Recurring Event Identification Using Server-Side Structured Data Analysis
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Solution Overview
Problem
Identifying recurring events in structured data consumes significant power and processing resources at user devices, which often lack access to additional data sets for training, leading to high error rates and inefficiencies.
Innovation Solution
A remote server leverages more accurate rules and machine learning models to identify recurring events, conserving power and processing resources at user devices by performing pattern identification more efficiently and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If user devices perform pattern identification locally, then processing speed may be maintained, but power consumption and processing resource usage increase significantly
Solution Approach 1:
The patent introduces a server as an intermediary between the user device and the data source. The server performs the computationally intensive pattern identification tasks using its greater processing resources, while the user device only needs to communicate with the server. This mediator approach transfers the energy-intensive processing from the user device to the server, reducing power consumption at the user device while maintaining processing speed through efficient server-side computation and response.
2Reliability
If user devices perform pattern identification with limited data access, then device autonomy is maintained, but identification accuracy decreases due to high error rates
Solution Approach 1:
The patent merges the data access capabilities of the server with the processing needs of the user device. The server has access to multiple data sources including training data sets that individual user devices lack. By combining the server's data access advantages with its processing capabilities, the system achieves high identification accuracy without requiring individual user devices to have complex data access configurations.
3Measurement precision
If more processing resources are allocated for pattern identification, then accuracy improves, but resource consumption at user devices increases
Solution Approach 1:
The patent segments the pattern identification system into two distinct parts: the user device that initiates requests and receives results, and the server that performs the actual heavy processing. This segmentation allows the processing power requirements to be separated from the user device and concentrated at the server, which has access to greater computational resources. The user device maintains low power consumption while the server provides high-accuracy processing through its allocated resources.
Data Source
AI summary
In some implementations, a server may receive, from a user device, one or more credentials associated with a data source. Accordingly, the server may receive, from the data source and using the one or more credentials, a set of structured data including a plurality of entries. The server may identify at least one recurring event based on one or more entries in the plurality of entries, and may determine, for the at least one recurring event, one or more derived properties. The server may generate a data structure indicating the at least one recurring event and the one or more derived properties, and may transmit, to the user device, the generated data structure.