How artificial intelligence is reshaping patent portfolio benchmarking

The evolution of patent classification

Patent classification has long been a cornerstone of intellectual property (IP) strategy. It’s how organizations make sense of sprawling portfolios, identify competitive threats and uncover opportunities for licensing, acquisition, or innovation. Whether you’re segmenting assets by technology domain, aligning patents to business units, or mapping whitespace in a crowded field, classification is what turns raw data into strategic insight.

Over the past decade, purpose-built tools have dramatically improved how this work gets done. They’ve replaced spreadsheets and static reports with dynamic dashboards, semantic search and customizable taxonomies. These platforms have empowered IP teams to benchmark portfolios, visualize trends and collaborate across functions — all with greater speed and confidence.

But the demands on classification are changing.

Portfolios are growing not just in size but in complexity. Innovation is increasingly interdisciplinary, spanning multiple technologies, industries and jurisdictions. Business strategies shift more frequently, requiring taxonomies to evolve in real time. As organizations seek to align IP with R&D, product and commercial goals, the need for more tailored, flexible classification frameworks is only intensifying.

The challenge isn’t that traditional classification is broken — it’s that it needs to scale and adapt to keep pace with the speed of innovation. That’s why the conversation is shifting from “how do we classify?” to “how can we classify better, faster, and more strategically?” The answer isn’t to abandon existing tools or workflows. It’s to enhance them — and that’s where AI enters the picture.

How AI enhances patent classification

AI isn’t here to reinvent classification — it’s here to help it evolve.

At its core, AI classification uses machine learning and natural language processing to categorize patents based on patterns in language, structure and context. Unlike traditional systems that rely on fixed taxonomies or manual tagging, AI can learn from examples, adapt to new inputs and apply classification rules at scale — often across tens of thousands of patents in minutes.

This brings several key advantages to portfolio benchmarking:

• Scalability: AI can process and classify large volumes of patent data far faster than human analysts, making it ideal for first-pass analysis or ongoing monitoring.
• Customization: AI can be trained on a company’s own taxonomy — aligning patent data with how the business actually thinks about its technologies and strategic priorities.
• Consistency: AI models apply classification logic uniformly, reducing the variability that often comes with manual tagging.
• Context-awareness: By leveraging natural language understanding, AI can go beyond keywords to interpret meaning, intent, and nuance — helping uncover relationships and trends that might otherwise be missed.

Importantly, AI doesn’t replace expert judgment. It enhances it. The most effective implementations treat AI as a partner — accelerating the classification process, surfacing insights faster, and freeing up human experts to focus on higher-value analysis and decision-making.

Real-world applications of AI classification

Across industries, IP teams are integrating AI classification into their portfolio benchmarking workflows — not to replace their existing tools, but to extend their capabilities.

Organizations that already use structured platforms for patent analytics are now layering in AI to accelerate first-pass classification, apply custom taxonomies at scale, and maintain consistency across evolving business units or product lines. This is especially valuable in scenarios like:

• Competitive benchmarking: Applying your own lens to a competitor’s portfolio to understand how their innovation aligns (or diverges) from your strategic focus.

• Whitespace analysis: Rapidly segmenting large datasets to identify underexplored areas of technology or opportunity.
• Post-merger integration: Reclassifying acquired portfolios to fit your internal taxonomy — without months of manual effort.

One example of this in action is the AI Classifier within Innography, which allows users to define their own taxonomy, input training data (or use natural language definitions), and classify thousands of patents in minutes. In one test, the tool categorized over 2,500 patents in less than 18 minutes — a task that could have taken days or even weeks manually.

What makes this approach effective isn’t just the speed — it’s flexibility. Users can train models on their own terms, apply them across different datasets, and visualize the results using familiar tools. Because AI classifier is explainable and editable, teams retain full control over how insights are generated and used, all within Innography’s existing analysis and visualization environment.

A smarter way to benchmark your patent portfolio

Patent classification is evolving — not because the fundamentals have changed, but because the demands have. As portfolios grow, strategies shift and innovation accelerates, IP teams need tools that can keep pace without compromising clarity, control, or strategic alignment.

AI classification offers a way forward. It builds on the strengths of traditional analytics platforms, adding speed, scalability and adaptability to workflows that already deliver value. When implemented thoughtfully — with human oversight, custom taxonomies and integration into existing systems — AI becomes more than a technical upgrade. It becomes a strategic enabler.

At Clarivate, we’ve built these principles into the design of Innography AI Classifier — a feature that empowers IP professionals to classify patents at scale, using their own lens, with full transparency and control. Whether you’re benchmarking competitors, mapping whitespace, or aligning portfolios to business strategy, Innography helps you move from data to decision — faster.

The future of classification isn’t about choosing between tradition and technology. It’s about combining both — to see your portfolio, and your competitive landscape, more clearly than ever before.

Explore how Innography’s AI Classifier can enhance your portfolio benchmarking strategy by reading more about it on our solution page.

patent analytics Patent landscape Portfolio strategy

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