Automated Survey Attribute Definitions for Dynamic Data Analysis
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Solution Overview
Problem
Conventional digital survey systems face challenges in accurately interpreting and analyzing large volumes of dynamic survey data, are inefficient in processing survey responses, and lack flexibility in adapting to real-time changes and diverse data formats.
Innovation Solution
A survey attribute definition system that intelligently applies attribute definitions to digital survey data, aligning it with a global labeling schema, and provides efficient user interfaces for generating survey analyses, accommodating dynamic changes and diverse data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional digital survey systems process large volumes of survey data using individual administrator tagging, then comprehensive data collection is achieved, but processing efficiency deteriorates due to excessive time and computing resources required
Solution Approach 1:
The patent replaces the mechanical manual tagging system with an automated machine learning-based attribute definition system. The system uses trained models to automatically identify and apply attribute definitions to survey data, eliminating the need for individual administrator tagging while maintaining comprehensive data processing capability.
Solution Approach 2:
The system enables self-service processing where the survey data automatically receives attribute definitions through the machine learning model without requiring human intervention for each data point. The model autonomously processes large volumes of survey data, applying appropriate attributes based on learned patterns from training data.
2Measurement precision
If conventional systems use rigid individual tagging processes, then data accuracy can be monitored, but flexibility deteriorates in adapting to dynamic survey data changes
Solution Approach 1:
The patent implements a dynamic attribute definition system where the machine learning model can be retrained and updated as survey data evolves. The system adapts to changing survey formats, contexts, and labels by incorporating new training data, allowing it to maintain accuracy while flexibly responding to dynamic changes in survey methodologies and data structures.
3Reliability
If conventional systems process survey responses through complex manual workflows, then thorough analysis is achieved, but resource consumption increases excessively
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive survey data before deployment. The models learn attribute definition patterns in advance, enabling them to efficiently process new survey data without requiring complex real-time computational workflows. This pre-processing phase consolidates the computational effort, reducing ongoing resource consumption while maintaining thorough analysis capability.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for applying attribute definitions to various survey data and utilizing attribute definitions to generate survey analyses. In particular, in one or more embodiments, the disclosed systems intelligently determines attribute definitions to apply to digital surveys, digital survey questions, and digital survey responses. The disclosed systems can determine attribute definitions to apply to survey data based on user input, associated survey templates, associated prior use of attribute definition, analysis of the text of digital surveys, and a variety of other attributes of survey data. The disclosed systems can generate digital survey analysis by utilizing attribute definitions applied to a variety of types of survey data.