Text-to-Text Transformation of Qualitative Responses
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Solution Overview
Problem
Existing technologies face challenges in effectively summarizing and interpreting qualitative responses from multiple user devices, particularly when responses vary in length and lack accompanying quantitative data, making it difficult to determine consensus or generate relevant summaries.
Innovation Solution
A system and method for text-to-text transfer transformation that includes a transformation computer processing a plurality of thought objects, reducing redundant objects, calculating clusters based on summary length and object quantity, and generating summaries using semantic vector representation and confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If summarization tools process qualitative responses with increasing amounts of textual information, then the summary should become more comprehensive, but the summarization tools find it difficult to provide relevant summarization and have limitations on the amount of text that can be processed
Solution Approach 1:
The patent segments the qualitative responses by first clustering them into groups based on semantic similarity, then selecting representative samples from each cluster. This segmentation allows the system to handle large amounts of text by processing them in manageable groups rather than attempting to process all text at once, thereby maintaining summarization relevance while increasing the quantity of text that can be processed.
Solution Approach 2:
The patent extracts and removes redundant thought objects from the qualitative responses before summarization. By identifying and removing duplicate or highly similar responses, the system reduces the amount of text that needs to be processed while maintaining the essential information, thus improving summarization relevance and enabling processing of larger text quantities.
2Quantity of substance
If the system processes a large number of qualitative responses, then the summary should be more comprehensive, but the computational overhead increases and processing time increases
Solution Approach 1:
The patent performs preliminary actions by clustering qualitative responses into groups and selecting representative samples before the actual summarization process. This preliminary organization of data reduces the computational complexity of processing large numbers of responses, as the system only needs to process representative samples from each cluster rather than all individual responses, thereby reducing processing time while maintaining comprehensive coverage.
Solution Approach 2:
The patent creates a simplified representation (copy) of the qualitative responses through clustering and sampling. Instead of processing all original responses directly, the system works with a reduced set of representative samples that capture the essence of the larger dataset, thereby reducing computational overhead and processing time while maintaining summary comprehensiveness.
3Quantity of substance
If the system processes qualitative responses without filtering, then the summary should be more comprehensive, but redundant thought objects increase computational overhead and reduce efficiency
Solution Approach 1:
The patent extracts and removes redundant thought objects from the qualitative responses through clustering and sampling. By identifying and excluding duplicate or highly similar responses, the system maintains summary comprehensiveness while significantly reducing the number of items that need to be processed, thereby improving processing efficiency and productivity.
Solution Approach 2:
The patent creates a condensed copy of the qualitative responses by selecting representative samples from clustered groups. This sampling approach preserves the essential information needed for comprehensive summarization while reducing the volume of data to be processed, thus maintaining completeness while improving processing efficiency.
Data Source
AI summary
A system and method for text-to-text transformation of thought objects. A transformation computer receives a plurality of thought objects from user devices. The thought object contains qualitative responses. Transformation computer processes the received thought objects into a semantic vector representation. Redundant thought objects are removed to generate a reduced plurality of thought objects. The reduced thought objects are then clustered using semantic vector representation. One or more of the thought objects are selected for transformation from the clusters. A transformer generates a summary using one or more of the selected thought objects.