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IA-001

Invention Analysis Benchmark

Patent Practice

Designingv0.1Aug 2026~12 publications (provisional)

A benchmark for evaluating AI systems on structured invention analysis, using a focused set of patent publications.

~12

Publications

Systems Tested

Evaluators

Question

Can AI systems produce invention analyses that are accurate, complete, and reliable enough for an attorney to use as a genuine starting point — not just plausible-sounding text?

Task Definition

TBD — the precise task definition (what input a system receives, and what output it must produce) is still being designed.

Dataset / Inputs

The benchmark is built around a focused set of patent publications — currently planned at approximately 12 — selected to represent a range of invention types. The exact publications, selection criteria, and dataset structure have not yet been finalized.

Gold Standard

TBD — the Laboratory is still determining how gold-standard invention analyses will be created and by whom. This section will eventually distinguish the reference answer used for comparison from the evaluation rubric used to judge a system's output.

Method

TBD — the evaluation procedure, including how a system's output is compared against the gold standard and how disagreement between evaluators is resolved, has not yet been finalized.

Limitations

TBD — like any benchmark, this one will have limitations: dataset size, subject-matter coverage, evaluator agreement, and how well performance here predicts performance on other legal tasks. Those limitations will be documented explicitly once the benchmark exists in a form that has them.

Evaluation Rubric

Provisional dimensions

These are the Laboratory's working evaluation dimensions, not this benchmark's finalized rubric.

  1. 01

    Accuracy

    Does the output correctly address the task?

  2. 02

    Completeness

    Does it identify the material issues, facts, or considerations?

  3. 03

    Reasoning / Analysis

    Does the analysis appropriately connect facts, rules, and conclusions?

  4. 04

    Consistency

    Does the system produce reasonably stable results across comparable inputs?

  5. 05

    Evidence / Support

    Are conclusions supported by the relevant source material?

  6. 06

    Workflow Compliance

    Did the system follow the defined workflow?

  7. 07

    Human Reviewability

    Can an attorney efficiently inspect and evaluate the output?

  8. 08

    Failure Severity

    When the system fails, how consequential is the failure?

Results

Results — Coming Soon

No runs have been completed for this benchmark yet.

Failure Analysis

Where — and how badly — did it fail?

  • Missed issue
  • Incorrect reasoning
  • Unsupported conclusion
  • Hallucination
  • Incomplete analysis
  • Context failure
  • Instruction-following failure
  • Workflow failure
  • Inconsistent output
  • Human-review concern

Provisional categories — not yet formally adopted

Related

Related Experiment

EXP-001

Invention Analysis Workflow

Explore Experiment →