Search Result Reordering via User Action Correlation
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Solution Overview
Problem
Existing applications fail to monitor and log all types of object-level actions across multiple unrelated applications, and they do not analyze relationships between events or objects, nor enable searching based on observed actions.
Innovation Solution
A system with monitoring software that tracks user and computer-initiated actions across various objects, analyzes relationships, and reorders search results based on user action data, using a correlation determining system to infer relationships and adjust search results accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If comprehensive monitoring of all object-level actions across multiple applications is implemented, then the quantity and completeness of action data increases, but the device complexity and data processing requirements increase
Solution Approach 1:
The monitoring system is divided into multiple components: event loggers in each application that capture local actions, a central data collection module that aggregates logs, and an analysis engine that processes the combined data. This segmentation allows comprehensive monitoring without requiring a monolithic complex system, as each component handles a specific subset of functions independently.
Solution Approach 2:
The monitoring system employs universal data structures and interfaces that can handle multiple types of actions across different applications through a common framework. The event log format and data collection mechanisms are designed to be application-agnostic, allowing the same system architecture to monitor diverse object-level actions without requiring application-specific customization for each data source.
2Measurement precision
If relationship analysis between events and objects is performed, then the quality and relevance of search results improves, but the computational time and processing resources increase
Solution Approach 1:
The system pre-computes and stores relationship metadata between events and objects during the data collection phase, creating an indexed relationship database before search operations are needed. This preliminary analysis includes establishing co-occurrence patterns, temporal relationships, and contextual associations between monitored actions and objects, so that during search operations, the system can quickly retrieve pre-analyzed relationships rather than performing computationally intensive analysis in real-time.
3Ease of operation
If search results are reordered based on user action data, then the usefulness and relevance of search results increases, but the additional processing and analysis requirements increase
Solution Approach 1:
The system implements feedback loops where user interactions with search results and monitored actions continuously inform the reordering algorithm. The monitoring data provides feedback about user preferences, frequently accessed objects, and contextual patterns that dynamically adjust search result ordering. This feedback mechanism allows the system to improve search usefulness over time without requiring complex manual configuration, as the algorithm automatically adapts based on observed user behavior patterns.
Data Source
AI summary
The invention is directed to a system and method of reordering conventional search results provided by a remote search engine such as Google. The invention may leverage a chronological timeline of user action information to infer relationships between objects in a search result. The search result reordering process does not require direct interaction by the user.