Predictive Messaging Engine for M2M Sensor Data Analysis
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Solution Overview
Problem
Current communications networks do not capture or analyze machine-to-machine (M2M) communications, leading to a burden and cost for recipients to process and store the high volume of M2M messages, which are simply passed through the network.
Innovation Solution
Implementing a predictive messaging engine within the communications network to store, analyze, and generate predictive messages based on M2M communications, allowing for the identification of potential future events and reducing the need for third-party subscribers to handle the analysis and storage of these messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the communications network simply passes M2M communications along without capturing or analyzing them, then the network infrastructure remains simple and costs are low, but recipients bear the burden and cost of processing and storing high volume M2M messages
Solution Approach 1:
The patent introduces an intermediary component within the communications network that captures and analyzes M2M communications before they reach recipients. This intermediary service handles the processing burden, freeing recipients from storing and analyzing raw M2M data while the network infrastructure provides the analytical processing capability.
Solution Approach 2:
The patent extracts the analysis and processing function from the recipient devices and places it within the communications network infrastructure. By taking out the computational burden from endpoints and centralizing it in the network, the system reduces recipient processing requirements while maintaining enhanced analytical capabilities.
2Reliability
If the communications network captures and analyzes M2M communications, then predictive messaging can be generated to identify potential future events, but the network infrastructure complexity and costs increase
Solution Approach 1:
The patent implements preliminary analysis of M2M communications within the network to generate predictive messages about future events. By performing analysis in advance and generating predictions before issues occur, the system improves reliability and enables proactive responses, while the network infrastructure provides the analytical processing capability.
Solution Approach 2:
The patent introduces an intermediary component within the communications network that captures and analyzes M2M communications before they reach recipients. This intermediary service handles the processing burden, freeing recipients from storing and analyzing raw M2M data while the network infrastructure provides the analytical processing capability.
3Quantity of substance
If high volume M2M communications are sent daily, then comprehensive data collection is achieved, but the messages become cumbersome for recipients to process and store
Solution Approach 1:
The patent extracts the analysis and processing function from the recipient devices and places it within the communications network infrastructure. By taking out the computational burden from endpoints and centralizing it in the network, the system reduces recipient processing requirements while maintaining enhanced analytical capabilities.
Solution Approach 2:
The patent introduces an intermediary component within the communications network that captures and analyzes M2M communications before they reach recipients. This intermediary service handles the processing burden, freeing recipients from storing and analyzing raw M2M data while the network infrastructure provides the analytical processing capability.
Data Source
AI summary
A method, computer-readable storage device and apparatus for generating a predictive message in a communications network are disclosed. For example, the method receives at least one machine-to-machine communication in the communications network, stores the at least one machine-to-machine communication in a database of the communications network, analyzes the at least one machine-to-machine communication, and generates the predictive message based upon the at least one machine-to-machine communication that is analyzed.


