公开(公告)号：US11741400B1

公开(公告)日：2023-08-29

申请号：US17127752

申请日：2020-12-18

Applicant: Beijing DiDi Infinity Technology and Development Co., Ltd.

Inventor： Conghui Fu ,  Zihan Yi ,  Zetian Ni ,  Xin Chen

IPC: G06Q10/02 ,  G06Q50/30 ,  G10L17/06 ,  G06N20/00 ,  G06F18/213 ,  G06F18/214

CPC classification number: G06Q10/02 ,  G06F18/213 ,  G06F18/214 ,  G06N20/00 ,  G06Q50/30 ,  G10L17/06

Abstract: Techniques for automatically detecting when a ride requester has requested a ride-share ride on behalf of a guest rider using some or all of the communications between the driver and ride requester are described herein. For example, a server can obtain chat logs between a ride requester and a driver and process the chat logs to identify whether the ride requester has requested a ride on behalf of a guest rider. In particular, the server can train an artificial intelligence model (e.g., a machine learning model) to predict potential guest rider behavior. Once trained, the server can obtain chat logs comprising chat messages sent between a driver and a ride requester, and apply a representation of the chat logs as an input to the trained artificial intelligence model to determine whether guest rider behavior is detected.

公开(公告)号：US20220188723A1

公开(公告)日：2022-06-16

申请号：US17123103

申请日：2020-12-15

Applicant: BEIJING DIDI INFINITY TECHNOLOGY AND DEVELOPMENT CO., LTD.

Inventor： Conghui Fu ,  Wei Wang ,  Zetian Ni ,  Zihan Yi ,  Kaidan Yang ,  Zhendong Li ,  Yifang Yuan ,  Xin Chen

IPC: G06Q10/06 ,  G06N20/00

Abstract: Embodiments of the disclosure provide systems and methods for processing a transportation service request. An exemplary system may include a communication interface configured to receive the transportation service request from a terminal device. The system may further include at least one processor. The at least one processor may be configured to generate a passenger score based on the received transportation service request using a first machine learning model trained with sample passenger data associated with past impacted drivers. The at least one processor may further be configured to generate a trip score based on the generated passenger score and the received transportation service request using a second machine learning model trained with sample trip data associated with the past impacted drivers. The at least one processor may also be configured to allow or block the received transportation service request based on the generated trip score.