CTFR 18/484,269 CTFR 100812 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. Response to Arguments Applicant’s arguments/amendments with respect to the rejection of claims under 35 USC § 103 have been considered but are not persuasive . Applicant’s arguments: “the compression rates, as described by Chi, are not the same as, and do not render obvious, the "first bandwidth size and a second bandwidth size smaller than the first bandwidth size," as recited in independent claim 1.” “Chi ... fails to teach different machine learning models for different purposes (corresponding to different-sized bandwidths). Chi fails to teach a machine learning model "for channel projection" and a machine learning model "for vector compression." Thus, Chi cannot be relied upon to teach or suggest the features of independent claim 1.” The Examiner’s Response Applicant asserts “the compression rates, as described by Chi, are not the same as, and do not render obvious, the "first bandwidth size and a second bandwidth size smaller than the first bandwidth size," as recited in independent claim 1.” Examiner respectfully disagrees. Claim 1, disclose that “communicate first signaling indicating a first bandwidth size and a second bandwidth size that is less than the first bandwidth size, the first bandwidth size corresponding to a first machine learning model for channel projection and the second bandwidth size corresponding to a second machine learning model for vector compression;” . “communicate” and “bandwidth size” can be understand as UE receive value(s) (e.g. “bandwidth size” ) “corresponding to” a ML model configuration” . Chi teaches “compression rate” as a value corresponding to an ML models configuration (“pre-trained encoding neural network”). Chi, Fig. 6 – 7, further explain that “[0158] CSI compression rates in the collection are arranged in a descending order from large to small”. Applicant asserts “Chi ... fails to teach different machine learning models for different purposes (corresponding to different-sized bandwidths). Chi fails to teach a machine learning model "for channel projection" and a machine learning model "for vector compression." Thus, Chi cannot be relied upon to teach or suggest the features of independent claim 1 ”. Examiner respectfully disagrees. Claim 1 , disclosed UE communicate with signals indicate “bandwidth size” (e.g. value ) corresponding to different machine learning models which is ML "for channel projection" and ML "for vector compression” . However functionality of “channel projection” and “vector compression” are not clearly defined, these two function purpose could be understood as functions/models participated in a channel projection compression process . Chi teaches multiple ML model (e.g. “pre-trained encoding neural network”) for “channel projection” and “vector compression” . Chi [0038] teaches UE “ may compress CSI by using a pre-trained encoding neural network”. Chi [0037] further explains that these ML models (e.g. “pre-trained encoding neural network”) is a solution for a channel projection problem “ the application of the mMIMO technology” . Applicant is reminded that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See in re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones , F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR international Co. v. Teleflex, Inc ., 550 U.S. 398, 82 USPQ2d 1385 (2007). Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-21-aia AIA Claim s 1-5, 11, 13-18, 25, and 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over Chi (US 20250330223 A1), hereinafter Chi in view of Zhu (US20230100253A1), hereinafter Zhu . Regarding to claim 1, Chi teaches a user equipment (UE), comprising: one or more memories; and one or more processors coupled with the one or more memories and individually or collectively configured to ([0006] apparatus for acquiring channel quality) communicate first signaling indicating a first bandwidth size and a second bandwidth size that is less than the first bandwidth size, the first bandwidth size corresponding to a first machine learning model for channel projection and the second bandwidth size corresponding to a second machine learning model for vector compression ([0038] , the terminal device may compress CSI by using a pre-trained encoding neural network .... [0071] the terminal device may receive a first compression parameter sent by the network device; and determine the CSI compression parameter according to the first compression parameter. For instance, the terminal device may receive the first compression parameter through radio resource control (RRC) signaling (e.g., broadcast signaling, or, signaling dedicated for the terminal device)....[0145-0146] a collection of the preset CSI compression rates may include σ={σ.sub.1, σ.sub.2, . . . , σ.sub.T}, and the preset CSI compression rates in the collection are arranged in a descending order from large too small. ... For example, the preset CSI compression rate σ.sub.1 corresponding to the sub-encoder 1 may be 1/2, the preset CSI compression rate σ.sub.2 corresponding to the sub-encoder 2 may be 1/4, ...and so on); receive second signaling indicating channel state information ([0063] terminal device may obtain a space domain channel matrix H by measurement (may also be called estimation) according to a received CSI-RS) . Chi does not explicitly teach transmitting a channel state feedback message based at least in part on the second signaling, the channel state feedback message indicating a sub-space of a codebook for a projection of at least a portion of the channel state information corresponding to the first bandwidth size based at least in part on the first machine learning model, a compression of a subset of the projection corresponding to the second bandwidth size based at least in part on the second machine learning model, or both. Zhu teaches transmitting a channel state feedback message based at least in part on the second signaling ([0096] a capability to perform a (machine learning-based) channel state information (CSI) measurement procedure ... [0113] A UE model may be configured together with the network model, for example, to compress and decompress channel state feedback (CSF) transmitted across the wireless); the channel state feedback message indicating a sub-space of a codebook for a projection of at least a portion of the channel state information corresponding to the first bandwidth size based at least in part on the first machine learning model, a compression of a subset of the projection corresponding to the second bandwidth size based at least in part on the second machine learning model, or both ([0031] compression and decompression of channel state feedback may be implemented with neural network models running on both the UE and the network entity... [0090]A neural network function (NNF) may be defined as a function Y=F(X) to be supported by a neural network model. ... .Examples of these machine learning-based wireless communications management procedures may include .... channel state feedback, compression...[0105-0106] the first machine learning model and the second machine learning model may comprise matched machine learning models whereby the output of one of the machine learning models is used as an input to the other machine learning model.... selecting the at least one machine learning model (e.g., the first machine learning model) corresponding to the at least one NNF, the CU-XP 716 may additionally determine a second machine learning model for the base station 110 to perform at least a portion of the machine learning-based wireless communications management procedure). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Zhu to the teaching of Chi. The motivation for such an addition would be to improve channel estimate ([0087] Zhu). Regarding to claim 2, Chi and Zhu teach the UE of claim 1, Chi further teaches wherein, to communicate the first signaling, the one or more processors are individually or collectively configured to: receive configuration signaling indicating the first bandwidth size, the second bandwidth size, or both for the channel state feedback message ([0146] For example, the preset CSI compression rate σ.sub.1 corresponding to the sub-encoder 1 may be 1/2, the preset CSI compression rate σ.sub.2 corresponding to the sub-encoder 2 may be 1/4, the preset CSI compression rate σ.sub.3 corresponding to the sub-encoder 3 may be 1/8, the preset CSI compression rate σ.sub.4 corresponding to the sub-encoder 4 may be 1/16, the preset CSI compression rate σ.sub.5 corresponding to the sub-encoder 5 may be 1/32, the preset CSI compression rate σ.sub.6 corresponding to the sub-encoder 6 may be 1/64, and so on. It is to be noted that, the values of the preset CSI compression rates here are all examples, and specific values are not limited in the disclosure). Regarding to claim 3, Chi and Zhu teach the UE of claim 1, Chi does not explicitly teach wherein, to communicate the first signaling, the one or more processors are individually or collectively configured to: transmit a request for the first bandwidth size, the second bandwidth size, or both; and receive, in response to the request, configuration signaling indicating the first bandwidth size, the second bandwidth size, or both for the channel state feedback message. Zhu teaches wherein, to communicate the first signaling, the one or more processors are individually or collectively configured to: transmit a request for the first bandwidth size, the second bandwidth size, or both; and receive, in response to the request, configuration signaling indicating the first bandwidth size, the second bandwidth size, or both for the channel state feedback message ([0007] a method of wireless communication by a user equipment (UE) includes transmitting, to a base station, a request for a machine learning configuration for a network-based neural network model). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Zhu to the teaching of Chi . The motivation for such an addition would be to improve channel estimate ([0087] Zhu). Regarding to claim 4, Chi and Zhu teach the UE of claim 1, Chi does not explicitly teach wherein, to communicate the first signaling, the one or more processors are individually or collectively configured to: select the first bandwidth size, the second bandwidth size, or both for the channel state feedback message; and transmit an indication of the first bandwidth size, the second bandwidth size, or both based at least in part on the selecting. Zhu further teaches wherein, to communicate the first signaling, the one or more processors are individually or collectively configured to select the first bandwidth size, the second bandwidth size, or both for the channel state feedback message; and transmit an indication of the first bandwidth size, the second bandwidth size, or both based at least in part on the selecting ([0088] The neural network model may include a model structure and model parameters. Additionally, dynamic configuration may provide the base station with flexibility to selectively choose, at any given time and for a particular scenario, which NNF(s) and/or corresponding model(s) to use for performing one or more machine learning-based wireless communications management procedures. Moreover, dynamic configuration may allow the base station to dynamically update neural network models for NNFs). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Zhu to the teaching of Chi . The motivation for such an addition would be to improve channel estimate ([0087] Zhu). Regarding to claim 5, Chi and Zhu teach the UE of claim 1, Chi does not explicitly teach wherein communicating the first signaling is based at least in part on a change in one or more channel metrics. Zhu further teaches wherein communicating the first signaling is based at least in part on a change in one or more channel metrics. ( [0150] The method of Aspect 14 or 15, further comprising receiving, from the base station, a list of events triggering the UE to report a change in conditions that initiate an update of the network-based neural network model.) It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Zhu to the teaching of Chi . The motivation for such an addition would be to improve channel estimate ([0087] Zhu). Regarding to claim 11, Chi and Zhu teach the UE of claim 1, Chi does not explicitly teach wherein the one or more processors are individually or collectively further configured to transmit capability information for the UE indicating that the UE supports the first machine learning model for the channel projection and the second machine learning model for the vector compression, wherein communicating the first signaling is based at least in part on the capability information for the UE. Zhu teaches wherein the one or more processors are individually or collectively further configured to transmit capability information for the UE indicating that the UE supports the first machine learning model for the channel projection and the second machine learning model for the vector compression, wherein communicating the first signaling is based at least in part on the capability information for the UE ([0006]The neural network model includes a model structure and a parameter set, based on the NNF, the UE machine learning capability). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Zhu to the teaching of Chi . The motivation for such an addition would be to improve channel estimate ([0087] Zhu). Regarding to claim 13, Chi and Zhu teach the UE of claim 1, Chi further teaches wherein the codebook comprises a non-discrete Fourier transform codebook of a set of non-discrete Fourier transform codebooks (fig. 4, and [0037] With the application of the mMIMO technology, in order to reduce the overhead of CSI reporting, a CSI compression technique based on discrete Fourier transform (DFT) may be adopted, where a terminal device performs CSI compression before reporting to a network device. ... [0109] The CSI compression model 41 may compress an input first channel matrix according to a CSI compression parameter and then output a target channel matrix (the target channel matrix may also be called a codeword)). Claims [14-18, 25, and 27] (apparatus claim at network entity), [28-29] (method claim at UE), and [30] ( method claim at network entity) are rejected under the same reasoning as claims [1-5, 11, and 13] apparatus claim at UE, where Zhu teaches both apparatus and method at UE and network entity ([0009]) . 07-21-aia AIA Claim s 6 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chi in view of Zhu and further in view of Elshafie (US 20230081579 A1), hereinafter Elshafie . Regarding to claim 6, Chi and Zhu teach the UE of claim 5, Chi and Zhu do not explicitly teach wherein the one or more processors are individually or collectively further configured to transmit third signaling requesting an update to a resource allocation for the channel state feedback message based at least in part on the change in the one or more channel metrics. Elshafie teaches wherein the one or more processors are individually or collectively further configured to: transmit third signaling requesting an update to a resource allocation for the channel state feedback message based at least in part on the change in the one or more channel metrics ([0096]-[0097] UE 115- a may report or request other parameters, such as different resource allocations or resource patterns, among others. UE 115- a may transmit the request 205 in an uplink shared channel message, an uplink control channel message, an RRC message, a MAC CE, or any combination thereof. In some cases, the request 205 may be included with a CSI report... if UE 115- a detects frequent changes to channel conditions, UE 115- a may request to transmit CSI reports at a higher periodicity to provide more frequent and more up-to-date channel characteristics to base station 105- a , despite the presence of interference). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Elshafie to the teaching of Chi and Zhu . The motivation for such an addition would be to improve channel conditions or reduce unnecessary resource consumption ([0047] Elshafie). Claim [19] (apparatus claim at network entity) is rejected under the same reasoning as claim [6] apparatus claim at UE, where Zhu teaches both apparatus and method at UE and network entity ([0009]) . Regarding to claim 20, Chi and Zhu and Elshafie teach the UE of claim 19, Chi and Zhu do not explicitly teach wherein the one or more processors are individually or collectively further configured to receive fourth signaling requesting an update to the resource allocation for the channel state feedback message, wherein transmitting the third signaling updating the resource allocation is further based at least in part on the fourth signaling. Elshafie further teaches wherein the one or more processors are individually or collectively further configured to receive fourth signaling requesting an update to the resource allocation for the channel state feedback message, wherein transmitting the third signaling updating the resource allocation is further based at least in part on the fourth signaling ([0096-0097] UE 115- a may report or request other parameters, such as different resource allocations or resource patterns, among others. UE 115- a may transmit the request 205 in an uplink shared channel message, an uplink control channel message, an RRC message, a MAC CE, or any combination thereof. In some cases, the request 205 may be included with a CSI report... if UE 115- a detects frequent changes to channel conditions, UE 115- a may request to transmit CSI reports at a higher periodicity to provide more frequent and more up-to-date channel characteristics to base station 105- a , despite the presence of interference). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Elshafie to the teaching of Chi and Zhu. The motivation for such an addition would be to improve channel conditions or reduce unnecessary resource consumption ( [0047] Elshafie) . 07-21-aia AIA Claim s 7-8, 10, 21-22, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Chi in view of Zhu and further in view of Vitthaladevuni (US20230254773A1), hereinafter Vitthaladevuni . Regarding to claim 7, Chi and Zhu teach the UE of claim 1, Chi and Zhu do not explicitly teach wherein the one or more processors are individually or collectively further configured to receive third signaling indicating a configuration for transmitting the channel state feedback message, wherein transmitting the channel state feedback message is based at least in part on the configuration. Vitthaladevuni teaches wherein the one or more processors are individually or collectively further configured to receive third signaling indicating a configuration for transmitting the channel state feedback message, wherein transmitting the channel state feedback message is based at least in part on the configuration ([0134] UE 120 a may receive, from base station 110, signaling identifying a threshold power level at which to switch from a relatively power-intensive channel state feedback processing type to a less power-intensive channel state feedback processing type.... and ... [0136] UE 120 a may switch from transmitting type-III channel state information to transmitting type-I or type-II channel state information, which may each be associated with reduced power consumption relative to generating type-III channel state information). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Vitthaladevuni to the teaching of Chi and Zhu . The motivation for such an addition would be to provide for less CSI feedback overhead and improve performance ([0066] Vitthaladevuni). Regarding to claim 8, Chi and Zhu and Vitthaladevuni teach the UE of claim 7, Chi further teaches wherein, to transmit the channel state feedback message, the one or more processors are individually or collectively configured to transmit, based at least in part on the configuration, an aperiodic channel state feedback message comprising a first compression of a first parameter indicating the sub-space of the codebook for the projection, a second compression of a second parameter indicating the compression of the subset of the projection , or both ([[0147] the collection of the preset CSI compression rates may include σ={1/4, 1/16, 1/32, 1/64} ). Regarding to claim 10, Chi and Zhu and Vitthaladevuni teach the UE of claim 7, Chi and Zhu do not explicitly teach wherein the third signaling comprises a radio resource control message, a medium access control message, or a downlink control information message. Vitthaladevuni teaches wherein the third signaling comprises a radio resource control message, a medium access control message, or a downlink control information message ([0134] UE 120 a may receive signaling, such as radio resource control (RRC) signaling, medium access control (MAC) control element (CE) signaling, downlink control information (DCI) signaling ). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Vitthaladevuni to the teaching of Chi and Zhu. The motivation for such an addition would be to provide for less CSI feedback overhead and improve performance ([0066] Vitthaladevuni). Claims [21-22, and 24] (apparatus claim at network entity) are rejected under the same reasoning as claims [7-8, and 10] apparatus claim at UE, where Zhu teaches both apparatus and method at UE and network entity ([0009]) . 07-21-aia AIA Claim s 9 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Chi in view of Zhu, and further in view of Vitthaladevuni, and further in view of Ji (US 20130273954 A1), hereinafter Ji . Regarding to claim 9, Chi and Zhu and Vitthaladevuni teach the UE of claim 7, Chi and Zhu and Vitthaladevuni do not explicitly teach wherein the configuration indicates a first periodicity and a second periodicity different from the first periodicity, and wherein, to transmit the channel state feedback message, the one or more processors are individually or collectively configured to: transmit, according to the first periodicity, a first channel state feedback message comprising a first compression of a first parameter indicating the sub-space of the codebook for the projection; and transmit, according to the second periodicity, a second channel state feedback message comprising a second compression of a second parameter indicating the compression of the subset of the projection. Ji teaches wherein the configuration indicates a first periodicity and a second periodicity different from the first periodicity, and wherein, to transmit the channel state feedback message, the one or more processors are individually or collectively configured to: transmit, according to the first periodicity, a first channel state feedback message comprising a first compression of a first parameter indicating the sub-space of the codebook for the projection; and transmit, according to the second periodicity, a second channel state feedback message comprising a second compression of a second parameter indicating the compression of the subset of the projection ([0074] the UE may differentiate between the `shorter periodicity` and `longer periodicity` designations based on some predefined or dynamically defined periodicity threshold, and may utilize a first CSF generation technique (e.g., using a first CSF filtering or averaging constant or set of constants) for shorter periodicity and a second CSF generation technique (e.g., using a second CSF filtering or averaging constant or set of constants) for longer periodicity). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Ji to the teaching of Chi and Zhu and Vitthaladevuni . The motivation for such an addition would be to optimize throughput ([0059] Ji). Claims [23] (apparatus claim at network entity) is rejected under the same reasoning as claims [9] apparatus claim at UE, where Zhu teaches both apparatus and method at UE and network entity ([0009]) . 07-21-aia AIA Claim s 12 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Chi in view of Zhu and further in view of Vitthaladevuni (US 20230188302 A1), hereinafter Vitthaladevuni76 . Regarding to claim 12, Chi and Zhu teach the UE of claim 1, Chi and Zhu do not explicitly teach wherein the channel state feedback message comprises a first set of bits indicating a first compression of a first parameter indicating the sub-space of the codebook for the projection, a second set of bits indicating a second compression of a second parameter indicating the compression of the subset of the projection, or both; and a first quantity of the first set of bits is larger than a second quantity of the second set of bits. Vitthaladevuni76 teaches wherein the channel state feedback message comprises a first set of bits indicating a first compression of a first parameter indicating the sub-space of the codebook for the projection, a second set of bits indicating a second compression of a second parameter indicating the compression of the subset of the projection, or both; and a first quantity of the first set of bits is larger than a second quantity of the second set of bits ([0066] measuring the level of compression achieved by the encoder neural network (e.g., comparing the number of bits associated with the encoded CSI feedback to the number of bits associated with the input measurements, or channel realization)). It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teaching of Vitthaladevuni76 to the teaching of Chi and Zhu. The motivation for such an addition would be to configure the UE to report CSI with an appropriate level of accuracy ([0005] Vitthaladevuni76). Claims [26] (apparatus claim at network entity) is rejected under the same reasoning as claims [12] apparatus claim at UE, where Zhu teaches both apparatus and method at UE and network entity ([0009]) . Conclusion 07-39 AIA 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 VAN T NGUYEN whose telephone number is (571)272-6178. The examiner can normally be reached 8:00 AM - 5:00 PM (EST). 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, Ayman A Abaza can be reached at (571) 270-0422. 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. /VAN TA NGUYEN/Examiner, Art Unit 2465 /AYMAN A ABAZA/Primary Examiner, Art Unit 2465 Application/Control Number: 18/484,269 Page 2 Art Unit: 2465 Application/Control Number: 18/484,269 Page 3 Art Unit: 2465 Application/Control Number: 18/484,269 Page 4 Art Unit: 2465 Application/Control Number: 18/484,269 Page 5 Art Unit: 2465 Application/Control Number: 18/484,269 Page 6 Art Unit: 2465 Application/Control Number: 18/484,269 Page 7 Art Unit: 2465 Application/Control Number: 18/484,269 Page 8 Art Unit: 2465 Application/Control Number: 18/484,269 Page 9 Art Unit: 2465 Application/Control Number: 18/484,269 Page 10 Art Unit: 2465 Application/Control Number: 18/484,269 Page 11 Art Unit: 2465 Application/Control Number: 18/484,269 Page 12 Art Unit: 2465 Application/Control Number: 18/484,269 Page 13 Art Unit: 2465 Application/Control Number: 18/484,269 Page 14 Art Unit: 2465 Application/Control Number: 18/484,269 Page 15 Art Unit: 2465 Application/Control Number: 18/484,269 Page 16 Art Unit: 2465 Application/Control Number: 18/484,269 Page 17 Art Unit: 2465 Application/Control Number: 18/484,269 Page 18 Art Unit: 2465 Application/Control Number: 18/484,269 Page 19 Art Unit: 2465 Application/Control Number: 18/484,269 Page 20 Art Unit: 2465
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