Help Resource Selection Using Context-Aware Relevance Ranking
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Solution Overview
Problem
Current help systems for computer-based applications are labor-intensive and inefficient, as they provide absolute referencing, leading to repetitive information and lack of user-specific content, failing to adapt to user interactions and external user data.
Innovation Solution
An apparatus and method that enhance help resource selection by parsing help content into basic elements, using modules to determine user context, calculate relevance, and access external data to provide tailored help resources, including a selection logic module that tracks user access and adjusts resource display based on user knowledge and interaction history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If absolute referencing is used to provide help content, then help resources are immediately available to users, but the same information is repeated each time and cannot be tailored to specific users
Solution Approach 1:
The patent segments help content into modular help resources that can be independently selected and assembled. Instead of providing complete static help files, the system breaks down help content into discrete resources that can be tailored to individual user needs based on user profile data, allowing both immediate availability and user-specific customization.
Solution Approach 2:
The system changes parameters by using user profile attributes (such as skill level, experience, preferences) to dynamically select and customize help resources. This allows the same help system to adapt its content presentation, depth, and type based on varying user parameters while maintaining immediate accessibility.
2Loss of information
If all help resources from the penumbra are provided to users, then complete information is available, but users are overwhelmed and must search through excessive content
Solution Approach 1:
The system uses feedback from user profile data and interaction history to intelligently filter and prioritize help resources. By analyzing user characteristics and previous help-seeking behavior, the system provides targeted recommendations that deliver complete relevant information while minimizing unnecessary content, thus reducing search time without losing essential information.
Solution Approach 2:
The system performs preliminary filtering and organization of help resources based on user profiles before users need to search. By pre-processing and curating help content according to user characteristics in advance, the system presents only the most relevant resources, eliminating the need for users to wade through excessive unfiltered content.
3Reliability
If help content is updated manually for each topic, then complete information is maintained, but the process is labor-intensive and inefficient
Solution Approach 1:
The patent implements a universal help resource structure where a single help resource can serve multiple topics and contexts. By creating modular, reusable help components that can be referenced across different help scenarios, the system maintains information accuracy through centralized updates while dramatically improving update efficiency, as one update can benefit multiple help topics simultaneously.
Solution Approach 2:
The system uses copying and referencing mechanisms where help resources are defined once and then referenced multiple times across different help topics. This allows maintainers to update a single source definition and have changes automatically reflected wherever that resource is referenced, ensuring consistency and accuracy while eliminating repetitive manual updates.
Data Source
AI summary
An apparatus, system, and method are disclosed for enhancing help resource selection for a user that accesses a help function within a computer application. The method of enhancing the help resource selection comprises determining a list of help resources applicable to the current context of the user interaction with the computer application and the help feature. Then, the method ranks applicable help resources according to relevance criteria set by the practitioner of the invention. For example, the practitioner may wish to provide certain help resources based upon the skill level of the user and whether the user has accessed specific help resources in the past. The method would preferentially select those help resources that matched the user skill level and past access criteria set by the practitioner.