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 and 11 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.
In re page 8, applicant argues that the rejection of claim 2 is deficient because the Examiner's mapping is conclusory and does not show the recited features arranged as in the claim. Claim 2 requires "integrating a security maintenance system with one or more security systems comprising the one or more security devices." The Office Action points only to Laixin's general "camera anomaly detection system based on visual Al," without identifying any disclosure of integrating a distinct security maintenance system with one or more separate security systems. Merely identifying Laixin's overall system does not establish disclosure of the specific integration recited in claim 2.
In response, the examiner respectfully disagrees. It is noted that claim 2 recites “integrating a security maintenance system with one or more security systems comprising the one or more security devices”. Since claim 2 recites “one or more”, Laixin’s general “camera anomaly detection system based on visual AI” shown in Fig. 4 anticipates the claimed “integrating a security maintenance system”.
In re page 8, applicant argues that the rejection of claim 4 is conclusory. Claim 4 requires that "the one or more predetermined thresholds are predetermined based on video recordings stored in one or more databases”. The Office Action points only to Laixin's "preset threshold." Laixin discloses a stored template image and a preset similarity threshold, but does not disclose that the threshold itself is predetermined based on video recordings stored in one or more databases. The Examiner's mapping conflates a stored template image with a threshold derived from stored video recordings, and therefore fails to show the element as claimed.
In response, the examiner respectfully disagrees. As recognized by applicant, Laixin discloses a stored template image and a preset similarity threshold. This similarity threshold is not just any threshold but it based on the stored template image. Thus, the similarity threshold of Laixin anticipates the claimed "the one or more predetermined thresholds are predetermined based on video recording stored in one or more databases” as recited in claim 4.
In re pages 8-9, applicant argues, with respect to claims 7 and 17, neither Laixin nor Niikura teaches or suggests "training the Al model based on the periodically retrieved security data; classifying the periodically retrieved data; and based on the classifying, detecting the one or more anomalies”. Laixin employs "training the Al model based on the periodically retrieved security data; classifying the periodically retrieved data; and based on the classifying, detecting the one or more anomalies." Laixin employs pre-trained residual (ResNet) networks trained on a "massive dataset" to detect occlusion and dirt; Laixin does not describe training the model on periodically retrieved security data, nor a classification step on which subsequent anomaly detection is based. Niikura does not cure this deficiency. Niikura's only reference to machine learning is the construction of an algorithm for extrapolating a predicted cleaning timing from contamination indices over time; Niikura does not disclose training an anomaly-detection model on periodically retrieved security data or a classify- then-detect process.
In response, the examiner respectfully disagrees. Laixin discloses in page 6 that “In some embodiments, the ResNet neural network model adopts massive data sets of training, with ensures the accuracy of the algorithm”. It is recognized that “ResNet neural network model” inherently include the claimed “training the Al model based on the periodically retrieved security data; classifying the periodically retrieved data; and based on the classifying, detecting the one or more anomalies”. See page 8, paragraph #0075 and page 10, paragraph #0091 of US 2025/0372236 A1 for ResNet neural network model.
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 1-5 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yang Laixin et al. (CN 114286082 A) in view of Faulkner et al. (US Patent No. 11,457,271 B2).
In considering claim 1, Laixin et al. discloses all the claimed subject matter, note 1) the claimed acquiring security data from one or more security devices is met by the acquired image of the camera to the data layer (Figs. 1-3, page 7, lines 1-24), 2) the claimed assessing security data quality by conducting one or more tests that compare the security data to one or more predetermined thresholds is met by the image matching algorithm of the camera, and the similarity between the current image of the camera and the template image is compared to the preset threshold (Figs. 1-3, page 7, line 1 to page 8, line 26), 3) the claimed and assigning one or more anomaly indicators to security devices if the security data quality fails to meet the one or more predetermined thresholds is met by if the similarity between the current image of the camera and the template image is less than the preset threshold, then Output alarm information, and update the template image after the camera is adjusted correctly (Figs. 1-3, page 7, line 1 to page 8, line 26), and 4) the claimed generating an automated maintenance report for the one or more security devices including the one or more anomaly indicators, wherein the automated maintenance report identifies the particular one or more security devices associated with each anomaly indicator is met by using the camera anomaly detection system based on visual AI (Artificial Intelligence) technology, it can automatically detect camera anomalies in real time 7*24, output alarm information and statistical data are transmitted to the presentation layer (Figs. 1-3, page 7, line 1 to page 8, line 26).
However, Laixin et al. explicitly does not disclose the claimed includes one or more recommended maintenance actions corresponding to the one or more anomaly indicators.
Faulkner et al. teaches that in the same field of display of video streams having an user interface for displaying one or more recommended maintenance actions corresponding to the one or more anomaly indicators (Fig. 3A, col. 14, lines 27-51).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the predictive maintenance user interface for displaying one or more recommended maintenance actions as taught by Faulkner et al. into Yang Laixin et al.’s system in order to correct the anomaly of the camera.
In considering claim 2, the claimed wherein acquiring security data from one or more security devices further comprises integrating a security maintenance system with one or more security systems comprising the one or more security devices is met by using the camera anomaly detection system based on visual AI (Artificial Intelligence) technology (Figs. 1-3, page 7, line 1 to page 8, line 26 of Laixin et al.).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 3, the claimed wherein the security devices comprise one or more video cameras and the security data comprises one or more video recordings captured by the one or more video cameras is met by the video cameras (page 9, lines 4-27 of Laixin et al.).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 4, the claimed wherein the one or more predetermined thresholds are predetermined based on video recordings stored in one or more databases is met by the preset threshold (Figs. 1-3, page 7, line 1 to page 8, line 26 of Laixin et al.).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 5, the claimed wherein conducting one or more tests comprises conducting one or more tests that detect one or more of tampering, lens obstruction, frame clarity, brightness levels, and blur levels is met by detect the camera anomalies includes steering, occlusion and stains (Figs. 1-3, page 7, line 25 to page 8, line 26 of Laixin et al.).
The motivation to combine the references has been discussed in claim 1 above.
Claim 11 is rejected for the same reason as discussed in claim 1 above and further the claimed comprising one or more computer readable memories, and one or more processors is met by the processor and the memory of the electronic device (Fig. 4, page 9, line 28 to page 10, line 7).
Claims 12-15 are rejected for the same reason as discussed in claims 2-5, respectively.
.
Claims 6-10 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yang Laixin et al. (CN 114286082 A) in view of Faulkner et al. (US Patent No. 11,457,271 B2) and further in view of NIIKURA et al. (US 2019/0369031 A1).
In considering claim 6, Yang Laixin et al. discloses all the claimed subject matter, note 1) the claimed utilizing an artificial intelligence (Al) model to detect one or more anomalies is met by using the camera anomaly detection system based on visual AI (Artificial Intelligence) technology (Figs. 1-3, page 7, line 1 to page 8, line 26). However the combination of Yang Laixin et al. and Faulkner et al. explicitly do not disclose the claimed further comprising: periodically retrieving one or more security data sets from the one or more security devices, and generating one or more predictive maintenance suggestions.
NIIKURA et al. teach that the contamination degree determination unit 42 transmits information related to the contamination degree to the cleaning timing prediction unit 43 in order to calculate a cleaning timing to be performed in the future when the contamination degree is equal to or smaller than the threshold (step S12: NO), in step S14, the cleaning timing prediction unit 43 calculates the cleaning timing (that is, a predicted cleaning timing) to be performed in the future and the display device 23 notifies of the content thereof, after that, the flow returns to step S11, this process is repeated automatically every arbitrary period determined in advance by the operator or at a timing based on a manual operation of the operator (Fig. 8, page 6, paragraph #0078- #0082).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the predictive maintenance as taught by NIIKURA et al. into the combination of Yang Laixin et al. and Faulkner et al.’s system in order to notify the user to perform maintenance in an appropriate timing.
In considering claim 7, the claimed wherein utilizing an AI model to detect anomalies further comprises: training the Al model based on the periodically retrieved security data; classifying the periodically retrieved data; and based on the classifying, detecting the one or more anomalies is met by using the camera anomaly detection system based on visual AI (Artificial Intelligence) technology to detect the camera anomalies includes steering, occlusion and stains (Figs. 1-3, page 7, line 1 to page 8, line 26 of Yang Laixin et al.).
The motivation to combine the references has been discussed in claim 6 above.
In considering claim 8, the claimed further comprising: triggering one or more notifications if one or more anomalies are detected is met by outputting alarm information and statistical data are transmitted to the presentation layer (Figs. 1-3, page 7, line 1 to page 8, line 26 of Yang Laixin et al.).
The motivation to combine the references has been discussed in claim 6 above.
In considering claim 9, the claimed wherein the one or more notifications comprise an alarm is met by outputting alarm information and statistical data are transmitted to the presentation layer (Figs. 1-3, page 7, line 1 to page 8, line 26 of Yang Laixin et al.).
The motivation to combine the references has been discussed in claim 6 above.
In considering claim 10, the claimed further comprising one or more of providing the one or more predictive maintenance suggestions to an authorized user; and performing one or more predictive maintenance actions automatically is met by the cleaning timing prediction unit 43 calculates the cleaning timing (that is, a predicted cleaning timing) to be performed in the future and the display device 23 notifies of the content thereof (Fig. 8, page 6, paragraph #0078- #0082 of NIIKURA et al.).
The motivation to combine the references has been discussed in claim 6 above.
Claims 16-20 are rejected for the same reason as discussed in claims 6-10, respectively.
Conclusion
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 TRANG U TRAN whose telephone number is (571)272-7358. The examiner can normally be reached M-F 10:00AM- 6:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN W. MILLER can be reached at 571-272-7353. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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August 22, 2026
/TRANG U TRAN/Primary Examiner, Art Unit 2422
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