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. Finance your developments. Meet investors.

  • 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 the different types of licensing strategies
    • Understand the issues faced by MSEs and Start-ups
    • 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 in data sciences


    For investors in tech companies:

    • Discover avenues for enhancing IP assets of portfolio companies
    • Understand IP issues during investment evaluation
    • 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 h 30

    Welcome, registration, coffee and networking​

    8 h

    Opening remarks and introductions​

    8 h 15

    What is a data science research project?

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

    9 h

    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 h 15

    Networking break

    10 h 30

    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)?
    • Where value is built in the ecosystem according to licensing strategies

    11 h 45

    Keynote Speaker

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

    12 h 15



    University IP management strategies to help ensure a sustainable ecosystem

    This session is presented by BCF.

    13 h 15

    Introductory remarks and afternoon objectives​

    13 h 25

    Presentations on licensing strategies and approaches used at different universities​

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

    15 h

    Networking break

    15 h 15

    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

    15 h 45

    Business growth, funding, business valuation​

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


    How to engage with universities as an SME or start‑up

    This session is presented by NSERC.

    13 h 15

    Opening remarks and session objectives​

    13 h 25

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

    13 h 55

    Brief presentations on available enabling technologies​

    14 h 20

    Expert panel: How to engage with universities

    • Advantages
    • Financial leverage
    • Mechanisms
    • Research contracts and IP management

    15 h

    Networking break

    15 h 15

    The financing of the R&D for SMEs

    Session organized by TechnoMtl and Prompt and presented by Finalta.


    16 h 15

    How to create a dynamic and agile ecosystem?

    16 h 45

    Closing remarks​

    17 h à 19 h

    Networking cocktail reception with booths

  • Context
    Challenges concerning the licensinf and transfer of IP developed by universities

    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.

  • Price : $120*

    This includes access to presentations,

    breakfast, lunch and cocktail.

    *After April 20th, the price will be $240.

  • Accommodation

    As the Espace CDPQ a great partner of the May 17th Symposium, the participants of the event can take advantage of a preferential rate of 189$ plus taxes per room per night at Fairmont The Queen Elizabeth. For reservation, on-line or by phone at 1 866 540‑4483, just mention the code UNI0517.

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