Strategic Patent Counsel for Advanced AI Systems
Modern artificial intelligence and machine learning systems are layered technical architectures. They span model training pipelines, optimization and tuning workflows, production inference environments, data engineering workflows, and iterative feedback systems. Patent protection that treats these systems as isolated algorithms or functional outputs often fails to capture the technical innovation embedded in their structure, operation, and learning processes.
When that underlying structure is not reflected in claim design, portfolios may be vulnerable during examination and lose strategic coverage as competing architectures evolve. My work centers on designing durable AI patent portfolios by aligning patent strategy with how technical advantage is actually created — across architecture, algorithms, data, and system integration — and how it evolves over time.
This approach supports companies developing machine learning models, reinforcement learning systems, large-scale inference infrastructure, and production AI platforms operating in competitive, rapidly evolving markets.
Advantage-Aligned Protection
Effective claim design begins with structural clarity. In AI systems, technical advantage may reside in architecture, training methodologies, model optimization strategies, data pipelines, deployment environments, or their interaction. Protection that reflects how advantage is created is more likely to capture the sources of differentiation that matter in the market.
By grounding claims in system structure — rather than abstract functional descriptions — portfolios are positioned to withstand examination scrutiny and reduce exposure to design-arounds as competing architectures evolve and technical approaches diversify.
Patent protection should track the technical advantages that distinguish a system in the market.
Integrated Legal Survivability
Durability is not achieved by addressing eligibility or obviousness issues reactively. Instead, AI patent portfolios are strongest when legal resilience is engineered into the portfolio from the outset.
Claim strategy must anticipate abstraction concerns, functional overbreadth, and the increasing combinability of prior art in rapidly evolving AI fields. Structural drafting, layered claim sets, and disciplined continuation planning contribute to long-term resilience across examination, enforcement, and competitive scrutiny.
Patent protection for AI should be structured to withstand scrutiny before scrutiny arises.
Portfolio-Level Strategy
AI innovation rarely resides in a single algorithm. It emerges across training methodologies, model architecture, infrastructure configuration, and system integration as architectures are incrementally refined or fundamentally redesigned.
Durable AI patent portfolios are structured to reflect how technical advantage unfolds across the innovation lifecycle, distinguishing among core architectural innovations, algorithmic advances, implementation variants, infrastructure differentiation, and product evolution. Designed as coherent systems of protection rather than isolated filings, they preserve strategic coverage as competing architectures evolve and seek differentiation.
Portfolio design should anticipate competitive evolution.
About
James T. Fisher is a patent attorney with over fifteen years of experience advising technology companies on complex software and artificial intelligence systems. He has drafted and prosecuted over one hundred patent applications for enterprise clients, with work spanning machine learning, generative AI, cloud infrastructure and security, anomaly detection, signal processing, and distributed systems.
With a background in computer science and formal study of artificial intelligence, his practice bridges model architecture, algorithmic innovation, and large-scale production environments. He regularly collaborates with engineering teams to translate technical advances — from multivariate anomaly detection and model training methodologies to large language model integration, post-generation validation layers, and training dataset curation — into durable, advantage-aligned patent portfolios.
He practices at the Kraguljac Law Group, a boutique intellectual property firm with nearly three decades of focused experience in complex software and computing technologies. There, he develops strategically aligned AI and software patent portfolios built for long-term legal and competitive resilience.