Patent Publication Number: US-11651459-B2

Title: Computer-controlled precision education and training

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/712,213 filed on Dec. 12, 2019, the complete disclosure of which, in its entirety, is hereby incorporated by reference. 
    
    
     BACKGROUND 
     Technical Field 
     The embodiments herein generally relate to computerized Internet-based education and training systems, and more particularly to a computer-controlled remote-based learning system for facilitating execution of workflow tasks. 
     Description of the Related Art 
     Schools are a great way of learning and training. However, several modern technology requirements and environments for training systems are dependent on its context and reference. This means someone may not need to be taught everything in all possible ways. The context demands at least some level of customization for the training and education delivery process in a digital environment. 
     Therefore, there is a need of a new intelligent and evolved digital system for education and training that more efficiently allows execution of certain workflow tasks requiring training and education. 
     SUMMARY 
     In view of the foregoing, an embodiment herein provides a system for live digital streaming of one or more contextual micro-content blocks in real-time for a workflow task to facilitate task performance by a user. The system includes a context sensing engine that processes one or more context inputs associated with the user and converts them into processed inputs, and generates an output based on the one or more context inputs received from a front-end context monitoring appliance. The front-end context monitoring appliance is communicatively coupled to the context sensing engine at a remote location and includes a context sensor that detects a context of the task performance and a Global Positioning Service (GPS) device that detects geographical coordinates of a device associated with the user. The system further includes a processing circuit. The processing circuit includes a navigation engine that navigates through one or more digital information sources accessible over a network and searches for information that matches one or more parameters of relevance for the workflow task. The processing circuit further includes a computerized data collection wireless appliance that extracts computer-executable information files from the one or more digital information sources that matches the one or more parameters of relevance for the workflow task. The processing circuit further includes an information processing engine communicatively coupled to the computerized data collection wireless appliance. The information process engine digitally processes the collected computer-executable information files into a plurality of processed information blocks. The processing circuit further includes a micro-content blocks creator that generates the one or more contextual micro-content blocks from the plurality of processed information blocks based on the output generated by the context sensing engine. The system further includes a micro-content communication component that transmits the one or more contextual micro-content blocks relating to the workflow task to the device associated with the user at a time when the plurality of micro-tasks of the workflow task are about to start. 
     The system may further include an AI/ML (artificial intelligence/machine learning) system remotely connected to the front-end context monitoring appliance and communicatively coupled to the context sensing engine. The AI/ML system may receive a signal from the context sensing engine containing the processed context inputs and the output generated based on the context inputs, process the context inputs to determine a contextual pattern for the workflow task utilizing a plurality of intelligent and machine learning-based tools, and transmit a digital signal containing information pertinent to the contextual pattern to the processor. 
     The processing circuit may further include a filtering circuit that filters the plurality of processed information blocks based on the contextual pattern associated with the user and the workflow task as indicative through the output generated by the context sensing engine communicatively coupled to the processing circuit and the AI/ML system. 
     The one or more micro-content blocks may include at least one of a context-based micro-content block, a location-based micro-content block, a role-based micro-content block, and a skills-based micro-content block such that the one or more contextual micro-content blocks matches to a plurality of micro-tasks of the workflow task. 
     The one or more contextual micro-content blocks may be time-stamped before transmission by the micro-content communication component to the device associated with the user for real-time delivery according to an occurrence of the plurality of micro-tasks of the workflow task. 
     The one or more parameters of relevance may be defined based on one or more of a plurality of digital inputs stored in a memory device, the plurality of digital inputs including a digital input indicative of nature of the workflow task, a digital input indicative of specific micro-tasks associated with the workflow task, a digital input indicative of location of execution or performance of the workflow task, a digital input indicative of actor performing the task, a digital input indicative of context of the workflow task, a digital input indicative of skill-sets of the actor performing the workflow task, a digital input indicative of role of the actor performing the workflow task. 
     The GPS device may retrieve real-time tracking location coordinates associated with an event occurrence for the plurality of micro-tasks of the workflow task. The GPS device may include a radio-navigation system. 
     The front-end context monitoring appliance may include an agent device coupled communicatively and operatively with the context sensing engine. 
     The agent device may be operated by deploying an installable agent, configured as a browser plugin, at the device associated with the user. 
     The system may further include a blockchain device that interacts with the context sensing engine through a plurality of blockchain configured distributed access points over a blockchain network. 
     The blockchain device may include a distributed trusted ledgers system containing a plurality of distributed blockchain ledgers associated with a plurality of computing terminals such that each ledger stores a copy of a computer-executable file containing the context inputs and the one or more contextual micro-content blocks associated with the workflow task. 
     The blockchain device may include a blockchain database that stores the collected computer-executable information files from the one or more digital information sources and the one or more contextual micro-content blocks. 
     Another embodiment herein provides a blockchain-enabled information management server for live digital streaming of one or more contextual micro-content blocks in real-time for a workflow task to facilitate task performance by a user. The information management server includes a context sensing engine that may process one or more context inputs associated with the user and convert them into processed inputs, and generate an output based on the one or more context inputs received from a front-end context monitoring appliance. The front-end context monitoring appliance may be communicatively coupled to the context sensing engine at a remote location. The front-end context monitoring appliance may include a context sensor that detects a context of the task performance and a GPS device that detects geographical coordinates of a device associated with the user. The system further includes a processing circuit. The processing circuit includes a navigation engine that navigates through one or more digital information sources accessible over a network and searches for information that matches one or more parameters of relevance for the workflow task. The processing circuit further includes a computerized data collection wireless appliance that extracts computer-executable information files from the one or more digital information sources that matches the one or more parameters of relevance for the workflow task. The processing circuit includes an information processing engine communicatively coupled to the computerized data collection wireless appliance. The information processing engine digitally processes the collected computer-executable information files into a plurality of processed information blocks. The processing circuit includes a micro-content blocks creator for generating the one or more contextual micro-content blocks from the plurality of processed information blocks based on the output generated by the context sensing engine. 
     The system further includes a micro-content communication component that transmits the one or more contextual micro-content blocks relating to the workflow task to the device associated with the user at a time when the plurality of micro-tasks of the workflow task are about to start. The system further includes a blockchain device that interacts with the context sensing engine through a plurality of blockchain configured distributed access points over a blockchain network. 
     The blockchain device may include a distributed trusted ledgers system containing a plurality of distributed blockchain ledgers associated with a plurality of computing terminals such that each ledger stores a copy of a computer-executable file containing the context inputs and the one or more contextual micro-content blocks associated with the workflow task. 
     These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating exemplary embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which: 
         FIG.  1    illustrates an example of a computer architecture in which various embodiments herein may operate; 
         FIG.  2    illustrates a schematic diagram of an information management server in accordance with an embodiment herein; 
         FIG.  3    illustrates a front-end context monitoring appliance connected with a context sensing engine in accordance with an embodiment herein; 
         FIG.  4    illustrates a blockchain-configured ecosystem architecture containing one or more components of the system of  FIG.  1    in accordance with an embodiment herein; and 
         FIG.  5    is a block diagram illustrating a computer system used in accordance with the embodiments herein. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein. 
     Referring now to the drawings, and more particularly to  FIGS.  1  through  5   , where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments. In the drawings, the size and relative sizes of components, layers, and regions, etc. may be exaggerated for clarity. 
       FIG.  1    illustrates an example of a computer architecture in which various embodiments herein may operate. The computer architecture may include a system  102  for live digital streaming of one or more contextual micro-content blocks in real-time for a workflow task  114  to facilitate task performance by a user. The system  102  may include an information management server  104  configured for navigating through a plurality of information sources  106   a ,  106   b , and  106   c  (collectively referred to as information sources  106 , for the purpose of description) distributed remotely from one another and storing digital files executable by a computer (such as the information management server  104 ) in a plurality of respective storage devices such as a storage device  108   a , a storage device  108   b , and a storage device  108   c  collectively referred to as storage devices  108 , for the purpose of description. An information source of the information sources  106  may store its respective digital files in one or more storage devices  108  that are associated with the respective information source. The information sources  106  and the storage devices  108  shown in  FIG.  1    are for illustration purposes only, otherwise the number of information sources  106  and the number of storage systems  108  may be different. In some embodiments, there may be large number of information sources  106  and storage systems  108  and a different number of information sources  106  compared with the storage systems  108 . 
     The information management server  104  may be connected to user devices associated with a plurality of users. As an example, for the purpose of describing an embodiment, the information management server  104  is shown to be communicatively connected with one user device  112  through a communication network  110 , such as the Internet or an Intranet. The user may perform a workflow task at a particular time and location such that the performance of the workflow task (alternatively referred to as a “task” without limitations herein)  114  occurs at a location remote from the location of the information management server  104 . The user device  112  is associated with the user and is also located remote from the information management server  104 . 
     The workflow task  114  may involve a series of tasks called micro-tasks such as a first task, a second task, a third task, and a fourth task, etc. without limitations in number. Each of these micro-tasks may be performed in a particular sequence at particular locations and particular time slots. These micro-tasks may be independent of one another, or totally or partially dependent on at least some of the other micro-tasks. 
     The information management server  104  may be configured to monitor and receive details pertinent to the workflow task  114  based on certain inputs received from the user device  112  and/or based on certain automated transfer of digital messages from the user device  112  and its associated sensors and monitoring agents such as a front-end context monitoring appliance  310  as shown in  FIG.  3    and described later. The information management server  104  may serve the user device  112  one or more micro-content blocks  116  based on the inputs received and/or the digital messages, which may be indicative of the workflow task  114  along with associated respective micro-tasks and the context of the user device  112 , user, and the performance of the workflow task  114  (referred to as contextual patterns). 
     The micro-content blocks  116  may be of a variety of types based on the types of contextual patterns associated with the user device  112  and the workflow task  114 . For example, the micro-content blocks  116  may include at least one or more task context-based micro-content blocks  118 , one or more role-based micro-content blocks  120 , one or more skill-based micro-content blocks  122 , and one or more location-based micro-content blocks  124 . In an example, the various micro-content blocks  116  may include information elements generated by mapping onto each micro-task for relevancy, the role of the user interacting for executing a specific micro-task for relevancy, and the level of experience or skill sets of the user for a particular micro-task that is contained within the workflow task  114  such that the one or more contextual micro-content blocks  116  match to the plurality of micro-tasks of the workflow task. Therefore, the various micro-content blocks  116  provides a set of structured digital information blocks that are relevant and related and deeply maps onto various micro-tasks when looked in association with the skillets of the user, location of the user, nature of micro-tasks and the overall workflow task  114 , role of the user in various micro-tasks, and the like. The micro content blocks  116  are further discussed below. 
     The illustrated information sources  106  may include online web sources and databases connected over the Internet, an electronic medical record (EHR), a medical information exchange (HIE), an image archiving communication system storing images, other localized but accessible data stores and the like without limitations. The information sources  106  provide various types of digital information including medical information to the information management server  104 . For example, EHRs and web pages can each provide information such as medical information, diagnostic information, radiographs, and the like. 
       FIG.  2   , with reference to  FIG.  1   , illustrates a schematic diagram of the information management server  104  in an embodiment. The information management server  104  may include a processing circuit also referred to as a processor  202  interchangeably and a context sensing engine  204 . The processing circuit  202  may include a navigation engine  206 , a computerized data collection wireless appliance  208 , an information processing engine  210 , and a micro-content blocks generator  212 . The processing circuit  202  is configured to perform a variety of specialized processing tasks including navigating for relevant information, extracting the relevant information from the plurality of distributed information sources  106 , and creating information elements called micro-information blocks as will be discussed later for delivery to the user device  112  in accordance with the execution or performance of the specific micro tasks. The processing circuit  202  may be capable of executing pre-programmed instructions for performing specialized tasks associated with operation of the information management server  104 . The various components of the processing circuit  202  are discussed hereafter in the document. 
     The processor  202  receives a portion of the digital information that the information management server  104  identifies as relevant and performs specific tasks for processing such as but not limited to indexing, semantic meta tagging, matching, relevancy checking, curating, summarizing, indexing, organizing, processing, standardizing, transforming into micro-content blocks etc. 
     The navigation engine  206  is configured to navigate through one or more of the digital information sources  106  accessible over the network  110  and search for the digital information that matches one or more parameters of relevance for the workflow task  114 . The navigation engine  206  may crawl through millions of web pages and other localized repositories for searching relevant information. The one or more parameters of relevance may be defined based on the nature and context of the workflow task  114 , specific micro-tasks associated with the workflow task  114 , location of execution or performance of the workflow task  114 , actor performing the workflow task  114 , context of the workflow task  114 , skill-sets of the person performing the workflow task  114 , role of the person performing the workflow task  114 , and the like, each indicated through a respective digital input. These parameters may together be indicative of a contextual pattern for the workflow task  114 . An AI/ML (artificial intelligence/machine learning) system  214  may utilize the various parameters of relevance associated with the workflow task  114  and create a unified score for determining the contextual pattern associated with the workflow task  114 . The AI/ML system  214  is communicatively coupled to the context sensing engine  204  and the processor  202 . 
     The contextual pattern may be determined based on one or more context inputs received and processed by the context sensing engine  204  as monitored by the front-end context monitoring appliance  310  (shown in  FIG.  3   ). The context sensing engine  204  may be configured to process the one or more context inputs associated with the workflow task  114  (including associated user and systems) and generate an output signal based on the one or more context inputs. The context sensing engine  204  may include or be coupled to a receiving circuit  234  to receive the one or more context inputs including any manually provided user inputs indicative of the user behaviour etc. The context sensing engine  204  may utilize the context inputs obtained from signals received from the front-end context monitoring appliance  310  (of  FIG.  3   ) indicative of the context in which the workflow task  114  is being performed. 
     As illustrated in  FIG.  3   , with reference to  FIGS.  1  and  2   , the front-end context monitoring appliance  310  may include a context sensor  302  to detect a context of the task performance, and a GPS device  304  to detect geographical coordinates of a user device  306  associated with the user. 
     The Global Positioning Service (GPS) device  304  of the front-end context monitoring appliance  310  may help in real-time tracking of location coordinates associated with various event occurrences or performance of the workflow task  114  or respective micro-tasks by collecting their location details. Real-time tracking may offer different challenges in the tracking of the event occurrences or tasks depending on the complexity of the event occurrences when it is performed manually and takes time, resources and manpower. Location data may be collected in most cases by the GPS device  304  using, for example, a radio-navigation system; though in some other specific cases different location technologies can be used. For example, in an ambulance service of a hospital, the GPS device  304  may help in real-time tracking of a patient being transferred, journey reports, stop reports, alerts, and scheduled reports for future and associated tasks performed by different participants. 
     The front-end context monitoring appliance  310  may include an agent device  312  that may be coupled communicatively and operatively with the information management server  104 . Similar agent devices may be coupled with or deployed at other devices too that may be connected with the information management server  104 . The agent device  312  may be operated by deploying an installable agent  314  at the user device  306 . In an example, the installable agent  314  may be defined in the form of a browser plugin installed by a user on the user device  306 . 
     The processor  202  and the context sensing engine  204  are communicatively connected with the AI/ML system  214  in an embodiment. In an embodiment, the AI/ML system  214  may be an integral part of the processor  202  or the context sensing engine  204 . In accordance with an embodiment, the AI/ML system  214  may receive a signal from the context sensing engine  204  containing the processed context inputs. The AI/ML system  214  may process the context inputs to determine a contextual pattern for the workflow task  114  utilizing a plurality of intelligent and machine learning-based tools. The AI/ML system  214  may include an automatic control system  218 , an artificial intelligence machine  220 , and machine learning tools  222 . These components are discussed below. 
     In an example, the AI/ML system  214  may allow decision-making and determining the contextual pattern of the workflow task  114  based on real-time evidence as generated by the context sensing engine  204  utilizing and processing the context inputs received from the front-end context monitoring appliance  310 . The real-time evidence may be generated based on the context inputs monitored by the context sensor  302 , GPS device  304 , and other devices. The context inputs allow the AI/ML system  214  to perform complex decision-making tasks to determine the contextual pattern associated with the workflow task  114 . The AI/ML system  214  may perform simple and tactical tasks smartly in the absence of humans with the use of the artificial intelligence machine  220  and the machine learning tools  222  because the computer-executable context inputs are trustworthy and are obtained through direct environments associated with the user, user device  306 , and the workflow task scenario. The AI/ML system  214  may generate the contextual pattern using certain predefined computer-executable rules that may be defined either by human manually or generated by the network  110  and/or based on learning by the AI/ML system  214  based on past transactions and operations over time. The contextual pattern may be indicative of the actual situation in which the workflow task  114  and its associated user may behave while performing the workflow task  114  or the challenges that may come across in task execution or the learning and training that the user may require for performing the workflow task  114  or respective micro-tasks effectively. Accordingly, appropriate steps may be taken by the processor  202  for ensuring necessary support is provided to the user or the user device  306  at the right time. 
     In an example, the AI/ML system  214  may perform an automated analysis of the context inputs to determine the contextual pattern. The AI/ML system  214  may generate AWL-based predictions of future expected behavior and learning requirements by the user using the artificial intelligence machine  220  and the machine learning tools  222  to timely guide at appropriate time in advance when certain information is required by the user. The artificial intelligence machine  220  is configured to generate an output (such as the contextual pattern) smartly using a set of inputs such as the context inputs so that the generated output address requirements of the user for executing a task more precisely and accurately in a real-time scenario. In the process, the artificial intelligence machine  220  utilizes the machine learning tools  222 . The machine learning tools  222  are configured to train the artificial intelligence machine  220  how to learn over time with more repeat requests for the micro content blocks by different users in different contexts performing different types of tasks. The machine learning tools  222  are given access to data and allowed to learn on its own based on historical records and processing etc. 
     In an example, the AI/ML system  214  may carry out a predetermined inference on the basis of the aggregated context inputs, and take action in accordance with certain inference results generated as a result of the analysis by the AI/ML system  214 . The automatic control system  218  may be provided and adapted for a target action to be taken by the artificial intelligence machine  220  of the AI/ML system  214  on the basis of the aggregated context inputs and the inference results and generate a control output for taking a target action such as generating the micro-content blocks  116  as will be discussed later. In an example, the AI/ML system  214  may be adapted to drive the artificial intelligence machine  220  on the basis of the inference results and the control output for past events stored in a memory circuit  224 . 
     The AI/ML system  214  may generate the contextual pattern and transmit a digital signal  203  containing information pertinent to the contextual pattern to the processor  202 . The processor  202  may perform a set of processing tasks as described herein. 
     As discussed above, the pages and other data repositories are crawled by the navigation engine  206  to identify the relevant information in view of the workflow task  114  based on the one or more parameters of relevance. The processor  202  includes the computerized data collection wireless appliance  208  that is configured to extract computer-executable information files from the one or more digital information sources  106  that matches the one or more parameters of relevance for the workflow task  114  as identified and crawled by the navigation engine  206 . 
     The computerized data collection wireless appliance  208  may perform certain data collection tasks digitally to extract computer-executable information files from the one or more digital information sources  106  that matches the one or more parameters of relevance for the workflow task  114 . In the example described herein, the computerized data collection wireless appliance  208  may be permitted to generate and/or collect data from the information sources  106  that are connected to the network  110  and permitted to be accessed by the processor  202  or associated entity such as an organization. The network  110  may be a wireless or a physical network configured to operate as a peer network in some embodiments or a global internetwork. 
     The data collection wireless appliance  208  or the processor  202  may be coupled communicatively with a local data reservoir  226  such that data collection wireless appliance  208  may be configured to collect, store, and digitally manage data in the local data reservoir  226  that is extracted or collected from the information sources  106  in context of the workflow task  114  that may be a computer-executable task in some embodiments. 
     The information processing engine  210  that is communicatively coupled to the computerized data collection wireless appliance  208  is configured to digitally process the collected computer-executable information files into a plurality of processed information blocks. The process of transformation of the collected computer-executable information files into the processed information blocks may involve a series of steps such as including, without limitations, meta tagging of the information files, summarizing the information files, curation, high level relevancy analysis for the workflow task  114 , through an automated process with or without utilizing one or more operations by the AI/ML system  214 , and the like. 
     The micro-content blocks creator  212  of the processor  202  is configured to generate the one or more contextual micro-content blocks  116  from the plurality of processed information blocks based on the output generated by the context sensing engine  204  and/or the AI/ML system  214  indicative of the one or more contextual patterns derived from the context inputs received in real-time or in advance from the workflow task  114  scenarios, associated user, and the associated user device  306 . The processing circuit  202  may further include a filtering circuit  232  configured to filter the plurality of processed information blocks based on the one or more contextual patterns and the processed content inputs associated with the user as indicative through the output generated by the context sensing engine  204  and/or the AI/ML system  214  communicatively coupled to the processing circuit  202 . This may allow more accurate and relevant information to be used for generating the micro-content blocks  116 . 
     The filtering circuit  232  removes redundant or unwanted information from an information stream (such as the processed information blocks) using automated or computerized methods prior to presentation to the user in the form of the micro content blocks. The filtering circuit  232  manages the information overload and increment of the semantic signal-to-noise ratio. The filtering circuit  232  compares the processed information blocks with certain reference characteristics such as the contextual pattern to determine what is noise versus what is important for a particular workflow task or a series of micro tasks. 
     The one or more micro-content blocks  116  may include context-based micro-content blocks that are generated in view of the nature of the workflow task  114  and the situation in which the workflow task  114  is being executed and the various micro-tasks associated with the workflow task  114  and the nature of each micro-task thereof. The one or more micro-content blocks  116  may include the location-based micro-content block  124  that is generated in view of the location of actual execution of the workflow task  114 . The one or more micro-content blocks  116  may include the role-based micro-content block  120  that is generated in view of the role of the user in performance of the workflow task  114 . The one or more micro-content blocks may include the skills-based micro-content block  122  that is generated in view of the skill-sets of the user. Many additional types of micro-content blocks  116  may be generated without limitations. 
     In an example, the various micro-content blocks  116  may include information elements extracted from the processed information blocks by mapping onto each micro-task for relevancy, role of the user interacting for executing a specific micro-task for relevancy, level of experience or skill sets of the user for a particular micro-task that is contained within the workflow task  114  such that the one or more contextual micro-content blocks  116  match to the plurality of micro-tasks of the workflow task. Therefore, the various micro-content blocks  116  provides a set of structured digital information blocks that are relevant and related and deeply maps onto various micro-tasks when looked in association with the skillets of the user, location of the user, nature of micro-tasks and the overall workflow task  114 , role of the user in various micro-tasks, and the like. This may permit providing relevant information to the user when a particular task such as the workflow task  114  is performed such that even if the user is not very familiar with how to perform the workflow task  114 , the user device  306  may be permitted to receive necessary inputs from the processor  202  in terms of the micro-content blocks  116  that guides the user about the task execution not only at a broad level but at specific micro steps or micro-tasks levels. 
     Each micro step or micro-task may be of a few seconds or minutes duration or may be longer. The duration of a micro-task may depend on the level of guidance it needs as a standalone step independent of other steps or micro-tasks. The workflow task  114  may be broken into the micro-tasks based on the level of guidance a portion of the entire workflow task  114  may require for the user to effectively execute it. For example, a task may involve a series of five steps such that the first step involves understanding a particular function in order to start a medical device, while the remaining four steps are merely to monitor the different values generated by the device. In an example, the task may be broken into two micro-tasks: a first micro-task involving the first step, and a second micro-task involving the remaining four steps. In an embodiment, each step may be defined by a separate micro-task or more than one micro-task. The procedure of determining the various micro-tasks based on the workflow task  114  is performed by a task manager  228 . 
     The task manager  228  may be configured to manage various tasks such as delivery of the micro content blocks in accordance with schedule of the workflow task  114 . The task manager  228  is communicatively coupled to the processing circuit  202 . The task manager  228  may schedule the delivery of the micro-content blocks  116  according to schedule of the tasks and the associated micro-tasks and accordingly tie different tasks on a time series and identifies timelines associated with the delivery of the micro-content blocks  116  for the different tasks. 
     The task manager  228  examines the status of the micro-tasks on the time series and generates an automated output indicative of the task&#39;s status and respective micro-content blocks delivery. The task manager  228  may automatically notify to the user about status of the micro-content blocks delivery for the tasks as soon as they are delivered and/or their tasks are complete or if the delivery is pending after due time. The task manager  228  may, in general, organize scheduling of the tasks as they are to occur and accordingly connect with other systems and components for allowing the delivery of the micro-content blocks  116  without any delay and/or conflict. 
     In an embodiment, the task manager  228  may serve as a command center and can track activities of the users such as who is reading, who is learning, and who is spending what time on what content as indicated through tracking of the time spent by the users on the different micro-content blocks  116  delivered to them. Accordingly, intelligent recommendations may be sent to the users based on their education habits and those items that are more useful to their activities and tasks. 
     The micro-content blocks  116  that match and are related precisely to specific micro-tasks associated with the workflow task  114  are communicated by the processor  202  to the user device  306 . The micro-content communication component  230  of the processor  202  transmits the one or more contextual micro-content blocks relating to the specific micro-tasks of the workflow task  114  to the user device  306  associated with the user at a time when the workflow task  114  or a micro-task is about to occur. The one or more contextual micro-content blocks  116  may be time-stamped before transmission to the user device  306  associated with the user for real-time delivery according to the occurrence of the plurality of micro-tasks. 
     In various examples, the information sources  106  may be hosted by applications such as websites or electronic applications or mobile applications or computing machines associated with third-party companies or third-party vendors (or merely third parties for simplicity of description). The information blocks obtained from the information sources  106  may be transformed into the micro-content blocks  116  based on context data analysis, application data analysis, user data analysis, AI/ML by the information management server  104  so as to generate necessary information presentable on a user screen in the form of the micro-content blocks  116  by the information management server  104 . 
     The recorded or monitored context sensitive, application sensitive, or user sensitive information (together referred to as contextual data or information or context sensitive data or information or contextually sensitive data or information) by the front-end context monitoring appliance  310  may be supplied to the information management server  104  for further processing of the information and generating and delivering the micro-content blocks  116  to the user device  306  as discussed above. The contextual data monitored by the front-end context monitoring appliance  310  may change with time and may also be different for different tasks. 
     In some embodiments, the computerized data collection wireless appliance  208  may be configured to perform certain data collection tasks within the network  110  digitally. In an example, the computerized data collection wireless appliance  208  may be permitted to generate and/or collect data from various devices such as medical devices, web pages, local data repositories, etc. together referred to as the information sources  106  that operate within the network  110 . The computerized data collection wireless appliance  208  may be configured to collect, store, and digitally manage data at the information management server level that is extracted or collected from the information sources  106 . 
     The network  110  may broadly represent one or more LANs, WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellular technologies, etc.), global internet, and/or networks using any of wired, wireless, terrestrial microwave, or satellite links, and may include the public Internet. 
     In  FIG.  2   , a network layer  236  may be provided that may use a push gateway system  238  and/or a pull gateway system  240  to collect the data from the information sources  106 . In the pull gateway system  240 , the network layer  236  may be permitted to send or ask queries to the information sources  106 . In response to the queries, a computer-executable file containing information may be pulled in by the network layer  236  from the information sources  106 . In the push gateway system  238 , the network layer  236  may monitor various data elements at the information sources  106  and the information sources  106  may push relevant data in the form of the computer-executable files into the network layer  236  to get transferred to a central database or a local data reservoir  226 . 
     The data collected from the information sources  106  may be stored in the local data reservoir  226  either for a small period of time just before it gets processed by the processor  202  or for a longer time such as a retention period as necessary. 
     The devices or machines that may generate the digital data extracted by the computerized data collection wireless appliance  208  or its components (exporters) thereof may include such as medical devices, FHIR-capable systems (EHRs), HL7-capable systems (EHRs or electronic health record systems), Non-HL7/FHIR EHRs, etc., source databases, safety/error reporting systems, global web pages, social media posts, local data repositories and the like. 
       FIG.  4   , with reference to  FIGS.  1  through  3   , illustrates an exemplary blockchain-configured ecosystem architecture  400  containing one or more components of the system  102  and also contain additional components so as to allow integrity of transactions and the digital data (including the information blocks and the micro-content blocks) shared/processed during the transfer or storage as discussed above in the document. The blockchain-configured ecosystem architecture  400  may provide a crowdsourced integrity network for storing the data accessed or extracted or transformed for sharing or storing across the network  110  instead of locally stored information by different participants or databases that may be tampered with. 
     The ecosystem architecture  400  may be blockchain-configured involving various blockchain devices. For example, the information management server  104  may interact with a blockchain device  402  through a plurality of blockchain configured distributed access points  404 . A network that facilitates interaction across all components may be a blockchain integrity network. The blockchain network may build trust among the various participants or entities or systems or components thereof and their associated computing terminals or devices even if the devices/terminals or machines etc. may not “know” one another. The blockchain network may allow connections and transactions and recording and sharing of the data, information blocks, micro-content blocks, and various codes/token generated during an entire transaction including service tokens and authorization tokens in a trusted mode. A record of transactions and sharing and data from various terminals/devices stored on the blockchain in the form of computer-executable distributed ledgers  406  may provide proof to command the necessary trust among the terminals/devices (such as those associated with various participants/nodes etc. without limitations) to cooperate through a peer-to-peer or peer-to-client distributed digital ledger technology system. The ecosystem architecture  400  may include a distributed trusted ledgers system  414  containing the distributed blockchain ledgers  406  associated with a plurality of computing terminals and devices such that each ledger stores a copy of computer-executable files  416  containing the context inputs, context patterns, micro-content blocks, information blocks, information extracted from the information sources  106 , and various other details corresponding to the tasks such as the workflow task  114  and the trust notes for defining security and trust among the computing terminals and devices across the network so that each computing terminal trusts the other computing terminal through the blockchain. The distributed ledgers system  414  enables coding of rules-based contracts that execute when specified conditions are met. The distributed ledgers  406  make it easier to create cost-efficient networks where any device or any evidence associated with a task execution or transaction may be tracked, without requiring a central point of control. 
     The various computing terminals or devices in the network serve as distributed peer-to-peer nodes and connections. The information management server  104  and its components thereof may be configured to perform the task of processing the context inputs and the information blocks further through the blockchain network based on the rules as defined and discussed herein. Each terminal/device/node in the ecosystem architecture  400 , etc. may receive a copy of the blockchain which may get downloaded automatically upon joining the blockchain integrity network. Every permissioned node or the device in the network is an administrator of the blockchain, and may join the network voluntarily so that the network is decentralized. 
     The blockchain may eliminate the risks that come with data being held centrally by storing data across the network which may include the computer-executable files  416  containing the information blocks, context inputs, context patterns, etc. and/or the various tokens/codes including transaction codes. The blockchain security use encryption technology and validation mechanisms for security and integrity verification. The security may be enabled through public and private keys. A public key may define a user&#39;s address on the blockchain. The private key may give its owner an access to various digital assets in the network. 
     In an embodiment, the distributed ledgers  406  may enable coding of smart contracts (with the use of such as smart contract systems) that will execute when specified conditions are met. These smart contracts may protect various information pieces associated with the service deliveries and other transactions and data processing/storage and eliminate the risk of files copying and redistribution without protecting privacy rights. 
     The blockchain-configured ecosystem architecture  400  may provide a private view for the various devices and the entities operating in the network through the private data store  418  so that each permissioned device such as the information management server  104  may privately access the computer-executable files  416  associated with a task based on various policies such as based on their respective identities. The information management server  104  may access the computer-executable files  416  through the dedicated private data store  418  available through the plurality of distributed blockchain-configured access points  404 , which may be enabled in the form of distributed blocks as shown in  FIG.  4   , with each block providing the ability to access the features of the blockchain-configured ecosystem architecture  400  by different terminals and devices at the same time based on defined and granted access rights. 
     The private data store  418  may provide a virtual storage to facilitate interaction, information exchange, reviewing, and presentation of the computer-executable files  416 . For example, the private data store  418  may allow a virtual storage and presentation of only limited executable files or portions of the executable files for access by particular entities or participants in accordance with permissions granted for reviewing. The private data store  418  may be configured to auto-hash review interactions at any required interval. This compartmentalization of the computer-executable files  416  ensures that the computer-executable files  416  are secured and private as per access rights authorized to the nodes. The data presented on the private data store  418  of the blockchain serves as a secure way to ensure that the private data store  418  is in sync with any permissioned access. 
     In an embodiment, the blockchain-configured digital ecosystem architecture  400  may provide a federated blockchain comprising of several entities/participants (including the user) and their associated computers and devices (such as the user device  306 ) and sensors that jointly interact to process transfers of data through a trusted, secured and distributed network of the blockchain-configured access points  404 . 
     The various components described herein and/or illustrated in the figures may be embodied as hardware-enabled modules and may be a plurality of overlapping or independent electronic circuits, devices, and discrete elements packaged onto a circuit board to provide data and signal processing functionality within a computer. An example might be a comparator, inverter, or flip-flop, which could include a plurality of transistors and other supporting devices and circuit elements. The modules that include electronic circuits process computer logic instructions capable of providing digital and/or analog signals for performing various functions as described herein. The various functions can further be embodied and physically saved as any of data structures, data paths, data objects, data object models, object files, database components. For example, the data objects could include a digital packet of structured data. Example data structures may include any of an array, tuple, map, union, variant, set, graph, tree, node, and an object, which may be stored and retrieved by computer memory and may be managed by processors, compilers, and other computer hardware components. The data paths can be part of a computer CPU that performs operations and calculations as instructed by the computer logic instructions. The data paths could include digital electronic circuits, multipliers, registers, and buses capable of performing data processing operations and arithmetic operations (e.g., Add, Subtract, etc.), bitwise logical operations (AND, OR, XOR, etc.), bit shift operations (e.g., arithmetic, logical, rotate, etc.), complex operations (e.g., using single clock calculations, sequential calculations, iterative calculations, etc.). The data objects may be physical locations in computer memory and can be a variable, a data structure, or a function. Some examples of the modules include relational databases (e.g., such as Oracle® relational databases), and the data objects can be a table or column, for example. Other examples include specialized objects, distributed objects, object-oriented programming objects, and semantic web objects. The data object models can be an application programming interface for creating HyperText Markup Language (HTML) and Extensible Markup Language (XML) electronic documents. The models can be any of a tree, graph, container, list, map, queue, set, stack, and variations thereof, according to some examples. The data object files can be created by compilers and assemblers and contain generated binary code and data for a source file. The database components can include any of tables, indexes, views, stored procedures, and triggers. 
     In an example, the embodiments herein can provide a computer program product configured to include a pre-configured set of instructions, which when performed, can result in actions as stated in conjunction with various figures herein. In an example, the pre-configured set of instructions can be stored on a tangible non-transitory computer readable medium. In an example, the tangible non-transitory computer readable medium can be configured to include the set of instructions, which when performed by a device, can cause the device to perform acts similar to the ones described here. 
     The embodiments herein may also include tangible and/or non-transitory computer-readable storage media for carrying or having computer-executable instructions or data structures stored thereon. Such non-transitory computer readable storage media can be any available media that can be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as discussed above. By way of example, and not limitation, such non-transitory computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions, data structures, or processor chip design. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or combination thereof) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable media. 
     Computer-executable instructions include, for example, instructions and data which cause a special purpose computer or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps. 
     The techniques provided by the embodiments herein may be implemented on an integrated circuit chip (not shown). The chip design is created in a graphical computer programming language, and stored in a computer storage medium (such as a disk, tape, physical hard drive, or virtual hard drive such as in a storage access network. If the designer does not fabricate chips or the photolithographic masks used to fabricate chips, the designer transmits the resulting design by physical means (e.g., by providing a copy of the storage medium storing the design) or electronically (e.g., through the Internet) to such entities, directly or indirectly. The stored design is then converted into the appropriate format (e.g., GDSII) for the fabrication of photolithographic masks, which typically include multiple copies of the chip design in question that are to be formed on a wafer. The photolithographic masks are utilized to define areas of the wafer (and/or the layers thereon) to be etched or otherwise processed. 
     The resulting integrated circuit chips can be distributed by the fabricator in raw wafer form (that is, as a single wafer that has multiple unpackaged chips), as a bare die, or in a packaged form. In the latter case the chip is mounted in a single chip package (such as a plastic carrier, with leads that are affixed to a motherboard or other higher level carrier) or in a multichip package (such as a ceramic carrier that has either or both surface interconnections or buried interconnections). In any case the chip is then integrated with other chips, discrete circuit elements, and/or other signal processing devices as part of either (a) an intermediate product, such as a motherboard, or (b) an end product. The end product can be any product that includes integrated circuit chips, ranging from toys and other low-end applications to advanced computer products having a display, a keyboard or other input device, and a central processor. 
     Furthermore, the embodiments herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk—read only memory (CD-ROM), compact disk—read/write (CD-R/W) and DVD. 
     A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. 
     Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters. 
     A representative hardware environment for practicing the embodiments herein is depicted in  FIG.  5   , with reference to  FIGS.  1  through  4   . This schematic drawing illustrates a hardware configuration of an information handling/computer system  500  in accordance with the embodiments herein. The system  500  comprises at least one processor or central processing unit (CPU)  10 . The CPUs  10  are interconnected via system bus  12  to various devices such as a random access memory (RAM)  14 , read-only memory (ROM)  16 , and an input/output (I/O) adapter  18 . The I/O adapter  18  can connect to peripheral devices, such as disk units  11  and tape drives  13 , or other program storage devices that are readable by the system. The system  500  can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein. The system  500  further includes a user interface adapter  19  that connects a keyboard  15 , mouse  17 , speaker  24 , microphone  22 , and/or other user interface devices such as a touch screen device (not shown) to the bus  12  to gather user input. Additionally, a communication adapter  20  connects the bus  12  to a data processing network, and a display adapter  21  connects the bus  12  to a display device  23  which may be embodied as an output device such as a monitor, printer, or transmitter, for example. Further, a transceiver  26 , a signal comparator  27 , and a signal converter  28  may be connected with the bus  12  for processing, transmission, receipt, comparison, and conversion of electric or electronic signals. 
     The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims.