Zi Heng Wang

20220207478, Jun 30, 2022

Dec 29, 2020

17/136358

- San Francisco CA, US
Lizhu Zhang - San Francisco CA, US
Lei Kang - San Francisco CA, US
Zi Wang - San Francisco CA, US
Raghav Gupta - San Francisco CA, US

G06Q 10/08
G06N 20/00
G06N 5/04
G06Q 10/06

A computing system can facilitate an on-demand delivery service by receiving menu item requests and transmit corresponding order requests to menu item preparers. The system can execute one or more trained predictive models using a set of current predictive metrics for the menu item preparer to generate probability curves for driver wait time and menu item sit time against a logical cost to the on-demand delivery service. The system may then utilize the curves to determine an optimal arrival time for a selected delivery provider to pick up the menu items for delivery.

20230034221, Feb 2, 2023

Jul 29, 2021

17/389281

- San Francisco CA, US
Ji Chen - Mountain View CA, US
Zi Wang - Mountain View CA, US
Soren Zeliger - Oakland CA, US
Ganesh Krishnan - San Francisco CA, US
Wa Yuan - Alameda CA, US
Michael Scheibe - San Francisco CA, US

G06Q 30/02
G06Q 30/06
G06Q 10/06
G06N 20/00
G06N 5/04

An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.

20200409543, Dec 31, 2020

Jun 28, 2019

16/456097

- Redmond WA, US
Zi Wang - Mountain View CA, US
Fermin Simeon - San Jose CA, US
Albert Ho - Sunnyvale CA, US
Viktar Starynski - Sunnyvale CA, US
Peng Yuan - San Francisco CA, US
Giathang Dao - Fremont CA, US

G06F 3/0484
G06F 9/54
G06F 9/451

Methods, systems, and computer programs are presented for implementing configurable processing of data by an enterprise platform. One method includes an operation for receiving via an Application Programming Interface (API) of a frontend of an enterprise platform, a configuration file for configuring a custom property. Further, the method includes identifying services in a backend of the enterprise platform for implementing the custom property by the enterprise platform, the services comprising a storage service for storing values of the custom property. The method further includes operations for configuring program handlers for the custom property in the identified services, and for receiving a request, via the API of the frontend, to present a user interface (UI) for setting a value of the custom property. A presentation of the custom property is set in the UI based on the configuration of the custom property, and the UI is presented on a display.

20200402184, Dec 24, 2020

Jun 19, 2020

16/906289

- St. Louis MO, US
Anand Pramod Deshmukh - Albany CA, US
Jesse B. Grote - Boone IA, US
Hongwei Luo - St. Louis MO, US
Aviral Shukla - Defiance MO, US
Zi Wang - Chesterfield MO, US
Yiduo Zhan - Chesterfield MO, US
Hui Zhang - Ballwin MO, US
Xiaobo Zhou - Chesterfield MO, US

Monsanto Technology LLC - St. Louis MO

G06Q 50/02
A01D 41/127
G06Q 10/06

A routing processor implements a multi-stage prescriptive routing model engine based on harvest input data relating to the harvesting of crops at a plurality of locations by a plurality of combines and based on a harvest characteristic representing an attribute of the crops to be harvested by the combines. The multi-stage prescriptive routing model engine generates a combine routing program prescribing the movement of each combine between the locations and includes a demand stage configured to identify combine harvesting demand as a function of the harvest input data and the harvest characteristic and a scheduling stage configured to generate the combine routing program as a function of the harvesting demand.