Artificial intelligence and intellectual property considerations


Financier Worldwide Magazine

January 2018 Issue

Artificial intelligence (AI) is pushing innovation in new ways and accelerating with technological advancements in computing power, data and algorithms. As technology advances, so too does the ability to use AI tools in previously unachievable ways. This has led to a recent uptrend in AI deployment by companies ranging from startups to long-established institutions. Intellectual property (IP) protects and encourages innovation and creativity. As such companies, investors, and entrepreneurs should be aware of key intellectual property considerations as applied to AI innovation.

What is AI?

AI is a field of computer science that includes machine learning, natural language processing, speech processing, expert systems, robotics and machine vision. AI tools automate decision making using programming rules and, in some cases, training data sets. For example, AI tools can derive credit score measurements from disparate data sets, and detect and recognise objects from image data.

Human subject matter experts can provide feedback on results as part of a training or testing process. Machine learning can adapt its programming based on feedback. The data can be represented by various graph and network structures. For example, an artificial neural network or neural net is a system designed to process information by simulating the framework of biological brains.

IP for AI

IP relates to intangible assets, including inventions, brands, new technologies, source code and artistic works. More specifically, IP pertains to patents, trade marks, copyright and industrial design. IP also extends to trade secrets and confidential information. However, these latter two categories are not governed by a specific statute in Canada, unlike the other kinds of IP. In this article we will focus on patents, trade marks and copyrights, although all types of IP have relevance to AI.

Companies and research institutions should clearly define and protect their IP with registrations and documentation, especially when working with multiple third parties. A company may then better control use of its IP rights, including permitted use under licensing and collaborative arrangements. In the AI context, the legislative protection has not yet advanced as quickly as the technology, which makes early and ongoing IP portfolio management of particular importance.


Copyright relates to new original artistic, literary, dramatic or musical works. This includes computer programme code, compilations of data and graphics. Copyright provides the exclusive legal right to produce, reproduce, publish or perform an original literary, artistic, dramatic or musical work.

Copyright is an important IP asset for AI, as it protects the technology product (code and data) from unauthorised use and reproduction. Contributors to the technology should be identified and tracked. Ownership and confidentiality of the copyright should clearly be set out in a written agreement.

Companies may also benefit from placing digital locks on their products and services for security. Circumvention of digital locks is an offence in some jurisdictions and may provide relief against unauthorised parties. Companies should have policies for developers incorporating third-party copyright, even if inadvertently, as it may impact ownership of the technology and freedom to operate. Employees or a contracted developer, for example, may incorporate third-party source code without authorisation, which may impact ownership and could create inadvertent liability of infringement of other’s IP rights.

AI systems involve large data sets which can be protected by copyright as compilations of data. These data sets and underlying algorithms are important IP assets for the company. Contractual terms with end users and third parties should clearly specify permitted use.

AI systems can also generate new works protectable by copyright, such as creating new artwork or music. However, most copyright statutes do not yet not clearly define who owns machine-generated works. It is currently a point of contention in respect of some such works whether the work is generated by a machine, and or the role played by the humans in creation of the work. To this end, agreements should attempt to clarify ownership when possible. Further, an AI system may act or operate autonomously in a manner that infringes third-party IP rights. If existing laws do not extend liability to a machine, then a related stakeholder (such as the owner, developer, operator or another supply chain participant) may be responsible.


A trade mark is unique and identifies the source of the goods and services with which it is associated. It may consist of a combination of letters, words, sounds or designs that distinguishes one company’s goods or services from those of others in the marketplace.

A strong brand helps AI companies differentiate their products and services from competitors and establish a strong reputation in the market. AI technology and algorithmic accountability can help a company develop goodwill for its brand. AI companies are often stewards of important data  assets, and documentation should consider these as valuable assets and document and register IP when possible. A reputable brand may be of paramount importance to customers.

An AI tool can be a ‘black box’ device embedded within a finished product offered by a third party. This can make it difficult for the end customer to recognise the brand of the company supplying the ‘black box’. A co-branding agreement can provide for use of the mark associated with the ‘black box’ on the finished product offered by the third party. This can help the ‘black box’ provider become recognisable by the end consumer.


Patents provide a time-limited protection for an invention. A patent entitles the patent owner to the exclusive right to make, use and sell his or her invention in exchange for full and clear disclosure on how to work the invention.

Patents provide a mechanism to exclude others from making, using or selling the patented technology, which may help companies obtain or maintain market share, and protect research and development investments. Patents can provide a competitive advantage, and may also be used defensively as a negotiation tool. Patent publications can also be cited against subsequently filed applications to prevent grant.

A technology development strategy should consider if patent protection is available for core technology innovation. Companies should also be aware of other publications and litigations, as competitors and other players may have their own patents or pending applications. In contrast with trade secrets, granted patents may be enforced against third parties that make, use or sell the claimed invention, despite independent development. Given the quickly evolving AI market, obtaining early priority dates is important in view of the ‘first to file’ nature of the patent system.

Patent eligibility

AI involves software which is increasingly difficult to patent. Patent offices, along with the courts, have struggled with establishing clear delineations of what is patentable and what is not patentable. The claims have to clearly define the patent eligible innovation with the patent description clearly describing how to make and use the innovation. As an example, courts held that a method for automatically animating lip synchronisation and facial expressions to be patent eligible and specific inventive animation rules were described to enable a computer to do something it could previously.

Highlighting salient technical features such as technical advantages and practical implementation details can increase the likelihood of success during patent examination. The description should highlight discernible effects generated by the AI innovation or use case. An example can relate to moving a physical machine to pick up an object. The AI tool can also be embodied in a physical form factor, such as a medical device.

A company making, using or selling AI tools should also consider its freedom to operate to avoid encroaching on existing patents covering AI innovation. A patent landscape assessment is helpful to understand the scope of third-party rights to mitigate risk.


Given the importance of data analytics, companies continue to invest in research and develop in AI to advance their processing and data mining capabilities. An IP strategy for AI systems will layer IP rights to protect different aspects of the innovation. Companies can clearly define and protect their IP with registrations and documentation. Clear agreements on IP rights should be established between third parties to manage risk.


Maya Medeiros and Jordana Sanft are partners at Norton Rose Fulbright LLP. Ms Medeiros can be contacted on +1 (416) 216 4823 or by email: Ms Sanft can be contacted on +1 (416) 216 4798 or by email:

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