公开(公告)号：US20250148364A1

公开(公告)日：2025-05-08

申请号：US18815781

申请日：2024-08-26

Applicant: Google LLC

Inventor： Joshua Brian Braverman ,  Camille Wormser ,  César Augusto Naranjo ,  Alexey Vaysburd ,  Dorothea Wiesmann Rothuizen ,  Brian Michael Burdick ,  Jason Sean Krueger ,  Xiaoxuan Zhang ,  Sergiu Ion Goschin

IPC: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling artificial intelligence to display responses in a conversational user interface that are tailored to a user of the interface, to predicted future states, and/or to predicted trajectories that include transitions between multiple states. In one aspect, a method includes initiating a user session with a conversational user interface of an artificial intelligence system that displays, within the conversational user interface, responses to user interactions received during the user session, the responses being generated using one or more machine learning models of the artificial intelligence system. During the user session, the system receives data indicating one or more user interactions within the conversational user interface by a user. The system updates a state record that represents a first state. The system processes the state record to determine potential trajectories for the user session.

公开(公告)号：US20250005656A1

公开(公告)日：2025-01-02

申请号：US18658397

申请日：2024-05-08

Applicant: Google LLC

Inventor： Alexander Kerelsky ,  Sergiu Ion Goschin ,  Dong Lin ,  Jie Han

IPC: G06Q30/08

Abstract: This technology generally relates to a method for leveraging a measure of bidding model uncertainty to directly improve automatic bidding. The methods may include measuring the inherent uncertainty of automatic bidding models using techniques, such as quantile regression. Further, the measure of bidding model uncertainty may be incorporated into bid formulas to inform the generated bids for an auction. The method may be further formulated to modify the bids to be more conservative when the bidding model uncertainty is higher. Once the uncertainty level of the bidding model is reduced to a more stable level, the bidding method will resume generating bids with more efficiency.