Dynamic Help Content System for Software Applications
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Solution Overview
Problem
Current help content systems face inefficiencies due to manual creation of content in multiple formats, leading to wasted time and storage space, as well as challenges in updating and combining content, resulting in low-quality or costly content development.
Innovation Solution
A dynamic help content system that enables a 'write-once, present-everywhere' approach by using content units, expert and user ratings to train machine learning models, allowing for customized content generation and storage optimization, and combining content units for improved user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual creation of content in multiple formats is used, then content can be provided for various software features and errors, but time and storage space are wasted
Solution Approach 1:
The patent segments help content into reusable content units that can be independently created and then combined in multiple formats. Instead of manually creating separate content for each format, the system divides content into modular units that can be assembled dynamically, reducing creation time while maintaining format versatility.
Solution Approach 2:
The patent creates templates and reusable content units that can be copied and adapted across different formats. Once a content unit is created, it can be replicated and presented in various formats without requiring manual recreation, thus saving time while providing multi-format support.
2Adaptability or versatility
If manual creation of content in multiple formats is used, then content can be provided for various software features and errors, but storage space is wasted
Solution Approach 1:
The patent merges multiple format representations into a single unified content unit structure. Instead of storing separate content files for each format, the system combines them into one standardized unit that can be rendered in different formats, reducing storage requirements while maintaining format versatility.
Solution Approach 2:
The patent creates universal content units that serve multiple functions and formats simultaneously. A single content unit can be presented in various formats and contexts without requiring separate storage for each version, achieving multi-functionality with reduced storage space.
3Reliability
If static help content is included in the application, then users can access error information, but the content cannot be dynamically customized or combined
Solution Approach 1:
The patent transforms static help content into dynamic content units that can be assembled and presented differently based on user context, device type, and error characteristics. The content remains reliable and available while gaining the ability to be dynamically customized and combined for different scenarios.
4Measurement precision
If separate content is provided for each searched error/feature, then specific error information is available, but user experience is fragmented
Solution Approach 1:
The patent merges multiple separate error contents into a unified help response by combining relevant content units. Instead of presenting separate content for each error, the system integrates them into a coherent, continuous user experience while maintaining accurate error-specific information.
Data Source
AI summary
A method may include accessing a ratings datastore, the rating datastore including ratings for users with respect to a feature of a software application; transmitting a request for generating a content unit with respect to the feature to a first user of the users based on the first user's ratings for the feature in the ratings datastore; receiving the content unit from the first user; selecting a set of users from a first class of users to review the received content unit based on the set of users' respective ratings for the feature in the ratings datastore; storing the received content unit in a content datastore as associated with a content rating for the feature based on ratings received from the set of users.