Intermediary System for Meaningful Content Update Detection
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Solution Overview
Problem
Current computing devices struggle to effectively detect and notify users of meaningful updates to network-accessible content, such as web pages, as they often receive unnecessary changes, burdening users with irrelevant information.
Innovation Solution
An intermediary system analyzes user interactions and content changes to identify important portions of a page, providing personalized notifications and visual treatments for substantive updates, while excluding minor changes, using a content analysis module, content rendering engine, and user interaction data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clients request content changes from servers, then content updates are received, but users are burdened with unnecessary notifications of minor changes
Solution Approach 1:
The system applies different notification treatments to different portions of content based on their significance. Meaningful portions (e.g., article text, headlines) trigger notifications, while non-meaningful portions (e.g., advertisements, navigation elements) are filtered out. This selective approach ensures users are notified only of important changes rather than all changes uniformly.
Solution Approach 2:
The patent introduces an intermediary system between the client and server that analyzes content changes before notifying users. This intermediary evaluates whether changes are meaningful by comparing updated content with previous versions and determining if the changes warrant user notification, thereby filtering out irrelevant updates.
2Loss of information
If all content changes are notified to users, then completeness of information is improved, but user experience deteriorates due to excessive notifications
Solution Approach 1:
The system changes the parameter of notification significance by analyzing content characteristics. It evaluates whether a content change meets a threshold of meaningfulness based on factors such as content type, change magnitude, and user interests. Only changes that satisfy these parameter criteria trigger notifications, balancing information completeness with user experience.
3Loss of time
If clients frequently check for content updates, then update timeliness is improved, but network bandwidth and energy consumption increase
Solution Approach 1:
The system performs preliminary analysis of content changes on the server side or intermediary system before transmitting notifications to clients. By pre-evaluating whether changes are meaningful and preparing targeted notifications in advance, the system reduces the need for clients to frequently poll for updates, thereby reducing energy consumption while maintaining timely notification of important changes.
Data Source
AI summary
Features are disclosed for detecting meaningful updates to network accessible content, including but not limited to web pages. The portion or portions of content that are meaningful can be automatically determined based on a previously defined content profile, an analysis of user interactions with the content, algorithms and automated content analysis techniques, some combination thereof, or other techniques. Content can be monitored and determinations can be made regarding whether and to what extent the content has changed. Client devices or users thereof may be notified of detected meaningful content updates. Notifications can include updated portions of the content. The updated content may be displayed to the user on a client device, and visual treatments may be applied to the updated portions to draw the users' attention to the presence and substance of the updates.