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 Arguments
Applicant's arguments filed August 7, 2026 with respect to claims 1-20 have been fully considered but they are not persuasive.
Regarding independent claims 1 and 14, Applicant argues that prior art does not teach any adjustment in the operation of an RRU. More specifically with respect to Bisaria et al. (US 11,553,502), applicant argues that there is no teaching that the cited predictions result in any adjustment in the operation of the RRU. Applicant notes that the modification of resource assignments for the slice using predictions/projections of Bisaria et al. do not occur in the RRU but instead occur in the BBU.
Examiner agrees that the resource assignment modifications, using the predictions/projections, occur in the BBU. Examiner disagrees that this functionality does not result in any adjustment in the operation of the RRU. As is well known in the art, a network slice uses resources provided by the RRU. Figure 5 of Bisaria et al. shows the RRUs 520 corresponding to respective slices 570. The modified resource assignments occurring in the BBU are further provided by the respective RRU to the respective slice. Therefore, the operation at the RRU is adjusted in some way in order for the RRU to provide the modified resource assignments to the slice, even if the adjustment is as simple as providing the modified resource assignments to the slice as opposed to providing previous/past resource assignments to the slice.
Accordingly, the claimed invention remains rejected as outlined in the previous non-final rejection of May 7, 2026 with updates to reflect any claim amendments
Claim Rejections - 35 USC § 103
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.
The factual inquiries 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.
Claim(s) 1-3, 10-12 and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisaria et al. (US 11,553,502) in view of O’Shea et al. (US 2020/0343985).
Regarding claim 1, Bisaria et al. disclose a system (Figure 5), comprising:
at least one baseband unit (BBU) (Column 9 line 36 and figure 5, vBBUs 560A-C);
one or more radio units communicatively coupled to the at least one BBU (Column 9 line 33 and figure 5, RRUs 520A-C coupled to vBBUs 560A-C via fronthaul connection 545);
one or more antennas communicatively coupled to the one or more radio units (Column 9 line 33 and figure 5, RRUs 520A-C inherently have coupled antennas);
wherein the at least one BBU, the one or more radio units, and the one or more antennas are configured to implement a base station (Column 2 lines 44-55, radio network node include, but are not limited to, base stations (BS), multi-standard radio (MSR) nodes such as MSR BS, gNodeB, eNode B, network controllers, radio network controllers (RNC), base station controllers (BSC), relay, donor node controlling relay, base transceiver stations (BTS), access points (AP), transmission points, transmission nodes, remote radio units (RRU) (also termed radio units herein), remote ratio heads (RRH), and nodes in distributed antenna system (DAS)) for wirelessly communicating with user equipment (Column 9 lines 31-32 and figure 5, RRUs 520A-C wirelessly coupled to user devices 540A-C); and
a computing system (Figure 3, network device 150) configured to:
receive traffic data (Figure 3 and column 6 line 66 – column 7 line 45, historical data store 310 storing usage data including bandwidth utilization by slices allocated to devices that are being monitored and other utilization measures that affect predictions as to future utilization of the network slice); and
determine a predicted radio resource usage of the base station based on the traffic data (Column 7 lines 33-45, network device 150, Usage data, as used to describe some embodiments herein, can broadly include, but is not limited to, bandwidth utilization by slices allocated to monitored devices and other utilization measures that can affect predictions as to future utilization of the network slice…approaches to data collection and analysis can result in predictions (e.g., projections) that can used to modify capacity assigning for network slices before any service degradation for the network slice occurs);
wherein the system is configured to adjust operation of at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station (Column 7 lines 40-45, approaches to data collection and analysis can result in predictions (e.g., projections) that can be used to modify capacity assigning for network slices before any service degradation for the network slice occurs; Figure 5 and column 9 lines 25-36, wherein a network slice 570 [572] corresponds to a RRU 520; Abstract, based on the monitored slice performance, facilitating recalibration of the resource profile in accordance with a condition associated with the network service type, resulting in a modification of the capacity of the resource assigned to the network slice; Column 7 lines 63-67, monitoring of slice utilization data 495, evaluation of usage data (e.g., by capacity prediction component analyzing real time data store 490, and modifying capacity assigning of resources based on usage data, for the different levels; Claim 1, based on the monitored slice performance and a prediction of future utilization of the network slice, facilitating recalibration of the resource profile in accordance with a condition associated with the network service type, resulting in a modification of the capacity of the resource assigned to the network slice).
Bisaria et al. do not disclose the following limitations that are disclosed by O’Shea et al.: wherein each respective radio unit of the one or more radio units is communicatively coupled to a respective subset of the one or more antennas (O’Shea et al., Figure 6 and paragraph 132, a machine-learning network such as the machine-learning network 605 within a system such as the system 600. The machine-learning network approach scales from small numbers of antennas, e.g., 1, 2, or 4, up to larger Massive MIMO systems, e.g., with 32, 64, 256 or more antennas; Paragraph 122, antennas per sector); a machine learning computing system (O’Shea et al., Abstract, A prediction corresponding to the data signal transmitted through the communications channel is obtained from the machine-learning network; Figure 6 with machine-learning network 605 incorporated); received time data (O’Shea et al., Paragraph 124, a machine-learning network can learn to equalize many different channel response from different user equipment (UE) but which all draw from some distribution for the full sector. This distribution may change or be conditioned on other aspects over time, such as time of day, day of week, event activity, or other physical phenomena which can change the overall distribution of channel statistics for all user allocations within the cell); and prediction based on time data (O’Shea et al., Paragraph 124, conditioning based on time of day, day of week, event activity, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bisaria et al. with the cited disclosure from O’Shea et al. in order to advantageously use a machine-learning network approach to scale from a small number antennas to massive MIMO systems with a large number of antenna elements (O’Shea et al., Paragraph 20).
Regarding claim 2, Bisaria et al. in view of O’Shea et al. disclose wherein the time data and traffic data includes: time of day; day of week (O’Shea et al., Paragraph 124, conditioning based on time of day, day of week, event activity, etc.); and a number of user equipment wirelessly communicating with the base station (Bisaria et al., Figure 3, historical data store 310 storing usage history respective user devices).
Regarding claim 3, Bisaria et al. disclose wherein at least some of the time data and/or the traffic data are provided by one or more devices external to the system (Figure 4 and column 7 lines 61-67, elements of network device 150 can be used at one or more levels of RAN 400, e.g., to advantageously distribute and replicate the monitoring of slice utilization data 495, evaluation of usage data (e.g., by capacity prediction component analyzing real time data store 490, and modifying capacity assigning of resources based on usage data, for the different levels; Also see figure 1).
Regarding claim 10, O’Shea et al. discloses wherein the machine learning computing system is configured to utilize the time data and the traffic data as inputs to a plurality of machine learning models (Paragraph 131 and figure 6, machine-learning network 605 is used by two DUs 604 and 606, indicating a plurality machine-learning network instances; Paragraphs 46, 47, 49, 58, 60, 72, 75, 93, 94, 96, 104, 134, 136, data inputs into the machine-learning network operating at each of the two DUs to obtain predictions related to the inputs [Paragraph 94]), wherein each machine learning model of the plurality of machine learning models is directed to a specific sub-area of a service area, a specific frequency band, and/or a specific operator (O’Shea et al., Paragraph 131 and figure 6, machine-learning network 605 is used by two DUs 604 and 606; Paragraph 129, the DU performs High-PHY processing for a number of sectors. Therefore, each machine-learning network in the respective DU is directed to a specific number of sectors [i.e. specific sub-area of a service area]).
Regarding claim 11, Bisaria et al. disclose wherein the one or more radio units includes a plurality of radio units (Column 9 line 33 and figure 5, RRUs 520A-C coupled to vBBUs 560A-C via fronthaul connection 545), wherein the one or more antennas includes a plurality of antennas (Column 2 line 55, nodes in distributed antenna system (DAS)).
Regarding claim 12, O’Shea et al. disclose wherein the BBU includes a central unit communicatively coupled to a distributed unit (Figure 6, CU 602 coupled to DUs 604 and 606), wherein the distributed unit is communicatively coupled to the one or more radio units (Figure 6, DUs 604 and 606 coupled to Rus 610-616 via network 608).
Regarding claim 14, Bisaria et al. disclose a method, comprising:
receiving traffic data (Figure 3 and column 6 line 66 – column 7 line 45, historical data store 310 storing usage data including bandwidth utilization by slices allocated to devices that are being monitored and other utilization measures that affect predictions as to future utilization of the network slice);
determining a predicted radio resource usage of a base station based on the traffic data (Column 7 lines 33-45, network device 150, Usage data, as used to describe some embodiments herein, can broadly include, but is not limited to, bandwidth utilization by slices allocated to monitored devices and other utilization measures that can affect predictions as to future utilization of the network slice…approaches to data collection and analysis can result in predictions (e.g., projections) that can used to modify capacity assigning for network slices before any service degradation for the network slice occurs), wherein the base station includes at least one baseband unit (BBU) (Column 9 line 36 and figure 5, vBBUs 560A-C), one or more radio units communicatively coupled to the at least one BBU (Column 9 line 33 and figure 5, RRUs 520A-C coupled to vBBUs 560A-C via fronthaul connection 545), and one or more antennas communicative coupled to the one or more radio units (Column 9 line 33 and figure 5, RRUs 520A-C inherently have coupled antennas), wherein the at least one BBU, the one or more radio units, and the one or more antennas are configured to implement a base station (Column 2 lines 44-55, radio network node include, but are not limited to, base stations (BS), multi-standard radio (MSR) nodes such as MSR BS, gNodeB, eNode B, network controllers, radio network controllers (RNC), base station controllers (BSC), relay, donor node controlling relay, base transceiver stations (BTS), access points (AP), transmission points, transmission nodes, remote radio units (RRU) (also termed radio units herein), remote ratio heads (RRH), and nodes in distributed antenna system (DAS)) for wirelessly communicating with user equipment (Column 9 lines 31-32 and figure 5, RRUs 520A-C wirelessly coupled to user devices 540A-C); and
adjusting operation of at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station (Column 7 lines 40-45, approaches to data collection and analysis can result in predictions (e.g., projections) that can be used to modify capacity assigning for network slices before any service degradation for the network slice occurs; Figure 5 and column 9 lines 25-36, wherein a network slice 570 [572] corresponds to a RRU 520; Abstract, based on the monitored slice performance, facilitating recalibration of the resource profile in accordance with a condition associated with the network service type, resulting in a modification of the capacity of the resource assigned to the network slice; Column 7 lines 63-67, monitoring of slice utilization data 495, evaluation of usage data (e.g., by capacity prediction component analyzing real time data store 490, and modifying capacity assigning of resources based on usage data, for the different levels; Claim 1, based on the monitored slice performance and a prediction of future utilization of the network slice, facilitating recalibration of the resource profile in accordance with a condition associated with the network service type, resulting in a modification of the capacity of the resource assigned to the network slice).
Bisaria et al. do not disclose the following limitations that are disclosed by O’Shea et al.: wherein each respective radio unit of the one or more radio units is communicatively coupled to a respective subset of the one or more antennas (O’Shea et al., Figure 6 and paragraph 132, a machine-learning network such as the machine-learning network 605 within a system such as the system 600. The machine-learning network approach scales from small numbers of antennas, e.g., 1, 2, or 4, up to larger Massive MIMO systems, e.g., with 32, 64, 256 or more antennas; Paragraph 122, antennas per sector); received time data (O’Shea et al., Paragraph 124, a machine-learning network can learn to equalize many different channel response from different user equipment (UE) but which all draw from some distribution for the full sector. This distribution may change or be conditioned on other aspects over time, such as time of day, day of week, event activity, or other physical phenomena which can change the overall distribution of channel statistics for all user allocations within the cell); and prediction based on time data (O’Shea et al., Paragraph 124, conditioning based on time of day, day of week, event activity, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bisaria et al. with the cited disclosure from O’Shea et al. in order to advantageously use a machine-learning network approach to scale from a small number antennas to massive MIMO systems with a large number of antenna elements (O’Shea et al., Paragraph 20).
Regarding claim 15, Bisaria et al. in view of O’Shea et al. disclose wherein the time data and traffic data includes: time of day; day of week (O’Shea et al., Paragraph 124, conditioning based on time of day, day of week, event activity, etc.); and a number of user equipment wirelessly communicating with the base station (Bisaria et al., Figure 3, historical data store 310 storing usage history respective user devices).
Regarding claim 16, Bisaria et al. disclose wherein receiving time data and traffic data includes: receiving at least some of the time data from one or more devices external to the base station; and/or receiving at least some of the traffic data from one or more devices external to the base station (Figure 4 and column 7 lines 61-67, elements of network device 150 can be used at one or more levels of RAN 400, e.g., to advantageously distribute and replicate the monitoring of slice utilization data 495, evaluation of usage data (e.g., by capacity prediction component analyzing real time data store 490, and modifying capacity assigning of resources based on usage data, for the different levels; Also see figure 1).
Claim(s) 4, 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisaria et al. in view of O’Shea et al. as applied to claims 1 and 14 above, and further in view of Graves et al. (US 2014/0161447).
Regarding claim 4, Bisaria et al. in view of O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Graves et al.: wherein the system is configured to power on at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station (Graves et al., Paragraphs 53-60 discuss provisioning of RRUs and BBUs based on traffic predictions for certain times of day and days of the week by provisioning [power on] BBUs and RRUs, switching off BBUs and RRUs, or taking BBUs and RRUs out of service in order to enable service areas to meet their traffic peaks with economic provisioning).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Graves et al. in order to perform economic provisioning of RRUs, enabling service areas to meet their traffic peaks (Graves et al., Paragraph 60).
Regarding claim 5, Bisaria et al. in view of O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Graves et al.: wherein the system is configured to power off at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station (Graves et al., Paragraphs 53-60 discuss provisioning of RRUs and BBUs based on traffic predictions for certain times of day and days of the week by provisioning BBUs and RRUs, switching off [power off] BBUs and RRUs, or taking BBUs and RRUs out of service [power off] in order to enable service areas to meet their traffic peaks with economic provisioning).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Graves et al. in order to perform economic provisioning of RRUs, enabling service areas to meet their traffic peaks (Graves et al., Paragraph 60).
Regarding claim 17, Bisaria et al. in view of O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Graves et al.: wherein adjusting operation of at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station includes: powering on at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station (Graves et al., Paragraphs 53-60 discuss provisioning of RRUs and BBUs based on traffic predictions for certain times of day and days of the week by provisioning [power on] BBUs and RRUs, switching off BBUs and RRUs, or taking BBUs and RRUs out of service in order to enable service areas to meet their traffic peaks with economic provisioning); and/or powering off at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station (Graves et al., Paragraphs 53-60 discuss provisioning of RRUs and BBUs based on traffic predictions for certain times of day and days of the week by provisioning BBUs and RRUs, switching off [power off] BBUs and RRUs, or taking BBUs and RRUs out of service [power off] in order to enable service areas to meet their traffic peaks with economic provisioning).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Graves et al. in order to perform economic provisioning of RRUs, enabling service areas to meet their traffic peaks (Graves et al., Paragraph 60).
Claim(s) 6-8, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisaria et al. in view of O’Shea et al. as applied to claims 1 and 14 above, and further in view of Bye et al. (US 2022/0132320).
Regarding claim 6, Bisaria et al. in view O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Bye et al.: wherein the system is configured to activate one or more frequency bands utilized by the one or more radio units based on the predicted radio resource usage of the base station (Bye et al., Paragraph 50, resources including spectrum and bandwidth are reserved [activated] for a peak load occasion [predicted usage] and released when peak load is not present; Paragraphs 5 and 7, RU activating BWP based on needed spectrum usage compared to a threshold over a period of time).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Bye et al. in order to perform dynamic cellular network spectrum sharing (Bye et al., Title).
Regarding claim 7, Bisaria et al. in view O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Bye et al.: wherein the system is configured to deactivate one or more frequency bands utilized by the one or more radio units based on the predicted radio resource usage of the base station (Bye et al., Paragraph 50, resources including spectrum and bandwidth are reserved for a peak load occasion [predicted usage] and released [deactivated] when peak load is not present; Paragraphs 5 and 7, RU activating BWP based on needed spectrum usage compared to a threshold over a period of time; Paragraph 6, The network may be configured to determine that spectrum allocated to the secondary operator RU may not be currently needed. The network may be configured to release the spectrum; Paragraph 59, a determination may be made, such as by a component of the secondary operator's network (or by an authorized user of the secondary operator's network) that indicates spectrum that is not currently needed…For example, if less than 50% of the available radio resources are unused for a defined period of time, a percentage of those available radio resources may be released).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Bye et al. in order to perform dynamic cellular network spectrum sharing (Bye et al., Title).
Regarding claims 8 and 19, Bisaria et al. in view of O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Bye et al.: wherein the system is further configured to: periodically determine whether a deactivated frequency band is needed (Bye et al., Paragraph 62, determination reaches the opposite conclusion in that some amount of reserved spectrum is now needed…the determination could be based on a comparison to a threshold over time. For instance, if more than 75% of the available spectrum of the second RU is used on average for over a defined period of time, spectrum that was previously released may be reclaimed); and reactivate the deactivated frequency band in response to a determination that the deactivated frequency band is needed (Bye et al., Paragraph 62, At block 520, spectrum that was released or leased at block 505 may be reclaimed [reactivate]…determination reaches the opposite conclusion in that some amount of reserved spectrum is now needed…spectrum that was previously released may be reclaimed…The secondary operator, via its RU, may send new or activate different BWP definitions at its UE so that the reclaimed spectrum begins being used).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Bye et al. in order to perform dynamic cellular network spectrum sharing (Bye et al., Title).
Regarding claim 18, Bisaria et al. in view O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Bye et al.: wherein adjusting operation of at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station includes: activating one or more frequency bands utilized by the one or more radio units based on the predicted radio resource usage of the base station (Bye et al., Paragraph 50, resources including spectrum and bandwidth are reserved [activated] for a peak load occasion [predicted usage] and released when peak load is not present; Paragraphs 5 and 7, RU activating BWP based on needed spectrum usage compared to a threshold over a period of time); and/or deactivating one or more frequency bands utilized by the one or more radio units based on the predicted radio resource usage of the base station (Bye et al., Paragraph 50, resources including spectrum and bandwidth are reserved for a peak load occasion [predicted usage] and released [deactivated] when peak load is not present; Paragraphs 5 and 7, RU activating BWP based on needed spectrum usage compared to a threshold over a period of time; Paragraph 6, The network may be configured to determine that spectrum allocated to the secondary operator RU may not be currently needed. The network may be configured to release the spectrum; Paragraph 59, a determination may be made, such as by a component of the secondary operator's network (or by an authorized user of the secondary operator's network) that indicates spectrum that is not currently needed…For example, if less than 50% of the available radio resources are unused for a defined period of time, a percentage of those available radio resources may be released).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Bye et al. in order to perform dynamic cellular network spectrum sharing (Bye et al., Title).
Claim(s) 9 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisaria et al. in view of O’Shae et al. as applied to claims 1 and 14 above, and further in view of Sundaresan et al. (US 2014/0031049).
Regarding claims 9 and 20, Bisaria et al. in view of O’Shae et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Sundaresan et al.: wherein the system is configured to determine whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse based on the predicted radio resource usage (Sundaresan et al., Paragraph 29, obtaining, from each small cell in said each sector, aggregate traffic demand, determining a minimum set of resources needed for distributed antenna systems (DAS) and fractional frequency reuse (FFR) configurations based on first traffic demand from mobile traffic and second traffic demand from sector-exterior traffic, respectively, determining optimal multiplexing of the DAS and FFR configurations for said each sector, determining baseband processing unit (BBU) resource usage metric (RU metric) for said each sector, clustering the plurality of sectors two at a time based on the RU metrics of the two sectors, and applying, through a front-haul configuration on allocated resources, DAS and FFR strategies to each small cell in the two sectors; Paragraph 80, operations performed for both uplink and downlink).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Sundaresan et al. in order to enhance network capacity using dynamic frequency reuse techniques (Sundaresan et al., Paragraph 127).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisaria et al. in view of O’Shea et al. as applied to claim 12 above, and further in view of Tofighbakhsh et al. (US 2020/0314821).
Regarding claim 13, Bisaria et al. in view of O’Shea et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Tofighbakhsh et al.: wherein the machine learning computing system is implemented in a radio access network intelligent controller (Tofighbakhsh et al., Paragraph 33, machine learning algorithm/model downloaded to a running instance of a RIC (radio access network intelligent controller) [Paragraph 33]) in order to produce a hybrid distributed and centralized model to increase spectrum efficiency and provide new service opportunities [Paragraph 34]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Bisaria et al. and O’Shea et al. with the cited disclosure from Tofighbakhsh et al. in order to order to produce a hybrid distributed and centralized model to increase spectrum efficiency and provide new service opportunities (Tofighbakhsh et al., Paragraph 34).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Notargiacomo et al. (US 2021/00400527) disclose a BBU changing its RU priority over time depending on expected usage (Paragraph 47).
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OTIS L THOMPSON, JR whose telephone number is (571)270-1953. The examiner can normally be reached Monday - Friday, 6:30am - 7:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chirag G. Shah can be reached at (571)272-3144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OTIS L THOMPSON, JR/Primary Examiner, Art Unit 2477
October 8, 2026
