Attorney-Led Invention Discovery Is the Missing Intake Layer

The old intake model waits too long

Traditional invention intake waits for an engineer to recognize an invention, document it, and submit it to counsel. That model made more sense when technical work moved through slower, more formal milestones.

Modern software and AI teams work differently. Invention signal is scattered across repositories, tickets, experiments, design docs, and meeting notes. A meaningful technical improvement may be split across several pull requests and described only indirectly in a sprint ticket.

If counsel waits for a polished IDF, the most valuable work may never enter the review queue.

Attorney-led invention discovery changes the starting point. Instead of waiting for disclosures, counsel proactively discovers candidate inventions from the engineering record.

Discovery is not drafting

Attorney-led discovery is a front-end intake layer. It is not a replacement for patent drafting, prior art search, or prosecution strategy.

Its job is earlier:

• Find technical work that might be patent-relevant
• Package the candidate with source evidence
• Give counsel enough context to triage
• Pull inventors into focused review only when needed

That distinction matters. Many patent tools help after the invention is already known. Attorney-led discovery helps answer the question before that: what should we even evaluate?

Why attorneys should lead

Engineers understand the system, but attorneys understand the patent question.

An engineer may describe a change as a workaround, optimization, or refactor. An attorney may see a claimable method, a novel architecture, a technical constraint solved in a non-obvious way, or a commercially important improvement that should be preserved.

That does not mean attorneys should work without engineers. It means attorneys should not depend on engineers to decide when the patent process begins.

The best workflow lets counsel lead discovery, then invites engineers to validate facts and fill technical gaps.

What the discovery layer reads

Attorney-led discovery works when it reads the same artifacts engineering teams already produce.

For software and AI companies, useful sources include:

• Pull requests and code review comments
• Commit messages and branch histories
• Jira tickets, epics, and sprint notes
• PRDs and architecture docs
• Technical retrospectives and incident reviews
• Experiment logs and model evaluation notes

These artifacts are valuable because they preserve the work in context. They show what problem the team was solving, what alternatives were available, what constraints mattered, and when the solution emerged.

What counsel should receive

The output of attorney-led discovery should be a candidate package, not a raw data dump.

A strong package includes:

• A concise candidate title
• A plain-English technical summary
• The problem the team solved
• The implemented solution
• Source citations tied to the artifacts
• A confidence or priority score
• A first-pass disclosure draft

This package gives counsel a fast way to decide whether to reject, monitor, interview inventors, or move toward a formal IDF.

The engineer conversation gets better

When counsel starts with a candidate package, inventor interviews become more useful.

Instead of asking broad discovery questions, counsel can ask targeted validation questions:

• Is this summary technically accurate?
• What alternatives did you reject?
• What part of the implementation was novel or difficult?
• Which source artifact best supports this claim?
• Did this work ship, and when?

Those questions respect the engineer’s time. They also help counsel move faster because the conversation starts from evidence instead of memory.

It also improves outside counsel workflows

Attorney-led discovery is not only for in-house teams. IP firms can use the same model with clients that generate large amounts of technical work but provide inconsistent disclosures.

For outside counsel, the value is preparation. Instead of arriving at an invention harvesting meeting with a blank agenda, the attorney arrives with candidate summaries, citations, and a focused list of questions.

That changes the client experience. The attorney is not asking the client to do the first pass. The attorney is bringing work product.

AI belongs in triage, not final judgment

AI makes attorney-led discovery practical because it can read far more engineering material than a person can review manually.

But AI output is useful only when it stays traceable. A candidate without citations creates more attorney work, not less. Counsel needs to know where the candidate came from and why the system flagged it.

The right pattern is evidence-first:

1. Ingest engineering artifacts.
2. Identify possible technical novelty.
3. Generate a candidate package with citations.
4. Let attorneys review, reject, refine, or advance.

AI accelerates the first pass. Attorneys keep the decision.

A new intake operating model

Attorney-led invention discovery turns intake from an inbox into a review queue.

The old question was, “Did anyone submit an IDF?”

The better question is, “What did engineering build this week that counsel should evaluate?”

That question is more aligned with how modern technical work happens. It also gives IP teams a more systematic way to capture inventions before timing, memory, or competing priorities erase the signal.

ALID is built as that missing intake layer. It reads GitHub, Jira, and documents, then gives attorneys a queue of cited invention candidates with draft disclosure language. Read why engineers do not file invention disclosures, or request access to test the workflow on your own engineering data.