Work Visit Detection Using Movement State Machines
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Solution Overview
Problem
Current mobile computing devices face challenges in effectively utilizing movement-related signals to detect work-related visits in a resource-efficient manner, as existing systems lack precision and efficiency in identifying and categorizing such visits.
Innovation Solution
A computer-implemented technique that utilizes movement-sensing mechanisms and state machines to detect work-related visits by analyzing location and motion data, determining visit commencements and endings, and employing a context-determining component to classify visits as work-related, with features like user interface presentations, power management, and data sharing options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If movement-sensing mechanisms continuously track location and motion data to detect work-related visits, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The system uses periodic sampling of movement data at defined intervals rather than continuous tracking, allowing the device to monitor for visit conditions while consuming less power. The state machine transitions between active monitoring and lower-power states based on detected movement patterns.
Solution Approach 2:
The system dynamically adjusts monitoring intensity based on detected conditions. When movement patterns suggest a potential visit (e.g., arrival at a location followed by stationary period), the system increases monitoring accuracy. During non-visit periods, monitoring is reduced to conserve power.
2Measurement precision
If the state machine continuously monitors movement data with high accuracy, then visit detection precision is improved, but device complexity increases
Solution Approach 1:
The monitoring system is divided into discrete states (e.g., idle, traveling, arrived, visiting, departed) with clear transition conditions. Each state has specific monitoring requirements, allowing complex detection logic to be managed through modular state definitions rather than a monolithic complex system.
Solution Approach 2:
The state machine acts as an intermediary layer between raw movement data and visit detection logic. It processes and filters movement data through defined states, simplifying the overall system architecture by separating data processing from decision-making about visit detection.
3Measurement precision
If the system collects and processes detailed movement data to classify visits, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system pre-defines visit characteristics and classification criteria before monitoring begins. Movement patterns associated with work-related visits are identified in advance, allowing the state machine to quickly match observed patterns against predefined templates rather than performing complex analysis in real-time.
Solution Approach 2:
The system replaces complex real-time analytical processing with pattern-matching logic based on pre-defined movement signatures. By substituting detailed continuous analysis with predefined pattern recognition, the system achieves accurate classification with reduced processing overhead.
Data Source
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AI summary
A computer-implemented technique is described herein for detecting work-related visits. The technique includes receiving movement data from one or more movement-sensing mechanisms associated with a local computing device carried by a user. The technique further includes: determining, using a state machine, and based on the movement data, that a user has commenced a candidate visit, the candidate visit being described by visit data; determining, using the state machine, and based on the movement data, that the user has completed the candidate visit; and determining, using a context-determining component, and based on the visit data, whether the candidate visit corresponds to a work-related visit for which work has been completed. The technique further includes mechanisms that allow the user to add a content item to the visit data of an ongoing visit, prevent private data from leaving the local computing device, and efficiently consume power of the local computing device.