Predicting trade secret case outcomes using argument schemes and learned quantitative value effect tradeoffs

Abstract

This paper presents the Value Judgment Formalism and its experimental implementation in the VJAP system, which is capable of arguing about, and predicting outcomes of, a set of trade secret misappropriation cases. VJAP creates an argument graph for each case using argument schemes and a representation of values underlying trade secret law and effects of facts on these values. It balances effects on values in each case and analogizes it to tradeoffs in precedents. It predicts case outcomes using a confidence measure computed from the graph and generates textual legal arguments justifying its predictions. The confidence propagation uses quantitative weights learned from past cases using an iterative optimization method. Prediction performance on a limited dataset is competitive with common machine learning models. The results and VJAP's behavior are discussed in detail.