Collaboration Medium Asset Pointer Filtering
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Solution Overview
Problem
Existing collaboration mediums fail to effectively share relevant assets between entities based on user profiles and interactions, relying on search engines and keyword-based algorithms that do not account for contextual relevance and user preferences.
Innovation Solution
A method and system that analyze metadata from user interactions, filter assets on local systems according to user and environment profiles, and display pointers to relevant assets in a collaboration medium, enabling peer-to-peer sharing of relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If search engines and keyword-based algorithms are used to find assets, then assets can be located on the network, but users are unable to share relevant assets based on their preferences and profiles
Solution Approach 1:
The system performs preliminary actions by analyzing user information and identifying metadata before the actual asset sharing occurs. User profiles and environment profiles are pre-established, and assets are pre-filtered based on these profiles, so that when users interact in the collaboration medium, the relevant assets are already prepared and ready for immediate sharing without requiring real-time processing
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between users and assets. This intermediary analyzes user information, matches assets to user profiles, and filters assets based on environment profiles before presenting them to users. The intermediary handles the complex matching logic, allowing users to benefit from personalized asset sharing without directly implementing the matching algorithms themselves
2Quantity of substance
If all users' assets are made available in peer-to-peer collaboration, then comprehensive asset sharing is achieved, but information overload and irrelevance increase
Solution Approach 1:
The system applies local quality by making each user's asset sharing experience unique and tailored to their specific needs. Instead of applying a uniform approach to all users, the system customizes the asset selection for each user based on their individual profile, current environment, and interaction context. This ensures that each user receives a curated subset of assets that is highly relevant to their specific situation
Solution Approach 2:
The system dynamically changes parameters such as user profile attributes, environment conditions, and interaction context to determine asset relevance. By monitoring changes in these parameters in real-time, the system can adjust which assets are presented to each user, ensuring that the asset recommendations remain relevant as conditions change during the collaboration process
3Measurement precision
If real-time asset filtering based on profiles is implemented, then asset relevance is improved, but system complexity increases
Solution Approach 1:
The system performs complex profile analysis and asset matching operations in advance, before users need the assets. User profiles and environment profiles are established beforehand, and assets are pre-tagged and categorized. This preliminary processing reduces the complexity of real-time operations, as the system only needs to retrieve and present pre-processed information when users interact in the collaboration medium
Data Source
AI summary
A method for providing assets in a collaboration medium includes receiving information from an entity; analyzing the information to identify metadata; searching a system to locate assets relevant to the identified metadata; filtering the assets located on the system according to at least one of a predetermined user profile or a environment profile; sending pointers to the filtered assets on the entity's system to a second entity's system; and displaying pointers to the filtered assets in the collaboration medium.