Group Preference Control for Network Devices
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Solution Overview
Problem
Existing network-connected devices, such as IoT devices, struggle to optimize settings for multiple users with varying preferences and tolerance ranges, leading to suboptimal user experiences in shared environments.
Innovation Solution
A processing system that employs machine learning to detect and learn individual and group preferences and tolerance ranges, adjusting device settings to create an optimal environment by combining user data from mobile devices and biometric sensors, using a hierarchical temporal memory structure to model user behaviors and adapt settings dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual user profiles with specific preferences are created, then user experience for single users is optimized, but the system fails when multiple users are present
Solution Approach 1:
The system dynamically adapts device settings based on which users are currently present in the zone. User profiles are not static but are actively selected and combined based on real-time presence detection, allowing the system to transition between different user configurations seamlessly.
Solution Approach 2:
The system changes the parameters of device settings by computing a group profile that aggregates individual user preferences. When multiple users are detected, the system modifies the device parameters based on the combined group profile rather than individual profiles, effectively transforming how preferences are applied.
2Adaptability or versatility
If device settings are customized for individual users, then individual preferences are satisfied, but conflicts arise in shared environments
Solution Approach 1:
The system merges multiple individual user profiles into a unified group profile when multiple users are present. This combination process integrates preferences from different users to create a consolidated set of device settings that represent the group's collective preferences, resolving conflicts between individual customizations.
Solution Approach 2:
The group profile acts as an intermediary between individual user profiles and device settings. Rather than directly applying individual preferences that may conflict, the system uses the group profile as a mediator to compute and apply settings that satisfy multiple users simultaneously.
3Extent of automation
If the system learns and adapts to user preferences, then user experience improves over time, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting user presence, selecting appropriate profiles, and adjusting device settings without manual intervention. The learning mechanism operates autonomously, observing user behaviors and preferences to refine the group profile and improve settings over time.
Solution Approach 2:
The system implements feedback loops where user interactions with devices and environmental data are continuously monitored. This feedback informs the learning algorithm, allowing the system to adapt and refine group profiles based on actual user responses and environmental conditions, improving automation while managing complexity through iterative optimization.
Data Source
AI summary
A processing system including at least one processor may detect the presence of at least two users in a zone containing a network-connected device, obtain preferences and tolerance ranges of the at least two users with respect to the network-connected device, select a setting for the network-connected device in accordance with the preferences and tolerance ranges of the at least two users, and apply the setting to the network-connected device. The processing system may further detect a change of the setting, and adjust at least one of the preferences and tolerance ranges of the at least two users in response to the change of the setting.