7-eleven, Inc.

Xinan Wang

Shahmeer Ali Mirza

Kyle Dalal

Sailesh Bharathwaaj Krishnamurthy

Crystal Maung

Matthew Raymond Magee

Matthew O'Daniel Redmond

John Robert Keller

Tejas Pradip Rode

Fahad Mirza

A system includes a door sensor that provides a status of whether a door of a refrigeration system is open or closed, a temperature sensor that measures a temperature of a food compartment of the refrigeration system, a power sensor that measures an amount of power consumed by the refrigeration system, and a compressor sensor that provides acoustic data about a compressor of the refrigeration system. The system further includes a remote computing system configured to send an alert indicating that the refrigeration system needs servicing when the temperature of the food compartment of the refrigeration system is determined to be above a predetermined temperature while: the door of the refrigeration system is closed, the amount of power consumed by the refrigeration system is within a predetermined power range, and acoustic signals of the compressor of the refrigeration system are within a predetermined acoustic range.

Systems and methods for monitoring refrigeration systems

An image sensor is positioned such that a field-of-view of the sensor encompasses portion of a beverage machine. The field-of-view includes a zone associated with operating the beverage machine. A subsystem receives a signal indicating beverage is dispensed and angled-view images from the image sensor. First image(s) associated with a start of beverage being dispensed are determined by detecting a hand of a person entering the zone associated with operating the beverage machine. A first identifier is determined for the person in the first image(s). Second image(s) associated with an end of the beverage being dispensed are determined by detecting a hand of the person exiting the zone. A second identifier is determined for the person in the second image(s). If the first identifier is the same as the second identifier, the beverage is assigned to the person with the first identifier.

Self-serve beverage detection and assignment

An image sensor is positioned such that a field-of-view of the sensor encompasses portion of a beverage machine. The field-of-view includes a first zone associated with operating the beverage machine to dispense the beverage and a second zone in which a cup is placed to receive the beverage. A beverage assignment subsystem receives angled-view images from the image sensor. An event associated with an object entering one or both of the first zone and the second zone is detected. In image(s) associated with a start of the event, it is determined that both a hand of a person enters the first zone and the cup is placed in the second zone. If the cup remained in the second zone for at least a threshold time, the beverage is assigned to the person whose hand entered the first zone.

Image-based self-serve beverage detection and assignment

An apparatus includes a memory and processor. The memory stores a machine learning algorithm configured to decide whether to use a virtual shopping cart to verify all or a portion of a transaction performed with an algorithmic shopping cart. The processor receives feedback for a decision made by the algorithm, indicating whether the algorithmic and virtual carts match. The processor assigns a reward to the feedback. A first positive reward is assigned when the virtual shopping cart is not used for verification, and the feedback indicates that the carts match. A second positive reward is assigned when the virtual cart is used for verification, and the feedback indicates that the carts do not match. A first negative reward is assigned when the virtual shopping cart is not used for verification, and the feedback indicates that the carts do not match. A second negative reward is assigned when the virtual cart is used for verification, and the feedback indicates that the carts match. The processor uses the reward to update the algorithm.

Machine learning algorithm trained to identify algorithmically populated shopping carts as candidates for verification

A system includes a weight sensor and a weight server. The weight sensor generates a signal indicative of a weight of at least one of an item. The weight server detects an event corresponding to a weight change on the weight sensor when a quantity of the item is removed from the weight sensor. The weight server determines the item quantity by calculating a result of dividing the weight change over a unit weight of the item. If the result is within a threshold range from an integer that is closest to the result, the weight server determines that a quantity of the item with the amount of the integer is removed from the weight sensor. If the result is not within the threshold range from the integer, the weight server uses weight change patterns of historically observed signals to determine item quantity that was removed from the weight sensor.

Adaptive item counting algorithm for weight sensor using sensitivity analysis of the weight sensor

An apparatus includes a memory and processor. The memory stores a machine learning algorithm configured to decide between using an algorithmic and a virtual cart to process a transaction. The processor receives feedback for a decision made by the algorithm, indicating whether the algorithmic and virtual carts match. The processor assigns a reward to the feedback. A first positive reward is assigned when the algorithmic cart is selected, and the feedback indicates that the carts match. A second positive reward is assigned when the virtual cart is selected, and the feedback indicates that the carts do not match. A first negative reward is assigned when the algorithmic cart is selected, and the feedback indicates that the carts do not match. A second negative reward is assigned when the virtual cart is selected, and the feedback indicates that the carts match. The processor uses the reward to update the algorithm.

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Dallas, TX, United States of America

Xinan Wang