Intelligent initial provisioning for user equipment with one or more subscriber identity modules

1 . One or more non-transitory computer-readable media storing one or more computer programs, the one or more computer programs configured to cause at least one processor to:

perform intelligent initial provisioning for User Equipment (UE) to select a home network for the UE by analyzing intelligent initial provisioning criteria; and

send the home network selection from the intelligent initial provisioning to the UE, wherein

the intelligent initial provisioning criteria comprise a home location of the UE and hardware and software capabilities of the UE, and

the home network selection comprises whether to provision onto the home network directly or through a third party.

2 . The one or more non-transitory computer-readable media of claim 1 , wherein the one or more computer programs are further configured to cause the at least one processor to:

receive an intelligent initial provisioning request from a management platform, the initial provisioning request comprising the hardware and software capabilities of the UE; and

use the received intelligent initial provisioning request from the management platform in the intelligent initial provisioning.

3 . The one or more non-transitory computer-readable media of claim 2 , wherein the intelligent initial provisioning request is an intent service request and the intelligent initial provisioning comprises:

executing an intent service comprising a data gathering service and a rules engine service; and

outputting the home network selection.

4 . The one or more non-transitory computer-readable media of claim 3 , wherein

the intent service request comprises an address, UE device details, and Subscriber Identity Module (SIM) details.

5 . The one or more non-transitory computer-readable media of claim 3 , wherein

the data gathering service returns an economic area, a UE device capability, and a network coverage list, and

the rules engine service returns the home network selection.

6 . The one or more non-transitory computer-readable medium of claim 1 , wherein the intelligent initial provisioning criteria further comprise phone number eligibility for Mobile Network Operators (MNOs), average signal strengths of the MNO networks in the home location of the UE, average congestion over time on the MNO networks, a number or percentage of subscribers of an entity performing the intelligent initial provisioning that are on each of the MNO networks, cost criteria, subscription criteria, contract criteria between the entity performing the intelligent initial provisioning and the MNOs, policy criteria, a highest service quality among the MNOs, a lowest voice and Short Message Service (SMS) service cost among the MNOs, forecasts of future traffic on the MNO networks, canary factors, input from artificial intelligence (AI)/machine learning (ML) models, or any combination thereof.

7 . The one or more non-transitory computer-readable media of claim 1 , wherein

the intelligent initial provisioning criteria are weighted, and

the intelligent initial provisioning comprises applying a scoring system to the weighted intelligent initial provisioning criteria for each MNO network in the home location of the UE.

8 . The one or more non-transitory computer-readable media of claim 7 , wherein the one or more computer programs are further configured to cause the at least one processor to:

override the home network selection from the intelligent initial provisioning based on override criteria.

9 . The one or more non-transitory computer-readable media of claim 8 , wherein the override criteria comprise a number or percentage of subscribers required to meet contractual obligations with a Mobile Network Operator (MNO), a number or percentage of subscribers required in an MNO network based on canary factors, or an MNO falling below a network performance threshold based on maximum tolerated congestion or a minimum acceptable signal strength.

10 . The one or more non-transitory computer-readable media of claim 1 , wherein the home network selection comprises a home network for voice and Short Message Service (SMS) services and another home network for data services.

11 . The one or more non-transitory computer-readable media of claim 1 , wherein the one or more computer programs are further configured to cause the at least one processor to:

train one or more artificial intelligence (AI)/machine learning (ML) models using signal strength data, location data, congestion data, contractual data for one or more contracts between an entity performing the intelligent initial provisioning and one or more Mobile Network Operators (MNOs), or any combination thereof;

deploy the one or more trained AI/ML models; and

use the one or more AI/ML models as part of the intelligent initial provisioning.

12 . A computer-implemented method, comprising:

receiving an intelligent initial provisioning request from a management platform, by one or more computing systems, the initial provisioning request comprising hardware and software capabilities of User Equipment (UE);

performing intelligent initial provisioning for the UE to select a home network for the UE by analyzing intelligent initial provisioning criteria using the received intelligent initial provisioning request from the UE, by the one or more computing systems; and

sending the home network selection from the intelligent initial provisioning to the UE, by the one or more computing systems, wherein

the intelligent initial provisioning criteria comprise a home location of the UE and hardware and software capabilities of the UE, and

the home network selection comprises whether to provision onto the home network directly or through a third party.

13 . The computer-implemented method of claim 12 , wherein the intelligent initial provisioning request is an intent service request and the intelligent initial provisioning comprises:

executing an intent service comprising a data gathering service and a rules engine service; and

outputting the home network selection.

14 . The computer-implemented method of claim 13 , wherein the intent service request comprises an address, UE device details, and Subscriber Identity Module (SIM) details.

15 . The computer-implemented method of claim 13 , wherein the data gathering service returns an economic area, a UE device capability, and a network coverage list, and the rules engine service returns the home network selection.

16 . The computer-implemented method of claim 12 , wherein the intelligent initial provisioning criteria further comprise phone number eligibility for Mobile Network Operators (MNOs), average signal strengths of the MNO networks in the home location of the UE, average congestion over time on the MNO networks, a number or percentage of subscribers of an entity performing the intelligent initial provisioning that are on each of the MNO networks, cost criteria, subscription criteria, contract criteria between the entity performing the intelligent initial provisioning and the MNOs, policy criteria, a highest service quality among the MNOs, a lowest voice and Short Message Service (SMS) service cost among the MNOs, forecasts of future traffic on the MNO networks, canary factors, input from artificial intelligence (AI)/machine learning (ML) models, or any combination thereof.

17 . The computer-implemented method of claim 12 , further comprising:

overriding the home network selection from the intelligent initial provisioning, by the one or more computing systems, based on override criteria, wherein

the override criteria comprise a number or percentage of subscribers required to meet contractual obligations with a Mobile Network Operator (MNO), a number or percentage of subscribers required in an MNO network based on canary factors, or an MNO falling below a network performance threshold based on maximum tolerated congestion or a minimum acceptable signal strength.

18 . The computer-implemented method of claim 12 , further comprising:

training one or more artificial intelligence (AI)/machine learning (ML) models, by the one or more computing systems, using signal strength data, location data, congestion data, contractual data for one or more contracts between an entity performing the intelligent initial provisioning and one or more Mobile Network Operators (MNOs), or any combination thereof;

deploying the one or more trained AI/ML models, by the one or more computing systems; and

using the one or more AI/ML models as part of the intelligent initial provisioning, by the one or more computing systems.

19 . One or more computing systems, comprising:

memory storing computer program instructions; and

at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause at least one processor to:

receive an intelligent initial provisioning request from a management platform, the initial provisioning request comprising hardware and software capabilities of User Equipment (UE),

perform intelligent initial provisioning for the UE to select a home network for the UE by analyzing intelligent initial provisioning criteria using the received intelligent initial provisioning request from the UE, and

send the home network selection from the intelligent initial provisioning to the UE, wherein

the intelligent initial provisioning criteria comprise a home location of the UE and hardware and software capabilities of the UE,

the intelligent initial provisioning request is an intent service request, and

the intelligent initial provisioning comprises executing an intent service comprising a data gathering service and a rules engine service.

20 . The one or more computing systems of claim 19 , wherein the intelligent initial provisioning criteria further comprise phone number eligibility for Mobile Network Operators (MNOs), average signal strengths of the MNO networks in the home location of the UE, average congestion over time on the MNO networks, a number or percentage of subscribers of an entity performing the intelligent initial provisioning that are on each of the MNO networks, cost criteria, subscription criteria, contract criteria between the entity performing the intelligent initial provisioning and the MNOs, policy criteria, a highest service quality among the MNOs, a lowest voice and Short Message Service (SMS) service cost among the MNOs, forecasts of future traffic on the MNO networks, canary factors, input from artificial intelligence (AI)/machine learning (ML) models, or any combination thereof.