Data platform for a network connected dispensing device

Patent metadata

Field	Value
Publication number	US-10692323-B2
Application number	US-201615169522-A
Country	US
Kind code	B2
Filing date	May 31, 2016
Priority date	May 31, 2016
Publication date	Jun 23, 2020
Grant date	Jun 23, 2020

Abstract

An apparatus may receive data. The data may include first data and second data. The first data may be received from a set of sensor devices located within one or more dispensing devices. The second data may be received from one or more devices located external to the one or more dispensing devices. The apparatus may process the data to generate a model. The apparatus may receive sensor data. The apparatus may determine a response action associated with the particular dispensing device based on the sensor data and the model. The apparatus may provide information associated with causing the response action to be implemented.

First claim

What is claimed is:

1. A method comprising:
receiving, by an apparatus, data,
the data including first data and second data,
the first data being received from a plurality of sensor devices located within one or more dispensing devices,
the second data being received from one or more devices located external to the one or more dispensing devices;

processing, by the apparatus, the data to generate a predictive model,
the predictive model providing a value indicating a likelihood, based on the data, that a compressor component associated with the data is likely to fail;

receiving, by the apparatus, sensor data,
the sensor data relating to a particular dispensing device of the one or more dispensing devices;

determining, by the apparatus and based on the sensor data and the predictive model, that the particular dispensing device is associated with a particular value indicating a particular likelihood of compressor component failure;
determining, by the apparatus, that the particular value satisfies a threshold likelihood of failure;
determining, by the apparatus, a response action associated with a compressor controller component of the particular dispensing device based on determining that the particular value satisfies the threshold likelihood of failure,
the response action being associated with altering a configuration of the compressor controller component of the particular dispensing device; and

causing, by the apparatus, the response action to be implemented.

2. The method of claim 1 ,
where determining the response action comprises:
identifying a set of locations corresponding to a set of user devices associated with a set of technicians;
selecting a particular technician, of the set of technicians, based on a corresponding location of the set of locations; and
determining the response action to cause an alert to be transmitted to a particular user device, of the set of user devices, associated with the particular technician.

3. The method of claim 1 , further comprising:
determining, based on the predictive model, that satisfaction of a threshold internal temperature of the one or more dispensing devices corresponds to a threshold quantity of items of product being dispensed;
determining, based on the sensor data, that an internal temperature for the particular dispensing device does not satisfy the threshold internal temperature; and
where determining the response action comprises:
determining that the internal temperature for the particular dispensing device is to be altered based on determining that the internal temperature for the particular dispensing device does not satisfy the threshold internal temperature.

4. The method of claim 1 , where causing the response action to be implemented comprises:
providing information to the particular dispensing device to cause the particular dispensing device to alter the configuration of the compressor controller component of the particular dispensing device.

5. The method of claim 1 , where causing the response action to be implemented comprises:
providing information to a user device to cause information to be provided for display via the user device.

6. The method of claim 1 , further comprising:
determining that the particular dispensing device has been moved from a first location to a second location based on the sensor data,
the second location being at least a threshold distance from the first location;

obtaining information identifying a set of locations of a set of user devices based on determining that the particular dispensing device has been moved,
the set of user devices being utilized by a set of inspectors;

selecting a particular inspector, of the set of inspectors based on a corresponding location of a user device, of the set of user devices, associated with the particular inspector; and
transmit information to the user device identifying the second location to dispatch the particular inspector to inspect the particular dispensing device.

7. The method of claim 1 , further comprising:
generating a user interface,
the user interface including a first set of user interface elements associated with providing information associated with the sensor data and the predictive model,
the user interface including a second set of user interface elements associated with receiving one or more interactions associated with selecting the response action;

providing the user interface for display;
detecting an interaction, of the one or more interactions, with the second set of user interface elements,
the interaction being associated with selecting the response action; and

where determining the response action comprises:
determining the response action based on detecting the interaction with the second set of user interface elements.

8. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to:
receive, via a network connection to a vending device, information identifying sensor data relating to the vending device,
the sensor data being associated with identifying a status of a compressor component of the vending device;

determine, based on a predictive model related to a group of vending devices and based on the sensor data, a value indicating a likelihood that the compressor component will fail within a particular period of time;
determine that the value satisfies a threshold likelihood of failure;
determine, based on the value satisfying the threshold likelihood of failure and the sensor data, an alteration to a configuration of the vending device,
the alteration being associated with a compressor controller component associated with the compressor component; and

cause the alteration to the configuration of the vending device based on determining the alteration to the configuration of the vending device.

9. The non-transitory computer-readable medium of claim 8 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
receive data from the group of vending devices,
the data relating to a set of sensors integrated into the group of vending devices;

process the data based on a processing technique to generate the predictive model; and
where the one or more instructions, that cause the one or more processors to receive the information identifying the sensor data relating to the vending device, cause the one or more processors to:
receive the information identifying the sensor data after processing the data to generate the predictive model.

10. The non-transitory computer-readable medium of claim 9 , where the one or more instructions, that cause the one or more processors to process the data to generate the predictive model, cause the one or more processors to:
process the data based on a particular processing technique,
the particular processing technique including at least one of:
a machine learning technique,
a heuristic technique,
an image processing technique,
a regression technique, or
an optimization technique.

11. The non-transitory computer-readable medium of claim 8 , where the one or more instructions, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
obtain data relating to a technician,
the data including location data regarding a user device associated with the technician, traffic data relating to a traffic condition, or inventory data relating to an inventory of components stored via a vehicle associated with the technician;

select the te

Assignees

• Accenture Global Solutions Ltd

Inventors

• Torresani Giorgio
• Sawyer Jeffrey Scott
• Sawhney Puneet
• Groves Austin S
• PATEL Drashti Sunit

Classifications

• G07F9/009

  User recognition or proximity detection · CPC title

• G07F9/002 Primary

  Vending machines being part of a centrally controlled network of vending machines · CPC title

• G07F9/001

  Interfacing with vending machines using mobile or wearable devices · CPC title

• G06Q10/087

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06Q10/06311

  Scheduling, planning or task assignment for a person or group · CPC title