PANORAMA-NOC4PC-Multimodal

A multimodal extension of the NOC4PC benchmark from PANORAMA (Lim et al., PANORAMA: A Dataset and Benchmarks Capturing Decision Trails and Rationales in Patent Examination, NeurIPS 2025 Datasets & Benchmarks).

The original NOC4PC task asks a model to judge a patent claim's patentability (§102 / §103 / allow) from text only (claim + cited prior-art text). This dataset adds the drawings that real examiners actually look at — for both the patent application and every cited prior-art reference — so the task can be studied as a true vision-language problem.

Why two configs (reference layout)

In NOC4PC the same drawing is shared by many rows (a single application appears in ~18 claim-level rows on average; popular prior-art patents appear in hundreds). Embedding the image in every row would duplicate it dozens of times and blow the dataset up past 600 GB. Instead — exactly like VQAv2 / DocVQA / OK-VQA — we store each unique drawing once and let task rows reference it by image_id:

config	one row =	key columns
noc4pc	one claim-level examination instance	..., application_image_ids[], prior_art_image_ids[]
drawings	one rendered drawing page	image_id, source, ref_number, page, image

image_id format: app_{applicationNumber}_p{N} (application) and cited_{patentNumber}_p{N} (prior art), where N is the 1-based page number. Drawings are rendered from the original USPTO drawing PDFs at 120 dpi (all pages).

Columns

noc4pc

• application_number, claim_number — the target claim
• context (JSON string) — application title / abstract / claims
• prior_art_specifications (JSON string) — cited prior art (title, abstract, claims, cited paragraph)
• answer (JSON string) — {"code": "102" | "103" | "ALLOW", "reason": ...} (gold label)
• application_image_ids (list[str]) — drawing pages of the application
• prior_art_image_ids (list[str]) — drawing pages of all cited prior arts

drawings

• image_id (str), source (application | prior_art), ref_number (str), page (int), image (datasets.Image)

Usage

from datasets import load_dataset

REPO = "sungjae98/PANORAMA-NOC4PC-multimodal"

# 1) load both configs
noc = load_dataset(REPO, "noc4pc", split="test")
draw = load_dataset(REPO, "drawings", split="train")   # the image bank

# 2) build an image_id -> PIL.Image lookup (lazy; decode on access)
id2idx = {iid: i for i, iid in enumerate(draw["image_id"])}
def get_image(image_id):
    return draw[id2idx[image_id]]["image"]   # PIL.Image

# 3) assemble one multimodal example
ex = noc[0]
app_imgs   = [get_image(i) for i in ex["application_image_ids"]]
prior_imgs = [get_image(i) for i in ex["prior_art_image_ids"]]
print(ex["application_number"], ex["claim_number"], "->", eval(ex["answer"])["code"])
print("application pages:", len(app_imgs), " prior-art pages:", len(prior_imgs))
# feed ex["context"] + ex["prior_art_specifications"] + app_imgs + prior_imgs to a VLM

Tip: for memory-tight training, don't materialize all of draw; index it once and decode images on demand, or filter draw to the image_ids you need per batch.

Splits (noc4pc)

split	rows
train	136,211
validation	7,392
test	2,884

Provenance & license

Drawings and text are derived from the public USPTO record and the PANORAMA dataset. Released under CC-BY-NC-4.0, following the original PANORAMA license. This is an unofficial, research-oriented extension and is not affiliated with the original authors or LG AI Research.

If you use this, please cite the original PANORAMA paper.