Post-Processing Layer

The Post-Processing Layer is the final stage of Orn’s architecture, where annotated outputs are standardized, normalized, and packaged into structured formats that can be consumed by downstream systems. If ingestion secures authenticity, pre-processing polishes quality, and annotation extracts meaning, post-processing ensures that the resulting data is consistent, interoperable, and ready for deployment. This is where Orn transforms structured labels into true datasets.

The first responsibility of this layer is label normalization. Since annotations are generated from a mix of automated models and human-in-the-loop feedback, inconsistencies may appear in the way objects, actions, or environments are described. Post-processing resolves these variations by aligning all labels to a unified ontology, ensuring that “cup,” “mug,” and “glass” are treated consistently as a single category where appropriate. This normalization guarantees that the dataset speaks a single, standardized language, which is essential for both internal use and external distribution.

Next, the system formats data into interoperable structures aligned with robotics and AI training standards. Temporal action sequences, object bounding boxes, and task outcomes are converted into widely accepted formats such as JSON ontologies, ROS-compatible data, or video-label pairing structures. By packaging annotated outputs in these familiar formats, Orn ensures that the resulting datasets can be plugged directly into machine learning pipelines, robotics simulations, or research workflows without additional engineering overhead.

The Post-Processing Layer also incorporates quality weighting and scoring. Every dataset entry is tagged with metadata that reflects its quality, confidence level, and reviewer validation status. Higher-quality clips, verified with strong human-AI agreement, are weighted more heavily, while lower-confidence entries are flagged for optional review. This creates a transparent record of dataset reliability, giving downstream consumers control over the tradeoff between dataset size and precision.

Finally, this stage manages distribution and integration. Normalized datasets are stored in structured repositories, linked with contributor reputation scores, and made accessible through APIs, marketplaces, or direct licensing agreements. At this point, the content ceases to be just a video or a label — it becomes a plug-and-play dataset that can be consumed by robotics companies, AI researchers, and ecosystem partners.

By the time a submission leaves the Post-Processing Layer, it has traveled the full Orn pipeline: from raw gallery upload, through ingestion and validation, to cleaning and privacy enforcement, to annotation and structuring, and finally to standardized, high-quality output. The result is a dataset that is not only authentic and diverse but also consistent, interoperable, and immediately usable for both in-game progression and external AI applications.

All thresholds, parameters, and detection methods described are subject to continuous refinement as technology advances and as the requirements of the ecosystem evolve.