Establishing a tokenized lesson package for a virtual environment

Abstract

A method includes a computing device of a computing infrastructure determining a plurality of effectiveness metrics for learning objects and selecting a set of learning objects based on the effectiveness metrics to produce a lesson package. The method further includes interpreting a request from a learning object owner computing device to make available for licensing a set of learning objects of the lesson package to produce an object basics record of a smart contract for the set of learning objects. The method further includes establishing, with an accreditation authority computing device, accreditation of the lesson package based on the object basics record. When the object basics record is accredited, the method further includes establishing available license terms of the smart contract for the lesson package and causing generation of a non-fungible token associated with the smart contract in an object distributed ledger.

What is claimed is: 
     
         1 . A computer-implemented method of using a computing infrastructure for utilizing an object distributed ledger, the method comprises:
 determining, by a marketplace computing device of the computing infrastructure, a plurality of effectiveness metrics for a plurality of learning objects associated with a common topic, wherein each learning object includes a unique set of descriptive asset digital video frames that portray an aspect of a corresponding set of knowledge bullet-points points of the common topic for the learning object;   selecting, by the marketplace computing device, a set of learning objects of the plurality of learning objects based on the plurality of effectiveness metrics to produce a lesson package;   interpreting, by the marketplace computing device, a request from a learning object owner computing device of the computing infrastructure to make available for licensing a set of learning objects of the lesson package to produce an object basics record of a smart contract for the set of learning objects, wherein the learning object owner computing device is distinct from the marketplace computing device, wherein the object basics record includes a learning object set identifier of the set of learning objects, an identifier of the lesson package, the effectiveness metrics for the set of learning objects, and at least one learning object owner identifier associated with the set of learning objects;   establishing, by the marketplace computing device with an accreditation authority computing device of the computing infrastructure, accreditation of the lesson package based on the object basics record; and   when the object basics record is accredited:
 establishing, by the marketplace computing device, available license terms of the smart contract for the lesson package, and 
 causing, by the marketplace computing device, generation of a non-fungible token associated with the smart contract in the object distributed ledger. 
   
     
     
         2 . The method of  claim 1 , wherein the determining the plurality of effectiveness metrics for the plurality of learning objects associated with the common topic comprises one or more of:
 identifying a retention test score associated with a first learning object of the plurality of learning objects to produce a retention metric for the first learning object;   identifying a first learning entity rating of the first learning object to produce a first user rating metric for the first learning object;   generating a group rating metric for the first learning object based on the first learning entity rating of the first learning object and a second learning entity rating of the first learning object; and   comparing a learning objective of a third learning object of the plurality of learning objects to a learning objective of the lesson package to produce a fit metric for the third learning object.   
     
     
         3 . The method of  claim 2 , wherein the selecting the set of learning objects of the plurality of learning objects based on the plurality of effectiveness metrics to produce the lesson package comprises at least one of:
 selecting the first learning object when the retention metric for the first learning object is greater than a retention metric minimum threshold level;   selecting the first learning object when the first user rating metric for the first learning object is greater than a user rating metric minimum threshold level;   selecting the first learning object when the group rating metric for the first learning object is greater than a group rating metric minimum threshold level; and   selecting the third learning object when the fit metric for the third learning object is greater than a fit metric minimum threshold level.   
     
     
         4 . The method of  claim 1 , wherein the interpreting the request from the learning object owner computing device to make available for licensing the set of learning objects of the lesson package to produce the object basics record of the smart contract for the set of learning objects comprises one or more of:
 identifying a set of learning object identifiers for the set of learning objects;   generating the learning object set identifier of the set of learning objects based on the set of learning object identifiers;   identifying a set of learning object owner identifiers associated with the set of learning objects, wherein the set of learning object owner identifiers includes the at least one learning object owner identifier;   determining a set of training areas for the set of learning objects, wherein each training area is associated with one or more learning objects of the set of learning objects;   identifying, for each learning object of the set of learning objects, a corresponding accreditation authority computing device; and   identifying, for each learning object of the set of learning objects, a valid timeframe of the learning object.   
     
     
         5 . The method of  claim 1 , wherein the establishing the accreditation of the lesson package based on the object basics record comprises:
 identifying the accreditation authority computing device based on a first identified corresponding accreditation authority of the object basics record for a first learning object of the set of learning objects;   exchanging accreditation information with the accreditation authority computing device for the first learning object; and   indicating that the object basics record is accredited for the first learning object when the accreditation information is substantially the same as the object basics record for the first learning object.   
     
     
         6 . The method of  claim 1 , wherein the causing the generation of the non-fungible token associated with the smart contract in the object distributed ledger comprises:
 determining whether to indirectly or directly update the object distributed ledger;   when indirectly updating the object distributed ledger:
 issuing a non-fungible token generation request to an object ledger computing device of the computing infrastructure serving as a blockchain node of the object distributed ledger, wherein the non-fungible token generation request includes the smart contract; and 
   when directly updating the object distributed ledger:
 obtaining a copy of the object distributed ledger, 
 hashing the smart contract utilizing a receiving public key of the object distributed ledger to produce a next transaction hash value, 
 encrypting the next transaction hash value utilizing a private key of the marketplace computing device to produce a next transaction signature, 
 generating a next block of a blockchain of the object distributed ledger to include the smart contract and the next transaction signature, and 
 causing inclusion of the next block as the non-fungible token in the object distributed ledger. 
   
     
     
         7 . A marketplace computing device of a computing infrastructure, the marketplace computing device comprises:
 an interface;   a local memory; and   a processor operably coupled to the interface and the local memory, wherein the processor performs functions to:
 determine a plurality of effectiveness metrics for a plurality of learning objects associated with a common topic, wherein each learning object includes a unique set of descriptive asset digital video frames that portray an aspect of a corresponding set of knowledge bullet-points points of the common topic for the learning object; 
 select a set of learning objects of the plurality of learning objects based on the plurality of effectiveness metrics to produce a lesson package; 
 interpret a request from a learning object owner computing device of the computing infrastructure to make available for licensing a set of learning objects of the lesson package to produce an object basics record of a smart contract for the set of learning objects, wherein the learning object owner computing device is distinct from the marketplace computing device, wherein the object basics record includes a learning object set identifier of the set of learning objects, an identifier of the lesson package, the effectiveness metrics for the set of learning objects, and at least one learning object owner identifier associated with the set of learning objects; 
 establish, with an accreditation authority computing device of the computing infrastructure, accreditation of the lesson package based on the object basics record; and 
 when the object basics record is accredited:
 establish available license terms of the smart contract for the lesson package, and cause generation of a non-fungible token associated with the smart contract in an object distributed ledger. 
 
   
     
     
         8 . The marketplace computing device of  claim 7 , wherein the processor performs functions to determine the plurality of effectiveness metrics for the plurality of learning objects associated with the common topic by one or more of:
 identifying a retention test score associated with a first learning object of the plurality of learning objects to produce a retention metric for the first learning object;   identifying a first learning entity rating of the first learning object to produce a first user rating metric for the first learning object;   generating a group rating metric for the first learning object based on the first learning entity rating of the first learning object and a second learning entity rating of the first learning object; and   comparing a learning objective of a third learning object of the plurality of learning objects to a learning objective of the lesson package to produce a fit metric for the third learning object.   
     
     
         9 . The marketplace computing device of  claim 8 , wherein the processor performs functions to select the set of learning objects of the plurality of learning objects based on the plurality of effectiveness metrics to produce the lesson package by at least one of:
 selecting the first learning object when the retention metric for the first learning object is greater than a retention metric minimum threshold level;   selecting the first learning object when the first user rating metric for the first learning object is greater than a user rating metric minimum threshold level;   selecting the first learning object when the group rating metric for the first learning object is greater than a group rating metric minimum threshold level; and   selecting the third learning object when the fit metric for the third learning object is greater than a fit metric minimum threshold level.   
     
     
         10 . The marketplace computing device of  claim 7 , wherein the processor performs functions to interpret the request from the learning object owner computing device to make available for licensing the set of learning objects of the lesson package to produce the object basics record of the smart contract for the set of learning objects by one or more of:
 identifying a set of learning object identifiers for the set of learning objects;   generating the learning object set identifier of the set of learning objects based on the set of learning object identifiers;   identifying a set of learning object owner identifiers associated with the set of learning objects, wherein the set of learning object owner identifiers includes the at least one learning object owner identifier;   determining a set of training areas for the set of learning objects, wherein each training area is associated with one or more learning objects of the set of learning objects;   identifying, for each learning object of the set of learning objects, a corresponding accreditation authority computing device; and   identifying, for each learning object of the set of learning objects, a valid timeframe of the learning object.   
     
     
         11 . The marketplace computing device of  claim 7 , wherein the processor performs functions to establish the accreditation of the lesson package based on the object basics record by:
 identifying the accreditation authority computing device based on a first identified corresponding accreditation authority of the object basics record for a first learning object of the set of learning objects;   exchanging accreditation information with the accreditation authority computing device for the first learning object; and   indicating that the object basics record is accredited for the first learning object when the accreditation information is substantially the same as the object basics record for the first learning object.   
     
     
         12 . The marketplace computing device of  claim 7 , wherein the processor performs functions to cause the generation of the non-fungible token associated with the smart contract in the object distributed ledger by:
 determining whether to indirectly or directly update the object distributed ledger;   when indirectly updating the object distributed ledger:
 issuing a non-fungible token generation request to an object ledger computing device of the computing infrastructure serving as a blockchain node of the object distributed ledger, wherein the non-fungible token generation request includes the smart contract; and 
   when directly updating the object distributed ledger:
 obtaining a copy of the object distributed ledger, 
 hashing the smart contract utilizing a receiving public key of the object distributed ledger to produce a next transaction hash value, 
 encrypting the next transaction hash value utilizing a private key of the marketplace computing device to produce a next transaction signature, 
 generating a next block of a blockchain of the object distributed ledger to include the smart contract and the next transaction signature, and 
 causing inclusion of the next block as the non-fungible token in the object distributed ledger. 
   
     
     
         13 . A non-transitory computer readable memory comprises:
 a first memory element that stores operational instructions that, when executed by a processing module, causes the processing module to:
 determine a plurality of effectiveness metrics for a plurality of learning objects associated with a common topic, wherein each learning object includes a unique set of descriptive asset digital video frames that portray an aspect of a corresponding set of knowledge bullet-points points of the common topic for the learning object; 
   a second memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 select a set of learning objects of the plurality of learning objects based on the plurality of effectiveness metrics to produce a lesson package; 
   a third memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 interpret a request from a learning object owner computing device of a computing infrastructure to make available for licensing a set of learning objects of the lesson package to produce an object basics record of a smart contract for the set of learning objects, wherein the learning object owner computing device is distinct from the processing module, wherein the object basics record includes a learning object set identifier of the set of learning objects, an identifier of the lesson package, the effectiveness metrics for the set of learning objects, and at least one learning object owner identifier associated with the set of learning objects; 
   a fourth memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 establish, with an accreditation authority computing device of the computing infrastructure, accreditation of the lesson package based on the object basics record; and 
   a fifth memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 when the object basics record is accredited:
 establish available license terms of the smart contract for the lesson package, and cause generation of a non-fungible token associated with the smart contract in an object distributed ledger. 
 
   
     
     
         14 . The non-transitory computer readable memory of  claim 13 , wherein the processing module functions to execute the operational instructions stored by the first memory element to cause the processing module to determine the plurality of effectiveness metrics for the plurality of learning objects associated with the common topic by one or more of:
 identifying a retention test score associated with a first learning object of the plurality of learning objects to produce a retention metric for the first learning object;   identifying a first learning entity rating of the first learning object to produce a first user rating metric for the first learning object;   generating a group rating metric for the first learning object based on the first learning entity rating of the first learning object and a second learning entity rating of the first learning object; and   comparing a learning objective of a third learning object of the plurality of learning objects to a learning objective of the lesson package to produce a fit metric for the third learning object.   
     
     
         15 . The non-transitory computer readable memory of  claim 14 , wherein the processing module functions to execute the operational instructions stored by the second memory element to cause the processing module to select the set of learning objects of the plurality of learning objects based on the plurality of effectiveness metrics to produce the lesson package by at least one of:
 selecting the first learning object when the retention metric for the first learning object is greater than a retention metric minimum threshold level;   selecting the first learning object when the first user rating metric for the first learning object is greater than a user rating metric minimum threshold level;   selecting the first learning object when the group rating metric for the first learning object is greater than a group rating metric minimum threshold level; and   selecting the third learning object when the fit metric for the third learning object is greater than a fit metric minimum threshold level.   
     
     
         16 . The non-transitory computer readable memory of  claim 13 , wherein the processing module functions to execute the operational instructions stored by the third memory element to cause the processing module to interpret the request from the learning object owner computing device to make available for licensing the set of learning objects of the lesson package to produce the object basics record of the smart contract for the set of learning objects by one or more of:
 identifying a set of learning object identifiers for the set of learning objects;   generating the learning object set identifier of the set of learning objects based on the set of learning object identifiers;   identifying a set of learning object owner identifiers associated with the set of learning objects, wherein the set of learning object owner identifiers includes the at least one learning object owner identifier;   determining a set of training areas for the set of learning objects, wherein each training area is associated with one or more learning objects of the set of learning objects;   identifying, for each learning object of the set of learning objects, a corresponding accreditation authority computing device; and   identifying, for each learning object of the set of learning objects, a valid timeframe of the learning object.   
     
     
         17 . The non-transitory computer readable memory of  claim 13 , wherein the processing module functions to execute the operational instructions stored by the fourth memory element to cause the processing module to establish the accreditation of the lesson package based on the object basics record by:
 identifying the accreditation authority computing device based on a first identified corresponding accreditation authority of the object basics record for a first learning object of the set of learning objects;   exchanging accreditation information with the accreditation authority computing device for the first learning object; and   indicating that the object basics record is accredited for the first learning object when the accreditation information is substantially the same as the object basics record for the first learning object.   
     
     
         18 . The non-transitory computer readable memory of  claim 13 , wherein the processing module functions to execute the operational instructions stored by the fifth memory element to cause the processing module to cause the generation of the non-fungible token associated with the smart contract in the object distributed ledger by:
 determining whether to indirectly or directly update the object distributed ledger;   when indirectly updating the object distributed ledger:
 issuing a non-fungible token generation request to an object ledger computing device of the computing infrastructure serving as a blockchain node of the object distributed ledger, wherein the non-fungible token generation request includes the smart contract; and 
   when directly updating the object distributed ledger:
 obtaining a copy of the object distributed ledger, 
 hashing the smart contract utilizing a receiving public key of the object distributed ledger to produce a next transaction hash value, 
 encrypting the next transaction hash value utilizing a private key of the processing module to produce a next transaction signature, 
 generating a next block of a blockchain of the object distributed ledger to include the smart contract and the next transaction signature, and 
 causing inclusion of the next block as the non-fungible token in the object distributed ledger.