公开(公告)号：US20250036886A1

公开(公告)日：2025-01-30

申请号：US18766812

申请日：2024-07-09

Applicant: Google LLC

Inventor： Chen-Yu Lee ,  Alexander Ratner ,  Tomas Pfister ,  Chun-Liang Li ,  Yasuhisa Fujii ,  Ranjay Krishna ,  Cheng-Yu Hsieh ,  Si-An Chen

IPC: G06F40/40 ,  G06N3/0475

Abstract: Using a large language model to comply with a user request. The large language model receives tool documentation for each of one or more tools, and analyzes the tool documentation for each of the one or more tools to determine, for each tool, one or more tasks that the tool is operable to perform. Upon receiving a request from a user, the large language model generates a plan for complying with the request by using one or more of the tools, the plan including performance of one or more of the tasks.

公开(公告)号：US20240249192A1

公开(公告)日：2024-07-25

申请号：US18417556

申请日：2024-01-19

Applicant: Google LLC

Inventor： Sercan Omer Arik ,  Si-An Chen ,  Nathanael Christian Yoder ,  Chun-Liang Li

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The present disclosure provides an architecture for time series forecasting. The architecture is based on multi-layer perceptrons (MLPs), which involve stacking linear models with non-linearities between them. In this architecture, the time-domain MLPs and feature-domain MLPs are used to perform both time-domain and feature-domain operations in a sequential manner, alternating between them. In some examples, auxiliary data is used as input, in addition to historical data. The auxiliary data can include known future data points, as well as static information that does not vary with time. The alternation of time-domain and feature-domain operations using linear models allows the architecture to learn temporal patterns while leveraging cross-variate information to generate more accurate time series forecasts.