Various systems and methods for providing a proximity measurement system are provided herein. A proximity measurement system includes: a user tracking service to calculate a travel area of a user, the travel area representing an area that the user has traveled within during a period of time, a proximity service to: identify a reminder task created by the user, the reminder task having an associated point of interest; identify a plurality of geographical locations of the point of interest; and calculate an effective proximity measure based on the travel area of the user and the plurality of geographical locations; and a notification service to transmit a notification to the user when the user is within the effective proximity measure of any one of the plurality of geographical locations.

Aspects of the subject technology relate to a technique automatically adapting a vehicle HMI to a user preference. The HMI includes a touchscreen displaying selectable icons associated with functions. Every time the HMI detects a selection of a first icon by a user among the icons, the HMI sends to the server, first information on a first function associated with the first icon, when the first icon is selected, and where the HMI is located when the first icon is selected. The server analyzes the first information to recognize a pattern of behavior taken by the user in selecting the first icon, generating a first user preference. The HMI updates an arrangement of the icons on the touchscreen based on the first user preference. The first user preference includes parameters that includes a certain day of a week, a certain time of that day, and a location.

In some implementations, a computing device can be configured to automatically tum off notifications when generating a notification would cause a disturbance or be unwanted by a user. The device can be configured with quiet hours during which notifications that would otherwise be generated by the computing device can be suppressed. In some implementations, quiet hours can be configured as a time period with a start time and an end time. In some implementations, quiet hours can be derived from application data. For example, calendar data, alarm clock data, map data, etc. can be used to determine when quiet hours should be enforced. In some implementations, the device can be configured with exceptions to quiet hour notification suppression. In some implementations, the user can identify contacts to which the quiet hours notification suppression should not be applied.

In some implementations, a computing device can be configured to automatically tum off notifications when generating a notification would cause a disturbance or be unwanted by a user. The device can be configured with quiet hours during which notifications that would otherwise be generated by the computing device can be suppressed. In some implementations, quiet hours can be configured as a time period with a start time and an end time. In some implementations, quiet hours can be derived from application data. For example, calendar data, alarm clock data, map data, etc. can be used to determine when quiet hours should be enforced. In some implementations, the device can be configured with exceptions to quiet hour notification suppression. In some implementations, the user can identify contacts to which the quiet hours notification suppression should not be applied.

In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

A mobile communication device operates by: generating an interactive interface that is presented for display via a display device associated with the mobile communication device; receiving reminder data via user interaction with the interactive interface, wherein the reminder data indicates a reminder associated with one of a plurality of contacts; receiving communication event data in response to a communications event associated a communication, via the mobile communication device, with the one of the plurality of contacts; generating, in response to the communication event, a notification that includes the reminder data; and presenting the notification for display via the interactive interface.

An event information processing method and a first electronic device for implementing the method are provided, and relate to the field of communication technologies. The method includes: A first electronic device receives a first message sent by a second electronic device. The first message carries a trust level of the second electronic device. The first electronic device determines whether the trust level of the second electronic device meets a preset condition. When the trust level of the second electronic device meets the preset condition, the first electronic device sends a first event to the second electronic device. According to the technical solution, selective event synchronization can be implemented.

Embodiments are described for a pattern-based control system that learns and applies device usage patterns for identifying and disabling devices exhibiting abnormal usage patterns. The system can learn a user’s normal usage pattern or can learn abnormal usage patterns, such as a typical usage pattern for a stolen device. This learning can include human or algorithmic identification of particular sets of usage conditions (e.g., locations, changes in settings, personal data access events, application events, IMU data, etc.) or training a machine learning model to identify usage condition combinations or sequences. Constraints (e.g., particular times or locations) can specify circumstances where abnormal pattern matching is enabled or disabled. Upon identifying an abnormal usage pattern, the system can disable the device, e.g., by permanently destroying a physical component, semi-permanently disabling a component, or through a software lock or data encryption.

Embodiments are described for a pattern-based control system that learns and applies device usage patterns for identifying and disabling devices exhibiting abnormal usage patterns. The system can learn a user’s normal usage pattern or can learn abnormal usage patterns, such as a typical usage pattern for a stolen device. This learning can include human or algorithmic identification of particular sets of usage conditions (e.g., locations, changes in settings, personal data access events, application events, IMU data, etc.) or training a machine learning model to identify usage condition combinations or sequences. Constraints (e.g., particular times or locations) can specify circumstances where abnormal pattern matching is enabled or disabled. Upon identifying an abnormal usage pattern, the system can disable the device, e.g., by permanently destroying a physical component, semi-permanently disabling a component, or through a software lock or data encryption.