Amazon Technologies, Inc.

Samarth Bhargava

Vinaya Nadig

Anjishnu Kumar

Supriya Medapati

Mohit Nayak

Prakash Kumar Jha

Omar Zia Khan

Bhaskara Kiran Kumar Kommalapati

Yugandhar Maram

Zheng Zheng

Rafael Colorado Vargas

Techniques for determining relevant and personalized supplemental content for a user are described. A system may prompt a language model to generate queries based on an entity (or entities) representing a user's affinity. In some examples, the language model may be prompted to generate an initial query corresponding to the entity and then generate one or more enriched queries based on the initial query. The one or more enriched queries may be used to search one or more content providers for supplemental content results. The supplemental content results may be aggregated, may be validated to ensure correspondence to the enriched query, and may be stored for future output. A system component may request supplemental content for output, at which time the system may select one or more supplemental content from storage, where the selection may be made to optimize relevancy for current user and situational context.

Determining supplemental content for output

Techniques for coordinating output of inferred content using various components and systems are described. A supplemental content system and a notification system may each receive inferred content to be output. When the supplemental content system or the notification system outputs the inferred content, the respective system stores a record of the output of the content in a historical output storage. Thereafter, when the other system is ready to output the inferred content, the other system may prevent the inferred content from being output based on the inferred content having already been output, as represented in the historical output storage.

Voice user interface notification rendering

A system is provided for determining customized notification output based on user preferences and event information. A user may request a system to provide a notification when a future event occurs. The system may determine a custom notification/output template for the notification, where a portion of the template is determined based on a user preference. When the event occurs, the system may generate a notification using the template, where portions of the notification are determined using event information and user preferences.

Generating event output

Techniques for rendering notification and announcement content for read time are described. A notification system provides non-natural language notification content. Sometime thereafter, a user input to output the notification content is received. In response, template-based or natural language generation processing is performed to convert the non-natural language notification content into natural language notification content including updated time information. The natural language notification content is then output to the user. Alternatively, a notification system provides non-natural language announcement content. Sometime thereafter, the template-based or natural language generation processing is performed to convert the non-natural language announcement content into natural language announcement content including updated time information. The natural language announcement content is then proactively output to an intended recipient user or group of users.

Dynamically rendered notifications and announcements

Techniques for ordering the output of notification summaries are described. A system may receive multiple notifications intended for a same user or group of users. In response to receiving a user input requesting output of notifications (or in response to multiple notifications expiring soon), the system may identify multiple notifications intended for the user or group of users. The system generates natural language summaries of the notifications, and orders the natural language summaries based on one or more default ordering rules, one or more user preferences, one or more notification provider preference, and/or user feedback. The system then outputs the ordered natural language summaries to the user.

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Redmond, WA, United States of America

Samarth Bhargava