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 .
Response to Amendment
This office action is in response to the amendment filed June 19, 2026 (6/19/2026).
Claims 1-2, 4, 7, 10-11, 13, 17-18, 20-31, 33, 35-36, 39-40, 42, and 46-47 are currently pending.
Claims 1-2, 7, 29-31, 33, 36, 39-40, and 42 are amended.
No previous claims were canceled.
No new claims were added.
Response to Arguments
Applicant’s arguments and amendments, see Remarks Pg. 12, filed June 19, 2026 (6/19/2026), with respect to claims 2, 7, 10, 31, 33, 35, 36, 39, and 40 have been fully considered and are persuasive. The 35 U.S.C. 112 rejection of claims 2, 7, 10, 31, 33, 35, 36, 39, and 40 has been withdrawn.
Applicant’s arguments with respect to claims 1, 4, 11, 13, 17-18, 20, 29-30, 33, 39, 42, and 46 have been considered but are moot because the new ground of rejection necessitated by the amendments does not rely on the reference or combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In response to applicant's argument that , the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
While the Examiner respectfully disagrees that modifying Hu to transmit the ML model to the AMF would be counter-intuitive and contrary to Hu’s deliberate architectural design, the test for obviousness would not depend on incorporating Kumar 1 into the design of Hu. Hu teaches the collection of “history paging information”, and with that information a machine learning model is then determined for paging (Pg. 9-10). Kumar 1 teaches transferring a ML model to the AMF, specifically teaching “The transmission, at 568, to the at least one of the first base station (e.g., 554) or the AMF 555 of the core network (e.g., OAM core network 556) may be configured to initiate the at least one of the ML procedure or the NN procedure, the at least one of the ML procedure or the NN procedure being at least one of a signaling procedure or a management procedure” (Par. [0091]). The transmission of the ML model to the AMF for the AMF to use in a signaling procedure (taught in Kumar 1) would suggest to one of ordinary skill in the art that after collecting paging information and determining a ML model for paging (taught in Hu), the ML model could then be transmitted to the AMF to use for paging which is a signaling procedure.
This action is FINAL.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim 47 recites the limitation "the NWDAF" in "the NWDAF is deployed as a custom application…" There is insufficient antecedent basis for this limitation in the claim as neither Claim 47 nor the claim it depends on, Claim 42, mention an NWDAF. Examiner notes that similar claims 7 and 36, were amended to include the limitation “the first network node is a Network Data Analytics function (NWDAF)” and similar amendments to claim 47 would provide appropriate antecedence for the limitation.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 13, 17, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (CN 110505688) in view of Kumar et al. (US 2022/0182263) (hereinafter Kumar 1).
Regarding Claim 1, Hu teaches a method at a first network node for facilitating a second network node in paging a user equipment (UE),
the method comprising collecting, from one or more network nodes, paging information for the UE (Pg. 10, “Step 301: obtaining the data sample of UE; Here, the data sample is the history paging area information of the UE”),
determining a machine learning (ML) model at least partially based on the paging information (Pg. 11, “For example, it is possible to select a machine learning algorithm, training the data samples to obtain a linear regression model, and then using the obtained model to predict the paging optimization information (paging area) in a specific time period”).
Hu, however, does not explicitly teach transmitting, to the second network node, the determined ML model for use by the second network node in paging the UE.
In the same field of endeavor, Kumar 1 teaches transmitting, to the second network node, the determined ML model for use by the second network node in paging the UE (Par. [0091] “For example, referring to FIGS. 5A-5B, the OAM 506/556 may transmit, at 566a, the ML/NN model and features to the AMF via a signaling-based ML/NN technique or transmit, at 568, the ML/NN model and features to the base station 554 via a management-based ML/NN techniques. The transmission, at 568, to the at least one of the first base station (e.g., 554) or the AMF 555 of the core network (e.g., OAM core network 556) may be configured to initiate the at least one of the ML procedure or the NN procedure, the at least one of the ML procedure or the NN procedure being at least one of a signaling procedure or a management procedure”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kumar 1’s transferring of a ML model to a second node to have said node execute the model with Hu’s collection of paging information and determination of a ML model for paging to expectedly improve telecommunication technologies.
Regarding Claim 13, Hu in view of Kumar 1 teaches the invention of Claim 1, where Hu further teaches disclosing the paging information comprises at least one of: - mobility information for one or more UEs comprising the UE (100); - statistical paging information for the one or more UEs, wherein the statistical paging information comprises at least one of a paging success ratio in each paging phase, a number of paging messages in each paging phase, and paging attempts in each paging phase; - core network information indicating relationship between each tracking area (TA) and NB/gNB for a core network to which the first network node belongs; and- supplemental information that facilitates a Mobility Management Entity (MME) or an Access and Mobility Function (AMF) in linking the ML model to an Operation and Maintenance (OAM) configuration (Pg. 10 "The time addition, during practical application, UE id a periodic registration timer is according to the behavior of the UE (e.g., mobility of the UE) to update. For example, the time when especially frequent mobility of the UE, the UE id of the periodic registration timer needs to be shortened, the UE moving frequently, the time the UE id of the periodic registration timer may be longer, i.e., the cell in the cell list and the tracking area list can be updated once for a long time").
Regarding Claim 17, Hu in view of Kumar 1 teaches the invention of Claim 1 where Hu further teaches determining the ML model for the UE comprises:
analyzing mobility information for the UE; (Pg. 10 "The time addition, during practical application, UE id a periodic registration timer is according to the behavior of the UE (e.g., mobility of the UE) to update. For example, the time when especially frequent mobility of the UE, the UE id of the periodic registration timer needs to be shortened, the UE moving frequently, the time the UE id of the periodic registration timer may be longer, i.e., the cell in the cell list and the tracking area list can be updated once for a long time"),
evaluating statistical paging information to simulate paging at one or more confidence levels; (Pg. 11, “For example, it is possible to select a machine learning algorithm, training the data samples to obtain a linear regression model, and then using the obtained model to predict the paging optimization information (paging area) in a specific time period”),
and determining the ML model for the UE at least partially based on the analyzed mobility information and/or the evaluated statistical paging information (Pg. 11, “For example, it is possible to select a machine learning algorithm, training the data samples to obtain a linear regression model, and then using the obtained model to predict the paging optimization information (paging area) in a specific time period”).
Regarding Claim 29, Hu discloses a first network node configured to facilitate a second network node in paging a user equipment (UE), the first network node comprising:
a processor; (Pg. 5 “The embodiment of the present invention further claims a network equipment, comprising: a second processor”),
a memory storing instructions which, when executed by the processor, cause the first network node to: (Pg. 5 “The embodiment of the invention further claims a network equipment, comprising: a second processor and second memory of computer program for storage can be run on a processor, wherein the second processor is configured to run the computer program, any one side step executes the NWDA method”),
collect, from one or more network nodes, paging information for the UE; (Pg. 10 “Step 301: obtaining the data sample of UE; Here, the data sample is the history paging area information of the UE”),
determining a machine learning (ML) model at least partially based on the paging information; (Pg. 11 “For example, it is possible to select a machine learning algorithm, training the data samples to obtain a linear regression model, and then using the obtained model to predict the paging optimization information (paging area) in a specific time period”),
Hu, however, does not explicitly teach transmitting, to the second network node, the determined ML model for use by the second network node in paging the UE.
In the same field of endeavor, Kumar 1 teaches transmitting, to the second network node, the determined ML model for use by the second network node in paging the UE (Par. [0091] “For example, referring to FIGS. 5A-5B, the OAM 506/556 may transmit, at 566a, the ML/NN model and features to the AMF via a signaling-based ML/NN technique or transmit, at 568, the ML/NN model and features to the base station 554 via a management-based ML/NN techniques. The transmission, at 568, to the at least one of the first base station (e.g., 554) or the AMF 555 of the core network (e.g., OAM core network 556) may be configured to initiate the at least one of the ML procedure or the NN procedure, the at least one of the ML procedure or the NN procedure being at least one of a signaling procedure or a management procedure”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kumar 1’s transferring of a ML model to a second node to have said node execute the model with Hu’s collection of paging information and determination of a ML model for paging to expectedly improve telecommunication technologies.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (CN 110505688) in view of Kumar et al. (US 2022/0182263) (hereinafter Kumar 1) in further view of Stojanovski et al. (US 2019/0191409).
Regarding Claim 4, Hu in view of Kumar 1 teaches the invention of Claim 1, where Hu further teaches wherein the paging information comprises at least one of: - location information in terms of tracking area (TA), eNB/gNB, or cell; - time information; and- UE service type (Pg. 11 “That is, NWDA uses the history of the UE paging region information collected, comprising a paging range corresponding at a time by the user, based on the machine learning algorithm, prediction UE paging optimisation information within a certain time period, that accurately predicts UE related to mobility mode and regular UE tracking area”).
Hu however, does not teach receiving, from a collocated mobility management module, paging information for the UE.
Stojanovski teaches receiving, from a collocated mobility management module, paging information for the UE (Par. [0018] “Other arrangements are possible, including arrangements in which two or more of the gNB-CU 106, CU-CP 107, CU-UP 108, gNB-DU 109 are co-located” and Par. [0069] “In accordance with some embodiments, a gNB 105 of a 3GPP network may include: memory; and processing circuitry. The gNB 105 may be configured with logical nodes. The logical nodes may include a gNB-CU 106 and a gNB-DU 109. The processing circuitry may decode a first paging message, wherein the first paging message is received at the gNB-CU 106 from an access management function (AMF) entity”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stojanovski’s co-located nodes receiving paging information with Kumar 1’s transferring of a ML model and Hu’s paging information including paging region and corresponding time to improve communications between a UE and network node.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (CN 110505688) in view of Kumar et al. (US 2022/0182263) (hereinafter Kumar 1) in further view of Liu et al. (US 2022/0361262).
Regarding Claim 11, Hu in view of Kumar 1 teaches the invention of Claim 1, but does not teach the first network node is an AI server that is located separately from the second network node and wherein the paging information that has been collected is anonymized.
In the same field of endeavor, Liu teaches the first network node is an AI server that is located separately from the second network node and wherein the paging information that has been collected is anonymized (Par. [0022] “The AI entity could be described as a logical function entity that enables intelligent control and optimization of Radio Access Network (RAN) elements and resources via data collection. The AI entities can be in different geographic locations, integrated in different RAN nodes, or as separate entities, e.g., AI servers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Liu’s first node being an AI server that is located separately with Kumar 1’s transferring of a ML model and Hu’s network node system to support a much wider range of use-case characteristics and provide a more complex and sophisticated range of access requirements and flexibilities.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (CN 110505688) in view of Kumar et al. (US 2022/0182263) (hereinafter Kumar 1) in further view of Kumar et al. (US 2022/0377844) (hereinafter Kumar 2).
Regarding Claim 18, Hu in view of Kumar 1 teaches the invention of Claim 17, but does not teach an initial configuration of the ML model is configured by an OAM module, and wherein the method further comprises: providing the OAM module with at least one of history of confidence levels, performance of the current paging procedure, and suggestion for paging profiles.
In the same field of endeavor, Kumar 2 teaches teach an initial configuration of the ML model is configured by an OAM module, and wherein the method further comprises: providing the OAM module with at least one of history of confidence levels, performance of the current paging procedure, and suggestion for paging profiles (Par. 0089] “The OAM 602 may subsequently transmit, at 620, a model training response and a model training configuration to the model repository 612 (e.g., associated with aggregated weights and/or updated model rules)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kumar 2’s OAM module with Kumar 1’s transferring of a ML model and Hu’s determination of a paging model to better improve ML model training.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (CN 110505688) in view of Kumar et al. (US 2022/0182263) (hereinafter Kumar 1) in further view of Narayanan et al. (US 2024/0187127).
Regarding Claim 20, Hu in view of Kumar 1 teaches the invention of Claim 1, but does not teach training the ML model based on a cost function that is determined at least partially based on an amount of signaling for successfully paging the UE and/or a paging latency.
In the same field of endeavor, Narayanan teaches training the ML model based on a cost function that is determined at least partially based on an amount of signaling for successfully paging the UE and/or a paging latency (Par. [0242] "a WTRU may be configured to adapt AI processing such that the AI model performance may be traded-off to achieve one or more desired objective. For example, the AI model may learn from experience (e.g., observing data and/or environment) over a period of time. The performance of an AI model may evolve over a time period. A WTRU may adapt AI processing wherein the adaption may lead to a reduction in one or more of the following: a power consumption, memory usage, a latency, overhead or processing requirement(s), for example, at the cost of a reduction in AI model inference performance and/or an increase in signaling overhead. For example, when AI processing may be applied to wireless functions (e.g., one or more of the following: channel estimation, demodulation, RS measurements, HARQ, CSI feedback, positioning, beam management etc.), it may be possible to perform granular adjustment to tradeoff a model performance to achieve an objective. For example, the WTRU may adapt the processing to accomplish one or more of the following: a reduction in power consumption, a reduction memory/storage utilization, a reduction in Latency, or a reduction in processing power (e.g., computational resources)").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Narayanan’s AI training based on latency with Kumar 1’s transferring of a ML model and Hu’s machine learning model receiving UE paging information to improve the performance of Hu’s machine learning model.
Claims 30 and 42 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 2022/0182263) (hereinafter Kumar 1) in view of Hu et al. (CN 110505688).
Regarding Claim 30, Kumar 1 teaches a method at a second network node for paging a user equipment (UE), the method comprising:
receiving, from a first network node, a machine learning (ML) model, for paging the UE; (Par. [0091] “For example, referring to FIGS. 5A-5B, the OAM 506/556 may transmit, at 566a, the ML/NN model and features to the AMF via a signaling-based ML/NN technique or transmit, at 568, the ML/NN model and features to the base station 554 via a management-based ML/NN techniques. The transmission, at 568, to the at least one of the first base station (e.g., 554) or the AMF 555 of the core network (e.g., OAM core network 556) may be configured to initiate the at least one of the ML procedure or the NN procedure, the at least one of the ML procedure or the NN procedure being at least one of a signaling procedure or a management procedure”).
Kumar 1, however, does not explicitly teach determining a paging profile at least partially based on the received ML model and initiating a paging procedure for the UE at least partially based on the determined paging profile.
In the same field of endeavor, Hu teaches determining a paging profile at least partially based on the received ML model (Pg. 10 “Here, the determined paging optimization information is used for AMF to determine paging range and paging the UE according to the paging range; The determined optimization information characterizes the movement of the UE within a particular time period”),
and initiating a paging procedure for the UE at least partially based on the determined paging profile (Pg. 10 “Here, the determined paging optimization information is used for AMF to determine paging range and paging the UE according to the paging range; The determined optimization information characterizes the movement of the UE within a particular time period”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kumar 1’s transferring of a ML model to a second node to have said node execute the model with Hu’s utilization of a ML model for paging to expectedly improve telecommunication technologies.
Regarding Claim 42, Kumar 1 teaches a second network node configured for initiating a paging procedure for a user equipment (UE), the second network node comprising:
a processor; (Par. [0027] “By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors”)
a memory storing instructions which, when executed by the processor, the second network node to: (Par. [0028] “Accordingly, in one or more example embodiments, the functions described may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium”),
receive, from a first network node, a machine learning (ML) model for paging the UE; (Par. [0091] “For example, referring to FIGS. 5A-5B, the OAM 506/556 may transmit, at 566a, the ML/NN model and features to the AMF via a signaling-based ML/NN technique or transmit, at 568, the ML/NN model and features to the base station 554 via a management-based ML/NN techniques. The transmission, at 568, to the at least one of the first base station (e.g., 554) or the AMF 555 of the core network (e.g., OAM core network 556) may be configured to initiate the at least one of the ML procedure or the NN procedure, the at least one of the ML procedure or the NN procedure being at least one of a signaling procedure or a management procedure”).
Kumar 1, however, does not explicitly teach determine a paging profile at least partially based on the received ML; and initiate the paging procedure for the UE at least partially based on the determined paging profile.
In the same field of endeavor, Hu teaches determine a paging profile at least partially based on the received ML; (Pg. 10 “Here, the determined paging optimization information is used for AMF to determine paging range and paging the UE according to the paging range; The determined optimization information characterizes the movement of the UE within a particular time period”),
and initiate the paging procedure for the UE at least partially based on the determined paging profile (Pg. 10 “Here, the determined paging optimization information is used for AMF to determine paging range and paging the UE according to the paging range; The determined optimization information characterizes the movement of the UE within a particular time period”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kumar 1’s transferring of a ML model to a second node to have said node execute the model with Hu’s utilization of a ML model for paging to expectedly improve telecommunication technologies.
Claim 40 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 2022/0182263) (hereinafter Kumar 1) in view of Hu et al. (CN 110505688) in further view of Devlic et al. (WO 2020/239195).
Regarding Claim 40, Kumar 1 in view of Hu teaches the invention of Claim 30, but does not teach the first network node is a Network Data Analytic Function (NWDAF) which is deployed as an application or service on a MEC platform at a MEC host or collocated with a MEC orchestrator; and the second network node is a User Plane Function (UPF).
In the same field of endeavor, Devlic teaches the first network node is a Network Data Analytic Function (NWDAF) which is deployed as an application or service on a MEC platform at a MEC host or collocated with a MEC orchestrator; (Pg. 17, Lines 6-7 “At step VII in Fig. 5, the NWDAF 300 receives the third control message 530 and therefrom derives the selected MEC host”),
and the second network node is a User Plane Function (UPF) (Pg. 17, Lines 7-8 “Based on the selected MEC host the NWDAF 300 selects a UPF which is collocated with the selected MEC host”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Devlic’s NWDAF being deployed at an MEC host and selection of a UPF with Kumar 1’s transferring of a ML model to a second node to have said node execute the model with Hu’s to better meet low latency requirements in a wireless network.
Claim 46 is rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (CN 110505688) in view of Kumar et al. (US 2022/0182263) (hereinafter Kumar 1) in further view of .
Regarding Claim 46, Hu in view of Kumar 1 teaches the invention of Claim 29, but does not teach training the ML model based on a cost function that is determined at least partially based on an amount of signaling for successfully paging the UE and/or a paging latency.
In the same field of endeavor, Narayanan teaches training the ML model based on a cost function that is determined at least partially based on an amount of signaling for successfully paging the UE and/or a paging latency (Par. [0242] "a WTRU may be configured to adapt AI processing such that the AI model performance may be traded-off to achieve one or more desired objective. For example, the AI model may learn from experience (e.g., observing data and/or environment) over a period of time. The performance of an AI model may evolve over a time period. A WTRU may adapt AI processing wherein the adaption may lead to a reduction in one or more of the following: a power consumption, memory usage, a latency, overhead or processing requirement(s), for example, at the cost of a reduction in AI model inference performance and/or an increase in signaling overhead. For example, when AI processing may be applied to wireless functions (e.g., one or more of the following: channel estimation, demodulation, RS measurements, HARQ, CSI feedback, positioning, beam management etc.), it may be possible to perform granular adjustment to tradeoff a model performance to achieve an objective. For example, the WTRU may adapt the processing to accomplish one or more of the following: a reduction in power consumption, a reduction memory/storage utilization, a reduction in Latency, or a reduction in processing power (e.g., computational resources)").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Narayanan’s AI training based on latency with Kumar 1’s transferring of a ML model and Hu’s machine learning model receiving UE paging information to improve the performance of Hu’s machine learning model.
Allowable Subject Matter
Claims 2, 7, 10, 21-28, 31, 33, 35-36, and 39 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 47 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Antipolis (Sophia Antipolis, "TR 23.791: Update of Solution 5 to Avoid Biased Data Sample" August 20-24 2018, SA WG2, Pgs. 1-3 (Year: 2018)) (found in IDS) discloses a method for preventing bias in paging failure prediction to better optimize the service experience (See Pg. 3, Par. 6.5.1.1 General).
Jaeseong et al. (Jaeseong Jeong et al., "Mobility Prediction for 5G Core Networks," March 2021, IEEE Communications Standards Magazine, Pgs. 58-61 (Year: 2021)) discloses a method for ML-assisted adaptive paging using predicted future trajectories in a mobile communication system (See Pg. 59, Par. Use Case 1: Paging).
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.
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/J.N.D./Examiner, Art Unit 2641
/CHARLES N APPIAH/Supervisory Patent Examiner, Art Unit 2641
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