Task-Oriented Activity System for Search Relevance
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Solution Overview
Problem
Existing search engine technologies are inadequate in providing relevant results, especially when users need to locate specific information on personal resources or in contexts where popularity metrics are not applicable, and they fail to accurately match user perceptions of their current tasks due to implicit context discovery and limited expressiveness.
Innovation Solution
Integrating a Task-Oriented Activity System (TOAS) with search engines to include task-related metadata, which predicts the user's current tasks and enhances indexing and query processing to provide task-aware search results by pre-processing queries and post-processing search results with task-related information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search engine technology uses keyword matching and link popularity metrics, then search results can be generated quickly, but the relevance and accuracy of results deteriorate when users need to locate specific information in personal resources or non-popular contexts
Solution Approach 1:
The system performs preliminary actions by automatically discovering user contexts and tasks in advance, building context profiles and task models before search queries are submitted. This allows the search engine to have pre-computed contextual information ready to improve result relevance without adding complexity to the search execution process.
Solution Approach 2:
The patent introduces context profiles and task models as intermediary structures between the user and the search engine. These intermediaries capture user-specific contextual information and task states, mediating between raw search queries and search results to improve relevance without requiring fundamental changes to the core search engine architecture.
2Extent of automation
If search engines use implicit context discovery techniques, then automated search can be performed, but the context accuracy deteriorates because implicitly discovered contexts never completely match each individual's organization of their own activity
Solution Approach 1:
The system employs feedback mechanisms where user interactions with search results and system predictions are continuously monitored. This feedback is used to refine and update context profiles and task models, progressively improving context accuracy while maintaining automated operation. The feedback loop allows the system to learn from user corrections and adjustments.
Solution Approach 2:
The context profiles and task models are designed to be dynamic rather than static, continuously adapting to user behavior patterns and correcting discrepancies between discovered and actual user contexts. This dynamic adjustment allows the system to maintain high automation while improving context accuracy over time through continuous refinement.
3Productivity
If search systems use keyword profiles or probability distributions to represent contexts, then automated processing is enabled, but the expressiveness deteriorates leading to either less specific search results or lack of results due to over-constraint
Solution Approach 1:
The patent combines multiple representational approaches into a composite context model that integrates keyword profiles, probability distributions, and structured task models. This composite structure leverages the automated processing capabilities of statistical methods while adding the expressiveness of structured task representations, avoiding the limitations of using any single approach alone.
Solution Approach 2:
The context representation is segmented into multiple components: keyword-level information, document-level contextual features, and task-level structured data. This segmentation allows each component to be processed optimally while maintaining overall context expressiveness, enabling automated processing without over-constraint.
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 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 and/or to enhance search queries to include task-related search criteria.