Kilpatrick’s Tyler McAllister recently spoke on a panel at The IP Strategy Summit in Seattle. The panel, “AI and Your Patent Management, Strategy & Portfolio” covered how you can leverage AI for efficient patent portfolio management, exploring data analytics for IP strategy, and uncovering emerging AI technologies reshaping patent prosecution. Gain insights into predictive analytics for anticipating patent office actions and preparing for the future of patent professionals in the AI era.
Tyler’s key takeaways from the presentation include:
1. Organizations are increasingly adopting AI to enhance IP workflows, driven by top-level leadership mandates. Real-world applications include internal GPT-like tools for invention intake, preparation of invention disclosure forms, review of patent applications. The tools are aimed at reducing internal bottlenecks in the patent review process. Other tools are being developed in-house for portfolio strategy, AI-driven analytics, and classification systems to identify high-value patents early.
2. Law firms face pressure to deliver more value at lower costs. While AI tools promise efficiency, many third-party options lack substance, requiring careful vetting to ensure quality. Law firms should think how the technology provides additional client value and be prepared to articulate this value in concrete terms (e.g., insights relating to how patent claims relate to client’s products or those of competitors).
3. The overwhelming number of AI products and lack of universal standards complicate decision-making. Effective selection requires a health amount of skepticism, real-world testing, oversight to maintain quality, and balancing the benefits of building internal tools versus purchasing external solutions for complex tasks. Companies working from the ground up might in this space may end up winning the day, compared to those trying to sell a “bolton” AI feature to accompany an already existing product. Threshold questions for any new vendor should include confidentiality, data security, and privacy, in addition to features.
4. Business leaders and patent teams are not always aligned. Use of AI tools, including summarizations, mappings, and categorizations of patent assets can be useful to help business leaders understand how patents relate to their spend (e.g., features, services, etc.).
5. With AI evolving faster than legislation, companies must remain agile to navigate dynamic changes. Staying ahead requires anticipating technological shifts and adapting strategies to meet emerging challenges in the IP landscape.
