Free Wearable Corpus

A structured prior art commons covering wearable devices and body measurement — watches, bracelets, rings, eyewear, hearables, headbands, patches, garments, exoskeletons, implantables, and ingestibles — disclosed in private patents, open source, science fiction (film/TV/comics/games/ literature), academic literature, regulatory filings (FDA 510(k), De Novo, CE MDR), and standards bodies.

The corpus IS the prior art commons. Every entry, at the moment of timestamped commit and quarterly release, is a defensive publication. The structured data here, combined with the cryptographic timestamping in releases/, is itself the invalidity-contention material.

The corpus is a lexicon, not a catalog. Retrieval equals citation.

License

CC0-1.0. Public domain dedication. See LICENSE.

Two design commitments that differ from the Free Humanoid Corpus

Multi-axis tagging

Wearable patent claims read on component combinations: form factor × sensor × algorithm. The corpus indexes each axis independently. Cross-cuts are generated per single tag and per (tag × tag) intersection.

Component cross-cuts + one combination template

Combining a known sensor with a known wearable form factor is per se obvious under KSR / MPEP § 2143 Rationale A. The §103 argument for any (form factor × sensor) tuple is boilerplate — see OBVIOUSNESS_TEMPLATE.md. Entries supply component disclosures. The template supplies the combination argument. The corpus is component cross-cuts plus one template, not thousands of pre-built combinations.

What's in here

SCHEMA.md               — schema spec (currently v0.1)
OBVIOUSNESS_TEMPLATE.md — canonical §103 combination argument
TIMESTAMPING.md         — release ceremony for cryptographic timestamping
RELEASE_RUNBOOK.md      — step-by-step release procedure
CONTRIBUTING.md         — how to add an entry
README.md               — this file

corpus.jsonl            — master corpus (one JSON object per line)
private.jsonl           — generated mirror, entries with corpus="private"
open.jsonl              — generated mirror
fictional.jsonl         — generated mirror
academic.jsonl          — generated mirror
regulatory.jsonl        — generated mirror
standards.jsonl         — generated mirror

CORPUS_INDEX.md         — generated alphabetical index
lineage.json            — generated ancestor/descendant DAG

cross_cuts/             — generated per-tag prior art views
  INDEX.md              — table of all cross-cuts with counts
  <tag>.md              — chronological view of all entries disclosing <tag>
  <tag1>__<tag2>.md     — chronological view of (tag1 ∩ tag2) entries

tools/
  validate.py           — schema and quality-bar enforcement
  index.py              — generates CORPUS_INDEX.md, lineage.json, per-corpus mirrors
  cross_cuts.py         — generates single-axis and intersection cross-cuts
  lookup.py             — patent-claim → matching prior art entries
  new_entry.py          — interactive entry scaffolder

release.sh              — quarterly release ceremony with timestamping

Quick start

# Validate the corpus
python3 tools/validate.py corpus.jsonl --strict

# Regenerate derived artifacts after editing corpus.jsonl
python3 tools/index.py .
python3 tools/cross_cuts.py

# Look up prior art for a wearable claim
python3 tools/lookup.py "wristworn device with photoplethysmography sensor and processor configured to detect atrial fibrillation"

# Run a quarterly release with cryptographic timestamping
./release.sh 2026.Q3

Quality bar

An entry is commons-grade (not draft: true) when:

1. disclosure_citation resolves to a primary source verifiable by a third party.
2. first_disclosure_date is the earliest verifiable public disclosure, defensible against challenge.
3. prior_art_notes reads as a 102/103 anticipation analysis someone unrelated to us could cite without rewriting.
4. sources cite primary references.
5. For patented entries, ip_citations lists actual patent numbers.

Entries that don't yet clear this bar are merged with draft: true and have explicit work items in their notes field.

Cross-cuts as the working tool

The cross-cut files (cross_cuts/<tag>.md) are the operational form of the commons. Each file lists every entry disclosing a given component, in chronological order, with prior_art_notes and disclosure_citation inline.

Use them when assessing wearable claims:

• A claim about wristworn PPG? Open cross_cuts/watch__sensor-ppg.md — anchored at the earliest disclosed wristworn PPG implementation, every intervening disclosure listed chronologically.
• A claim about EEG eyewear? Open cross_cuts/glasses__sensor-eeg.md.
• A claim about ring-form HRV? Open cross_cuts/ring__sensor-ppg__algo-hrv.md.

For each, drop the cited entries into OBVIOUSNESS_TEMPLATE.md and the §103 argument is complete.

Contributing

Contributions are welcome under CC0-1.0. By submitting an entry you dedicate the contribution to the public domain.

A good entry holds up as defensive publication. Briefly:

• Cite primary sources, not Wikipedia.
• Identify specific components disclosed using the taxonomy in SCHEMA.md: form factor, sensors, algorithms, outputs.
• Write prior_art_notes as element-by-element anticipation analysis.
• For patented entries, point back at academic / fictional / regulatory / standards prior art that anticipates the claims.

If unsure, mark the entry draft: true and document the work needed to clear the quality bar.

Provenance

Originated within Open Interface Engineering (OpenIE) as a sister project to the Free Humanoid Corpus and Free eVTOL Corpus, applying the same defensive-publication methodology to a different patent-thicket field. The corpus serves the wearable and body-measurement field broadly.