Multi-dimensional Data Tagging for Query Response Automation
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Solution Overview
Problem
Responding to data requests is time-consuming and resource-intensive due to the repetitive processing of large volumes of information, with missed opportunities to leverage previously generated information for further insights.
Innovation Solution
A computing platform applies multi-dimensional data tags to response data, allowing efficient identification and reuse of relevant data through artificial intelligence and natural language processing, enabling automated responses to similar queries and trend detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing of data requests is used, then data accuracy and user validation are maintained, but time consumption and resource intensity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically tagging data with multiple dimensions (industry, location, time period, etc.) during data ingestion, so that when data requests arrive, the filtering and retrieval operations can be performed efficiently on pre-organized data rather than raw data, reducing the time needed for each request while maintaining accuracy through user validation
Solution Approach 2:
The system creates structured copies of data metadata and tags that can be rapidly queried and matched against request criteria without moving or copying the actual large-volume data, enabling fast preliminary processing while the original data remains stationary for accurate validation when needed
2Productivity
If comprehensive data tagging is applied, then data retrieval efficiency and reuse capability improve, but system complexity and processing overhead increase
Solution Approach 1:
The tagging system segments data identification into multiple independent dimensions (industry type, geographic location, time period, data category, etc.), where each dimension can be tagged, stored, and queried separately. This modular approach improves retrieval efficiency by enabling targeted filtering while reducing overall system complexity compared to a monolithic tagging system
Solution Approach 2:
The multi-dimensional tag structure serves multiple functions simultaneously: it enables efficient filtering and search, supports data reuse identification, facilitates trend analysis across different dimensions, and provides a framework for automated response generation. This universal tagging framework reduces the need for separate systems for each function
3Productivity
If automated response generation is implemented, then resource intensity and time consumption are reduced, but the ability to handle novel or complex queries may be compromised
Solution Approach 1:
The system incorporates feedback loops where user interactions with automated responses (validation, corrections, approvals) are used to refine and improve the automated response generation process over time. This allows the system to learn from actual usage patterns and improve its ability to handle novel queries while maintaining high productivity
Solution Approach 2:
The system dynamically adjusts its operation mode based on query characteristics and confidence levels. For routine, well-structured queries, it operates in fully automated mode for high productivity. For novel or complex queries, it transitions to semi-automated or manual modes, maintaining adaptability. The tagging system enables this dynamic behavior by providing structured information that helps determine the appropriate level of automation
Data Source
AI summary
Aspects of the disclosure relate to multi-dimensional data tagging and reuse. A computing platform may receive first response data associated with responses to a first set of queries. Subsequently, the computing platform may apply data tags to the first response data, which may include tagging the first response data based on multiple dimensions. Then, the computing platform may prompt a user of a computing device from which the data originated to validate the data tags applied to the first response data. Next, the computing platform may analyze a second set of queries which may be associated with the same content type. Thereafter, the computing platform may generate second response data associated with responses to the second set of queries based on the data tags applied to the first response data and send the second response data in response to the second set of queries.