Natural Language Data Pipelines Through Structured Query Translation

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Solution Overview

Problem

Existing data processing systems lack efficient methods for generating and describing data pipelines using natural language queries, limiting the flexibility and accuracy in data processing and visualization.

Innovation Solution

A system and method for generating data pipelines and descriptions using natural language queries, involving the use of computing models like GPT-3 to convert NL queries into standard query languages and generate data pipelines, and then describe these pipelines in NL format.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If natural language queries are used to generate data pipelines, then ease of operation is improved, but manufacturing precision deteriorates

Engineering Contradiction:

Improveease of operationVSAvoidprecision

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent introduces an intermediary system comprising computing models (such as GPT-3) and processing modules that mediate between natural language queries and data pipeline generation. This intermediary translates NL queries into structured query languages, generates appropriate data pipelines, and ensures precision through model-based processing, thereby resolving the contradiction between ease of operation and manufacturing precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If computing models are used to convert NL queries to standard query language, then productivity is improved, but device complexity increases

Engineering Contradiction:

ImproveproductivityVSAvoidcomplexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical or manual data pipeline construction methods with computing models (AI/ML systems). These models automatically convert natural language queries into executable data pipelines, significantly improving productivity. The complexity is managed through software-based solutions rather than physical or manual processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of time

If data pipelines are generated automatically from NL queries, then loss of time is reduced, but reliability may deteriorate

Engineering Contradiction:

ImprovetimeVSAvoidreliability

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The patent implements feedback mechanisms where the system processes NL queries through computing models, generates data pipelines, and can refine or adjust the output based on results or user input. This feedback loop ensures that while automatic generation saves time, the reliability is maintained through iterative improvement and validation of the generated pipelines.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250272287A1Systems and methods for generating and displaying a data pipeline using a natural language query, and describing a data pipeline using natural language

Publication Date: 2025.08.28 PALANTIR TECHNOLOGIES INC

AI summary

System and method for generating and displaying data pipelines according to certain embodiments. For example, a method includes: receiving a natural language (NL) query; receiving a model result generated based on the NL query, the model result including a query in a standard query language, the model result being generated using one or more computing models; and generating the data pipeline based at least in part on the query in the standard query language, the data pipeline comprising one or more data pipeline elements, at least one data pipeline element of the one or more pipeline elements being corresponding to a query component of the query in the standard query language.