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 with respect to claims 1, 11, and 18 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
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.
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.
Claims 1-7 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Likar et al US 20180184345 (hereinafter Likar) in view of Mattaparti et al US 20230300687 (hereinafter Mattaparti) and Chang et al US 20160112942 (hereinafter Chang).
Regarding claim 1, Likar discloses a method comprising:
identifying, by a network device (110, see fig. 1A, [0019]) fig. 1A, [0020]) corresponding to a source AP (121B, see fig. 1A, [0020]) of a client device (140, see fig. 1A, [0019]) in the network infrastructure (Internet-subnet security module receives the results of a neighbor scan that reveals nearby access points, including access point 131 in a different subnet, see fig. 1, [0021]);
determining, by the network device, first roaming provisioning credentials for the first set of roaming targets (security keys, see [0021]-[0022], [0039]-[0040]); and
transmitting, by the network device, the first roaming provisioning credentials to the first set of roaming targets in advance of the client device roaming to any of the first set of roaming targets (security key distribution to out of IP subnet access points, see figs. 1 and 6-7, [0021]-[0022], [0024], [0039]-[0040]).
Likar does not disclose accessing, by a network device, a machine learning (ML) model built based on a dataset characterizing past roaming of client devices among access points (APs) in a network infrastructure; and
identifying, by the network device using the ML model, a first set of roaming targets comprising one or more non-radiofrequency (RF) neighbor access points (APs) corresponding to a source AP of a client device in the network infrastructure.
In the same field of endeavor, Mattaparti discloses accessing, by a network device (130, see fig. 1, [0053], [0055]), a machine learning (ML) model built based on a dataset characterizing past roaming of client devices among access points (APs) in a network infrastructure (see [0044]-[0045], [0047]-[0048], [0051]-[0052]); and
identifying, by the network device using the ML model, a first set of roaming targets comprising one or more corresponding to a source AP of a client device in the network infrastructure (see fig. 2, step 214, [0051]-[0052], [0055]-[0056]).
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Mattaparti with Likar with a reasonable expectation of success by using/accessing a machine learning model to predict the access point(s) that a mobile device will roam to, for the benefit of enabling fast roaming by distributing security keys to the access point(s) before the mobile device roams to the access point(s).
The combination of Likar and Mattaparti does not specifically disclose wherein the non-RF neighbor APs operate outside the source AP’s RF neighborhood.
In a similar field of endeavor, Chang discloses identifying a set of roaming targets comprising one or more non-radiofrequency (RF) neighbor access points (neighbor report 112 comprising AP3, AP4, and AP5, see fig. 1, [0021]) corresponding to a source AP of a client device in a network infrastructure (AP2 118, that mobile device 102 is moving away from, see fig. 1, [0015]-[0016], [0021]), wherein the non-RF neighbor APs operate outside the source AP’s RF neighborhood (AP3, AP4, and AP5 are away from the area of coverage of AP2 118, see fig. 1, [0021]).
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Chang with the combination of Likar and Mattaparti with a reasonable expectation of success by determining neighbor APs as APs that are outside the coverage area of a source AP using the system disclosed by Chang of generating a neighbor list as the client device roams (see [0015]-[0016], [0021]-[0022]), for the benefit of improving wi-fi roaming performance by reducing the time spent performing channel scanning for access points.
Regarding claim 11, Likar discloses a network device (110, see fig. 1A, [0019]), comprising:
a machine-readable storage medium storing executable instructions (618, 614, see figs. 1 and 6, [0077]-[0078]); and
a processing resource coupled to the machine-readable storage medium (616, see figs. 1 and 6, [0078]) and configured to execute one or more of the instructions to:
identify,
determine first roaming provisioning credentials for the first set of roaming targets (security keys, see [0021]-[0022], [0039]-[0040]);
and transmit the first roaming provisioning credentials to the first set of roaming targets in advance of the client device roaming to any of the first set of roaming targets (security key distribution to out of IP subnet access points, see figs. 1 and 6-7, [0021]-[0022], [0024], [0031], [0039]-[0040]).
Likar does not disclose accessing, by a network device, a machine learning (ML) model built based on a dataset characterizing past roaming of client devices among access points (APs) in a network infrastructure; and
identifying, by the network device using the ML model, a first set of roaming targets comprising one or more non-radiofrequency (RF) neighbor access points (APs) corresponding to a source AP of a client device in the network infrastructure.
In the same field of endeavor, Mattaparti discloses accessing, by a network device (130, see fig. 1, [0053], [0055]), a machine learning (ML) model built based on a dataset characterizing past roaming of client devices among access points (APs) in a network infrastructure (see [0044]-[0045], [0047]-[0048], [0051]-[0052]); and
identifying, by the network device using the ML model, a first set of roaming targets comprising one or more
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Mattaparti with Likar with a reasonable expectation of success by using/accessing a machine learning model to predict the access point(s) that a mobile device will roam to, for the benefit of enabling fast roaming by distributing security keys to the access point(s) before the mobile device roams to the access point(s).
The combination of Likar and Mattaparti does not specifically disclose wherein the non-RF neighbor APs operate outside the source AP’s RF neighborhood.
In a similar field of endeavor, Chang discloses identifying a set of roaming targets comprising one or more non-radiofrequency (RF) neighbor access points (neighbor report 112 comprising AP3, AP4, and AP5, see fig. 1, [0021]) corresponding to a source AP of a client device in a network infrastructure (AP2 118, that mobile device 102 is moving away from, see fig. 1, [0015]-[0016], [0021]), wherein the non-RF neighbor APs operate outside the source AP’s RF neighborhood (AP3, AP4, and AP5 are away from the area of coverage of AP2 118, see fig. 1, [0021]).
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Chang with the combination of Likar and Mattaparti with a reasonable expectation of success by determining neighbor APs as APs that are outside the coverage area of a source AP using the system disclosed by Chang of generating a neighbor list as the client device roams (see [0015]-[0016], [0021]-[0022]), for the benefit of improving wi-fi roaming performance by reducing the time spent performing channel scanning for access points.
Regarding claims 2 and 12 as applied to claims 1 and 11, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses wherein the dataset comprises information corresponding to a plurality of roaming events, wherein, for a given client device of the client devices, a roaming event specifies one or more of an identifier of a respective source AP, an identifier of a target AP, a timestamp of the roaming event, a Media Access Control (MAC) address of the given client device, or device type of the given client device (historical data, see [0052], [0070]-[0072], [0074]).
Regarding claims 3 and 13 as applied to claims 1 and 12, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses extracting model features based on the dataset, wherein the model features comprise one or more of a first device identifier of a source AP, a second device identifier of a target AP, a roaming event time, a third device identifier of the client device, or a client device type (historical behavior, see [0045], [0047], [0055]-[0056]); and training the ML model using the dataset during a learning phase (see [0051]-[0052]).
Regarding claims 4 and 14 as applied to claims 3 and 13, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses wherein the training the ML model comprises first finetuning model features during the learning phase (feedback and updating of the machine learning model, see [0051]-[0052], [0059], [0061]).
Regarding claim 5 as applied to claim 4, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses wherein identifying the first set of roaming targets comprises inferring, during an inference phase, the one or more non-RF neighbor APs based on the time of the day and using the ML model (see [0047], [0051]-[0052]).
Regarding claims 6 and 15 as applied to claims 5 and 14, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses second finetuning, by network device, the model features based on roaming events reported during the inference phase (see [0047], [0051]-[0052]).
Regarding claim 7 as applied to claim 1, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses wherein the first roaming provisioning credentials corresponding to the client device comprise a target encryption key, and wherein determining the first roaming provisioning credentials corresponding to the client device comprises calculating the target encryption key for each of the first set of roaming targets based on a client encryption key corresponding to the client device and respective Basic Service Set Identifiers (BSSIDs) of the first set of roaming targets (see [0026], [0045]-[0046]).
Regarding claims 9 and 16 as applied to claims 1 and 11, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Likar further discloses:
identifying, by the network device, a second set of roaming targets comprising one or more RF neighbor APs of the source AP (neighbor scans to identify neighbors (e.g., 121A), see [0024]-[0025]);
determining, by the network device, second roaming provisioning credentials for the second set of roaming targets (security keys generated and uploaded to the cloud control element, see fig. 1A, [0025]); and
transmitting, by the network device, the second roaming provisioning credentials to the second set of roaming targets in advance of the client device roaming to any of the first set of roaming targets (distribution of security keys to neighbors, see fig. 1A, [0025]).
Regarding claims 10 and 17 as applied to claims 9 and 16, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Likar further discloses wherein identifying the second set of roaming targets comprises selecting one or more APs having Received Signal Strength Indicator (RSSI) values at the source AP greater than a threshold value as the second set of roaming targets (see fig. 1A, [0027]).
Regarding claim 18, Likar discloses a networked system (100A, see fig. 1A) comprising:
a plurality of access points (APs) (121A, 121B, 131, see fig. 1A) comprising a source AP (121B, see fig. 1A, [0020]) facilitating wireless connectivity to a client device (140, see fig. 1A, [0020]) in a network infrastructure (100A, see fig. 1, [0019]); and
a network device communicatively coupled to the plurality of Aps (110, see fig. 1A, [0019]), wherein the network device is configured to:
determine first roaming provisioning credentials for the first set of roaming targets (security keys, see [0021]-[0022], [0039]-[0040]); and
transmit the first roaming provisioning credentials to the first set of roaming targets in advance of the client device roaming to any of the first set of roaming targets (security key distribution to out of IP subnet access points, see figs. 1 and 6-7, [0021]-[0022], [0024], [0031], [0039]-[0040]), wherein a roaming target of the first set of roaming targets authenticates the client device using a respective one of the first roaming provisioning credentials when the client device associates with the roaming target (640, see fig. 6, [0039]-[0040]).
Likar does not disclose accessing, by a network device, a machine learning (ML) model built based on a dataset characterizing past roaming of client devices among access points (APs) in a network infrastructure; and
identifying, by the network device using the ML model, a first set of roaming targets comprising one or more non-radiofrequency (RF) neighbor access points (APs) corresponding to a source AP of a client device in the network infrastructure.
In the same field of endeavor, Mattaparti discloses accessing, by a network device (130, see fig. 1, [0053], [0055]), a machine learning (ML) model built based on a dataset characterizing past roaming of client devices among access points (APs) in a network infrastructure (see [0044]-[0045], [0047]-[0048], [0051]-[0052]); and
identifying, by the network device using the ML model, a first set of roaming targets comprising one or more
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Mattaparti with Likar with a reasonable expectation of success by using/accessing a machine learning model to predict the access point(s) that a mobile device will roam to, for the benefit of enabling fast roaming by distributing security keys to the access point(s) before the mobile device roams to the access point(s).
The combination of Likar and Mattaparti does not specifically disclose wherein the non-RF neighbor APs operate outside the source AP’s RF neighborhood.
In a similar field of endeavor, Chang discloses identifying a set of roaming targets comprising one or more non-radiofrequency (RF) neighbor access points (neighbor report 112 comprising AP3, AP4, and AP5, see fig. 1, [0021]) corresponding to a source AP of a client device in a network infrastructure (AP2 118, that mobile device 102 is moving away from, see fig. 1, [0015]-[0016], [0021]), wherein the non-RF neighbor APs operate outside the source AP’s RF neighborhood (AP3, AP4, and AP5 are away from the area of coverage of AP2 118, see fig. 1, [0021]).
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Chang with the combination of Likar and Mattaparti with a reasonable expectation of success by determining neighbor APs as APs that are outside the coverage area of a source AP using the system disclosed by Chang of generating a neighbor list as the client device roams (see [0015]-[0016], [0021]-[0022]), for the benefit of improving wi-fi roaming performance by reducing the time spent performing channel scanning for access points.
Regarding claim 19 as applied to claim 18, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses wherein the network device is configured to:
identify a second set of roaming targets comprising one or more RF neighbor APs of the source AP (neighbor scans to identify neighbors (e.g., 121A), see [0024]-[0025]);
determine second roaming provisioning credentials for the second set of roaming targets (security keys generated and uploaded to the cloud control element, see fig. 1A, [0025]); and
transmit the second roaming provisioning credentials to the second set of roaming targets in advance of the client device roaming to any of the first set of roaming targets (distribution of security keys to neighbors, see fig. 1A, [0025]).
Regarding claim 20 as applied to claim 18, the combination of Likar, Mattaparti, and Chang disclose the claimed invention. Mattaparti further discloses wherein the network device is further configured to:
first finetune model features derived based on the dataset (feedback and updating of the machine learning model, see [0051]-[0052], [0059], [0061]);
infer, during an inference phase, the one or more non-RF neighbor APs based on the time of the day and using the ML model (see [0047], [0051]-[0052]); and
second finetune the model features based on roaming events reported during the inference phase (see [0047], [0051]-[0052]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Likar et al US 20180184345 (hereinafter Likar) in view of Mattaparti et al US 20230300687 (hereinafter Mattaparti) and Chang et al US 20160112942 (hereinafter Chang) as applied to claim 7 above, and further in view of Van Wageningen et al US 20230111629 (hereinafter Van Wageningen).
Regarding claim 8 as applied to claim 7, the combination of Likar, Mattaparti, and Chang disclose the claimed invention except wherein the client encryption key is an R0 key and the target encryption key is an R1 key specified in the Institute of Electrical and Electronics Engineers (IEEE) 802.11r Specification. In the same field of endeavor, Van Wageningen discloses client encryption key being an R0 key and the target encryption key is an R1 key specified in the Institute of Electrical and Electronics Engineers (IEEE) 802.11r Specification (see [0147]).
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Van Wageningen with the combination of Likar, Mattaparti, and Chang with a reasonable expectation of success by deriving encryption keys R0 and R1, based on IEEE 802.11r amendment, for the benefit of using the encryption keys to aid in fast and seamless access point transition or handoff.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ficara et al US 20250331038 discloses predicting potential roaming trajectories.
Lovison et al US 20240306054 discloses directional neighbor reports for roaming optimization.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 OLUMIDE T AJIBADE AKONAI whose telephone number is (571)272-6496. The examiner can normally be reached Monday-Friday 8AM-4PM.
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/OLUMIDE AJIBADE AKONAI/Primary Examiner, Art Unit 3648
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