原始名称记载: GOOGLE LLC

Methods are disclosed for making installable web applications more widely available, helping users to find and install web applications on their computing devices. Some methods help surface an install control to initial installation. Some methods provide generate manifest files for web sites not associated with an installable web application. Some methods provide default offline capabilities for installable web applications.

Systems and methods for providing information responsive to a user search are provided. Such a method includes receiving a search query from a client device and generating a plurality of categories associated with the search query using a first artificial intelligence (AI) model. The method also includes selecting a plurality of sponsored content options that are responsive to the search query and, for each category, generating respective freeform descriptive text for the category using a second AI model. The method also includes causing the client device to present a user interface that includes (i) the respective freeform descriptive text for each category and (ii) links to third party information resources associated with the selected plurality of sponsored content options. An arrangement of the links within the user interface indicates which of the plurality of sponsored content options correspond to which of the plurality of categories.

Implementations relate to handling visual content across a multi-turn dialog. A user input that includes natural language content and visual content is received during the dialog. If the visual content is being received for the first time in the dialog, the visual content is processed to generate a corresponding tokenized representation of the visual content. The corresponding tokenized representation can be cached in a database in association with the dialog, or in association with a user account of a user of the user query. If the visual content is subsequently referenced in the dialog, the corresponding tokenized representation of the visual content is retrieved from the database. The corresponding tokenized representation of the visual content, corresponding tokenized representations of natural language content, and optionally other metadata can be processed, using a generative model, to generate a response responsive to the user input.

Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.

Implementations set forth herein relate to an automated assistant that can operate as an interface between a user and a separate application to search application content of the separate application. The automated assistant can interact with existing search filter features of another application and can also adapt in circumstances when certain filter parameters are not directly controllable at a search interface of the application. For instance, when a user requests that a search operation be performed using certain terms, those terms may refer to content filters that may not be available at a search interface of the application. However, the automated assistant can generate an assistant input based on those content filters in order to ensure that any resulting search results will be filtered accordingly. The assistant input can then be submitted into a search field of the application and a search operation can be executed.

A filter bank comprising filters is obtained. For pixels of a degraded frame, respective sets of combining scalars for combining the filters of the filter bank are obtained. For the pixels of the degraded frame, respective pixel-specific filters are obtained by combining the filters of the filter bank using the respective sets of combining scalars. A restored frame is obtained by filtering the pixels of the degraded frame using the respective pixel-specific filters. The respective sets of combining scalars may be obtained using a machine-learning model that receives the degraded frame as an input, where the machine-learning model is a convolutional neural network. The machine-learning model may be trained to minimize an error between restored frames and corresponding source frames.

A user device operating in dual-mode connectivity with a master node and a secondary node detects a failure of a secondary cell group associated with the secondary node (). In response, the user device suspends an secondary cell group transmission (). The user device generates a message related to a status of the SCG for transmission to the MN () and transmitting the message to the MN when the SCG transmission is suspended ().