Strategic IP Counsel for AI, Machine Learning, and Data‑Driven Technologies
Artificial Intelligence is transforming how organizations build products, deliver services, and compete. From machine learning and large language models (LLMs) to computer vision, agentic AI, and autonomous systems, protecting AI innovation requires intellectual property strategies that align with both technical reality and evolving legal standards.
Marshall Gerstein has been advising clients on AI patenting and IP protection since 2006, supporting companies across numerous industries as AI patent filings continue to grow at an unprecedented pace. At Marshall Gerstein, we help companies protect, commercialize, and enforce AI‑driven innovations through sophisticated intellectual property (IP) strategies tailored to today’s rapidly evolving technical and legal landscape.
AI Intellectual Property Services at Marshall Gerstein
We advise clients throughout the entire AI IP lifecycle, from early research and development through commercialization and enforcement.
End‑to‑End AI IP Counsel
Our AI practice includes:
- AI patent drafting for machine learning, generative AI, and AI‑enabled systems (training, inference, deployment, and integration)
- Patent prosecution before the USPTO, including sophisticated § 101 and § 112 strategies
- Portfolio development and competitive landscaping, including prior art analysis and filing roadmaps
- Patentability, freedom‑to‑operate (FTO), and invalidity opinions
- Trade secret and data protection strategies, including AI governance and confidentiality controls
- Technology transactions, including data licensing, model licensing, joint development agreements, and open‑source compliance
- IP due diligence for investment, mergers and acquisitions, and strategic partnerships
- IP enforcement and litigation strategy in coordination with specialized trial teams
- Trademark protection and brand strategy for AI products and services
Our team includes attorneys, patent agents, and technical specialists with advanced degrees in computer science, engineering, and related disciplines—allowing us to translate complex AI systems into enforceable legal rights.
Artificial Intelligence: A Transformative Technology Across Industries
AI is no longer an emerging technology—it is foundational to modern products, services, and infrastructure. Organizations are deploying AI across diverse applications, including:
- Large language models (LLMs) such as OpenAI’s ChatGPT and Claude‑class systems for search, decision support, summarization, and automation
- Machine learning and deep learning systems for prediction, optimization, and anomaly detection
- Computer vision and image processing, including convolutional neural networks (CNNs) for robotics, manufacturing inspection, and autonomous driving
- Agentic AI systems that plan, act, and iterate using multi‑step reasoning and tool orchestration
- Neural network applications in pharmaceuticals and life sciences, including drug discovery and bioinformatics
Since filing its first AI‑related patent applications in 2006, Marshall Gerstein has remained at the forefront of AI intellectual property protection. AI patent filings have grown dramatically—averaging approximately 32% year‑over‑year growth from 2012 to 2024, with continued acceleration across nearly all technical sectors.
AI Patent Experience Across High‑Growth Technology Sectors
While AI innovation spans virtually every industry, patent activity is particularly concentrated in:
- Telecommunications and networking
- Transportation and autonomous systems
- Consumer electronics and computing platforms
- Medical devices and digital health
- Life sciences and pharmaceuticals
- Manufacturing and industrial automation
- Security, document management, and enterprise software
- Engineering and infrastructure systems
Our attorneys and patent agents routinely work with AI technologies including machine learning, generative AI, LLMs, agentic AI, computer vision, natural language processing, robotics, control systems, and optimization algorithms.
Representative AI Industries and Technologies We Support
Marshall Gerstein advises companies developing and deploying AI in a wide range of technical domains, including:
- Autonomous vehicles and advanced driver‑assistance systems (ADAS)
- Robotics and drones (navigation, perception, manipulation, and fleet management)
- Manufacturing, industrial IoT, and predictive maintenance
- Computer graphics, simulation, and GPU‑accelerated computing
- Computer vision, imaging, and video analytics
- Natural language processing and speech technologies
- Digital health, MedTech, and software‑based medical devices (SaMD and SiMD)
- Pharmaceuticals, bioinformatics, and laboratory automation
- E‑commerce platforms and consumer personalization
- Financial services, insurance, and risk analytics
- Cybersecurity and identity technologies
- Telecommunications, networking, and edge computing
- Energy, utilities, agritech, and smart infrastructure
- Blockchain, distributed ledgers, and quantum‑adjacent software
How We Approach AI Patent Drafting and Prosecution
Our AI patent strategy is designed to convert technical innovation into durable and enforceable claim scope.
Our AI Patent Methodology
- Workflow mapping
Identify invention points across the AI lifecycle—data ingestion, training, inference, deployment, and monitoring. - Structured claim architecture
Develop layered claim sets (system, method, and non‑transitory computer‑readable medium) with core and fallback embodiments. - Technically rich specifications
Include implementation detail, experimental results where available, alternative embodiments, and domain‑specific examples. - Proactive prosecution strategy
Prepare for § 101 and § 112 challenges with clearly articulated technical improvements and supporting evidence.
Understanding Artificial Intelligence from an IP Perspective
Artificial intelligence refers to computational techniques that enable systems to perform tasks traditionally requiring human judgment—such as recognizing patterns, interpreting language, forecasting outcomes, and controlling machines.
Common AI Modalities
- Machine learning (ML): supervised, unsupervised, and reinforcement learning
- Large language models (LLMs) and retrieval‑augmented generation (RAG)
- Agentic AI and autonomous workflow orchestration
- Deep learning and neural networks
- Computer vision and image analysis
- Natural language processing (NLP)
- Generative AI for text, images, audio, and code
- Planning, scheduling, and robotics systems
AI Workflows and Innovation Opportunities
Most AI systems follow a common lifecycle:
- Data acquisition and rights management
- Data preprocessing and feature engineering
- Model training and tuning
- Inference, deployment, and system integration
- Monitoring, governance, and iterative improvement
From an IP perspective, innovation—and patentable subject matter—can arise at any stage of this workflow, including data pipelines, training methods, inference optimization, system integration, and domain‑specific applications.
Legal Considerations for AI in the United States
Patent Eligibility, Inventorship, and Disclosure
AI inventions are often software‑implemented and must be carefully drafted to address patent eligibility under 35 U.S.C. § 101, as well as disclosure and enablement requirements under § 112.
We focus on:
- Claims anchored to concrete technical improvements
- Specific AI workflows and system behavior
- Measurable performance gains and real‑world applications
AI‑Assisted Innovation and Inventorship
Under U.S. law, inventors must be natural persons. When AI tools assist development, inventorship turns on human conception and decision‑making, making documentation of human contributions essential.
Beyond Patents: Copyrights, Trade Secrets, and Data Rights
A robust AI IP strategy often combines multiple forms of protection:
- Copyright for software and certain data compilations
- Trade secret protection for datasets, model weights, prompts, and deployment pipelines
- Contractual controls for data licensing, open‑source compliance, and confidentiality
- Trademarks for AI product names and branding
AI Governance and IP Readiness
AI governance is increasingly linked to IP value. We help clients align product design, documentation, and commercialization strategies with evolving regulatory, privacy, and market expectations.
Visit our AI Regulations page for the latest developments and resources.
AI IP Readiness Checklist
- Confirm ownership and licensing rights for training and deployment data
- Document human inventive contributions and engineering decisions
- Decide what to patent versus protect as a trade secret
- Prepare technical disclosure materials early
- Implement clear AI tool usage and confidentiality policies
Related Reading
- How to Patent an Artificial Intelligence (AI) Invention: Guidance from the U.S. Patent Office (USPTO)
- The USPTO Issues Guidance on Patenting Artificial Intelligence (AI)-related Inventions per 35 U.S.C. § 101 (Subject Matter Eligibility)
- The Federal Circuit hints at Enablement requirements for Artificial Intelligence (AI) Inventions
- Artificial Intelligence (AI) Patenting Handbook: Version 3.0
- Artificial Intelligence (AI) Policy Considerations
Frequently Asked Questions
Are AI inventions patentable in the U.S.?
Many AI‑enabled inventions are patentable, but success depends on thoughtful claim drafting and prosecution strategy.
Can an AI system be named as an inventor?
No. U.S. patent law requires human inventors, and focuses on the human contributions made to the invention. AI systems are treated as tools.
Should we rely on patents or trade secrets for AI?
Most organizations adopt a blended strategy, balancing enforceability with confidentiality.
How do we protect proprietary datasets and model weights?
Protection typically combines contracts, trade secret measures, and selective patent filings.
What about generative AI output and training data risk?
Copyright and licensing issues require proactive policies, provenance controls, and careful data governance.