Intellectual property: training data, outputs, patents and trade secrets

Generative AI has put intellectual property law at the centre of AI governance. Models are trained on vast collections of text, images, music and code, much of it protected by copyright; they produce outputs that may resemble existing works; and they raise new questions about who, if anyone, owns what they create. Courts in several countries are deciding cases that will shape the industry, and legislatures and copyright offices are studying reforms. Beyond copyright, AI raises questions about patents for AI-assisted inventions, trade secret protection for models and data, and the use of people's voices and likenesses. This lesson covers copyright in training data, copyright in outputs, infringing outputs and how to manage them, patents, trade secrets, personality rights, and licensing of models and data.

Copyright basics

Copyright protects original expression (literary, artistic, musical and dramatic works, including software) but not ideas, facts or methods. It gives the owner exclusive rights to reproduce, adapt, distribute and communicate the work, subject to exceptions. Training a model involves making copies of works (to collect, clean and process them), and a model's outputs may reproduce or adapt them, so both stages can engage copyright. The key legal questions are whether training copies fall within an exception, whether models themselves contain copies, and whether particular outputs infringe.

Training data: exceptions and litigation

Canada's Copyright Act has no specific exception for text and data mining. Training would need to fit within fair dealing, which permits dealing with a work for the purposes of research, private study, education, parody, satire, criticism or review, or news reporting, if the dealing is fair. The Supreme Court of Canada, in CCH Canadian v. Law Society of Upper Canada (2004), set out factors for fairness: the purpose of the dealing, its character, the amount taken, alternatives available, the nature of the work, and the effect of the dealing on the work; it has interpreted the purposes, especially research, broadly. Whether commercial model training is fair dealing has not yet been decided by Canadian courts. The federal government consulted on copyright and generative AI in 2023 and 2024 without legislating, and in November 2024 a group of Canadian news publishers sued OpenAI in the Ontario Superior Court for using their articles in training. Canadian developers training on content from other jurisdictions must also consider those countries' laws.

The European Union has text and data mining exceptions under its 2019 Copyright in the Digital Single Market Directive: one for research organizations, and a general one that applies unless rights holders have reserved their rights in a machine-readable way (an opt-out). The EU AI Act requires general-purpose AI model providers to have a policy to respect these opt-outs and to publish a summary of training content (Lesson 3). Japan and Singapore have broad exceptions for computational analysis. The global picture is therefore fragmented, and developers increasingly license data from publishers, image libraries, music companies and platforms, partly to reduce legal risk and partly to access higher-quality data.

Copyright in outputs

Can AI-generated outputs be protected by copyright, and if so, who owns them? In most jurisdictions, copyright requires human authorship.

In Canada, the Copyright Act does not define "author", but courts have linked originality to the exercise of skill and judgment (CCH, 2004), which implies a human author. The Canadian Intellectual Property Office registered a work listing an AI system as co-author in 2021, but registration in Canada is not examined for validity, and this registration has been challenged in court. The prevailing view is that works generated autonomously by AI are not protected, while works in which a human exercises sufficient skill and judgment, for example by selecting, arranging or substantially modifying AI outputs, may be protected as to the human contribution.

Practical consequences follow. Content generated purely by AI may be freely copied by others; organizations that need exclusive rights (for marketing, entertainment or software) should ensure meaningful human creative contribution and document it. Provider terms of service usually assign whatever rights exist in outputs to the user, but cannot create rights the law does not grant.

Infringing outputs

Regardless of how training is treated, an output that reproduces a substantial part of a protected work, or is substantially similar to it, can infringe, and the user who publishes it may be liable as well as, potentially, the provider. Models can memorize and reproduce training text, code, images (such as famous artworks or characters) and lyrics, especially for works duplicated many times in training data. Providers mitigate with deduplication of training data, filters that block reproduction of known protected content, and refusals for requests to reproduce works. Users mitigate by checking outputs, especially code and published content.

# Checking generated text for near-verbatim reproduction of a protected source, using word n-gram overlap
def shingles(text, n=6):
    w = text.lower().split()
    return {tuple(w[i:i + n]) for i in range(len(w) - n + 1)}
source = ("the old lighthouse keeper climbed the spiral stairs each evening to light the lamp "
          "that guided the fishing boats home through the narrow rocky channel before the storms of autumn")
outputs = {
    "close paraphrase": "each evening the old keeper of the lighthouse climbed the winding stairs to light the lamp for the boats",
    "verbatim passage": "she remembered how the old lighthouse keeper climbed the spiral stairs each evening to light the lamp that guided the fishing boats home",
    "original text": "a ferry crossed the bay at dawn while gulls circled above the fish market",
}
src = shingles(source)
for name, text in outputs.items():
    sh = shingles(text)
    overlap = len(sh & src) / max(len(sh), 1)
    flag = "review for copying" if overlap > 0.3 else "low overlap"
    print(f"{name:18s} 6-gram overlap with source {overlap:5.1%}  {flag}")

close paraphrase   6-gram overlap with source  0.0%  low overlap
verbatim passage   6-gram overlap with source 83.3%  review for copying
original text      6-gram overlap with source  0.0%  low overlap

The verbatim passage shares 83 % of its six-word sequences with the source and should be reviewed, while the original text shares none. The close paraphrase also shares none, which shows the limitation of simple n-gram checks: copyright infringement can occur through substantial similarity of expression without exact copying, as with paraphrases of distinctive passages, and detecting it requires human judgement. Similar tools exist for code, where licence compliance tools compare generated code against open-source repositories to detect copied code carrying licence obligations.

Many AI vendors now offer copyright indemnities to business customers, promising to defend and compensate them for claims that outputs infringe, subject to conditions such as using the vendor's content filters and not deliberately trying to reproduce protected works. Organizations should read these conditions carefully (Lesson 11).

Patents and AI

Patents protect inventions that are new, non-obvious (inventive) and useful, for a limited term in exchange for public disclosure. Two AI issues arise. First, inventorship: courts and patent offices in the United States, the United Kingdom, the EU and elsewhere, in the DABUS cases, have held that an inventor must be a natural person, so an AI system cannot be named as inventor; Canadian patent authorities have taken the same position. Inventions made with AI assistance can be patented if a human made a significant contribution to the conception of the claimed invention, though the guidance on what counts has evolved and should be checked. Second, patentability of AI itself: algorithms and mathematical methods are not patentable as such, but AI-based inventions that produce a technical effect or a practical application, such as improved image processing or control of a physical process, may be patentable, with jurisdictions drawing the line differently. Organizations must also weigh patents against trade secrecy: patents require disclosure, while much AI value lies in data, weights and know-how that are easier to keep secret.

Trade secrets and confidential information

Trade secret law protects commercially valuable information kept secret through reasonable measures. Model weights, training data, data pipelines, prompts and evaluation sets are often protected this way, and their protection depends on security, access controls, confidentiality agreements and careful contracts with partners. Two common risks deserve attention. First, employees pasting confidential information into public AI tools may disclose it to the provider and lose trade secret protection; several companies restricted employee use of public chatbots after incidents of this kind, and many now provide enterprise tools with contractual protections. Second, outputs of a model can sometimes reveal training data or system prompts, so confidential data used in fine-tuning or retrieval needs safeguards (AI-430). Model extraction, where an attacker queries a model to train a copy, raises both technical and legal questions; providers' terms usually prohibit using outputs to develop competing models.

Voices, likenesses and personality rights

Generative AI can imitate a person's voice, face and style, raising questions beyond copyright, which does not protect a voice or a style as such.

In Canada, the tort of appropriation of personality, recognized in Ontario and elsewhere, and statutory privacy torts in British Columbia, Saskatchewan, Manitoba, Newfoundland and Labrador and Quebec's Civil Code protect against unauthorized commercial use of a person's name, likeness or voice. Deceptive use of a likeness in advertising can also breach the Competition Act. Proposed federal online harms legislation would address harmful deepfakes, and criminal law prohibits non-consensual distribution of intimate images, including, through recent amendments and proposals, synthetic ones.

Licensing models and data

Much AI is built on licensed components. Open models are released under licences ranging from permissive open-source licences (such as Apache 2.0 or MIT) to custom licences that restrict certain uses, require attribution, or limit use by very large companies; some "open-weight" licences are not open source in the traditional sense. Datasets carry their own licences and terms. Before using a model or dataset, organizations should check the licence for permitted uses (commercial use, modification, redistribution), attribution and notice requirements, restrictions on use cases, obligations passed to downstream users, and any terms about using outputs to train other models. Keeping a record of components and their licences, a software or AI bill of materials, supports compliance and due diligence.