CTNF 18/567,199 CTNF 85700 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status This office action is in response to the communication(s) filed on 01/29/2026 . Claim(s) 1-4, 6-17, and 19-20 is/are currently presenting for examination. Claim(s) 1 and 14 is/are independent claim(s). Claim(s) 1-4, 6-11, 13-17, and 19-20 is/are rejected. Claim(s) 12 is/are objected to. This action has been made NON-FINAL. Response to Arguments Applicant's arguments filed on 01/29/2026 have been considered but are moot in view of the new ground(s) of rejection. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA 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. 07-21-aia AIA Claim (s) 1-4, 6-7, 11, and 13-17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US_20190239238_A1_Calabrese in view of CN_103686748_A_Yang (with English translation) . Regarding claim 1 , Calabrese discloses a network node device, comprising: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the network node device to (Calabrese figure 6, paragraphs 215-220) : generate at least one client cluster comprising at least one client device served by the network node device according to at least one clustering criterion (Calabrese figure 3, paragraph 93, “…the agent node 210 may also be configured to determine a control action associated with the radio cell 215, 235-1, 235-2, 235-3 in the communication system 200 based on the control policy, a set of available control actions, and at least one feature representing the state of the communication system 200, or a subset thereof. The agent node 210 may further be configured to configure the radio resource parameter associated to the radio cell 215, 235-1, 235-2, 235-3 based on the determined control action”, paragraph 96 , “… the radio resource parameter may be a control parameter associated with allocation of shared radio resources of one or more radio cells 215, 235-1, 235-2, 235-3 to one or more user devices 220, such as time-frequency resource blocks”) ; assign the at least one control algorithm instance to the at least one client cluster (Calabrese figure 3, paragraph 79, “The action taken by the agent node 210 according to the learned control policy may comprise either the change of a radio resource parameter (e.g., downlink transmission power of one or more eNodeBs, uplink transmit power of one or more user device 220, electrical tilt of one or more eNodeBs, etc.), the change of the parameter of a given control algorithms (e.g., one threshold value for performing hand over or selecting one or more radio frequency carrier to be assigned to the user device 220…”, and paragraph 96) ; and control at least one transmission power control parameter of the at least one client device in the at least one client cluster using the at least one control algorithm instance (Calabrese figure 3, paragraph 95, “Further, the radio resource parameter may be a control parameter associated with uplink power control in one or more use devices 210, such a power control adjustment value, a fractional pathloss adjustment value, an open-loop power control configuration value…”, paragraph 96, paragraph 154, “The trainer node 400 can determine the optimal control policy 430 based on a Reinforcement Learning (RL) algorithm. The reinforcement learning algorithm solves the problem of associating the experienced reward to the control actions that, taken in a given state of the system 200, lead to that reward. The control policy 430, resulting from a reinforcement learning algorithm, maps a given system state to the action to be taken…”) , wherein the at least one control algorithm instance is configured to, in each iteration, when in an exploration mode: change the at least one transmission power control parameter of at least one client device in the at least one client cluster, monitor a change in at least one performance indicator of the at least one client device in response to the change, and update a policy according to the change in at least one performance indicator (Calabrese paragraphs 76-79, “…a method for autonomously learning different radio resource management strategies using measurements and Key Performance Indicators (KPIs) collected from an algorithmic interaction with the radio environment. Such algorithmic interaction with the radio environment…The action taken by the agent node 210 according to the learned control policy may comprise either the change of a radio resource parameter (e.g., downlink transmission power of one or more eNodeBs, uplink transmit power of one or more user device 220, electrical tilt of one or more eNodeBs, etc.), the change of the parameter of a given control algorithms…”, and paragraphs 136-137, “…the agent node 210 may be configured to determine whether to apply the control policy 430 based on the exploration-to-exploitation control parameter ϵ. The exploration-to-exploitation control parameter ϵ may indicate how often in average the control policy 430 should be used compared to an alternative control policy 430…”) , and wherein the at least one control algorithm instance is configured to, when in an exploitation mode, iteratively change the at least one transmission power control parameter of at least one client device in the at least one client cluster according to the policy (Calabrese paragraphs 76-79, “…a method for autonomously learning different radio resource management strategies using measurements and Key Performance Indicators (KPIs) collected from an algorithmic interaction with the radio environment. Such algorithmic interaction with the radio environment…The action taken by the agent node 210 according to the learned control policy may comprise either the change of a radio resource parameter (e.g., downlink transmission power of one or more eNodeBs, uplink transmit power of one or more user device 220, electrical tilt of one or more eNodeBs, etc.), the change of the parameter of a given control algorithms…”, and paragraphs 136-137, “…the agent node 210 may be configured to determine whether to apply the control policy 430 based on the exploration-to-exploitation control parameter ϵ. The exploration-to-exploitation control parameter ϵ may indicate how often in average the control policy 430 should be used compared to an alternative control policy 430…”), but does not discloses wherein the at least one clustering criterion comprises a reference signal received power, RSRP, difference metric of the at least one client device, wherein the RSRP difference metric comprises a difference between an RSRP received by the at least one client device from the network node device and an RSRP received by the at least one client device from another network node device. Yang discloses wherein the at least one clustering criterion comprises a reference signal received power, RSRP, difference metric of the at least one client device, wherein the RSRP difference metric comprises a difference between an RSRP received by the at least one client device from the network node device and an RSRP received by the at least one client device from another network node device (Yang, translation, page 2, “Set b: UE set of users whose reported difference PPR between the RSRP of the serving cell and the RSRP of the strongest neighbor cell is less than the predetermined PPR threshold”) . Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Yang’s grouping the UEs based on their reported difference PPR between the RSRP of the serving cell and the RSRP of the strongest neighbor cell is less than the predetermined PPR threshold in Calabrese’s system for accurately dividing edge users to improve the performance of interference coordination algorithms (Yang page 2 lines 12-14). This method for improving the system of Calabrese was within the ordinary ability of one of ordinary skill in the art based on the teachings of Yang. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Calabrese and Yang to obtain the invention as specified in claim 1. Regarding claim 2 , Calabrese and Yang disclose the network node device according to claim 1, and Calabrese further discloses wherein the at least one transmission power control parameter comprises normalized transmit power density and/or a path-loss compensation factor (Calabrese paragraphs 42, 95-96, " ... Further, the radio resource parameter may be a control parameter associated with uplink power control in one or more use devices 210, such a power control adjustment value, a fractional path loss adjustment value, an open-loop power control configuration value ... a control parameter associated with allocation of shared radio resources of one or more radio cells 215, 235-1, 235-2, 235-3 to one or more user devices 220, such as time-frequency resource blocks…") . Regarding claim 3 , Calabrese and Yang disclose the network node device according to claim 1, and Calabrese further discloses wherein the at least one clustering criterion comprises a quality of service and/or radio conditions of the at least one client device (Calabrese paragraphs 76, 111, “... Such algorithmic interaction with the radio environment is based on ideas from machine learning and, more specifically, reinforcement learning and tailors them to the specifics of the radio communication system 200. Using the methodology proposed in this solution it is possible to design better solutions for the management of radio resources which are more performing and adaptive to the changing radio environment conditions ... " and " ... The advantage of this embodiment is to enable centralized control of a plurality of radio network nodes 230, with the aim of improving KPls like the spectral efficiency of the system 200, the satisfaction of Quality and Service (QoS) requirements and the fairness of the distribution of resources across user devices 220 ... ") . Regarding claim 4 , Calabrese and Yang disclose the network node device according to claim 1, and Calabrese further discloses wherein the at least one clustering criterion comprises reference signal received power of the at least one client device (Calabrese paragraphs 115-116, “ ... the set of features may comprise a list of radio control parameters associated with one or more radio resource management algorithms to be controlled by the agent node 210 ... Thereby, the radio environmental measurement received from user devices 220 with the radio cell 215, 235-1, 235-2, 235-3 controlled by the agent node 210 may comprise at least one or more in the group of: a measurement of the RSRP associated to at least one radio cell 215 controlled by the agent node 210 (specifically, useful signal); a measurement of RSRP associated to at least one neighbouring cell (i.e., interference); a measurement of the SNR associated to at least one radio cell 215 controlled by the agent node 21 O; and/or a measurement of the SINR associated to at least one neighbouring cell controlled by the agent node 210... " ) . Regarding claim 6 , Calabrese and Yang disclose the network node device according to claim 1, and Calabrese further discloses wherein the at least one control algorithm instance comprises a plurality of control algorithm instances configured to coordinate with each other the changes in the at least one transmission power control parameter (Calabrese figure 3, paragraphs 76-79, 86, “... More specifically, it discloses a method for autonomously learning different radio resource management strategies using measurements and Key Performance Indicators (KPls) collected from an algorithmic interaction with the radio environment. .. The action taken by the agent node 210 according to the learned control policy may comprise either the change of a radio resource parameter ... the change of the parameter of a given control algorithms ... or any similar measurement or ratio related to a comparison of the power level of a desired signal with the level of undesired background noise, measured by the user device 220; ... " and "... One agent node 210 may further control a plurality of radio cells either co-located 215 or not co-located 235-1, 235-2, 235-3...”) . Regarding claim 7 , Calabrese and Yang disclose the network node device according to claim 1, and Calabrese further discloses wherein the at least one control algorithm instance is configured to coordinate the changes in the at least one transmission power control parameter with at least one other control algorithm instance in another network node device (Calabrese figure 3, paragraphs 76-79, 86, “... More specifically, it discloses a method for autonomously learning different radio resource management strategies using measurements and Key Performance Indicators (KPls) collected from an algorithmic interaction with the radio environment. .. The action taken by the agent node 210 according to the learned control policy may comprise either the change of a radio resource parameter ... the change of the parameter of a given control algorithms ... or any similar measurement or ratio related to a comparison of the power level of a desired signal with the level of undesired background noise, measured by the user device 220; ... " and "... One agent node 210 may further control a plurality of radio cells either co-located 215 or not co-located 235-1, 235-2, 235-3...”) . Regarding claim 11 , Calabrese and Yang disclose the network node device according to claim 1, and Calabrese further discloses wherein the at least one performance indicator comprises a sum of average throughput over the at least one client cluster or throughput in a selected client cluster in the at least one client cluster (Calabrese paragraphs 118-121, “... In one embodiment, the agent node 210 determines the performance measurement rt associated with the controlled radio cell 215, 235-1, 235-2, 235-3 as weighted sum of the state observations ... the function and hi(xi) represents the average data throughput of user i in the radio cell 215, 235-1, 235-2, 235-3 and the reward rt(x) in equation [1] can be approximated for different values of a and weights wi with e.g. any, some or all of the following expressions: The average data throughput associated with the user devices 220 in the radio cell 215, 235-1, 235-2, 235-3 ... Each reward expression enables the agent node 210 to optimize a different performance metric that can either be associated with individual user devices 220, to radio cells 215, 235-1, 235-2, 235-3, or the communication system 200 as a whole ....”) . Regarding claim 13 , Calabrese and Yang disclose the limitations as set forth in claim 1. Regarding claim 14 , Calabrese and Yang disclose the non-transitory computer-readable medium comprising computer program instructions encoded thereon, said computer program instructions comprising program code configured to perform the method according to claim 13, when the computer program instructions are executed on a computer (Calabrese paragraphs 222, 256) . Regarding claim 15 , Calabrese and Yang disclose the limitations as set forth in claim 2. Regarding claim 16 , Calabrese and Yang disclose the limitations as set forth in claim 3. Regarding claim 17 , Calabrese and Yang disclose the limitations as set forth in claim 4. Regarding claim 19 , Calabrese and Yang disclose the limitations as set forth in claim 6. Regarding claim 20 , Calabrese and Yang disclose the limitations as set forth in claim 7 . 07-21-aia AIA Claim (s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US_20190239238_A1_Calabrese in view of CN_103686748_A_Yang (with English translation) and US_20150181519_A1_Klockar . Regarding claim 8 , Calabrese and Yang disclose the network node device according to claim 1, but do not disclose wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the network node device to perform the at least one control algorithm in the exploitation mode as long as the at least one performance indicator of the at least one client device is above a preconfigured threshold. Klockar discloses wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the network node device to perform the at least one control algorithm in the exploitation mode as long as the at least one performance indicator of the at least one client device is above a preconfigured threshold (Klockar figure 7, paragraphs 74-75, 91-93, 98, “... the current uplink signal quality for the low power RBS is below a predefined threshold. This means that the UEs connected to the low power RBS are severely interfered by the UEs connected to the macro RBS .... If this is the case, the network node adjusts the uplink power control setting for the low power RBS upwards in order for those UEs to achieve an improved uplink signal quality... " and there is" ... the adjusting unit 425 is adapted to adjust the current uplink power control setting for the low power RBS is also based on the current uplink signal quality for the low power RBS ... " and " ... network node checks if uplink quality measurements indicate that the uplink signal quality for the low power RBS is below a first predefined threshold, Z. If this condition is fulfilled, it means that the low power UE(s) experience substantial interference from the macro UE(s). However, if this is not the case, then the uplink power control setting for the low power RBS, P0_LP, does not require to be updated as illustrated in step 770 and the method goes back to step 730 to perform uplink quality measurements...”) . Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Klockar’s the current uplink power control setting for the low power RBS is adjusted upwards in case the current uplink signal quality for the low power RBS is below a predefined threshold in Calabrese and Yang’s system to improve uplink signal quality. This method for improving the system of Calabrese and Yang was within the ordinary ability of one of ordinary skill in the art based on the teachings of Klockar. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Calabrese, Yang and Klockar to obtain the invention as specified in claim 8. Regarding claim 9 , Calabrese, Yang and Klockar disclose the network node device according to claim 8, and Klockar further discloses wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the network node device to, in response to at least one performance indicator dropping below the preconfigured threshold, check validity of the at least one client cluster (Klockar figure 7, paragraphs 74-75, 91-93, 98, “... the current uplink signal quality for the low power RBS is below a predefined threshold. This means that the UEs connected to the low power RBS are severely interfered by the UEs connected to the macro RBS .... If this is the case, the network node adjusts the uplink power control setting for the low power RBS upwards in order for those UEs to achieve an improved uplink signal quality... " and there is" ... the adjusting unit 425 is adapted to adjust the current uplink power control setting for the low power RBS is also based on the current uplink signal quality for the low power RBS ... " and " ... network node checks if uplink quality measurements indicate that the uplink signal quality for the low power RBS is below a first predefined threshold, Z. If this condition is fulfilled, it means that the low power UE(s) experience substantial interference from the macro UE(s). However, if this is not the case, then the uplink power control setting for the low power RBS, P0_LP, does not require to be updated as illustrated in step 770 and the method goes back to step 730 to perform uplink quality measurements...”) . Regarding claim 10 , Calabrese, Yang and Klockar disclose disclose the network node device according to claim 8, and Calabrese further discloses wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the network node device to, in response to the at least one client cluster being invalid, re-cluster the at least one client device in the at least one client cluster (Calabrese paragraphs 24, 79, 112. Also see Klockar figure 2, paragraphs 26, 31) . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 12 is/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. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEIBIN HUANG whose telephone number is (571)270-3695. The examiner can normally be reached Monday - Friday 9:30AM - 6:00PM. 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, Sujoy Kundu can be reached at (571)272-8586. 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. /W.H/Examiner, Art Unit 2471 /SUJOY K KUNDU/Supervisory Patent Examiner, Art Unit 2471 Application/Control Number: 18/567,199 Page 2 Art Unit: 2471 Application/Control Number: 18/567,199 Page 4 Art Unit: 2471 Application/Control Number: 18/567,199 Page 5 Art Unit: 2471 Application/Control Number: 18/567,199 Page 6 Art Unit: 2471 Application/Control Number: 18/567,199 Page 7 Art Unit: 2471 Application/Control Number: 18/567,199 Page 9 Art Unit: 2471 Application/Control Number: 18/567,199 Page 10 Art Unit: 2471 Application/Control Number: 18/567,199 Page 11 Art Unit: 2471 Application/Control Number: 18/567,199 Page 13 Art Unit: 2471 Application/Control Number: 18/567,199 Page 14 Art Unit: 2471 Application/Control Number: 18/567,199 Page 15 Art Unit: 2471 Application/Control Number: 18/567,199 Page 16 Art Unit: 2471
Prosecution Insights