Context-Aware Mobile Message Blocking System
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Solution Overview
Problem
Existing systems fail to effectively block irrelevant electronic messages from being displayed on mobile devices by not considering the contextual and cognitive states of users in physical venues, leading to unwanted advertisements and notifications.
Innovation Solution
A processor-implemented method that identifies a user's contextual history and cognitive state within a physical venue to block irrelevant electronic messages related to target products by analyzing factors such as notification history, location, purchase history, financial status, shopping list, shopping mode, device activity, and biometric data, using a system that integrates environmental, GPS, and biometric sensors to determine when messages are irrelevant to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If electronic messages are sent to mobile devices in physical venues, then marketing reach and advertising exposure are improved, but user annoyance and information relevance deteriorate due to irrelevant messages
Solution Approach 1:
The system performs preliminary identification of contextual history and cognitive state before sending messages. By analyzing user location, purchase history, and current cognitive state in advance, the system determines message relevance before delivery, preventing irrelevant messages from reaching users and thus avoiding annoyance while maintaining marketing effectiveness.
Solution Approach 2:
The system continuously monitors user cognitive state and contextual information as feedback to determine whether to block or deliver messages. This real-time feedback mechanism allows the system to adapt message delivery based on current user state, ensuring messages are relevant and reducing user annoyance from irrelevant notifications.
2Ease of operation
If message blocking based on contextual analysis is implemented, then message relevance and user experience are improved, but system complexity and processing requirements worsen
Solution Approach 1:
The system segments the complex analysis task into distinct components: contextual history identification (location, purchase history) and cognitive state identification (current user state). These segmented analyses are performed independently and then integrated to make blocking decisions, making the overall system more manageable and implementable despite the complexity.
Solution Approach 2:
The system introduces an intermediary processing layer that receives raw sensor data and contextual information, processes it through cognitive state analysis, and outputs blocking decisions. This intermediary layer simplifies the interface between diverse data sources and the message blocking function, reducing system complexity while maintaining comprehensive analysis capabilities.
Data Source
AI summary
A processor-implemented method, system, and/or computer program product blocks irrelevant message targeted to mobile electronic device. One or more processors identify a contextual history and a cognitive state of a person while in a physical venue. The processor(s) block the electronic message targeted to a mobile electronic device in use by the person based on the contextual history of the person and the cognitive state of the person while in the physical venue, such that the electronic message is deemed to be an irrelevant message.