Task-Oriented Search Query Enhancement via Activity Context
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Solution Overview
Problem
Current search engine technologies are inadequate in providing relevant results as they rely on keyword matching and popularity metrics, failing to accurately identify resources in personal networks and desktop environments, and are limited by implicit context discovery and keyword-based representations.
Innovation Solution
Integration of task-related information into the search process through a Task-Oriented Activity System (TOAS) that predicts the user's current task and enhances search queries with metadata, allowing for task-oriented query extensions and post-processing of search results to filter and rank results based on task relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If keyword matching and popularity metrics are used for search, then search engine technology can handle general web search effectively, but it fails to accurately identify resources in personal networks and desktop environments
Solution Approach 1:
The patent applies local quality by transitioning from generic search metrics to context-specific evaluation. Instead of using universal popularity metrics, the system evaluates resources based on their relevance to the user's current task and personal context, creating locally optimized search results that adapt to individual needs and environments.
Solution Approach 2:
The patent changes the parameters used for search evaluation from keyword frequency and popularity metrics to task-relevance-based parameters. The system monitors user activity, identifies current tasks, and uses these task parameters to rank and filter search results, fundamentally changing how search relevance is measured and determined.
2Extent of automation
If implicit context discovery techniques are used, then search results can be automatically generated, but the contexts discovered do not match the user's own perception of their context
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors user interactions with resources and uses this feedback to refine its understanding of user context and tasks. This continuous feedback loop allows the system to automatically discover contexts while maintaining alignment with user perceptions by adapting to actual user behavior patterns.
Solution Approach 2:
The system performs self-service by automatically monitoring and analyzing user activity without requiring explicit input. It autonomously discovers contexts by observing user interactions with resources, applications, and files, generating search results that reflect the user's own organizational patterns and perceptions.
3Productivity
If keyword profiles or probability distributions are used to represent search contexts, then search results can be generated efficiently, but the expressiveness is limited leading to less specific or insufficient search results
Solution Approach 1:
The patent adds new dimensions to context representation by incorporating task identifiers, resource relationships, and temporal information alongside traditional keyword profiles. This multi-dimensional representation maintains efficiency while significantly increasing expressiveness and preserving critical context details that would otherwise be lost.
Solution Approach 2:
The system uses composite representations that combine multiple types of information—keyword profiles, task contexts, resource metadata, and usage patterns—into a unified search context model. This composite approach preserves the efficiency of keyword-based searching while adding the richness of task-aware context information.
Data Source
AI summary
Methods for using task-related information to enhance digital searching are provided. A task-oriented user activity system maintains task-related information about resources accessed by a user and current user task. This task-related information is used to enhance search queries to include task-related search criteria that improve relevance of search results. The task-related information can also be used to include task-related metadata in search engine index, e.g., by storing the metadata in the index or by storing it in resources which are subsequently indexed. Task-related information can also be used to enhance search results by filtering and ranking results to increase relevance with respect to a user's current task.