Measuring the originality of intellectual property assets based on estimated inter-asset distances

Abstract

Originality criteria are frequently used to assess the validity of intellectual property (IP) rights such as copyright and design rights. However, the concept of originality is not universally nor unambiguously defined in the IP arena. 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. A simple and suitably bounded formula is obtained, in which the originality of an asset writes as a ratio of two average distances: the harmonic mean of the distances from this asset to its comparands divided by the harmonic mean of the distances between the sole comparands. Accordingly, the originality of objects such as IP assets can be simply estimated based on distances computed thanks to machine learning extraction techniques or other distance computation algorithms. Application is made to various types of IP assets, including emojis, mobile phones, typeface designs, paintings, and novel titles. The results obtained illustrate how automatic originality assessments can be used to gather additional facts, which may be taken into consideration by IP professionals when assessing the validity of IP rights such as copyrighted works and design rights.