Dynamic Personalization Profile Engine for Decision Accuracy
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Solution Overview
Problem
Traditional decision engines rely on static user profiles and large amounts of parametric data, leading to inaccuracies and increased processing times, which complicates user interactions and requires more powerful devices.
Innovation Solution
The implementation of dynamic user profiles that utilize known contextual relevance, incorporating location, co-located people, and time-of-day parameters to enhance the accuracy of decision engines, allowing for more precise predictions and reduced device requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static user profiles with large amounts of parametric data are used, then decision engine accuracy is improved, but processing time increases and device complexity increases
Solution Approach 1:
The patent extracts only the most relevant contextual parameters (location, time-of-day, co-located people) from the full user profile, discarding unnecessary parametric data. This selective extraction maintains decision accuracy while reducing processing time and device requirements.
Solution Approach 2:
The patent transitions from static user profiles to dynamic profiles that adapt based on contextual factors like current location, time-of-day, and nearby people. This dynamic approach improves accuracy by capturing relevant state changes without requiring processing of all possible profile parameters.
2Measurement precision
If static user profiles with large amounts of parametric data are used, then decision engine accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential contextual parameters needed for accurate decision-making, eliminating the need to process large amounts of unnecessary parametric data. This reduces the computational power and device complexity required while maintaining decision accuracy.
Solution Approach 2:
The dynamic profile approach processes only currently relevant contextual information rather than maintaining and processing complete static profiles, reducing device complexity while improving accuracy through context-aware adaptations.
3Measurement precision
If dynamic user profiles with contextual parameters are used, then decision accuracy is improved, but profile complexity increases
Solution Approach 1:
The patent segments the user profile into a core static component and dynamic contextual components. This segmentation organizes complexity by separating permanent user attributes from transient contextual factors, making the profile structure more manageable while improving decision accuracy.
Solution Approach 2:
The patent introduces dynamic elements to the profile structure that adapt based on contextual parameters like location and time-of-day. This controlled dynamic complexity improves accuracy by capturing relevant state changes without creating unmanageable profile structures.
Data Source
AI summary
The present invention generally relates to utilization of at least two known location addresses yielding accurate determination of a user's mode of operations and/or preferences. In one embodiment, the present invention utilizes proximity and/or vector between a current location address and a known home or work address to determine preferences that dynamically change based on the current location and inter-relationship between the current location and at least one other known location address.