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US 12,705,633 B2Granted
Contextually aware devices and machine learning-driven optimization systems
AssigneeToshiba Global Commerce Solutions, Inc.
InventorsAkshay Kumar Pandit, Manda Miller, Kristen M. Chung, Albert D. Bennah, Ashley Wehr
Life of this patent
Estimating the term…
Abstract
Techniques for intelligent system optimization are provided. Capacity data of one or more exit devices at a physical location is received. Demand data that indicates a current demand for exit service at the physical location is received from one or more monitoring devices. A decision is generated based on the capacity data and the demand data using a machine learning (ML) model. Commands corresponding to the decision are transmitted to the one or more exit devices for implementation.
Claims18
A method comprising:
- receiving, by one or more processors, capacity data of one or more exit devices at a physical location;
- receiving, by one or more processors, demand data from one or more monitoring devices that indicates a current demand for exit service at the physical location;
- monitoring, by one or more processors, an efficiency metric, wherein the efficiency metric represents a speed at which one of the exit devices generates a weight for an item and an accuracy level of the weight;
- processing, by one or more processors, the capacity data and the demand data using a machine learning (ML) model to identify correlations between the capacity data and the demand data;
- determining, by one or more processors, using the machine learning (ML) model, the efficiency metric satisfies a threshold;
- responsive to identifying the correlations between the capacity data and the demand data and determining the efficiency metric satisfies the threshold, generate a control output configured to drive one or more corrective actions;
- transmitting the control output configured to drive the one or more corrective actions to the one or more exit devices for execution:
- responsive to transmitting the control output configured to drive the one or more corrective actions, executing, by one or more processors, the one or more corrective actions at the one or more exit devices, wherein executing the one or more corrective actions at the one or more exit devices comprises:
- automatically rebooting the one or more exit devices and automatically recalibrating a scale associated with the one or more exit devices.
6 more elements
A system comprising:
- one or more memories collectively storing computer-executable instructions;
- and
- one or more processors configured to collectively execute the computer-executable instructions and cause the system to:
- receive capacity data of one or more exit devices at a physical location;
- receive demand data from one or more monitoring devices that indicates a current demand for exit service at the physical location;
- monitor an efficiency metric, wherein the efficiency metric represents a speed at which one of the exit devices generates a weight for an item and an accuracy level of the weight;
- process the capacity data and the demand data using a machine learning (ML) model to identify correlations between the capacity data and the demand data;
- determine, using the machine learning (ML) model, the efficiency metric satisfies a threshold;
- responsive to identifying the correlations between the capacity data and the demand data and determining the efficiency metric satisfies the threshold, generate a control output configured to drive one or more corrective actions;
- transmit the control output configured to drive the one or more corrective actions to the one or more exit devices for execution:
- responsive to transmitting the control output configured to drive the one or more corrective actions, executing, by one or more processors, the one or more corrective actions at the one or more exit devices, wherein executing the one or more corrective actions at the one or more exit devices comprises:
- automatically rebooting the one or more exit devices and automatically recalibrating a scale associated with the one or more exit devices.
9 more elements
A computer program product, comprising:
- computer-readable storage mediums having computer-readable program code executable to cause the computer program product to:
- receive capacity data of one or more exit devices at a physical location;
- receive demand data from one or more monitoring devices that indicates a current demand for exit service at the physical location;
- monitor an efficiency metric, wherein the efficiency metric represents a speed at which one of the exit devices generates a weight for an item and an accuracy level of the weight;
- process the capacity data and the demand data using a machine learning (ML) model to identify correlations between the capacity data and the demand data;
- determine, using the machine learning (ML) model, the efficiency metric satisfies a threshold;
- responsive to identifying the correlations between the capacity data and the demand data and determining the efficiency metric satisfies the threshold, generate a control output configured to drive one or more corrective actions;
- transmit the control output configured to drive the one or more corrective actions to the one or more exit devices for execution:
- responsive to transmitting the control output configured to drive the one or more corrective actions, executing, by one or more processors, the one or more corrective actions at the one or more exit devices, wherein executing the one or more corrective actions at the one or more exit devices comprises:
- automatically rebooting the one or more exit devices and automatically recalibrating a scale associated with the one or more exit devices.
7 more elements
Dependencies are inferred from the claim text.