Google Inc.

Arash Sadr

Alice Au Quan

Matthew Nirvan Sharifi

Victor Carbune

Harshit Kharbanda

Christopher Kelley

Belinda Zeng

Minsang Choi

Jieming Yu

Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.

Search with machine-learned model-generated queries

Systems and methods for generating and providing outputs in a multi-device system can include leveraging environment-based prompt generation and generative model response generation to provide dynamic response generation and display. The systems and methods can obtain input data associated with one or more computing devices within an environment, can obtain environment data descriptive of the plurality of computing devices within the environment, and can generate a prompt based on the input data and environment data. The prompt can be processed with a generative model to generate a model-generated output. The model-generated output can then be transmitted to a particular computing device of the plurality of computing devices.

Ambient multi-device framework for agent companions

Growing community of inventors

Belmont, CA, United States of America

Arash Sadr