Dynamic and Semantic Database of Effects for Technology Transfer

A dynamic and semantic database of effects has been developed. Scientific effects databases are collections of effects that can be used in technology transfer for identifying alternative way to perform a same function. Usually effects consists in static lists that searchers have to manually browse imagining which effects are useful for their applications. In TRIZ community, more advanced tools (function-oriented), as the pointer to effects, have been developed. These tools link physical effects to a narrow set of functions contained in the database. These functions not always match perfectly the function required by searchers and in some cases the matching is inexistent. Another drawback is that also these databases are static. Indeed, the links between effects and functions are defined a priori and they are independent from the application field in order to gain a general validity. Pre-defined links can lead to the suggestions of physical effect unusable for a specific field or in the worst cases usable effects can be missing. To overcome limitations, the authors have developed a dynamic pointer to physical effects that integrates a new library of effects and a semantic search engine called Kompat. It works with any function (pair verb-object). The link between function and physical effect is created by searching on technical or scientific literature, such as patents. The semantic engine automatically extracts all documents containing the physical effect to perform the selected function, and create a list of Effects linked to the initial function. The number of documents is also an index of the feasibility and the degree of maturity of the technology using that effect. Technology transfer is done comparing the list of effects already extracted in our initial search domain, with effects coming from a larger pool of documents; these effects are not yet known in starting domain but already used in others for performing the same function. This article presents an application of the proposed dynamic effects database to a case study of eye inspection with the goal of identifying new promising technologies for measuring the temperature of the cornea.