Reference Link-Based Q/A Similarity Prediction
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Solution Overview
Problem
Conventional techniques for identifying relevant content in response to user questions often fail to recognize latent or implicit similarities due to terminological variations, leading to inefficient and costly live support sessions.
Innovation Solution
A method involving machine learning techniques, specifically training a model based on reference links between question and answer pairs, to predict relevant Q/A pairs for a given question, thereby overcoming semantic analysis limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If semantic analysis techniques are used to identify relevant content, then the system can process questions automatically, but it fails to identify latent or implicit similarities due to terminological variations
Solution Approach 1:
The patent introduces reference links as an intermediary element that connects Q/A pairs. These reference links serve as explicit mediators that bridge terminological variations, allowing the system to identify relevant content even when semantic analysis alone would fail due to different terminology. The reference links act as a mediator that translates between different terminological representations of the same underlying concept.
Solution Approach 2:
The system performs preliminary action by pre-processing Q/A pairs to extract and store reference links before the actual similarity detection process. This preliminary extraction of reference relationships creates a structured foundation that enables more accurate identification of relevant content later, without requiring complex real-time semantic analysis of terminological variations.
2Device complexity
If conventional semantic analysis is used, then processing is relatively simple, but relevant content is not identified leading to increased live support costs
Solution Approach 1:
The patent segments the content analysis process into distinct components: extracting reference links from Q/A pairs, building a reference graph structure, and performing similarity detection based on reference relationships. This segmentation allows the system to handle complexity in a modular way, where each component focuses on a specific aspect of the analysis, improving overall reliability without requiring monolithic complex processing.
Solution Approach 2:
The patent adds another dimension to the analysis by incorporating reference link structures alongside traditional semantic analysis. Instead of relying solely on semantic similarity in the original text space, the system creates a new dimension based on reference relationships, allowing it to identify relevant content through multiple pathways and thereby improve reliability.
3Measurement precision
If manual review is used to ensure accurate content identification, then relevance is guaranteed, but the process requires human intervention and is time-consuming
Solution Approach 1:
The system implements self-service by automatically extracting reference links from Q/A pairs and using these references to identify relevant content without human intervention. The automated system serves itself by creating and utilizing the reference structure, eliminating the need for manual review while maintaining high accuracy through the structured reference-based approach.
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated computational system that uses reference link analysis. This substitution maintains high accuracy in content relevance identification while dramatically reducing processing time and eliminating the need for human intervention in the content matching process.
Data Source
AI summary
Aspects of the present disclosure provide techniques for predicting content relevant to questions based on reference links. Embodiments include receiving a set of question and answer (Q/A) pairs and identifying a set of references in the set of Q/A pairs that link pairs of Q/A pairs of the set of Q/A pairs. Embodiments include identifying popular Q/A pairs of the set of Q/A pairs based on the set of references. The popular Q/A pairs may be referenced by a subset of the set of Q/A pairs and each respective Q/A pair of the subset of the set of Q/A pairs may comprise a respective question of a plurality of questions. Embodiments include training a model based on the plurality of questions, the popular Q/A pairs, and the set of references, to predict Q/A pairs of the set of Q/A pairs that are relevant to a given question.