WO2024097475A1 - Commutation intelligente entre modules d'identification de l'abonné (cartes sim) - Google Patents

Commutation intelligente entre modules d'identification de l'abonné (cartes sim) Download PDF

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Publication number
WO2024097475A1
WO2024097475A1 PCT/US2023/074585 US2023074585W WO2024097475A1 WO 2024097475 A1 WO2024097475 A1 WO 2024097475A1 US 2023074585 W US2023074585 W US 2023074585W WO 2024097475 A1 WO2024097475 A1 WO 2024097475A1
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WIPO (PCT)
Prior art keywords
sim
profile
switch
carrier
devices
Prior art date
Application number
PCT/US2023/074585
Other languages
English (en)
Inventor
In-Kyung Kim
Eric Hieb
Robert Urbanek
Original Assignee
Dish Wireless L.L.C.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US18/357,535 external-priority patent/US20240147216A1/en
Application filed by Dish Wireless L.L.C. filed Critical Dish Wireless L.L.C.
Publication of WO2024097475A1 publication Critical patent/WO2024097475A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/18—Selecting a network or a communication service
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/30—Security of mobile devices; Security of mobile applications
    • H04W12/37—Managing security policies for mobile devices or for controlling mobile applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/40—Security arrangements using identity modules
    • H04W12/45—Security arrangements using identity modules using multiple identity modules
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/02—Terminal devices
    • H04W88/06—Terminal devices adapted for operation in multiple networks or having at least two operational modes, e.g. multi-mode terminals

Definitions

  • the present invention generally relates to communications, and more specifically, to intelligent subscriber identity module (SIM) switching for user equipment (UE) devices.
  • SIM subscriber identity module
  • UE user equipment
  • IMSI international mobile subscriber identity
  • the SIM identifies which service provider network that the UE connects with.
  • Network service providers may provide customers with a device with a preinstalled SIM for their network, or users may insert a SIM for the network into their Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) own device.
  • the SIM may also be associated a phone number for the device.
  • a SIM may be a physical SIM (pSIM) or an embedded SIM (eSIM).
  • pSIM is a physical card that is inserted into an associated slot in the UE.
  • eSIM is a digital version of a pSIM including a profile that can be downloaded to a mobile device to provide functionalities of a pSIM.
  • 5G fifth generation
  • one or more non-transitory computer-readable media store one or more computer programs for performing intelligent SIM switching for a UE device using a current SIM or SIM profile.
  • the one or more computer programs are configured to cause at least one processor to receive instructions from a rules engine of the UE device, or from a home network and/or an intelligent subscriber management server of the UE device, to switch from the current SIM or SIM profile to a different SIM or SIM profile.
  • the rules engine is configured to determine which SIM or SIM Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) profile from a plurality of SIMs or SIM profiles that the UE device should switch to in accordance with a policy.
  • the one or more computer programs are also configured to cause at least one processor to, responsive to the instructions from the rules engine, configure the UE device for the different SIM or SIM profile and an associated carrier and switch the UE device to the other SIM or SIM profile for communications services provided by the associated carrier.
  • a computer-implemented method for performing intelligent SIM switching for a UE device using a current SIM or SIM profile includes receiving, by the UE device, instructions from a rules engine of the UE device, or from a home network and/or an intelligent subscriber management server of the UE device, to switch from the current SIM or SIM profile to a different SIM or SIM profile.
  • the rules engine is configured to determine which SIM or SIM profile from a plurality of SIMs that the UE device should switch to in accordance with a policy.
  • the computer- implemented method also includes, responsive to the instructions from the rules engine, configuring the UE device for the different SIM or SIM profile and an associated carrier, by the UE device.
  • the computer-implemented method further includes switching the UE device to the other SIM or SIM profile for communications services provided by the associated carrier, by the UE device.
  • the policy is predictive and uses deterministic logic based on observations over time, probabilistic logic of one or more artificial intelligence (AI) / machine learning (ML) models, or both.
  • a computing system includes memory storing computer program instructions for performing intelligent SIM switching for a UE device using a current SIM or SIM profile and at least one processor configured to Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) execute the computer program instructions.
  • the computer program instructions are configured to cause the at least one processor to use a trained AI/ML model for intelligent SIM switching to assist in determining a SIM or SIM profile of a plurality of SIMs or SIM profiles to switch to, by a rules engine of a home network, an intelligent subscriber management server, or the UE device.
  • the rules engine is configured to determine which SIM or SIM profile from the plurality of SIMs or SIM profiles that the UE device should switch to in accordance with a policy.
  • the computer program instructions are also configured to cause the at least one processor to receive instructions from the rules engine to switch from the current SIM or SIM profile to a different SIM or SIM profile.
  • the computer program instructions are further configured to cause the at least one processor to, responsive to the instructions from the rules engine, configure the UE device for the different SIM or SIM profile and an associated carrier, and switch the UE device to the other SIM or SIM profile for communications services provided by the associated carrier.
  • the trained AI/ML model is trained using hardware information from a plurality of UE devices, connection information from the plurality of UE devices, network congestion information, information pertaining to upcoming events, or any combination thereof.
  • an intelligent subscriber management server includes memory storing computer program instructions configured to facilitate roaming intelligent SIM switching for a plurality of UE devices and at least one processor configured to execute the computer program instructions.
  • the computer program instructions are configured to cause the at least one processor to monitor traffic from the plurality of UE devices routed through home network infrastructure via a radio Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) access network (RAN) of the home network infrastructure, or routed through the home network infrastructure from carrier network infrastructure of another carrier.
  • the computer program instructions are also configured to cause the at least one processor to determine that a UE device of the plurality of UE devices should switch from switch from a current SIM or SIM profile to a different SIM or SIM profile in accordance with a policy.
  • the computer program instructions are further configured to cause the at least one processor to instruct the UE device of the plurality of UE devices to switch to the different SIM or SIM profile.
  • a computer-implemented method for facilitating roaming intelligent SIM switching for a plurality of UE devices includes monitoring, by an intelligent subscriber management server, traffic from the plurality of UE devices routed through home network infrastructure via a RAN of the home network infrastructure, or routed through the home network infrastructure from carrier network infrastructure of another carrier.
  • the computer-implemented method also includes determining, by the intelligent subscriber management server, that a UE device of the plurality of UE devices should switch from switch from a current SIM or SIM profile to a different SIM or SIM profile in accordance with a policy.
  • the computer- implemented method further includes instructing the UE device of the plurality of UE devices to switch to the different SIM or SIM profile, by the intelligent subscriber management server.
  • a non-transitory computer-readable medium stores a computer program for facilitating roaming intelligent SIM switching for a plurality of UE devices.
  • the computer program is configured to cause at least one Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) processor to monitor traffic from the plurality of UE devices routed through home network infrastructure via a RAN of the home network infrastructure, or routed through the home network infrastructure from carrier network infrastructure of another carrier.
  • P2022-11-32.PCT Attorney Docket No. 1687.0008PCT
  • the computer program is also configured to cause the at least one processor to determine that a UE device of the plurality of UE devices should switch from switch from a current SIM or SIM profile to a different SIM or SIM profile in accordance with a policy taking into account locations of the plurality of UE devices, hardware capabilities of the plurality of UE devices, compatibility of the plurality of UE devices with carrier networks associated with a plurality of SIMs or SIM profiles, services that the plurality of UE devices are configured to use, carrier networks that are available in respective locations of the plurality of UE devices, network quality criteria, subscription criteria, cost criteria, or any combination thereof.
  • the computer program is further configured to cause the at least one processor to instruct the UE device of the plurality of UE devices to switch to the different SIM or SIM profile.
  • the policy is predictive and uses deterministic logic based on observations over time, probabilistic logic of one or more artificial intelligence (AI) / machine learning (ML) models, or both, and/or the computer program is configured to determine the policy based on the one or more AI/ML models.
  • AI artificial intelligence
  • ML machine learning
  • FIG. 1A is an architectural diagram illustrating carrier networks configured to implement intelligent SIM switching, according to an embodiment of the present invention.
  • FIG. 1B is an architectural diagram illustrating carrier networks configured to implement intelligent SIM switching in a roaming scenario, according to an embodiment of the present invention.
  • FIG. 2 illustrates the mobile device with N SIMs notionally depicted, according to an embodiment of the present invention.
  • FIG.3 is a flow diagram illustrating a process for configuring a UE device to switch to a different SIM, according to an embodiment of the present invention.
  • FIG. 4 is a flow diagram illustrating a process for intelligent SIM switching, according to an embodiment of the present invention.
  • FIG. 5 is a flow diagram illustrating another process for intelligent SIM switching, according to an embodiment of the present invention.
  • FIG. 6 is a flow diagram illustrating yet another process for intelligent SIM switching, according to an embodiment of the present invention.
  • FIG. 7 is a flow diagram illustrating still another process for intelligent SIM switching, according to an embodiment of the present invention.
  • Attorney Docket No. 1687.0008PCT P2022-11-32.PCT
  • FIG. 8A illustrates an example of a neural network that has been trained to assist with intelligent SIM switching, according to an embodiment of the present invention.
  • FIG. 8B illustrates an example of a neuron, according to an embodiment of the present invention.
  • FIG. 9 is a flowchart illustrating a process for training AI/ML model(s), according to an embodiment of the present invention.
  • FIG. 10 is an architectural diagram illustrating a computing system configured to intelligent SIM switching or aspects thereof, according to an embodiment of the present invention.
  • FIG. 11 is a flowchart illustrating a process for intelligent SIM switching, according to an embodiment of the present invention.
  • similar reference characters denote corresponding features consistently throughout the attached drawings.
  • Some embodiments pertain to intelligent SIM switching for UE devices.
  • the UE may have multiple SIMs for multiple carrier networks (i.e., mobile network operators (MNOs) that provide wireless network infrastructure, such as radio access networks (RANs)) or a universal SIM. It may be desirable to switch between SIMs for various reasons, such as based on network quality criteria, subscription criteria, agreement criteria between MNOs, etc. These criteria may inform a policy for determining which SIM to use.
  • MNOs mobile network operators
  • RANs radio access networks
  • a rules engine is used to determine whether to switch SIMs and the associated carrier networks in accordance with a policy.
  • the rules engine may apply business logic to determine the networks and configuration to be used.
  • the rules engine may take into account the location, UE device capabilities and compatibility with the carrier networks, the services that the UE uses, which carrier networks are available in the location, network quality criteria, subscription criteria, cost criteria, etc.
  • the rules engine then returns the network to which the UE should switch.
  • the policy may cause the UE to always use the SIM for the lowest cost available carrier network that meets certain minimum quality criteria (e.g., a minimum signal strength, signal-to-noise ratio (SNR) signal to interference and noise ratio (SINR), etc.).
  • minimum quality criteria e.g., a minimum signal strength, signal-to-noise ratio (SNR) signal to interference and noise ratio (SINR), etc.
  • the SIM for the highest quality available carrier network may be selected. More complex, tiered policies are also possible. For instance, certain carrier networks may be preferred in certain areas, different carrier networks and their SIMs may be preferred for different services (e.g., there may be a list of preferred carrier networks for voice and short message service (SMS) and another list of preferred carrier networks for data services), artificial intelligence (AI) / machine learning (ML) models may be employed (e.g., to predict congestion at different times of the day/week/month, network quality, weather impacts, etc.), etc. [0031] Some embodiments may be reactive or predictive.
  • SMS voice and short message service
  • ML machine learning
  • Reactive embodiments may switch SIMs based on detected criteria, such as a drop in signal strength below a Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) minimum threshold or detection that a local RAN for a carrier has gone down, a change in the location of the UE, detection that an antenna of the UE is no longer working such that a certain band can no longer be used, etc.
  • Predictive embodiments may use deterministic logic based on observations over time and/or probabilistic logic in the form of AI/ML models. Such predictive approaches may be more beneficial for cars, drone delivery networks, etc. However, any suitable UE device, such as smart phones, personal computers, etc., may benefit from predictive techniques without deviating from the scope of the invention.
  • the policy that is used may be determined for the current UE conditions using AI/ML models. For instance, AI/ML models may be used to learn that when a certain policy to prefer a certain carrier SIM that has less cost for a brand associated with the smart phone is used in a first area, users tend to cancel their subscriptions and switch carriers at a high rate. However, in a second another area, the number of cancellations is low.
  • the AI/ML model may intelligently recommend that a policy that tends not to recommend the lower cost carrier be used in the first area and a policy that recommends a higher cost carrier be used in the second area.
  • information regarding current conditions detected by the UE device may be provided to the network infrastructure for the carrier that is implementing the intelligent SIM switching.
  • Server-side logic may then be employed to analyze network loading and congestion, subscription information, carrier contract information, apply AI/ML Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) models, etc. in real time or near-real time and make a choice regarding which SIM identity to use at a given time.
  • active network metrics are captured from UE devices to gain insight into the performance of different carrier networks and build network data profiles over time, as well as current performance in real time or near-real time. This information may be used to identify trends for the carrier networks, to provide training data to train/retrain AI/ML models, etc.
  • different policy rules may be weighted. For instance, for a lower cost subscription, a relatively heavy static weight may be applied to the priority of selecting the lowest cost network. Another weight may be applied to network quality metrics that decreases as network quality decreases (e.g., a linear, exponential, or step function tied to signal strength, SNR, SINR, congestion, etc.).
  • weights for various criteria may be learned and provided by AI/ML models.
  • a SIM for a certain carrier network may be selected by default if that carrier network is available and meets certain minimum quality criteria at the UE location.
  • different rules may be applied to different brands, and these rules may at least partially depend on the hardware capabilities of the given carrier network.
  • the rules could be venue based if there is a managed private network. For instance, customers may prefer the managed private network over other networks.
  • To perform intelligent SIM switching radio connections to available RANs for each SIM are maintained in accordance with a list of carrier networks. This list is Attorney Docket No.
  • 1687.0008PCT (P2022-11-32.PCT) provided to the modem of the UE to scan and connect to the RANs. If a carrier network is not available, the carrier network is dropped from the scan list. Conversely, if a carrier network becomes available, the carrier network is added to the scan list. The UE periodically checks which carrier networks are available such that an updated list is maintained for the UE location. [0038] In some embodiments, the primary roaming network in the SIM profile configures the UE with the optimum parameters for the initial network that the UE connects to at activation. Multiple considerations, such as the location of the subscriber, the network loading, the quality of the services, etc., may be taken into account for the initial network selection decision in some embodiments.
  • the UE may be optimally configured for that network initially. In other words, software and hardware of the UE would be optimized for that carrier. If optimized for AT&T ® , the UE may not support band n71 and outer bands, and AT&T ® may also have a priority list for the bands it supports, a certain packet size that it uses, etc. [0039] The UE then dynamically reconfigures itself based on the actual network that the UE is attached to.
  • the policy (e.g., the network quality, the service grade, the device location, the subscription, etc.) drives dynamic reconfiguration of the UE.
  • Intelligent, dynamic SIM switching occurs when the UE is actively used by the subscriber after the initial activation of the UE and may take into account temporal Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) and geographic factors (e.g., typical network loading at certain times and locations, which may be learned by one or more AI/ML models).
  • a specific network may provide the best service to the user.
  • switching requires a number porting processes. Hence, from user experience perspective, a data-only device is most practical since switching can be done without user intervention.
  • dual SIM dual standby (DSDS) may be used since the voice service can stay on an initially provisioned network on one SIM while data networks can be switched on the other SIM as desired. See, for example, U.S. Patent Application No. 18/174,216.
  • a data voice application such as WhatsApp ®
  • a data voice application may be used on data-only devices, which allows any desired data network to be selected.
  • IP Internet Protocol
  • IMS Multimedia Subsystem
  • IP Internet Protocol
  • IMS Internet Multimedia Subsystem
  • IP Internet Protocol
  • VoIP Internet Protocol
  • IMS Multimedia Subsystem
  • the network order information is static and fixed.
  • some embodiments update the network for voice and/or data dynamically, depending on the aforementioned conditions. This operation can be achieved by updating the configuration in a given SIM, but conceptually, it can be achieved by switching the SIM as well.
  • Such techniques may be applicable to both single SIM and multi-SIM devices (e.g., DSDS devices).
  • FIG. 1A is an architectural diagram illustrating a wireless communications system 100 with multiple (N) networks and UE 110 that are configured to implement intelligent SIM switching, according to an embodiment of the present invention.
  • User data traffic is shown with solid lines and subscriber management traffic is shown with dashed lines.
  • UE 110 includes three SIMs in this embodiment – SIM 112 for carrier 1, SIM 114 for carrier 2, SIM 116 for carrier N, and SIM 118 for the home network. Any desired number of SIMs may be included in UE 110 without deviating from the scope of the invention.
  • SIMs 112, 114, 116 may be pSIMs, eSIMs, or a combination thereof.
  • a universal SIM may be used in place of multiple SIMs, and the appropriate eSIM may be downloaded after the network to switch to is selected.
  • SIMs 112, 114, 116 respectively, allow UE 110 to communicate with carrier 1 network infrastructure 130, carrier 2 network infrastructure 132, carrier N network infrastructure 134, and home network infrastructure 136 (i.e., the carrier network cores) via respective RANs 120, 122, 124, 126.
  • Home network infrastructure 136 and carrier network infrastructure 130, 132, 134 also communicate with an intelligent subscriber management server 150. While a single intelligent subscriber management server 150 is shown, multiple servers may be used in some embodiments, depending on traffic and processing requirements. Intelligent subscriber management server 150 may perform the network and SIM switching functionality disclosed herein.
  • UE 110, computing systems of RANs 120, 122, 124, and/or computing systems of carrier network infrastructure 130, 132, 134 and home network infrastructure 136 may be computing system 1000 of FIG. 10.
  • Attorney Docket No. 1687.0008PCT P2022-11-32.PCT
  • Carrier network infrastructure 130, 132, 134 and home network infrastructure 136 may include computing systems and other equipment associated with pass-through edge data centers (PEDCs), breakout edge data centers (BEDCs), regional data centers (RDCs), national data centers (NDCs), etc.
  • Carrier network infrastructure 130, 132, 134 and home network infrastructure 136 may provide various network functions (NFs) and other services.
  • PEDCs pass-through edge data centers
  • BEDCs breakout edge data centers
  • RRCs regional data centers
  • NDCs national data centers
  • Carrier network infrastructure 130, 132, 134 and home network infrastructure 136 may provide various network functions (NFs) and other services.
  • NFs network functions
  • BEDCs may break out User Plane Function (UPF) data traffic (UPF-d) and provide cloud computing resources and cached content to UE 110, such as providing NF application services for gaming, enterprise applications, etc.
  • RDCs may provide core network functions, such as UPF for voice traffic (UPF-v) and Short Message Service Function (SMSF) functionality.
  • NDCs may provide a Unified Data Repository (UDR) and user verification services, for example.
  • UPF User Plane Function
  • UPF-d User Plane Function data traffic
  • SMSF Short Message Service Function
  • UDR Unified Data Repository
  • IP-SM gateway IP-SM gateway
  • E-SMLC enhanced serving mobile location center
  • PCRF policy and charging rules function
  • MME mobility management entity
  • SGW signaling gateway
  • SGW-C signaling gateway
  • SGW-U user data plane
  • PGW packet data network gateway
  • PGW-U user data plane
  • HSS home subscriber server
  • UPF + PGW-U access and mobility management
  • AMF access and mobility management
  • UDM unified data management
  • SMF session management function
  • SMF session management function
  • SMF session management function
  • SMF short message service center
  • PCF policy control function
  • the intelligent SIM switching process in some embodiments may be initiated by intelligent subscriber management server 150 via control messages delivered through a regular Internet connection of UE 110, for example.
  • UE 110 may switch SIMs and connect to a different carrier network based on the control messages, or may switch to another network itself if the home network cannot be reached.
  • UE 110 may reach home network infrastructure 136 and intelligent subscriber management server 150 through the Internet 140 (connection not shown) or through another carrier network.
  • the home network may be a mobile virtual network operator (MVNO).
  • MVNO mobile virtual network operator
  • home network infrastructure 136 may be used for data services and carrier network infrastructure 130, 132, 134 may be used for voice services.
  • Intelligent subscriber management server 150 may be one or more of its own servers, and potentially other network infrastructure. However, in some embodiments, intelligent subscriber management server 150 is part of home network infrastructure 136.
  • a policy for the UE implemented on intelligent subscriber management server 150 may determine that UE 110 should switch to a Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) different carrier network. For instance, if the policy indicates that UE 110 should switch to carrier 2, intelligent subscriber management server 150 sends control messages to UE 110 via the Internet 140 or RAN 126 to switch to carrier 2.
  • FIG. 1B is an architectural diagram 160 illustrating carrier networks configured to implement intelligent SIM switching in a roaming scenario, according to an embodiment of the present invention.
  • UE 110 includes SIMs 112, 114, 116, 118, RANs 120, 122, 124, carrier network infrastructure 130, 132, 134, home network infrastructure 136, and intelligent subscriber management server 150.
  • UE 110 may use RAN 120, 122, or 124 and respective carrier network infrastructure 130, 132, or 134 to route all traffic (e.g., voice/SMS, data, and subscriber management traffic) to home network infrastructure 136, and vice versa.
  • all traffic e.g., voice/SMS, data, and subscriber management traffic
  • the traffic flow for data traffic from UE Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) 110 to home network infrastructure 136 would be UE 110 to RAN 120 to carrier 1 network infrastructure 130 to home network infrastructure 136 to the Internet 140.
  • the roaming scenario does not necessarily involve SIM switching.
  • UE 110 may communicate with carrier network infrastructure 136 for voice/SMS and subscriber management traffic via the Internet 140 without going through another carrier network in some embodiments.
  • UE 110 may also send data traffic to and receive data traffic from the Internet 140 directly.
  • SIM 118 (which is used for voice in this case) could roam into carrier network 1 once SIM 118 loses home network coverage (not involving SIM switching).
  • SIM used for data SIM 114 or SIM 116 in this case
  • the operation in roaming may be the same as the home network situation except for situations such as the DSDS case, where one SIM is attached to a carrier network for voice and SMS (e.g., carrier network 1) and the other SIM is switched among the remaining carriers.
  • this SIM switches among the carrier networks, with a preference for cannier network 1, where communications could be home routed.
  • FIG. 2 illustrates a mobile device 200 with multiple SIMs, according to an embodiment of the present invention.
  • mobile device 200 may be UE 110 of FIG. 1 and/or computing system 1000 of FIG. 10.
  • Mobile device 200 has N SIMs in this embodiment (e.g., SIM 1220, Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) SIM 2222, ..., SIM N 224).
  • SIMs 1 to N may be a pSIM or an eSIM and is associated with a respective MNO.
  • FIG. 3 is a flow diagram illustrating a process 300 for configuring a UE device to switch to a different SIM, according to an embodiment of the present invention.
  • the process begins with receiving instructions to switch to a different SIM at 305. This may include downloading an eSIM to switch to in some embodiments.
  • the UE framework e.g., the operating system
  • binding refers to launching the carrier application and connecting to a service running within the carrier application. From that point onwards, the framework is able to invoke the APIs that are exposed by the service.
  • the carrier application determines which SIMs to use for different services in some embodiments, such as one SIM for data and another SIM for voice and SMS. However, in certain embodiments, only a single SIM is switched to. The carrier application may also help to facilitate download of the eSIM profile when an eSIM is used.
  • the carrier configuration for the brand for the SIM to switch to is returned at 315, including the brand name and carrier certificate array 320.
  • the access point names (APNs) for the brand are added at 325 and a customization service is started at 330.
  • the customization service operations 335 include obtaining the brand preloaded contacts, the brand wallpaper, and the application enablement/disablement settings.
  • the network configuration is then started at 340.
  • the configuration operations 345 may include retrieving an activation code, configuring the UE for the Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) bands used by the network MNO, configuring the packet size to that used by the MNO, and downloading a policy for the user’s subscription to be used on the UE side.
  • the policy may govern which MNO the UE uses for which services and under what conditions in some embodiments.
  • the UE then operates with the MNO(s) that it switched to at 350 using the SIM(s) for those MNO(s).
  • FIG. 4 is a flow diagram illustrating a process 400 for intelligent SIM switching, according to an embodiment of the present invention.
  • the system includes UE 410, carrier networks (e.g., RANs, PEDCs, BEDCs, RDCs, NDCs, etc.) CN 1420 through CN N 430, and home network / intelligent subscriber management server 440.
  • carrier networks e.g., RANs, PEDCs, BEDCs, RDCs, NDCs, etc.
  • Home network / intelligent subscriber management server 440 determines which carrier network to switch to and instructs UE 410 to switch to the SIM for that CN.
  • CN 1420 has been selected.
  • UE 410 configures itself to switch to CN 1420. In some embodiments, this may include downloading an eSIM for CN 1 420.
  • a device carrier application with the correct privileges can use a local profile assistant (LPA) to switch the eSIM profile that is enabled.
  • LPA local profile assistant
  • the LPA is a set of functions in the UE that are responsible for providing the capability to download encrypted profiles to the SIM.
  • the pSIM can operate in a similar manner to the eSIM if the pSIM is loaded with the eSIM profile. If the pSIM is loaded with the conventional pSIM profile, SIM switching would mean physically removing one pSIM and inserting Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) another pSIM.
  • a consumer eSIM profile uses a PULL model (i.e., the device/client is put in control for profile management and the LPA plays the role in the device), whereas an M2M eSIM profile uses a PUSH model (i.e., control occurs at the backend/server).
  • M2M machine- to-machine
  • An M2M eSIM profile may be used on a pSIM in some embodiments, and the server/backend (e.g., the SM-DP) may use SMS to control the eSIM profile.
  • UE 410 requests to attach to CN 1420, and CN 1420 attaches UE 410 to the network. CN 1420 notifies UE 410 that the attachment was successful, and UE 410 informs home network / intelligent subscriber management server 440 that the SIM switch was successful. UE 410 then uses CN 1420 for communications services.
  • FIG. 5 is a flow diagram illustrating another process 500 for intelligent SIM switching, according to an embodiment of the present invention.
  • home network / intelligent subscriber management server 440 makes the determination that UE 410 should switch rather than UE 410 doing so.
  • UE 410 sends data pertaining to UE 410 (e.g., hardware information, bands that UE 410 supports, etc.) and connection data to available carrier networks to home network / intelligent subscriber management server 440.
  • Home network / intelligent subscriber management server 440 may use this information as part of the SIM switching decision process.
  • CN 1420 has been selected.
  • Attorney Docket No. 1687.0008PCT P2022-11-32.PCT
  • FIG. 6 is a flow diagram illustrating yet another process 600 for intelligent SIM switching, according to an embodiment of the present invention.
  • UE 410 uses one carrier network for voice and SMS and another carrier network for data. The process begins with UE 410 informing home network / intelligent subscriber management server 440 that SIM switches should occur.
  • Home network / intelligent subscriber management server 440 determines which carrier networks to switch to for voice and SMS and for data, respectively, and instructs UE 410 to switch to the SIMs for those CNs.
  • CN 1420 has been selected for voice and SMS and CN N 430 has been selected for data.
  • UE 410 configures itself to switch to CN 1420.
  • UE 410 requests to attach to CN 1420 and CN N 430, and CN 1420 and CN N 430 attach UE 410 to their respective networks.
  • CN 1420 and CN N 430 notify UE 410 that the attachment was successful, and UE 410 informs home network / intelligent subscriber management server 440 that the SIM switches were successful.
  • FIG. 7 is a flow diagram illustrating still another process 700 for intelligent SIM switching, according to an embodiment of the present invention.
  • UE 410 uses one carrier network for voice and SMS and another carrier network for Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) data.
  • home network / intelligent subscriber management server 440 makes the determination that UE 410 should switch SIMs rather than UE 410 doing so.
  • UE 410 sends data pertaining to UE 410 and connection data to available carrier networks to home network / intelligent subscriber management server 440.
  • Home network / intelligent subscriber management server 440 may use this information as part of the SIM switching decision process.
  • CN 1 420 has been selected for voice and SMS and CN N 430 has been selected for data.
  • UE 410 configures itself to switch to CN 1420.
  • UE 410 requests to attach to CN 1420 and CN N 430, and CN 1420 and CN N 430 attach UE 410 to their respective networks.
  • CN 1420 and CN N 430 notify UE 410 that the attachment was successful, and UE 410 informs home network / intelligent subscriber management server 440 that the SIM switches were successful.
  • UE 410 then uses CN 1420 for voice and SMS and CN N 430 for data.
  • FIG. 8A illustrates an example of a neural network 800 that has been trained to suggest a carrier for voice and SMS, suggest a carrier for data, predict duration that the carrier(s) in the Attorney Docket No.
  • Neural network 800 also includes a number of hidden layers. Both DLNNs and shallow learning neural networks (SLNNs) usually have multiple layers, although SLNNs may only have one or two layers in some cases, and normally fewer than DLNNs. Typically, the neural network architecture includes an input layer, multiple intermediate layers, and an output layer, as is the case in neural network 800.
  • a DLNN often has many layers (e.g., 10, 50, 200, etc.) and subsequent layers typically reuse features from previous layers to compute more complex, general functions.
  • a SLNN tends to have only a few layers and train relatively quickly since expert features are created from raw data samples in advance. However, feature extraction is laborious. DLNNs, on the other hand, usually do not require expert features, but tend to take longer to train and have more layers. [0070] For both approaches, the layers are trained simultaneously on the training set, normally checking for overfitting on an isolated cross-validation set. Both techniques can yield excellent results, and there is considerable enthusiasm for both approaches. The optimal size, shape, and quantity of individual layers varies depending on the problem that is addressed by the respective neural network. [0071] Returning to FIG.8A, UE hardware information, UE connection information, congestion information, information pertaining to upcoming event(s), etc.
  • Hidden layer 2 receives inputs from hidden layer 1
  • hidden layer 3 receives inputs from hidden layer 2, and so on for all hidden layers until the last hidden layer provides its outputs as inputs for the output layer.
  • numbers of neurons I, J, K, and L are not necessarily equal, and thus, any desired number of layers may be used for a given layer of neural network 800 without deviating from the scope of the invention. Indeed, in certain embodiments, the types of neurons in a given layer may not all be the same.
  • Neural network 800 is trained to assign a confidence score to appropriate outputs. In order to reduce predictions that are inaccurate, only those results with a confidence score that meets or exceeds a confidence threshold may be provided in some embodiments.
  • neural networks are probabilistic constructs that typically have confidence score(s). This may be a score learned by the AI/ML model based on how often a similar input was correctly identified during training. Some common types of confidence scores include a decimal number between 0 and 1 (which can be interpreted as a confidence percentage as well), a number between negative ⁇ and positive ⁇ , a set of expressions (e.g., “low,” “medium,” and “high”), etc.
  • Neurons in a neural network are implemented algorithmically as mathematical functions that are typically based on the functioning of a biological neuron. Neurons receive weighted input and have a summation and an activation function that governs whether they pass output to the next layer. This activation function may be a nonlinear thresholded activity function where nothing happens if the value is below a threshold, but then the function linearly responds above the threshold (i.e., a rectified linear unit (ReLU) nonlinearity).
  • ReLU rectified linear unit
  • Summation functions and ReLU functions are used in deep learning since real neurons can have approximately similar activity functions. Via linear transforms, information can be subtracted, added, etc. In essence, neurons act as gating functions that pass output to the next layer as governed by their underlying mathematical function. In some embodiments, different functions may be used for at least some neurons.
  • FIG. 8B An example of a neuron 810 is shown in FIG. 8B. Inputs ⁇ ⁇ , ⁇ ⁇ , ... , ⁇ ⁇ from a preceding layer are assigned respective weights ⁇ ⁇ , ⁇ ⁇ , ... , ⁇ ⁇ .
  • 1687.0008PCT (P2022-11-32.PCT) input from preceding neuron 1 is ⁇ ⁇ ⁇ ⁇ .
  • These weighted inputs are used for the neuron’s summation function modified by a bias, such as: ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ + ⁇ ⁇ 1 ⁇ [0077]
  • ⁇ ⁇ ⁇ ⁇ may be given by: 1 if ⁇ ⁇ + ⁇ ⁇ 0 [0078]
  • the output ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ + ⁇ ⁇ 3 ⁇ [0079]
  • any suitable neuron type or combination of neuron types may be used without deviating from the scope of the invention.
  • the ranges of values of the weights and/or the output value(s) of the activation function may differ in some embodiments without deviating from the scope of the invention.
  • a goal, or “reward function,” is often employed.
  • a reward function explores intermediate transitions and steps with both short-term and long-term rewards to guide the search of a state space and attempt to achieve a goal (e.g., finding the best carrier for a give service, determining a timing strategy for turning on a SIM radio in order to save power, determining when the network is likely to be congested, etc.).
  • a goal e.g., finding the best carrier for a give service, determining a timing strategy for turning on a SIM radio in order to save power, determining when the network is likely to be congested, etc.
  • a cost function such as mean square error (MSE) or gradient descent may be used to punish predictions that are slightly wrong much less than predictions that are very wrong.
  • MSE mean square error
  • gradient descent may be used to punish predictions that are slightly wrong much less than predictions that are very wrong.
  • Backpropagation is a technique for optimizing synaptic weights in a feedforward neural network. Backpropagation may be used to “pop the hood” on the hidden layers of the neural network to see how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights, and vice versa. In other words, backpropagation allows data scientists to repeatedly adjust the weights so as to minimize the difference between actual output and desired output.
  • the backpropagation algorithm is mathematically founded in optimization theory.
  • supervised learning training data with a known output is passed through the neural network and error is computed with a cost function from known target output, which gives the error for backpropagation. Error is computed at the output, and this error is transformed into corrections for network weights that will minimize the error.
  • the gradient descent procedure requires the computation of output o given an input x corresponding to a known target output t, and producing an error o ⁇ t.
  • This global error is then propagated backwards giving local errors for weight updates with computations similar to, but not exactly the same as, those used for forward propagation.
  • the learning rate A is chosen with respect to machine learning considerations. Below, A is related to the neural Hebbian learning mechanism used in the neural implementation.
  • the AI/ML model may be trained over multiple epochs until it reaches a good level of accuracy (e.g., 97% or better using an F2 or F4 threshold for detection and approximately 2,000 epochs). This accuracy level may be determined in some embodiments using an F1 score, an F2 score, an F4 score, or any other suitable technique without deviating from the scope of the invention.
  • a good level of accuracy e.g. 97% or better using an F2 or F4 threshold for detection and approximately 2,000 epochs.
  • This accuracy level may be determined in some embodiments using an F1 score, an F2 score, an F4 score, or any other suitable technique without deviating from the scope of the invention.
  • the AI/ML model may be tested on a set of evaluation data that the AI/ML model has not encountered before.
  • the AI/ML model may not be known what accuracy level is possible for the AI/ML model to achieve. Accordingly, if the accuracy of the AI/ML model is starting to drop when analyzing the evaluation data (i.e., the model is performing well on the training data, but is starting to perform less well on the evaluation data), the AI/ML model may go through more epochs of training on the training data (and/or new training data). In some embodiments, the AI/ML model is only deployed if the accuracy reaches a certain level or if the accuracy of the trained AI/ML model is superior to an existing deployed AI/ML model.
  • a collection of trained AI/ML models may be used to accomplish a task. This may collectively allow the AI/ML Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) models to enable semantic understanding to better predict event-based congestion or service interruptions due to an accident, for instance.
  • Some embodiments may use transformer networks such as SentenceTransformersTM, which is a PythonTM framework for state-of-the-art sentence, text, and image embeddings. Such transformer networks learn associations of words and phrases that have both high scores and low scores. This trains the AI/ML model to determine what is close to the input and what is not, respectively. Rather than just using pairs of words/phrases, transformer networks may use the field length and field type, as well.
  • Natural language processing (NLP) techniques such as word2vec, BERT, GPT-3, Open AI, etc. may be used in some embodiments to facilitate semantic understanding.
  • Other techniques such as clustering algorithms, may be used to find similarities between groups of elements.
  • Clustering algorithms may include, but are not limited to, density-based algorithms, distribution-based algorithms, centroid-based algorithms, hierarchy-based algorithms.
  • K-means clustering algorithms the DBSCAN clustering algorithm, the Gaussian mixture model (GMM) algorithms, the balance iterative reducing and clustering using hierarchies (BIRCH) algorithm, etc.
  • GMM Gaussian mixture model
  • BIRCH balance iterative reducing and clustering using hierarchies
  • FIG. 9 is a flowchart illustrating a process 900 for training AI/ML model(s), according to an embodiment of the present invention.
  • the process begins with providing UE hardware data, connection data, congestion data, event data, etc. at 910, whether labeled or unlabeled.
  • Other training data used in addition to or in lieu of the training data shown in FIG. 9 may include, but is not limited to, available networks at Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) the location, customer rate plans that determine the quality of service and thus impact the network that is selected, etc. Indeed, the nature of the training data that is provided will depend on the objective that the AI/ML model is intended to achieve.
  • the AI/ML model is then trained over multiple epochs at 920 and results are reviewed at 930. [0094] If the AI/ML model fails to meet a desired confidence threshold at 940, the training data is supplemented and/or the reward function is modified to help the AI/ML model achieve its objectives better at 950 and the process returns to step 920. If the AI/ML model meets the confidence threshold at 940, the AI/ML model is tested on evaluation data at 960 to ensure that the AI/ML model generalizes well and that the AI/ML model is not over fit with respect to the training data. The evaluation data includes information that the AI/ML model has not processed before. If the confidence threshold is met at 970 for the evaluation data, the AI/ML model is deployed at 980.
  • FIG. 10 is an architectural diagram illustrating a computing system 1000 configured to perform intelligent SIM switching or aspects thereof, according to an embodiment of the present invention.
  • computing system 1000 may be one or more of the computing systems depicted and/or described herein, such as UE, a carrier server, a computing system of a RAN, etc.
  • Computing system 1000 includes a bus 1005 or other communication mechanism for communicating information, and processor(s) 1010 coupled to bus 1005 for processing information.
  • Processor(s) 1010 may be any type of general or specific purpose processor, including a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Graphics Processing Unit (GPU), multiple Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) instances thereof, and/or any combination thereof.
  • Processor(s) 1010 may also have multiple processing cores, and at least some of the cores may be configured to perform specific functions. Multi-parallel processing may be used in some embodiments.
  • at least one of processor(s) 1010 may be a neuromorphic circuit that includes processing elements that mimic biological neurons.
  • Computing system 1000 further includes a memory 1015 for storing information and instructions to be executed by processor(s) 1010.
  • Memory 1015 can be comprised of any combination of random access memory (RAM), read-only memory (ROM), flash memory, cache, static storage such as a magnetic or optical disk, or any other types of non-transitory computer-readable media or combinations thereof.
  • RAM random access memory
  • ROM read-only memory
  • flash memory flash memory
  • static storage static storage
  • Non- transitory computer-readable media may be any available media that can be accessed by processor(s) 1010 and may include volatile media, non-volatile media, or both. The media may also be removable, non-removable, or both.
  • computing system 1000 includes a communication device 1020, such as a transceiver, to provide access to a communications network via a wireless and/or wired connection.
  • communication device 1020 may be configured to use Frequency Division Multiple Access (FDMA), Single Carrier FDMA (SC-FDMA), Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), Orthogonal Frequency Division Multiplexing (OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Global System for Mobile (GSM) communications, General Packet Radio Service (GPRS), Universal Mobile Attorney Docket No.
  • FDMA Frequency Division Multiple Access
  • SC-FDMA Single Carrier FDMA
  • TDMA Time Division Multiple Access
  • CDMA Code Division Multiple Access
  • OFDM Orthogonal Frequency Division Multiplexing
  • OFDMA Orthogonal Frequency Division Multiple Access
  • GSM Global System for Mobile
  • GPRS General Packet Radio Service
  • PCT Telecommunications System
  • UMTS Telecommunications System
  • W-CDMA Wideband CDMA
  • HSDPA High-Speed Downlink Packet Access
  • HSUPA High-Speed Uplink Packet Access
  • HSPA High-Speed Packet Access
  • LTE Long Term Evolution
  • LTE Advanced LTE-A
  • 802.11x Wi-Fi, Zigbee, Ultra-WideBand (UWB), 802.16x, 802.15, Home Node-B (HnB), Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Near-Field Communications (NFC), fifth generation (5G), New Radio (NR), any combination thereof, and/or any other currently existing or future- implemented communications standard and/or protocol without deviating from the scope of the invention.
  • RFID Radio Frequency Identification
  • IrDA Infrared Data Association
  • NFC Near-Field Communications
  • 5G Fifth generation
  • NR New Radio
  • communication device 1020 may include one or more antennas that are singular, arrayed, phased, switched, beamforming, beamsteering, a combination thereof, and or any other antenna configuration without deviating from the scope of the invention.
  • Processor(s) 1010 are further coupled via bus 1005 to a display 1025, such as a plasma display, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, a Field Emission Display (FED), an Organic Light Emitting Diode (OLED) display, a flexible OLED display, a flexible substrate display, a projection display, a 4K display, a high definition display, a Retina ® display, an In-Plane Switching (IPS) display, or any other suitable display for displaying information to a user.
  • a display 1025 such as a plasma display, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, a Field Emission Display (FED), an Organic Light Emi
  • Display 1025 may be configured as a touch (haptic) display, a three-dimensional (3D) touch display, a multi- input touch display, a multi-touch display, etc. using resistive, capacitive, surface- acoustic wave (SAW) capacitive, infrared, optical imaging, dispersive signal technology, acoustic pulse recognition, frustrated total internal reflection, etc.
  • Any Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) suitable display device and haptic I/O may be used without deviating from the scope of the invention.
  • a keyboard 1030 and a cursor control device 1035 are further coupled to bus 1005 to enable a user to interface with computing system 1000.
  • a physical keyboard and mouse may not be present, and the user may interact with the device solely through display 1025 and/or a touchpad (not shown). Any type and combination of input devices may be used as a matter of design choice.
  • no physical input device and/or display is present. For instance, the user may interact with computing system 1000 remotely via another computing system in communication therewith, or computing system 1000 may operate autonomously.
  • Memory 1015 stores software modules that provide functionality when executed by processor(s) 1010.
  • the modules include an operating system 1040 for computing system 1000.
  • the modules further include an intelligent SIM switching module 1045 that is configured to perform all or part of the processes described herein or derivatives thereof.
  • Computing system 1000 may include one or more additional functional modules 1050 that include additional functionality.
  • a “computing system” could be embodied as a server, an embedded computing system, a personal computer, a console, a personal digital assistant (PDA), a cell phone, a tablet computing device, a quantum computing system, or any other suitable computing device, or combination of devices without deviating from the scope of the invention. Presenting the above-described functions as being performed by a “system” is not intended to limit the scope of the Attorney Docket No.
  • a module may be implemented as a hardware circuit comprising custom very large scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components.
  • VLSI very large scale integration
  • a module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, graphics processing units, or the like.
  • a module may also be at least partially implemented in software for execution by various types of processors.
  • An identified unit of executable code may, for instance, include one or more physical or logical blocks of computer instructions that may, for instance, be organized as an object, procedure, or function.
  • modules need not be physically located together, but may include disparate instructions stored in different locations that, when joined logically Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) together, comprise the module and achieve the stated purpose for the module.
  • modules may be stored on a computer-readable medium, which may be, for instance, a hard disk drive, flash device, RAM, tape, and/or any other such non-transitory computer-readable medium used to store data without deviating from the scope of the invention.
  • a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices.
  • FIG. 11 is a flowchart illustrating a process 1100 for intelligent SIM switching, according to an embodiment of the present invention.
  • the process begins with training an AI/ML model for intelligent SIM switching to assist in determining the SIM or SIM profile (e.g., in the case of a virtual SIM, where one SIM may be used for multiple carriers) of the plurality of SIMs or SIM profiles to switch to at 1110.
  • the AI/ML model is trained using hardware information from a plurality of UE devices, connection information from a plurality of UE devices, network congestion information, information pertaining to upcoming events, or any combination thereof.
  • the UE device sends information pertaining to conditions detected by the UE device to the home network of the UE device at 1120.
  • the rules engine may Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) be configured to use the information pertaining to the conditions detected by the UE device to determine which SIM(s) or SIM profile(s) from the plurality of SIMs or SIM profiles to switch to.
  • the trained AI/ML model for intelligent SIM switching is used by a rules engine to assist in determining the SIM or SIM profile of the plurality of SIMs or SIM profiles to switch to at 1130.
  • the UE device receives instructions from the rules engine (e.g., of the UE device or of a home network of the UE device) to switch from the current SIM or SIM profile to a different SIM or SIM profile at 1140.
  • the instructions may include more than one SIM (e.g., one SIM for voice and SMS and another SIM for data).
  • the rules engine is configured to determine which SIM(s) or SIM profile(s) from a plurality of SIMs or SIM profiles that the UE device should switch to in accordance with the policy.
  • the UE device configures itself for the different SIM(s) or SIM profile(s) and associated carrier(s) at 1150.
  • the UE device is then switched to the other SIM(s) or SIM profile(s) for communications services provided by the associated carrier(s) at 1160.
  • the rules engine is configured to take into account a location of the UE device, hardware capabilities of the UE device, compatibility of the UE device with carrier networks associated with the plurality of SIMs or SIM profiles, services that the UE device is configured to use, carrier networks that are available in a location of the UE device, network quality criteria, subscription criteria, cost criteria, or any combination thereof, when determining which SIM or SIM profile from the plurality of SIMs or SIM profiles to switch to.
  • the rules engine selects a SIM or SIM profile of the plurality of SIMs or SIM profiles with a lowest cost Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) available carrier network that meets minimum quality criteria or a highest quality according to quality criteria.
  • the policy is a tiered policy comprising a carrier network preference order based on location or a preference for different carrier networks for different services.
  • the policy is reactive and the rules engine is configured to cause the UE to switch to the different SIM or SIM profile based on a drop in signal strength below a minimum threshold, detection that a RAN for a carrier of the current SIM or SIM profile has gone down, a change in a location of the UE, detection that an antenna of the UE is no longer working such that a band can no longer be used, or any combination thereof.
  • the policy is predictive and uses deterministic logic based on observations over time, probabilistic logic of one or more AI/ML models, or both.
  • the rules engine is configured to determine the policy based on one or more artificial AI/ML models. In some embodiments, the rules engine is configured to analyze network loading and congestion, subscription information, carrier contract information, or any combination thereof, to determine which SIM(s) or SIM profile(s) from the plurality of SIMs or SIM profiles to switch to. [0109]
  • the process steps performed in FIGS. 3-7 and 11 may be performed by computer program(s), encoding instructions for the processor(s) to perform at least part of the process(es) described in FIGS. 3-7 and 11, in accordance with embodiments of the present invention.
  • the computer program(s) may be embodied on non-transitory computer-readable media.
  • the computer-readable media may be, but are not limited to, a hard disk drive, a flash device, RAM, a tape, and/or any other such medium or combination of media used to store data.
  • the computer program(s) may include Attorney Docket No. 1687.0008PCT (P2022-11-32.PCT) encoded instructions for controlling processor(s) of computing system(s) (e.g., processor(s) 1010 of computing system 1000 of FIG. 10) to implement all or part of the process steps described in FIGS.3-7 and 11, which may also be stored on the computer- readable medium.
  • the computer program(s) can be implemented in hardware, software, or a hybrid implementation.
  • the computer program(s) can be composed of modules that are in operative communication with one another, and which are designed to pass information or instructions to display.
  • the computer program(s) can be configured to operate on a general purpose computer, an ASIC, or any other suitable device.

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Abstract

L'invention concerne une commutation intelligente entre modules d'identification de l'abonné (cartes SIM) pour des dispositifs d'équipement utilisateur (UE). L'UE peut avoir de multiples cartes SIM pour des réseaux de multiples opérateurs (c'est-à-dire, des opérateurs de réseau mobile (MNO) qui fournissent une infrastructure de réseau sans fil, telle que des réseaux d'accès radio (RAN)) ou une carte SIM universelle. Il peut être souhaitable de commuter entre cartes SIM pour diverses raisons, par exemple sur la base de critères de qualité de réseau, de critères d'abonnement, de critères d'accord entre MNO, etc. Ces critères peuvent informer une politique pour déterminer quelle carte SIM utiliser.
PCT/US2023/074585 2022-10-31 2023-09-19 Commutation intelligente entre modules d'identification de l'abonné (cartes sim) WO2024097475A1 (fr)

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US18/357,535 US20240147216A1 (en) 2022-10-31 2023-07-24 Intelligent subscriber identity module (sim) switching
US18/357,446 US20240147215A1 (en) 2022-10-31 2023-07-24 Intelligent subscriber identity module (sim) switching

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Citations (4)

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Publication number Priority date Publication date Assignee Title
US20160149605A1 (en) * 2014-11-25 2016-05-26 Red Hat, Inc. Subscriber identity module (sim) selection in multi-sim communication devices
EP3840439A2 (fr) * 2019-12-19 2021-06-23 Yokogawa Electric Corporation Dispositif mtc, procédé, programme et appareil
US20220286839A1 (en) * 2021-03-03 2022-09-08 Qualcomm Incorporated Techniques for an optimized dual subscriber identity module dual active mode
US20220295343A1 (en) * 2021-03-10 2022-09-15 Apple Inc. System selection for high-throughput wireless communications

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160149605A1 (en) * 2014-11-25 2016-05-26 Red Hat, Inc. Subscriber identity module (sim) selection in multi-sim communication devices
EP3840439A2 (fr) * 2019-12-19 2021-06-23 Yokogawa Electric Corporation Dispositif mtc, procédé, programme et appareil
US20220286839A1 (en) * 2021-03-03 2022-09-08 Qualcomm Incorporated Techniques for an optimized dual subscriber identity module dual active mode
US20220295343A1 (en) * 2021-03-10 2022-09-15 Apple Inc. System selection for high-throughput wireless communications

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