ML Content Retrieval Using Distilled Datasets for Similar Case Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information processing systems lack efficient techniques for content analysis and retrieval, particularly in enterprise customer service systems, where technical support agents need guidance on resolving similar issues.
Innovation Solution
The implementation of machine learning techniques to generate a distilled dataset from an input dataset, allowing for the computation of similarities between incoming content and previously solved cases, thereby retrieving relevant past solutions for technical support agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If technical support agents manually review past cases to find similar issues, then they can access previous resolution procedures, but the process is time-consuming and reduces productivity
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning-based content analysis system. The system automatically analyzes incoming content, compares it with the distilled dataset, and retrieves similar past cases without human intervention, thereby eliminating the time-consuming manual search while preserving access to previous resolution procedures.
Solution Approach 2:
The system performs preliminary actions by pre-processing and distilling the training dataset into a compact representation before actual use. This preprocessing step creates an optimized dataset that enables faster similarity computation during runtime, allowing the system to quickly retrieve relevant past cases without compromising the quality of information access.
2Reliability
If a large training dataset is used for machine learning, then the model can learn from more examples, but the computational complexity and processing time increase
Solution Approach 1:
The patent extracts the essential information from the large training dataset by creating a distilled dataset that captures the most important patterns and features. This extraction process removes redundant data while retaining the core knowledge needed for accurate similarity computation, thereby reducing computational complexity while maintaining model reliability.
Solution Approach 2:
The system changes the parameter of data representation by transforming the original large dataset into a distilled format with reduced dimensionality. This parameter transformation maintains the essential information needed for accurate content analysis while significantly reducing the computational resources required for processing.
3Loss of information
If traditional content retrieval methods are used, then the system can search through all available data, but the retrieval speed is slow and does not scale well
Solution Approach 1:
The patent replaces traditional linear search methods with a machine learning-based similarity computation system. The system uses the distilled dataset to efficiently compute similarities between incoming content and past cases, providing both complete search coverage and fast retrieval speeds that scale well with increasing data volume.
Data Source
AI summary
A method comprises generating a distilled dataset from an input dataset, wherein the input dataset comprises a plurality of first data samples, and the distilled dataset comprises a plurality of second data samples. In the method, an input data sample is received, and similarities are computed between the input data sample and respective ones of the plurality of second data samples. The method further comprises selecting one or more second data samples of the plurality of second data samples based, at least in part, on the computed similarities, and retrieving one or more first data samples of the plurality of first data samples from the input dataset based, at least in part, on the selected one or more second data samples. The generating is performed, at least in part, using one or more machine learning models.