Automatic management of multiple contexts

Users interacting with computing devices often multi-task across and navigate between multiple contexts. Users often report distraction and fatigue due to such switching of contexts. For example, it is hard for users to remember an earlier context and resume work at a later time. It has been argued that a user is better off focusing on one context at a time to improve productivity. However, current computers are blind to the different contexts for a user. Applications and programs relating to several current contexts are displayed without taking into account potential for distractions. Techniques of this disclosure enable automatic grouping of software applications on a user’s computer such that only applications that are relevant to a current context are visible. When switching contexts, the corresponding applications for the context are automatically made available. Such user interface enables a user to focus on a current context is strengthened while providing the ability to switch quickly to other contexts as necessary

Distinguishing Bots from Human Callers

Telephone users frequently receive unwanted calls from sales, advertising, or spam callers. Such calls may be made by automated agents or bots of such sophistication that they are often difficult to distinguish from human callers. This disclosure presents machine-learning techniques that enable differentiation of bots from human callers. Machine-learning models are trained to recognize artifacts that distinguish bot callers. Users can report callers as bots or humans, thereby enabling federated learning of differences between human callers and bots. A suspected bot caller is challenged with audio or visual captchas to further filter out bots. An incoming call that is confirmed as bot-initiated is either not delivered to the user, or the call recipient is alerted that the caller is likely a bot

A logical layer to interpret user interactions

Some computing user interfaces include a large number of user-selectable options which can make it difficult for a user to locate the right option. Another frequent user interface problem is inadvertent or unintended invocation of actions, e.g., by selection of an incorrect icon from two icons placed close to each other. In some user interfaces, simple actions sometimes require a disproportionately tedious sequence of interactions. This disclosure introduces a layer between the user and the application such that user commands or interactions are interpreted in light of past interactions, and corrected, filtered or automated as appropriate. Past interactions are utilized, and interpretation or corrective action is performed only upon permission from the user. For ease of interaction, such permission is obtained, e.g., at initial setup, and is modifiable

On-Device and System-Wide Audio Live Captioning with Language Translation

This publication describes techniques and apparatuses that enable an electronic device (e.g., a smartphone) to provide on-device (e.g., offline), system-level (e.g., operating system), live captioning with language translation in a language that a user can choose (select). Therefore, the smartphone enables the user to read live captioning in the language of their choice without relying on an internet connection, cellular data, or any wired and/or wireless communication with a remote server. Also, the smartphone enables the user to read live captioning in the language of their choice on any medium with audio content supported by the smartphone