• Licensing in Artificial Intelligence

    and Data Sciences








  • The day will articulate around 3 axes

    1. Understanding and licensing IP in the field of data science
    2. Universities: Developing an IP licensing strategy that promotes a sustainable ecosystem
    3. SMEs and start-ups: Working with universities to build your IP. Identify sources of funding for R&D

  • Symposium Objectives


    For all participants:

    • Discover the key elements of a project in artificial intelligence
    • Become more knowledgeable about IP in the field of data science
    • Learn about potential IP protection strategies
    • Recognize how and when to use each strategy

    For universities:

    • Become familiar with different types of licensing strategies
    • Understand the issues faced by members of the ecosystem
    • Examine simple and effective licensing agreement processes
    • Determine policy guidelines for negotiation and IP management

    For SMEs and Start-ups:

    • Understand how to engage with universities when licensing technologies or signing research contracts
    • Learn about benefits and available financial levers
    • Identify available university technologies and potentially valuable expertise
    • Discover financing options for your R&D


    For investors in tech companies:

    • Discover avenues for enhancing IP assets of portfolio companies
    • Understand IP issues during investment evaluation
    • Identify how to build and secure value in portfolio companies.

  • Target Audience

    1. SMEs and start-ups (core AI or user AI)
    2. University technology transfer offices, research offices, academic administrators, research group managers, university research commercialization agencies
    3. Investors in the tech sectors (Angels, VC and growth)

    4. IP, legal and strategic advisors

    • Regions: Canada and USA
    • Simultaneous interpretation services from French to English will be available.
    • Presentations will be filmed for future reference.

  • Schedule


    Understanding IP licensing in the field of artificial intelligence

    This session is presented by ROBIC.

    7:30 AM

    Welcome, registration, coffee and networking​

    8:00 AM

    Opening remarks and introductions​

    8:15 AM

    What is a data science research project?

    • Development and discovery of new algorithms, technical uses, basic and applied research. Challenges for access to data.
    • Examples in deep learning, operational research, and business intelligence

    9:00 AM

    Artificial intelligence and intellectual property

    • The taxonomy of artificial intelligence and data science (toward a shared vocabulary). Where does data science IP stand as far as TRL (algorithms, raw data, modified data, trained engines, private and public data)?
    • Explanation of the principles, methods, limitations, and uses of protection tools (patents, copyrights, know‑how, trade secrets, MTA)
    • Open source 101 and data licensing (types of licences, pros and cons, pitfalls to avoid)

    10:15 AM

    Networking break

    10:30 AM

    Busting myths

    • Landscape of leaders in AI (technological, geopolitical and IP races)   
    • Who patents what, current and best practices (universities, SMEs, major industries)
    • Strategies used by industry leaders (open source, copyright, licensing, patents, trade secrets). How important is IP for tech companies (start‑ups, SMEs, large businesses)?
    • Impact of IP management strategies in SMEs. According to the approach "where are we creating value in the ecosystem?"

    11:45 AM

    Daniel Dardani, MIT Technology Licensing Office

    • Approaches and strategies for IP protection and licensing used at MIT
    • Feedback

    12:15 PM



    University IP management strategies to help ensure a sustainable ecosystem

    This session is presented by BCF.

    1:15 PM

    Introductory remarks and afternoon objectives​

    1:25 PM

    Presentations on licensing strategies and approaches used at different universities​

    IP management in research contracts, IP licensing, data management, ethics management regarding research use

    3:00 PM

    Networking break

    3:15 PM

    What issues and realities do tech SMEs and start‑ups face?​

    • The role of IP in their growth
    • The IP development process and ways to speed it up
    • Wariness of university IP licensing
    • Licencing conditions needed to access university IP: scope, exclusivity, revenue structure
    • University IP as a potential strategic asset/advantage for start‑ups
    • Positive transfer mechanisms

    3:45 PM

    Business growth, funding, business valuation​

    • IP challenges in an investment
    • Use of open source (future risks - acquisition)
    • Building value proposition and unique business offering
    • What investors like and don’t like to see in IP and licensing contracts


    For SME and start-ups, the strategic financing of the R&D, the technologies in AI available for licensing and how to deal with universities

    1:15 PM

    Opening remarks and session objectives​

    1:20 PM

    Flash presentations of available enabling technologies​ and academic expertise available to SMEs

    The financing of the R&D for SMEs

    This part of session B is organized by TechnoMtl and Prompt and presented by Finalta.

    1:30 PM

    Strategic planning for financing R&D

    How do industry leaders do it?

    2:15 PM

    Flash presentations of available enabling technologies and academic expertise available to SMEs​

    2:25 PM

    The secrets of a good R & D strategy in a collaboration business-university

    Possible scenarios to maximize each private dollar invested.

    3:05 PM

    Flash presentations of available enabling technologies​ and academic expertise available to SMEs​

    3:15 PM

    Networking break

    Business with universities: simpler and more valuable than you think

    This part of session B is presented by NSERC.

    3:30 PM

    The keys to understanding technology transfer. It’s simpler and more valuable than you think!

    3:45 PM

    Panel of Experts: How to engage with universities

    • Advantages
    • Mechanisms
    • Drafting contract to search
    • Sharing IP rights
    • What to expect and not expect from a university partner
    • Access to resources

    4:15 PM

    How to create a dynamic and agile ecosystem?

    4:45 PM

    Closing remarks​ with Pierre Boivin, MILA

    Overview of the Quebec Provincial Strategic Plan for the Development of the AI Ecosystem and the Development of the New MILA

    5:00 to 7:00 PM

    Networking cocktail reception with booths

  • 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 the development of your strategy in AI.

  • Accommodation

    Since Espace CDPQ is the main partner of the May 17th Symposium, event participants can take advantage of a preferred rate of $189, plus taxes, per room per night at Fairmont The Queen Elizabeth. When registering by phone at 1-866-540‑4483, simply mention the code UNI0517. For online reservation:


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