Datasets:

arbitrdatasets
/

arbitr-uspto-ev-sample

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
note: string
patents: struct<source_dataset: string, updated: string, source: string, rows: int64>
applications: struct<source_dataset: string, updated: string, source: string, rows: int64>
vs
publication_number: string
publication_id_full: string
claims: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 246, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              note: string
              patents: struct<source_dataset: string, updated: string, source: string, rows: int64>
              applications: struct<source_dataset: string, updated: string, source: string, rows: int64>
              vs
              publication_number: string
              publication_id_full: string
              claims: string

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We reply within 24 hours. Enterprise licensing available.

Arbitr USPTO EV Dataset — Sample Release

This repository provides a lightweight sample of the full Arbitr USPTO EV Dataset, a production-grade, commercially licensed dataset derived from USPTO bulk exports (PTGRDT + APPDT), curated specifically for the Electric Vehicle (EV) domain.

The sample lets you inspect:

Schema & field normalization

Directory layout

CPC-domain filtering

Provenance structure

Licensing terms

before purchasing the full commercial dataset.

The full dataset (not included here) supports R&D teams, automotive OEMs, investors, consultants, and AI/ML pipelines that need clean, ready-to-use, thematically filtered patent intelligence.

What the Full Dataset Is

The Arbitr USPTO EV Dataset is a structured, high-quality subset of USPTO patents and applications with thematic focus on:

Electric vehicles (Y02T)

Battery technologies (H01M)

Charging systems (H02J)

Power electronics (H02P, H02K)

EV propulsion & control (B60K, B60L, B60W)

It contains:

Granted patents (PTGRDT)

Patent applications (APPDT)

Text layers (abstracts, claims, descriptions)

CPC/IPC classifications

Assignee & inventor normalization

References / citations graph fields

Provenance & manifest metadata

Delivered as Parquet + JSONL, compatible with pandas, Spark, DuckDB, Trino, ML training pipelines, and enterprise IP analytics platforms.

What’s Inside This Sample

This sample is deliberately small (<1 MB) but structurally identical to the full dataset:

data/patents/patents_2025-10.parquet → 200-row slice of the Oct-2025 granted-patent export.

data/applications/applications_2025-10.parquet → 200-row slice of the corresponding application export.

data/texts/texts_sample.jsonl.gz → Claims/description sample from the full text layer.

metadata/ → Machine-readable dataset manifest, CPC whitelist, schemas.

data/provenance/ → Download log + transformation summary.

LICENSE_Arbitr_Commercial.txt → Arbitr Inc Commercial Dataset License v1.1.

CHANGELOG.md → Version history for this sample.

This structure mirrors the production dataset 1:1.

Purpose of the Sample

Provide a safe preview of the dataset structure.

Allow integration testing without downloading multiple GBs.

Demonstrate the CPC-driven EV-domain curation.

Showcase the commercial licensing model for enterprise users.

Usage Example

import pandas as pd

patents = pd.read_parquet("data/patents/patents_2025-10.parquet")
applications = pd.read_parquet("data/applications/applications_2025-10.parquet")

Any pipeline built against this sample will run on the full dataset (subject to licensing).

Commercial Edition (Full Dataset)

The full dataset (not included here) provides:

Hundreds of thousands of EV-domain patent records

2015–2025 coverage (configurable)

Normalized, cleaned, de-duplicated fields

Rich text layers (claims, descriptions where available)

CPC/IPC EV-domain whitelist applied at scale

Provenance logs & versioned manifest

Commercial license (perpetual internal use)

This product is designed for:

Automotive & EV OEMs / suppliers

VC/PE investors mapping EV innovation

Consulting firms (tech strategy, due diligence)

AI/ML labs training models on structured patent corpora

Market intelligence teams needing competitive landscapes

Pricing

Full dataset commercial license:

➡️ USD 4,990 (one-time, perpetual internal use)

Includes:

Full access to the dataset

Minor version updates (e.g., 1.0 → 1.0.x)

Rights to train internal AI models on the dataset

Use of derived analytics, embeddings, graphs, dashboards

Commercial use inside your organization

Enterprise upgrades available:

Annual updates subscription

Multi-entity licensing

Additional USPTO domains (AI, Battery, MED, Energy, etc.)

Custom CPC filters

LLM-friendly embeddings layer

Getting the Full Dataset

Contact Arbitr Inc at:

👉 [email protected] or open an Issue/Discussion in this repository.

We respond within 24 hours.

License

See LICENSE_Arbitr_Commercial.txt.

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