# About Paul Roberts ![[Paul Headshot.png]] # Software Intellectual Property Attorney I am an intellectual property attorney focused on the intersection of patent law, artificial intelligence, and technology risk. My work centers on designing patent strategies for complex software and AI systems, advising on AI governance and compliance, and structuring technology transactions that balance innovation, risk control, and long-term business value. I work closely with engineering, product, and executive teams to translate complex technical systems into defensible intellectual property, while maintaining strong governance over how AI is developed, deployed, and relied upon in high-stakes environments. --- ### Intellectual Property & AI Law My practice emphasizes patent strategy for software-driven systems, including machine learning pipelines, large language models, data-driven platforms, and automated decision systems. I focus on aligning patent portfolios with real system architectures, business objectives, and regulatory constraints—rather than drafting patents in isolation from how technology is actually built and used. I regularly advise on patent eligibility, obviousness, written description, and enablement issues as they apply to modern AI systems, and I approach prosecution strategy with an understanding of examiner behavior, art unit dynamics, and downstream enforcement risk. --- ### AI Governance & Risk Management I work at the governance layer of AI systems—where legal judgment, technical reality, and organizational risk intersect. My focus is on ensuring that AI is used as a decision-support tool rather than an unaccountable authority, particularly in areas involving intellectual property, compliance, and high-impact business decisions. This includes evaluating AI failure modes, managing hallucination and provenance risk, maintaining human-in-the-loop controls, and designing workflows that remain auditable and explainable as systems scale. --- ### Technology Transactions & Contracting In addition to patent strategy, I advise on technology transactions involving AI and software systems, including vendor agreements, licensing, open-source compliance, and government contracting. My approach emphasizes long-term flexibility, avoidance of vendor lock-in, protection against unintended IP leakage, and clear allocation of risk as technologies evolve. --- # AI Skills & Capabilities The following sections summarize my AI-related skills and capabilities at a systems level. They reflect hands-on design, operation, and governance of AI-assisted workflows in legal and technical contexts, rather than theoretical familiarity or policy commentary. --- ### Artificial Intelligence Systems Architecture & Strategy - Design and operation of domain-specific AI systems for legal workflows - End-to-end AI workflow architecture (inputs → models → outputs → oversight) - Human-in-the-loop system design for high-risk decisions - AI risk management (hallucination, bias, provenance, drift) - AI adoption strategy for engineering, legal, and executive stakeholders --- ### Advanced Prompt Engineering & Instruction Design - Multi-stage prompt chaining and task decomposition - Constraint-driven prompting (legal accuracy, format, compliance) - Instruction hierarchy design (system-level, workflow-level, task-level) - Few-shot and example-driven prompting using authoritative corpora - Prompt debugging and failure-mode mitigation across LLM platforms --- ### Retrieval-Augmented Generation (RAG) & Knowledge Engineering - RAG pipeline design using structured legal and technical corpora - Semantic retrieval from code, specifications, templates, and prior filings - Authority control between authoritative and non-authoritative sources - Document chunking, versioning, and update propagation - Template-based RAG for claims, specifications, and figures --- ### AI-Assisted Patent & IP Workflows - AI-assisted patent drafting (claims, specifications, embodiments) - Patent eligibility (§101), obviousness (§103), and §112 analysis - Claim and specification updates informed by identified prior art - Codebase-to-claims translation and system architecture abstraction - Art unit prediction and prosecution path optimization --- ### Legal Knowledge Engineering & Compliance AI - Encoding patent law frameworks into AI instruction systems - Automated compliance validation across claims, specifications, and figures - Cross-document consistency enforcement - AI governance and internal usage policy design - Third-party AI vendor risk and licensing assessment --- ### Multi-Modal & Document Intelligence - Large-scale legal document generation (12–50+ page artifacts) - Filing-ready figure generation and integration - Structured legal formatting aligned with USPTO practice - Executive summaries and technical reports - Cross-modal consistency between text, figures, and claims --- ### Workflow Automation & Process Design - Multi-phase AI workflow orchestration (10+ stages) - Conditional branching based on legal and technical analysis - AI-assisted quality assurance pipelines - Version control and document lifecycle management - Process redesign to reduce cost without increasing risk --- ### AI Governance, Risk, & Ethics - AI authorship, copyright, and training-data risk analysis - Fair-use assessment for AI-generated outputs - Likeness and publicity risk evaluation for image and video AI - Regulatory trajectory monitoring for AI, copyright, and patents - Auditability and explainability by design --- ### AI Education & Translation - Translation of AI concepts for legal, engineering, and executive audiences - Internal training on AI-assisted patent strategy and governance - Use of AI outputs as decision-support rather than authority - Development of AI-based training and reference materials