7-Eleven, Inc.

Nicholas E Evangelopoulos

A data prediction subsystem stores hourly event potential data indicating an expected amount of removal events as a function of time of day. Based at least in part on event data, it is determined that a first item associated with a first location has an empty status at a time after a start of a day. For the day, an anticipated event value is determined for the first item at the first location. Using the anticipated event value and the hourly event potential data, a time-adjusted event value is determined. Based at least in part on the time-adjusted event value, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

Proactive request communication system with improved data prediction with time of day adjustment

A data prediction subsystem receives event data indicating an amount of an item removed from each of a plurality of locations over a previous period of time. For each location, prediction data is determined using the event data. The prediction data includes, for each day over a future period of time, a non-integer value indicating an anticipated amount of the item that will be removed from the location. An improved rounding process is used to round the prediction value for each day. The resulting prediction data is used to proactively request items with improved communication and computational efficiency.

Data prediction and proactive request system using artificial intelligence

A data prediction subsystem stores event data indicating amounts of items removed from locations over a previous period of time and event-to-status transition rules for each location. An event is detected at a first location. The detected event is associated with a change in status of a first item. Based on the detected event and the event-to-status transition rules, an anticipated item status is determined for the first item, indicating whether the first item is believed to be present at the first location at a time during the previous period of time of the event data. Based at least in part on the anticipated item status for the first item, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

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Denton, TX, United States of America

Nicholas E Evangelopoulos