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 .
DETAILED ACTION
This communication is responsive to Amendment filed 03/03/2026.
Claims 1-20 are pending in this application. Claims 1, 8, and 15 are independent claims. In Amendment, claims 1, 8, and 15-20 are amended. This Office Action is made final.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1, 8, and 15 is/are directed to an abstract idea under the mental process wherein the limitations of “identifying…”, “performing…”, “computing…”, “determining…”, “determine…” and “rank…” can be mentally done in human mind with help of pen and paper. Given the required data/parameters information, one ordinary skill in the art can analyze these information according to the steps identified above with some calculations by observing, evaluating, and making judgement as required under Prong I step 2A. However, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations “implementing a correlation model with supervised machine learning” (claims 1, 8 and 15), “processing resource…cause the processing resource” (claim 8) and “receiving…” (claim 15) are considered as additional elements. These additional elements are merely considered as general software tools for implementation, generic and well-known computer system components for implementation, and extra-activities solution respectively without significantly amount to the judicial exception under Prong II step 2A. Thus, it does not integrate into the practical application. Under step 2B, these additional elements are merely considered as general software tools for implementation, generic and well-known computer system components for implementation, and extra-activities solution respectively without significantly amount to the judicial exception as evidently seen in MPEP 2106.05(d) and (f).
Re claims 2-7, 9-14 and 16-20, these claims are similarly rejected as directing to an abstract idea under the mental process as seen above wherein these claims are either further elaborate with additional abstract idea limitations or insignificant amount additional elements to the judicial exception. Thus, individually or in combination of these additional elements does not integrate into the practical application.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Greene, Jr (U.S. 2019/0245754 A1) in view of Stayner et al. (U.S. 2019/0095965 A1).
Re claim 1, Greene, Jr. discloses in Figures 1-16 a computer-implemented method, comprising: identifying features including access point (AP) parameters and client device parameters (e.g. paragraphs [0007-0008, 0042 and 0054] wherein various/all network nodes including client devices and networking equipment’s parameters are identified and collected for analysis) that indicate a network health of a network of one or more access points (AP) and client devices (e.g. Figures 1-2, abstract, and paragraphs [0004 and 0037-0038] which monitor the heathy/correction/normal of the target network); performing, with one or more processing resources of a network appliance, feature goodness classification (FGC) to classify each identified feature independently with feature weights (e.g. paragraphs [0048-0049 and 0070] which classifying the collected features/data with assigning coefficient as corresponding feature weight); computing, with the one or more processing resources of the network appliance, for each feature including access point (AP) parameters and client device parameters (e.g. paragraphs [0035 and 0042] wherein all parameters of network elements are collected and computed); determining if a network problem is identified based on the cumulative scores (e.g. paragraph [00038 and 0049] wherein network failure/problem is analyzed and identified); and implementing a correlation model with supervised machine learning to determine correlation criteria based on implicit relationships between various different network parameters and remediation actions based on features contributing to an identified network problem (e.g. Figure 3, abstract and paragraphs [0006, 0008, and 0049] wherein remediation is identified and implemented). Greene, Jr. discloses the normalized values of each feature (e.g. paragraph [0074] for equalizing features) but fails to explicitly disclose computing cumulative scores periodically as a weighted sum of normalized values of each feature. However, Stayner et al. disclose in Figures 1-4 computing cumulative scores periodically as a weighted sum of normalized values of each feature (e.g. abstract and paragraphs [0051-0062]). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to add computing cumulative scores periodically as a weighted sum of normalized values of each feature as conceptually seen in Stayner et al.’s invention into Greene, Jr.’s invention because it would effectively and accurately isolate the failure of the network.
Re claim 2, Greene, Jr. in view of Stayner et al. disclose implementing a reinforcement learning (RL) based remediation model to rank intersection regions for correlation criteria based on rewards; and initially setting remediation actions to an equal reward (e.g. Greene, Jr. paragraph [0070] and Stayer et al. – (paragraphs [0042-0044]).
Re claim 3, Greene, Jr. in view of Stayner et al. disclose consulting a remediation matrix to determine remediation actions available for the correlation criteria; and selecting a remediation action with a highest rank (e.g. Greene, Jr. paragraph [0070] and Stayer et al. – paragraph [0066]).
Re claim 4, Greene, Jr. in view of Stayner et al. disclose applying the selected remediation action to the network (e.g. Greene, Jr. – abstract and Figures 1-2).
Re claim 5, Greene, Jr. in view of Stayner et al. disclose monitoring network parameters over time (e.g. Greene, Jr. – abstract and paragraphs [0006-0007]); and determining a reward calculation and updating a reward based on a reward calculation (e.g. Stayner et al. – paragraph [0083]).
Re claim 6, Greene, Jr. in view of Stayner et al. disclose the AP parameters comprise channel utilization, transmit retries if an AP does not receive acknowledgement of a transmitted data frame, data frame discards, noise level, or CPU stats (e.g. Greene, Jr. – paragraph [0091]).
Re claim 7, Greene, Jr. in view of Stayner et al. disclose the client device parameters comprise RSSI, retries, discards, or noise level (e.g. Greene, Jr. – paragraphs [0003-0004 and 0007-0008]).
Re claim 8, it is a system claim having similar limitations as cited in claim 1. Thus, claim 8 is also rejected under the same rationale as cited in the rejection of claim 1 above.
Re claim 9, it is a system claim having similar limitations as cited in claim 2. Thus, claim 9 is also rejected under the same rationale as cited in the rejection of claim 2 above.
Re claim 10, it is a system claim having similar limitations as cited in claim 3. Thus, claim 10 is also rejected under the same rationale as cited in the rejection of claim 3 above.
Re claim 11, it is a system claim having similar limitations as cited in claim 4. Thus, claim 11 is also rejected under the same rationale as cited in the rejection of claim 4 above.
Re claim 12, it is a system claim having similar limitations as cited in claim 5. Thus, claim 12 is also rejected under the same rationale as cited in the rejection of claim 5 above.
Re claim 13, it is a system claim having similar limitations as cited in claim 6. Thus, claim 13 is also rejected under the same rationale as cited in the rejection of claim 6 above.
Re claim 14, it is a system claim having similar limitations as cited in claim 7. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 7 above.
Re claim 15, Greene, Jr. discloses in Figures 1-16 a non-transitory computer readable medium having stored therein instructions being executable by one or more processing resources of one or more network appliances cause the one or more processing resources to (e.g. abstract): implement a correlation model with supervised machine learning to determine correlation criteria based on implicit relationships between various different network parameters and remediation actions based on features (e.g. Figure 3, abstract and paragraphs [0006, 0008, and 0049] wherein remediation is identified and implemented) contributing to an identified network problem of a network having one or more access points and client devices (e.g. paragraphs [0007-0008, 0042 and 0054] wherein various/all network nodes including client devices and networking equipment’s parameters are identified and collected for analysis); receive, with a reinforcement learning (RL) based remediation model, correlation criteria and remediation actions (e.g. Figures 1-3 and paragraphs [0006-0008]). Greene, Jr. discloses the rank intersection regions of different network parameters for correlation criteria including one or more of interference, load balancing, ratio frequency (RF) coverage, anomalies for a wired network, service level agreement breaches, and application experience (e.g. paragraphs [0070-0071]) but, Greene, Jr. fails to disclose explicitly rank intersection regions for correlation criteria based on rewards. However, Stayner et al. disclose rank intersection regions for correlation criteria based on rewards (e.g. paragraphs [0066, 0042-0043, 0047 and 0083]). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to add rank intersection regions for correlation criteria based on rewards as conceptually seen in Stayner et al.’s invention into Greene, Jr.’s invention because it would effectively and accurately isolate the failure of the network.
Claim 16, Greene, Jr. in view of Stayner et al. disclose the instructions being executable by the one or more processing resources cause the one or more processing resources to: initially set remediation actions to an equal reward or update rewards based on input parameters including problem status from a problem detection model, correlation criteria, and remediation actions (e.g. Greene, Jr. – abstract and Figures 1-2 and Stayner et al. – paragraph [0083]).
Re claim 17, it is a medium claim having similar limitations as cited in claim 3. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of claim 3 above.
Re claim 18, it is a medium claim having similar limitations as cited in claim 4. Thus, claim 18 is also rejected under the same rationale as cited in the rejection of claim 4 above.
Re claim 19, it is a medium claim having similar limitations as cited in claim 5. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of claim 5 above.
Re claim 20, Greene, Jr. in view of Stayner et al. disclose the instructions being executable by the one or more processing resources cause the one or more processing resources to: determine the reward calculation by incrementing a reward if the applied remediation positively impacted the network (e.g. Stayner et al. – paragraph [0083]).
Response to Arguments
Applicant's arguments filed 03/03/2026 have been fully considered but they are not persuasive.
The applicant argues in pages 7-8 for claims rejected under 35 USC 101 that these claims are claiming a specific, technical improvement to network infrastructure functionality that provides a particular network appliance-based, automated, closed-loop feedback mechanism as seen in specification paragraph [0068].
The examiner respectfully submits that the current claim language does not provide any limitation that would integrate into the practical application at all. At most the claims is claiming to perform a remediation actions broadly without any specific detail or what are the remediation actions implement in the appliance as argued. All the limitations identified above are considered mental abstract idea using the ML as software tool to perform the mental processes.
The applicant argues in pages 9-10 for independent claim 1 that the cited reference, particularly Greene, fails to anticipate the amended limitations.
The examiner respectfully submits that Greene reference reasonably discloses the amended portion as fully addressed in the rejection above wherein the features are scaled/weighted with corresponding coefficient (0-1) to show the impact of that particular features as conceptually taught in paragraphs [0070-0072] and the features are derived from parameters of all the network elements/equipment in paragraphs [0042] wherein the AP and client device are considered as part of the network elements/equipment.
The applicant argues in pages 10-11 for claims that the references cannot be combined as Greene is focusing optimizing network configuration and Stayner is tracking service requests.
The examiner respectfully submits that there is no explicitly statement/limitation/feature that would prevent the combination of these references wherein Stayner is not only tracking service requests as alleged by the applicant but rather it discloses as similar algorithm for finding normalized score based on weighted components as seen in paragraphs [0051-0062 and 0075].
Conclusion
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.
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/PHUOC H NGUYEN/Primary Examiner, Art Unit 2451