Amazon Technologies, Inc.

Da Teng

Naresh Narayanan

Adrian Evans

Bharath Bhimanaik Kumar

Farah Shariff

Muhammad Bilal Khokhar

This disclosure proposes systems and methods for processing natural language inputs using data associated with multiple language recognition contexts (LRC). A system using multiple LRCs can receive input data from a device, identify a first identifier associated with the device, and further identify second identifiers associated with the first identifier and representing candidate users of the device. The system can access language processing data used for natural language processing for the LRCs corresponding to each of the first and second identifiers, and process the input data using the language processing data at one or more stages of automatic speech recognition, natural language understanding, entity resolution, and/or command execution. User recognition can reduce the number of candidate users, and thus the amount of data used to process the input data. Dynamic arbitration can select from between competing hypotheses representing the first identifier and a second identifier, respectively.

Natural language processing using context

Techniques for prioritizing resources of various users, associated with a device, when responding to a user input received from the device are described. When a user input is received from a device, a system may generate a resource list for a group profile (e.g., a household profile) and each user profile (including any guest user profile) associated with the device. Each resource list may include the catalogs of resources (e.g., songs of a playlist, contacts of a contact list, etc.) of the group profile or user profile. The system may also generate a weight matrix including a respective weight for each catalog of each resource list. Various processing components (e.g., an automatic speech recognition component, a natural language understanding component, and an entity resolution component) may process using the resource lists and the weight matrix to determine an output responsive to the user input.

Resource selection for processing user inputs

Techniques for improving profile switching and/or account switching using resource grouped architecture. For example, a system may group multiple applications and/or features together and associate them with a container that includes a set of resources shared between the features. Thus, all of the applications/features in a container may use the same set of resources, which enables the system to update the resources for multiple features at a time. In addition, the system may maintain multiple containers simultaneously, enabling the system to perform profile/account switching by moving an individual application/feature from a first container to a second container. Thus, this resource grouped architecture simplifies profile/account switching and enables additional functionality for care givers. For example, the system may associate some applications/features with a care giver profile/account and other applications/features with a care recipient profile/account, enabling the care giver to control both accounts without signing out.

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Sammamish, WA, United States of America

Da Teng