Amazon Technologies, Inc.

Hersh Nagar

Srinivasan Sundar Raghavan

Douglas Scott Cerny

Neha Goswami

Santosh Vasant Bhosale

Stephanie Weeks

Satya S Mishra

Daniel Micol-Ponce

Vikash Kumar Jain

Brandon Le

Noelia Moron

Kian Leong Ong

Steven David Kann

Jaime Vallori

Abhinav Kasliwal

Vamsi Krishna Vutukuru

Manish Kumar

Vijay Chaudhari

Vijaya Prasad Reddy Avula

Ankur Anand

Yi Lu

A customer eProcurement system enables a blanket purchase order on a supplier website to establish a budget, which buyers can use without the need to take their cart back to the customer eProcurement system. The budget value for a buyer or a group of buyers allows them to perform their buying functions without the need for further internal processing by the customer system so long as purchases are within the budget. The blanket purchase order allows buyers to place the orders directly on the supplier website, while at the same time following policies of the customer eProcurement system and allows easier order reconciliation within the customer system.

Budget-based approval-less ordering for punchout users in an ecommerce marketplace

Devices and techniques are generally described for dynamic policy determination using machine learning. In some examples, first goal data may be received. A first machine learning model may generate a first computer-executable policy based at least in part on the first goal data. In some examples, a first search query may be received. In various examples, the first search query may be modified by the first computer-executable policy to generate a modified search query. In some examples, first feedback data related to user interaction with search results of the modified search query may be determined. In various examples, at least one parameter of the first machine learning model may be updated based at least in part on the first feedback data to generate a second machine learning model.

Machine learning system for dynamic generation of computer-implemented policies

Devices and techniques are generally described for a speech processing procurement system. In various examples, recommendations may be provided by back-end systems. The recommendations may be ranked by a machine learning model, and may be sorted and filtered for a particular output device. Various voice guidance workflows may be invoked through user interaction with a speech processing procurement system. The workflows may be used to surface pre-recorded voice memos and/or to determine recommendations based on dynamically updated procurement policies.

Speech processing procurement system

Growing community of inventors

Snoqualmie, WA, United States of America

Hersh Nagar