Work Schedule Detection via User Signal Analysis
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Solution Overview
Problem
Users' actual work schedules are often not accurately reflected in calendar updates, leading to a need for determining work schedules based on various user signals such as location, activity patterns, and device context.
Innovation Solution
A system and method for determining a user work schedule by retrieving and evaluating user signals from device and user contexts, generating activity patterns, and applying weights to these patterns to determine the user's work schedule at different granularities, which can be shared and updated dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If calendar updates are used to reflect work schedules, then work schedule information can be shared with other users, but the accuracy of work schedule representation deteriorates because users do not update calendars to reflect actual work patterns
Solution Approach 1:
The system automatically determines work schedules by analyzing user signals from devices and contexts without requiring manual calendar updates. The work schedule component retrieves user signals, generates activity patterns, and determines work schedules autonomously, eliminating the need for users to manually maintain accurate calendar information while reflecting actual work patterns
Solution Approach 2:
The system continuously monitors user signals and dynamically updates work schedules based on detected activity patterns. This feedback mechanism allows the system to adapt to changing work patterns automatically, maintaining accuracy without requiring manual intervention from users to update their calendars
2Measurement precision
If user signals are collected from device and user contexts to determine work schedules, then work pattern granularity is improved, but user privacy and security concerns worsen
Solution Approach 1:
The work schedule component acts as an intermediary that processes user signals locally on the device rather than transmitting raw data to external servers. This intermediary approach enables detailed work pattern analysis while keeping sensitive user data localized, reducing privacy risks associated with data collection and transmission
Solution Approach 2:
The system processes and analyzes user signals locally on the user's device rather than centralized server processing. This local quality approach allows detailed work pattern determination while maintaining user control over their data and reducing the security risks associated with centralized data storage and transmission
3Adaptability or versatility
If work schedule determination is implemented on a server, then access to remote user signals is improved, but security and privacy preservation worsen
Solution Approach 1:
The work schedule component serves as a local intermediary that processes user signals on the device before any external communication occurs. This intermediary architecture enables the system to maintain detailed work schedule determination capabilities while preserving user data security by keeping sensitive information localized and minimizing external data transmission
4Measurement precision
If manual calendar updates are required to reflect work schedules, then calendar information can be maintained, but the system fails to capture actual work patterns including working from home, conference calls while driving, and varying daily schedules
Solution Approach 1:
The system automatically captures and analyzes user signals from device contexts and user behaviors to determine work schedules without requiring manual calendar maintenance. This self-service approach accurately captures diverse work patterns including remote work, travel-related work activities, and varying schedules by analyzing actual user behavior rather than relying on manual updates
Solution Approach 2:
The system continuously monitors and analyzes user signals in real-time to maintain up-to-date work schedules that reflect current work patterns. This continuous action ensures that work schedule information remains accurate and current without requiring periodic manual updates, capturing evolving work patterns as they occur
Data Source
AI summary
One or more techniques and/or systems are provided for determining a user work schedule. A user may seldom update actual work hours, such as within a calendar, to reflect an actual schedule of the user, which may result in erroneous information being exposed to services that may notify other users as to when the user is inside or outside work hours. Accordingly, user signals (e.g., a current device location, execution of a work-related app, access to a work VPN, participation in a conference call from home, etc.) may be evaluated to generate a set of user activity patterns that may be indicative of recurring work patterns of the user. A user work schedule for the user may be determined based upon the set of user activity patterns. The user work schedule may be used to modify user interfaces (e.g., a conferencing website, a phone app, etc.) exposed to other users.