Natural Language Data Pipelines Through AI Query Translation

The patent introduces computing models (AI/ML models) as intermediaries that translate natural language queries into executable data pipeline configurations. This mediator layer enables users to interact with complex data processing systems using simple natural language without needing to understand the underlying computational complexity, thus improving ease of operation while managing device complexity through automated translation.

The patent replaces traditional mechanical interaction methods (clicking through GUIs, writing code) with natural language processing. Users can describe desired data processing operations in natural language instead of manually configuring complex pipeline elements, significantly improving ease of operation while the system handles the complexity of translating these descriptions into functional data pipelines.

The system employs self-service computing models that automatically translate natural language queries into data pipeline configurations without requiring manual intervention from experts. This automation improves productivity by enabling rapid pipeline generation, while the models handle the complexity of understanding natural language semantics and mapping them to appropriate data processing operations.

The patent utilizes parameter changes in computing models (such as adjusting model confidence thresholds, translation parameters, and pipeline configuration parameters) to optimize the balance between productivity and complexity. By dynamically adjusting these parameters, the system can improve productivity through more accurate and efficient query-to-pipeline translation while managing the underlying device complexity.