Nov 7, 2025Struggling to show “technical effect” for AI at the EPO without drifting into business KPIs? This lesson equips you to draft examiner-ready effect statements that prove machine-level improvements and tie them causally to claimed features. You’ll get a concise framework (purpose → quantified outcome → causality), high-signal examples, and targeted exercises to stress‑test credibility, scope, and evidence alignment. Expect precise phrasing guides, benchmark and ablation checklists, and wording templates designed to reduce objections and accelerate allowance.

Nov 7, 2025Struggling to distinguish mere preference from true teaching-away in AI prior art—or to prove unexpected results with legal weight? By the end of this lesson, you’ll diagnose dissuasion language with precision, build secondary-consideration records that tie claim elements to metrics, and draft examiner-ready phrasing that survives §§102/103. You’ll get crisp explanations, corpus-driven templates, high-signal examples, and targeted exercises to validate your judgment and tighten your claim charts. The approach is discreet and surgical—focused on measurable ROI: fewer OAs, stronger nonobviousness, faster allowances.

Nov 6, 2025Do your AI claims lean on “configured to” and invite ambiguity, objections, or weak enforcement? In this lesson, you’ll learn to replace results-only phrasing with precise, testable structures that align actors, anchor functions to technical means, and meet USPTO/EPO sufficiency and clarity standards. Expect a focused diagnostic framework, corpus-driven rewrite templates (hardware, software, model-centric), jurisdictional stress tests, and crisp examples—plus targeted exercises to lock in the edits. Outcome: cleaner claims, fewer §112/Art.84 headaches, and stronger, faster prosecution.

Nov 5, 2025Struggling to keep AI examiner interviews focused—especially when training data drives §101 and §103? This lesson equips you to run a surgical, written agenda that isolates claim interpretation, ties eligibility to reproducible data-driven improvements, and tests obviousness via dataset assumptions. You’ll get crisp frameworks with timing and scripts, high-signal question stems, and documentation checklists—plus targeted exercises to validate your approach. Finish ready to shorten prosecution and memorialize commitments that support AFCP, after-final, or appeal pathways.

Nov 5, 2025Struggling to turn cutting-edge AI work into a defensible, reproducible invention story? In this lesson, you’ll learn to build a complete AI invention narrative using a professional disclosure form template—covering overview, novelty, architecture, data lineage, training, evaluation, deployment, safety/compliance, IP, and confidentiality. Expect crisp explanations, corpus-driven model language patterns, high-signal examples, and targeted exercises to lock in reproducibility and legal readiness. Leave with a disclosure that is auditable, claim-oriented, and enterprise-ready.

Nov 5, 2025Struggling with AI training claims that draw §112 rejections or won’t translate cleanly to EPO two‑part format? This lesson gives you a measurable, template‑driven playbook to draft definitive, enabled, and well‑supported claims—complete with bounded datasets, hyperparameters, stopping rules, and metric‑linked acceptance. You’ll get surgical explanations, corpus‑tested examples, and concise exercises to validate phrasing and spot red flags before filing. Outcome: produce U.S. single‑part and EPO two‑part training claims that withstand scrutiny and cut office actions.