Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is in response to application filed on 01/19/2024. Claims 1-20 were previously pending. Claims 1-20 are rejected.
Information Disclosure Statement
3. The information disclosure statement(s) (IDS) submitted on 04/25/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) is/are being considered by the examiner.
Drawings
4. The drawings FIG.1 are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “102” has been used to designate both * and *.
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Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
5. 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 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.
5.1. 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 of this title, 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.
5.2. 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.
5.3. Claim(s) 1-4, 11-14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Phillippe, (“Phil”, US 2022/0408271 A1) in view of Vasseur et al., (“Vas”, US 2019/0116485 A1).
Regarding Claim 1, Phil teaches, a computer-implemented method comprising:
obtaining a first layout of a set of access points (APs) to be deployed within an environment (Phil, [0025-26]: an office environment can include 4 APs equally distributed across multiple floors in a building; FIG.1, network environment 100, AP 102, [0030-32]: an network environment 100, including multiple APs 102a-d. It is obvious to a person of ordinary skill in the art to obtain a floor plan (“1st layout”) including 4 APs);
determining, based on evaluating the first layout with a machine learning model, that at least a first AP of the set of APs, will be associated with one or more sticky clients (Phil, FIG.1, static device 104, mobile 106, [0038-39]: determine whether the first static network device 104a (“sticky client”) corresponds with a prioritized wireless access point 102 using weighted averages based on a historical derived proximity of the wireless access points in the network environment 100);
generating a second layout of the set of APs to be deployed within the environment using the machine learning model (Phil, [0024]: In a multi-AP wireless deployment where the APs are capable of coordinating actions or a status of each AP, a moving average of RSSI values may be calculated for each network device to associate with each given AP. Over time, based either on a simple moving average or a machine learning (ML) model (“2nd layout”), the frequency of associations for each network device to any other AP weighted according to a set of weights for each AP).
Phil does not expressly teach
transmitting information associated with the second layout.
Vas teaches (Vas, FIG.3, network data collection platform 304, collected data 334, [0046]: transmitting collected data 334 from local locations to network data collection platform 304)
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “network data collection platform” of Vas into the invention of Phil. The suggestion/motivation would have been enable a mechanism that helps to reduce or eliminate roaming failures in the wireless network (Vas, [0042-45]). Including the “network data collection platform” of Vas into the invention of Phil was within the ordinary ability of one of ordinary skill in the art based on the teachings of Vas.
Regarding Claim 2, Phil-Vas teaches, the computer-implemented method of claim 1, wherein the machine learning model is trained to predict a likelihood that an AP will be associated with one or more sticky clients (Vas, FIG.5, client device 504, mobility path failure modeler 508, [0081]: mobility path failure modeler 508 predicts the approximate path of travel of client device 504 and its final AP).
Regarding Claim 3, Phil-Vas teaches, the computer-implemented method of claim 2, wherein the determination that at least the first AP will be associated with one or more sticky clients is based on determining that the likelihood predicted by the machine learning model is greater that a threshold (Vas, FIG.5, client device 504, [0085]: the set of mobility path(s) that client device 504 is predicted to take may also be constrained to APs for which the expected signal quality from client device 504 will be above a given threshold).
Regarding Claim 4, Phil-Vas teaches, the computer-implemented method of claim 2, wherein the machine learning model is trained on a dataset comprising (i) a plurality of AP identifiers, and (ii) for each AP identifier, an indication of whether the respective AP is associated with one or more sticky clients (Vas, FIG.3, machine learning-based analyzer 312, APs 320, 328, [0049-51]: machine learning-based analyzer 312 may be able to extract patterns of Wi-Fi roaming in the network and roaming behaviors (e.g., the “stickiness” of clients to APs 320, 328, “ping-pong” clients, the number of visited APs 320, 328, roaming triggers, etc.).).
Regarding Claim 11, Phil teaches, a system comprising:
one or more memories collectively storing computer-executable instructions; and one or more processors communicatively coupled to the one or more memories, the one or more processors being collectively configured to execute the computer- executable instructions to cause the system to perform an operation comprising (Phil, Claim 8: A system comprising: a processor; and a non-transitory computer-readable medium including instructions that, when executed by the processor, cause the processor to:):
obtaining a first layout of a set of access points (APs) to be deployed within an environment (Phil, FIG.1, network environment 100, AP 102, [0025-26, 30-32]: an office environment 100 includes multiple APs 102a-d equally distributed across multiple floors in a building. It is obvious to a person of ordinary skill in the art to obtain a floor plan (“1st layout”) including 4 APs);
determining, based on evaluating the first layout with a machine learning model, that at least a first AP of the set of APs, will be associated with one or more sticky clients (Phil, FIG.1, static device 104, mobile 106, [0038-39]: determine whether the first static network device 104a (“sticky client”) corresponds with a prioritized wireless AP 102 using weighted averages based on a historical derived proximity of the wireless APs in the network environment 100);
generating a second layout of the set of APs to be deployed within the environment using the machine learning model (Phil, [0024]: In a multi-APs wireless deployment where the APs are capable of coordinating actions or a status of each AP, a moving average of RSSI values may be calculated for each network device to associate with each given AP. Over time, based either on a simple moving average or a machine learning (ML) model (“2nd layout”), the frequency of associations for each network device to any other AP weighted according to a set of weights for each AP).
Phil does not expressly teach
transmitting information associated with the second layout.
Vas teaches (Vas, FIG.3, network data collection platform 304, collected data 334, [0046]: transmitting collected data 334 from local locations to network data collection platform 304)
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “network data collection platform” of Vas into the invention of Phil. The suggestion/motivation would have been enable a mechanism that helps to reduce or eliminate roaming failures in the wireless network (Vas, [0042-45]). Including the “network data collection platform” of Vas into the invention of Phil was within the ordinary ability of one of ordinary skill in the art based on the teachings of Vas.
Regarding Claim 12, Phil-Vas teaches, the system of claim 11, wherein the machine learning model is trained to predict a likelihood that an AP will be associated with one or more sticky clients (Vas, FIG.5, client device 504, mobility path failure modeler 508, [0081]: mobility path failure modeler 508 predicts the approximate path of travel of client device 504 and its final AP).
Regarding Claim 13, Phil-Vas teaches, the system of claim 12, wherein the determination that at least the first AP will be associated with one or more sticky clients is based on determining that the likelihood predicted by the machine learning model is greater than a threshold (Vas, FIG.5, client device 504, [0085]: the set of mobility path(s) that client device 504 is predicted to take may also be constrained to APs for which the expected signal quality from client device 504 will be above a given threshold).
Regarding Claim 14, Phil-Vas teaches, the system of claim 12, wherein the machine learning model is trained on a dataset comprising (i) a plurality of AP identifiers, and (ii) for each AP identifier, an indication of whether the respective AP is associated with one or more sticky clients (Vas, FIG.3, machine learning-based analyzer 312, APs 320, 328, [0049-51]: machine learning-based analyzer 312 may be able to extract patterns of Wi-Fi roaming in the network and roaming behaviors (e.g., the “stickiness” of clients to APs 320, 328, “ping-pong” clients, the number of visited APs 320, 328, roaming triggers, etc.)).
Regarding Claim 20, Phil teaches, a non-transitory computer-readable medium comprising computer-executable instructions, which when collectively executed by one or more processors of a computing system cause the computing system to perform an operation comprising (Phil, Claim 16, A non-transitory computer-readable medium including stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process to):
obtaining a first layout of a set of access points (APs) to be deployed within an environment (Phil, FIG.1, network environment 100, AP 102, [0025-26, 30-32]: an office environment 100 includes multiple APs 102a-d equally distributed across multiple floors in a building; It is obvious to a person of ordinary skill in the art to obtain a floor plan (“1st layout”) including 4 APs);
determining, based on evaluating the first layout with a machine learning model, that at least a first AP of the set of APs, will be associated with one or more sticky clients (Phil, FIG.1, static device 104, mobile 106, [0038-39]: determine whether the first static network device 104a (“sticky client”) corresponds with a prioritized wireless AP 102 using weighted averages based on a historical derived proximity of the wireless APs in the network environment 100);
generating a second layout of the set of APs to be deployed within the environment using the machine learning model ((Phil, [0024]: In a multi-APs wireless deployment where the APs are capable of coordinating actions or a status of each AP, a moving average of RSSI values may be calculated for each network device to associate with each given AP. Over time, based either on a simple moving average or a machine learning (ML) model (“2nd layout”), the frequency of associations for each network device to any other AP weighted according to a set of weights for each AP).
Phil does not expressly teach
transmitting information associated with the second layout.
Vas teaches (Vas, FIG.3, network data collection platform 304, collected data 334, [0046]: transmitting collected data 334 from local locations to network data collection platform 304)
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “network data collection platform” of Vas into the invention of Phil. The suggestion/motivation would have been enable a mechanism that helps to reduce or eliminate roaming failures in the wireless network (Vas, [0042-45]). Including the “network data collection platform” of Vas into the invention of Phil was within the ordinary ability of one of ordinary skill in the art based on the teachings of Vas.
5.4. Claim(s) 5, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Phillippe, (“Phil”, US 2022/0408271 A1) in view of Vasseur et al., (“Vas”, US 2019/0116485 A1)., and further in view of Wang et al., (“Wang”, US 2022/0330047 A1).
Regarding Claim 5, Phil-Vas teaches, the computer-implemented method of claim 4, but not expressly teaches wherein the indication of whether the respective AP is associated with one or more sticky clients is based on (i) a first number of clients associated with the AP that are labeled as sticky clients and (ii) a second number of clients associated with the AP that are labeled as non-sticky clients.
Wang teaches (Wang, Table 2, [0123]: detect service level expectation (SLE) metrics associated with sticky clients and eliminate them from consideration when assigning AP scores).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “service level expectation (SLE) metrics” of Wang into the invention of Phil-Vas. The suggestion/motivation would have been to enable automatically detecting coverage holes within the wireless network based on SLE metrics for client devices in the wireless network (Wang, [0004-6]). Including the “SLE metrics” of Wang into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Wang.
Regarding Claim 15, Phil-Vas teaches, the system of claim 14, but not expressly teaches wherein the indication of whether the respective AP suffers from sticky clients is based on (i) a first number of clients associated with the AP that are labeled as sticky clients and (ii) a second number of clients associated with the AP that are labeled as non-sticky clients.
Wang teaches (Wang, Table 2, [0123]: detect service level expectation (SLE) metrics associated with sticky clients and eliminate them from consideration when assigning AP scores).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “service level expectation (SLE) metrics” of Wang into the invention of Phil-Vas. The suggestion/motivation would have been to enable automatically detecting coverage holes within the wireless network based on SLE metrics for client devices in the wireless network (Wang, [0004-6]). Including the “SLE metrics” of Wang into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Wang.
5.5. Claim(s) 6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Phillippe, (“Phil”, US 2022/0408271 A1) in view of Vasseur et al., (“Vas”, US 2019/0116485 A1), and further in view of Likar et al.. (“Lik”, US 2019/0297546 A1).
Regarding Claim 6, Phil-Vas teaches, the computer-implemented method of claim 1, but not expressly teaches wherein a sticky client is a client that (i) is associated to an AP with a signal strength lower than a threshold, (ii) the signal strength is lower than a signal strength to a neighboring AP, and (iii) the association has persisted for a threshold amount of time.
Lik teaches (Lik, Claim 1: (i) detecting a measured RSSI value between the first access point and the specific wireless station (“sticky client”) below the dynamic RSSI threshold; (ii) a dynamic RSSI threshold indicating when the specific wireless station is to be handed off from the first access point to one of the neighboring access points, based on a highest of RSSI values for the neighboring access points;).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “sticky-client” of Lik into the invention of Phil-Vas. The suggestion/motivation would have been to enable forcing transitions between APs sticky-client stations of a cloud-controlled Wi-Fi network (Lik, [0002]). Including the “sticky-client” of Lik into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lik.
Regarding Claim 16, Phil-Vas teaches, the system of claim 11, but not expressly teaches wherein a sticky client is a client that (i) is associated to an AP with a signal strength lower than a threshold, (ii) the signal strength is lower than a signal strength to a neighboring AP, and (iii) the association has persisted for a threshold amount of time.
Lik teaches (Lik, Claim 1: (i) detecting a measured RSSI value between the first AP and the specific wireless station (“sticky client”) below the dynamic RSSI threshold; (ii) a dynamic RSSI threshold indicating when the specific wireless station is to be handed off from the first AP to one of the neighboring APs, based on a highest of RSSI values for the neighboring APs).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “sticky-client” of Lik into the invention of Phil-Vas. The suggestion/motivation would have been to enable forcing transitions between APs sticky-client stations of a cloud-controlled Wi-Fi network (Lik, [0002]). Including the “sticky-client” of Lik into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lik.
5.6. Claim(s) 7-10, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Phillippe, (“Phil”, US 2022/0408271 A1) in view of Vasseur et al., (“Vas”, US 2019/0116485 A1), and further in view of Chandail et al., (“Chan”, US 10,794,983 B1).
Regarding Claim 7, Phil-Vas teaches, the computer-implemented method of claim 1, but not expressly teaches wherein:
the first layout of the set of APs comprises an indication of a respective proposed deployment location within the environment for each AP; and
the second layout of the set of APs comprises, for at least one AP of the set of APs, a different proposed deployment location within the environment for the at least one AP.
Chan teaches
the first layout of the set of APs comprises an indication of a respective proposed deployment location within the environment for each AP (Chan, FIG.6, heatmap 600, col. 8, lines 20-53: Each AP's measurement/heatmap contributes independently to a composite/aggregate heatmap for all APs (e.g., heatmap 600) from the reception of a STA transmission (Tx). The techniques herein are thus poised to determine which AP(s) to exclude from and include in creation of this composite heatmap (for finding the XY location for any single STA given such measurements)); and
the second layout of the set of APs comprises, for at least one AP of the set of APs, a different proposed deployment location within the environment for the at least one AP (Phil, [0024]: calculating a moving average of RSSI value of each network device (“client data”) to associate with each give AP. It is obvious to a person of ordinary skill in the art that the proposed deployment location for the Aps might be different when the layout of the set of APs trained by different machine learning algorithm).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “location information” of Chan into the invention of Phil-Vas. The suggestion/motivation would have been for enhancing accuracy of angle-of-arrival (AoA) device locating through machine learning. Including the “location information” of Chan into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Chan.
Regarding Claim 8, Phil-Vas teaches, the computer-implemented method of claim 1, but not expressly teaches wherein the information associated with the second layout comprises at least one of: (i) a number of the set of APs of the second layout, (ii) a respective location of each of the set of APs of the second layout, or (iii) a maximum transmit power of each of the set of APs of the second layout.
Chan teaches (Chan, Abstract: obtaining a machine learning model indicative of how to focus on particular location information from a plurality of radio frequency (RF) elements (“APs”) to provide an accurate location estimate of a wireless client based at least in part on angle-of-arrival (AoA) information of the wireless client).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “location information” of Chan into the invention of Phil-Vas. The suggestion/motivation would have been for enhancing accuracy of angle-of-arrival (AoA) device locating through machine learning. Including the “location information” of Chan into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Chan.
Regarding Claim 9, Phil-Vas teaches, the computer-implemented method of claim 1, but not expressly teaches wherein the first layout of the set of APs is evaluated with the machine learning model without a set of client data associated with the first layout of the set of APs.
Chan teaches (Chan, Abstract, col. 9, lines 4-16: obtains a machine learning model indicative of how to focus on particular location information from a plurality of radio frequency (RF) elements (“APs”)to provide an accurate location estimate of a wireless client based at least in part on angle-of-arrival information of the wireless client; col. 16, lines 5-24: obtains a machine learning model (e.g., based on an artificial neural network (ANN)) indicative of how to “focus” on particular location information from a plurality of radio frequency (RF) elements to provide an accurate location estimate of a wireless client based at least in part on angle-of-arrival (AoA) information of the wireless client).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “location information” of Chan into the invention of Phil-Vas. The suggestion/motivation would have been for enhancing accuracy of angle-of-arrival (AoA) device locating through machine learning. Including the “location information” of Chan into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Chan.
Regarding Claim 10, Phil-Vas teaches, the computer-implemented method of claim 1, but not expressly teaches further comprising transmitting an indication of the first AP to a computing system.
Chan teaches (Chan, FIG.6, heatmap 600, col. 8, lines 20-53: Each AP's measurement/heatmap contributes independently to a composite/aggregate heatmap for all APs (e.g., heatmap 600) from the reception of a STA transmission (Tx). The techniques herein are thus poised to determine which AP(s) to exclude from and include in creation of this composite heatmap (for finding the XY location for any single STA given such measurements))
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “location information” of Chan into the invention of Phil-Vas. The suggestion/motivation would have been for enhancing accuracy of angle-of-arrival (AoA) device locating through machine learning. Including the “location information” of Chan into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Chan.
Regarding Claim 17, Phil-Vas teaches, the system of claim 11, but not expressly teaches wherein:
the first layout of the set of APs comprises an indication of a respective proposed deployment location within the environment for each AP; and
the second layout of the set of APs comprises, for at least one AP of the set of APs, a different proposed deployment location within the environment for the at least one AP.
Chan teaches
the first layout of the set of APs comprises an indication of a respective proposed deployment location within the environment for each AP (Chan, FIG.6, heatmap 600, col. 8, lines 20-53: Each AP's measurement/heatmap contributes independently to a composite/aggregate heatmap for all APs (e.g., heatmap 600) from the reception of a STA transmission (Tx). The techniques herein are thus poised to determine which AP(s) to exclude from and include in creation of this composite heatmap (for finding the XY location for any single STA given such measurements); and
the second layout of the set of APs comprises, for at least one AP of the set of APs, a different proposed deployment location within the environment for the at least one AP (Phil, [0024]: calculating a moving average of RSSI value of each network device (“client data”) to associate with each give AP. It is obvious to a person of ordinary skill in the art that the proposed deployment location for the Aps might be different when the layout of the set of APs trained by different machine learning algorithm).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “location information” of Chan into the invention of Phil-Vas. The suggestion/motivation would have been for enhancing accuracy of angle-of-arrival (AoA) device locating through machine learning. Including the “location information” of Chan into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Chan.
Regarding Claim 18, Phil-Vas teaches, the system of claim 11, but not expressly teaches wherein the first layout of the set of APs is evaluated with the machine learning model without a set of client data associated with the first layout of the set of APs.
Chan teaches (Chan, Abstract: obtaining a machine learning model indicative of how to focus on particular location information from a plurality of radio frequency (RF) elements (“APs”) to provide an accurate location estimate of a wireless client based at least in part on angle-of-arrival (AoA) information of the wireless client).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “location information” of Chan into the invention of Phil-Vas. The suggestion/motivation would have been for enhancing accuracy of angle-of-arrival (AoA) device locating through machine learning. Including the “location information” of Chan into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Chan.
Regarding Claim 19, Phil-Vas teaches, the system of claim 11, but not expressly teaches wherein the information associated with the second layout comprises at least one of:
(i) a number of the set of APs of the second layout, (ii) a respective location of each of the set of APs of the second layout, or (iii) a maximum transmit power of each of the set of APs of the second layout.
Chan teaches (Chan, Abstract, col. 9, lines 4-16: obtains a machine learning model indicative of how to focus on particular location information from a plurality of radio frequency (RF) elements (“APs”)to provide an accurate location estimate of a wireless client based at least in part on angle-of-arrival information of the wireless client; col. 16, lines 5-24: obtains a machine learning model (e.g., based on an artificial neural network (ANN)) indicative of how to “focus” on particular location information from a plurality of radio frequency (RF) elements to provide an accurate location estimate of a wireless client based at least in part on angle-of-arrival (AoA) information of the wireless client).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “location information” of Chan into the invention of Phil-Vas. The suggestion/motivation would have been for enhancing accuracy of angle-of-arrival (AoA) device locating through machine learning. Including the “location information” of Chan into the invention of Phil-Vas was within the ordinary ability of one of ordinary skill in the art based on the teachings of Chan.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Ohlarik et al., US 2019/0230620 A1, Method For Configuring Client Device E.g. Cell Phone, Involves Applying Operations To Client Device At Current Location Of Client Device Relative To Preceding Location Or Succeeding Location Of Client Device Based On Evaluating.
Cisco Wireless Network. Solution Guide, © 2023 Cisco and/or its affiliates, total pages 202.
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/C.L/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446