IP Attorney
Draft a patent application for a software invention
What You Do Today
Work with inventors to understand the technology, conduct a prior art search, draft claims from broadest to narrowest, write the specification with sufficient enablement, and prepare figures.
AI That Applies
Patent drafting AI generates initial claim sets and specification language from invention disclosures, referencing prior art to position claims and ensuring specification supports claim scope.
Technologies
How It Works
The system ingests invention disclosures as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output — initial claim sets and specification language from invention disclosures — surfaces in the existing workflow where the practitioner can review and act on it. You still craft the claiming strategy, make judgment calls about claim breadth vs.
What Changes
First drafts come together faster. AI suggests claim language based on similar granted patents and identifies specification gaps that could limit future prosecution options.
What Stays
You still craft the claiming strategy, make judgment calls about claim breadth vs. prosecution risk, work with inventors to capture the true invention, and ensure Alice/Mayo compliance for software patents.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for draft a patent application for a software invention, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long draft a patent application for a software invention takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your general counsel or managing partner
“What content do we produce the most of that follows a repeatable structure?”
They set the firm's AI adoption posture
your legal technology manager
“What's our current review and approval process, and would AI-generated first drafts change the bottleneck?”
They manage the tools and can show you capabilities you don't know exist
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.