Measuring the originality of intellectual property assets based on machine learning outputs

TL;DR

This paper introduces a novel method to quantify the originality of intellectual property assets by analyzing the distances between assets and their comparands using concepts from entropy and surprisal, enabling simple and effective originality estimation.

Contribution

It formulates originality as a ratio of average distances based on machine learning-derived metrics, providing a new quantitative approach for assessing IP originality.

Findings

Originality can be estimated using distance ratios from machine learning outputs.

The method applies to diverse assets like emojis, typefaces, paintings, and titles.

A simple, bounded formula for originality based on pairwise distances is proposed.

Abstract

Originality criteria are frequently used to compare assets and, in particular, 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, the originality function is defined according to the surprisal associated with a given asset. Creative assets can be justifiably compared to particles that repel each other via an electrostatic-like pair potential. This allows a very simple, suitably bounded formula to be obtained, in which the originality of an asset writes as the ratio of a reference energy to an interaction energy imparted to that asset. In particular, the originality of an asset can be expressed as a ratio of two average distances, i.e., 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 unsupervised machine learning techniques or other distance computation algorithms. Application is made to various types of assets, including emojis, typeface designs, paintings, and novel titles.