Best Practices for Creating a Technology-Based Patent Classification System (Part 2)

In today's rapidly evolving patent landscape, creating a well-structured and effective technology-based patent classification system is more important than ever. With the rise of artificial intelligence (AI) and its growing application in patent portfolio management, it's crucial to design your classification scheme with AI auto-classification in mind. By following best practices and considering the specific requirements of AI-driven systems, you can unlock valuable insights, make informed decisions, and align your patent strategy with your business objectives.

As a follow-up to Part 1 of our series on Patent Classification, below are recommended best practices for creating a technology-based patent classification system optimized for AI auto-classification. These practices are based on Anaqua's experience working with organizations of various sizes and industries and have proven to be effective in developing robust and meaningful classification schemes that leverage the power of AI.

Alignment with Business Objectives:

• Ensure that the classification scheme aligns with your organization's business objectives, product lines, and strategic priorities.

Collaboration and Stakeholder Input:

• Involve relevant stakeholders, such as technical experts, business leaders, and patent attorneys, in the design process to ensure the classification scheme meets the needs of different departments and functions.

Coverage and Exclusivity:

• Exhaustive Coverage: The classification scheme should cover all relevant technology areas in your patent portfolio. Ensure that there are no gaps or missing categories.
• Mutually Exclusive Categories: Each category should be distinct and non-overlapping to avoid ambiguity in classification. Patents should fit clearly into one category without belonging to multiple categories.

Classification Structure:

• Correct Level of Detail: Strike the right balance between breadth and depth. The classification should be detailed enough to provide meaningful insights but not so granular that it becomes unwieldy.
• Manageable Number of Categories: Aim for a reasonable number of categories that cover the key technology areas without being overly complex.
• Sufficient Patent Coverage per Class: Ensure that each category has 50 to 100 patents to build reliable models. This can include both your own patents and relevant third-party patents.
• Clear and Concise Titles: Category titles should be clear, concise, and accurately reflect the content of the category. Avoid ambiguous or overlapping titles that may cause confusion.
• Consistency and Coherence: Maintain a consistent naming convention and structure throughout the classification scheme. Use a logical hierarchy and avoid inconsistencies in terminology or granularity.

Adaptability and Flexibility:

• Design the classification scheme to be adaptable to future changes and emerging technologies. Allow room for adding new categories or modifying existing ones as the technology landscape evolves without the unnecessary burden of doing so.

Implementation with Artificial Intelligence (AI)

Implementing these best practices when creating your technology-based patent classification system will lay a solid foundation for effective patent portfolio management in the era of AI. By designing your classification scheme with AI auto-classification in mind, you can harness the power of machine learning to streamline processes, improve accuracy, and gain valuable insights.

However, it's important to be aware of potential pitfalls and challenges that can arise during the classification design process, especially when considering the specific requirements of AI-driven systems. In the next article, we'll delve into these pitfalls and discuss strategies to overcome them, ensuring that your classification system is not only well-designed but also optimized for AI auto-classification.

Next Steps

In final post in this series, I explore the common pitfalls to avoid when designing your technology-based patent classification system in the context of AI auto-classification. You'll get actionable advice to navigate these challenges successfully and ensure that your classification scheme is future-proof and ready to leverage the full potential of AI in patent portfolio management.

Read more:

• Part 1: Why Your Company Needs a Technology-based Patent Classification System
• Part 3: Avoiding the Pitfalls: Designing a Patent Classification System for AI Efficiency
• Anaqua's Patent AI Auto-Classifier

Written by Matt Troyer, AcclaimIP Senior Director, Product & Innovation