Unsubmitted Input Data Relevance Scoring for Task Performance
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Solution Overview
Problem
Users often edit their input data multiple times before submission, leading to lost unsubmitted data that may be insightful for determining user intent, but utilizing this data can be unreliable and result in unintended answers.
Innovation Solution
A method that identifies unsubmitted user data terms and determines their relevance to user intent using a graphical semantic model based on submitted input data, selecting only relevant terms to supplement the submitted data for improved task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unsubmitted user data is utilized to supplement submitted input data, then task performance quality is improved, but reliability deteriorates due to potential unintended answers
Solution Approach 1:
A graphical semantic model serves as an intermediary between unsubmitted user data and the task processing system. The model evaluates unsubmitted terms against the submitted input data to determine relevance, acting as a filter that mediates which unsubmitted data should be incorporated. This resolves the contradiction by introducing a reliability-checking mechanism that prevents unintended answers while still allowing quality improvements from relevant unsubmitted data.
Solution Approach 2:
The system changes the parameter of unsubmitted data from 'unevaluated raw terms' to 'relevance-scored terms'. By applying a relevance scoring mechanism based on the graphical semantic model, the system transforms unsubmitted data into a controlled format where only terms meeting relevance thresholds are incorporated. This parameter transformation maintains reliability while improving task performance quality.
2Loss of information
If all unsubmitted terms are incorporated into submitted data, then information completeness is improved, but data precision deteriorates due to inclusion of irrelevant terms
Solution Approach 1:
The graphical semantic model acts as an intermediary filtering mechanism that evaluates each unsubmitted term against the submitted input data. It prevents irrelevant terms from being incorporated while allowing relevant terms to pass through, thus resolving the contradiction between information completeness and data precision by selectively filtering unsubmitted data based on semantic relevance.
Solution Approach 2:
The system applies different quality standards to different portions of unsubmitted data. Instead of uniformly incorporating or rejecting all unsubmitted terms, the relevance scoring mechanism evaluates each term individually and applies local quality control. Terms that meet the relevance threshold are incorporated with high precision, while irrelevant terms are excluded, achieving both completeness and precision.
3Productivity
If unsubmitted data is analyzed and evaluated, then task performance is improved, but system complexity increases
Solution Approach 1:
The graphical semantic model serves as a pre-built intermediary structure that simplifies the analysis of unsubmitted data. Rather than implementing complex real-time analysis algorithms, the system uses the semantic model as a ready-made evaluation framework. This resolves the contradiction by providing a structured approach that improves task performance without requiring excessive system complexity.
4Manufacturing precision
If unsubmitted terms are filtered and evaluated for relevance, then data quality is improved, but processing time increases
Solution Approach 1:
The graphical semantic model is constructed and prepared in advance, creating a ready-made framework for evaluating unsubmitted terms. This preliminary action allows the system to quickly assess relevance during the actual processing phase without requiring complex real-time analysis. The contradiction is resolved by shifting work to the preliminary model-building phase, enabling faster quality filtering during execution.
Data Source
AI summary
Methods, systems and computer program products are provided. Terms of unsubmitted input data entered by a user during composition of submitted input data for the performance of a task are identified. For an identified unsubmitted term, a relevance score indicating the relevance of the unsubmitted term to user intent is determined. The relevance score of the unsubmitted term is determined using a graphical semantic model based on the submitted input data as a representation of user intent. The identified unsubmitted term is selected, for use in supplementing the submitted input data for the performance of the task, based on the determined relevance score.