Sequenced Instructions for Captive Portal Auto-Connection
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Solution Overview
Problem
Existing solutions for connecting to network access points, such as Wi-Fi hotspots, lack a seamless auto-connect experience and fail to leverage collective user interaction data for improved connection processes.
Innovation Solution
A cloud service crowdsources interaction data from mobile devices to generate and distribute sequenced instructions for connecting to network access points, enabling automated and seamless connections by replaying learned user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual connection processes are used for captive portals, then users can connect to network access points, but the process is tedious and requires significant user intervention
Solution Approach 1:
The system performs preliminary actions by capturing and analyzing interaction data from previous connection attempts, generating sequenced instructions in advance. When a device needs to connect to a captive portal, the pre-analyzed instruction set is immediately applied, eliminating the need for users to manually go through the tedious connection process again.
Solution Approach 2:
The system creates a copy of the successful connection process by capturing interaction data from one or more devices and generating sequenced instructions that replicate the connection steps. These instructions are then applied to subsequent connection attempts, allowing automatic replay of the connection process without user intervention.
2Adaptability or versatility
If traditional connection methods are used, then individual devices can connect to networks, but collective user interaction data is not leveraged for improvement
Solution Approach 1:
The system implements feedback by capturing interaction data from connection attempts, analyzing this data to generate improved sequenced instructions, and applying these instructions to subsequent connections. This closed-loop process continuously learns from real-world usage and adapts to handle variations in different captive portals, turning previously unused interaction data into a valuable resource for improvement.
3Extent of automation
If automated connection is implemented, then user intervention is minimized, but the system must process and analyze large amounts of interaction data
Solution Approach 1:
The system introduces an intermediary component that captures interaction data from multiple devices, analyzes this data to generate sequenced instructions, and distributes these instructions back to devices. This intermediary layer handles the complex data processing and analysis tasks, allowing individual devices to maintain simple automated connection functionality while benefiting from collective learning.
Data Source
AI summary
Embodiments produce a set of instructions for connecting to a network through a network access point based on data crowdsourced from mobile computing devices. The crowdsourced data describes interactions between the mobile computing devices and the network access point when establishing a connection to the network. A cloud service analyzes the crowdsourced data to identify a set of instructions for association with the network access point. The mobile computing devices replay the set of instructions when subsequently attempting to connect to the network access point.