An automated method for identifying TRIZ evolution trends from patents

Trend analysis of the Theory of Inventive Problem Solving (Russian acronym: TRIZ) identifies the evolutionary status of systems to seek directions for further improvement of technology by relating properties and functions obtained from patents to TRIZ trends. The property, which is a specific characteristic of a system, is usually described using adjectives; the function, which is an action that changes a feature of an object, is usually described using verbs. Methods exist to facilitate identification of TRIZ trends, but they rely heavily on human intervention to identify specific trends and trend phases. Therefore, this paper proposes a method that automates identification of TRIZ trends. The proposed method consists of (1) extracting binary relations of the ‘adjective + noun’ or ‘verb + noun’ forms from patents using natural language processing, (2) defining a ‘reasons for jumps’ rule base that arranges trend-specific binary relations for trend identification, and (3) determining specific trends and trend phases by measuring semantic sentence similarity between the binary relations from patents and the binary relations in the rule base. The final output of the method depicts the evolutionary potential as a normalized radar plot, which can be used as input for technology forecasting based on TRIZ trends.