Patent Landscape: 3D/4D Reconstruction × Robot Manipulation
Sources: Session notes, 2026-05-19 Raw: US Patent Landscape 3D Reconstruction Updated: 2026-08-12
Overview
A systematic US patent search covering “3D/4D generative methods × robot manipulation × safety assessment” confirms five white spaces with no corresponding granted or pending US patents as of May 2026. The methodology finding is equally important: robot ML patents rarely cite academic papers, making academic-citation-based prior art searches ineffective in this domain.
Methodology Note
Robot ML patents use broad functional claim language and minimal NPL (non-patent literature) citations — unlike biomedical or chemical patents. “Reverse citation search from academic papers” is largely ineffective here. Recommended search strategy: IPC classification codes + major assignee filtering.
Key IPC codes for this domain:
| Technology | Primary IPC |
|---|---|
| Video / image generation | G06T 13/xx |
| 3D model / point cloud | G06T 17/xx |
| Deep learning generative models | G06N 3/045 |
| Robot program control | B25J 9/16 |
| Robot simulation | B25J 9/1697 |
| Robot safety devices | B25J 19/06 |
| ML simulation | G06F 30/27 |
Search pattern: IPC:B25J9/1697 AND IPC:G06N3/045 targets deep learning + robot simulation.
Existing Patents by Direction
Robot Safety (HRI Collision)
- US12420422 — Situation-aware Safety Assessment of Robot-Human Activities: visual sensing → collision probability × severity → risk score → action control. Most direct granted case for “visual assessment of human-robot collision safety.”
- US11872702B2 (Dexai, 2024) — Robot Interaction with Human Co-workers: 3D model geometric overlap + neural network collision prediction.
AI-Generated Safety Constraints
- US20250042032A1 (pending, 2025) — LLM prompt + text environment description → safety constraints → control signal modification. Input is text, not visual image.
- US20240253224A1 / US20240359319A1 (Sanctuary AI, 2024) — LLM-driven robot system, IPC safety device classification.
Simulation-Driven Control
- US11938638B2 (Google/GDM, 2024) — Simulation Driven Robotic Control: 3D point cloud simulated environment → candidate action evaluation → real robot execution. Closest granted case to “pre-execution rollout” concept. Uses static point clouds, not generative.
Transformer + VLM → Action
- US20240189994A1 (Google, 2024) — Real-world Robot Control Using Transformer NNs (RT series)
- US20240078429A1 (Google, 2024) — Control Policies for Robotic Agents: learned predictive model + MPC
Diffusion Policy
- US20250312914A1 (2025) — Transformer Diffusion for Robotic Task Learning
- US20250353169A1 (2025) — Semi-supervised Learning of Robot Control Policies (NPL cites RT-1/RT-2, not Diffusion Policy original paper)
3D Reconstruction Foundations
- US12145617B2 (NVIDIA) — 3D Surface Reconstruction with Point Cloud Densification Using AI
Confirmed White Spaces (as of May 2026)
- 3DGS / NeRF → robot manipulation planning — completely empty
- Video generation as pre-execution simulation — completely empty (V-Dreamer, Cosmos Policy, GPC exist only as academic papers)
- Point cloud generation for manipulation planning — nearly empty (US11938638B2 uses static point clouds, not generative)
- VLM visual assessment of robot action safety (image-input scoring) — empty; US20250042032A1 is closest but uses text input
- 3D/4D geometric world model × pre-execution safety scoring integrated pipeline — empty
Claim Strategy: VLM vs. Functional Language
Writing “VLM” directly in independent claims narrows protection scope — competitors can substitute equivalent architectures. Recommended layering:
Independent claim (broad):
“a multimodal machine learning model configured to process visual observations and generate structured evaluation outputs”
Dependent claim (narrow):
“wherein the multimodal machine learning model is a vision-language model (VLM)” “wherein the evaluation output comprises a numerical safety score and a natural language explanation”
Key Prior Art Distinctions
The most dangerous prior art for a claim combining “3D input → simulated trajectory → visual feature scoring”:
| Patent | 3D Input | Simulated Trajectory | Visual Feature Scoring |
|---|---|---|---|
| US11938638B2 (Google) | ✓ | ✓ | ✗ (state matching) |
| US20240025035A1 (Intrinsic) | ✗ | ✓ | ✗ (physical threshold) |
| US10766136 (Amazon) | ✗ | ✗ | ✓ (real execution only) |
| Target combination | ✓ | ✓ | ✓ |
Amazon US10766136: CNN analyzes real robot execution video → task success score → RL reward. Uses CNN (not visual+linguistic joint model); assesses real execution (not simulated pre-execution).
Distinction strategy for a visual pre-execution safety claim:
- Evaluation target: action feasibility/safety (not environment description)
- Output form: structured score + language explanation + downstream control signal
- Temporal position: pre-execution (not post-execution reward)
- Input modality: 3D point cloud or 4D video (not 2D image)
Open Questions
- As 3DGS and NeRF-to-manipulation papers publish in 2025-2026, how quickly does the white space close? First-mover timing matters.
- Does the VLM visual safety scoring white space extend to European (EPO) and Chinese (CNIPA) patent jurisdictions?
- US20250042032A1 (text-input safety constraints) is pending — if granted, does its claim scope reach visual-input variants?