Predictive Model Framework for Mobile Device Behavioral Adaptation
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Solution Overview
Problem
Existing approaches for implementing dynamic adjustment in mobile applications are inefficient and often result in inaccurate and undesirable functionality, degrading user experience and battery performance due to the difficulty in identifying and responding to user behavioral patterns.
Innovation Solution
A framework that enables application developers to establish and update predictive models on mobile devices, optimizing their deployment and management by interfacing with other software entities to identify appropriate times for updates, such as when the device is not in use or charged, and caching predictive models as contiguous files to reduce mapping work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If application developers build frameworks to identify and respond to user behavioral patterns, then dynamic adjustment capabilities are provided, but the frameworks deliver inaccurate functionality and degrade user experience
Solution Approach 1:
The patent pre-processes user interaction data during application usage to build behavioral pattern models in advance. Instead of analyzing raw data in real-time, the system continuously updates user profiles, contact hierarchies, and communication patterns during idle periods, so that accurate predictions are ready when needed for dynamic adjustment.
Solution Approach 2:
The patent introduces an intermediary layer (user profile database and behavioral pattern engine) between raw user interactions and the application's dynamic adjustment mechanisms. This intermediary processes, stores, and structures user behavior data into organized patterns, improving both accuracy and efficiency of behavioral analysis.
2Adaptability or versatility
If frameworks continuously monitor and update behavioral patterns, then dynamic adjustment is improved, but battery performance degrades due to inefficient power consumption
Solution Approach 1:
The patent implements periodic action by scheduling model updates and behavioral analysis to occur during idle periods and low-power states. The system monitors user behavior continuously but performs computationally intensive processing only when the device is idle or connected to power, reducing real-time power consumption while maintaining pattern accuracy.
Solution Approach 2:
The system performs preliminary processing of user behavior data during idle periods before it is needed for predictions. By pre-computing behavioral patterns and updating models during low-power states, the system reduces the computational burden during active usage, thereby conserving battery power.
3Reliability
If predictive models are updated frequently to maintain accuracy, then user experience is improved, but device responsiveness degrades due to processing overhead
Solution Approach 1:
The patent updates predictive models periodically during idle periods rather than continuously in real-time. The system schedules model training and updates to occur when the device is not actively being used, maintaining model accuracy while avoiding interruptions to device responsiveness during user interactions.
Solution Approach 2:
The system performs preliminary model updates and data processing in advance during idle periods. By preparing updated predictive models before they are needed, the system ensures accurate predictions are available without causing delays or responsiveness issues during active device usage.
4Adaptability or versatility
If developers implement comprehensive behavioral analysis frameworks, then dynamic adjustment capabilities are enhanced, but the complexity of framework development increases
Solution Approach 1:
The patent extracts and separates the complex behavioral analysis functionality into a standalone predictive model engine that operates independently from the application logic. This modular approach allows developers to integrate dynamic adjustment capabilities without embedding complex analysis frameworks within their applications, reducing development complexity.
Solution Approach 2:
The system implements self-service by automatically collecting, processing, and analyzing user behavior data without requiring developer intervention. The predictive model engine autonomously builds and updates user profiles, identifies behavioral patterns, and provides predictions to applications, freeing developers from the complexity of implementing comprehensive behavioral analysis frameworks.
Data Source
AI summary
Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.