v13s/golden-fto-layer-a

Layer A — Office Action Triples for FTO Evaluation

A public dataset of (invention → cited prior art → outcome) triples extracted from the USPTO Office Action Research Dataset (OARD)

• USPTO Open Data Portal (ODP) API (US slice) and the EPO Open Patent Services (OPS) Register service (EP slice). Built as the agent-evaluation substrate for Parallax, an AI-native Freedom-to- Operate (FTO) and defensive-publication platform for individual inventors and small teams.

Curated by Vox (org: v13s). Parallax is a Vox product; the curation layer (annotations, severity tagging, schema, manifest) is © Vox 2026 under CC-BY-4.0. The underlying USPTO patent data is public domain.

TL;DR

• 5 450 rows (v1.2.20260508-r2): 5 000 US Office Actions (filing years 2011–2017) + 450 EP search reports (filing years 2014–2020, IPC-stratified across 23 buckets)
• 22 Parquet shards in the cases partition, partitioned by jurisdiction × filing_year, plus a prior_art_index/<jurisdiction>/ sibling partition (v1.2+) aggregating cross-citations
• Schema: (case_id, invention, examination, prior_art[], outcome, provenance) — see Schema below
• v1.2 additions: prior_art[].categories[] for multi-category ST.14 splits (XY → ["X","Y"]) + prior_art_index sibling partition for cross-citation aggregation
• Two jurisdictions live (US, EP); JP gated on INPIT bulk credential (申請 sent 2026-05-08)
• License: CC-BY-4.0 on the curation; underlying patent documents remain in the public domain
• SHA-256 manifest at MANIFEST.json for byte-level reproducibility

Quick start

from datasets import load_dataset

# Default config returns both jurisdictions (5 450 rows).
ds = load_dataset("v13s/golden-fto-layer-a", split="train")
print(len(ds))                           # 5450

# Per-jurisdiction configs are also available.
ds_us = load_dataset("v13s/golden-fto-layer-a", "us", split="train")
ds_ep = load_dataset("v13s/golden-fto-layer-a", "ep", split="train")
print(len(ds_us), len(ds_ep))            # 5000, 450

row = ds[0]
print(row["case_id"])                    # e.g. "US-13004847-0"
print(row["invention"]["title"])         # "SYSTEM AND METHOD FOR ..."
print(row["examination"]["oa_type"])     # "rejection" | "search_report"
print(row["examination"]["rejection_reasons"])  # ["obviousness_103"]
for ref in row["prior_art"]:
    print(ref["ref_id"], ref["severity"])
    # US: "US9123456B2", "obviousness"
    # EP: "US6825941",   "novelty_destroying"

Schema

Each row is a single Office Action event linked to its prior-art citations. The full schema lives at data-pipeline/src/layer_a/schema.py in the source repo.

Field	Type	Description
case_id	string	Stable id: <jurisdiction>-<application_number>-<oa_seq>
schema_version	string	Per-row schema version (1.0 legacy / 1.2 post-2026-05-07)
jurisdiction	string	US or EP; JP in future versions
source_dataset	string	uspto_oard (US rows) or epo_ops (EP rows)
extracted_at	timestamp[s, UTC]	When this row was emitted
invention	struct	Application metadata — title, abstract, IPC/CPC codes, claims, applicant
examination	struct	OA event — oa_date, oa_type ∈ {rejection, allowance, search_report}, rejection_reasons[], examiner_id
prior_art	list	Cited references — ref_id, ref_type, source (examiner/applicant), rejection_basis, claims_blocked[], severity, categories[] (v1.2+), metadata
outcome	struct	Final disposition — final_disposition, disposition_date, granted_claims[], amendments_made, decision_source (v1.1+)
provenance	struct	Audit trail — parser_version, source_file, manifest_sha256, validation_status, validation_notes[]

Severity enum (prior_art[].severity)

A 3-value severity enum that downstream consumers can join across jurisdictions. Each jurisdiction has its own source signal:

Severity	US (OARD signal)	EP (WIPO ST.14 search-report category)
novelty_destroying	rejection_102=1 AND citation_in_oa=1	X or E (incl. multi-char XY, XYI)
obviousness	rejection_103=1 AND citation_in_oa=1	Y
background	otherwise (PTO-892, PTO-1449 IDS)	A, P, D, T, L, O, I

The EP search-report category sometimes concatenates multiple codes (e.g. "XY" means the citation is BOTH novelty-relevant AND obviousness-relevant). The lowering preserves the raw string and the extractor maps the most-severe component to severity.

prior_art[].categories (v1.2+)

The single-string severity collapses multi-character ST.14 codes to one band (e.g. XY → novelty_destroying, dropping the inventive-step signal). To preserve the full set, v1.2 adds a categories: list<string> field with each code as its own alphabetically-sorted entry:

Source category	severity	categories
X	novelty_destroying	["X"]
Y	obviousness	["Y"]
XY	novelty_destroying	["X", "Y"]
XYI	novelty_destroying	["I", "X", "Y"]
A	background	["A"]

Legacy v1.0 / v1.1 rows have categories = [] (empty). Jurisdictions whose source data doesn't expose ST.14 codes (US OARD uses 35 USC § sections, not ST.14) also leave the field empty. Filter for len(categories) > 0 to query only ST.14- exposed rows.

Query example — find multi-category citations (citations where the examiner cited the same document under both novelty AND inventive-step grounds):

from datasets import load_dataset

ds_ep = load_dataset("v13s/golden-fto-layer-a", "ep", split="train")

multi_cat_rows = []
for row in ds_ep:
    for ref in row["prior_art"]:
        cats = set(ref["categories"])
        if {"X", "Y"}.issubset(cats):
            multi_cat_rows.append(
                (row["case_id"], ref["ref_id"], ref["categories"])
            )
print(f"{len(multi_cat_rows)} XY-cited references in EP slice")
# e.g. ("EP-3290023A1-0", "US10721059", ["X", "Y"])

Without categories[] (v1.0.x consumers) you'd see severity = "novelty_destroying" for every XY citation, indistinguishable from a pure-X citation.

Cross-citation index (v1.2+)

A sibling partition prior_art_index/<jurisdiction>/index.parquet aggregates the cases partition by (ref_id, citing_jurisdiction) so consumers can ask "how often has document X been cited" without walking the cases data row-by-row.

from datasets import load_dataset

idx = load_dataset(
    "v13s/golden-fto-layer-a", "prior_art_index", split="train",
)
# Top-cited refs in EP search reports
top = sorted(
    [r for r in idx if r["citing_jurisdiction"] == "EP"],
    key=lambda r: r["citation_count"], reverse=True,
)[:10]
for r in top:
    print(r["ref_id"], r["citation_count"], r["citing_case_ids"])

Index schema:

Field	Type	Description
ref_id	string	Cited document id (e.g. US10721059)
citing_jurisdiction	string	Where the citing examiner sits (EP, US)
citation_count	int32	Total times this ref appears in prior_art[] across cases
citing_case_ids	list	Sorted set of case_id values that cite this ref
severity_distribution	struct	Count by severity band (novelty_destroying, obviousness, background)
first_cited_date	date	Earliest examination.oa_date across citing cases
last_cited_date	date	Latest examination.oa_date across citing cases

Per-jurisdiction subdirs (prior_art_index/EP/, prior_art_index/US/) keep the index sharded by which extractor produced it. To get a cross-jurisdiction view, union the partition or use the default config above which includes both.

Rejection reason codes

Canonical 3-letter codes consistent across jurisdictions:

Code	USC §	Description
anticipation_102	35 USC §102	Lack of novelty (single-reference)
obviousness_103	35 USC §103	Obviousness (multi-reference combination)
subject_matter_101	35 USC §101	Patent-eligible subject matter (Alice/Mayo/Bilski)
indefiniteness_112	35 USC §112	Written description / definiteness
double_patenting	non-statutory	Same invention claimed twice

Future EP/JP releases add their statute-equivalent codes (novelty_epc_54, inventive_step_epc_56, novelty_jp_29_1, etc.) without breaking the schema.

How was this built?

US slice (5 000 rows)

1. OARD bulk download (the 4M-row USPTO Office Action Research Dataset, frozen at the 2017 release): manually browser-downloaded from research.uspto.gov, mirrored to v13s/oard-2017-mirror for repeatable fetches
2. office_actions.csv scan for the first 5 000 unique application IDs in chronological order
3. citations.csv filter pass to keep only those 5 000 apps' citation rows (~50 MB filtered from a 4 M-row, 5 GB unfiltered source)
4. USPTO ODP API enrichment per app (60 RPM rate limit; ~85 minutes wall-clock for the full pass)
5. Triple construction — the OARD's pre-classified rejection_* boolean columns + the citation rows + the ODP metadata combine into a LayerATriple per OA event

EP slice (450 rows, v1.0.2 → v1.2 expansion)

1. EP publications list auto-curated via IPC-stratified OPS published-data/search queries across 23 (IPC, year-range) buckets covering G06F (16/17/21/40), H04L67, H04W4, G06Q30, G06N (3/20), G06V20, A61K (9/39/47), B60W30, B60K35, G05D1, G01S17, C07K16, C12N15 — filing years 2014–2020. Quality gate keeps only candidates with ≥ 1 search-phase reg:citation and ≥ 3 claim-text entries
2. OPS published-data full-cycle for biblio + claims (epodoc/docdb format, kind-suffix fallback for older publications)
3. OPS Register service (/rest-services/register/publication/ epodoc/{pub}/biblio) for search-report citations — these carry the WIPO ST.14 category codes, mapped to severity via the table above and the full multi-character string split into categories[] (v1.2)
4. Two-endpoint merge per publication: full-cycle gives the bibliographic context; the Register service gives the prior_art[] list. Filtered to @cited-phase == "search" to keep the high-signal X/Y/A subset
5. Triple construction — same LayerATriple shape as the US slice; oa_type = "search_report", outcome.final_disposition = "pending" for EP rows (a separate legal-status enrichment path resolves to granted / lapsed_fee / withdrawn in the live Parallax agent's priorArtReferences table; the Layer A public dataset keeps the conservative default)
6. Index reduction (v1.2) — after the cases shards land, a build_index_for_staging pass walks them once and writes the prior_art_index/EP/index.parquet sibling partition with per-(ref_id, citing_jurisdiction) citation_count + severity_distribution aggregates

Common steps (both slices)

6. Validation: every row passes a linking validator that checks temporal sanity (cited prior art filed before the invention), severity coherence (novelty-destroying citations on a granted+unamended application would be an inconsistency), and schema round-trip
7. Parquet emit partitioned by jurisdiction × filing_year, with a SHA-256 manifest for byte-level reproducibility
8. HuggingFace push under v13s/golden-fto-layer-a

The full pipeline source lives in the public repo at parallax/data-pipeline. The release runner is bin/local-extract-v1.sh.

Known limitations

• Sample size: 5 450 rows. The full OARD has 4 M+ Office Actions; ramp-up to 50 K+ US rows is planned alongside JP-slice landing. The EP slice grew 11 → 450 (v1.0.2 → v1.2) via IPC-stratified auto-curation; further expansion gated on OPS /claims 413 attrition handling for long-claim publications.
• OPS /claims 413 attrition (EP curation): G06F16 / H04L67 publications with very long claim lists exceed OPS's /claims payload size limit. The curation quality gate currently drops these candidates rather than partial-fetching, biasing the EP slice toward pharma/mechanical/control IPCs.
• Sparse claim text: The ODP search endpoint returns bibliographic metadata (title, applicant, IPC) but not full claim text. Some rows have invention.claims = [] or placeholder markers; full claim extraction needs a separate ODP call (planned).
• JP not yet shipped: JP slice gated on INPIT bulk credential approval (申請 sent 2026-05-08); see docs/07-partnerships/inpit-bulk-data-application.md.
• EP claim ranges: The Register service embeds claim ranges in the citation's bibliographic text annotation ([Y] 5,12). v1.0.3+ extracts these into prior_art[].claims_blocked; legacy v1.0.2 rows leave the list empty.
• Mixed schema_version partition: rows from v1.0 / v1.1 cron cycles carry schema_version="1.0" and an empty categories[], while v1.2+ rows carry schema_version="1.2" and populated categories[] (when the source supports ST.14). Filter on schema_version if you need a single-version partition.
• US prior_art_index not yet populated: The v1.2 sibling index Parquet currently exists for EP only. US index lands on the next US re-extract (next weekly cron, Wed 06:00 UTC).
• EP outcome field is conservative: Without joining the OPS legal-status endpoint, outcome.final_disposition defaults to pending for EP rows. The live Parallax agent resolves these via a separate legal-status enrichment path; the public Layer A dataset keeps the conservative default.
• US outcome field is conservative: HUPD-derived outcome enrichment provides granted / rejected / pending for ~99 % of US rows (filing 2011-2017); rows beyond HUPD coverage default to pending.

Versioning

Semantic versioning per golden-dataset-plan.md:

• MAJOR — schema-incompatible (field removed, type changed)
• MINOR — new fields, new jurisdictions, ≥10 % data growth
• PATCH — parser bugfix, individual case re-validation

The HuggingFace dataset repo's git history is the canonical release ledger. To pin a specific version in your code:

ds = load_dataset("v13s/golden-fto-layer-a", revision="v1.2.20260508-r2")

Citation

If you use this dataset in academic work, please cite:

@dataset{vox_layer_a_2026,
  author       = {Hara, Yoichiro and {Vox}},
  title        = {Layer A — Office Action Triples for
                  Freedom-to-Operate Evaluation},
  year         = 2026,
  publisher    = {Hugging Face},
  version      = {{1.2.20260508-r2}},
  url          = {https://huggingface.co/datasets/v13s/golden-fto-layer-a},
  note         = {Curated under CC-BY-4.0; underlying patent
                  data in the public domain}
}

License

• Curation layer (this dataset): CC-BY-4.0 — the schema, severity tagging, and triple construction are © Vox 2026 and may be used / redistributed with attribution.
• Underlying patent documents: public domain (USPTO).
• OARD source data: public domain (USPTO Office of the Chief Economist).

Contact

• Curator: Yoichiro Hara (Co-Founder & CEO, Vox Technologies / Vox Japan株式会社) — yo@vox.delivery
• Org: Vox (HF: v13s)
• Source repo: https://github.com/masterleopold/parallax
• Issues: https://github.com/masterleopold/parallax/issues
• Product surface: https://parallax.3mergen.com

For takedown requests on specific patent applications, file an issue or email the curator. Public-domain patent data is included in good faith; the curation layer can be redacted on request.