PASSWORD CHECKING SYSTEM

A password checking system can be used to log into a device or service by entering a password in the form of a character sequence on a first keyboard layout. In case a user enters a password that does not match the correct password, the system retrieves a list of alternate keyboard layouts. Then the system uses the user’s touch location pattern from the password entry on one of the alternate keyboard layouts in the list, and the system converts the touch location pattern to another character sequence. The system then checks whether the converted character sequence matches the correct password. Then, if the converted character sequence matches the correct password, the system unlocks the device or service. Thus, if a user attempts to unlock a device or service fails due to incorrect password entry, the system tries the converted character codes based on other keyboard layouts and checks for a password match

Automatic detection of spurious touch inputs

Many users rest the palm of their hand on the screen when writing or interacting with a tablet or other touchscreen device. Spurious strokes can be generated from such touch, causing problems such as accidental activation of application modes or incorrect input. This disclosure describes the use of machine learning techniques to differentiate between valid and spurious strokes

Using a convertible device as desktop peripheral

The techniques of this disclosure enable input devices, e.g., keyboard and trackpad of a convertible laptop and other computing devices to act as peripherals for an external display, e.g., external monitor, television, etc. The techniques, as disclosed herein, determine if a convertible laptop is connected to an external monitor, whether it is in a particular mode, and is upside down. When these conditions are met, the keyboard and trackpad of the convertible laptop are configured to act as peripherals for the external display

User Activity Prediction for Device Screen and Power Management

To save battery, consumer devices, e.g., laptops, smartphones, etc., enter low-power states based on user inactivity. Typically, a device enters a low-power state after a fixed time has elapsed since the last user activity, e.g., keyboard or touchscreen activity. However, a fixed timeout does not work evenly for all users; for example, it is found that a substantial fraction of users reactivate the device immediately after the device screen is dimmed or the device has entered sleep state. This disclosure describes machine learning techniques to predict the transition to a low-power state based on user activity patterns and the state of the device. The techniques result in improved user experience due to better prediction of the start of user inactivity and increased battery life due to accurate power management

Memory Management Using Tab Discard and Reload Prediction

Browsers and other multi-tab applications discard tabs when there is insufficient memory. When a tab has been discarded, the user is forced to reload the tab to continue interaction. Selection of tabs to discard can be based on simple heuristics; however, such selection can lead to discarding tabs that the user is likely to use. Incorrectly discarded tabs are disruptive to users. This disclosure describes the use of machine learning techniques to generate more accurate predictions to select the tab to be discarded. Selectively discarding tabs in this manner can improve memory management while also providing a better user experience