Network Device Predictive Modeling via Contextual Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automation networks for electronic devices lack the ability to predict and automatically execute user intentions and preferences without manual input, leading to undesirable automated operations that users may find uncomfortable.
Innovation Solution
A computer-implemented method and system that analyzes interaction data and contextual data to identify correlations between user interactions and contexts, generating predictions for future interactions and automating network device functions based on confirmed correlations, allowing for gradual trust-building in automated operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automation networks operate only in accordance with manually provided rules and settings, then users maintain control and comfort, but the system cannot predict or automatically execute user intentions without manual input
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing interaction data and contextual data to identify correlations before automatically executing functions. The predictive modeling approach pre-processes user behavior patterns and contextual information to generate predictions that can be tested and refined over time, allowing the system to learn user intentions in advance without forcing immediate automation
Solution Approach 2:
The system implements feedback mechanisms by testing predictions against actual user interactions and refining the predictive model based on observed outcomes. The confidence level associated with predictions provides feedback on the reliability of automated decisions, allowing the system to adjust its automation extent based on accumulated experience and user responses
2Productivity
If the system generates automated predictions without user input, then productivity and efficiency improve, but users may find the automated operations uncomfortable or undesirable
Solution Approach 1:
The system applies partial automation by generating predictions with associated confidence levels rather than fully automated execution. This allows the system to perform automated operations only when confidence thresholds are met, balancing productivity gains with user comfort by avoiding over-automation in uncertain situations
Solution Approach 2:
The system dynamically adjusts the extent of automation based on confidence levels and accumulated learning. As the predictive model gains accuracy through continued data collection and testing, the system can progressively increase automated operations, creating a dynamic balance between productivity and user acceptance that evolves over time
3Measurement precision
If the system collects and analyzes interaction data and contextual data to identify correlations, then predictive accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the predictive modeling process into distinct functional modules: interaction data collection, contextual data collection, correlation analysis, prediction generation, and prediction testing. This segmentation allows each component to be independently developed, tested, and optimized, managing overall system complexity while maintaining predictive accuracy
Data Source
AI summary
Techniques and systems are provided for predictive modeling based on interactions with a network device. For example, a method may include generating a prediction including a correlation between an interaction with a network device and a context, wherein the interaction is associated with a function performed by the network device. Confidence parameters associated with the prediction can be determined. The prediction can be tested by analyzing received interaction data and contextual data, and the analysis can include determining whether the interaction with the network device occurred in the correlated context. A confidence value can be calculated based on the testing outcome, and can be compared to the confidence parameters. A message relating to modification of the confidence parameters can be transmitted.