrates a numeral broken down into primitives for proper identification. 
         FIG. 37  illustrates the two scenarios before and after contour tracing. 
         FIG. 38  illustrates the filtering process. 
         FIG. 39  illustrates the strokes used for dyslexic character recognition in the table. 
         FIG. 40  illustrates the ranges of the straight lines with various degree of geometric accuracy. 
         FIG. 41  illustrates the reference segments and matching scores as part of recognition. 
         FIG. 42  illustrates the matching matrix. 
         FIG. 43  illustrates the relative matching scores for specified dyslexic primitive codes. 
         FIGS. 44A and 44B  illustrates the average dyslexic pattern matching scores. 
     
    
    
     Other features of the present embodiments will be apparent from the detailed description that follows. 
     DETAILED DESCRIPTION 
     Several systems and process for informational, integrated and interactive Dyslexia testing modules using hardware that includes servers, mobile devices and software to screen for Dyslexia and to manage the outcome after the use by the user/individual by the doctors are disclosed. Although the present embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the various embodiments. 
     In this work, a tablet PC based multimedia framework has been used where 4 different dyslexia testing modules, reading, writing, drawing and eye tracking have been proposed. The test can be administered by a school on a national level, where each individual test is graded by a licensed dyslexia therapist. 
     Each user interactions with the test modules are stored as a video along with the audio and gaze or pupil movement. At the end of a test session, the tablet PC communicates with the cloud based big data environment where the multimedia data consisting of user drawn text, user interaction with different test modules captured as an audio, video and screen images are uploaded for a set of methods to detect dyslexia phenomena. The methodology automatically detects dyslexia from the available multimedia files and shares the results with a dyslexia therapist for further analysis. 
     The test modules have been tested with a Samsung tablet, Samsung Galaxy Note  4 , and a Microsoft Surface Pro 4 tablet PC with a stylus pen as support. In tablets and smartphones, we save images in .jpg format in 896*530 resolution and save video in .mp4 format. The video is created using FFMPEG codec library. Generally video size for 3 min is 30-50 KB with a rate of 30 frames per second. We have developed a library, which can detect user drawn numbers from 1 to 12 and the position and angles of hour and minute hands of a clock. The library is built using python based open CV image processing library. KNN method is used for number and hand detection. Although at the current stage the library is working fine, but it lacks sufficient training data from actual testing modules. The more the training data we will have; the more accurate result will be produced by the analytics engine. 
     The eye tracking module has been implemented using Eyetribe with EyeTribe SDK version 0.9.56. The big data repository has been implemented using Amazon Infrastructure as a Service (IaaS). The Amazon EC2 instances allow quickly scaling capacity both up and down as the computing requirements changes, which is a fundamental case for our application due to variable usage. The API server is deployed on the EC2 instances where scaling and load balancing is configured by creating AWS Auto scaling Group, which is monitored and triggered when the average utilization of EC2 is high or low. This is done using the Cloud Watch for scaling activities. Elastic Load Balancing is used to distribute traffic to instances within Auto scaling Group to get the optimum utilization of the resources and cost. The media files are stored in Amazon S3 big data repository while the relational database that stores user profile, dyslexia profile, session metadata, and other relational data is implemented in PostGreSQL Database. 
       FIG. 1  is a block diagram of an example of a dyslexia screening system, according to one or more embodiments. Particularly, the system is supported by server  104 , computing devices  106 ,  104 ,  108 , and  112  (some, such as device  112 , which may be mobile devices), a network  102 , a database  110 . In another embodiment, the user hard ware such as a PDA, computer or a mobile phone or any wireless device, or an electronic book (e-book) may be connected with each other or work independently to allow the user to use the multimedia tool for education and testing. A network  102  may be a local area network (LAN), wide area network (WAN), metropolitan area network (MAN), extranet, intranet, internet, peer-to-peer network or the like or a combination thereof. In the case of a wireless network,  102  may comprise, but need not be limited to HomeRF, HiperLAN, Bluetooth, Zigbee, WiMAX, Wibree, FM, AM, 802.11 (G, N), WiFi and satellite, Wireless ISP, Satellite Broadband, Mobile Broadband, Local Multipoint Distribution Service and satellite communication systems etc. In one embodiment, the system may be a server-client system with processing occurring on one or more computers or mobile devices connected through a network  102 . In another embodiment, the system may be a peer-to-peer system with processing occurring in all computers or mobile devices connected through a network  102 . In one embodiment, data is aggregated and stored in a central database server  110  connected to one or more computers or servers either directly or through a network  102 . In another embodiment, data stores may be distributed among various devices and servers in the system. 
       FIG. 2  is a block diagram illustrating the different modules that may reside in a processor or a server  200  for Dyslexia screening and management. Collectively the software represented as modules residing and processed by the hardware are input module  202 , classification module  204 , analysis module  206 , recommendation module  208 , display module  210 , management module  212 , communication module  214 , collaboration module  216 , server module  218  and cloud management module  220 . Modules in  FIG. 2  have interfaces to the users, and one-to-one connectivity to the backend intelligence. The modules in backend intelligence that provide active support and computation work based on stimuli from the blocks in  FIG. 2 . For example, the user interface for input module  202  receives user input and relays to intelligent Dyslexia analytics system  606  component User graphical user interface (GUI) management  1002 . Similarly, classification module in  204  interfaces with back-end classification module  1006 . Further information of back-end intelligence is provided in  FIG. 10 . 
     Various modules of the Dyslexia analytics system as a software resident in the computing device used by the end user who is taking the test are illustrated in  FIG. 2 . Input Module  202  may receive inputs from other systems within or external to the application, and from users and experts such as doctors. Classification Module  204  may contain system templates and may request data from other modules within the dyslexia test application to create associations among data using system templates. Analysis Module  206  may receive data from other modules and process that data to generate individual or aggregated performance metrics to be relayed to the backend intelligence through client interfaces. Recommendation Module  208  may calculate recommended performance goals or activities for that user or group based on results of classification and analysis to be relayed to the Dyslexia analytics system. Display Module  210  may present a user interface to a user to visually display results generated by the dyslexia test application. The display user interface may be interactive allowing the user to view, add, edit, configure, copy, store, remove, send or comment upon displayed results. In one embodiment, automatic performance review documentation uses these methods. 
       FIG. 2  also illustrates the client side management module  212  that manages the data acquired through user interfaces, to be collected and relayed to Dyslexia analytics system. Similarly, management module  212  also receives information from the Dyslexia analytics system to be displayed over user interfaces. Communication module  214  uses the network resources to communicate with the backend server and Dyslexia analytics system over network. 
     Collaboration module  216  maps the user to groups taking similar tests. Server module  218  uses the communication module  214  to reach its cohort in the Dyslexia analytics system for relaying the results. Cloud management module  220  interfaces the client side to the relational and NoSQL data bases in the server side. 
       FIG. 3  shows the high level performance metric impacts for a user as an individual  302 , team or group  306  and community  304  at large and finally the country. The proposed Dyslexia analytics system/Dyslexia management system operates on the individual  302  performance of Dyslexia test tracking metrics such as clock drawing test, spelling test, reading test, picture test, listening test and letter writing test. 
       FIG. 3  also shows the analysis at the group  306  level, where the group metrics to measure variance, statistics, group patterns and mistake distribution. The productivity measurements affect individuals  302  as their performance constitutes group impact. Hence tracking test results at individual level, holistically leads to group statistics. 
       FIG. 3  also illustrates the community level analysis, where metrics such as dyslexia distribution over time period, group patterns, investment impacts and correlation over various group at various time and time management that can be tracked. In addition, metrics compared to other communities can provide improvement areas such as screening efficacy, compensation, investment and impact. 
       FIG. 4  illustrates an embodiment where feedback is taken real time in a deterministic and stochastic random fashion. In another embodiment, feedback is taken non-real-time in a deterministic and random fashion, when the test is completed. 
       FIG. 4  illustrates the different feedback types and the impact it has depending on who the feedback represents. Feedbacks are of two types, Language  404  and Abstract  402 . Language  404  test feedbacks are taken through online user interface where tests concentrate on the reading, writing, drawing and spelling aspects. The individual user  406  is tested whether the competency to properly interpret and answer the language aspects through letter writing test, spelling test and reading test. The second type of feedback is more of abstract  402  type, where the test concentrates on the user competency to listen, draw and interpret. Here the user takes a test using the online user interface comprising of listening test, family drawing test and clock drawing test. 
       FIG. 4  also illustrates the statistics pertaining to individual  406  and group or community  408 . The performance feedback for group/community  408  is more filtered and distilled across multiple individuals. For example, the collection of individual language  404  tests are represented at group/community level  408  through age statistics, letter pattern analysis results, distribution and group patterns. For more abstract level feedbacks of group/community  408  are obtained through filtered representation of results at test performance and goal tracking. 
       FIG. 5  illustrates the multimedia data flow. From the individual&#39;s  406  perspective, the dyslexia test in class is taken online  502 . In one embodiment,  FIG. 5  illustrates users to use a hand held smart phone  504  to take the test. In another embodiment, the user can take a test using laptop, desktop, tablet or any other electronic, physical and mechanical means. The multimedia data is stored in the electronic device before being uploaded to the backend server  506  through the network. The backend intelligence uses the big data analytics engine in the server to store and process the data  508 . The data is viewed by an expert using expert client  510 . The expert performs an autograding and multimedia based review before updating the results for view  512  over the network. In one embodiment, the results are available for view over electronic medium. In another embodiment, the results are also available to be viewed in a physical (such as paper) or other (such as audio, braille and broadcast) mediums. In another embodiment, the results are made available to a group. 
       FIG. 5  shows the high-level architecture of the proposed system. In one embodiment, a school wishing to administer the dyslexia test modules among a group of students first obtains the dyslexia test licenses from the big data server and installs necessary software into required number of tablet PCs. After the test is finished, the multimedia files containing each test module within each tablet PC is uploaded to the big data server, which processes each incoming multimedia test session file and makes the processed raw media files and the auto-grading metadata available for a subject matter medical expert to further view and approve. The auto-grading process at the big data server uses state of the art image processing and gaze tracking methods to assist the medical doctor in filtering and flagging each test module that shows symptoms of dyslexia. The final result is made available to the respective stakeholders. 
       FIG. 6  illustrates the intelligent dyslexia analytics system/solution components. The users  602  participate in the dyslexia test online. The users could be an individual  612  or a group  614 . The tests are administered locally first and then uploaded to the backend online over Internet  610 . The test vectors are captured by the client modules and the test vectors are transferred to intelligent Dyslexia analytics system  606  for analysis. The information is stored in the database system  608 . The data is open for expert analysis over cloud based expert system  620  through expert client  622 . The results for individual  612 , group  614 , community  616  and country  618  are used by policy makers and researchers  604 . 
       FIG. 7  illustrates the intelligent dyslexia analytics solution components. The client side test is taken by individuals  612  and the results are compiled for group  614 , community  616  and country  618 . The client side components directly communicate with Dyslexia analytics system  606  that is intelligent. Dyslexia analytics system  606  has abstraction for user interface  708 , classification  710 , synchronization layer  712 , analysis platform  714 , dyslexia engine  716  and web services layer  718 . Dyslexia analytics system  606  provides the analytics capability, user interface capability and authentication capability. The backend intelligence interfaces with cloud based expert system  620  through which experts and policy experts access the intelligence system to receive the analytical results for the performance metrics. The Dyslexia analytics system uses redundant knowledgebase  608  to store the data. 
       FIG. 8  illustrates the intelligent dyslexia analytics client components. The clients as an individual  612  or group  614  or a community  616  or a country  618  use client components such as laptop  812 , desktop  814 , smart devices  816  and tablets  818  to perform the test administration. In one embodiment, non-electronic test methods are possible, but needs to be entered into the system manually. The client interfaces, as components within the client devices  810 , provide the interfaces to the users  802 , hardware layer  804 , group managers  806  and the multimedia  808  in backend intelligence system. 
       FIG. 9  illustrates the system components impacted during a testing cycle. The hardware layer  804  in the tablet PC has the test modules and the eye tracker module. In another embodiment, the hardware layer  804  can host the test modules in laptop, desktop and other electronic devices. The test modules provide audio, video and images to the users through multimedia handler  808 . The multimedia handlers also provide the eye pupil coordinates through estimation. Both the functions interface directly with the backend intelligence  606  and the visualization interface to the user  708 . The audio, video and images are customized per test and obtained from the dyslexia engine  716  in Dyslexia analytics system  606 . Special eye movement parser intelligence in dyslexia engine  716  is resident in the backend intelligence  606 . Synchronization layer  712  provides the media and augmented reality towards visualization  708 . The web services layer provides the total data management  718  interfacing with the Dyslexia analytics system  606 . The media controller subsystem provides the intelligence necessary to handle the customized multimedia test modules. The analytics engine provides the number crunching and performance metric estimation and the session file handlers interface with various databases to handle big data analysis. The media controller talks to with visual interface to provide distilled user performance information. 
       FIG. 9  shows the software components of the proposed system. The Hardware Layer consists of the test environment and the eye tracker sensor. The Multimedia Handler incorporates software library components that handle text, audio, image, and video frames captured during the test time. It also stores user&#39;s pupil movement co-ordinates while the user interacts with different test modules. The multimedia is used for capturing rich visual and semantic cues for the doctor analyzer during dyslexia decision making. The Dyslexia analytics system keeps track of individual test modules and stores the multimedia frames per test module in the local memory. It also employs a parser library for the pupil tracker that stores the spatial location of the pupil coordinates with respect to each test module. It attaches test module-based pupil data in the memory. Finally, the multimedia and pupil data are augmented together at the Synchronization Layer to make the final multimedia element as a video file per test module. The video file combines a rich user interaction with the test module elements and eye movement, which is used as a semantic source of the doctor who analyzes the dyslexia pattern. This video is also used at the server side to automatically grade each test module and classify a user as dyslexic or not. 
     In one embodiment, the Web Services Layer  718  consists of software components that allow receiving any multimedia video test session file, unpacking it, processing different media within the session packet, employing analytics to parse the video files available from different test modules to identify a set of dyslexia identifiers, and finally saving the dyslexia metrics as well as individual media files within relational databases and big data repositories respectively. Media Controller acts as a controller in the MVC Web Services Layer by acting as a proxy between the Synchronization Layer and that of the Visualization Interface. The Media Controller maintains the flow of data at the server side. The user submitted test session file is parsed by the Session File Handler where the raw media (multimedia and pupil movement) is separated and stored in the Hadoop Big Data Cluster whereas the session metadata is stored in the Session Repository. The session metadata is sent to the Analytics Engine, which performs various analyses on the video and pupil data and stores the results in the Session Statistics. User and Disability Profile stores user profile, types of dyslexia patterns observed during a test, types and levels of exam difficulty assigned by the doctor, etc. 
       FIG. 10  illustrates the intelligent dyslexia analytics system server components of the Dyslexia analytics system  606 . In one embodiment, the user management is provided through Graphical User Interface (GUI) module  1002 . The admin interface  1004  provides the high level authentication at user and admin level. It also provides interfaces from where one can buy single or bulk volume test licenses. The classification module  1006  groups the users to various classifications such as school, community and sections. The registration module  1008  provides the registration of users, doctors, experts and policy makers. 
       FIG. 10  also illustrates the feedback mechanism for various test modules. The feedback analysis is conducted for both deterministic  1012  analysis and stochastic analysis  1014 . Confidence interval  1016  for the stochastic analysis is performed for accuracy of the statistical analysis. Spatial eye movement parser  1022  provides the dyslexia engine the capability to monitor the eye movement and analyze. Network communication module  1024  handles the backend server connectivity. Automated reports  1026  generates reports for experts, doctors and policy experts in specified formats. Feedback communication  1028  module interfaces with multimedia handlers to display through visual interface to the end user. Data gathering  1032  module interfaces with client devices to get the test data. Multimedia data module  1034  interfaces with multimedia handler  1056 . Hysteresis  1036  module handles the analysis of the data over various users in group and over various time intervals for drawing intelligent conclusions. Forensic analysis  1038  module provides the function to analyze data that have been in the past to understand the trend in a particular group or community. Redundancy management module  1030  provides the interface to manage the redundant database  608 . Data analytics engine  1042  the brain module to analyze the data using the web services layer  718  components such as Hadoop big data cluster  1052 , session file handler  1058 , knowledge base  1040  and heuristic engine  1048 . The data analytics  1042  provides trends, performance graphs and other useful data for drawing proper conclusions towards policies and goals. The performance metrics are managed through performance management  1044 . The individual and users are grouped into logical equivalence classes using dependency grouping  1046  to track the dyslexia impact within a group. The group profiles are managed for security using user profile management  1054 . 
       FIG. 11  illustrates the expert system architecture. The cloud based expert system  620  provides the important function for experts and analysts to further research and analyze the data collected by Dyslexia analytics system  606  and use the data analytics obtained through the Dyslexia analytics system  606  to draw important macro level conclusions. The expert system  620  interfaces with expert system user knowledgebase  1120  which contains important user level information. It also interfaces with the redundant education knowledgebase  1122  that consists of the learning due to dyslexia patters within groups and communities for training doctors, experts, social workers, and other health care workers. The expert system  620  consists of knowledgebase interface  1102 , network interface  104 , redundancy management  1106 , feedback analyzer  1108  and authentication  1110 . The expert clients  622  login through network  1104  and the expert user interface  1112  and are authenticated  1110  before accessing the feedback  1108  using redundant knowledgebase  1120  and  1122 . The redundancy and fault tolerance of the knowledgebase  1120  and  1122  is handled through redundancy management  1106 . 
       FIG. 12  illustrates the control and data flow to user interface  1002 . The visual interface  708  for individual users and groups captures performance, reports, events, benchmarks and activities. The data is fed as individual data  1212 , group data  1214  and community data  1216 . The individual data  1212  is received through test modules  804  from user interfaces in client device components. The group data  1214  and community data  1216  are received through group statistics  1224  and community statistics  1226  received from the backend intelligent dyslexia analytics  1042 . The user interface data is provided based on profiles  1054 . The data are stored in knowledgebase  1120  and  1122 . The user interface also collects data through expert system  620  analyses and the feedback based on the analysis  1210 . 
       FIG. 13  illustrates the message sequence chart for user data flow  1002  as part of feedback gathering and analysis. User enters the login certificates  1004  and the user interface  708  authenticates and the profile is checked  1006 . The web services layer  718  receives the data and the authentication process is completed  1008 . The end user starts inputting the dyslexia test data, which are gathered  1034  and sent to dyslexia backend engine  716 , for analysis  1022  and synchronization  1018 . The data is stored in knowledgebase  608 . The analytics metrics derived is communicated back  1056  to visual interface. 
       FIG. 14  illustrates an embodiment of user interface screen. The granularity of the data  1402  is at patient, school, community or country level. Time period of the analysis is entered  1408  for analytics. The comparison can be done between patient/user and the average group  1412  as graph. The menu can be used  1404  to specify the metric that needs to be compared. The performance  1406  can be either for overall country level, group level or individual level for mean analysis. 
       FIG. 15  illustrates the dyslexia test types using tablet  1502 . In another embodiment, the four tests can be conducted using any electronic device with a touch screen and camera attached or built-in for gaze/pupil tracking. 
       FIG. 16A  illustrates the clock drawing test using tablet  1602 . In  FIG. 16B  the data is captured using stylus  1604  in a proper figure pattern as done by the end user for accurate depiction. This test presents an empty circle and a button to press to give a subject a verbal instruction to fill in the clock to show e.g. 12: 20 am. While a user performs the test, methodology tracks the drawing anomaly and allocates marks for different strokes. Different kinds of anomaly patterns are described in the doctor assessment module. 
       FIG. 17A  illustrates the writing test, where the user uses stylus to write  1702  the answer. The answer in terms of incremental stroke images is captured by the device  1704  for further analysis as shown in  FIG. 17B . A number of writing anomalies are detected through this module. For instance, there is a word cat and numbers 69345. For each section, the methods look for reversed and missing letters. Since the way they are written are stored as multimedia screencast videos, the examiner can see how the letters e.g. e, a, p and b are written. By retracing the video of the test as it is written to see where the start and end is, a judgment about sloppy, missing, reversed, and uneven writing can also be made. 
       FIG. 18A  illustrates the reading test, where the eye movement is monitored using camera  1802 . Number  1806  specifically shows the position of the pupil while taking the test. The data of eye movement concentration area within the sentence is captured  1804  for further analysis in  FIG. 18B . Dyslexics tend to show unorganized vertical or horizontal or circular eye movements. This test indicates abnormal eye movement and the examiner can go back and listen to the recorded speech and also see the eye movements recorded as video while hearing it to see if the candidate could follow the written materials. The  FIG. 1802  shows how the eye coordinates are superimposed on top of the text while it is read and finally sent to the server side for the doctor analyzer to observe. 
       FIG. 19  illustrates the pupil movement tracking for the complete paragraph in the tablet  1902 . The pupil movement captured through camera is highlighted in the screen  1904  for further analysis. The projected shared area  1904  in front of the monitor or screen shows the field of view of the camera in which the best pupil recognition takes place. 
       FIG. 20A  illustrates the drawing family members test, where the stylus is used by the user to capture the illustration of the family member  2002 . In  FIG. 20B  the user interaction data on the screen is captured as images by the device for further analysis  2004  at the Dyslexia analytics system  606  is shown. A subject will be asked to draw two family members e.g. father and mother. The way the subject draws the family members is tracked and the method identifies whether there is dyslexic pattern observed. The pupil movement data is also saved as multimedia for assisting the doctor analyzer. Different kinds of granularity of the drawn objects are observed by an auto-grading method. For example, how many features are drawn by a student: is it just a bare skeleton, or have hairs, nails, dressed up, all fingers, eye brow, flesh, and complete number of salient body part. 
       FIG. 21A  illustrates the data analysis of the complete user test by an expert using automatic grading from video analysis  2108 . The backend intelligence provides the captured data through expert client to view the analytics and the raw data over user interface for making important conclusions. At the end of each test module, the interaction data is captured by saving the screencast video, and audio stream, storing them locally at the tablet, and attaching user profile and session metadata. Once all the test modules are performed within the given time, the multimedia files containing dyslexia patterns can be uploaded to the big data repository.  FIG. 21B  shows a sample instance where one test module is being uploaded to the server. 
       FIG. 21  shows the user interfaces of different doctor modules. User interface  2102 , shows the result of automatically tagging each student&#39;s submitted test results. Different color index shows different tagging e.g. red circle shows a confirmed dyslexia pattern found by the auto-grading method, yellow represents a possibility and hence requires the doctor to confirm it and a green means the test results exhibit no dyslexia patterns. This color scheme is used to assist a doctor with a high level overview of the test results for mass screen. A doctor can always go to any particular or all test results to approve the dyslexia patterns manually. In  FIG. 21B  user interaction video  2104  and the automatic grading done by server side methods on the basis of positioning of clock hour, hour hand arrow marking, minute hand arrow marking, hour hand arrow size, and minute hand arrow size is shown. The video shows the incremental buildup of the clock, which will give the doctor an idea how the drawing was performed over time as well. In  FIG. 21C  a Clock drawing test video  2106  synchronized with the spatial eye movement pattern during the test is shown. The eye movement pattern and the formation of different parts of the body give an indication whether the subject is dyslexic. A doctor can mark his/her comments on the observed video and gaze data. In  FIG. 21D  a Writing test  2110  and the auto marking of the writing, based on the video or image analysis on reverse, missing, sloppy and uneven letters is presented. In  FIG. 21E  an Automatic augmentation of pupil  2112  coordinates superimposed on the reading test content in spatio-temporal dimensions, which will give a Dr. analyzer the movement order of the pupil is shown as an example. The doctor can accept the automatic grading or can manually enter the grading for the eye movement test. 
       FIG. 22A  illustrates the use case diagram for the user to complete the test. The registration process for the student is illustrated in  2202 , where the test administrated registers the school and students. The student takes the test from the tab that has been set up by the administrator. The client component in the tab transfers the results and data to backend intelligence over cloud/Internet through network interface. In  FIG. 22B  the data analysis  2204  in the Dyslexia analytics system  606  goes through analysis using auto grading services and displayed as results. The final review is done through experts such as doctors and analyzers. The final reports are sent to policy handlers and stakeholders. 
       FIG. 23A  illustrates examiner registration and login process. The registration  2302  is directly authenticated at the backend intelligence system. In  FIG. 23B  is shown that the user name and password is verified in a secure fashion directly over network interface. This ultimately has to meet the HIPPA rules of United States for privacy and individual medical records protection as well. 
       FIG. 24A  illustrates the school registration process  2402 , student registration process  2404 , in  FIG. 24B , and the authentication  2404 , in  FIG. 24C , by the administrator before the student takes the test. 
       FIG. 25A  illustrates the dyslexia test module procedure for ages 10 and above. The authentication  2502  is followed by the administration of the four tests  2504  ( FIG. 25C  shows the choices a user can make before they start the test). Each test has timeline as shown in  2506 . After the administration of the clock test  2512  (shown in  FIG. 25B ), spelling test  2506  ( FIG. 25E ), reading test  2516  ( FIG. 25F ) and family member drawing test  2514  ( FIG. 25D ), the data is submitted to Dyslexia analytics system. 
       FIG. 26A  illustrates the dyslexia test for young kids, where the authentication  2602  is administered followed by the four tests  2604  ( FIG. 26C ). The tests are simpler compared to those of age 10 and above.  FIG. 26B  shows Clock drawing is  2612  which is simpler. The writing test  2606  is confined to alphabet repetition.  FIG. 26D  actually provides space for a drawing test. 
       FIG. 27A  illustrates the experts and doctors registration and login process. Doctor registration  2702  and login  2704  ( FIG. 27B ) are authenticated by the administration modules in the backend intelligence system  606 . 
       FIG. 28A  illustrates the test result dashboard at visual interface  708 . The dashboard for the doctor provide various schools and communities  2802  and on choice of a school or community various users, students and individuals are listed  2804  ( FIG. 28B ) with their respective performance analyzed through intelligence engine and doctors input. 
       FIG. 29  illustrates the dyslexia test result for clock drawing test  2902  for an individual user as seen from the visual interface  708  of doctor for further analysis. 
       FIG. 30  illustrates the spelling test result  3002  as seen by visual interface  708  seen by the doctor. The individual data  3004  is clearly shown as captured by the client component. 
       FIG. 31A  illustrates the reading test result for an individual  3102 . In  FIG. 31B  and  FIG. 31C , the normal pattern of pupil movement is shown  3104  and compared to the captured pupil movement  3104  the examined user or student. 
     While the user reads the text, the pupil tracker reads the pupil movement co-ordinates and stores in a temp file. Once the reading module is finished, this X, Y values are sent to the server side API. The server side API simply plots the X, Y values and makes a JPG file, which will be made available to the doctor&#39;s module (same like we show the video for other three modules). Once plotted, the patterns should look like a continuous z pattern as following. Once the pupil coordinates are sent from the tab to the server, a server function receives the excel or csv or json file, passes it to the plotting function, returns a JPG image and stored in the web server folder, which is shown in the respective reading test web page in the doctor module. Not only this image is made available for the doctor to manually observe the dyslexic eye pattern, but also a new audio module is introduced where the audio is also be captured. It means, when the student looks at the text and read aloud (he/she has to read it loudly while he reads the text by eye movement). At the end of the test, both pupil movement coordinates and audio is stored and made available at the server side doctor module. The doctor now can see the pupil plot superimposed on the text he/she read and hear the audio. 
     In one embodiment,  FIG. 31  has the following methodology for reading module:
         User puts correct license code and open the app   User chooses Reading module and language   Timer starts counting T minutes.   User starts reading texts with eye and loudly using voice from left (non-Arabic) or from right (Arabic)
           i) CaptureVoice function captures the audio stream and stores in a temporary file   ii) CapturePupilCoordinate function captures pupil coordinates 3102 and stores in a temporary file   iii) a timer and normalized aspect ratio is used to synchronize the reading text location and pupil movement and make them augmentable and make the pupil coordinate superimposed on top of the text  3102 .   
           User clicks on done or the timer expires   A session multimedia file along with user profile is created that contains the synchronized text, audio and pupil movement data, which is ready for uploading to the server.       

       FIG. 32  illustrates the drawing family member test result as seen by the dyslexia doctor  3202 . The data for the user  3204  as captured by the client component is shown in the visual interface of doctor  708  for further analysis.  FIG. 33  illustrates the listening test result of an individual or student  3302  as captured by the client component  3304 . This is displayed in visual interface  708  so doctor or expert can further analyze it.  FIG. 34  illustrates the letter writing test result of an individual or student  3402  as captured by the client component  3402 . The data  3404  is reviewed by the doctor through visual interface  708  for further analysis. 
       FIG. 35  illustrates the system organization and the mathematical foundation of the proposed dyslexic writing pattern detection method.  FIG. 35  depicts a high-level overview of our proposed dyslexic character recognition system, which consists of four main components. In the reference dyslexic characters database component  3502 , individual characters, provided by different dyslexic patterns of characters available from Dr. analyzer  3510 , are scanned, pre-processed and then stored in the reference characters&#39; database as 48×48 matrix format. In stroke generation component  3504 , the reference character is broken into its primitive strokes such as curves and straight lines. The curvature analysis and string generation component  3506  implements curvature analysis methodology to differentiate between curves and lines and generates the appropriate string consisting of numeric codes of each stroke that uniquely identifies a character from its structural representation. Finally, the string matching and character recognition component  3508  applies the dynamic programming paradigm to recognize the character. 
       FIG. 36  illustrates the methodology that takes the image containing the letters  3606  and breaks down each number and letters  3604  into its primitives  3602 . The Figure captures the input and preprocessing. The captured stroke image file containing test characters is either enhanced/compressed or restored to form a 48×48 pixel size image. Once stored in the cloud database, the process proceeds by scanning alternately from left to right and from top to bottom along various rows and columns of the input array. This continues until a black byte is encountered. A byte is taken as black if it has more than one black point. 
     To recognize a given dyslexic pattern within a typed character, the character needs first to be digitized into a matrix of binary format for ease of handling. The digitizer methodology converts a physical sample to a pattern vector as following: X=[X1 X2 X3 . . . Xn]. Where n is the number of measurements. Component Xi of the vector X assumes the value 1 or 0 depending on the state of the i-th position for a particular input. The representation is in the form of a matrix, whose entries have one of the two values 0 (Zero) or 1 (One) corresponding to white and black points in the original image respectively. 
       FIG. 37  illustrates the contour tracing and filtering methodology. Contour tracing is the process of finding a series of black points on the boundary of a black region in a white field. The white to black transition points of the letter are obtained by scanning the digitized character from left to right and from top to bottom each time and storing the obtained values in a memory location. The zero (0) to one (1) transition  3702  and contour traced output  3704  are shown. The next task towards dyslexic pattern recognition is to filter out all the isolated 1&#39;s (ones), called stray points, which are presented in the contour map of the character to be recognized. These stray points are those that are not associated with the alphabet or letter; however, they might be generated by stylus pane mistakenly touching the tablet window during the writing process and must be eliminated before the actual dyslexic recognition process starts. During the filtering process, a ‘1’ is considered to be isolated if there is no similar ‘1’ in its neighborhood region, defined as seven digits above and below the row of the reference digit. 
     In  FIG. 38 , the middle row corresponds to the reference binary 1 under consideration. Both digits ‘1’ in the upper and lower rows  3802  are not neighbours of the reference digit ‘1’ because they do not lie within the seven digits region. Both upper and lower row 1&#39;s are recognized as neighbours of the reference digit  3804 . Therefore, we keep them in the matrix. The process of filtering out unneeded digits is performed by applying the binary operator ‘And’ for each byte with the mask of its top and bottom bytes then applying the ‘Or’ binary operator on the computed results. The resulting value is stored in the matrix. For the first and last lines of the matrix, only the bottom and the top bytes are taken respectively since neither the first line is bordered by a line above it nor the last line by one below it. 
       FIG. 38  also illustrates the Strokes Extraction Process methodology. To extract the drawn dyslexic character strokes, all x and y co-ordinates of each pixel in the filtered file needs to be saved to the recognition engine. As described above, the recognition process starts from the top left corner and proceeds towards the bottom in order to find continuous points. Points in a line are deemed continuous if three bits on either side in its immediate upper line are black, as depicted. If a discontinuity occurs, scanning proceeds to the next line with the assumption that the void in the current line is due to improper scanning. If the next line presents continuity, a black point is assumed in the previous line because improper scanning may result in loss of pixel and its coordinates are saved. Otherwise, the stroke is assumed to have terminated. All black points ‘1’ of the stroke are terminated by a ‘0’ during the storage process of the coordinates so that they are not reconsidered when the scanning process of searching for the next stroke starts again from the upper left corner. The scanning strokes process continues until no new strokes are found. The result is to produce the collection of stroke coordinates. We need this extraction process because we have to split up the image into several parts. 
       FIG. 39  illustrates the Strokes encoding process methodology. For the purpose of dyslexic character recognition, it is desired to generate a numeric code for each character. The first step in this procedure is the selection of strokes and the generation of code for each stroke in a character. The selection of strokes is illustrated. In our methodology, each input pattern is resolved into primitive structural elements called strokes or morphs. The first step is the selection of appropriate morphs in terms of which the dyslexic patterns of interest can be represented. To simplify the selection process, we aim at spotting strokes that are simple enough to recognize and minimum in number to be easy to find. For our purpose and analysis, six simple strokes were chosen to represent dyslexic character patterns and 10 numeric digits. These strokes are shown in  FIG. 39 . 
       FIG. 40  illustrates the wide range of deviation that is allowed in each of the six defined strokes. The intuition behind choosing these strokes is obvious since all dyslexic patterns consist of lines or curves. Though there are characters that have some form of a circle shape, this is disregarded because when we side trace the letter, we only obtain the vertical convex or vertical concave curves. So, we take those lines or curves as a principle part of the character. As a result, a wide range of deviation is allowed in each of the six defined strokes. 
     Finding the strokes codes is an important procedure within the methodology. The coordinates of a particular stroke are now analyzed to find the appropriate code of that stroke. The strokes under consideration are either curves or straight lines with the main assumption that the characters of any alphabet in any language can be represented by a combination of those. Later, the co-ordinates of continuous strokes are assembled together to find the appropriate numeric codes. To form an equation from some points, we set the points in the equation, then we get a path. The equation of curve fitting is
 
 x=a+by+cy   2   Equation 1
 
where a, b, and c are as follows:
 
     
       
         
           
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     Where N=Total number of points on the stroke. The point on the stroke which has maximum curvature (κ) is calculated by using the formula
 
κ= x   2 /(1+ x   1   2 ) 3/2   Equation 2
 
     Here x 1 =dx/dy &amp; x 2 =d 2 x/dy 2    
     After extensive research and curve analysis we have found that if the value of κ&gt;=0.9, then the stroke is assumed to be a curve, otherwise (κ&lt;0.9), it is assumed that the stroke is a straight line. This new approach in differentiating the curve and straight line based on the value of κ will result in a significant amount of error minimization and enhanced recognition as we will demonstrate later. Again, code 5 or 6 is assigned according to the value of a constant c, whether positive or negative. 
     The slope of each line is calculated according to the following equation
 
 x=a+by   Equation 3
 
     Transforming equation 3 to y=mx+c form, we get y=(x/b)−(a/b). So the slope of the line=1/b, where a, b are defined as follows: 
     
       
         
           
             
               
                 
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     Again, N is the number of points in a stroke (N) of a given character. If N is less than or equal to 4, the stroke code is considered to be 0. This is explained by the fact that improper touch on the tablet screen by the stylus can create up to four points. So setting that condition on the value of N makes up for this. A stroke with code ‘0’ is created due to the existence of noise in the surrounding of the character. 
     The methodology to encoding the characters includes the representation of characters in the form of a numeric string. The numerals in the code not only indicate strokes which build the characters but also show the relationship among the strokes. The strokes in the characters are traversed exactly one time from left to right and from top to bottom along various rows and columns. All strokes are traversed exactly once. The letter code consists of the stroke codes written down in the order in which they were encountered. It is possible to get an idea about the structure of the character from its code. The methodology starts scanning process from the top left corner and stopped at the bottom right corner. 
       FIG. 41  illustrates the recognition of characters using matching scores. The characters represented by numeric string codes are now ready to be recognized. A search is conducted on a dictionary of codes called reference strings to find a matching code corresponding to that generated from the input character. The reference segment and the matching scores for the corresponding input segments are shown in  FIG. 41 . Reference segment  4102  and the corresponding possible input segments  4104  are illustrated. 
       FIG. 42  illustrates the methodology for matching matrix. In this process, the reference pattern is thought of as a sequence of segments. In order to compare it with the input string, we need a reference string. We represent the reference string R by
 
 R={R seg 1   ,R seg 2   , . . . ,R seg k }  Equation 3
 
     Whereas the input string I, which is also a list of segments is represented by
 
 I={I seg 1   ,I seg 2   , . . . ,I seg n }  Equation 3
 
       FIG. 42  is of a matching matrix that we have included to ‘weigh’ the minimum score the input deserves. The matrix&#39;s rows are referenced by the input segments, the columns by the reference segments since we must compare with previous data. The intersection of the i-th input segment and j-th reference segment holds the matching score between the i-th and j-th input segments. The matching matrix is shown in  FIG. 42 , where Si,j denotes the matching score. 
       FIG. 43  illustrates the relative matching scores for specified dyslexic primitive codes are shown in Table. In the methodology to compute optimal score, after we designed and built the matching matrix, optimal path is found through the matrix in order to achieve maximum dyslexic pattern recognition scores. The methodology works as follow: 
     If we are currently in M (i,j) , we compute: Equation 4
 
 Sl=M   (i,j) +max(( M   (i+1,j)   +M   (i+2,j) ),( M   (i+1,j)   +M   (i+2,j+1) ))
 
 Sr=M   (i,j) +max(( M   (i,j+1)   +M   (i,j+2) ),( M   (i,j+1)   +M   (i+1,j+2) ))
 
 Sd=M   (i,j) +( M   (i+1,j+1) +max( M   (i+1,j+2) ,( M   (i+2,j+2)   ,M   (i+2,j+1) )
 
Where,
 
     Sl denotes a shift down. 
     Sr denotes a Shift right. 
     Sd denotes a Shift diagonally. 
     Then according to the maximum value found−max (Sl, Sr, Sd) we move down, right or diagonally along the matrix to the direction of the maximum value. 
       FIG. 44A  illustrates the average dyslexic pattern matching score with 100% possibility  4402  and the average dyslexic pattern matching score with 62% possibility  4404  ( FIG. 44B ). The methodology to decision taking includes optimal score computation. After completing the optimal score computation as described above, we calculate the average score of matching the input string with each reference dyslexic pattern string. This is called the Average matching score. An average matching score (S av ) of 90% or more is the cutoff. Any outcome of S av  is disregarded, and the character in question considered erroneous and hence cannot be recognized with the autograding methodology, which can be done using manual grading by the Dr. Analyzer. Note that the reference string (character) that gives the maximum S av  with the input string is considered as the recognized and accepted dyslexic character. 
     INDUSTRIAL APPLICABILITY 
     Until this innovation, Dyslexia used to be a nightmare to properly detect at the age of 4 or 5 when a child starts going to school. Since these gifted children have difficulty in reading, writing, and drawing, they fail to properly follow the class lectures, prepare homework and write exams. Hence, the dyslexic kids starts getting isolated from the others, gets bad grades, drops out and cannot take part in innovation and other future studies. However, if they are detected in early stage, this can be addressed through assistive technologies and they can take part in regular schools. Although some single modality of screening dyslexia is available, none is available that can be applied to a mass level such as city or country or world. Hence, our proposed innovation can provide screening tests for the whole world as it is based on cloud and independent of languages. There are very significant application and superior benefits for calculating health metrics to improve for an individual. More specifically the present invention describes a more effective method and system for obtaining data regarding a user&#39;s dyslexic activity and interactions, and a novel method and system for processing such data using system templates to generate timely, accurate, relevant and actionable analytic metrics that participants, doctors and policy makers can use to guide them towards better treatments and outcomes. This technology may be used for other applications but not limited to, such as product launch, patient feedback, and customer need evaluation etc. 
     In addition, it will be appreciated that the various operations, processes, apparatuses and methods disclosed herein may be embodied in a machine-readable medium and/or a machine accessible medium compatible with a data processing system (e.g., a computer system), and may be performed in any order (e.g., including using means for achieving the various operations). Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.