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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/12/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 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.
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.
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.
Claim(s) 1-2, 4, 6-10, 12, 14-17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over VRIND et al. US Patent Pub. No.: 20200260404, hereinafter, ‘VRIND’ and further in view of YANG, Helin et al., "Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management," 2020, IEEE Journal on Selected Areas in Communications, hereinafter , ‘Yang’.
Consider Claims 1 and 10, VRIND teaches a method for aerial-assisted federated learning at a Federated Learning (FL) server (e.g., see structural components – processor, txcvr, etc. in at least figure 1 and 4 and 0037 - each of the UE, drone, and base station may be a network node in accordance with the Third Generation Partnership Project (3GPP) standards and comprises a transceiver and processor.)comprising: receiving, from a plurality of User Equipment (UEs), a plurality of parameter sets, respectively, wherein each of the plurality of parameter sets includes a terrestrial link Channel Quality Indicator (CQI), location information, and capability information of a corresponding UE to handle Dual Communication (DC) with an aerial link (e.g., see at least 0007 –0009 “receiving from one or more UEs at least one feedback parameter pertaining to at least one of: a Predicted Buffer in uplink and downlink, a Prioritized Traffic type, a Channel Quality Measurement, and a Minimum Guaranteed service based on quality of service…identifying UEs capable” 0052- “ …the terrestrial network based on the UE capability, can dynamically control”); receiving, from an aerial cell, trajectory information indicating a coverage range of the aerial cell (e.g., see determining aerial trajectory – 0009, 0023,0025, 0043-0044 and 0079); selecting at least one UE from the plurality of UEs based on the received plurality of parameter sets and the received trajectory information (see UE selection – 0006-0009 “selecting the UE to use an aerial network through at least one drone base station cell based on a service requirement of the UE determined from the at least one feedback parameter”); and triggering an activation of the aerial link between the aerial cell and the selected at least one UE to include the selected at least one UE to a set of federated UEs associated with the FL server (e.g., see at least 0006 – “establishing an aerial communication link between the UE and the at least one drone base station cell by deploying one or more LAPS as a drone base station in accordance with the determined position to augment a network connectivity of the selected UE”.);wherein selecting the at least one UE from the plurality of UEs comprises: determining, based on the location information of the corresponding UE and the received trajectory information, that the at least one UE among the plurality of UEs is in the coverage range of the aerial cell(e.g., see FIG. 4 illustrates a message exchange between a User Equipment (UE) and a network for the FACT based Zone-based Drone Positioning and Trajectory – see also location and trajectory information in at least 0054-0055); and selecting the at least one UE from the plurality of UEs wherein: the terrestrial link CQI of the at least one UE is lower than a threshold value; and the capability information indicates that the at least one UE can handle the DC with the aerial link (e.g., see at least 0032 – “LAPS based drone cell may be deployed when the serving terrestrial cell is unable to support a quality of service (QoS) threshold for a UE or a set of UEs” – see also 0022 – “he UE is selected for availing an aerial network through a drone base station (DBS) cell based on service requirement of the UE determined from the feedback parameter. Further, a position for at least one DBS-cell is determined with respect to the at least one selected UE based on the at least one parameter to serve the selected UE. An aerial-communication link is established between the selected UE and the DBS cell by deploying one or more Low Altitude Platform Station as a drone base station (DBS) in accordance with the determined position of DBS-cell to thereby augment a network connectivity of the selected UE.”).
However, assuming arguendo, the Examiner’s interpretation of UE capability information and FL server above are not reasonable.
Yang further teaches a FL UAV server (e.g., see page 2 last paragraph) “the optimal dynamic scheduling and resource management of UAVs to complete the tasks,
subject to the resource constraints of UAVs and different computational capabilities of devices.
Joint management of both UAVs trajectory (or UAVs placement) and resource management
has been widely studied to optimize the network performance, such as in [23]–[28]. In [23]
and [24], a joint optimization for user association, resource allocation, and UAV placement
was studied in multi-UAV wireless networks, to guarantee the quality of services (QoS) of mobile devices.” (e.g., see at least pages 4 and 22).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein selecting the at least one UE from the plurality of UEs comprises: determining, based on the location information of the corresponding at least one UE and the received trajectory information, that the at least one UE among the plurality of UEs is in the coverage range of the aerial cell; and selecting the at least one UE from the plurality of UEs when: the terrestrial link CQI of the at least one UE is lower than a threshold value; and the capability information indicates that the at least one UE can handle the DC with the aerial link for the purpose of federating learning in a network.
Consider Claim 2, Vrind teaches wherein: the terrestrial link CQI is measured by the corresponding UE based on measurement parameters received from a primary cell, and the terrestrial link CQI indicates a channel quality of the terrestrial link between the primary cell and the corresponding UE (e.g., this is met based on at least 0041 – “Through UE feedback, the QoS information of each UE is used to predict UL and DL traffic from and to each UE and a channel quality is determined”).
Consider Claim 4 and 12, Vrind teaches each of the plurality of parameter sets includes training resource information of the corresponding UE, and the training resource information includes at least one of information of computation capability of the corresponding UE or information of training data at the corresponding UE (e.g., see at least 0043 and table 1 that illustrates parameters using a trained RNN model).
Consider Claims 6 and 14, Vrind teaches wherein triggering the activation of the aerial link comprises: sending the selected at least one UE to a primary cell for activating the aerial link with the aerial cell hosted on a Low Altitude Platform (LAP) (e.g., see at least 0006 – “establishing an aerial communication link between the UE and the at least one drone base station cell by deploying one or more LAPS as a drone base station in accordance with the determined position to augment a network connectivity of the selected UE”. – see LAP - 0051).
Consider Claims 7 and 15, Vrind teaches receiving, from the primary cell, a response indicating that the DC is activated for the selected at least one UE; and adding, upon receiving the response, the selected at least one UE to the set of federated UEs for an FL process (See at least 0051 – “At step 108, an aerial communication link is established between the selected UE and the DBS cell by deploying one or more Low Altitude Platform Station (LAPS) as a DBS in accordance with the determined position of DBS-cell to thereby augment network connectivity of the selected UE. The aerial-communication link is established between the selected UEs and the drone cell.”)
Consider claim 8, Vrind teaches performing with the selected at least one UE the FL process using the aerial link(See at least 0051 – “At step 108, an aerial communication link is established between the selected UE and the DBS cell by deploying one or more Low Altitude Platform Station (LAPS) as a DBS in accordance with the determined position of DBS-cell to thereby augment network connectivity of the selected UE. The aerial-communication link is established between the selected UEs and the drone cell.”).
Consider Claim 9, Vrind teaches wherein performing the FL process comprises: sending a global model distribution to the selected at least one UE; performing, by the selected at least one UE, a machine learning training process based on the global model distribution; and receiving a training result report from the selected at least one UE based on the machine learning training process (e.g., see at least 0047 – “The feedback parameters are determined by UEs at least based on a model. For example, the feedback parameters may be determined by UEs at least based on a neural-network-based model In an example, the feedback parameters have been defined at least through earlier presented Table 1.”- see also 0071).
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over VRIND et al. US Patent Pub. No.: 20200260404, hereinafter, ‘VRIND’ and further in view of YANG, Helin et al., "Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management," 2020, IEEE Journal on Selected Areas in Communications, hereinafter , ‘Yang’ and further in view of LI US Patent Pub. No.: 2023/0262706.
Consider Claims 5 and 13, VRIND teaches the claimed invention except wherein the one or more processors are further configured to: select at least one UE from the plurality of UEs when the training resource information indicates that the computation capability of the at least one UE is greater than a threshold capability.
In analogous art, Li teaches in 0052 – “reporting method for the user equipment assistance information provided by the example of the disclosure, the user equipment carries the own computation capability resource assistance information (such as the computation capacity remaining amount information, the current electric quantity remaining amount, the consumed computation capacity remaining amount of the UE side expected or estimated for accessing of current AI/ML model training, and the electric quantity consumption remaining amount of the UE side expected or estimated for accessing of current AI/ML model training) in the MAC CE or the radio resource control (RRC) signaling, and sends the computation capability resource assistance information to the network device through the control channel carrying the MAC CE or the RRC signaling. When the network device performs AI/ML operation based on the computation capability resource assistance information reported by the UE, an operation adapted to the computation capability resource assistance information reported by the UE can be allocated to the UE, so that the UE can quickly complete the operation based on its own computation capability, and can thus better cooperate with the network device to achieve an AI/ML inference task”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the one or more processors are further configured to: select at least one UE from the plurality of UEs when the training resource information indicates that the computation capability of the at least one UE is greater than a threshold capability for the purpose of addressing computational limitations introduced by high intensive computing operations.
Claim(s) 16-17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over VRIND et al. US Patent Pub. No.: 20200260404, hereinafter, ‘VRIND’ and further in view of YANG, Helin et al., "Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management," 2020, IEEE Journal on Selected Areas in Communications, hereinafter , ‘Yang’ and further in view of Gordaychik US Patent Pub. No.: 20190363843 A1 and further in view of Kwon US Patent Pub. No.: 20160066284 A1.
Consider Claim 16, VRIND teaches a method for aerial-assisted federated learning at a User Equipment (UE) comprising: sending, to a Federated Learning (FL) server, a plurality of parameter sets, respectively, wherein the plurality of parameter sets includes a terrestrial link Channel Quality Indicator (CQI), capability information of the UE to handle Dual Communication (DC) with an aerial link(e.g., see at least 0007 –0009 “receiving from one or more UEs at least one feedback parameter pertaining to at least one of: a Predicted Buffer in uplink and downlink, a Prioritized Traffic type, a Channel Quality Measurement, and a Minimum Guaranteed service based on quality of service…identifying UEs capable” 0052- “ …the terrestrial network based on the UE capability, can dynamically control”), and location information, wherein the FL server selects the UE for the DC with the aerial link when the terrestrial link CQI of the UE is lower than a threshold value and the capability information indicates that the UE can handle the DC with the aerial link(e.g., see at least 0032 – “LAPS based drone cell may be deployed when the serving terrestrial cell is unable to support a quality of service (QoS) threshold for a UE or a set of UEs” – see also 0022 – “the UE is selected for availing an aerial network through a drone base station (DBS) cell based on service requirement of the UE determined from the feedback parameter. Further, a position for at least one DBS-cell is determined with respect to the at least one selected UE based on the at least one parameter to serve the selected UE. An aerial-communication link is established between the selected UE and the DBS cell by deploying one or more Low Altitude Platform Station as a drone base station (DBS) in accordance with the determined position of DBS-cell to thereby augment a network connectivity of the selected UE.”); receiving, from a primary cell upon selection of the UE by the FL server, a cell configuration of the aerial cell for synchronization of the UE with the aerial cell for enabling the UE to handle DC with the aerial link; and performing, with the FL Server, an FL process using the aerial link (e.g,, see at least 0009 –“selecting a set of UEs from the plurality of UEs to use an augmented terrestrial network through one or more drone base station cells based at least in part on one or more service requirements of the UE determined from the at least one parameter; and determining a position for at least one drone base station cell in a 3D space with respect to the at least one selected set of UEs, the position based on one or more of: a 2D coverage zone on ground with a maximum number of UEs, a maximum weighted average of a buffer-requirement for each of the selected set of UEs, and an aerial scheduling period with respect to the at least one drone base station cell determined in real time based on a mobility and traffic pattern” see also 0032).
However, assuming arguendo, the Examiner’s interpretation of UE capability information and FL server above are not reasonable.
Yang further teaches a FL UAV server (e.g., see page 2 last paragraph) “the optimal dynamic scheduling and resource management of UAVs to complete the tasks,
subject to the resource constraints of UAVs and different computational capabilities of devices.
Joint management of both UAVs trajectory (or UAVs placement) and resource management
has been widely studied to optimize the network performance, such as in [23]–[28]. In [23]
and [24], a joint optimization for user association, resource allocation, and UAV placement
was studied in multi-UAV wireless networks, to guarantee the quality of services (QoS) of mobile devices.” (e.g., see at least pages 4 and 22).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein selecting the at least one UE from the plurality of UEs comprises: determining, based on the location information of the corresponding at least one UE and the received trajectory information, that the at least one UE among the plurality of UEs is in the coverage range of the aerial cell; and selecting the at least one UE from the plurality of UEs when: the terrestrial link CQI of the at least one UE is lower than a threshold value; and the capability information indicates that the at least one UE can handle the DC with the aerial link for the purpose of federating learning in a network.
However, Vrind does not specifically teach receiving, from a primary cell providing a Master Cell Group (MCG) upon selection of the UE by the FL server, a measurement configuration including cell information of an aerial cell receiving, from the primary cell, a configuration of the aerial cell for the aerial cell to provide a Secondary Cell Group (SCG) to the UE; performing downlink and uplink synchronization with the aerial cell and reporting a Power Headroom (PHR) Medium Access Control (MAC) Control Element (CE) for both the primary cell and the aerial cell to activate the DC with the aerial link.
In analogous art, Gordaychik teaches “…UE in dual connectivity may have a master cell group and secondary cell group” – paragraph 0130, “A ground based unit may report CQI/PMI 902 and RI similar to that of LTE 906. For example, a legacy LTE device 906 may report CQI between 1 and 160 subframes. A UAV may receive a configuration via RRC signaling and use this configuration to determine periodicity for measurement reporting” – paragraph 0233.
In analogous art, Kwon uplink and downlink synchronization in paragraph 0063 and power headroom report MAC control element - paragraph 0113.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date, based on the teachings of Gordaychik and Kwon to receiving, from a primary cell providing a Master Cell Group (MCG) upon selection of the UE by the FL server, a measurement configuration including cell information of an aerial cell receiving, from the primary cell, a configuration of the aerial cell for the aerial cell to provide a Secondary Cell Group (SCG) to the UE; performing downlink and uplink synchronization with the aerial cell and reporting a Power Headroom (PHR) Medium Access Control (MAC) Control Element (CE) for both the primary cell and the aerial cell to activate the DC with the aerial link for the purpose of efficiently utilizing resources in a dual connectivity environment.
Consider Claim 17, Vrind teaches each of the plurality of parameter sets includes training resource information of the corresponding UE, and the training resource information includes at least one of information of computation capability of the corresponding UE or information of training data at the corresponding UE (e.g., see at least 0043 and table 1 that illustrates parameters using a trained RNN model).
Consider Claim 19, Vrind teaches wherein performing the FL process comprises: sending a global model distribution to the selected at least one UE; performing, by the selected at least one UE, a machine learning training process based on the global model distribution; and receiving a training result report from the selected at least one UE based on the machine learning training process (e.g., see at least 0047 – “The feedback parameters are determined by UEs at least based on a model. For example, the feedback parameters may be determined by UEs at least based on a neural-network-based model In an example, the feedback parameters have been defined at least through earlier presented Table 1.”- see also 0071).
Consider Claim 20, Vrind teaches wherein: the terrestrial link CQI is measured by the corresponding UE based on measurement parameters received from a primary cell, and the terrestrial link CQI indicates a channel quality of the terrestrial link between the primary cell and the corresponding UE (e.g., this is met based on at least 0041 – “Through UE feedback, the QoS information of each UE is used to predict UL and DL traffic from and to each UE and a channel quality is determined”).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over VRIND et al. US Patent Pub. No.: 20200260404, hereinafter, ‘VRIND’ and further in view of YANG, Helin et al., "Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management," 2020, IEEE Journal on Selected Areas in Communications, hereinafter , ‘Yang’ and further in view of Gordaychik US Patent Pub. No.: 20190363843 A1 and further in view of Kwon US Patent Pub. No.: 20160066284 A1 and further in view of LI US Patent Pub. No.: 2023/0262706.
Consider Claim 18, VRIND teaches the claimed invention except wherein the one or more processors are further configured to: select at least one UE from the plurality of UEs when the training resource information indicates that the computation capability of the at least one UE is greater than a threshold capability.
In analogous art, Li teaches in 0052 – “reporting method for the user equipment assistance information provided by the example of the disclosure, the user equipment carries the own computation capability resource assistance information (such as the computation capacity remaining amount information, the current electric quantity remaining amount, the consumed computation capacity remaining amount of the UE side expected or estimated for accessing of current AI/ML model training, and the electric quantity consumption remaining amount of the UE side expected or estimated for accessing of current AI/ML model training) in the MAC CE or the radio resource control (RRC) signaling, and sends the computation capability resource assistance information to the network device through the control channel carrying the MAC CE or the RRC signaling. When the network device performs AI/ML operation based on the computation capability resource assistance information reported by the UE, an operation adapted to the computation capability resource assistance information reported by the UE can be allocated to the UE, so that the UE can quickly complete the operation based on its own computation capability, and can thus better cooperate with the network device to achieve an AI/ML inference task”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the one or more processors are further configured to: select at least one UE from the plurality of UEs when the training resource information indicates that the computation capability of the at least one UE is greater than a threshold capability for the purpose of addressing computational limitations introduced by high intensive computing operations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20140177507 A1 teaches There are many existing network integration technologies, such as the integration technology of the WiFi/WiMAX and the 3GPP networks. A technology uses the NAS protocol, wherein the UE first transmits the WLAN capabilities to the service node (SGSN) in GPRS (General Packet Radio Service), the SGSN then forwards it to the WAG, to solve how to transmit the WLAN capabilities of the wireless transmit/receive unit (WTRU) for dual-mode mobile phone GPRS/Wireless LAN (WLAN) or Universal Mobile Telecommunications System (UMTS)/WLAN to the network side.
US 20180220470 - A first method includes transmitting, to a network, an indication of a capability to operate in an uplink aggregation dual connectivity mode or in a fast switching dual connectivity mode; and receiving, from the network, a dual connectivity configuration for the UE based at least in part on the indication of the capability.
EP 1213941 A2 teaches The present invention relates to a method of supporting proper hand-off of a dual-mode mobile terminal based on terminal capability and communication status to guarantee continuous mobility in a mixed communication network where a synchronous and an asynchronous network system coexist
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES TERRELL SHEDRICK whose telephone number is (571)272-8621. The examiner can normally be reached 8A-5P.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew D 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.
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/CHARLES T SHEDRICK/Primary Examiner, Art Unit 2646
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