公开(公告)号：US20240020573A1

公开(公告)日：2024-01-18

申请号：US17862765

申请日：2022-07-12

Applicant: Google LLC

Inventor： Wangyang Zhang ,  Leyou Zhang ,  Rajarishi Sinha ,  Michael Peter Perrone ,  Andrew James McGehee ,  Dawei Jia ,  Jingtao Wang

IPC: G06N20/00 ,  G06Q10/04

CPC classification number: G06N20/00 ,  G06Q10/04

Abstract: Aspects of the disclosure are directed to an approach for extending forecasting models to various levels of granularity. The approach can include receiving a target level of granularity for distributing a forecast, performing forecast modeling at an aggregated level of granularity, and determining a distribution method to distribute results of the forecast model at the target level of granularity. The approach can improve performance over existing forecasting models with minimal overhead.

公开(公告)号：US20230297899A1

公开(公告)日：2023-09-21

申请号：US18183291

申请日：2023-03-14

Applicant: Google LLC

Inventor： Jingtao Wang ,  Wangyang Zhang ,  Michael Peter Perrone

IPC: G06Q10/04

CPC classification number: G06Q10/04

Abstract: A method for optimal time-to-event (TTE) modeling includes obtaining a forecast request requesting performance of a TTE forecast forecasting an amount of time an event will occur after a starting point in time. The method includes obtaining a cutoff value representing an amount of time after the starting point in time that the event has not occurred. The method also includes forecasting, using an uncertainty forecasting model, the amount of time the event will occur after the starting point in time and updating the forecasted amount of time based on the cutoff value. The method also includes returning the updated forecasted amount of time the event will occur after the starting point in time.

公开(公告)号：US20240362583A1

公开(公告)日：2024-10-31

申请号：US18307084

申请日：2023-04-26

Applicant: Google LLC

Inventor： Ramesh Natarajan ,  Wangyang Zhang ,  Julian Everett Robinson

IPC: G06Q10/087

CPC classification number: G06Q10/087

Abstract: A method for prioritizing inventory checks and audits includes receiving a plurality of product identifiers. Each respective product identifier of the plurality of product identifiers is associated with a respective product of a plurality of products. For each respective product identifier of the plurality of product identifiers, the method also includes predicting, using an inventory predictor model, a mixture probability distribution over possible quantities for the associated respective product, and generating, using the mixture probability distribution, a respective inventory confidence score. Here, the respective inventory confidence score indicates a confidence estimation of an actual inventory of the respective associated product. The method further includes selecting, using each respective inventory confidence score for each respective product, a list of candidate products from the plurality of products, the list of candidate products ordering the candidate products based on an uncertainty of the actual inventory of the respective candidate product.