Unintentional Call Detection Using Button Timing and Force Analysis
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Solution Overview
Problem
Private and public emergency services face challenges in distinguishing between intentional and unintentional calls, particularly with clients who may not realize they have made a call, leading to inefficient follow-up processes and potential delays in providing necessary assistance.
Innovation Solution
A method that involves recognizing user interactions with a device to determine if a call is intentional or unintentional by collecting and analyzing information such as timing of button presses, force sensors, and environmental data, and using this information to automatically signal the user or forward data to a response center for evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all calls are thoroughly followed up to ensure urgent assistance is provided, then reliability of emergency response is improved, but loss of time and resources increase due to significant cost of following up on unintentional calls
Solution Approach 1:
The system performs preliminary analysis of call characteristics (duration, silence detection, background noise, caller behavior patterns) before initiating follow-up procedures. By evaluating multiple parameters during and after the initial call, the system pre-determines whether a call is likely intentional or unintentional, allowing selective follow-up only on calls that require human intervention. This preliminary classification mechanism resolves the contradiction by filtering out unintentional calls before they consume follow-up resources, while maintaining thorough follow-up for potentially urgent intentional calls.
2Measurement precision
If manual review of each call is performed to distinguish intentional from unintentional calls, then measurement precision of call intent is improved, but productivity of the service decreases due to significant resources consumed
Solution Approach 1:
The system implements self-service through automated analysis of call characteristics including call duration, periods of silence, background noise levels, and caller behavior patterns. The automated system independently evaluates these parameters to classify calls as intentional or unintentional without requiring manual review for every call. This self-service mechanism maintains high measurement precision by analyzing multiple data points, while simultaneously improving productivity by processing calls automatically rather than requiring human reviewers for each case.
Solution Approach 2:
The system incorporates feedback loops where call outcomes and follow-up results are analyzed to continuously refine the classification algorithms. By feeding back information about which calls were actually unintentional versus intentional, the system improves its future detection accuracy. This feedback mechanism enables the automated system to achieve high measurement precision over time, reducing the need for manual review and thereby maintaining high service throughput.
3Productivity
If automated systems are used to detect unintentional calls, then productivity increases by reducing manual follow-up, but measurement precision may deteriorate without thorough human evaluation
Solution Approach 1:
The automated system performs multiple functions simultaneously: it analyzes call duration, detects silence periods, evaluates background noise, assesses caller behavior patterns, and classifies call intent all in one integrated process. This multi-functional approach allows the system to maintain high measurement precision by considering multiple indicators of call intent, while achieving high productivity through automated processing. The universal system handles both detection and classification without requiring separate manual evaluation steps.
Solution Approach 2:
The system replaces manual mechanical evaluation with automated electronic analysis of call characteristics. Sensors and software automatically detect and analyze call parameters such as duration, silence periods, and background noise levels, substituting human reviewers with electronic detection systems. This substitution maintains measurement precision through consistent application of detection algorithms while dramatically improving productivity by processing calls automatically without human intervention for routine classifications.
Data Source
AI summary
Methods and systems for detecting unintentional calls placed to a call center. In some implementations, a device with telephone capability recognizes that it is being used to place a call, and automatically collects information usable to estimate whether the call is intentional or unintentional. For example, the device may record the timing of presses of a button used to initiate the call, the force used to press the button, or other parameters. The device may analyze the information, or may forward at least some of the information to the call recipient for analysis. When a suspected unintentional call is detected, the device may signal the user of the device, or other actions may be taken.