Saurabh Gupta

US Patent:

20180082355, Mar 22, 2018

Filed:

Nov 30, 2017

Appl. No.:

15/827458

Inventors:

- Armonk NY, US
Ajay A. Deshpande - White Plains NY, US
Saurabh Gupta - Irving TX, US
Arun Hampapur - Norwalk CT, US
Ali Koc - White Plains NY, US
Yingjie Li - Chappaqua NY, US
Dingding Lin - Beijing, CN
Xuan Liu - Yorktown Heights NY, US
Christopher S. Milite - Oxford CT, US
Brian L. Quanz - Yorktown Heights NY, US
Chek Keong Tan - Danbury CT, US
Dahai Xing - White Plains NY, US
Xiaobo Zheng - Shanghai, CN

International Classification:

G06Q 30/06
G06Q 10/06

Abstract:

A method for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics—to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.

US Patent:

20170206481, Jul 20, 2017

Filed:

May 13, 2016

Appl. No.:

15/154007

Inventors:

- Armonk NY, US
Ajay A. Deshpande - White Plains NY, US
Saurabh Gupta - Irving TX, US
Arun Hampapur - Norwalk CT, US
Steve Igrejas - Nashua NH, US
Ali Koc - White Plains NY, US
Yingjie Li - Chappaqua NY, US
Ding Ding Lin - Beijing, CN
Xuan Liu - Yorktown Heights NY, US
Christopher S. Milite - Oxford CT, US
Brian L. Quanz - Yorktown Heights NY, US
Vadiraja S. Ramamurthy - Allen TX, US
Sachin Sethiya - Billerica MA, US
Chek Keong Tan - Danbury CT, US
Dahai Xing - White Plains NY, US
Michael Yesudas - Plano TX, US
Xiaobo Zheng - Shanghai, CN

International Classification:

G06Q 10/06
G06N 5/04

Abstract:

A predictive engine on a computer environment comprising a shared pool of configurable computing resources is executed to perform a predictive analysis on data pipelined into the computer environment, the data received from a plurality of sources and in a plurality of different formats, the predictive engine generating a network level cost information based on the predictive analysis on a dynamic and continuous basis. Asynchronous communication comprising the network level cost information from the predictive engine is received and a set of candidate nodes for order fulfillment is generated based on the network level cost information and a defined distance between the set of candidate nodes and a target destination. An optimization engine on the computer environment is invoked that filters the set of candidate nodes. A number of fulfillment nodes that meet one or more of a constraint and preconfigured rule is output.

US Patent:

20170206592, Jul 20, 2017

Filed:

Jan 13, 2017

Appl. No.:

15/406323

Inventors:

- Armonk NY, US
Ajay A. Deshpande - White Plains NY, US
Saurabh Gupta - Irving TX, US
Arun Hampapur - Norwalk CT, US
Ali Koc - White Plains NY, US
Yingjie Li - Chappaqua NY, US
Dingding Lin - Beijing, CN
Xuan Liu - Yorktown Heights NY, US
Christopher S. Milite - Oxford CT, US
Brian L. Quanz - Yorktown Heights NY, US
Chek Keong Tan - Danbury CT, US
Dahai Xing - White Plains NY, US
Xiaobo Zheng - Shanghai, CN

International Classification:

G06Q 30/06
G06Q 10/06

Abstract:

A system, method and computer program product for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics—to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.