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
1Extent of automation
If user devices perform pattern identification locally, then processing autonomy is improved, 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 allows the user device to maintain processing autonomy in terms of initiating requests and receiving results, while offloading the energy-intensive computations to the server.
2Loss of time
If user devices perform pattern identification locally, then response time may be reduced, but processing resources and power are consumed inefficiently
Solution Approach 1:
The patent implements a centralized service model where the server creates and maintains pattern identification models that can be copied or shared across multiple user devices. Instead of each device independently performing computations, the server processes data centrally and distributes results. This copying approach maintains responsiveness for users while dramatically improving overall processing efficiency through resource sharing and avoidance of redundant computations.
3Adaptability or versatility
If user devices lack access to training data sets, then device independence is improved, but identification accuracy decreases due to high error rates
Solution Approach 1:
The patent shifts the problem from a single-device dimension to a networked dimension by introducing a server that aggregates data from multiple sources. The server accesses training data sets that no individual user device would have access to alone. This dimensional shift from isolated device processing to centralized networked processing allows user devices to maintain independence while achieving high identification accuracy through the server's access to comprehensive training data.
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.