Data Shape Confidence for Query Response Filtering

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Solution Overview

Problem

Existing data analysis systems face challenges in determining the relevance and completeness of datasets received from various sources to accurately respond to queries, particularly in complex scenarios like medication adherence monitoring, where data sources provide incomplete or irrelevant information.

Innovation Solution

A method that involves determining the expected data shape based on a query, comparing it to the received dataset, and calculating a confidence factor to assess the dataset's relevance and completeness, ensuring only relevant data is transmitted in response to the query.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If data is received from various sources to respond to queries, then data availability increases, but data relevance and completeness deteriorate

Engineering Contradiction:

Improvedata availabilityVSAvoiddata relevance and completeness

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system implements feedback by comparing the received data shape against the expected data shape derived from the query requirements. This comparison generates a confidence factor that feeds back into the decision-making process, determining whether to accept, reject, or request additional data, thereby ensuring data reliability while maintaining availability

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes the parameter of data evaluation from simple presence/absence to a confidence factor based on shape comparison. By transforming the data assessment criterion into a quantifiable confidence metric, the system can dynamically adjust data acceptance decisions based on the degree of match between received and expected data shapes

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If all received data is transmitted in response to queries, then data completeness improves, but data quality and relevance worsen

Engineering Contradiction:

Improvedata completenessVSAvoiddata quality and relevance

Core Design Contradiction:

Quantity of substanceVSLoss of information

Solution Approach 1:

The system extracts only the relevant data elements that match the expected shape from the received dataset. By separating relevant data from irrelevant data through shape comparison, the system transmits only the necessary information, maintaining completeness of relevant data while eliminating irrelevant content that would reduce quality

Inventive Principle:

Principle #2Taking out (Extraction)

3Measurement precision

If data shape comparison is performed to determine confidence factor, then data accuracy improves, but system complexity worsens

Engineering Contradiction:

Improvedata accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the data validation process into distinct components: extracting expected shape from query, comparing received data shape, calculating confidence factor, and making transmission decisions. By dividing the complex validation task into manageable segments, the system achieves high measurement precision while keeping each individual component relatively simple

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11748354B2Data shape confidence

Publication Date: 2023.09.05 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A system determines a shape of incoming data, and determines, based on the shape, whether the data should be passed on to support answering a query. If the shape of the data suggests that the data is insufficient or may even be misleading, the system can gate the data or prompt for more.