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, 12-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee et al (11,641,579) in view of Ravikanti et al (2023/0164541) or Velusamy (10,716,019).
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, wherein the rules engine is configured to determine (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. 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);
responsive to the instructions from the rules engine, configure the UE device for the (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 (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).
Regarding RCE 9/8/2026. Mukherjee does not explicitly teach maintain, on the UE device, attachment to both of the first and second SIMs or SIM profiles.
Ravikanti teaches a multi-SIM UE and maintain, on the UE, attachment to both of the first and second SIMs or SIM profiles (0017, 0034-0036, 0059-0060, 0062) wherein SIM1 is used for voice services and SIM2 is used for data service (0036, 0052, 0060, 0062). UE uses machine learning having rules for switching between SIMs OR SIM profiles (0058, 0083) wherein the rules consider SIM purpose, specified hours, application classification, location information, and usage restrictions (0053, 0055, 0058, 0066-0067, 0080).
Velusamy teaches UE equipped with two SIMs in a dual call/dual standby (DCSD) configuration which enables the dual-SIM user device to concurrently communicate with two different wireless carrier networks (col. 1 lines 55-67). The dual-SIM user device may be equipped with a comparison application (e.g., rules engine) is installed on the UE used to switch the dual-SIM user device to one or more carrier networks that provide lower voice call drops, a higher network signal strength, superior network data throughput rate, at one or more locations (col. 2 lines 36-67, col. 3 lines 32-45, col. 4 lines 50-66, col. 5 lines 22-37, col. 6 lines 60-63, col. 9 lines 47-56). In other words, the dual-SIM UE may switch from SIM1 associated with a first wireless carrier for voice to SIM2 associated with a second wireless carrier for data service based on UE location (col. 9 line 58 – col. 10 line 36, col. 15 line 54 – col. 16 line 48).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee to further use the machine learning criteria as taught by Ravikanti OR Velusamy in order to switch between SIM1 or SIM2 based on a certain purpose, historical user information, preconfigured SIM preferences, application classification schemes, and/or user location and/or time as taught by Ravikanti OR Velusamy thereby providing subscribers with faster or more reliable telecommunication features, as well as monetary savings.
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 (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. 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)
responsive to the instructions from the rules engine, configuring the UE device, the UE device for the (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) 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).
Regarding RCE 9/8/2026. Mukherjee does not explicitly teach maintaining, on the UE device, attachment to both of the first and second SIMs or SIM profiles.
Ravikanti teaches a multi-SIM UE and maintain, on the UE, attachment to both of the first and second SIMs or SIM profiles (0017, 0034-0036, 0059-0060, 0062) wherein SIM1 is used for voice services and SIM2 is used for data service (0036, 0052, 0060, 0062). UE uses machine learning having rules for switching between SIMs OR SIM profiles (0058, 0083) wherein the rules consider SIM purpose, specified hours, application classification, location information, and usage restrictions (0053, 0055, 0058, 0066-0067, 0080).
Velusamy teaches UE equipped with two SIMs in a dual call/dual standby (DCSD) configuration which enables the dual-SIM user device to concurrently communicate with two different wireless carrier networks (col. 1 lines 55-67). The dual-SIM user device may be equipped with a comparison application (e.g., rules engine) is installed on the UE used to switch the dual-SIM user device to one or more carrier networks that provide lower voice call drops, a higher network signal strength, superior network data throughput rate, at one or more locations (col. 2 lines 36-67, col. 3 lines 32-45, col. 4 lines 50-66, col. 5 lines 22-37, col. 6 lines 60-63, col. 9 lines 47-56). In other words, the dual-SIM UE may switch from SIM1 associated with a first wireless carrier for voice to SIM2 associated with a second wireless carrier for data service based on UE location (col. 9 line 58 – col. 10 line 36, col. 15 line 54 – col. 16 line 48).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee to further use the machine learning criteria as taught by Ravikanti OR Velusamy in order to switch between SIM1 or SIM2 based on a certain purpose, historical user information, preconfigured SIM preferences, application classification schemes, and/or user location and/or time as taught by Ravikanti OR Velusamy thereby providing subscribers with faster or more reliable telecommunication features, as well as monetary savings.
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 (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).
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 Ravikanti OR Velusamy further in view of Kojima et al (2021/0195392).
Regarding claims 4 and 14. Mukherjee in view of Ravikanti or Velusamy do 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 Ravikanti OR Velusamy 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 Ravikanti OR Velusamy further in view of Rice et al (2022/0053607).
Regarding claims 8 and 16. Mukherjee in view of Ravikanti OR Velusamy 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 Ravikanti OR Velusamy 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 Ravikanti OR Velusamy further in view of Pefkianakis et al (2022/0295343).
Regarding claim 9. Mukherjee in view of Ravikanti OR Velusamy 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 Ravikanti or Velusamy 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 Ravikanti OR Velusamy 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 Ravikanti OR Velusamy 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. Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Ravikanti OR Velusamy 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 is configured to determine (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. 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);
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 each of the (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) (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 (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).
Regarding RCE 9/8/2026. Mukherjee does not explicitly teach maintain, on the UE device, attachment to both of the first and second SIMs or SIM profiles.
Ravikanti teaches a multi-SIM UE and maintain, on the UE, attachment to both of the first and second SIMs or SIM profiles (0017, 0034-0036, 0059-0060, 0062) wherein SIM1 is used for voice services and SIM2 is used for data service (0036, 0052, 0060, 0062). UE uses machine learning having rules for switching between SIMs OR SIM profiles (0058, 0083) wherein the rules consider SIM purpose, specified hours, application classification, location information, and usage restrictions (0053, 0055, 0058, 0066-0067, 0080).
Velusamy teaches UE equipped with tow SIMs in a dual call/dual standby (DCSD) configuration which enables the dual-SIM user device to concurrently communicate with two different wireless carrier networks (col. 1 lines 55-67). The dual-SIM user device may be equipped with a comparison application (e.g., rules engine) is installed on the UE used to switch the dual-SIM user device to one or more carrier networks that provide lower voice call drops, a higher network signal strength, superior network data throughput rate, at one or more locations (col. 2 lines 36-67, col. 3 lines 32-45, col. 4 lines 50-66, col. 5 lines 22-37, col. 6 lines 60-63, col. 9 lines 47-56). In other words, the dual-SIM UE may switch from SIM1 associated with a first wireless carrier for voice to SIM2 associated with a second wireless carrier for data service based on UE location (col. 9 line 58 – col. 10 line 36, col. 15 line 54 – col. 16 line 48).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Mukherjee to further use the machine learning criteria as taught by Ravikanti OR Velusamy in order to switch between SIM1 or SIM2 based on a certain purpose, historical user information, preconfigured SIM preferences, application classification schemes, and/or user location and/or time as taught by Ravikanti OR Velusamy thereby providing subscribers with faster or more reliable telecommunication features, as well as monetary savings.
Mukherjee in view of Ravikanti OR Velusamy do not 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 Ravikanti OR Velusamy 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).
Response to Arguments
6. Applicant’s arguments with respect to claims 1-9, 11-16, and 18-19 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.
---(2020/0267779) Swami et al teaches multi-SIM/multi-operator UE configuration wherein SIM1 is for voice and associated with first MNO1 and SIM2 is for data services and associated with MNO2 and BOTH SIMS are active (e.g., maintain, on the UE, attachment to both of the first and second SIMs or SIM profiles) and the UE gets instructions from server to switch between SIMs and/or SIM profiles based on user location (e.g., user located in an airport) (0150-0152).
---(2021/0195686) Kaur teaches multi-SIM/multi-operator UE configuration wherein SIM1 is for voice and associated with first network operator (e.g., carrier) and second SIM2 is for data and associated with second network operator and the UE maintains connection/attachment to both of the first and second SIMS or SIM profiles and switches between the SIMs based at least on congestion, overload condition, device class preferences, traffic conditions, communication provider signal detection, UE movement, different geo-fencing (e.g., location) schemes, MNO RSPR, MNO RSRQ or any other signal measurement with MNOs or any other criteria (0002-0003, 0022-0024, 0046-0047, 0058, 0061) which is used by the UE to switch between SIMS/MNOs.
8. 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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Anderson can be reached at 571-272-4177. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BARRY W TAYLOR/Primary Examiner, Art Unit 2646
Prosecution Insights