Artificial Intelligence (?AI?) and the AI sub-field of Machine Learning (?ML?) are terms that originated in the fields of computer and data science but now form part of the common vernacular. AI has now found application in virtually every field.
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Some applications of AI have become part of our daily lives: virtual assistants, chatbots, search engines, online language translation and eCommerce all employ AI in various forms. Generative AI such as OpenAI?s products ChatGPT (natural language generation), Jukebox (music generation) and DALL-E2 (image generation) have captured the public attention to an enormous degree and can, indeed, do amazing things. A myriad of other applications of AI are found in disparate fields that, while not as visible on a daily basis, impact on our lives in a wide variety of ways.00With this rapidly-increasing impact comes not only exciting new technical capabilities but also new challenges for intellectual property (?IP?) law. Are current laws fit for purpose or is something new or different needed? This is not a new question; one need only look back to the early days of digital music, computer software and 3-D printing to find similar discussions of whether existing IP law is suited to emerging technologies. For the most part, the answer in the past has been ?yes?, with perhaps a tweak here and there. Whether the same will be true of AI is, as yet, an open question.00This book focuses specifically on AI and patents. Unsurprisingly, different jurisdictions have taken different approaches to patentability of AI-related inventions. Terminology (what is an ?AI-related invention??) also is inconsistent from one patent office to the next. These factors combine to create a maze of laws and regulations that patent applicants must navigate to secure protection for their innovations.00To facilitate comparison of laws and practices, this book introduces a taxonomy that separates AI-related inventions into five conceptual categories
