Edge is a company based in San Francisco that specializes in AI-driven patent writing tools. Edge aims to streamline the patent drafting process, helping inventors and legal professionals create high-quality patents more efficiently. The company's software assists in drafting claims, descriptions, and backgrounds for patents, potentially reducing errors and improving the overall quality of patent applications.
Garden Intelligence is an AI-powered platform designed to streamline and enhance the patent process for various stakeholders, including R&D organizations, inventors, patent prosecutors, and litigators. It combines AI reasoning models, a patent search index, and web scraping to provide tools for tasks such as invalidity searches, claim chart generation, and infringement analysis.
DeepIP, an AI-powered personal assistant designed to streamline the patent drafting process and manage responses to office actions. It aims to free intellectual property (IP) practitioners from tedious tasks, allowing them to focus on delivering greater value to their clients. DeepIP can summarize lengthy documents quickly, providing essential insights at a glance.
Patlytics a company specializing in AI-powered patent intelligence solutions. Patlytics offers a platform that assists with various aspects of the patent lifecycle, including patent drafting, prosecution, litigation, and portfolio management. The platform leverages AI and large language models (LLMs) to streamline patent-related processes and enhance efficiency for IP professionals.
Patented AI provides an essential tool to help individuals and companies protect against inadvertently sharing personal identifying information, trade secrets, and all other sensitive data with virtually all LLMs, enabling individuals across all industries to get on-device sensitive data checks and protection.
IP Copilot is an AI-powered platform designed to revolutionize intellectual property (IP) management, helping organizations discover, capture, curate, and protect their IP more efficiently. It uses AI to streamline the invention disclosure process, perform real-time prior art searches, and facilitate quick filing decisions.
This Perspective by Ben Chester Cheong (Singapore University of Social Sciences & Cambridge) offers a comprehensive legal–ethical review of transparency and accountability challenges in AI systems governing human wellbeing . It structures the discussion into four pillars—technical explainability methods, regulatory frameworks, ethical safeguards, and multi‑stakeholder collaboration—highlighting how each area plays a vital role in ensuring trust and societal resilience. Legal professionals will appreciate its actionable framework that bridges tech, ethics, and governance, making it a timely resource amid emerging regulations like GDPR “right to explanation” and EU AI Act mandates. By offering strategic clarity and policy cohesion, this article equips lawyers, compliance leaders, and policymakers with tools to embed transparency and accountability into AI systems that shape lives—making it a must‑read for anyone advising on responsible AI deployment.
This essay by Margot E. Kaminski and Meg Leta Jones explores how current legal frameworks actively construct AI-generated speech, rather than being passively disrupted by it. It introduces the “legal construction of technology” method—analyzing how laws like the First Amendment, content moderation, risk regulation, and consumer protection actively interpret and shape AI speech. This analysis matters to legal professionals because it reveals that existing institutions and norms already provide structured pathways for meaningful oversight, shifting the conversation from reactive problem-solving to proactive values-based policy design. By demonstrating that law and AI co-evolve through these intentional constructions, this piece empowers lawyers and policymakers to craft more effective, principled governance—prompting deeper engagement with the field.
This article by Graham H. Ryan analyzes how generative AI challenges the legal immunity conferred by Section 230 of the Communications Decency Act—and why that protection may crumble under new judicial scrutiny. Ryan argues that generative AI systems “create or develop content” and thus likely fall outside Section 230’s existing scope, exposing providers to increased liability for design decisions and algorithmic contributions. It matters for legal professionals because emerging case law may redefine liability standards—from co-authoring content to design-based claims—signaling a pivotal shift in AI governance and internet law that practitioners need to monitor closely. By framing generative AI as a catalyst for reevaluating the legal foundations of internet speech, the article urges lawyers to proactively reassess risk strategies and regulatory compliance in this evolving landscape.
This insightful analysis explores how China’s August 15, 2023 regulations on generative AI reflect a strategic choice to slow AI progress for social and political control. It reveals a key finding that Beijing’s cautious regulatory approach contrasts sharply with the innovation-first strategies of the U.S. and EU, granting other jurisdictions essential breathing room to develop responsible AI policies. Legal professionals will find this article timely and compelling, as it provides practical insight into how geopolitical AI maneuvering reshapes cross-border legal strategy, compliance, and tech governance. By positioning China’s policy as a global pivot point, this piece equips lawyers and policymakers with a nuanced understanding of how AI regulation is being shaped on the international stage—prompting further investigation and dialogue.
This analysis by Matt Blaszczyk, Geoffrey McGovern, and Karlyn D. Stanley explores how U.S. copyright law grapples with AI-generated content, addressing whether AI-assisted works qualify for protection and how training datasets are treated under both U.S. and EU doctrines. It highlights a key insight: U.S. law requires human “authorial contribution” for copyrightability, while the EU allows rights holders to challenge commercial AI training—underscoring a growing legal divide. Timely hearings in Congress and updates from the U.S. Copyright Office make this discussion urgent for legal professionals managing copyright risks in generative AI projects. The analysis empowers practitioners with a clear roadmap for navigating emerging policy debates, licensing strategies, and litigation landscapes around AI-authored content—making it essential reading for IP counsel advising on innovation-driven initiatives.
This expert analysis from Dentons partners Jennifer Cass, Anna Copeman, Sam Caunt, and David Wagget examines the unresolved IP challenges arising from generative AI in 2024 and the legal “cliffhangers” heading into 2025. It highlights key issues like copyright infringement during AI training, ownership of AI-generated works and inventions, and emerging litigation—such as Getty Images v. Stability AI. Legal professionals will find value in its forward-looking take on 2025 reforms, including licensing trends, contractual risk strategies, and pending court rulings. Written with practical insight, this piece equips lawyers with proactive tools to guide clients through rapidly evolving AI‑IP terrain.