Personalized ordering for presentation of help topics

Users often consult online help when they encounter problems. Many online help resources are organized as a list of question-answer pairs or topics presented in a fixed order. Upon user consent, the techniques of this disclosure utilize a machine learning model to personalize the order in which the list of question-answer pairs or topics is presented to the user. The personalized ordering utilizes the knowledge of the context of the user to sort the presented list of help content based on the match of the content with the problem the user is likely facing

Intelligent Radio Search

Radio search using traditional techniques such as tuning or switching between pre-stored radio stations is cumbersome and time-consuming. It is difficult for users to locate a radio station that is currently playing programs that are of interest to them. The techniques described in this disclosure employ voice recognition, audio fingerprinting, and on-the-fly recognition to identify available content. Further, the available content is matched with known user preferences, based on user consent. An intelligent, assistive user interface based on matched radio content is provided to the user. The interface enables the user to easily find and listen to radio content of interest

Persona Profiles for Prioritization of Content Output

A persona-specific ranking model creates a persona-specific profile layer in an operating system of a user equipment device that helps users to prioritize their ongoing activities by explicitly enabling a specific persona profile. A profile (e.g., user profile) can be used to manage a visual display of personal data associated with a specific user or a customized desktop environment. Much like today’s work profile, each persona profile is specific to a user context or activity, such as runner, reader, traveler, studier, house cleaner, philosopher, software engineer, rower, manager, skydiver, parent, and so on. The persona-specific ranking model creates a list of persona profiles suitable to the user based on user features (e.g., user interaction, activities, and priorities) and recognizes when a particular persona is active. A system layer is implemented to enable individual apps to expose information through the persona-specific ranking model, such that the most relevant information to the user’s current activity is prioritized accordingly within any application, including third-party applications. The persona profiles are manually, or autonomously, selectable

Enabling Repeat Purchases Via A Virtual Assistant

This disclosure describes a virtual assistant that can help users repeat prior purchases made via a shopping app or website. When making a repeat purchase, users expect the product or service to be identical to the previous purchase. Per techniques described herein, the virtual assistant automatically detects and highlights differences between a current order and past purchases of the same product or service prior to the user finalizing the purchase. The interaction between the user and the virtual assistant can take place via any device such as a smartphone, smart speaker, etc. Further, the actual purchase can be completed via any shopping app or website. Machine learning techniques are utilized to analyze past purchases, accessed with user permission; detect repeat orders; identify mismatches; and provide notifications and assistance with modifying and placing the order

Using Sensors to Improve User Interaction with Application Notifications

This publication describes techniques and methods for improving user interaction (e.g., dismissal, expansion) with application notifications on computing devices, such as smartphones, tablets, or smart glasses. The techniques incorporate the utilization of multiple on-device sensors (e.g., a camera sensor, an accelerometer) that can measure various means of user input. The methods as described herein afford users convenient and quick notification interaction options. Machine-learned (ML) models that employ gaze detection, custom gestures (e.g., hand movement, eye clipping), and/or user actions (e.g., shaking or rotating the device) can provide users the ability to expand, individually dismiss, or batch dismiss application notifications exclusive of or in conjunction with application notification priority levels (e.g., the urgency of the notification, preselected user importance levels)

Delivering Virtual Assistant Responses in a User-specified Alternate Language

Many households have multiple smart devices, with each device being tagged or named to indicate the room in which it is situated. Users typically interact with smart devices via voice-based virtual assistant software that provides voice responses to spoken queries, e.g., in the same language as that of the query. This disclosure describes techniques that enable users to configure their smart devices such that the smart device always responds in a fixed language, independent of the language in which input commands are provided. Such an operation allows users to receive virtual assistant responses in a language of their choice. Similarly, users are provided with options to specify a fixed region that is applicable regardless of the region in which the device is actually located. Automatic query and/or response translation is performed to provide the responses

INPUT METHOD EDITOR (IME) SUPPORTING MULTIPLE HYPOTHESES APPROACH

A system is described that enables an input method editor (IME) on a computing device (e.g., a wearable device or a mobile device) that provides multiple hypotheses. There are various different mechanisms for inputting a user’s input on a device, such as touch and swipe typing, voice input, handwriting recognition, camera input, optical character recognition (OCR), etc. The device may enable an IME to process a user’s input and return, to an application, multiple hypotheses in a standardized machine-readable form based on the input. The application may select one of the returned hypotheses to use as final input. By enabling the IME to return multiple hypotheses based on a user’s input, the device enables the application to use multiple hypotheses to provide an improved (e.g., more accurate) result

Data Saving Using Contextual Signals

Mobile device applications and operating systems offer a data-saving option such that online content that is believed to be unchanged since last load is not reloaded. However, this sometimes leads to a situation where online content that has actually changed is not reloaded or that stale content is reloaded. The misdetection of fresh content as stale or vice-versa occurs due to the heuristics that are used by the data-saving algorithms of the application/ OS, due to misconfigured servers, etc. This disclosure presents machine-learning techniques that determine if a web page or a portion thereof is to be reloaded. The techniques use various contextual signals, as permitted by the user, to make the reload decision, e.g., the content to be reloaded; surrounding content; their metadata and positions on a web-page; user interaction and behavior with the website; previously loaded content; etc. The techniques enable data-saving techniques that are robust and tailored to both user and website

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

Saving and Recalling Interaction Sequences of In-app Actions

When using apps, users can encounter difficulties in figuring out or remembering how to perform action sequences pertaining to specific tasks, especially tasks that are complicated or rarely performed. After successfully performing such tasks, it is cumbersome for the user to remember the details at a later time when they need to perform the task again. Currently, apps or devices do not provide users with the ability to capture and save these details for later recall and use. This disclosure describes techniques that, with user permission, save sequences of actions performed with an app on their device. The user can later recall the saved sequences as needed to navigate the app and/or to invoke automatic execution of the interaction sequence