Walmart Apollo, LLC

Manish Kumar Barnwal

Anindya Sankar Dey

Stephen Steel

Rahul Tripathi

Mainak Mitra

Pralabh Kumar

Nikesh K Srivasatava

Onzali Suba

Jeeban Kumar Swain

Yashas Bharadwaj

Aaron Ryan

Paul Schaefer

Ming Li

Lakshmi Bhaswanth Ramireddy

Kushal Mogathal Kumar

Shubham Sirothia

Mariana Pop

Nithya Swaminathan

An example distributed system for trip estimation is described. The system includes a plurality of sensors disposed in a plurality of geographically distributed facilities. Each of the plurality of geographically distributed facilities includes departments including retrievable items, and each one of the plurality of geographically distributed facilities includes at least one of the plurality of sensors in each of the departments. The system includes a computing system in communication with the plurality of sensors and the one or more databases. The computing system can be configured to receive an item and a source geographic location from a user device, select a subset of the geographically distributed facilities, establish a contemporaneous density of people at a particular department corresponding to the item received from the user device, and output to a user interface on the user device an estimated first transit value based on the contemporaneous density.

Distributed system for dynamic sensor-based trip estimation

In some embodiments, apparatuses and methods are provided herein useful to forecasting product demand. In some embodiments, a system comprise a forecasting control circuit to: apply each of a plurality of different models to forecast demand of a first product over a first historic period generating historic forecasted demands of the first product, wherein at least a first model uses selected one or more variables that are predicted to have an uncharacteristic effect on predicted demand; select one of the models and apply the model in generating a forecasted future demand, wherein the selection of the model is based on a difference between each of the generated historic forecasted demands and actual sales; and identify actions to modify inventory of the first product at the first shopping facility based on the forecasted future demand.

Systems and methods of utilizing multiple forecast models in forecasting customer demands for products at retail facilities

Examples of the disclosure provide a system and method for obtaining one or more current candidate resumes and one or more past candidate resumes associated with a role and analyzing full text of the obtained resumes to identify one or more items. Weighted values are determined for the identified items using a dimension reduction technique, and a probability score is calculated for each of the obtained current candidate resumes based on selection data associated with the obtained past candidate resumes. A keyword score is calculated for each of the obtained current candidate resumes based on a presence of one or more keywords associated with the role in the obtained current candidate resumes. A final candidate score is generated for each of the obtained current candidate resumes based on the keyword score and the calculated probability score, and the generated final candidate scores are output in association with the obtained current candidate resumes to a user interface.

Automated resume screening

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Burdwan, India

Manish Kumar Barnwal