Peer-to-Peer Feature Enablement via Infectious Device Tokens
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Solution Overview
Problem
Current software feature activation methods rely on centralized licensing servers, which are depersonalized and lack personal interaction, leading to a less engaging user experience and lower user satisfaction.
Innovation Solution
Implementing a peer-to-peer system where features are enabled through proximate contact between devices, using terms like 'patient-zero device' and 'infectious device' to spread enablement tokens, allowing users to share and activate hidden features within a population, similar to a viral spread, across various devices and platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If centralized licensing servers are used to control feature activation, then automation and scalability are improved, but user experience personalization and engagement deteriorate
Solution Approach 1:
The patent introduces peer devices as intermediaries in the feature activation process. Instead of direct centralized server control, feature enablement tokens are transmitted through peer-to-peer connections, allowing personal interaction while maintaining automated verification through the licensing server. This resolves the contradiction by adding a personal intermediary layer without eliminating automated backend control.
Solution Approach 2:
The system segments the feature activation process into two independent parts: (1) automated license verification with the central server, and (2) personal peer-to-peer token transmission. This segmentation allows both automation and personalization to operate in their respective domains simultaneously, resolving the contradiction between automated control and personalized user experience.
2Ease of operation
If peer-to-peer feature enablement is implemented, then user engagement and personalization are improved, but system complexity increases
Solution Approach 1:
The licensing server maintains its existing universal functionality by continuing to verify licenses and issue enablement tokens. The same server infrastructure handles both traditional centralized activation and the new peer-to-peer verification, avoiding additional system complexity while enabling personalized user engagement through peer devices.
Solution Approach 2:
The system creates a simplified copy of the license verification process that can be executed locally on peer devices. Instead of requiring full server functionality on each device, a lightweight token verification copy is implemented, allowing personalization without proportionally increasing system complexity.
3Ease of operation
If feature enablement tokens are transmitted through peer devices, then user satisfaction and retention are improved, but potential security risks increase
Solution Approach 1:
The system performs preliminary security verification by the central licensing server before any peer-to-peer transmission occurs. The server validates licenses and creates secure enablement tokens in advance, ensuring that only authorized features can be transmitted through peer devices. This preliminary security action prevents unauthorized access while allowing personalized feature sharing.
Solution Approach 2:
The licensing server provides continuous feedback by verifying each peer-to-peer transmission request against the original license authorization. This feedback mechanism ensures that peer device transmissions remain secure and authorized, allowing user satisfaction to improve through personalization without compromising security through unauthorized feature sharing.
Data Source
AI summary
The technology disclosed relates to identifying and notifying a user of nearby attendees at a mega attendance event who are in user's social graph by comparing the user's social graph to a list of event attendees. The identified attendees can be stratified into social graph tags that annotate, categorize and prioritize other users in the user's social graph. The technology disclosed also relates to identifying and notifying the user of nearby attendees of sessions at the event who meet introduction preferences of the user by finding matches between introduction preference attributes specified by the user and attributes of the attendees provided by the list of event attendees.