Inteligência Artificial e de Dados para impulso à inovação: do diagnóstico de potencialidade à transferência tecnológica com IA no PPGBIOTEC/UFAM

Abstract

This work employs an integrated set of data intelligence methods to support and promote Technology Transfer (TT) at the Federal University of Amazonas (UFAM), focusing on the Graduate Program in Biotechnology (PPGBIOTEC) and its insertion in the Amazonian bioeconomy. Following the Sequential Framework, a research method structured as a sequence of analyses aimed at identifying facts and signaling directions for the promotion of innovation, the first stage utilizes Data Envelopment Analysis (DEA) to produce a diagnosis of the relative efficiency of UFAM, as a Scientific, Technological and Innovation Institution (ICT), in the conversion of inputs into technological assets. Then, Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques are applied to the set of theses and dissertations of the PPGBIOTEC (TEDE/UFAM), automatically classifying them according to the codes of the International Patent Classification (CIP) of the Biotec-BR set and their respective Technological Maturity Levels (TRL). These procedures, employed on a strict methodological basis, uncover innovation potentials, thematic trends and opportunities for technological prospecting. The results reveal that 21.2% of the research analyzed has patenting potential, a significant volume that contrasts with the absence of deposits in the PATENTSCOPE database. The predominance of studies at TRL levels 1–3 (94.6%) confirms PPGBIOTEC's vocation for frontier research, while the 5.4% that achieve TRL 4+ show efforts at technological validation. The institutional efficiency analysis indicates that UFAM has room to expand its output results, having as feasible benchmarks a Virtual DMU composed of comparable Higher Education Institution (HEI). The integration of this evidence, through the Sequential Framework, allowed the identification of strategic assets, mapping thematic trends and organizing relevant information in a conceptual model of Interactive Dashboard, which synthesizes data from technological surveillance and open cooperation in the context of UFAM.