Smart assistance for meetings

The described techniques provide smart assistance to video conference meeting attendees. With user permission, such assistance includes replaying, transcribing, translating and/or summarizing all or part of a meeting, interpreting specified voice commands, and identifying topics or items of interest from the meeting recording or transcript. For example, meeting participants can provide keywords corresponding to topics of interest and seek notifications when such topics are discussed in the meeting. Also, users can query meeting recordings or transcripts for topics of interest or meeting action items. Present techniques can be utilized for video conferences, audio conferences, recorded talks, and face-to-face meetings

Screen brightness adjustment using machine learning

Mobile devices, e.g., smartphones, tablets, etc., often include a light sensor that senses ambient light. Data from this sensor is used to set the screen brightness. During ordinary usage of such devices, the light sensor sometimes gets covered, e.g., by the user\u27s fingers, which leads to a quick decrease in screen brightness. This makes for a poor user experience. This disclosure describes machine learning techniques that determine whether a detected change in ambient light is due to a true decrease in ambient light conditions or due to an occlusion of the light sensor. Screen brightness adjustments can be made on such determination

INTELLIGENT TELEPROMPTING SYSTEM FOR DELIVERING PRESENTATIONS

A system is proposed for intelligently and automatically advancing presentation text on a teleprompter User Interface (UI) as the presenter speaks. The system includes receiving, by a teleprompter system, a text file comprising speech of a presenter via the UI. The system further includes displaying contents of the text file on a UI screen. As the presenter speaks, the system further includes recording presenter speech according to predetermined criteria. The system also include running machine learning speech recognition tool to transcribe the recorded speech into recognized text. The system also includes semantically comparing text from the text file with the recognized text. If the recognized text matches content in the text file, the system includes advancing displayed text on the UI screen to show the following chunk of text to the presenter. On the other hand, if the recognized text does not match content in the text file, the system includes stopping the advancement of the text on the UI screen

Adaptive Ui Optimization Based On Automatic Detection Of Usage And Orientation Of A Mobile Phone

A system and method are disclosed that enable adaptive user interface (UI) optimization by automatically detecting usage and orientation of a mobile phone. The method includes detecting how the phone is oriented (portrait/landscape orientation) and by which hand (right or left) the phone is held and by which hand it is operated by the user. The input data such as raw touch input data, raw gyro sensor data, context of the phone (active application) or context of the user (time of day) are collected from the phone sensors and fed to a machine learning (ML) model. The ML model infers an easily accessible screen area. The system then optimizes the user interface to have the most relevant touch points or controls within the easily accessible area. The optimized adaptive UI is then presented to the user

Arbitrary precision user settings

A user of a mobile device often adjusts various user settings, e.g., speaker volume, screen brightness, etc., to reach a comfortable level. Sometimes, the user reaches, e.g., a volume level, that is a bit too high, and so hits the volume-down button. This results in a level that is too low, and so the user adjusts the volume back up, which is again unsatisfactorily high. The user repeats this loop for some time without truly finding a happy medium. This disclosure provides techniques that enable a user to reach a precise level for an electronic control setting. Upon detecting that a user is repeatedly adjusting a setting about a certain level, the techniques adjust a control step-size downwards. The user tunes into their desired level in finer steps. If the user is again found repeating an up-down sequence at the finer resolution, the step-size is again adjusted downwards. This process of adjusting downward the step-size continues until the user reaches a comfortable level. If the user stays at that level for a certain period of time, then the step-size is re-adjusted upwards to its original value

Improved Group Chat User Experience by Use of Topic Detection

As the number of participants in a group chat conversation increases, the problems of multiple topics and frequent topic switches become salient. These increase the burden on individual users to keep track of the conversation topics. These also lead to inefficiencies due to the disruption and interruptions caused by messages and notifications on topics that might be irrelevant or uninteresting to some of the chat participants. To address these problems, with user permission, this disclosure utilizes a machine learning model trained for automated detection of discussion topics in a chat. The detected topics are presented to the user to facilitate appropriate action based on current discussion topics or occurrences of topic switches. Model inference operations are performed entirely on the user’s device with specific user consent, thus preserving user privacy

Simultaneous multimodal user interface

This disclosure describes techniques to use a combination of voice based input with other input mechanism in human-computer interfaces. The techniques enable users to utilize in real-time, the suitable input mode(s) in a given context, without having to switch between input modes. With user permission, speech analysis techniques are utilized to analyze user speech and detect when speech includes user instructions, and to determine corresponding actions to be performed. By enabling simultaneous user input via multiple modes, the techniques facilitate effective navigation of complex tasks performed using a computing device

Offline Mobile Payments Using Public Key Cryptography

This disclosure describes techniques for the user of a mobile device to electronically pay another user using a payments app even in the absence of internet connectivity. At the payer’s end, a payment message that includes the payee’s identity, the amount, and other details is generated. The message is encrypted using the payer’s private key and provided to the payee’s device via a suitable mechanism such as peer-to-peer transfer, e.g., via Bluetooth. In another mechanism, the payer’s device generates an encoded image, e.g., a QR code, from the encrypted message. The message and/or the code is transferred to the payee’s mobile device, e.g., via a peer-to-peer connection or capturing an image of the code using a camera of the payee’s device. When either device connects to the Internet, the payment is decoded and verified using the payer’s public key. A message is sent to respective account providers to complete the payment

Restricting access to files by application programs

This disclosure describes techniques to enable users to restrict private data from display. Such private data can include images, videos, documents, and other files, by a user device. Private files can be manually or automatically designated, and the content of these files is hidden from user interfaces provided by application programs. Automatic designation is performed using user-permitted techniques for automatic analysis of files. Further, a variety of settings allow the user to control access to private data for display by designated application programs. Such features restrict another user that is viewing a device screen from viewing private data. Described techniques provide these features in ways that reduce the impact on usability, where authenticated access to private data is easily permitted via displayed prompts

Triggering alarms at dynamic and flexible times based on external factors

Users often set alarms to be reminded of events or tasks that occur at specific times. In addition to specifying a time, alarms can be set in the form of a timer that goes off after a specified period. Currently, setting an alarm requires that the user know in advance the specific time or period when the alarm is to go off. This disclosure describes the application of artificial intelligence (AI) techniques for setting alarms on a user’s device with the user’s permission. Such alarms can be set to go off at a time that can vary dynamically instead of a prespecified fixed time. The time at which the alarm goes off is determined based on various relevant external factors