Engineering Patent Applications

University License Agreement Common Mistakes. I recently helped out a friend negotiate a spin-out license agreement with his university. As a practicing lawyer, I negotiated dozens of these agreements. I thought that sharing some common mistakes could be helpful to others. 1. Not ensuring that the License grant contains all necessary rights. Not only does the company need a license to the patents, but also to any other university technology or materials that are necessary to commercialize the licensed patents (e.g, cell lines or know-how). 2. Not addressing future improvements. Ideally, get rights to any improvements that might be developed after the effective date of the license agreement. Alternatively, include a right for inventions related to the licensed patents over a particular period of time, or that relate to any improvements that are dominated by the licensed patents. At a minimum, get an option to negotiate a license to the improvements. 3. Not ensuring unencumbered sublicensing. Giving the University any right to approve a sublicense (including such approval shall not be unreasonably withheld) means you actually do not have the right to sublicense. Also, the University should assume all sublicensing agreements if the license agreement terminates before the sublicense terms expire. 4. Not limiting the scope of the University’s retained rights. University’s retained rights should be limited to non-commercial research and educational purposes, and should not allow conduct of clinical trials. To avoid sponsored research by a competitor allowing a competitor to gain access to improvement inventions, consider entering into a sponsored research agreement. 5. Not negotiating anti-stacking provisions for sublicenses. It is common to get the right to decrease the total amount of royalties paid to the university in a royalty period by a specified percentage if the company needs to obtain licenses from third parties to practice the licensed technology or commercially develop the licensed product. It is also important to reduce the sublicensing percentage owned to the university to reflect the number of licensors to be paid upon a sublicensing deal. Finally, it’s better to give equity than a right to a certain percentage of proceeds upon a liquidation event (IPO or sale). The equity grant is subject to dilution. Hope this helps! #licensing #techtransfer #entrepreneur #universityspinout

Coca Cola and WD-40 have something in common. Unlike many commercial products, they were never patented. This wasn't an oversight—it was a strategic decision. By skipping a patent, the creators ensured that the formula wouldn’t be made public. Patents require full disclosure of ingredients and processes, and once they expire, competitors are free to duplicate the product. WD-40 and Oca Cola makers chose long-term secrecy over temporary legal protection. This approach has worked for decades. Consider WD-40. Though it hit the market in 1958, its exact contents remain largely unknown. While some components like petroleum-based solvents are speculated, the full formulation is still locked in a vault at company headquarters. This decision has also helped WD-40 develop a kind of mystique. Its versatility—from loosening bolts to stopping squeaks to displacing moisture—is widely praised, yet its recipe remains out of reach. This kind of secrecy is rare in the chemical product industry, where patents are the norm. The formulae for both have been tested, reverse-engineered, and analyzed over the years, but no copy has gained the same traction. With no patent to read and no requirement to disclose more than the minimum for safety labeling, WD-40 and Coca Cola remain two of the best-kept trade secrets in consumer goods.

🚨 𝐀𝐮𝐬𝐭𝐫𝐚𝐥𝐢𝐚𝐧 𝐀𝐈 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐀𝐥𝐞𝐫𝐭!🚨 Fresh news from the Federal Government’s Digital Transformation Agency (DTA)! They've just rolled out a new standard for Agencies on how to draft their AI transparency statements. So, what's the scoop? Let's break it down! 👇 🗓️ 𝐌𝐚𝐫𝐤 𝐘𝐨𝐮𝐫 𝐂𝐚𝐥𝐞𝐧𝐝𝐚𝐫𝐬 By 28 February 2025, all Agencies must make their AI adoption transparency statements public. We knew they'd have to update these statements regularly, covering: - Compliance with the AI policy - How they monitor AI effectiveness - Their efforts to shield the public from any negative impacts But this new standard gives us a lot more information on what these statements will look like. 🤔 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 The aim here is to standardise these statements across the board, making it easier to build trust, understand the AI landscape, and compare approaches across different agencies. It’s all about transparency, baby! 🌐 📋 𝐖𝐡𝐚𝐭 𝐍𝐞𝐞𝐝𝐬 𝐭𝐨 𝐁𝐞 𝐈𝐧𝐜𝐥𝐮𝐝𝐞𝐝? The DTA demands that transparency statements are clear and in plain language – no technical jargon! This may not be an easy task when addressing AI! Here’s what the DTA wants to see in these transparency statements: 𝐈𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧𝐬: Why the agency is using (or thinking about using) AI 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Where and how AI is being used based on the AI use classification system in the standard (think decision-making, fraud detection, law enforcement, etc.) 𝐏𝐮𝐛𝐥𝐢𝐜 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧: Any AI the public might interact with directly, without a human in between 𝐀𝐈 𝐀𝐬𝐬𝐮𝐫𝐚𝐧𝐜𝐞: Governance and processes to ensure AI works as intended 𝐋𝐞𝐠𝐚𝐥 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: How the AI system follows all applicable laws (yes, this means a legal audit to understand what laws apply! 🕵️♂️) 𝐏𝐮𝐛𝐥𝐢𝐜 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Steps to protect people from any negative impacts 𝐏𝐨𝐥𝐢𝐜𝐲 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞e: Meeting all requirements under the responsible AI use policy 𝐔𝐩𝐝𝐚𝐭𝐞 𝐇𝐢𝐬𝐭𝐨𝐫𝐲: When the statement was last updated 🖥️ 𝐖𝐡𝐞𝐫𝐞 𝐭𝐨 𝐅𝐢𝐧𝐝 𝐈𝐭? These statements need to be front and centre on each Agency’s website—just like their Privacy Policies. And don’t forget, Agencies must also include a public contact email for any curious minds out there. 🔄 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐩𝐝𝐚𝐭𝐞? At least once a year, but more often if there are significant changes or new factors affecting the accuracy of the statement. 🤷♂️𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐚𝐥𝐥 𝐦𝐞𝐚𝐧? These new AI transparency statements are a goldmine for organisations aiming to work with the Federal Government. It helps them align with agency AI practices from day one, suggesting improvements and ensuring compliance. Plus, it could spark healthy competition among agencies, pushing everyone towards top-tier responsible AI use—a win for both the public and the agencies! 💼✨ Stay tuned! 🤖✨ #AI #ArtificialIntelligence #AIRegulation #Regulation #Policy

She took out a loan at 25% interest just to afford the patent office fee. What she created still holds the fashion industry together at the seams, more than a century later. Helen Blanchard wasn't supposed to be an inventor. Her family had been wealthy Portland shipping merchants until the financial panic of 1866 wiped them out. After her father died, they lost everything - their family home. She ended up working the floor of a Boston clothing factory. Day after day, the same thread running through every problem. Sewing machines could only sew straight. You couldn't reinforce a buttonhole. You couldn't work with stretchy fabrics. You couldn't seal fabric edges to prevent fraying. Everything that needed strength or flexibility had to be done by hand. Helen had no technical training, no engineering background, no capital. She went about the problem sideways. Helen changed the machine so the needle moved side-to-side as it went forward. ////\ instead of ———— That back-and-forth pattern made possible what straight stitches couldn't: seal edges, reinforce buttonholes, handle stretch fabrics. Everything that had required hand-sewing could now be done by machine. She wasn't a one-stitch wonder. Over the next 42 years, Helen was granted 28 patents. Twenty-two were adopted by large commercial factories - a 78% commercialization rate that remains extraordinary even today. Her machines became factory standard equipment and helped enable the growth of affordable ready-to-wear clothing. The financial success matched the technical achievement. Helen and her sister Louise founded two companies together. By the time she was 50, she'd earned enough to buy back her family's ancestral estate in Portland - the same property they'd lost decades earlier. But Helen recognized something her fellow industrialists didn't. Her labor-saving machines were displacing the women she'd worked alongside on factory floors. In the 1890s, she moved to New York and devoted herself to helping women who had lost work in clothing factories because of her inventions. She used her fortune to support them - not as charity, but as responsibility. AI is displacing workers at scale right now. Helen Blanchard faced the same dilemma 150 years ago - and actually tried to solve it. What can today's tech builders learn from her? #ipidity #patents #sheinvents #fashiontech #seamslegit #stitchesgetriches

Startups don’t fail because of bad ideas. They fail because of missed opportunities to protect and scale those ideas. If you're building a startup and don't have an Intellectual Property (IP) strategy, here's what you might be risking: ❌ Your tech gets copied before you even hit market ❌ Investors hesitate because your innovation isn’t protected ❌ You lose competitive advantage in your own space Now imagine this instead👇 ✅ You file a patent early → Your invention is protected ✅ You trademark your brand → Your identity is secure ✅ You build IP assets → Your company valuation goes up In 2025, IP isn't optional — it's your startup's shield and sword. The earlier you align your innovation with an IP strategy, the stronger your foundation for scaling, attracting funding, and entering global markets. 💡 Here’s what every startup should start with: ✅ Identify your core innovation ✅ Consult an IP expert (yes, early-stage!) ✅ File what's worth protecting — patents, trademarks, designs ✅ Align your IP with business goals ✅ Revisit and revise as you grow If you're a founder or working with a startup, this is your gentle nudge to take IP seriously. 🔁 Share with someone who needs to hear this. 💬 Got a question on startup IP strategy? Drop it below or DM — always happy to provide guidance. #Startup #IntellectualProperty #IPStrategy #Innovation #Patents #stanford #SJSU #Trademarks #StartupIndia #Founders #ViksitBharat #siliconvalley

𝗔 𝗽𝗿𝗼𝘃𝗶𝘀𝗶𝗼𝗻𝗮𝗹 𝗽𝗮𝘁𝗲𝗻𝘁 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗮𝗿𝗮𝗰𝗵𝘂𝘁𝗲—𝗶𝘁 𝗼𝗻𝗹𝘆 𝘀𝗮𝘃𝗲𝘀 𝘆𝗼𝘂 𝗶𝗳 𝗶𝘁'𝘀 𝗳𝘂𝗹𝗹𝘆 𝗽𝗮𝗰𝗸𝗲𝗱. "File a provisional—it's quick and cheap!" This advice destroys more patents than any examiner rejection. Here's what happens too often: • Company A rushes a two-page provisional before their investor demo. It describes their "AI-powered analytics platform" in marketing terms. No technical details. No implementation specifics. Cost saved: $10,000.    • Company B invests two extra weeks creating a complete provisional with technical architecture, algorithms, and multiple implementations. Cost: $15,000.    One year later, both convert to nonprovisional patent applications. Fast forward to patent litigation or acquisition due diligence: • Company A's patent gets invalidated—their provisional didn't "sufficiently describe" what they're claiming • Company B's patent survives every challenge That $5,000 "savings"? It just cost Company A everything. The provisional trap has three lethal variations: 𝗧𝗵𝗲 "𝗡𝗮𝗽𝗸𝗶𝗻 𝗦𝗸𝗲𝘁𝗰𝗵" 𝗧𝗿𝗮𝗽: Filing slideshows, marketing materials, or back-of-napkin drawings. Courts require enough detail that someone skilled in your field could build your invention. Your investor pitch deck doesn't qualify. 𝗧𝗵𝗲 "𝗞𝗶𝘁𝗰𝗵𝗲𝗻 𝗦𝗶𝗻𝗸" 𝗧𝗿𝗮𝗽: Dumping every document into a provisional without connecting them to specific claims. Volume doesn't equal validity. 𝗧𝗵𝗲 "𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗜𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻" 𝗧𝗿𝗮𝗽: Your product pivots significantly after filing, but you can't add new matter to claim priority. That provisional now protects something you don't sell. Two solutions that actually work: 1. 𝗙𝗶𝗹𝗲 𝗻𝗼𝗻𝗽𝗿𝗼𝘃𝗶𝘀𝗶𝗼𝗻𝗮𝗹 𝗽𝗮𝘁𝗲𝗻𝘁 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗵𝗲𝗻 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲. Yes, they cost more upfront. They're also much less like to explode later. 2. 𝗜𝗳 𝘆𝗼𝘂 𝗺𝘂𝘀𝘁 𝗳𝗶𝗹𝗲 𝗮 𝗽𝗿𝗼𝘃𝗶𝘀𝗶𝗼𝗻𝗮𝗹 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗮𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗮𝘀 𝘆𝗼𝘂 𝗰𝗮𝗻. Include architecture diagrams, code snippets, alternative implementations, use cases. Pretend you're explaining it to a competitor's engineering team—because someday, you might be. If you wouldn't jump out of a plane with a hastily-packed parachute, don't bet your company on a hastily-drafted provisional. Need to file fast but file right? Let's talk strategy. #patents #intellectualproperty #startups

For patent eligibility rejections at the USPTO, you are often at the mercy of the patent examiner. One common sticking point is whether the output of a result by a computer is sufficient to constitute a practical application, rendering a claim patent eligible. Usually, the output of a result by a computer is insufficient, but it can depend on the context. As I’ve mentioned before, I read ex parte appeal decisions from the PTAB on a daily basis, looking for decisions that can help me understand how the PTAB applies the law. A few months back, the PTAB issued a decision finding an output from computer (i.e., “an outputter”) was sufficient to constitute a practical application, in the context of physical devices recited at a high level of generality. I found the decision encouraging to an extent, and used it in an argument in a recent Response to Office Action that I filed for a client. The claims in Ex parte Taguchi, Appeal 2024-000087, Application 16/474,477 (March 2024) (nonprecedential) were directed to a failure diagnosis system that included a motor, a reduction gear, an encoder and a sensor recited at a high level of generality. The inventive concept in the claim was introduced by a processor that acquired data and determined whether the reduction gear indicates a sign of failure, as well as “an outputter that outputs a result of the determination of whether the reduction gear indicates the sign of failure.” (Pages 2 and 3). The PTAB compared the claim of Ex parte Taguchi to the claims in CardioNet LLC v. InfoBionic, Inc., 955 F.3d 1358, 1366 (Fed. Cir. 2020), stating: “The output of this system and process is the beneficial predictive determination of whether the reduction gear indicates the sign of failure. Claim 1 does not recite the further use of this output, but like the claims in CardioNet, this additional step is not required to improve the technology at issue. Simply put, reduction gear failure detectors, like cardiac monitors, are a technology to themselves, and can be improved without further use by other technologies.” I know most patent practitioners think it is pointless to cite nonprecedential ex parte appeal decisions from the PTAB in Office Action responses, but I have found it helpful throughout my career. Thus, if you are faced with an examiner who is telling you that your client’s claims need to recite using an output related to a machine to operate the machine in order to render the claims patent eligible, it can’t hurt to argue that CardioNet and Ex parte Taguchi say otherwise. #patents #patentlaw #uspto

Disclosure Letter in Mergers & Acquisitions (M&A) In an M&A transaction, a Disclosure Letter is a formal document provided by the seller to the buyer. It lists any exceptions, qualifications, or clarifications to the warranties and representations made in the main sale agreement. This letter ensures that the buyer is fully aware of any potential risks or liabilities before completing the deal, helping to protect the seller from future legal claims and providing transparency in the transaction. Scenario: ABC Corp. has agreed to acquire XYZ Ltd. for $50 million. As part of the acquisition agreement, XYZ Ltd. is required to provide certain warranties, such as: 1) The company has no ongoing legal disputes. 2) It has full ownership of all intellectual property. 3) There are no hidden liabilities or undisclosed financial obligations. However, XYZ Ltd. must disclose any exceptions to these warranties. This is where the Disclosure Letter comes in. XYZ Ltd.’s Disclosure Letter to ABC Corp: 1. Legal Disputes Disclosure: "XYZ Ltd. is currently facing a legal dispute with a former supplier regarding a contract termination. The claim is valued at $500,000 and is currently in settlement discussions." 2. Intellectual Property (IP) Rights Disclosure: "Some of XYZ Ltd.’s software includes third-party licensed technology, which requires an annual renewal fee of $200,000." 3. Financial Liabilities Disclosure: "XYZ Ltd. has an outstanding tax liability of $2 million, which is currently under review with tax authorities." 4. Employee Contracts Disclosure: "Certain key employees have ‘change-of-control’ clauses in their contracts. If they resign after the acquisition, they are entitled to severance payments totaling $1.5 million." (Note: This means that if the company is acquired by ABC Corp., these employees have the right to resign voluntarily and still receive a severance payment (a compensation package for leaving). #Disclosure #Letter #Acquisition #Employee #Payments #Tax #Liability #Financial #Conditions

𝗘𝗣𝗢 𝗕𝗼𝗮𝗿𝗱 𝗼𝗳 𝗔𝗽𝗽𝗲𝗮𝗹 𝗖𝗹𝗮𝗿𝗶𝗳𝗶𝗲𝘀 𝗗𝗶𝘀𝗰𝗹𝗼𝘀𝘂𝗿𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗲𝗻𝘁𝘀: 𝗔 𝗠𝘂𝘀𝘁-𝗥𝗲𝗮𝗱 𝗳𝗼𝗿 𝗣𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿𝘀 The EPO's recent decision in T 1669/21, concerning a method for predicting wear in metallurgical vessels using a "computational model", offers valuable insights into the current EPO approach to examining machine learning patent applications. The case, which was rejected due to insufficient disclosure, provides important lessons for patent practitioners. 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗽𝗮𝘁𝗲𝗻𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲? The decision underscores the need for meticulous attention to detail when drafting patent applications for machine learning inventions. Specificity is paramount. While the EPO accepts broad claims, these must be supported by a commensurately detailed and enabling disclosure. Here's what the EPO expects - 𝗖𝗹𝗲𝗮𝗿 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹: The type of model (e.g., neural network, support vector machine), its architecture, and the specific algorithms used must be explicitly stated. Simply referring to a generic "computational model" is insufficient. 𝗗𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗺𝗮𝗽𝗽𝗶𝗻𝗴: The application must provide clear guidance on how to select, pre-process, and represent input parameters within the model. This includes specifying how to handle time-varying or multi-dimensional parameters. Examples are crucial for illustrating these steps. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀: The description should cover the training data used, the training process, and the criteria for evaluating model performance. It should also address potential challenges such as data scarcity and the prevention of artefacts from random correlations. 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Where possible, include concrete, workable examples demonstrating the implementation of the invention. This could involve providing sample data, model configurations, and training scripts. 𝗪𝗵𝘆 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁? The EPO's focus on specificity stems from the Article 83 EPC requirement for sufficient disclosure. The patent application must enable a skilled person to carry out the invention without undue burden. This is particularly challenging for machine learning inventions, which often involve complex models and data-driven processes. 𝘓𝘪𝘯𝘬𝘴 𝘵𝘰 𝘧𝘶𝘭𝘭 𝘣𝘭𝘰𝘨 𝘱𝘰𝘴𝘵 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴. #EPO #MachineLearning #Patents #SufficiencyOfDisclosure #PatentPractice #CaseLaw

When America's most famous inventor, Thomas Edison, tried to claim credit for his patent in the 1880s, a self-taught genius named Granville T. Woods took him to court and won. In the late 19th century, train travel was incredibly dangerous. With no reliable way for moving trains to communicate, horrific collisions were tragically common. 🚂 Granville T. Woods, born in Ohio in 1856, saw this problem and used his brilliant mind to find a solution. He was a mostly self-taught electrical engineer with a gift for seeing how things worked. In 1887, Woods patented a revolutionary device he called the 'Synchronous Multiplex Railway Telegraph.' It was a system that allowed moving trains and stations to communicate with each other through telegraph lines. For the first time, dispatchers could know where trains were in real-time. This simple ability to communicate drastically reduced the risk of head-on and rear-end collisions, saving countless lives. But this incredible, life-saving invention caught the eye of Thomas Edison. Edison filed a legal claim against Woods, stating that he was the rightful owner of a similar technology. Woods, an African American man with far fewer resources, had to defend his patent in court against one of the most powerful and wealthy men in America. The courts sided with Woods, affirming him as the true inventor. After being defeated, it's said that Edison was so impressed he offered Woods a prominent position at one of his companies. Woods turned it down, choosing his own independence and integrity. Granville T. Woods, sometimes called the 'Black Edison,' went on to secure over 60 patents. His story is a powerful reminder of how character and truth can overcome even the biggest challenges. 🙏 Sources: Encyclopedia.com, National Archives