Systems and methods for troubleshooting equipment installation using artificial intelligence
The disclosure is directed to, among other things, systems and methods for troubleshooting equipment installations using machine learning. Particularly, the systems and methods described herein may be used to validate an installation of one or more devices (which may be referred to as “customer premises equipment (CPE)” herein as well) at a given location, such as a customer's home or a commercial establishment. As one non-limiting example, the one or more devices may be associated with a fiber optical network, and may include a modem and/or an optical network terminal (ONT). However, the one or more devices may include any other types of devices associated with any other types of networks as well.
1. A system comprising:
a processor; and
a memory storing computer-executable instructions, that when executed by the processor, cause the processor to:
receive, from a mobile device and by a machine learning system, image data of a customer premises equipment (CPE);
determine, using the machine learning system and based on the image data of the CPE, that a setting associated with the CPE is disabled;
automatically send, based on the determination that the setting is disabled, a signal to enable the setting on the CPE;
send, to the mobile device, a request for second image data of the CPE;
receive, by the machine learning system, the second image data of the CPE from the mobile device;
determine, by the machine learning system and based on the second image data of the CPE, that the setting is enabled; and
send, to the mobile device, a confirmation that the setting is enabled.
2. The system of claim 1 , wherein the computer-executable instructions further cause the processor to:
determine, by the machine learning, that the image data fails to meet a threshold quality level or fails to include the CPE;
send, to the mobile device, an indication to send third image data of the CPE;
receive the third image data from the mobile device;
determine that the third image data meets the threshold quality level or includes the CPE; and
send, to the mobile device, an indication that the third image data meets the threshold quality level or includes the CPE.
3. The system of claim 1 , wherein the computer-executable instructions further cause the processor to:
send, to the mobile device and by the machine learning system, a request for third image data including a component of the CPE;
receive the third image data from the mobile device; and
determine that the third image data includes the component of the CPE.
4. The system of claim 1 , wherein the computer-executable instructions further cause the processor to:
determine, by the machine learning system and based on the image data of the CPE, that a component of the CPE is incorrectly installed;
send, to the mobile device, an indication that the component of the CPE is incorrectly installed and a request for the mobile device to provide third image data of the CPE;
receive the third image data of the CPE; and
determine, based on the third image data of the CPE, that the component is correctly installed.
5. The system of claim 1 , wherein the computer-executable instructions further cause the processor to:
send, prior to receiving the image data from the mobile device, a signal to the CPE;
receive a response signal from the CPE; and
send, prior to receiving the image data from the mobile device and based on the response signal from the CPE, a request for the mobile device to capture image data of the CPE.
6. The system of claim 1 , wherein the computer-executable instructions further cause the processor to:
send, prior to receiving the image data from the mobile device, a signal to the CPE;
determine that a response signal is not received from the CPE; and
send, prior to receiving the image data from the mobile device and based on the response signal not being received from the CPE, a request for the mobile device to capture image data of the CPE.
7. The system of claim 1 , wherein the CPE is a modem or an optical network terminal (ONT), and wherein the setting is an Ethernet wide area networking (EWAN) mode.
8. A method comprising:
sending, by a mobile device and to a machine learning system, image data of a customer premises equipment (CPE);
receiving, by the mobile device and from the machine learning system, an indication that a component of the CPE has been incorrectly installed and a request for second image data of the CPE;
presenting, on a user interface of the mobile device, a visual indication of a portion of the CPE to capture in the second image data;
sending, by the mobile device and to the machine learning system, the second image data; and
receiving, by the mobile device and from the machine learning system, an indication that the component of the CPE has been correctly installed.
9. The method of claim 8 , further comprising:
receiving, by the mobile device, an indication that the image data fails to meet a threshold quality level or fails to include the CPE, and for the mobile device to provide third image data of the CPE;
sending the third image data to the machine learning system; and
receiving, by the mobile device, an indication that the third image data meets the threshold quality level or includes the CPE.
10. The method of claim 8 , further comprising:
receiving, from the machine learning system, a request for third image data including a component of the CPE; and
sending the third image data to the machine learning system.
11. The method of claim 8 , further comprising:
receiving, by the mobile device and based on a determination that a setting of the CPE is disabled, a request for second image data of the CPE; and
sending the second image data of the CPE to the machine learning system; and
receiving, by the mobile device and from the machine learning system, a confirmation that a setting is enabled on the CPE.
12. The method of claim 8 , further comprising:
receiving, prior to sending the image data to the machine learning system, a request to capture image data of the CPE.
13. The method of claim 8 , wherein the CPE is a modem or an optical network terminal (ONT).
14. A non-transitory computer-readable medium storing computer-executable instructions, that when executed by at least one processor, cause the at least one processor to:
receive, from a mobile device and by a machine learning system, image data of a customer premises equipment (CPE);
determine, using the machine learning system, that a component of the CPE is incorrectly installed;
send, to the mobile device, an indication of a change to make to the CPE;
request second image data of the customer premises equipment (CPE);
receive from the mobile device, the image data of the customer premises equipment (CPE);
determine, using the second image data of the customer premises equipment (CPE), that the component has been correctly installed; and
train the machine learning system using the image data and the second image data.
15. The non-transitory computer-readable medium of claim 14 , wherein the computer-executable instructions further cause the at least one processor to:
determine, using the machine learning system and based on the image data of the CPE, that a setting associated with the CPE is disabled;
automatically send, based on the determination that the setting is disabled, a signal to enable the setting on the CPE;
send, to the mobile device, a request for second image data of the CPE;
receive the second image data of the CPE from the mobile device;
determine, based on the second image data of the CPE, that the setting is enabled; and
send, to the mobile device, a confirmation that the setting is enabled.
16. The non-transitory computer-readable medium of claim 15 , wherein the CPE is a modem or an optical network terminal (ONT), and wherein the setting is an Ethernet wide area networking (EWAN) mode.
17. The non-transitory computer-readable medium of claim 14 , wherein the computer-executable instructions further cause the at least one processor to:
determine that the image data fails to meet a threshold quality level or fails to include the CPE;
send, to the mobile device, an indication for the mobile device to provide third image data of the CPE;
receive the third image data from the mobile device; and
determine that the third image data meets the threshold quality level or includes the CPE; and
send, to the mobile device, an indication that the third image data meets the threshold quality level or includes the CPE.
18. The non-transitory computer-readable medium of claim 14 , wherein the computer-executable instructions further cause the at least one processor to:
send, to the mobile device, a request for third image data including a component of the CPE;
receive the third image data from the mobile device; and
determine that the third image data includes the component of the CPE.
19. The non-transitory computer-readable medium of claim 14 , wherein the computer-executable instructions further cause the at least one processor to:
send, prior to receiving the image data from the mobile device, a signal to the CPE;
receive a response signal from the CPE; and
send, prior to receiving the image data from the mobile device and based on the response signal from the CPE, a request for the mobile device to capture image data of the CPE.
20. The non-transitory computer-readable medium of claim 14 , wherein the computer-executable instructions further cause the at least one processor to:
send, prior to receiving the image data from the mobile device, a signal to the CPE;
determine that a response signal is not received from the CPE; and
send, prior to receiving the image data from the mobile device and based on the response signal not being received from the CPE, a request for the mobile device to capture image data of the CPE.