Intellectual property
Intellectual property comprises creations of the mind, such as inventions, expressive works, designs, and distinctive marks, together with the legal rights that protect them.
Introduction to Generative AI, Second Edition
Intellectual property rights are legally enforceable rights over creations of the mind, such as inventions, expressive works, brands, or designs.
- How it is used
- Frames legal and practical questions about generative AI training data, outputs, and creators’ control over their work.
- How deep it goes
- Defines major IP protections and explains copyright and AI authorship at an overview level, while leaving training and output disputes unresolved.
- Examples
- Uses inventions, corporate logos, Coca-Cola’s formula, and creative works to distinguish kinds of protection.
- What you can do after
- Evaluate/understand competing rights and uncertainties when creative works are used to train AI or appear in its outputs.
Read how Introduction to Generative AI, Second Edition explains intellectual property →
Think Like a CTO
Intellectual property is a legally protected creation of the mind, such as software, inventions, written works, or designs.
- How it is used
- Guides build-versus-buy decisions and checks whether technology the business relies on is actually its own.
- How deep it goes
- Develops strategic, contracting, licensing, and due-diligence checks without setting out legal doctrine.
- Examples
- Illustrates risks through copied forum code, open-source code discovered during a deal, and ownership questions after outsourced development.
- What you can do after
- Evaluate/understand it by identifying distinctive technology, checking contractor assignments, and accounting for third-party code and licenses.
LLMs in Production
Intellectual property is a legally protectable intangible creation, information asset, or other proprietary business resource.
- How it is used
- Frames copyright and proprietary information as production risks for AI training, outputs, competition, and regulation.
- How deep it goes
- Connects copying behavior to liability, revenue, competition, and outdated law without resolving the underlying legal dispute.
- Examples
- Uses alleged New York Times articles in OpenAI training and reported word-for-word chatbot responses as the central case.
- What you can do after
- Evaluate how training data and generated outputs can create copyright exposure and participate in local regulatory discussions.
Read how LLMs in Production explains intellectual property →
Starting Data Analytics with Generative AI and Python
Creations of the mind, such as inventions, works, designs, and proprietary knowledge, that may be legally protected or commercially valuable.
- How it is used
- Guides decisions about submitting proprietary material to generative AI and using model-generated content commercially.
- How deep it goes
- Defines practical risks and review questions but defers jurisdiction-specific copyright interpretation to legal counsel.
- Examples
- Uses a proprietary algorithm mistakenly treated as non-sensitive because its underlying data was not shared.
- What you can do after
- Evaluate whether AI inputs and outputs require treatment, licensing checks, or legal review before use.
Read how Starting Data Analytics with Generative AI and Python explains intellectual property →
Build a Text-to-Image Generator (from Scratch)
Legal rights protecting creations of the mind, including artistic works and inventions.
- How it is used
- Frames text-to-image generation as a conflict among ownership, creativity, training-data use, and responsibility.
- How deep it goes
- Contrasts copying and transformation, explains fair-use arguments, and stops short of resolving the ongoing legal debate.
- Examples
- Uses copyrighted training collections, distinctive stylistic elements, and nearly identical images produced from related prompts.
- What you can do after
- Evaluate competing claims about whether generated images transform training patterns or reproduce protected works.
Read how Build a Text-to-Image Generator (from Scratch) explains intellectual property →
Related concepts
- generative modelingused by · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
- sensitive informationkind of · Sensitive information is information requiring protection because its unauthorized access, disclosure, alteration, or misuse could cause harm or violate obligations.
- build strategyuses · A build strategy is an approach for deciding which capabilities an organization should develop internally, especially those that provide unique competitive value.
- ethicspart of · Ethics is the study and application of principles for evaluating morally acceptable conduct, decisions, and outcomes.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.