Social Network Selection for Collaboration Artifacts
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Solution Overview
Problem
Existing mechanisms for sharing information across social networks often result in an inefficient or incorrect set of cooperating users due to the lack of consideration for relationship strength between users and candidate social networks, leading to suboptimal collaboration outcomes.
Innovation Solution
A method that identifies users associated with collaboration artifacts and determines the relationship strength between these users and candidate social networks, using similarity metrics to select the most relevant networks for effective information sharing and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing mechanisms are used to share information across social networks, then information sharing occurs, but the set of cooperating users is minimized and/or incorrect due to lack of relationship strength evaluation
Solution Approach 1:
The patent applies parameter changes by evaluating relationship strength as a quantitative parameter between users and candidate social networks. The system calculates relationship strength metrics (such as interaction frequency, connection density, or engagement levels) and uses these parameters to filter and select the most relevant social networks for information sharing, thereby improving both the accuracy of user selection and collaboration efficiency.
Solution Approach 2:
The patent replaces the mechanical/manual approach of selecting social networks for information sharing with an automated computational system. The system automatically calculates relationship strength between users and candidate networks, ranks the networks based on these metrics, and selects the optimal networks for sharing collaboration artifacts, eliminating the need for manual evaluation and improving both reliability and productivity.
2Ease of operation
If relationships are detected independently of relationship strength, then detection simplicity is maintained, but collaboration effectiveness is reduced
Solution Approach 1:
The system performs self-service by automatically evaluating relationship strength and selecting appropriate social networks without requiring manual intervention. The computational system autonomously calculates metrics, compares candidate networks, and makes selection decisions, maintaining ease of operation while significantly improving collaboration effectiveness through data-driven relationship assessment.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring relationship strength metrics and using this information to refine social network selections. The system evaluates the effectiveness of information sharing outcomes and adjusts its relationship strength calculations and network selections accordingly, improving collaboration effectiveness while maintaining operational simplicity through automated feedback loops.
Data Source
AI summary
Methods, products, apparatus, and systems may provide for identifying a set of users associated with one or more collaboration artifacts. Additionally, a set of networks including a plurality of candidate social networks may be identified. Moreover, a relationship strength may be determined between the set of users associated with the one or more collaborations artifacts and each of the candidate social networks to identify one or more relevant social networks from the candidate social networks. Determining the relationship strength may include calculating a similarity metric. In addition, at least one member affiliated with the one or more relevant social networks may become aware of the collaboration artifact.