String Transformation Program Generation via Example Clustering

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Solution Overview

Problem

Transforming alphanumeric strings in large datasets is a tedious, time-consuming, and error-prone process, especially for non-technical users, as existing automated tools often require ideal examples to generate accurate transformation programs, and it can be difficult to identify and manage improper transformations in large datasets.

Innovation Solution

The solution involves identifying candidate example input strings through clustering and recognition of distinguishing inputs, presenting them to users for transformation examples, and generating a string transformation program based on user-provided output strings to ensure intuitive and transparent data transformation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated transformation tools are used to transform strings in large datasets, then productivity is improved, but manufacturing precision deteriorates due to errors in transformation

Engineering Contradiction:

Improvestring transformation speedVSAvoidtransformation accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system automatically generates test cases from the dataset and uses the actual transformation results as feedback to verify and refine the transformation program. This closed-loop feedback mechanism ensures transformation accuracy while maintaining automated productivity by continuously validating results against expected outcomes.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs self-verification by automatically generating test cases and evaluating transformation results without requiring manual intervention. The transformation program self-corrects through automated testing and validation, enabling both high productivity and precision through self-service validation mechanisms.

Inventive Principle:

Principle #25Self-service

2Manufacturing precision

If manual transformation methods are used to ensure accuracy, then manufacturing precision is improved, but productivity deteriorates due to time-consuming processes

Engineering Contradiction:

Improvetransformation accuracyVSAvoidstring transformation speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system performs preliminary actions by automatically generating test cases and validation rules before executing the transformation on the entire dataset. This preliminary validation ensures accuracy is built into the transformation process from the start, maintaining precision while enabling automated high-speed processing of large datasets.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual mechanical transformation processes with automated computational methods that include built-in validation and verification mechanisms. This substitution maintains transformation accuracy through automated checking while dramatically improving productivity by eliminating manual processing bottlenecks.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Device complexity

If transformation programs are generated without proper example management, then device complexity is reduced, but manufacturing precision deteriorates due to improper transformations

Engineering Contradiction:

Improvetransformation program complexityVSAvoidtransformation accuracy

Core Design Contradiction:

Device complexityVSManufacturing precision

Solution Approach 1:

The system segments the transformation process into distinct components: example management, program generation, test case generation, and validation. This segmentation allows each component to be optimized independently, maintaining simplicity in the overall system while ensuring precision through dedicated validation mechanisms that operate separately from the transformation logic.

Inventive Principle:

Principle #1Segmentation

4Ease of operation

If user control over transformation examples is limited, then ease of operation is reduced, but device complexity is improved through automated processes

Engineering Contradiction:

Improveuser control over transformationVSAvoidexample management complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system introduces an intermediary layer between the user and the transformation program that automatically manages examples and generates test cases. This intermediary handles the complexity of example management internally while presenting a simplified interface to the user, thereby improving ease of operation without sacrificing the sophisticated validation and control mechanisms needed for precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11620304B2Example management for string transformation

Publication Date: 2023.04.04 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A method for transforming strings includes identifying one or more candidate example input strings from a database including a set of input strings. The candidate example input strings are presented for example transformation. For one or more of the candidate example input strings, an example output string corresponding to that example input string is received, where each example input string and its corresponding example output string define a transformation example in an example set. A string transformation program is generated based on transformation examples in the example set.