Dynamic Trust Level Adjustment for Mobile Devices

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Solution Overview

Problem

Existing trust level systems for mobile communication devices do not effectively account for dynamic spatial proximity changes between devices, leading to inadequate security measures for data access and communication.

Innovation Solution

A method that determines trust levels based on historical locational data, using machine logic rules to assess relative distance over time between communication devices, allowing for dynamic adjustments in trust levels regardless of constant or changing distances between devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If trust level is established based on static criteria, then security control is simplified, but the system cannot adapt to dynamic spatial proximity changes between devices

Engineering Contradiction:

Improveadaptability to spatial proximity changesVSAvoidcomplexity of trust level system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The trust level system transitions from static criteria to dynamic assessment by continuously monitoring spatial proximity between devices. The system calculates trust levels based on real-time distance measurements and historical proximity data, allowing trust levels to automatically adjust as devices move closer or farther apart, thereby achieving adaptability to spatial changes.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback mechanisms by continuously measuring device proximity through location services and using this information to recalculate trust levels. This closed-loop approach ensures that trust assessments remain current with actual spatial relationships, enabling the system to respond dynamically to changing proximity conditions while maintaining manageable complexity through automated calculations.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If historical locational data is continuously collected and analyzed, then trust level accuracy improves, but data processing requirements and system resource consumption increase

Engineering Contradiction:

Improveprecision of trust level determinationVSAvoidenergy consumption for data processing

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by selectively processing locational data based on predefined thresholds and significance criteria. Rather than continuously analyzing all proximity changes, the system focuses computational resources on meaningful spatial variations that genuinely impact trust levels, thereby improving precision while reducing unnecessary data processing and energy consumption.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system dynamically adjusts processing parameters such as data sampling intervals, historical data retention periods, and calculation complexity based on current operational context. This allows the system to optimize the balance between trust level precision and resource consumption, using more intensive processing only when spatial relationships indicate significant security considerations.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS9935965B2Establishing and using a trust level in mobile phones

Publication Date: 2018.04.03 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Automatically establishing and/or modifying a trust relationship between devices, including mobile devices, in communication, and customizing a user interface workflow based on the trust relationship. Trust relationships are based on numerous proximity-related factors including automatically gathered proximity data, length of time in proximity, and signals detected from a target communication device as well as other nearby communication devices.