Apparatus for proportional calculation regarding non-fungible tokens

Abstract

An apparatus for proportional calculation regarding non-fungible tokens is presented. The apparatus includes at least a processor and a memory. The memory is communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a non-fungible token representing a creative work. The memory further instructs the processor to calculate a contribution metric of the creative work and post a digitally signed assertion to an immutable sequential listing as a function of the contribution metric.

What is claimed is: 
     
         1 . An apparatus for proportional calculation regarding non-fungible tokens, the apparatus comprising:
 at least a processor; and   a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
 receive a non-fungible token that is representative of a creative work; 
 calculate a contribution metric of the creative work; and 
 post a digitally signed assertion to an immutable sequential listing as a function of the contribution metric. 
   
     
     
         2 . The apparatus of  claim 1 , wherein memory contains instructions further configuring the processor to embed NFT metadata into the non-fungible token, wherein the NFT metadata is representative of the contribution metric into the non-fungible token. 
     
     
         3 . The apparatus of  claim 1 , wherein the contribution metric comprises a value denoting an amount borrowed from the creative work. 
     
     
         4 . The apparatus of  claim 1 , wherein calculating the contribution metric comprises calculating the contribution metric as a function of a recursion. 
     
     
         5 . The apparatus of  claim 1 , wherein memory contains instructions further configuring the processor to calculate a creative value of the creative work as a function of the level of originality within the creative work. 
     
     
         6 . The apparatus of  claim 1 , wherein the non-fungible token comprises a plurality of non-fungible tokens, wherein the plurality of non-fungible tokens comprises:
 a primary non-fungible token representing a primary creative work; and   a secondary non-fungible token representing a secondary work, wherein the secondary creative work comprises at least a portion of the primary creative work.   
     
     
         7 . The apparatus of  claim 6 , wherein the primary creative work is included in the secondary creative work using at least one intervening creative work. 
     
     
         8 . The apparatus of  claim 7 , wherein calculating the contribution metric comprises generating the contribution metric as a function the at least an intervening creative work. 
     
     
         9 . The apparatus of  claim 1 , wherein memory contains instructions further configuring the processor to generate a quantitative requirement as a function of the contribution metric. 
     
     
         10 . The apparatus of  claim 9 , wherein generating the qualitative requirement comprises:
 training a quantitative machine-learning process using a quantitative requirement training set, wherein the requirement training set is configured to correlate a contribution metric as an input to a qualitative requirement as an output; and   outputting the qualitative requirement as a function of the quantitative machine-learning process.   
     
     
         11 . A method for proportional calculation regarding non-fungible tokens, the method comprising:
 receiving, by at least a processor, a non-fungible token that is representative of a creative work;   calculating, by the at least a processor, a contribution metric of the creative work; and   deploying, by the at least a processor, a digitally signed assertion to an immutable sequential listing as a function of the contribution metric.   
     
     
         12 . The method of  claim 11 , wherein method further comprises embedding, using at least a processor, NFT metadata into the non-fungible token, wherein the NFT metadata is representative of the contribution metric into the non-fungible token. 
     
     
         13 . The method of  claim 11 , wherein the contribution metric comprises a value denoting an amount borrowed from the creative work. 
     
     
         14 . The method of  claim 11 , wherein calculating the contribution metric comprises calculating the contribution metric as a function of a recursion. 
     
     
         15 . The method of  claim 11 , wherein the method further comprises calculating, using the at least a processor, a creative value of the creative work as a function of the level of originality within the creative work. 
     
     
         16 . The method of  claim 11 , wherein the non-fungible token comprises a plurality of non-fungible tokens, wherein the plurality of non-fungible tokens comprises:
 a primary non-fungible token representing a primary creative work; and   a secondary non-fungible token representing a secondary work, wherein the secondary creative work comprises at least a portion of the primary creative work.   
     
     
         17 . The method of  claim 16 , wherein the primary creative work is included in the secondary creative work using at least one intervening creative work. 
     
     
         18 . The method of  claim 17 , wherein calculating the contribution metric comprises generating the contribution metric as a function the at least an intervening creative work. 
     
     
         19 . The method of  claim 11 , wherein the method further comprises generating, using the at least a processor, a quantitative requirement as a function of the contribution metric. 
     
     
         20 . The method of  claim 19 , wherein generating the qualitative requirement comprises:
 training a quantitative machine-learning process using a quantitative requirement training set, wherein the requirement training set is configured to correlate a contribution metric as an input to a qualitative requirement as an output; and   outputting the qualitative requirement as a function of the quantitative machine-learning process.