Intellectual Property in Innovation

Lets Learn #Quantum – Post #16: Post-Quantum Cryptography (PQC) The Invisible Safe: Why Hackers Are Stealing Data They Can't Read Yet The biggest short-term impact of quantum computing isn't what it can create. It is what it can destroy. Right now, our digital world relies on encryption algorithms like RSA to protect banking, emails, and cloud data. Standard supercomputers would take thousands of years to crack them. But quantum computers change the rules. Running Shor’s Algorithm, a quantum computer could break today's encryption in hours. The Threat Happening Right Now Why care today if full-scale quantum computers are still year away? Because cybercriminals are actively executing a strategy known as Harvest Now, Decrypt Later (HNDL). Imagine a thief stealing a locked titanium safe. They cannot open it today, so they hide it in a basement and wait. Years from now, a new tool is invented that pops that safe open instantly. That is HNDL. Bad actors are intercepting and archiving sensitive enterprise data today, waiting for the day a quantum computer can unlock it. If your data needs to remain secret for the next decade, it is already at risk. Enter PQC: Upgrading the Locks Post-Quantum Cryptography (PQC) is the defense. It is a new generation of math shields designed to resist attacks from both conventional and quantum computers. The breakthrough? PQC runs seamlessly on your current servers, smartphones, and cloud platforms. Think of it as swapping out a traditional door lock for a multi-dimensional biometric scanner. The house stays the same; only the lock changes. Instead of traditional math, PQC relies on Lattice-Based Cryptography. Think of it like a maze with thousands of overlapping dimensions instead of two. Even a quantum computer gets completely lost trying to find the exit. The Strategic Reality You cannot swap out the security architecture of a global enterprise overnight. Migrating infrastructure takes years, which is why forward-thinking leaders are already auditing networks and testing PQC algorithms today using a hybrid approach. The quantum threat is not a future IT issue. It is a current strategic risk. The question for leadership is no longer: "When will a quantum computer be built?" The real question is: "Will our data still be secure when it arrives?" #QuantumTechnology #PostQuantumCryptography #PQC #QuantumSecurity #CyberSecurity #QuantumComputing #DigitalTransformation #DataProtection #TechnologyLeadership Co-authored with Atul Tripathi Sundar Ram, Sachin Arora, Himanshu Ghawri, Azizur Rahman, Shivendra singh, Prasun Nandy, Jaydeep Sarkar, Joydeep Roy, Arihant Garg, Amit Kumar, Hetal Shah, Arun Rangaraju, Sayantan Chatterjee, Rajesh Kumar Ojha, Dr. Raghav Manohar Narsalay, Praveen Sasidharan, Sundareshwar K (Sundar), Manu Dwivedi, Venkat Nippani, Himadri Ganguly, Ritesh Jain, Abhijit Chakraborty, Sumit Srivastav, Anit Shanker #soyoucan

Any organisation developing or fine tuning AI systems, particularly LLMs, needs to be ALL OVER how their IP is protected. IP protection for model components such as weights is a complex legal issue. Model weights in AI are numerical values that determine the strength of connections between nodes in a neural network. AI models adjust these weights during training to improve predictions or decisions based on input data. This adjustment process allows the model to "learn" from vast amounts of data, fine-tuning its accuracy in tasks such as image recognition or language translation. Traditionally, copyright does not extend to facts or the functional aspects of a creation. Model weights straddle this boundary, being both a product of immense computational effort and a representation of the underlying data on which the AI was trained. Current legal frameworks do not explicitly address the status of AI-generated data, including model weights (however in the USA it seems to be the case AI generated data is not protected by copyright). The principles underlying the EU’s Software and Database Directives provide a foundation for analysis. While the software enabling AI functionalities might be copyrightable as a literary work, the status of model weights is more ambiguous due to their nature as outputs from processing vast datasets, rather than direct human creation. The key question revolves around originality and the role of human authors in the creation process. According to the CJEU, for a work to be protected under copyright, it must be the author's own intellectual creation, reflecting the author's personality and choices (the Painer case). These criteria becomes blurred when considering model weights, where the "creation" process is largely automated and driven by algorithms following predefined objectives. Given the challenges associated with copyright protection, the sui generis rights established under the EU Database Directive offer an alternative approach. These rights protect substantial investments in obtaining, verifying, or presenting the contents of a database, regardless of originality. Model weights could potentially be viewed as part of a database, particularly if one considers the extensive computational resources and expertise required to train AI models. However, this interpretation hinges on whether model weights can be considered a "database" in the legal sense. The Directive's broad definition of databases may provide sufficient ground for this argument, especially given the structured nature of model weights within an AI's architecture. The potential application of copyright or sui generis database rights to AI model weights has significant implications for the commercialisation of AI technologies. Recognising these protections would grant developers legal mechanisms to control the use, distribution, and modification of their AI models, influencing licensing agreements and business models within the AI industry.

Everywhere I go lately (Phoenix, Chicago, Tulsa, Boise, Kansas), the same conversation happens once university leadership leaves the room. University IP commercialization isn’t “struggling.” It’s actively taxing innovation. It isn't working. The university commercialization model was built for a world where invention was rare, local, slow, and valuable on its own. None of that is true anymore. What is encouraging is that there are real alternatives and they’re already working in pieces around the world. One path is radical simplification: publicly funded research treated as public infrastructure. Publish it. Make IP open or royalty-free. Push commercialization downstream through founder-led companies, SBIR-style funding, and execution instead of toll booths. Defense Advanced Research Projects Agency (DARPA) figured this out decades ago. Another is founder-first ownership. Default IP to founders, cap university equity so it doesn’t block growth, eliminate upfront fees. Massachusetts Institute of Technology quietly moved this direction years ago, and its impact comes from companies formed — not patents licensed. A third is sponsored spinout studios, where universities stop pretending they can judge markets and partner instead with experienced operators who know how to build companies. Imperial College London and ETH Zürich are already doing this, treating professors as inventors and entrepreneurs as builders. There’s also time-bound IP reversion; if licensed research isn’t commercialized within a defined window, rights revert or enter a shared commons. This alone would end the practice of innovation dying in filing cabinets. Non-exclusive-first licensing is another underused lever. It lowers risk, increases experimentation, and speeds diffusion. NIH has shown it works, even if it caps upside for rent-seekers. And then there’s the option no one likes to admit: decoupling universities from commercialization entirely. Let universities focus on research and talent. Let markets, founders, and capital do the rest. Stanford University didn’t win because of its licensing office; it won because its graduates ignored it. The throughline here is simple: you cannot tax something into existence. You cannot committee your way to entrepreneurship. And you cannot confuse activity, demo days, or innovation districts with outcomes. If regions like New Mexico, Bentonville, Arizona, or Houston want to lead in their sectors, the move isn’t more branding or more centralized hubs. It’s stripping friction out of IP, stopping the handicapping of founders, and measuring results instead of attendance. Universities aren’t commercializing innovation. They’re taxing it. The longer we pretend that’s fine, the more valuable research we leave sitting on shelves. If you’re building startups, shaping policy, or working inside a university and know this needs to change...

From former USPTO Director Andrei Iancu: "The Trump administration will soon announce new leadership at the U.S. Patent and Trademark Office, the relatively low-profile but hugely influential federal agency that evaluates patent applications and sets intellectual property policies that affect the entire economy. The next USPTO director will have many issues to address, not least of which will be the loud agitation of activists who believe that the USPTO issues too many low-quality patents. But before federal policymakers cave to these activists and impose new regulations to crack down on the supposed torrent of “bad patents,” they would do well to recognize one crucial fact: the existence of a patent quality crisis is contradicted by the data." "Patents dramatically improve the odds of a company being able to take its invention to market. Startups that own a patent are 47% more likely to raise venture capital funding and 84% more likely to be acquired; the temporary market exclusivity conferred by a patent helps assure potential investors that they’ll be able to earn a return." "This leads to thousands of meritorious inventions being discarded each year for no good reason. It’s economic self-sabotage. China currently leads America in 37 out of 44 critical and emerging technologies, according to a recent study. Regaining the technological lead will require the United States to harness all the contributions its inventors make, not just some of them. That cannot happen without a robust intellectual property system." "The United States stacks up well against our peers when it comes to keeping out “bad patents.” But to succeed in the battle for 21st century technological supremacy, we’ll also need to ensure that we do not keep out good patents. The incoming administration and USPTO leadership can achieve this by strengthening patent protections and rejecting false narratives about the prevalence of low-quality patents." https://lnkd.in/gPH2f92C Originally published in The Well News with co-author David Kappos https://lnkd.in/g5pcWKjM USPTO, Andrei Iancu, The Richmond Observer, U.S. Office of Personnel Management (OPM), Office of Management and Budget, #fork, #patent

What Role Does Intellectual Property (IP Play in Business Performance a report by the European Patent Office (EPO) and the European Union Intellectual Property Office (EUIPO) has found the following: - Companies that own patents, trademarks, or designs generally outperform those without such IP rights. - SMEs produce a 44% higher revenue per employee when they own any IPR. - On average, IPR owners achieve 23.8% more revenue per employee.. These firms also pay salaries that are 22% higher. - Patent-owning firms show the largest revenue jump at 28.7%, along with notably higher wages. - Large companies often register multiple IPRs together, and fortify their competitive advantage. - Nearly half of large firms own IPRs, but fewer than 10% of SMEs own them. - Employment is almost doubled in IPR-owning firms compared to non-owners. - Information & communication and manufacturing sectors display the highest IPR ownership. - Improving access to IP protection for SMEs could significantly drive innovation and economic performance. The full report is attached for your reading, review, and sharing. #IntellectualProperty #EconomicPerformance #SMEs #Innovation #IPRights #RevenueGrowth #Employment #PatentOwnership #BusinessDevelopment #EUReport 

A distributed ledger built to last cannot rely on security assumptions that may not last. Quantum-safe preparation is necessary before the pressure arrives, because records and private keys must remain protected beyond today’s cryptography. The practical issue is time. Blockchain records can remain valuable for years, while the cryptographic methods that protect them may face new pressure as quantum computing advances. This does not mean organizations should panic or replace everything immediately. It means they need to understand which assets require long-term protection and where a migration path may be needed. Cryptographic signatures and private keys deserve special attention. If they protect ownership and access to assets, the question is not only whether they are safe today. The question is whether they can remain trusted across a longer horizon. Quantum-safe preparation is therefore a governance problem as much as a technical one. Organizations may need safer algorithms and migration planning, but the first step is knowing where the exposure sits. The goal is simple: preserve trust while security conditions change. For blockchain, durability is not enough if protection does not evolve with the system. #Blockchain #QuantumSecurity

Last week, Ethereum announced it is forming a post-quantum working group because they can read the room: cryptography isn’t a “future upgrade,” it’s a ticking dependency and a grown-up admission that digital trust has a shelf life. In 𝑵𝒐𝒘 𝑾𝒉𝒂𝒕? I called this the Big Crunch: the moment quantum collapses the economics of breaking today’s public-key cryptography. Unlike Y2K, this isn’t a bug you patch. It’s a global migration you either start early or you finish in panic. And timelines are already wobbling, Google research from 2025 suggested breaking RSA could need 20x fewer qubits than previously thought of. Unfortunately, most leaders treat quantum like a storm on the horizon: “interesting, but not today.” That’s a mistake. Attackers can already copy encrypted traffic and files now, store it, and unlock it later when quantum tools get good enough. That’s not theory. It’s a rational investment strategy from an adversary's perspective. And if a major system ever gets quietly cracked, you won’t hear about it when it happens. You’ll hear about it after someone has made money from it. After all, the incentives reward silence; think Enigma, but automated, monetized and at scale. The smart path is boring, but effective: start upgrading before the break, and form working groups like Ethereum to start today. It also means running hybrid encryption, today’s algorithms paired with post-quantum ones, across the places where trust lives: web connections (TLS), logins and identity, enterprise software, key management and HSMs, cloud services, and blockchain signatures. Do it early and you turn a cliff-edge event into a controlled rollout. Wait too long and it’s not just your future data at risk, old encrypted backups, archived emails, contracts, customer records, IP can become readable years later. In other words: you don’t just lose security going forward. You lose your history.

From Idea to Global Impact: Why Startups Can’t Ignore Patents Every startup begins with an idea. Some grow into global brands—others fade away. Here’s the difference nobody talks about: patents. ▶️ A founder in a co-working space patents her AI tool—not just in India, but also in the US & Europe. Investors lean in. ▶️A SaaS startup files early, creating a shield against copycats. Suddenly, they have leverage in partnerships. ▶️A health-tech company protects one breakthrough device—and that single patent becomes their ticket to global expansion. Patents aren’t just paperwork. They’re business assets that: ✔ Attract investors ✔ Build customer trust ✔ Open global markets The question is: Are you protecting just locally—or are you thinking global from Day 1? Your innovation is your future. Protect it like it matters—because it does. 👉 Startup founders: Ready to turn ideas into global advantage? Let’s talk IP strategy. #startups #invention #innovation #ipstrategy #patent #trademark #global #business #investors #funding

🎓 Are universities losing money by trying to make money? That’s the question posed by Joshua M. Pearce, Professor of Innovation at Western University, in this article published in The Conversation. His study uses full-cost accounting to assess the real return on university patenting. He shows that universities typically lose money on their patents, and the primary reason why is the indirect costs of faculty time. The Bayh-Dole Act (1980) allowed U.S. universities to patent publicly funded research, triggering an explosion in technology transfer offices. The assumption? That patents would deliver revenue and drive innovation. But Pearce’s worked example shows that once all costs are counted, including the hidden opportunity cost of faculty time, the ROI is often strongly negative. In one case, time diverted from grant writing to patenting cost over 33 times more than the IP income generated. Pearce’s analysis suggests universities should look at alternatives: 🔹 Transfer IP ownership to inventors: Let faculty commercialise their own research. This externalises cost and risk while preserving reputational and network benefits for the university. 🔹Focus where patents aren’t the best tool: In areas like software, IP isn’t the bottleneck. Support spinouts with lean, low-cost models instead of patent-heavy approaches. 🔹Target patenting strategically: Prioritise high-potential sectors like biotech, and avoid applying the same model across all disciplines. 🔹Outsource commercialisation: Third-party firms can manage IP on a performance basis, reducing overheads. 🔹Embrace open science and open licensing: Not all impact needs to be monetised. Sharing knowledge widely can accelerate innovation and improve public outcomes, without the cost of protection and litigation. The core insight is this: faculty time is not free. It’s the university’s scarcest and most productive resource. Using it inefficiently, especially in gaining patents that don't deliver ROI has serious opportunity costs for research, teaching, and real-world impact. As financial pressures intensify, universities must rethink how they pursue innovation. Not all IP is an asset, and not all impact requires a patent. Read Pearce’s full article (with easy to follow methodology): 👉 https://lnkd.in/gXfUkeGT #HigherEd #InnovationPolicy #TechnologyTransfer #FacultyTime #OpportunityCost #BayhDole #OpenScience #Spinouts #AcademicLeadership #IPStrategy #UniversityFinance #ResearchImpact #EntrepreneurshipInHigherEd https://lnkd.in/gS9jemm6

The data from the Mitsui & Co. Global Strategic Studies Institute presents a subtle but decisive shift in how #semiconductor leadership is being secured—not just through manufacturing scale, but through where knowledge is legally anchored. At a surface level, the numbers are straightforward: Most global leaders—Tokyo Electron, Samsung Electronics, Applied Materials, TSMC, and ASML—file a significant share of patents in the United States, often exceeding 70–90%. In contrast, Chinese entities like Chinese Academy of Sciences and NAURA Technology Group file almost entirely within China (~98%). But the strategic signal sits beneath this distribution. First insight: The US remains the global enforcement ground for IP. Filing in the US is not just about market access—it is about legal strength. The US patent system still acts as the most credible arena for defending high-value semiconductor innovations. This explains why even non-US companies anchor their IP there. Control in semiconductors is as much about litigation readiness as it is about fabrication capacity. Second insight: China is building a self-contained innovation loop. The near-total domestic filing by Chinese institutions signals a deliberate inward strategy. This is not a lag—it is a design choice. By concentrating patents locally, China is strengthening internal supply chains, reducing external dependency, and creating a protected innovation environment aligned with national priorities. Third insight: Two parallel IP ecosystems are forming. One is globally integrated, anchored around the US system. The other is domestically reinforced within China. Over time, this divergence could lead to limited interoperability—not just in technology standards, but in legal enforceability of innovation. Fourth insight: Patents are becoming strategic assets, not just legal instruments. In semiconductors, patents define control over process nodes, materials, lithography techniques, and equipment precision. Owning patents in the right jurisdiction determines who captures long-term economic value, who sets pricing power, and who controls ecosystem dependencies. This is where the conversation shifts from “innovation” to “ownership of innovation outcomes.” manufacturing builds the factory, but patents own the blueprint of the factory. One scales output, the other governs who is allowed to scale. For leadership teams, this has clear implications: R&D without a jurisdiction strategy is incomplete Market expansion must align with IP protection zones Partnerships need to account for where knowledge will be legally held National policy and corporate strategy are now tightly interlinked in deep tech sectors The semiconductor race is no longer only about nanometers. It is about where ideas are registered, defended, and monetized. Those who understand this will not just build technology—they will control its future value. DC* Dinwins