• Context
    Challenges of IP valuation and transfer from university research

    Universities seek to play an active role in helping their local ecosystems thrive in the emerging field of data science (artificial intelligence). They wish to contribute to value creation and support the growth of businesses while instilling a sense of stickiness to the local community. Using the licensing and transfer of research‑based IP as the foundation of this ecosystem brings up a number of questions:

    • What university IP management strategies are needed to provide the maximal impact and ensure this ecosystem is sustainable?
    • Should rules of access be developed in favour of local businesses and start‑ups?
    • How can chains of title be clearly defined for SMEs and start‑ups to protect their IP assets, give them a competitive edge, and protect them in the context of staff turnover, IP infringement, and open innovation with large corporations?
    • How can usage guidelines be developed to ensure that IP is used only for moral, ethical purposes approved by universities and not for malicious, criminal, discriminatory, military, or other unacceptable ends?
    • What constructive approaches would promote training, continuity of research, and fairness between inventors and ensure the recognition of student contributions?
    • What should be the focus of licences and potential products?
    • What distinctions need to be made between existing technologies and sponsored research?

    SMEs and start-ups face numerous challenges when it comes to integrating data science and artificial intelligence to enhance their business models, boost competitiveness, or develop new technologies. Establishing a solid IP portfolio is crucial to building value, developing partnerships, maintaining a competitive advantage, and securing funding. In addition to a shortage of qualified labour, labour mobility, and the pressure to develop immediate solutions, businesses must contend with mixed messages concerning the relevance of patents and the risks and benefits associated with using open source and relying on trade secrets. Is it shrewd or naïve to deliver data via the platforms, tools, and services of major suppliers such as Google, Amazon, and Microsoft? How can SEMs and start-ups benefit from massive government investment in university research despite lacking the stature of large organizations? Not enough is known about the financial leverage and catalysts for licensing university-developed enabling technologies of partnering with universities on R & D projects. A special track featured by TechnoMontreal and Prompt will cover financing opportunities to support development of your strategy in AI.

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