Mobile Content Delivery Adaptation via Sensor Activity Detection
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Solution Overview
Problem
Current mobile device technologies do not effectively customize content and notification delivery based on user physical activity and location, failing to leverage advanced shared computing infrastructure for enhanced customization.
Innovation Solution
A system and method that uses sensors on mobile devices to detect physical activity and adapt content delivery, with the option to define rules based on user preferences and access shared computing infrastructure to refine these rules using data from multiple users, determining most likely activities and adapting content accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content delivery is customized based on user physical activity and location using sensors and shared computing infrastructure, then user experience and content delivery relevance are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces an intermediary system (shared computing infrastructure/cloud platform) that handles complex rule creation, refinement, and activity recognition computations. The mobile device offloads heavy processing to this intermediary, which aggregates sensor data from multiple users, creates refined rules, and returns simplified delivery instructions to individual devices. This resolves the contradiction by maintaining high adaptability while reducing individual device complexity.
Solution Approach 2:
The shared computing infrastructure serves multiple functions: it acts as a data aggregation platform for sensor inputs from multiple users, a rule creation engine for generating content delivery rules, a rule refinement system for improving accuracy over time, and a distribution network for delivering refined rules to individual devices. This multi-functionality allows the system to achieve high customization without increasing individual device complexity.
2Measurement precision
If rules for content delivery are refined using data from multiple users via shared computing infrastructure, then rule accuracy and content delivery precision are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing sensor data from multiple users in the background, even when not actively delivering content. The shared computing infrastructure maintains ready-to-use activity profiles and pre-refined rules based on accumulated data, so when content delivery is needed, the system can quickly apply pre-computed rules without requiring time-consuming real-time analysis. This resolves the contradiction by trading some initial data collection time for faster subsequent decision-making.
3Adaptability or versatility
If sensor data from multiple users is collected and processed to determine most likely activities, then content delivery personalization is improved, but privacy concerns and data security requirements increase
Solution Approach 1:
The patent extracts and processes only the essential activity-related features from sensor data rather than handling complete raw datasets. The shared computing infrastructure processes aggregated, anonymized activity patterns rather than individual user identities or detailed personal information. This extraction approach maintains personalization capability by focusing on activity types and patterns while reducing privacy risks by removing or masking personally identifiable information from the processing pipeline.
Data Source
AI summary
A method (and structure) includes receiving an input from a sensor on a mobile device. Based on the sensor input, a processor determines whether a user of the mobile device is engaged in a specific physical activity. A control setting on the mobile device is set for delivering content during a period the specific physical activity is detected.