Ambient Information Display via Learned User Context
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Solution Overview
Problem
Users are required to repeatedly interact with displays to request ambient information, such as weather forecasts, which can be bothersome and lead to forgotten requests.
Innovation Solution
A computer-implemented method and system that record information about previously displayed ambient screens, build a probabilistic inference model based on this information, rank and select candidate ambient screens, and display them during idle timeslots without additional user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If displays require repeated user interactions to request ambient information, then user control and explicit information requests are maintained, but user convenience and information delivery timeliness deteriorate
Solution Approach 1:
The display system automatically monitors user context (location, time, device usage patterns) and autonomously determines when to present ambient information without requiring explicit user requests. The system serves itself by learning from past interactions and proactively delivering information during predicted idle timeslots, eliminating the need for repeated manual information requests.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns and contextual data to predict when users are most likely to be receptive to ambient information. By pre-calculating optimal delivery times and preparing information presentations in advance during idle timeslots, the system ensures timely information delivery without requiring last-minute user initiation.
2Productivity
If displays present ambient information automatically based on learned context, then information delivery timeliness and user experience improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of ambient information delivery into distinct functional modules: context data collection, user behavior analysis, idle time detection, information selection, and presentation scheduling. Each module processes specific aspects independently, reducing overall system complexity while maintaining high information delivery efficiency through coordinated operation of these specialized components.
Solution Approach 2:
The system introduces intermediary processing layers including probabilistic inference models and machine learning algorithms that act as mediators between raw context data and final information presentation decisions. These intermediaries translate complex user behavior patterns into actionable insights, managing system complexity by providing structured decision-making frameworks rather than requiring direct complex rule implementations.
3Loss of information
If displays monitor and analyze user behavior patterns continuously, then information personalization and relevance improve, but energy consumption and processing load increase
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic sampling of user context and behavior data at strategically determined intervals. The probabilistic inference model processes data in discrete batches corresponding to detected idle timeslots, reducing energy consumption while maintaining information relevance by analyzing patterns over time rather than requiring constant real-time processing.
Data Source
AI summary
Methods, computer readable media, and devices for auto scheduling of ambient information and apps based on learned user context and interaction are described. A method may include recording information corresponding to one or more ambient screens previously displayed to a user, building a probabilistic inference model based at least in part on the recorded information, ranking the one or more ambient screens based at least in part on the probabilistic inference model, selecting a candidate ambient screen from the ranked one or more ambient screens, and displaying the candidate ambient screen during an idle timeslot.