AI-ASSISTED GESTURE NAVIGATION FOR COMPUTING DEVICES

A computing device (e.g., a smartphone, a laptop computer, a tablet computer, a smartwatch, etc.) may use a machine learning model to classify user inputs as a back gesture for navigating with respect to graphical user interfaces (GUI) of the computing device. The computing device may apply a machine learning model to input data associated with the user input (e.g., (x,y) coordinates of the user inputs) and a context of the computing device (e.g., an application (“app”) that is currently executing on the computing device, the width of the computing device, the orientation of the computing device, etc.) to determine a degree of likelihood of the user input being a back gesture. If the degree of likelihood of the user input being a back gesture satisfies a threshold, the computing device may execute a back action associated with the back gesture. If the degree of likelihood does not satisfy the threshold, the computing device may execute a different action or may discard the user input. The machine learning model may be trained on a computing system (e.g., a remote server) distinct from the computing device while the trained machine learning model may be stored at the computing device

Fully Integrated On-Device Search and Web-Based Search Framework Incorporating Privacy Controls

This publication describes a framework, implemented on a computing device, for integrating search results provided by a user device system-level search with internet-based search results provided by an internet search engine application, while retaining the privacy of a user of the device. The search results may be ranked based on both the user device system-level search and the internet search

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

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Query-independent evidence in home page finding

Hyperlink recommendation evidence, that is, evidence based on the structure of a web's link graph, is widely exploited by commercial Web search systems. However there is little published work to support its popularity. Another form of query-independent evidence, URL-type, has been shown to be beneficial on a home page finding task. We compared the usefulness of these types of evidence on the home page finding task, combined with both content and anchor text baselines. Our experiments made use of five query sets spanning three corpora - one enterprise crawl, and the WT10g and VLC2 Web test collections. We found that, in optimal conditions, all of the query-independent methods studied (in-degree, URL-type, and two variants of PageRank) offered a better than random improvement on a content-only baseline. However, only URL-type offered a better than random improvement on an anchor text baseline. In realistic settings, for either baseline, only URL-type offered consistent gains. In combination with URL-type the anchor text baseline was more useful for finding popular home pages, but URL-type with content was more useful for finding randomly selected home pages. We conclude that a general home page finding system should combine evidence from document content, anchor text, and URL-type classification