US 12,462,298 B2
Computer-implemented systems and methods for real-time risk-informed return item collection using an automated kiosk
Yonghui Chen, San Diego, CA (US); Xin Jin, Sunnyvale, CA (US); and Yan Zhou, San Jose, CA (US)
Assigned to Coupang Corp., Seoul (KR)
Filed by COUPANG CORP., Seoul (KR)
Filed on May 11, 2021, as Appl. No. 17/316,764.
Application 17/316,764 is a continuation of application No. 16/845,239, filed on Apr. 10, 2020, granted, now 11,120,498.
Application 16/845,239 is a continuation of application No. 16/542,588, filed on Aug. 16, 2019, granted, now 10,657,591, issued on May 19, 2020.
Prior Publication US 2021/0264513 A1, Aug. 26, 2021
Int. Cl. G06Q 40/03 (2023.01); G06Q 20/20 (2012.01); G06Q 40/00 (2023.01)
CPC G06Q 40/03 (2023.01) [G06Q 20/208 (2013.01); G06Q 40/00 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A system for collecting return items based on a real time risk decision, comprising a return server, an automated kiosk, and a network interface, wherein:
the return server comprises:
one or more memory devices storing instructions; and
one or more processors configured to execute the instructions to perform operations in real-time, the operations comprising:
receiving, from the automated kiosk via the network interface, return item information and a request for return risk level relating to the return item information, wherein the return item information comprises at least one of: an order ID, an item ID, a product barcode, a pre-generated QR code, or a pre-generated return ID;
in response to receiving the request, preparing the return risk level using a supervised machine learning model trained on historical information including historical tags associated with return frauds by:
predicting a risk score of the return request based on applying the supervised machine learning model to the return item information;
determining a risk level based on the predicted risk score, wherein the risk level is a low risk level, a medium risk level, or a high risk level, and wherein the supervised machine learning model is trained to assign at least a predetermined threshold of return requests as low risk; and
transmitting the determined risk level to the automated kiosk;
in response to the determined risk level being the medium risk level, causing the automated kiosk to capture additional return item information via an imaging device, wherein the additional return item information includes at least one photo captured by the imaging device;
receiving, from the automated kiosk via the network interface, the additional return item information;
determining whether the additional return item information is valid;
in response to determining that the additional return item information is not valid, blocking electronic access to a user account associated with the return item by transmitting a first signal to a management system; and
transmitting a validation result to the automated kiosk, wherein transmitting the validation result causes the automated kiosk to display a return result, and wherein when the return result is an acceptance result, the operations further comprise causing the automated kiosk to eject a container for returning the return item; and
the automated kiosk comprises:
the imaging device;
a memory storing instructions; and
at least one processor configured to execute the instructions to automatically perform operations in real-time, the operations including:
automatically detecting a first condition, a second condition, or a third condition of the automated kiosk; and
based on automatically detecting the first condition, second condition, or third condition, automatically transmitting, via the network interface, a corresponding request to the return server,
wherein the first condition of the automated kiosk indicates that the automated kiosk has exceeded a predetermined storage capacity,
wherein the second condition of the automated kiosk indicates that automated kiosk is experiencing an electronic failure,
and wherein the third condition of the automated kiosk is automatically detected by the imaging device.