Canadian AI Patents

Canadian AI Patents

This PowerBI dashboard provides a comprehensive view of patent activity related to Artificial Intelligence (AI) across 15 OECD countries, including Canada, categorized by inventors and assignees. The classification, based on Cooperative Patent Classification (CPC) codes and the USPTO’s Artificial Intelligence Patent Dataset (AIPD 2023), segments patents into key AI subfields:

  1. Machine Learning: Algorithms enabling systems to learn and improve from data.

  2. Evolutionary Computation: Techniques inspired by natural evolution for optimization.

  3. Natural Language Processing (NLP): AI applications in understanding and generating human language.

  4. Vision: Technologies related to visual perception and image recognition.

  5. Speech: AI for recognizing and producing human speech.

  6. Knowledge Processing: Systems focused on reasoning and decision-making using organized knowledge.

  7. AI Hardware, Part 1: Specialized processors (e.g., GPUs, TPUs) designed for AI tasks.

  8. AI Hardware, Part 2: General-purpose hardware supporting AI applications.

This dashboard highlights both individual contributions (inventors) and organizational efforts (assignees), illustrating the depth of global innovation across AI subfields.

Source of data used in this Dashboard: United States Patent and Trademark Office (USPTO)

1. General Trends

  • Across all categories, patent filings surged significantly after 2010, demonstrating a sharp acceleration in AI innovation.

  • Assignees consistently outpace inventors in total patent filings, emphasizing the dominance of corporate and institutional R&D in advancing AI technologies.

2. Category-Specific Trends

  • Machine Learning leads all other categories, showcasing rapid growth in recent years as it forms the backbone of AI technologies.

  • AI Hardware, Part 1 shows similar explosive growth, reflecting increased demand for specialized hardware to support computationally intensive AI tasks.

  • Vision patents exhibit strong growth, underscoring the proliferation of applications in areas such as autonomous vehicles and surveillance.

  • Natural Language Processing (NLP) and Speech categories display steady growth, fueled by advancements in language models and conversational AI systems (e.g., chatbots).

  • Knowledge Processing shows consistent growth, reflecting the importance of reasoning systems for AI decision-making.

  • Evolutionary Computation has slower but stable growth, primarily tied to niche applications in optimization and simulation.

3. Assignees vs. Inventors

  • Assignees dominate in patents related to AI Hardware, particularly Part 1, signaling significant corporate investment in foundational infrastructure.

  • Inventors show relatively balanced contributions across categories, particularly in Machine Learning and NLP, highlighting the role of academic and independent research.