Multiuser Group Feeds for Shared Content Viewing and Scrolling
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Solution Overview
Problem
Existing applications for viewing and scrolling content items often only enable a single user system to view the content and do not permit participation from other users, with the selection of the next content item determined based on metadata associated with just the user of the application.
Innovation Solution
A group feed application that enables users on separate user systems to simultaneously view, react to, and scroll the same content item, using metadata from each user to determine the next content item, and allowing users to select content items from local or large collections, while detecting when friends are geographically near and offering to add them to the group feed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single user system views content items, then the content selection can be simplified to base on one user's metadata, but the functionality and user participation are limited
Solution Approach 1:
The patent merges multiple user systems into a single group feed context, where content is selected based on aggregated metadata from all users in the group rather than individual users. This combining approach enables multiple users to participate simultaneously while managing complexity through a unified content selection mechanism.
Solution Approach 2:
The group feed application serves multiple functions: it enables simultaneous viewing by multiple users, aggregates metadata from different users, determines content based on collective preferences, and facilitates social interaction. This multi-functionality addresses the versatility requirement while operating through a single integrated system.
2Measurement precision
If content selection is based on metadata from multiple users, then the content selection becomes more comprehensive and reflective of group preferences, but the complexity of determining the next content item increases
Solution Approach 1:
The system automatically aggregates metadata from multiple users and determines content selections without requiring manual input or complex processing logic from the user side. Each user's metadata self-contributes to the collective content determination, simplifying the overall process while improving selection accuracy.
Solution Approach 2:
The system uses feedback from user interactions (likes, views, engagement metrics) to continuously refine content selections. By analyzing metadata from multiple users' responses to content, the system iteratively improves content determination accuracy while managing complexity through automated feedback loops.
3Ease of operation
If the application enables multiple users to react to and scroll content simultaneously, then user interaction and engagement are enhanced, but the coordination and synchronization between users becomes more complex
Solution Approach 1:
The group feed application creates an equipotential environment where all users have equal access and interaction capabilities with the content, regardless of their individual user systems. This ensures smooth synchronization by treating all users uniformly in the interaction model, simplifying coordination while enhancing engagement.
Data Source
AI summary
Multiple users can simultaneously view and scroll content from a collection of content items working on separate user systems. Example methods include generating a group feed, determining, based on metadata associated with the first user and metadata associated with the second user, a first content item of the plurality of content items, and causing the first content item to be displayed on a first computing device and on a second computing device. The methods may further include accessing, from the first user or the second user, an indication of a reaction to the first content item, accessing, from the first user or the second user, an indication to scroll to a second content item of the plurality of content items, and determining, the second content item, based on the metadata of the first user, the metadata of the second user, and the reaction to the first content item.