Measuring the originality of intellectual property assets based on machine learning outputs
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Originality criteria are frequently used to assess the validity of intellectual property (IP) rights, such as copyright and design rights. In this work, the originality of an asset is formulated as a function of the distances between this asset and its comparands, using concepts of maximum entropy and surprisal analysis. Namely, this function is defined as the reciprocal of the surprisal associated with a given asset. Creative assets can justifiably be compared to particles that repel each other. This allows a very simple formula to be obtained, in which the originality of a given asset writes as the ratio of a reference energy to an interaction energy imparted to that asset. In particular, using an electrostatic-like pair potential makes it possible to rewrite the originality function as the ratio of two average distances, i.e., as the harmonic mean of the distances from the given asset to its comparands divided by the harmonic mean of the distances between the sole comparands. Thus, the originality of objects such as IP assets can be simply estimated based on distances computed according to vectors extracted thanks to unsupervised machine learning techniques or other algorithms. Application is made to various types of IP assets, including emojis, typeface designs, paintings, and novel titles.
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