公开(公告)号：US20220414736A1

公开(公告)日：2022-12-29

申请号：US17359353

申请日：2021-06-25

Applicant: eBay Inc.

Inventor： Ethan Benjamin Rubinson ,  Mark Jeffrey Weinberg ,  Senthil Kumar Padmanabhan ,  Parin Pankaj Jogani

IPC: G06Q30/06 ,  G06F16/248

Abstract: Various methods and systems for providing customized-item-specific interfaces of items on extended interface devices in a search system. A plurality of items—each having an extended interface configuration that indicates customized-item-specific interface instructions for extended presentation of items on extended interface devices—are accessed at a primary interface device associated with a set of extended interface devices including a second-user extended interface device that supports a social mode. Based on the first extended interface configuration of a first item, a determination is made that the first item is extendable to a first extended interface device in the set of extended interface devices. Based on determining that the first item is extendable, automatically causing generation of a customized-item-specific interface of the first item on the first extended interface device. Causing generation of the customized-item-specific interface can be based on communicating item extended interface data to the first extended interface device.

公开(公告)号：US20220414735A1

公开(公告)日：2022-12-29

申请号：US17359361

申请日：2021-06-25

Applicant: eBay Inc.

Inventor： Ethan Benjamin Rubinson ,  Mark Jeffrey Weinberg ,  Senthil Kumar Padmanabhan ,  Parin Pankaj Jogani

IPC: G06Q30/06 ,  G06N20/00

Abstract: Various methods and systems for providing predicted item conditions for items in an item listing system. A predicted item condition may indicate a calculated estimate of a descriptive state of the item based on item transaction features. Operationally, item transaction features of an item—associated with an item listing interface—are accessed at the item listing system. The item transaction features are communicated to an item condition prediction machine learning model of the item listing system. The item condition machine learning model is trained on historical item transactions comprising item condition features of historical item transactions, the historical item transactions are previous item transactions associated with the item listing system. Based on the item transaction features of the item, the item condition machine learning model is caused to generate a predicted item condition. The predicted item condition is communicated as a recommended item condition or required item condition.