Local Predictive Survey Participation Models for User Devices
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Solution Overview
Problem
Current methods for generating predicted survey participation data rely on centralized servers, leading to excessive data exchanges between user devices and servers, which can be inefficient and limit autonomy.
Innovation Solution
A method and system where a correlation server collects and analyzes behavioral and survey participation data to generate predictive survey participation patterns, which are then stored on user devices, allowing them to determine predicted survey participation data locally based on current behavioral data and stored patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predicted survey participation data are generated by a centralized server, then data processing capability is sufficient, but data exchange between user devices and server increases and user device autonomy decreases
Solution Approach 1:
The patent segments the data processing function by dividing the centralized server's predictive analytics capability into distributed predictive models that are deployed to individual user devices. Each user device independently executes its own predictive model to generate survey participation data locally, eliminating the need for continuous data exchange with the centralized server while maintaining autonomous operation.
Solution Approach 2:
The patent implements preliminary action by pre-computing and storing predictive models on user devices before they are needed. The centralized server performs the computationally intensive model training and analysis in advance, then distributes the resulting predictive models to user devices. This allows user devices to generate predicted survey participation data immediately without requiring real-time server communication.
2Productivity
If multiple exchanges occur between user device and centralized server, then predicted survey participation data can be generated, but system efficiency decreases
Solution Approach 1:
The patent extracts the predictive analytics functionality from the centralized server environment and embeds it directly into user devices as local predictive models. This extraction eliminates the need for multiple data exchange cycles between user devices and the server, as all predictive processing occurs locally on the user device, thereby improving system efficiency and reducing time loss.
3Reliability
If predictive survey participation patterns are stored on user devices, then reliance on centralized server reduces, but memory requirements at user device increase
Solution Approach 1:
The patent applies local quality by storing only the essential predictive models and their associated parameters on user devices, rather than storing complete datasets or complex processing algorithms. The predictive models are optimized to occupy minimal memory space while maintaining their predictive capability, allowing user devices to operate independently with reduced memory requirements.
Data Source
AI summary
A method for generating predicted survey participation data at a current user device. A server collects training behavioral data related to a website from a plurality of user devices. The server also collects training survey participation data related to the website from at least some of the plurality of user devices. The server analyzes the training survey participation data and the corresponding training behavioral data to infer correlations between the training survey participation data and the corresponding training behavioral data. The server further generates predictive survey participation patterns based on the inferred correlations. The server transmits the predictive survey participation patterns to the current user device. The current device collects current behavioral data related to the website. Then, the current user device determines predicted survey participation data for the current user device in relation to the website based on the current behavioral data and the predictive survey participation patterns.