Mechanism to Enable Participants to End Virtual Meetings Early

Virtual meetings sometimes stretch to the time initially allocated to them even though many or all participants would prefer to end the meeting early. However, due to social convention, no participant may be in a position to indicate that they would prefer to end the meeting earlier than scheduled. This disclosure describes a mechanism that can be included in virtual meeting software that enables meeting participants to indicate, during an ongoing meeting, that they would prefer to end the meeting early, e.g., any time starting from the provision of the indication. Upon receipt of such indications from a threshold number of participants, the meeting can be terminated automatically, or participants may be provided a message that they may leave the meeting

Automatic correction of timestamp and location information in digital images

Image storage and sharing services permit users to store and share images. Such applications typically organize images chronologically. However, chronological organization fails if the timestamp or location information stored in the image metadata (e.g., EXIF) is incorrect. Incorrect information is a common problem, e.g., when images are shared via messaging or social media, the shared image may have the time of sharing, not the time of capture. In another example, geolocation data may be deleted when an image is shared. Further, image timestamps can be inaccurate when multiple users contribute images to a single shared album from cameras that are not time synchronized. This disclosure describes techniques to determine the time and/or location of image capture by evaluating the image in the context of other images. Images with trustworthy time information are identified. Images with less trustworthy information are analyzed for content, and a determination is made as to whether they are ahead or behind a trustworthy image. In this manner, techniques of this disclosure enable chronological ordering of images

Surfacing Biased Portions of Multimedia Content Using Machine Learning

Multimedia enables content creators to communicate information by adding nuance which is difficult to convey through written language via voice tone, camera angle, content highlighting, etc. However, it can be difficult for content consumers to discern biased opinions included within multimedia content. This disclosure describes techniques to automatically detect and surface such biased opinions within multimedia content. The process involves examining publicly available multimedia and/or text content related to a given piece of multimedia content to identify and flag biased portions. The identified biased portions are surfaced to the user via a suitable user interface mechanism

User interface with automatic highlighting of content updates

Users that visit websites or other content at different times often see content with minor updates. For example, a shopping website may display a list of items and corresponding prices, and the prices may change between multiple user visits. While some websites include built-in functionality to notify users of such changes, no general purpose solution is available to alert users. This disclosure describes techniques to detect the context of a user visit to a website or other content, and determine changes to the webpage since a prior visit. With user permission, the UI of the website is modified to highlight the differences or the changes are indicated to the user, e.g., via a notification. Data from user visits is obtained and stored with user permission in order to determine changes

Automated Application Permissions Setting

Operating systems of computing devices include permission management features to grant software applications (apps) access to various hardware and software components. Permissions may be configured using permission sets that each specify different levels of access. A user can specify the level of access to an app by selecting a permission set. A conventional permission set either grants or restricts access to a component. Techniques are described that provide selective access to a component by automatically inferring fine-grained permissions from various user-specific and other available signals

Smart Button Actions On Mobile Devices

A system and method are disclosed that trigger a smart response to a button action when two or more running applications have applicable actions on a mobile device. The system includes a machine learning algorithm (MLA) built into the operating system that analyzes and learns from user actions. When the system detects a hardware button press, it retrieves possible actions/events that may be triggered for the current app and other running apps and processes. The actions are evaluated by computing a combination of scores involving machine-learned, rule-based scores provided by the apps and real-time signals such as location, time, user activity, etc. The system then triggers an action that has the highest score surpassing a predetermined threshold. The disclosed method would provide improved user experience, and also new, useful button actions that previously were not available to the user

SMART SCREEN ROTATION DETECTION FOR MOBILE DEVICES

A system and method to identify and implement applicationspecific screen rotation settings on a mobile are disclosed. The proposed method extends the operating system to use multiple signals, including sensor readings, accelerometer inputs, face detection algorithms, location based signals, or contextual signals as inputs. The rotation setting preferences specific to an application are aggregated based on the multiple signals feeding into a decision algorithm. The decision algorithm is configurable and learns rotation preferences specific to an application or application category. The decision algorithm can implement decisions based on a fixed combination of functions with manually chosen weights for the inputs. The combination of functions can also be learned using machine learning methods such as neural networks, support vector machines, or decision trees. The method further addresses the problem of erroneous rotation of screen content using a smart approach to detect the user’s preference

Visual User Interface (UI) Interaction Prediction

This publication describes an operating system (OS) level framework functionality, which can predict a user’s future interaction with first-party and third-party application software. This provides the user with an enhanced user experience (UX) by enabling first-party and third-party software to dynamically change the display in the user interface (UI) to aid the user to more-easily carry out the task at hand. The framework functionality at the OS level predicts the user’s future interactions using machine learning (ML). With this prediction, the supported application software can dynamically change the UI by presenting the user with relevant application software functions in a minimalist way