Pointers to effects are a group of TRIZ tools which helps the inventor to master a greater knowledge of scientific phenomena and laws, so to suggest him or her different directions to reach possibilities of solution. Pointers to geometric, chemical, and technological effects have been theorized, but only those to physical effects have ever had concrete developments at the research level and as commercial applications.
The aim of this work is twofold: on the one hand, to bring the pointer back to chemical effects (CE), recovering little-known texts that are difficult to find but also difficult to interpret, as they have never been translated from Russian. The other aim is to contextualize these tools in the light of the recent achievements of artificial intelligence technologies in the field of information retrieval. A combination of AI tools, as NER (Named Entity Recognition), RAG (Retrieval Augmented Generation) and LLM (Large Language Model) have been combined in order to identify chemical features from several chemical sources, to index documents in order to answer user’s questions, to interact with this Knowledge-Base by a chatbot and finally to generate a complete and standardized output.
A comparison is presented between recent commercial applications of AI and traditional pointers to CE from TRIZ literature. In this paper it is explained how the system works, which are the potentialities according to the AI technologies evolution and a comparative study between a SW infrastructure developed by the authors in collaboration with university spin-off software house and others current AI commercial players like GPT or Gemini based applications.
