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 .
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.
DETAILED ACTION
This communication is in response to Application No. 18/273,553 filed on 21 July 2023. The response filed 9 September 2025 amends claims 1, 3, 4, 6, and 8, cancels claim 7, adds claims 9 and 10, and presents arguments is hereby acknowledged. Claims 1-6 and 8-10 are presented for examination.
Response to Arguments
The response filed 9 September 2025 addresses the 35 USC 101 rejections made on the 24 July 2025 Non-Final Rejection. Applicant’s amendments, regarding the 35 USC 101 rejections, are considered. Applicant argues that the claims have been significantly amended to recite anomalies for maintenance engineers. Thus, these claims are directed to a judicial exception and qualifies as eligible subject matter under 35 USC 101. Therefore, all outstanding 35 USC 101 rejections are hereby removed.
Independent Claims 1, 4, and 8
On pages 10-11 of the response filed 9 September 2025, Applicant addresses the 35 U.S.C. 102 rejection made on the 24 July 2025 Non-Final Rejection. Applicant’s arguments, regarding the rejections under 35 U.S.C. 102, have been fully considered.
On pages 10-11, Applicant argues that the Dees system fails to teach “the system status information includes a plurality of parameters indicating a radio communication status in the radio system and a device status of a radio device included in the system” and “the anomaly situation information indicates presence or absence of anomaly for each of the plurality of parameters.” Examiner respectfully agrees and finds this argument persuasive. Dees fails to disclose the amended limitations. Therefore, Examiner finds this argument persuasive.
Dependent Claims 2, 3, 5, 6, 9, and 10
On pages 10-11 of the response filed 9 September 2025, Applicant addresses the 35 U.S.C. 102 rejection made on the 24 July 2025 Non-Final Rejection. Applicant submits that these claims are allowable at least as depending from an allowable independent claim, and further in view of the amendments to the independent claims, and the comments provided above. As per the comments above, Examiner found the arguments persuasive. With regards to allowability, Examiner has conducted a search and applied new art. Thus, a new rejection is established against the independent claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 5, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0250559 A1 to Dinh et al and in view of US PGPUB 2013/0286852 A1 to Bowler et al.
Regarding Claim 1, Dinh discloses a radio system maintenance support device for supporting maintenance of a radio system (FIG. 1, Intelligent Remediation System 105), the radio system maintenance device comprising: one or more processors (0062 provides for processors); and one or more memories (0062 provides for memory devices) in which system status information (0019 provides for system performance indicators), anomaly situation information (0019 and 0036 provides for anomalous behavior is KPI thresholds), failure analysis result information (0052 provides for labeling groups of remediation actions), and a failure analysis model are stored (0054 provides for action-reward valuation stage), wherein the failure analysis result information indicates at least one of a failure location in the radio system and a maintenance action required for the failure location (0052 provides for labeling groups of remediation actions), the failure analysis model is a trained model that receives at least the anomaly situation information and outputs the failure analysis result information, which is generated by learning based on the anomaly situation information and the failure analysis result information that are obtained in the past (0054-0055 provides for action-reward valuation stage uses anomalous events and outputs recommended actions, which is generated by a machine learning framework logging anomalous behavior), and the one or more processors are configured to: acquire the system status information (0019 provides for obtain system performance indicators); analyze the system status information to acquire the anomaly situation information (0019 and 0036 provides for detecting anomalous behavior that exceed KPI thresholds); acquire the failure analysis result information according to the anomaly situation information by using the failure analysis model (0054-0055 provides for acquire a recommended action according to the anomalous event by using the action-reward valuation stage); and present the acquired failure analysis result information to a maintenance engineer (0056 provides for output the recommended action to a system engineer), wherein maintenance on one of the radio system and the radio device is performed based on the acquired failure analysis result information presented to the maintenance engineer (0056 provides for output the recommended action to a system engineer). Dinh doesn’t explicitly disclose the system status information includes a plurality of parameters indicating a radio communication status in the radio system and a device status of a radio device included in the radio system, the anomaly situation information indicates presence or absence of anomaly for each of the plurality of parameters. Bowler, in a similar field of endeavor, discloses wherein the system status information includes a plurality of parameters indicating a radio communication status in the radio system and a device status of a radio device included in the radio system (FIG. 4, 0036-0037, and 0041 provides for the listing of alarms includes Channel Utilization Exceeded 36 and potential faults 14 of MAC addresses); and wherein the anomaly situation information indicates presence or absence of anomaly for each of the plurality of parameters (FIG. 4 and 0042 provides for the dashboard indicates a minor alarm as the presence, and a warning as the absence of an anomaly, wherein the dashboard can be expanded to show the number of network elements with the associated severity alarms). One of ordinary skill in the art before the effectively filed date of the claimed invention would have recognized the ability to utilize the teachings of Bowler for network maintenance activities. The network maintenance of Bowler, when implemented with the machine learning framework of the Dinh system, will allow one of ordinary skill in the art to capture specific parameters in order to match parameters with indicators of anomalous behavior. Therefore, the examiner concludes it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to utilize the network maintenance of Bowler with the machine learning framework of the Dinh system for the desirable purpose of customizing the display for a maintenance engineer.
Regarding Claim 2, the Dinh/Bowler system discloses the radio system maintenance support device according to claim 1, wherein the failure analysis model is a trained model that receives the system status information and the anomaly situation information and outputs the failure analysis result information, which is generated by learning based on the system status information, the anomaly situation information, and the failure analysis result information that are obtained in the past (Dinh, 0054-0055 provides for action-reward valuation stage uses anomalous events and outputs recommended actions, which is generated by a machine learning framework logging anomalous behavior), and the one or more processors are configured to acquire the failure analysis result information according to the system status information and the anomaly situation information by using the failure analysis model (Dinh, 0054-0055 provides for action-reward valuation stage uses anomalous events and outputs recommended actions, which is generated by a machine learning framework logging anomalous behavior).
Regarding Claim 4, similar rejection where the radio system maintenance support device of claim 1 teaches the radio system maintenance support method of claim 4.
Regarding Claim 5, similar rejection where the radio system maintenance support device of claim 2 teaches the radio system maintenance support method of claim 5.
Regarding Claim 8, similar rejection where the radio system maintenance support device of claim 1 teaches the non-transitory computer-readable recording medium of claim 8.
Regarding Claim 9, similar rejection where the radio system maintenance support device of claim 2 teaches the non-transitory computer-readable recording medium of claim 9.
Claims 3, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Dinh as applied to claims 1, 4, and 8 above, and further in view of US PGPUB 2019/0173743 A1 to Gerszberg et al.
Regarding Claim 3, the Dinh/Bowler system discloses the radio system maintenance support device according to claim 1. The Dinh/Bowler system doesn’t explicitly disclose wherein the parameters indicating the radio communication status include at least one of a reception level, a waveform, a signal-to-interference-noise ratio, a bit error rate, and fading, and the parameters indicating the device status of the radio device include an alarm output from the radio device. Gerszberg, in a similar field of endeavor, discloses wherein the parameters indicating the radio communication status include at least one of a reception level, a waveform, a signal-to-interference-noise ratio, a bit error rate (0198 provides for bit error rate threshold), and fading, and the parameters indicating the device status of the radio device include an alarm output from the radio device (0164 discusses sensors 1404 and outputting faults). One of ordinary skill in the art before the effectively filed date of the claimed invention would have recognized the ability to utilize the teachings of Gerszberg for performance thresholds to classify change in telemetry data. The bit error rate of Gerszberg, when implemented with the machine learning framework of the Dinh/Bowler system, will allow one of ordinary skill in the art to capture bit error rate in order to remediate faults for this category of anomalous behavior. Therefore, the examiner concludes it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to utilize the bit error rate of Gerszberg with the machine learning framework of the Dinh/Bowler system for the desirable purpose of remediating faults in this category of network performance.
Regarding Claim 6, similar rejection where the radio system maintenance support device of claim 3 teaches the radio system maintenance support method of claim 6.
Regarding Claim 10, similar rejection where the radio system maintenance support device of claim 3 teaches the non-transitory computer-readable recording medium of claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPUB 2022/0342788 A1 to Kanai et al discloses a failure factor locations for a plurality of apparatuses.
US PGPUB 2022/0245035 A1 to Minarik et al discloses displaying detected anomalies on a dashboard.
US PGPUB 2022/0182278 A1 to Vangapalli et al discloses determining root cause of connection failures.
US PGPUB 2024/0073708 A1 to Lu et al discloses frequency calibration in a mesh network.
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 SCHQUITA GOODWIN whose telephone number is (571)272-5477. The examiner can normally be reached M-F 9am - 5pm 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, Tonia Dollinger can be reached on (571) 272-4170. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SCHQUITA D GOODWIN/Primary Examiner, Art Unit 2459
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