# Patent Draft Quality and Three-Requirements Review

## Draft Completeness Review

Before saying a draft is ready, check:

1. The title matches the real inventive concept.
2. Claims, specification, abstract, and drawings use consistent terminology.
3. Each independent claim has a complete technical closed loop.
4. Every claim feature is supported in the specification.
5. The background states technical defects, not only business pain.
6. Technical effects correspond to technical features.
7. Alternatives avoid tying protection to one vendor, model, threshold, or scenario.
8. Implementation examples are concrete enough to show enablement.
9. Drawings cover all important flows/modules.
10. Confidential/public disclosure status is noted for the user to confirm.

## Novelty Review

Without a formal search, phrase the conclusion as preliminary.

Assess whether a single prior-art reference is likely to disclose all essential features in combination.

For AI/edge/cloud inventions, compare:

- local difficult-sample screening;
- scene-specific dataset generation;
- large model as teacher/labeler;
- label-to-sample conversion;
- scene/device-specific expert model training;
- OTA delivery;
- closed-loop feedback.

If the invention's novelty depends on a combination, keep that combination in the independent claim.

## Inventiveness Review

Identify possible closest prior art:

- active learning or low-confidence sample selection;
- pseudo-labeling or teacher/student training;
- knowledge distillation;
- edge AI model deployment;
- cloud training and OTA update;
- visual monitoring event detection.

Then explain why the invention is not a simple aggregation:

- it solves edge computing constraints and scene adaptation together;
- local screening reduces upload and labeling cost;
- large-model semantic labeling supplies fine-grained labels unavailable to the edge model;
- label conversion bridges semantic understanding and trainable small-model categories;
- device/scene binding makes model results different for identical hardware in different scenes;
- feedback closes the training/deployment loop.

## Practical Applicability Review

Confirm:

- devices/platforms can be implemented with known hardware/software;
- inputs, processing, outputs, and deployment are clear;
- the solution can be used in industrial/business scenarios;
- technical effects can be measured or observed.

## Common Risk Points

1. Claim only says "use AI" or "select model by scene" without a technical mechanism.
2. "Online training" is not supported by trigger conditions or data flow.
3. Large model output is not connected to small model training samples.
4. Effects are business-only, such as "better management", without technical metrics.
5. Embodiments overfocus on one business scenario and narrow the invention.
6. Claims overlimit to one named model/vendor.
7. Drawings include decorative images instead of technical diagrams.

## Recommended Final Summary

Use a concise conclusion:

- 新颖性：较好/一般/存在风险, with reason.
- 创造性：强/中等/偏弱, with closest-risk combinations and defense.
- 实用性：强/可满足/需补充, with implementation basis.

Add concrete recommendations before filing.
