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 .
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.
Claim(s) 1-5 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 2021/0310858 A1) in view of Salemi et al. (US 2020/0401784 A1), Zou et al. (US 2021/0397844 A1), Buerkle et al. (US 2021/0103738 A1) and Tatar et al. (US 2008/0266087 A1).
Regarding claims 1 and 11, Huang discloses a method (e.g. Abstract) and an integrated security system (e.g. Fig. 1 & [0003, 0023]) comprising:
a distributed fiber optic sensing system (e.g. [0023-0025] & Fig. 1: 401); and
one or more security cameras ([0025], e.g. Fig. 1: video surveillance system thus with cameras);
a central control system (e.g. Fig. 1: 102-104) configured to:
receive data from the distributed fiber optic sensing system (e.g. Fig. 1: 401, 103), and the one or more security cameras (e.g. Fig. 1: 101, 3.1);
wherein the integrated security system is configured to receive, integrate, and analyze data produced by the distributed fiber optic sensing system and the one or more security cameras (e.g. [0018, 0019, 0024, 0026]: DFOS receives and analyzes received data).
Huang fails to disclose the system comprising one or more aerial drones.
However, Salemi teaches it is known in the art to utilize aerial drones with distributed fiber sensing system to monitor and collect desirable data (e.g. [0020-0025]).
The advantage of adding a well-known data collecting means (e.g. drone) in a security system already comprises a combination of data collecting means (e.g. Fig. 1 of Huang) would be easily recognized by one skilled in the art, since it would provide a redundant data collecting means to improve accuracy and efficiency.
Thus, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the security system of Huang to include aerial drones as taught by Salemi, since adding aerial drones as data collecting means would improve data collecting accuracy and efficiency.
Huang discloses using A.I. engine to identifies an abnormal or other activity based on captured images from surveillance cameras (e.g. [0031-0035]). Huang fails to disclose, but Zou teaches:
perform multi-modal fusion on the received data (e.g. [0002, 0094, 0127, 0135]: modal fusion of received image data to identify an item); extract features from the received data (e.g. [0013]); detect a target based on the extracted features (e.g. [0032]: identifying items).
Thus, it would have been obvious to one skilled in the art to modify the teachings of Huang with the teachings of Zuo to utilize fusion modal to improve identification accuracy (e.g. Zuo: [0032]).
Huang fails to disclose, but Buerkle teaches:
in response to detecting the potential security threat, automatically dispatch at least one of the one or more aerial drones (e.g. [0015, 0019]: optionally deploy a drone to provide additional sensor to assist in tracking a threat after sensors 101, 106, 107 detected the threat) and
dynamically adjust surveillance coverage of at least one of the one or more aerial drones or the one or more security cameras (e.g. [0022, 0029, 0032]: adjust field of view of cameras).
Thus, it would have been obvious to one skilled in the art to modify the teachings of Huang with the teachings of Buerkle to provide additional sensors to assist in tracking a threat.
Huang fails to disclose, but Tatar teaches a security system for substation monitoring and detection (e.g. Abstract & [0028, 0053]), comprising: a distributed fiber optic sensing (DFOS) system (e.g. [0005, 0028]) installed along a substation perimeter and/or around one or more critical equipment locations (e.g. [0053]).
Thus, it would have been obvious to one skilled in the art to modify the teachings of Huang to apply the known in the art integrating security system including DFOS along substation perimeter and critical equipment locations, since both Huang and Tatar discloses the use of fiber optic as the sensing and monitoring system, where to use the sensing and monitoring system is merely intended use and is known in the art as taught by Huang and Tatar to be used along a stadium and/or to be used along perimeter of substation and/or critical locations.
Regarding claim 2, Huang and Tatar in combination discloses the distributed fiber optic sensing system (DFOS) is configured to monitor vibrations and/or acoustic signals (e.g. Huang: [0022]) along the substation perimeter and around the one or more critical equipment locations (e.g. Tatar: [0053]).
Regarding claim 3, Salemi teaches the one or more aerial drones provide real-time video feeds (e.g. [0020-0025]) for visual confirmation of incidents.
Regarding claim 4, Huang discloses the one or more security cameras provide real-time video feeds for visual confirmation of activities (e.g. Fig. 1: 101).
Regarding claim 5, Huang and Salemi in combination discloses provide machine learning and artificial intelligence techniques (e.g. [0024-0026]: machine learning) to the data received from the distributed fiber optic sensing system (e.g. Huang: Fig. 1 & [0024-0026]), the one or more aerial drones (e.g. Salemi: [0020-0025]), and the one or more security cameras (e.g. Huang: Fig. 1 & [0024-0026]).
Claim(s) 6-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 2021/0310858 A1) in view of Salemi et al. (US 2020/0401784 A1), Zou et al. (US 2021/0397844 A1), Buerkle et al. (US 2021/0103738 A1) and Tatar et al. (US 2008/0266087 A1) as applied to claims 1-5 above, and further in view of Morzhakov (US 2020/0349347 A1).
Regarding claim 6, Huang discloses machine learning configured to analyze collected data to identify events related to parking lot security, stadium intrusion and social sensing, etc (e.g. [0026-0027]).
Although it is known that machine learning extract relevant features from the received data, Huang fails to explicitly discloses the detail of the machine learning.
However, Morzhakov teaches machine learning extract relevant features from the received data including acoustic signatures (e.g. [0017]), visual cues (e.g. [0017]), and motion patterns (e.g. [0085, 0097]).
Thus, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify teachings of Huang with teachings of Morzhakov to utilize machine learning to extract relevant features from received data so as to detect abnormal activities.
Regarding claim 7, Morzhakov teaches the system configured to classify the extracted relevant features (e.g. [0061-0062]).
Regarding claim 8, Huang and Morzhakov in combination discloses the system configured to detect potential security threats from the classified extracted relevant features.
Huang in [0026-0027] discloses DFOS system utilizing machine learning to analyze received data to detect security threats (e.g. parking lot security and stadium intrusion); and, Morzhakov teaches machine learning is capable of classifying received data to detect abnormal activities. Thus, by modifying invention of Huang with machine learning classification technique as taught by Morzhakov, the combination would be able to detect potential security threats.
Regarding claim 9, Huang discloses the system configured to learn from historical data received from the distributed fiber optic sensing system, the one or more aerial drones, and the one or more security cameras (e.g. [0026-0027]: machine learning inherently discloses learning from historical data).
Regarding claim 10, Huang discloses the system configured to respond to detected potential security threats by initiating incident response procedures (e.g. [0026, 0030, 0038]: output notification to operators and/or attendees; provide urgent rescue; provide early warning and localization for fire prevention and/or detection/response including detecting gunshot and ultrasonic weapon).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-11 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.
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 KAM WAN MA whose telephone number is (571) 270-3693. The examiner can normally be reached M-F 9am-6pm.
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/KAM WAN MA/Examiner, Art Unit 2688
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