AI for the Patent Lifecycle: From Disclosure to Expiration
An intermediate course for tech-forward patent attorneys and legal-ops managers. Ground every prompt in the 89 FR 25609 duty-of-candor framework. 8 lessons + certificate.
This course is built for one specific reader: the tech-forward patent attorney or legal-operations manager who already knows what a patent is, what § 102 / § 103 / § 112 require, and how prosecution works — but who is now being asked to integrate AI into the practice without burning the firm down. You will not be told what an independent claim is. You will be told how Patlytics, DeepIP, and Solve Intelligence map onto the prosecution workflow, where each tool fails, and how to write the attorney-review checkpoints that keep you inside the duty of candor.
Eight lessons cover the full lifecycle. Lesson 1 establishes the empirical baseline — the Stanford RegLab hallucination floor, the Clarivate adoption numbers, the IPWatchdog “AI Squeeze” thesis from Gene Quinn (May 15 2026), and the lifecycle × AI capability matrix that organizes the next seven lessons. Lessons 2 through 4 walk through ideation and invention disclosure, prior-art search, and drafting / prosecution — including the empirical finding from arXiv 2406.19465 that GPT-class models produce high-quality first independent claims but degrade sharply on the dependent claim chain. Lessons 5 through 7 cover portfolio scoring, FTO screening, and competitive landscape mapping — with a downloadable scoring spreadsheet and a 10–50 patent renew-or-abandon decision tree. Lesson 8 is the capstone: a one-year AI-augmented operations playbook for a 25-patent portfolio, framed by the ABA Model Rules 1.1 / 1.6 framework, the state-bar matrix, and the sanctions precedents (Mata v. Avianca SDNY 22-cv-1461; the March 2026 Sixth Circuit $116K case).
What this course is not: a country-by-country patent-law tour, an advanced litigation or patent-monetization seminar, a “what is a patent” primer, or a generic AI 101 explainer. You are the audience that already lives in patent practice; the course teaches you how AI fits and where attorney judgment remains non-negotiable.
What You'll Learn
- Apply the 89 FR 25609 duty-of-candor framework to every AI-assisted USPTO submission
- Use Claude, ChatGPT, and Gemini to draft invention disclosures, independent claims, and Office Action responses with attorney-grade review checkpoints
- Evaluate prior-art search results from semantic AI tools against the Stanford 17–33% hallucination floor
- Build a portfolio scoring rubric using the Technology Relevance × Market Coverage methodology and translate it into renew-or-abandon decisions
- Design a Freedom-to-Operate workflow that slots AI into all six standard steps without losing the privileged-opinion structure
- Create a one-year AI-augmented patent operations playbook for a 10–50 patent portfolio
After This Course, You Can
What You'll Build
Course Syllabus
Prerequisites
- Working knowledge of US patent practice (you do not need to be USPTO-registered)
- Existing access to at least one general-purpose LLM (ChatGPT, Claude, or Gemini)
- Familiarity with at least one patent database (USPTO PAIR, Espacenet, or a commercial search tool)
Who Is This For?
- Patent attorneys at small to mid-size firms looking to augment drafting, prosecution, and FTO with AI without exposing the firm to malpractice risk
- In-house IP counsel at tech companies and product companies managing 10–50 patents
- Legal operations managers responsible for docketing, annuity decisions, and vendor selection across the patent portfolio
- Patent agents and registered practitioners evaluating Patlytics, DeepIP, Solve Intelligence, PatSnap, and similar platforms
- Engineering managers, R&D directors, and chief innovation officers who own the inventor-disclosure intake pipeline
Frequently Asked Questions
Do I need to be a registered patent practitioner to take this course?
No. The course is built for tech-forward patent attorneys, legal-operations managers, and in-house IP counsel. You need working knowledge of patent practice, not USPTO registration. Every AI prompt in the course is paired with an explicit attorney-review checkpoint, so a non-registered legal-ops manager learns where the registered practitioner must take over.
Which AI tools does the course cover?
General-purpose LLMs (Claude, ChatGPT, Gemini) for drafting and review tasks; specialized patent platforms (Patlytics, DeepIP, Solve Intelligence, PatSnap Eureka, Cypris, IPRally) for prior art, FTO, claim charting, and landscape work; and IP management platforms (Anaqua, Clarivate, FoundationIP, Questel) for docketing and annuities. The course teaches workflows, not vendor pitches — every recommendation is grounded in the underlying capability.
Will this course teach country-by-country patent law?
No, deliberately. The course focuses on US practice with USPTO-specific compliance (89 FR 25609, 37 CFR 1.56, 11.18, 11.303) and references the EPO and JPO only where the AI workflow changes substantially. Multi-jurisdiction strategy is a separate topic.
Will I get a certificate?
Yes. Complete all eight lessons and the capstone exercise to earn a verifiable PLM-prefix certificate. The certificate confirms you can apply the duty-of-candor framework to AI-assisted patent work and operate an AI-augmented portfolio of 10–50 patents.
How current is the regulatory and tooling content?
Built May 2026. Cites the USPTO April 2024 guidance (89 FR 25609), the November 2025 revised inventorship guidance, the Stanford RegLab Magesh et al. 2025 study on legal AI hallucination, and the May 15 2026 IPWatchdog 'AI Squeeze' article. The course carries a 3-month review cadence because the underlying AI tools and USPTO guidance change quickly.
