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README.md

@@ -1,62 +1,121 @@
 ---
-dataset_info:
-- config_name: drawings
-  features:
-  - name: image_id
-    dtype: string
-  - name: source
-    dtype: string
-  - name: ref_number
-    dtype: string
-  - name: page
-    dtype: int32
-  - name: image
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 39848628433
-    num_examples: 298935
-  download_size: 42150387144
-  dataset_size: 39848628433
-- config_name: noc4pc
-  features:
-  - name: application_number
-    dtype: string
-  - name: claim_number
-    dtype: int64
-  - name: context
-    dtype: string
-  - name: prior_art_specifications
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: application_image_ids
-    list: string
-  - name: prior_art_image_ids
-    list: string
-  splits:
-  - name: test
-    num_bytes: 117062704
-    num_examples: 2884
-  - name: validation
-    num_bytes: 292591832
-    num_examples: 7392
-  - name: train
-    num_bytes: 5605419085
-    num_examples: 136211
-  download_size: 898865796
-  dataset_size: 6015073621
+license: cc-by-nc-4.0
+task_categories:
+- image-text-to-text
+- visual-question-answering
+language:
+- en
+tags:
+- patent
+- patent-examination
+- multimodal
+- legal
+- uspto
+- novelty
+- non-obviousness
+pretty_name: PANORAMA-NOC4PC-Multimodal
+size_categories:
+- 100K<n<1M
 configs:
-- config_name: drawings
-  data_files:
-  - split: train
-    path: drawings/train-*
 - config_name: noc4pc
   data_files:
-  - split: test
-    path: noc4pc/test-*
+  - split: train
+    path: noc4pc/train-*
   - split: validation
     path: noc4pc/validation-*
+  - split: test
+    path: noc4pc/test-*
+- config_name: drawings
+  data_files:
   - split: train
-    path: noc4pc/train-*
+    path: drawings/train-*
 ---
+
+# PANORAMA-NOC4PC-Multimodal
+
+A **multimodal extension** of the NOC4PC benchmark from
+[PANORAMA](https://huggingface.co/datasets/LG-AI-Research/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
+
+```python
+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_id`s 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.