Abstract: Methods and systems for managing endpoint devices are disclosed. The endpoint devices may be managed by onboarding them. To onboard the endpoint devices, ownership vouchers may be configured by a current owner of the endpoint device to include configuration policies when the endpoint device is being transferred from the current owner to a subsequent owner. Such configuration policies may be included in delegation information stored in the ownership voucher that the endpoint device can use for ascertaining of the endpoint device's current and previous owners. Such configuration policies may also specify what actions the endpoint device can or cannot implement during the onboarding.

Abstract: Embodiments described herein provide systems and methods for correlating anonymous user data with in-person user sensor data. In some embodiments, a user interacts with a web interface which collects sensor data (e.g., video and audio) and stores that sensor data with other customer information (e.g., browsing history). The collected data is sent to a local interaction device which collects its own sensor data, which is compared to the data in the packets to correlate the in-person user with the online user, and continue the customer experience.

Abstract: Disclosed are techniques for modeling costs per pallet in a facility. A computer system can: receive, from a warehouse management system (WMS) and/or a refrigeration control system (RCS), time series data for a period of time for pallets in a facility, retrieve, from a data store, pallet movement data, changes in temperature data, labor usage data, and energy consumption data for each pallet amongst the pallets, determine costs per pallet over the period of time based on correlating the time series data with the retrieved data for each pallet amongst the pallets, the costs per pallet including at least one of energy costs per pallet or labor costs per pallet, determine projected costs per pallet based on modeling the costs per pallet over the period of time, the projected costs being determined for future time periods, and generate output indicating the projected costs per pallet for the future time periods.

Abstract: The present disclosure provides for using machine learning to evaluate flight demographics and predict part consumption. Historical aircraft part consumption data indicating prior consumption of aircraft parts is accessed, and historical aircraft flight demographics associated with the historical aircraft part consumption data are determined. A machine learning model is trained based on the historical aircraft part consumption data and the historical aircraft flight demographics, and the machine learning model is deployed to predict future aircraft part consumption.