Mobile Network Operator Inference Engine for Situational Information Push
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Solution Overview
Problem
Current portable devices, such as smartphones, lack the ability to provide situationally relevant information to users based on their location and external data, leading to ineffective targeting of content and advertisements, which can be annoying and irrelevant.
Innovation Solution
A system that utilizes a mobile device's GPS location, user profiles, browsing history, and social media information to infer the user's situation, allowing the Mobile Network Operator (MNO) to push relevant information and advertisements to the user through a unicast Internet Protocol connection, using an inference engine to analyze data and provide situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If portable devices provide information based on location and external data, then user engagement and relevance of information is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a Mobile Network Operator (MNO) as an intermediary service that handles the complex data processing and situational analysis. Instead of requiring the portable device itself to perform complex analysis of location data, browsing history, and social media information, the MNO's server system performs this processing and pushes relevant information back to the device, thereby reducing device complexity while maintaining high adaptability.
Solution Approach 2:
The MNO service platform is designed to handle multiple data sources and processing functions through a single unified system. The platform can process location data, browsing history, social media information, and push various types of content (information and advertisements) to multiple devices, providing universal functionality that reduces the need for device-specific complex processing.
2Adaptability or versatility
If targeted information and advertisements are pushed to users, then relevance and usefulness of content is improved, but user annoyance from irrelevant content increases
Solution Approach 1:
The system uses feedback from user behavior patterns and situational data to continuously refine the targeting accuracy. By analyzing browsing history, location patterns, and contextual information, the MNO adjusts the pushed content to better match user interests and needs, thereby improving relevance while reducing annoyance from irrelevant advertisements.
Solution Approach 2:
The patent applies local quality by tailoring content specifically to each user's local situation, location, and preferences rather than using generic broad-based advertisements. The system analyzes local contextual data (geographic location, nearby businesses, local events) and delivers customized content that is relevant to the user's immediate situation, thereby reducing annoyance from irrelevant content.
3Measurement precision
If multiple data sources are analyzed to infer user situation, then information targeting accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing user data, browsing history, and contextual information in databases before actual situation inference is needed. The MNO's server system maintains pre-organized data structures that can be quickly queried and combined during real-time situation analysis, reducing processing time while maintaining high accuracy through comprehensive data analysis.
Data Source
AI summary
A method involves an MNO receiving data from a mobile device representing a current situation of a user of the device. The MNO stores data characterizing products and services represented in a solution knowledgebase. The MNO stores data characterizing the user in a user knowledgebase. An inference engine analyzes the data in order to determine if the data corresponds to an actionable event relevant to the user. If so, a search of information relevant to the event and the mobile device data at least in the solution knowledgebase is conducted and the results are pushed to the mobile device. This abstract is not to be considered limiting, since other embodiments may deviate from the features described in this abstract.