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August 4, 2026Maiwald Chooses Solve Intelligence for Selected AI-Supported IP WorkflowsSolve Intelligence is proud to share that Maiwald, one of Germany’s largest and most progressive intellectual property firms, works with Solve Intelligence on selected AI-supported workflows in its patent and legal practice groups. Following an evaluation period during which selected functionalities were assessed for approved internal use cases, Maiwald and Solve have formed an enterprise partnership for 2026. The rollout reflects the same careful approach Maiwald brings to its client work. Solve enables customers in Europe to process data on servers located within Europe and holds recognized information-security certifications. In addition, Solve offers contractual commitments designed to protect confidential professional information in line with applicable statutory confidentiality obligations and professional conduct requirements. Against that background, any use of AI-supported tools took place only within approved governance structures and under applicable professional, contractual, confidentiality and data-protection requirements. That balance of innovation and professional judgment is characteristic of Maiwald. The firm has spent decades setting a standard for IP work in Europe: rigorous prosecution, a formidable contentious practice, and interdisciplinary teams of patent attorneys and attorneys-at-law working side by side. The firm approached AI the same way, methodically, with a clear sense of where it adds leverage and where an experienced attorney’s judgment remains irreplaceable.Read article
July 13, 2026How Much of Your Patent Practice Should You Codify?Put your time into more strategic codification and judgment that sets your work apart, and lets a specialist carry and maintain the shared foundation for your patent work. AI is changing how in-house teams and outside counsel approach patent work. One reason is that it has raised the payoff for turning a practice into AI templates, instructions, and review criteria that run at scale. Let’s call that codification. A purpose-built platform already encodes a great deal of best practice out of the box, and a team can layer its own on top with custom AI templates and instructions. So the real question is not whether to codify, but how much to do yourself and how much to leave to a platform built for the work. There is no fixed formula, only a sensible division of labor. Key Takeaways: • AI has raised the payoff for codifying patent practice: a written standard now guides AI-assisted drafting and review directly, not just junior training. • A purpose-built patent platform encodes a large body of best practice out of the box, so a team can run on that foundation without writing its own. • Solve Intelligence maintains its shared foundation across more than 700 IP teams, keeping it current far more efficiently than any single team could alone. • The strategic choice is how much of your own practice to codify on top; scarce expert time belongs on the standards and judgment that differentiate your work. • A named professional still signs off on every filing regardless of the tools used, so accountability for the result never moves to the AI.Read article
May 28, 2026AI X IP Thought Leader Exchange with Solve Intelligence, Haynes Boone, and 100+ Leading Voices in IPSolve Intelligence partnered with AmLaw 100 firm Haynes Boone to bring together 100+ senior IP leaders in Silicon Valley for a candid discussion on how AI is being used in patent practice today, and how that's shifting over the next 12 months. The evening was led by Leah Poynter Waterland (Sr. Director, Cisco), Jeff Draeger (Chief Patent Counsel, Intel), David McCombs (Partner and Board Member, Haynes Boone), Tony Capecci (Director of Practice Innovation, Haynes Boone), and Mallun Yen (Founder, Operator Collective; IP leader and Ex-Cisco). The room was 70% in-house, drawing Heads of IP and senior IP leaders from across enterprise software and cloud (Microsoft, IBM, Snowflake), semiconductors (Intel, Applied Materials), life sciences (Johnson & Johnson, Pfizer, Gilead), mobility and autonomy (Tesla, Waymo), connected devices and networks (Cisco, Samsung Research America), and AI labs (Mistral AI). The remaining 30% were outside counsel from firms including BCLP, Finnegan, Perkins Coie, Procopio, and Skadden. Key Insights Today, Solve supports global IP teams deploying AI agents for large-scale tasks including search across millions of patent and non-patent literature, portfolio analysis on thousands of assets, and claim charting, focussed on transparent source-backed and auditable outputs. Every IP leader in the room had either rolled out an AI patent platform or was in active procurement. Successful AI implementation requires collaboration and trusted long-term partnerships between IP teams, vendors, and outside counsel. Solve CEO Chris Parsonson expects per-user token usage to grow more than 1000x over the next 12 months.Read article
May 14, 2026Teva v. Eli Lilly: The Four Drafting Choices That Won $176.5MThe Federal Circuit Panel's April 16, 2026 decision in Teva v. Eli Lilly reinstates a $176.5M verdict and draws a clear distinction under §112 between method-of-treatment and composition claims. The decision highlights the importance of drafting choices, like those made by Teva 2006. Teva shows how much §112 outcomes turn on drafting choices rather than the volume of disclosure. From a specification describing a single humanized antibody, the patentee framed the invention as a method of treatment, anchored the genus to a known biological target, and relied on humanization as routine art within the POSA's knowledge. These are structural choices, made years before litigation, that shape how a patent reads in enforcement decades later. These choices are exactly what purpose-built tools like Solve Intelligence are designed to surface early on, during drafting, when they can be considered consciously and proactively. The alternative may be that issues only become apparent during litigation, when it is often too late to change a decision that was made previously, whether intentionally or not. Key takeaways Teva v. Eli Lilly (Fed. Cir. Apr. 16, 2026) marks the first clear limit on Amgen v. Sanofi. Amgen's §112 calculus does not extend to method-of-treatment claims reciting an antibody genus. Same specification, same genus, yet the antibody claims failed in IPR while the headache claims won at trial. One humanized antibody, a known biological target, and routine humanization techniques were enough to satisfy §112. Four drafting choices won the case: claim category, functional anchoring, routine art, one embodiment. Teva is a re-opening event for life sciences portfolios with genus claims.Read article
April 3, 2026How Solve Intelligence Handles Invention Disclosures and Unstructured DataIf you've been drafting patents for any length of time, you know the real bottleneck is often not the drafting itself. It's the messy inputs that precede it: partial forms, internal review decks, or email threads where the inventive aspects are buried. Getting from that to a coherent starting point for a draft consumes time most practices simply can't afford. AI can perform much of that translation work: extracting what matters, flagging what's missing, and generating the necessary follow-up questions based on holes and shortcomings. But it must operate inside proper confidentiality controls, and its output requires attorney review before going near a draft. This guide covers how that works in practice in Solve Intelligence's platform . Key takeaways The disclosure bottleneck is upstream; AI structures messy inputs before the drafting phase begins. AI extracts features, normalises terminology, surfaces gaps, and generates inventor questions, but attorney review is mandatory. The danger is plausible but fabricated detail, not obvious errors. Watch for AI-generated parameters or 'helpful' specifics. Disclosures contain trade secrets and unpublished IP. Use only tools with verified zero-training, zero-retention policies and enterprise-grade security. A sensible pilot, without client approval, uses anonymised or historical disclosures to define 'good' output and track key metrics over limited timeframe.Read article
March 12, 2026Marbury Law sees 3x-4x efficiency gain from using Solve IntelligenceWhen we sat down with Bob Hansen for this conversation, we knew it would be grounded in both legal depth and real-world business experience. Bob is a founding partner of The Marbury Law Group and has extensive experience across patent prosecution, litigation, licensing, portfolio strategy, and complex IP transactions. But what makes his perspective particularly compelling is that he also brings 20 years of real-world experience as an engineer, program manager, and business executive in Fortune 50 companies and start-ups. He understands firsthand how innovation moves from idea to product, and how intellectual property law fits into that journey. That dual lens is exactly why we wanted to have this discussion. Bob evaluates technology not just as a patent attorney, but as someone who has managed engineering teams, navigated acquisitions and divestitures, raised capital, and built businesses. When someone with that background says AI has been transformative and backs it up with measurable 3 to 4x efficiency gains, it’s worth listening. Key Insights AI adoption requires proof. Bob and his team tested multiple tools before committing, and only moved forward once they saw quantifiable results. 3 to 4x efficiency gains changed the business case. By tracking his own drafting time, Bob demonstrated that AI-enabled workflows made fixed-fee work viable at partner rates. Demonstration drives adoption. Live drafting sessions, client transparency, and side-by-side cost comparisons created full buy-in from both clients and colleagues. Integrated chat removes friction. Keeping research, drafting, and revisions inside one contextual workspace eliminated copy-paste workflows and saved significant time. Context is a force multiplier. AI performs best when it understands the full invention disclosure, file history, and drafting materials in one place. Speed expands strategic value. Faster drafting didn’t just save time - it enabled better coverage, stronger enablement, and real-time responsiveness to client needs. About Marbury Law The Marbury Law Group is a premier mid-size, full-service intellectual property and technology law firm in the Washington, D.C. area, with additional strength in commercial law, litigation, and trademark litigation. Recognized by Juristat as a top 35 law firm nationwide and holding Martindale-Hubbell’s AV® Preeminent™ Peer Review Rating, Marbury serves clients ranging from Fortune 500 companies and mid-size technology businesses to high-tech startups and inventors. Its practitioners bring unusually wide-ranging experience, including former technology executives, government R&D managers, startup founders, in-house counsel, “big-law” attorneys, USPTO patent examiners, and judicial clerks. Marbury delivers “big-law” service with the flexibility and personal attention of a smaller firm, pairing high-quality work with efficient, budget-aware billing. Based near the USPTO, the firm has drafted and prosecuted thousands of U.S. and foreign patent applications and trademarks, and advises on IP strategy, diligence, and licensing. Formed in 2009 through the merger of two established practices (with roots dating back to 1994), the firm takes its name from Marbury v. Madison (1803), the landmark Supreme Court case that established judicial review.Read article
March 10, 2026Potter Clarkson Enhances Patent Practice with Solve IntelligenceSolve Intelligence is deployed at Potter Clarkson as a practitioner-led platform, designed to enhance - not replace - the expertise of experienced patent attorneys. The firm uses the technology primarily at a senior level, where skilled practitioners are able to prompt and interrogate the system effectively to guide high-quality outputs. By combining advanced AI capability with deep technical and legal experience, the platform enables senior attorneys to work more efficiently while focusing their time and judgement on strategic advice, complex analysis and client value. This reflects the firm’s long-standing philosophy that technology should strengthen the role of the practitioner, not substitute professional expertise. “At Potter Clarkson, our priority is delivering technically rigorous and strategically sound advice to our clients. We use Solve Intelligence as a tool in the hands of experienced patent attorneys - professionals who understand how to guide, challenge and refine AI-generated outputs. It allows our senior teams to concentrate on the aspects of drafting and prosecution where their judgement adds the greatest value, while maintaining full control over quality and client strategy.” Peter Finnie, Partner, Potter Clarkson Since rolling out Solve Intelligence’s Patent Copilot, the firm has tailored the platform to reflect its established house styles and drafting standards. This customisation reduces administrative burden and supports consistency across teams, enabling practitioners to engage with AI efficiently without compromising on quality, client-specific requirements, or the firm’s distinctive approach.Read article
February 16, 2026PTAB Case Studies of AI Disclosure Requirements: Part IArtificial intelligence (AI) is a fast-evolving field with new technical methods, systems, and products constantly being developed. This growth has also been reflected in the dramatic increase in patent filings for AI-related inventions. According to Patents and Artificial Intelligence: A Primer from the Center for Security and Emerging Technology, more than ten times as many AI-related patent applications were published worldwide in 2019 than in 2013, and the increasing trend has only continued since. Although AI-related patent applications have been on the rise, explicit guidance on patentability requirements have only recently begun to be published by patent offices around the world. Indeed, as a burgeoning field of technology, AI inventions have unique features, such as the importance of training data and the lack of explainability and predictability of trained AI models, that differentiate such innovations from traditional types of computer-implemented inventions (CII). These features raise questions about the interpretation of disclosure requirements, among other patentability requirements, for AI-related inventions. For example, how much information, such as source code, training data sets, or machine learning model architectures, should be provided to satisfy the written description and enablement requirements of Title 35 of the U.S. Code § 112(a) or analogs in other patent jurisdictions? As we await further official guidance from the U.S. Patent & Trademark Office (USPTO) on disclosure requirements for AI-related inventions, we can gather initial indications from recent patent prosecution decisions from the Patent Trial & Appeal Board (PTAB) on such issues. In this article, we study a selection of PTAB appeals decisions for applications for AI-related inventions rejected under § 112. To set the background, we first review a classification of AI inventions and USPTO guidelines on disclosure requirements for computer-implemented inventions. After analyzing three case studies, we conclude with general takeaways and best practices, which emphasize that applicants must disclose specific algorithms and implementation details, not just desired outcomes, to satisfy written description requirements.Read article
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