Artificial Intelligence & Machine Learning

Artificial intelligence today is foundational infrastructure—shaping how organizations process data, make decisions, and deploy technology at scale. At Schmeiser Olsen, we work with clients advancing the frontiers of AI through novel architectures, efficient training methodologies, and high-performance inference systems.

Our patent counsel is grounded in both legal precision and deep technical understanding. We partner closely with inventors developing transformer-based models, multimodal systems, and specialized machine learning pipelines—delivering patent strategies that capture the full scope of innovation, from algorithmic breakthroughs to deployment optimization.

We recognize that AI innovation is not static—it’s a complex convergence of theory, data engineering, compute constraints, and real-world application. Our role is to translate that complexity into clear, enforceable patent protection that aligns with each client’s business objectives and technical vision.

Whether you’re filing your first AI patent or scaling a sophisticated ML platform, we help you define what’s protectable—and secure rights that support long-term business value.

Patent Strategy for Advanced AI Systems

For AI innovators, securing meaningful patent protection isn’t just about novelty—it’s about clearly articulating the technical contributions that differentiate your system. At Schmeiser Olsen, we work with in-house legal teams, CTOs, and founders to develop AI patent strategies that withstand both legal and technical scrutiny. We help clients protect the technologies that make intelligent systems work—not just what they do, but how they do it. Our AI patent practice covers a broad range of innovations, including:

Neural Architecture Innovations

From sparse attention transformers to cross-modal encoders, we protect model designs that improve training stability, scalability, and performance. This includes modular systems, weight-sharing techniques, and GPU-optimized architecture choices.

Training Optimization

We draft and prosecute patents for novel training workflows, such as curriculum learning, federated learning with privacy constraints, synthetic data augmentation, and semi-supervised or label-efficient approaches that reduce data requirements.

Inference System Design

When speed, size, and reliability matter, we help clients protect the methods that make deployment practical—quantization, pruning, dynamic batching, MoE routing, and runtime inference across distributed compute environments.

Data Representation & Pipelines

We support clients developing everything from sparse embedding frameworks to real-time annotation loops, feature stores, and graph-based processing systems that allow ML models to operate effectively at scale.

Explainability, Safety, and Control

We advise on patent strategies for auditability, attribution, adversarial robustness, and reinforcement learning with embedded human oversight—especially in regulated environments or sensitive applications.

practical insight

Navigating Patent Eligibility in AI (and Doing It Right)

Patent eligibility under 35 U.S.C. §101 remains a gating issue for AI innovation. The USPTO continues to scrutinize AI-related claims under the Alice/Mayo framework, particularly where inventions appear abstract or overly generalized. Our approach is to focus on how your invention improves computing technology—not just the result it achieves.

We help clients frame their AI inventions as system-level innovations—grounded in real engineering improvements, not theoretical goals.

We draft claims and specifications that clearly highlight concrete advantages, such as reduced compute load, more efficient gradient flow, or improved hardware integration. This might involve model optimization for specific inference accelerators (e.g., TPUs or ASICs), distributed execution across nodes, or memory management strategies during training.

To support eligibility, we incorporate implementation details, benchmark comparisons, and—where appropriate—ablation-style analysis to demonstrate the impact of specific model components. We also reference favorable case law (McRO, Enfish, DDR Holdings) to show that the invention solves a defined technical problem, not merely automates an abstract process.

The result is not just a patent application, but a resilient intellectual property asset—built for eligibility, defensibility, and long-term value.

Partner with Counsel Who Understand Artificial Intelligence & Machine Learning

At Schmeiser Olsen, we work with companies developing advanced models, scalable infrastructure, and ML-driven products to build strong, enforceable patent portfolios. Engage a team that speaks your language, understands your architecture, and protects what sets your AI apart.

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