Semantic Indexing of Refined AI Model Output Data
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Solution Overview
Problem
Raw output data from artificial intelligence models is often difficult to interpret due to voluminous and irrelevant information, making it challenging to extract meaningful insights.
Innovation Solution
A computing system refines and semantically indexes the output data from AI models by determining a refinement based on characteristics of the AI model and input data set, using hints and learned behavior, to generate a highly relevant semantic index that provides meaningful insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw output data from AI models is used directly, then the data volume is large and comprehensive, but the data is difficult to interpret and contains voluminous irrelevant information
Solution Approach 1:
The patent extracts only the relevant portions of AI model output data based on characteristics of the input data set and the AI model itself. By determining what information is relevant and extracting only that, the system eliminates voluminous irrelevant information while preserving essential insights, directly resolving the contradiction between information completeness and interpretability
Solution Approach 2:
The patent segments the raw output data into relevant and irrelevant portions, then further segments the relevant data into structured components that can be easily interpreted. This segmentation approach allows the system to maintain comprehensive information while organizing it in an interpretable format, addressing the contradiction between data volume and usability
2Ease of operation
If comprehensive raw output data is retained, then all information is preserved, but the usability and interpretability of the data decreases
Solution Approach 1:
The system extracts only the essential information from comprehensive raw output data by determining relevance based on input data set characteristics and AI model properties. This extraction process maintains data usability while reducing data volume, directly resolving the contradiction between interpretability and information completeness
Solution Approach 2:
The patent changes the parameters of the output data by transforming raw data into refined data with modified characteristics that enhance interpretability. By adjusting data parameters such as format, structure, and relevance criteria, the system improves ease of operation while maintaining adequate data quantity
3Loss of information
If refinement is applied to AI model output, then data relevance is improved, but additional processing steps are required
Solution Approach 1:
The patent performs preliminary refinement actions by determining relevant characteristics of the input data set and AI model before processing the output data. This preliminary action establishes criteria for relevance in advance, making the subsequent refinement process more efficient and reducing overall processing time while maintaining high information quality
Solution Approach 2:
The system uses characteristics of the AI model and input data set themselves to determine what refinement is needed, rather than requiring external analysis. This self-service approach to determining refinement parameters reduces processing overhead and time while still achieving high information quality in the refined output
Data Source
AI summary
The improved exercise of artificial intelligence by systematically refining and semantically indexing the output from AI models, so that the semantic index is highly relevant. To do this, the computing system obtains results of an input data set being applied to an AI model. The computing system then determines a refinement to apply to the obtained results. This determination may be based on one or more characteristics of the AI model and/or input data set. The determination may also be based on hints associated with that AI model, and/or learned behavior regarding how that AI model is typically used. The obtained results are then refined using the determined refinement. It is then this more relevant refined results that are semantically indexed to generate the semantic index. Thus, the semantic index represents, the more useful output from an AI model, which is semantically exposed so as to provide meaning.