Distribution List Proximity Filtering for Content Sharing
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Solution Overview
Problem
Existing methods for content sharing between co-located users are often cumbersome, requiring complex configurations and setups, and lack a user-centric approach, especially when compared to remote content sharing solutions.
Innovation Solution
A system that uses a 'wave' gesture to identify nearby users on a predefined distribution list, allowing for simple and intuitive content sharing by selecting a subset of users based on proximity or common characteristics, while excluding non-members from communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual copy and transfer methods are used for content sharing, then content can be shared between users, but the process becomes cumbersome and requires complex configurations
Solution Approach 1:
The system pre-establishes distribution lists containing user groups before content sharing is needed. When a user wants to share content, the system automatically identifies and selects users from these pre-configured lists based on their proximity, eliminating the need for manual recipient selection and complex configuration at the moment of sharing.
Solution Approach 2:
The system automatically performs user identification and selection based on proximity detection and pre-defined distribution lists, without requiring manual intervention. The apparatus autonomously determines which users to share content with by detecting nearby devices and matching them against stored distribution lists, making the sharing process self-service and eliminating cumbersome manual configurations.
2Adaptability or versatility
If all users on a distribution list are included in communication, then comprehensive coverage is achieved, but irrelevant users cannot be excluded
Solution Approach 1:
The system applies different selection criteria to different subsets of users within the distribution list based on their local characteristics, specifically their proximity to the sharing apparatus. Users are categorized into relevant (proximate) and irrelevant (non-proximate) groups, and content sharing is selectively applied only to the relevant subset, maintaining communication relevance while preserving the ability to adapt to different sharing scenarios.
3Measurement precision
If complex configuration methods are used for user selection, then precise user targeting is achieved, but the setup process becomes time-consuming
Solution Approach 1:
The system replaces manual mechanical user selection processes with automated electronic proximity detection. Instead of requiring users to manually configure and select recipients, the system uses device detection capabilities to automatically identify proximate users and match them against pre-defined distribution lists, achieving precise user targeting instantaneously without time-consuming setup procedures.
Data Source
AI summary
An apparatus, method, and computer program product are described that determine a subset of users from a predefined distribution list based on a common characteristic of members of the subset. A common characteristic may be the fact that members of the subset have been determined to be proximate the source user's device. The source user may communicate (e.g., share content) with members of the identified subset through selection of the distribution list, while non-members of the subset would be excluded from the communication.