Design Analysis for a Distributed Business Innovation System Employing Generated Expert Profiles, Matchmaking, and Blockchain Technology

Innovation ecosystems often face challenges such as inadequate coordination, insufficient protection of intellectual property, limited access to quality expertise, and inefficient matchmaking between innovators and experts. This paper provides an in-depth design analysis of SPARK-IT, a novel business innovation platform specifically addressing these challenges. The platform leverages advanced AI to precisely match innovators with suitable mentors, supported by a distributed web scraper that constructs expert profiles from reliable sources (e.g., LinkedIn and BrainMap). Data privacy and security are prioritized through robust encryption that restricts sensitive content exclusively to innovators and mentors, preventing unauthorized access even by platform administrators. Additionally, documents are stored encrypted on decentralized storage, with their cryptographic hashes anchored on blockchain to ensure transparency, traceability, non-repudiation, and immutability. To incentivize active participation, SPARK-IT utilizes a dual-token approach comprising reward and reputation tokens. The reward tokens, SparkCoins, are wrapped stablecoins with tangible monetary value, enabling seamless internal transactions and external exchanges. Finally, the paper discusses key design challenges and critical architectural trade-offs and evaluates the socio-economic impacts of implementing this innovative solution.