About
James T. Fisher is a patent attorney advising technology companies on strategic patent protection for advanced artificial intelligence and software systems. He has extensive experience drafting and prosecuting complex patent portfolios for enterprise technology clients, with work spanning machine learning and generative AI systems, inference architecture and production AI platforms, AI-driven cloud security, distributed computing environments, and multivariate anomaly detection and signal processing in production systems.
Across these domains, his work centers on identifying where and how technical advantage is structurally created — within model training workflows, inference environments, data pipelines, and system integration layers — and translating those structural advantages into patent strategies built for long-term competitive resilience.
He works directly with engineering leadership and product teams to ensure that patent protection reflects not just functional outcomes, but the architectural and operational distinctions that differentiate a system in the market.
James practices at the Kraguljac Law Group, a boutique intellectual property firm with nearly three decades of focused experience in complex software and computing matters.
Advancement-Driven Strategy
Effective AI patent strategy begins with identifying where competitive advantage is structurally created. In modern AI systems, differentiation may reside in model training methodologies, inference architectures, data pipelines, deployment environments, or the interaction among these layers.
He works directly with engineering leadership to isolate the architectural and operational distinctions that drive patentability and commercial advantage, translating those distinctions into layered claim strategies designed to withstand scrutiny and reduce exposure to strategic workarounds.
Patent protection should reflect the architectural distinctions that create durable competitive advantage.
Legal Strength by Design
AI patent durability is rarely achieved through reactive prosecution. Legal resilience must be engineered into the portfolio from the outset through structural drafting, layered claim hierarchies, and disciplined continuation planning.
His work anticipates recurring failure modes in fast-moving technical fields, including subject matter eligibility constraints, functional overbreadth, and the increasing combinability of prior art. The objective is not merely allowance, but durable portfolio integrity across examination, diligence, and adversarial contexts.
Patent protection for AI should be structured to withstand scrutiny before scrutiny arises.
Technical Focus Areas
His work spans advanced computing domains such as machine learning, multivariate anomaly detection, reinforcement learning systems, large language model and multimodal model integration, model training and fine-tuning workflows, distributed inference infrastructure, cloud platform integration, virtualization environments, and AI-driven cybersecurity systems.
Across these domains, his emphasis remains consistent: align claims with real system architectures and operational features, preserve strategic coverage as implementations evolve, and build patent families that function as coherent systems of protection rather than isolated filings.
Portfolio design should anticipate competitive evolution.
Credentials
James holds a Bachelor of Science in Computer Science from Vanderbilt University School of Engineering and a Juris Doctor from Case Western Reserve University School of Law, with studies focused on intellectual property and technology law.
He is registered to practice before the United States Patent and Trademark Office and is admitted to practice in Ohio and in multiple federal courts.