IP Challenges with Emerging Technologies

I came across a recent Science study that analyzed 77 patents from AI-first drug discovery companies. Compared to traditional, lab-based developers, these companies were significantly more likely to patent small-molecule compounds without conducting any in vivo studies. The study also found a consistent pattern: less biological data, fewer ADMET experiments, limited formulation detail, and earlier filings overall. Only 23% of these AI-driven patents included any animal testing. Yet many disclosed hundreds of molecules with minimal experimental validation. This raises a red flag. It looks less like a push toward de-risked innovation and more like broad IP positioning based on computational output. From a business standpoint, I understand the pressure to file early. Patents shape competitive advantage, attract capital, and signal pipeline momentum. But if filings outpace meaningful validation, the result is an innovation bottleneck. Untested molecules sit protected but undeveloped, blocking others from advancing or investing in them. AI has the potential to reshape drug discovery, but not by scaling noise. Its real commercial value lies in helping us prioritize high-quality candidates that are both novel and actionable. Filing before experimental validation shifts risk downstream, creates friction in licensing, and undermines investor confidence in the actual readiness of assets. If we want AI to accelerate not just ideation but actual development, we need to realign incentives. Strong IP should be linked to strong evidence. Otherwise, we risk building a patent landscape that looks impressive on paper but slows real progress. 📄 https://lnkd.in/eZdHvx-U #ai #drugdiscovery #biotech

Is code still intellectual property in a world where AI can generate it on demand? The rise of LLMs is transforming the foundation of software development, challenging traditional notions of intellectual property. Historically, IP was straightforward to identify: it was the code itself. Companies invested months or years building proprietary algorithms and implementations that represented their crystallized knowledge of how to solve complex problems. Today, LLMs can generate sophisticated, production-quality code in minutes rather than months. While this code still has inherent value in its utility, its nature as intellectual property has fundamentally changed. When multiple developers can prompt an LLM to generate similar solutions, the code itself no longer represents unique intellectual capital. This capability not only democratizes software development but raises a profound question: If code can be generated so easily, where does the real value in software development now lie? The answer is that intellectual property in the LLM era isn't in the code itself, but in the empirical knowledge of what actually works in practice. While LLMs can generate theoretically perfect code, they can't capture the practical insights that only come from deploying solutions in the real world. Consider a complex integration between enterprise systems - an LLM might generate syntactically correct code, but only practical experience reveals which approaches handle edge cases, maintain performance at scale, and remain maintainable. The true IP becomes this accumulated knowledge of real-world implementation challenges. This shift fundamentally changes how we build software businesses. Success is no longer about accumulating proprietary code, but about systematizing practical knowledge of making LLM-generated solutions work reliably at scale. When an LLM can generate ten different valid approaches to a problem, the competitive advantage lies in knowing which approach will actually succeed in production, how to implement it robustly, and how to maintain it efficiently. This knowledge, unlike code, can't be easily replicated or generated. We're entering an era where the winners won't be those who write the best code—that's becoming commoditized. Instead, they'll be those who most effectively accumulate and apply practical knowledge about making LLM-generated solutions work in production. This represents a profound shift: while LLMs can generate multiple, seemingly valid solutions to any problem, their non-deterministic nature means each generation might produce different code. This uncertainty actually increases the value of implementation knowledge - success requires understanding not just one solution, but the patterns and principles that work reliably across many possible implementations. The true intellectual property is the battle-tested knowledge of how to navigate from AI-generated possibilities to production-ready solutions.

The EUIPO - European Union Intellectual Property Office’s latest report offers a timely deep dive into the intersection of generative AI and copyright law — a space that’s rapidly evolving and fraught with legal, ethical, and technical challenges. As GenAI technologies become more pervasive, we must strike a balance between innovation and the protection of creators' rights. Key takeaways from the report include: ▪️Transparency Challenges: GenAI models often function as “black boxes,” making it difficult to trace how copyrighted content is used in training. This raises serious concerns about unauthorized usage. ▪️Legal Framework & Opt-Out Mechanisms: Clear opt-out provisions are essential so that creators can prevent their works from being swept into AI training datasets without consent. ▪️Identification of AI-Generated Content: There’s a growing need for robust tools to label and trace AI-generated content — a step toward better attribution and enforcement. ▪️Licensing Opportunities: The report envisions a structured licensing market for AI training data, enabling creators to participate in the AI ecosystem on fair terms. ▪️Policy Implications: These insights are meant to guide EU policymakers in shaping an AI-driven future that still upholds IP rights. Notably, the upcoming EUIPO Copyright Knowledge Centre, launching by the end of 2025, will serve as a vital resource for copyright holders looking to understand and protect their work in the AI era. If you want to learn how creators can implement their own opt-out mechanisms and effectively block AI tools from crawling and scraping their content, then comment “Opt-out” and I will send you a practical one-pager to help. https://lnkd.in/eVQNsNKZ #GenerativeAI #CopyrightLaw #EUIPO #DigitalInnovation #IntellectualProperty

AI is rewriting the rules of innovation. But who owns the future? The USPTO just unveiled its AI Strategy (January 2025), a blueprint for navigating AI’s role in intellectual property. With patents, trademarks, and copyrights at stake, this is about more than just technology—it’s about who controls the next era of innovation. Here are 5 takeaways: 1️⃣ AI Won’t Own Inventions—Yet AI can assist in innovation, but human inventors remain at the center of patent law. The USPTO is firm: AI can’t be listed as an inventor, but AI-generated work may influence patentability. The legal line is being drawn. 2️⃣ Patent Reviews Are Getting Smarter The USPTO is using AI to examine AI—leveraging machine learning for prior art searches, classification, and decision-making. This means faster approvals, better accuracy, and a more scalable patent process for the future. 3️⃣ IP Protection vs. AI Creativity: The Collision Generative AI is churning out text, images, music, and designs—but who owns it? Trademark and copyright laws weren’t built for AI-generated content. The USPTO is working to define rights and responsibilities in an AI-driven creative economy. 4️⃣ The U.S. is Playing for Global AI Leadership AI innovation is a geopolitical race. The USPTO is working with international partners to shape global AI patent standards, ensuring U.S. leadership in AI regulation, enforcement, and competition. The message? Innovation without protection is just an idea. 5️⃣ AI for All, Not Just Tech Giants The USPTO wants AI-driven innovation to be accessible, not just locked up by billion-dollar companies. From startups to underrepresented inventors, AI tools and patent protections need to be inclusive and equitable—or we risk leaving brilliant minds behind. What’s the bottom line? AI is not just a technology—it’s an economic force. The USPTO is positioning the U.S. to lead the next chapter of AI innovation while ensuring IP laws evolve to keep up. But will regulations accelerate AI’s potential—or slow it down?

Breaking down key IP findings in the Congressional report on #AI: 1️⃣ The issue of whether the training of AI models using copyrighted works constitutes copyright infringement is working its way through the court system. Main issue - Is it fair use and how does the Google Books case inform that analysis? Meanwhile, industry solutions are emerging: - Some AI developers have entered into creative licensing agreements with some rights holders - Some are using copyright clearinghouses - Start-ups are emerging to aggregate content Preliminary draft legislation in the PRC ▶️ use of copyrighted data for training is generally “a reasonable use of data” not requiring payment EU AI Act (effective Aug 2025) ▶️ requires transparency/sufficiently detailed summary of training data 2️⃣ USPTO & United States Copyright Office have been proactive in addressing the protectability of AI-generated ideas and content. Issues remain and more clarity is needed. These issues will be resolved, and clarity provided, over time by the agencies and the courts. The issue of AI-generated ideas or content infringing IP is also working its way through the courts (see again Google Books). 3️⃣ Stakeholders concerned that Alice decision could be read to prevent the patenting of AI-related inventions. They also have concerns that AI will raise the bar for patentability (more and more over time) because much more will be obvious in view of AI. 4️⃣ On transparency, some stakeholders want more (including documenting training data), whereas AI tech companies are worried about the expense and logistical and technical difficulties (+ disclosure of trade secrets or proprietary information). Some including White House calling for transparency (such as watermarking) if audio or visual output created or modified with AI. 5️⃣ Most high profile issue is abuse of identify-based rights or digital replicas/deep fakes. United States Copyright Office calls for urgent legislation to address gaps in current patchwork of laws and provides guidance on same. 🎙️ YOU can help shape the laws in the US, PRC and Europe (+). Contact the agencies (including attaches) who comment on and shape the work in Congress and in other countries. Use the amicus process in the courts even at the district court level. Full report ▶️ https://lnkd.in/gaRNe2NT #AI #IP #Congress

Over the holidays, I spent over 100 hours becoming more of a subject matter expert in AI, as I was already interested in the IP implications of artificial intelligence. Here is my take: AI is transforming patent law, and most practitioners aren't ready for it. Here's what's happening right now: 1. The USPTO won't recognize AI systems as inventors 2. Patent attorneys must adapt to new AI guidelines 3. AI tools are changing patent litigation completely 4. Only natural persons can be listed as inventors But that's just the start. The real changes are coming through: - International efforts to handle AI patent challenges - Strict rules about using AI in patent submissions - New examination procedures for AI-related patents - Updates to subject matter eligibility under 35 U.S.C. § 101 And here's what this means for patent practitioners: 1. Learn AI-related patent examination guidelines 2. Understand international AI patent standards 3. Stay current with USPTO's AI policies 4. Master ethical AI use in patent work The legal system is adjusting to these changes. Legislative reforms are coming. Patent practitioners who prepare now will be ready. Those who don't will fall behind. It's that straightforward. Don't wait to adapt. I will provide guidance in the coming days. #ai #ailaw #aipatents #aiip #artificialintelligence #patents #patentlitigation #BrownRudnick

𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈 𝐍𝐨𝐰 𝐓𝐫𝐚𝐝𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐥𝐥𝐞𝐜𝐭𝐮𝐚𝐥 𝐏𝐫𝐨𝐩𝐞𝐫𝐭𝐲 𝐑𝐢𝐠𝐡𝐭𝐬 A recent article from Decrypt sheds light on a significant development in AI: autonomous agents are now capable of trading intellectual property (IP) rights with each other. This advancement marks a new frontier in AI capabilities and could reshape how we approach innovation and IP management. 𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐚𝐧𝐝 𝐢𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 👉𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐍𝐞𝐠𝐨𝐭𝐢𝐚𝐭𝐢𝐨𝐧 AI agents are now demonstrating the ability to engage in complex negotiations without human intervention. This includes: ▪Understanding the value of different IP assets ▪Assessing risks and potential benefits of trades ▪Making strategic decisions based on long-term goals ▪Adapting negotiation strategies in real-time This level of autonomy in high-stakes decision-making represents a significant leap in AI capabilities. 👉𝐈𝐏 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 The integration of AI in IP trading could revolutionize how intellectual property is managed, licensed, and valued: ▪Automated IP audits could become more thorough and frequent ▪Real-time royalty tracking and distribution might become standard ▪AI could enhance IP enforcement by quickly identifying potential infringements ▪Valuation of IP portfolios could become more dynamic and market-responsive ▪Cross-licensing agreements could be negotiated and updated automatically These changes could lead to more efficient IP markets and reduced transaction costs. 👉𝐄𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐨𝐝𝐞𝐥𝐬 This technology opens the door to entirely new business models centered around AI-driven IP: ▪AI-generated inventions could become more common, raising questions about authorship and ownership ▪AI-facilitated IP marketplaces could emerge, allowing for instant trading of patents, copyrights, and other IP assets ▪AI-powered IP valuation services could provide real-time, market-driven assessments ▪Companies might develop AI agents specialized in managing and trading specific types of IP ▪New insurance products could arise to cover risks associated with AI-driven IP trade 👉𝐋𝐞𝐠𝐚𝐥 𝐚𝐧𝐝 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 This development raises important questions that will need to be addressed: ▪How will existing IP laws apply to AI-driven trades? ▪What safeguards are needed to prevent market manipulation by AI agents? ▪How can we ensure transparency and accountability in AI-driven IP transactions? ▪What are the implications for human inventors and creators? As AI agents become more sophisticated, their role in managing and trading IP will only expand. This marks the beginning of a fascinating new chapter in the intersection of AI and intellectual property. 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/guJsQsct #AI #DigitalTransformation #GenerativeAI #GenAI #Innovation  #ArtificialIntelligence #ML #ThoughtLeadership #NiteshRastogiInsights 

Dr. Saju Skaria's Weekly Reflections: 20/2023 Intellectual Property (IP) issues and Generative AI The use of Generative AI has taken the industry by storm. I thought it’s imperative to touch on this critical issue, i.e., intellectual property, before we close the discussions on AI. AI technology, leveraging data lakes and question snippets to recover patterns and relationships, is helping immensely in creative industries. However, we have a critical issue that the legal fraternity is trying to address: copyright infringement, ownership of AI-generated work, and leveraging unlicensed content in training data. Trained AI tools can replicate copies of original work (for example, paintings or photographs), which is a copyright infringement. A further challenge is that the users might create copies of the original that need to be more transformative, thereby causing the credibility of the original work. These unauthorized derivatives can cause significant penalties, and the courts are already dealing with such issues. There is significant debate around the “fair use doctrine” that allows reviewing the copyrighted without the owner’s permission for purposes like criticism (including satire), comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research,” and for transformative use of the copyrighted material in a manner for which it was not intended. A word of caution for companies is how to use Generative AI and leverage content. It’s a tightrope walk. Not even accidentally using copyrighted content, directly or unintendedly, without adequate protection can cause significant penalties. How could we reduce the risk of getting stuck in an IP violation? Here are a few recommended steps. 1.     AI developers (individuals /organizations) must ensure that they comply with the law regarding acquiring data to train their models. 2.     Creators, both individual content creators and brands that create content, should take steps to examine risks to their intellectual property portfolios and protect them. 3.     Businesses should evaluate their transaction terms to write protections into contracts. As a starting point, they should demand terms of service from generative AI platforms that confirm proper licensure of the training data that feed their AI. Finally, with appropriate protection, businesses can build portfolios of works and branded materials, meta-tag them, and train their generative AI platforms to produce authorized, proprietary (paid-up or royalty-bearing) goods as sources of instant revenue streams. I welcome your thoughts and views on the topic. #AI #Leadership Bharat Amin, NACD.DC ML Kabir Sandeep (Sandy) M. Krishnan CA Randhir Mazumdar Dr. Swati Karve, PhD Psychology Ashish Saxena Shiny Skaria