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.
RAND's analysis examines whether AI-generated works merit copyright protection and if training AI models on copyrighted content violates U.S. and international law. The research reveals emerging global divergence, with Beijing courts recognizing copyright for AI-generated images demonstrating human intellectual effort while U.S. approaches remain uncertain pending landmark litigation like NYT v. OpenAI. This comprehensive legal assessment provides critical insights for content creators and AI developers navigating the unresolved fair use questions that will determine billions in potential liability and the future of generative AI training practices.
RAND's policy analysis examines how AI algorithms' opacity creates fundamental privacy challenges around data collection, use, and decision-making, particularly in light of the EU AI Act's transparency requirements. The research explores regulatory approaches to address AI's lack of explainability while examining the tension between innovation and privacy protection under frameworks like GDPR and emerging U.S. state privacy laws. This authoritative government-sponsored research provides crucial insights for policymakers grappling with AI's transformative impact on privacy rights and the effectiveness of existing privacy legal frameworks.
FinTech Weekly's analysis contrasts global AI regulatory approaches, from the EU AI Act's comprehensive framework with €35 million penalties to China's strict government control versus Japan's flexible industry self-regulation. The piece examines Trump's pro-AI executive orders reversing Biden's 'safe and secure' AI policies while highlighting how financial services firms increasingly look to EU standards as 'best practice' despite compliance costs. This industry perspective illuminates the political volatility around AI regulation and the challenges facing financial services firms navigating divergent international regulatory philosophies.
Banking industry analysis reveals that while the EU AI Act establishes the world's first AI regulation in 2024, U.S. financial institutions face a 'fast-moving target' for compliance as regulatory frameworks remain unsettled. The assessment highlights SEC efforts to address AI conflicts of interest for investment advisors while emphasizing that loose regulatory frameworks create significant risks if AI isn't implemented diligently. This practitioner-focused piece underscores how compliance officers must monitor evolving AI requirements to protect data safety and security amid shifting national and global regulatory concerns.
Skadden's comprehensive analysis examines how the EU AI Act will govern financial services from 2024 while U.S. regulators rely on existing frameworks and guidance rather than new AI-specific legislation. The report details critical regulatory concerns including data quality, model risk, governance challenges, and consumer protection as 79% of UK financial firms have deployed machine learning applications beyond pilot phases. This authoritative assessment demonstrates the divergent regulatory approaches across jurisdictions and highlights the industry demand for harmonized international standards as financial institutions navigate complex compliance requirements.
Cooley's analysis reveals how Trump's reversal of Biden's AI policies eliminated federal guidance on workplace wearables and algorithmic bias, yet underlying anti-discrimination laws like Title VII and ADA remain fully applicable to AI systems. The piece emphasizes that while agencies removed AI-specific guidance documents, employers still face liability for discriminatory AI outcomes, particularly as state laws like Colorado's SB 24-205 impose additional AI compliance requirements. This practical legal assessment helps employers navigate the regulatory gap between federal policy shifts and persistent legal obligations in AI deployment.