DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
1. Claims1-3, 5-7, 10, 12-13, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee et al (11,641,579) in view of Chaudhary et al (2021/0282127) or Liu (2024/0155327).
Regarding claim 1. Mukherjee teaches one or more non-transitory computer-readable media storing one or more computer programs for performing intelligent subscriber identity module (SIM) switching for a user equipment (UE) device using a current SIM or SIM profile, the one or more computer programs configured to cause at least one processor to (figure 1, col. 6 line 46 – col. 7 line 48 wherein UE comprises processor, memory and program):
receive instructions from a rules engine of the UE device, or from a home
network and/or 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 configured to determine which SIM or SIM profile from a plurality of SIMs or SIM profiles that the UE device should switch to in accordance with a policy (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already);
responsive to the instructions from the rules engine, configure the UE device for the different SIM or SIM profile and an associated carrier (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already), wherein configuring the UE device for the associated carrier comprises configuring the UE for bands and (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already, col. 3 lines 1-20 – mobile network service provider packages which include bandwidth, voice minutes (e.g., packet sizes optimized for the associated network service provider (e.g., carrier), data capacity limit, frequency band indicator, air-interface indicator (e.g., LTE 5G), col. 11 line 65 – col. 12 line 27 – summarizes the differences between sticking to a single provider verses dynamically picking the best provider across the metrics measured (for each task and location) … dropped frames (e.g., packet sizes), col. 26 lines 60-67 – an eSIM is typically downloaded from a SM-DP+ … any number of profiles may be downloaded on the UE, col. 28 line 31 – bandwidth within frequency band, col. 29 lines 25-33 – provision users on demand and to update pricing and bands of offers. Different prices may be set on different frequency bands and for different band widths (e.g., packet sizes), col. 30 lines 46-54 – allow users to configure profiles that the agent should switch between, e.g., setting data cap (e.g,. packet sizes) and expiration rules, col. 33 lines 4-26 – network 1 identifier and associated bandwidth and network 2 identifier and associated bandwidth, col. 38 lines 25-41 – two SIMs associated with two providers (e.g., carriers) and manually enabling a SIM (and associated voice and data plan) before making a call or launching an app, col., 45 lines 15-30 – the “band” refers to, say, 5GHz or 3.5GHz, air interface is WiFi, LTE, 5G, the available bandwidth is an integer representing some number of units of bandwidth); and
switch the UE device to the other SIM or SIM profile for communications
services provided by the associated carrier (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Mukherjee does not explicitly use the term “packet sizes”.
Chaudhary teaches the MNOs 114 can represent different wireless service providers that provide specific services (e.g., voice and data) to which a user of the UE 102 can subscribe to access the service … the eUICC can be configured to store multiple eSIMs for access services offered by one or more MNOs 114. To be able to access the services offered by the MNOs, one or more eSIMs can be provisioned to the eUICC 108 of the UE. In some embodiments, policies can determine cost factors, data throughput rate limits (e.g., packet sizes), data capacity limits (e.g., packet sizes), application service compatibility and other criteria for determining whether one or more applications of the UE can access the 5G services (figure 1, paragraph 0027). The eUICC OS 206 can include an eSIM manager 210 (e.g., rules engine), which can perform management functions for various eSIMs (figure 2, 0028). eSIM policies may further include data throughput, QoS, latency, usage patterns, services, etc. to determine recommendations for 5G cell baseband resources (0031). A number of factors impact whether a given application, when used, can benefit from 5G connections including, for example, a known or expected amount of data to be transferred (e.g., packet sizes), data transfer rate cap … applicable cues can include intent cues that indicate an application’s intent to download a certain amount of data (e.g, packet sizes), such as for http(s)-based application that includes a content length entry-header field that indicates a size of an entity body to be transferred … the recommendation includes an indication of bandwidth usage for an application, e.g., a low or high bandwidth requirement or similar bandwidth ranking for the application (0032-0034).
Liu teaches UE has eSIMs configured for different Mobile Network Operators (MNOs) which enables a user to reduce phone plane cost or obtain a desired coverage, e.g., when traveling (0017) wherein SIM1 from AT&T is associated with FR1 band and SIM2 from Verizon is associated with FR2 band (0029, 0039).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee to further consider packet sizes, bandwidths, QoS, usage patterns, etc. as taught by Chaudhary or associate a first band to SIM1 from AT&T and a second band to SIM2 from Verizon as taught by Liu in order to further determine to switch the UE between SIM1 associated with a first MNO (e.g., first carrier/MNO associated with a first band, bandwidth, throughput, QoS for downloading a certain amount of data) or switch to another SIM2 associated with a second MNO (e.g, second carrier/MNO associated with a second band, second bandwidth, second throughput, second QoS for downloading a certain amount of data) as taught by Chaudhary (0003 – determine when to enable access to 5G cellular connections based on a variety of factors) or to switch to SIM1 from AT$T using FR1 band when at home and switch to SIM2 from Verizon using FR2 band when user is traveling as taught by Liu.
Regarding claim 12. Mukherjee teaches a computer-implemented method for performing intelligent subscriber identity module (SIM) switching for a user equipment (UE) device using a current SIM or SIM profile (figure 1, col. 6 line 46 – col. 7 line 48 wherein UE comprises processor, memory and program), comprising:
receiving, by the UE device, instructions from a rules engine of the UE device, or from a home network and/or 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 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 (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already);
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 (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already) wherein configuring the UE device for the associated carrier comprises configuring the UE for bands and (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already, col. 3 lines 1-20 – mobile network service provider packages which include bandwidth, voice minutes (e.g., packet sizes optimized for the associated network service provider (e.g., carrier), data capacity limit, frequency band indicator, air-interface indicator (e.g., LTE 5G), col. 11 line 65 – col. 12 line 27 – summarizes the differences between sticking to a single provider verses dynamically picking the best provider across the metrics measured (for each task and location) … dropped frames (e.g., packet sizes), col. 26 lines 60-67 – an eSIM is typically downloaded from a SM-DP+ … any number of profiles may be downloaded on the UE, col. 28 line 31 – bandwidth within frequency band, col. 29 lines 25-33 – provision users on demand and to update pricing and bands of offers. Different prices may be set on different frequency bands and for different band widths (e.g., packet sizes), col. 30 lines 46-54 – allow users to configure profiles that the agent should switch between, e.g., setting data cap (e.g,. packet sizes) and expiration rules, col. 33 lines 4-26 – network 1 identifier and associated bandwidth and network 2 identifier and associated bandwidth, col. 38 lines 25-41 – two SIMs associated with two providers (e.g., carriers) and manually enabling a SIM (and associated voice and data plan) before making a call or launching an app, col., 45 lines 15-30 – the “band” refers to, say, 5GHz or 3.5GHz, air interface is WiFi, LTE, 5G, the available bandwidth is an integer representing some number of units of bandwidth); and
switching the UE device to the other SIM or SIM profile for communications services provided by the associated carrier, by the UE device (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already),
wherein 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 (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Mukherjee does not explicitly use the term “packet sizes”.
Chaudhary teaches the MNOs 114 can represent different wireless service providers that provide specific services (e.g., voice and data) to which a user of the UE 102 can subscribe to access the service … the eUICC can be configured to store multiple eSIMs for access services offered by one or more MNOs 114. To be able to access the services offered by the MNOs, one or more eSIMs can be provisioned to the eUICC 108 of the UE. In some embodiments, policies can determine cost factors, data throughput rate limits (e.g., packet sizes), data capacity limits (e.g., packet sizes), application service compatibility and other criteria for determining whether one or more applications of the UE can access the 5G services (figure 1, paragraph 0027). The eUICC OS 206 can include an eSIM manager 210 (e.g., rules engine), which can perform management functions for various eSIMs (figure 2, 0028). eSIM policies may further include data throughput, QoS, latency, usage patterns, services, etc. to determine recommendations for 5G cell baseband resources (0031). A number of factors impact whether a given application, when used, can benefit from 5G connections including, for example, a known or expected amount of data to be transferred (e.g., packet sizes), data transfer rate cap … applicable cues can include intent cues that indicate an application’s intent to download a certain amount of data (e.g, packet sizes), such as for http(s)-based application that includes a content length entry-header field that indicates a size of an entity body to be transferred … the recommendation includes an indication of bandwidth usage for an application, e.g., a low or high bandwidth requirement or similar bandwidth ranking for the application (0032-0034).
Liu teaches UE has eSIMs configured for different Mobile Network Operators (MNOs) which enables a user to reduce phone plane cost or obtain a desired coverage, e.g., when traveling (0017) wherein SIM1 from AT&T is associated with FR1 band and SIM2 from Verizon is associated with FR2 band (0029, 0039).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee to further consider packet sizes, bandwidths, QoS, usage patterns, etc. as taught by Chaudhary or associate a first band to SIM1 from AT&T and a second band to SIM2 from Verizon as taught by Liu in order to further determine to switch the UE between SIM1 associated with a first MNO (e.g., first carrier/MNO associated with a first band, bandwidth, throughput, QoS for downloading a certain amount of data) or switch to another SIM2 associated with a second MNO (e.g, second carrier/MNO associated with a second band, second bandwidth, second throughput, second QoS for downloading a certain amount of data) as taught by Chaudhary (0003 – determine when to enable access to 5G cellular connections based on a variety of factors) or to switch to SIM1 from AT$T using FR1 band when at home and switch to SIM2 from Verizon using FR2 band when user is traveling as taught by Liu.
Regarding claims 2 and13. Mukherjee teaches wherein 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 (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Regarding claims 3. Mukherjee teaches wherein the rules engine selects a SIM or SIM profile of the plurality of SIMs or SIM profiles with a lowest cost available carrier network that meets minimum quality criteria or a highest quality according to quality criteria (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Regarding claims 5 and 15. Mukherjee teaches wherein 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 radio access network (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 (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Regarding claim 6. Mukherjee teaches wherein 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 (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Regarding claim 7. Mukherjee teaches wherein the rules engine is configured to determine the policy based on one or more artificial intelligence (AI) / machine learning (ML) models (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Regarding claim 10. Mukherjee teaches wherein the different SIM or SIM profile is for a first service, the instructions from the rules engine comprise selection of another different SIM or SIM profile for a second service, and the one or more computer programs 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 other different SIM or SIM profile and an associated carrier for the second service; and switch the UE device to the other SIM or SIM profile to use the second service provided by the associated carrier for the second service, wherein the first service comprises voice and short message service (SMS) and the second service comprises data, or vice versa (col. 1 line 64-66 – switch between SIMS wherein different SIM/carrier for phone calls and messages (e.g., SMS) and data, col. 2 line 42-54 – agent detects when user opens an application to make a phone call, determine which SIM card (mobile network operator) has cheaper voice rates, avoids roaming charges and/or has the most voice minutes available, and switch the UE to the mobile network service prover that optimizes utilization of the UE’s pre-purchased resources, col. 7 line 49 – col. 8 line 12 – user selects an application requiring resources (e.g., voice or data to run) … the selector then instructs a component of the UE to implement the selected resource package, e.g., by switching a mobile network component, such as a SIM card, col. 15 lines 44-49 – we simplify the provider selection problem by stipulating that as soon as the user launches an app (for brevity, making a call will also be considered “launching an app” in what follows), the user agent instantaneously selects one of, say k SIMs or eSIMs, and enables the selected SIM/eSIM if it was not enabled already, col. 38 lines 25-50 – it is a common usage scenario in many countries for a single user to carry two pre-paid SIM cards (corresponding to two providers) in a single dual-SIM device and manually enable a SIM (and associated voice and data plan) before making a call or launching an app … let us design a user agent to perform this task automatically).
Regarding claim 17. Mukherjee teaches wherein the different SIM or SIM profile is for a first service, the instructions from the rules engine comprise selection of another different SIM or SIM profile for a second service, and the method further comprises: responsive to the instructions from the rules engine, configuring the UE device for the other different SIM or SIM profile and an associated carrier for the second service, by the UE device; and switching the UE device to the other SIM or SIM profile to use the second service provided by the associated carrier for the second service, by the UE device, wherein the first service comprises voice and short message service (SMS) and the second service comprises data, or vice versa (col. 1 line 64-66 – switch between SIMS wherein different SIM/carrier for phone calls and messages (e.g., SMS) and data, col. 2 line 42-54 – agent detects when user opens an application to make a phone call, determine which SIM card (mobile network operator) has cheaper voice rates, avoids roaming charges and/or has the most voice minutes available, and switch the UE to the mobile network service prover that optimizes utilization of the UE’s pre-purchased resources, col. 7 line 49 – col. 8 line 12 – user selects an application requiring resources (e.g., voice or data to run) … the selector then instructs a component of the UE to implement the selected resource package, e.g., by switching a mobile network component, such as a SIM card, col. 15 lines 44-49 – we simplify the provider selection problem by stipulating that as soon as the user launches an app (for brevity, making a call will also be considered “launching an app” in what follows), the user agent instantaneously selects one of, say k SIMs or eSIMs, and enables the selected SIM/eSIM if it was not enabled already, col. 38 lines 25-50 – it is a common usage scenario in many countries for a single user to carry two pre-paid SIM cards (corresponding to two providers) in a single dual-SIM device and manually enable a SIM (and associated voice and data plan) before making a call or launching an app … let us design a user agent to perform this task automatically).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
2. Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Chaudhary or LIU further in view of Kojima et al (2021/0195392).
Regarding claims 4 and 14. Mukherjee in view of Chaudhary or LIU does not teach wherein 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.
Kojima teaches storing switching conditions for switching SIMs and communication carrier in association with each SIM (abstract, figure 3, 0040) which enables proper control in switching of communication by treating the set of the SIM and the communication carrier as one unit (0006). Different priority level(s) for home SIM(s) and roaming SIM(s) in conjunction with communication quality are provided (0032-0033, 0041, 0043, 0048) which enables the UE to quickly switch to the higher priority SIM when roaming.
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee in view of Chaudhary or LIU to associate a priority to each SIM and corresponding carrier as taught by Kojima thereby enabling the UE to quickly switch to the best SIM and corresponding carrier that provides the best communication quality when the UE roams.
3. Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Chaudhary or LIU further in view of Rice et al (2022/0053607).
Regarding claims 8 and 16. Mukherjee in view of Chaudhary or LIU do not teach wherein the one or more computer programs are further configured to cause the at least one processor to:
send information pertaining to conditions detected by the UE device to the home network and/or the intelligent subscriber management server of the UE device,
wherein the rules engine is configured to use the information pertaining to the conditions detected by the UE device to determine which SIM or SIM profile from the plurality of SIMs or SIM profiles to switch to.
Rice teaches training ML for switching SIMs (0063, 0112 – select SIM1/SIM2 based on signal strengths of cellular networks, based on available bandwidth, quality of service of the connection, stability of the connection, cost associated with the cellular network or any other characteristic). The UE and/or other UEs feedback real-time signal strength measurements to the network which is then used by the network to switch SIMs (0170).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee in view of Chaudhary or LIU to consider feedback from the UE and/or other UEs as taught by Rice thereby enabling the network to use real-time feedback regarding signal strengths of the network to select the best network/SIM that provides the highest quality of service and lowest cost.
4. Claims 9, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Chaudhary or LIU further in view of Pefkianakis et al (2022/0295343).
Regarding claim 9. Mukherjee in view of Chaudhary or LIU do not teach wherein the rules engine is configured to analyze network loading and congestion, subscription information, carrier contract information, or any combination thereof, to determine which SIM or SIM profile from the plurality of SIMs or SIM profiles to switch to.
Pefkianakis teaches using ML (0038-0039, 0127) trained using congestion information (0006, 0023, 0025) to switch between SIMs (0135) thereby increasing bandwidth available for communication by the UE and increases the reliability of data transfer (0004).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee in view of Chaudhary or LIU to consider network congestion as taught by Pefkianakis thereby increasing bandwidth available for communication by the UE and increases the reliability of data transfer (0004).
Regarding claims 11 and 18. Mukherjee teaches wherein the computer program instructions are further configured to cause the at least one processor to: use a trained AI/ML model for intelligent SIM switching to assist in determining the SIM or SIM profile of the plurality of SIMs or SIM profiles to switch to, by the rules engine (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already),
Mukherjee in view of Chaudhary or LIU do not explicitly teach wherein 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.
Pefkianakis teaches using ML (0038-0039, 0127) trained using congestion information (0006, 0023, 0025) to switch between SIMs (0135) thereby increasing bandwidth available for communication by the UE and increases the reliability of data transfer (0004).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee in view of Chaudhary or LIU to consider network congestion as taught by Pefkianakis thereby increasing bandwidth available for communication by the UE and increases the reliability of data transfer (0004).
5. Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Chaudhary or LIU further in view of Pefkianakis et al (2022/0295343).
Regarding claim 19. Mukherjee teaches a computing system, comprising: memory storing computer program instructions for performing intelligent subscriber identity module (SIM) switching for a user equipment (UE) device using a current SIM or SIM profile; and at least one processor configured to execute the computer program instructions (figure 1, col. 6 line 46 – col. 7 line 48 wherein UE comprises processor, memory and program), wherein the computer program instructions are configured to cause the at least one processor to:
use a trained artificial intelligence (AI) / machine learning (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 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, receive instructions from the rules engine to switch from the current SIM or SIM profile to a different SIM or SIM profile (abstract – ML aided autonomous control of mobile network access, col. 2 lines 1-12 – ML used to improve performance in mobile networks by predicting future signal strengths, figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider, col. 3 lines 5 – predicts which one of the plurality of resource packages meets the resource requirement and optimizes a reward function using a ML trained on previously implemented resource packages associated and associated QoE metrics OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already),
responsive to the instructions from the rules engine, configure the UE device for the different SIM or SIM profile and an associated carrier (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already) wherein configuring the UE device for the associated carrier comprises configuring the UE for bands and (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already, col. 3 lines 1-20 – mobile network service provider packages which include bandwidth, voice minutes (e.g., packet sizes optimized for the associated network service provider (e.g., carrier), data capacity limit, frequency band indicator, air-interface indicator (e.g., LTE 5G), col. 11 line 65 – col. 12 line 27 – summarizes the differences between sticking to a single provider verses dynamically picking the best provider across the metrics measured (for each task and location) … dropped frames (e.g., packet sizes), col. 26 lines 60-67 – an eSIM is typically downloaded from a SM-DP+ … any number of profiles may be downloaded on the UE, col. 28 line 31 – bandwidth within frequency band, col. 29 lines 25-33 – provision users on demand and to update pricing and bands of offers. Different prices may be set on different frequency bands and for different band widths (e.g., packet sizes), col. 30 lines 46-54 – allow users to configure profiles that the agent should switch between, e.g., setting data cap (e.g,. packet sizes) and expiration rules, col. 33 lines 4-26 – network 1 identifier and associated bandwidth and network 2 identifier and associated bandwidth, col. 38 lines 25-41 – two SIMs associated with two providers (e.g., carriers) and manually enabling a SIM (and associated voice and data plan) before making a call or launching an app, col., 45 lines 15-30 – the “band” refers to, say, 5GHz or 3.5GHz, air interface is WiFi, LTE, 5G, the available bandwidth is an integer representing some number of units of bandwidth), and
switch the UE device to the other SIM or SIM profile for communications services provided by the associated carrier (figure 1, col. 2 lines 42-67 wherein UE processor receives instructions from selector which includes ML model (e.g., rules engine) to determine which SIM card (mobile network provider) has cheaper voice rates and/or provides the best quality of experience (QoE) and/or select SIM based on a particular location and/or availability and switch the UE to the mobile network service provider OR col. 7 line 49 – col. 8 line 12 – wherein server uses ML to determine which SIM card provides the best quality of experience at the lowest cost and instructs the UE to switch a mobile network component, such as a SIM card, col. 15 lines 44-49 – the user selects/launches an App (e.g., makes a call) and the user agent (e.g., processor receives an instruction from ML model/rules engine) instantaneously selects one of, say, k SIMs or eSIMs and enables the selected SIM/eSIM if it was not enabled already).
Mukherjee does not explicitly use the term “packet sizes”.
Chaudhary teaches the MNOs 114 can represent different wireless service providers that provide specific services (e.g., voice and data) to which a user of the UE 102 can subscribe to access the service … the eUICC can be configured to store multiple eSIMs for access services offered by one or more MNOs 114. To be able to access the services offered by the MNOs, one or more eSIMs can be provisioned to the eUICC 108 of the UE. In some embodiments, policies can determine cost factors, data throughput rate limits (e.g., packet sizes), data capacity limits (e.g., packet sizes), application service compatibility and other criteria for determining whether one or more applications of the UE can access the 5G services (figure 1, paragraph 0027). The eUICC OS 206 can include an eSIM manager 210 (e.g., rules engine), which can perform management functions for various eSIMs (figure 2, 0028). eSIM policies may further include data throughput, QoS, latency, usage patterns, services, etc. to determine recommendations for 5G cell baseband resources (0031). A number of factors impact whether a given application, when used, can benefit from 5G connections including, for example, a known or expected amount of data to be transferred (e.g., packet sizes), data transfer rate cap … applicable cues can include intent cues that indicate an application’s intent to download a certain amount of data (e.g, packet sizes), such as for http(s)-based application that includes a content length entry-header field that indicates a size of an entity body to be transferred … the recommendation includes an indication of bandwidth usage for an application, e.g., a low or high bandwidth requirement or similar bandwidth ranking for the application (0032-0034).
Liu teaches UE has eSIMs configured for different Mobile Network Operators (MNOs) which enables a user to reduce phone plane cost or obtain a desired coverage, e.g., when traveling (0017) wherein SIM1 from AT&T is associated with FR1 band and SIM2 from Verizon is associated with FR2 band (0029, 0039).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee to further consider packet sizes, bandwidths, QoS, usage patterns, etc. as taught by Chaudhary or associate a first band to SIM1 from AT&T and a second band to SIM2 from Verizon as taught by Liu in order to further determine to switch the UE between SIM1 associated with a first MNO (e.g., first carrier/MNO associated with a first band, bandwidth, throughput, QoS for downloading a certain amount of data) or switch to another SIM2 associated with a second MNO (e.g, second carrier/MNO associated with a second band, second bandwidth, second throughput, second QoS for downloading a certain amount of data) as taught by Chaudhary (0003 – determine when to enable access to 5G cellular connections based on a variety of factors) or to switch to SIM1 from AT$T using FR1 band when at home and switch to SIM2 from Verizon using FR2 band when user is traveling as taught by Liu.
Mukherjee in view of Chaudhary or LIU do not explicitly teach wherein 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.
Pefkianakis teaches using ML (0038-0039, 0127) trained using congestion information (0006, 0023, 0025) to switch between SIMs (0135) thereby increasing bandwidth available for communication by the UE and increases the reliability of data transfer (0004).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee in view of Chaudhary or LIU to consider network congestion as taught by Pefkianakis thereby increasing bandwidth available for communication by the UE and increases the reliability of data transfer (0004).
Regarding claim 20. Mukherjee teaches wherein the different SIM or SIM 20. profile is for a first service, the instructions from the rules engine comprise selection of another different SIM or SIM profile for a second service, and 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 other different SIM or SIM profile and an associated carrier for the second service; and switch the UE device to the other SIM or SIM profile to use the second service provided by the associated carrier for the second service, wherein the first service comprises voice and short message service (SMS) and the second service comprises data, or vice versa (col. 1 line 64-66 – switch between SIMS wherein different SIM/carrier for phone calls and messages (e.g., SMS) and data, col. 2 line 42-54 – agent detects when user opens an application to make a phone call, determine which SIM card (mobile network operator) has cheaper voice rates, avoids roaming charges and/or has the most voice minutes available, and switch the UE to the mobile network service prover that optimizes utilization of the UE’s pre-purchased resources, col. 7 line 49 – col. 8 line 12 – user selects an application requiring resources (e.g., voice or data to run) … the selector then instructs a component of the UE to implement the selected resource package, e.g., by switching a mobile network component, such as a SIM card, col. 15 lines 44-49 – we simplify the provider selection problem by stipulating that as soon as the user launches an app (for brevity, making a call will also be considered “launching an app” in what follows), the user agent instantaneously selects one of, say k SIMs or eSIMs, and enables the selected SIM/eSIM if it was not enabled already, col. 38 lines 25-50 – it is a common usage scenario in many countries for a single user to carry two pre-paid SIM cards (corresponding to two providers) in a single dual-SIM device and manually enable a SIM (and associated voice and data plan) before making a call or launching an app … let us design a user agent to perform this task automatically).
Response to Arguments
6. Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
---(2024/0098504) Demonget et al teaches switching SIMs/carrier(s) (0050) using ML trained (0028 – ML trained over time) with data regarding voice service, content streaming, gaming service, etc. (0023, 0029), location based criteria (00024, 0038-0039, 0056), event-based criteria (0024, 0056), load-based criteria (0024, 0056), device type (0056).
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARRY W TAYLOR whose telephone number is (571)272-7509. The examiner can normally be reached Monday-Thursday: 7-5.
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/BARRY W TAYLOR/Primary Examiner, Art Unit 2646
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