Query Relevance Index Generation via Metric Segmentation
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Solution Overview
Problem
Current search engines lack effective methods to analyze and provide actionable insights from user queries and interaction data, limiting their ability to offer personalized and relevant results based on user metrics such as time, geographic source, and demographic variables.
Innovation Solution
A system that receives and measures user queries and interaction data, categorizes them, and provides customizable metric data to clients, allowing for graphical representation and comparison of query and result popularity over time, geographic source, and demographic variables, enabling businesses to gain intelligence on user sentiment and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If query tracking and user interaction measurement are implemented, then feedback for improving search result relevance is provided, but the ability to provide actionable business intelligence and insights is limited
Solution Approach 1:
The patent extracts and separates the measurement functionality from the traditional search feedback loop. It creates a distinct metric data collection and analysis system that pulls out query measurements, user interactions, and contextual information as separate analyzable entities, enabling independent business intelligence generation rather than just search optimization
Solution Approach 2:
The system segments query data into multiple measurable dimensions including time metrics, geographic source metrics, and demographic variable metrics. This segmentation transforms raw query data into structured, category-based measurements that can be independently analyzed and reported for different business purposes
2Adaptability or versatility
If comprehensive user data collection is performed, then user behavior analysis capability is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal metric data collection framework that handles multiple types of data (queries, user interactions, contextual information) through a single integrated system. This multi-functional approach consolidates what would otherwise require separate collection mechanisms for each data type, reducing overall system complexity while maintaining comprehensive analysis capability
Solution Approach 2:
The system introduces metric data structures and categorical frameworks as intermediary layers between raw user data and analysis processes. These intermediaries standardize and structure diverse user behavior data, making it easier to process and analyze without requiring complex custom handling for each data type
Data Source
AI summary
Methods and systems for generating query and result-based relevance indexes are provided. For one embodiment, a plurality of queries is received from a plurality of users. Each query of the plurality of queries is measured based on one or more metrics. The measured data of each query is stored. The queries are associated into topical query categories. A performance of a first query category is calculated based on at least one metric of the one or more metrics. A metric data request for a select category is received from a client. Lastly, the stored measured data of the selected category is transmitted to the client in response to the metric data request.