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
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 taught 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 1-3 and 7-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stoupis et al. (US 20160147209) (hereinafter Stoupis) in view of Richards et al. (US 20150304612) (hereinafter Richards).
Regarding claim 1, Stoupis teaches A real-time fault monitoring system, comprising:
one or more camera installations, each camera installation comprising: a thermal camera positioned to monitor a power system, and a wireless communication device in communication with the thermal camera and configured to receive a video signal from the thermal camera; and a server configured to receive the video signals of the thermal cameras from the wireless communication devices (see Stoupis paragraphs 13-22 regarding a system of inspecting power distribution elements, including a plurality of devices with cameras where video data is taken from thermal camera and wirelessly transmitted to server),
However, Stoupis does not explicitly teach a feed as needed for the limitations of claim 1.
Richards, in a similar field of endeavor, teaches store the video from the video signals, and provide the video as an internet video feed (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Stoupis to include the teaching of Richards so that in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis.
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 2, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 1, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis and Richards teaches wherein the server monitors the video signals of the thermal cameras in real-time and sends an alert to a client device when a hazardous condition is detected (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 3, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 2, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis and Richards teaches wherein hazardous conditions detected by the server include a temperature above a predetermined threshold (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 7, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 1, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis and Richards teaches wherein the thermal cameras are bi-spectrum cameras having a thermal imaging sensor and a visible light imaging sensor (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 8, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 1, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis and Richards teaches wherein each camera installation further comprises a mounting accessory configured to allow remote-controlled movement and adjustment of the position and angle of the thermal camera (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 9, Stoupis teaches A real-time fault monitoring system, comprising:
one or more client devices; a server; and a plurality of camera installations, each comprising: a thermal camera, and a wireless communication device in communication with the thermal camera and configured to receive a video signal from the thermal camera and provide it to the server (see Stoupis paragraphs 13-22 regarding a system of inspecting power distribution elements, including a plurality of devices with cameras where video data is taken from thermal camera and wirelessly transmitted to server),
However, Stoupis does not explicitly teach a detection protocol as needed for the limitations of claim 9.
Richards, in a similar field of endeavor, teaches wherein the server is configured to provide video from the video signals of the camera installations upon request to a client device of the one or more client devices, and wherein the server is further configured to monitor the video signals for hazardous conditions and send an alert to at least one of the client devices when a hazardous condition is detected (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time requested display viewing at the client device as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Stoupis to include the teaching of Richards so that in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis.
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 10, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 9, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis and Richards teaches wherein the thermal cameras are bi-spectrum cameras having a thermal imaging sensor and a visible light imaging sensor (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 11, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 9, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis and Richards teaches wherein the hazardous conditions detected by the server include a temperature above a predetermined threshold (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Claim(s) 4-6, 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stoupis et al. (US 20160147209) (hereinafter Stoupis) in view of Richards et al. (US 20150304612) (hereinafter Richards), further in view of Park et al. (US 20230201642) (hereinafter Park).
Regarding claim 4, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 3, and is analyzed as previously discussed.
However, the combination of Stoupis and Richards does not explicitly teach the hazard detection protocol as needed for the limitations of claim 4.
Park, in a similar field of endeavor, teaches wherein the hazardous conditions detected by the server further include smoke and fire (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the combination of Stoupis and Richards to include the teaching of Park so that in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines.
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 5, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 4, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein pattern recognition is employed by the server to detect smoke and to detect fire (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines).
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 6, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 5, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein the pattern recognition is performed using a deep learning algorithm (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines).
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 12, the combination of Stoupis and Richards teaches all aforementioned limitations of claim 11, and is analyzed as previously discussed.
However, the combination of Stoupis and Richards does not explicitly teach the hazard detection protocol as needed for the limitations of claim 12.
Park, in a similar field of endeavor, teaches wherein the hazardous conditions detected by the server further include smoke and fire (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the combination of Stoupis and Richards to include the teaching of Park so that in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines.
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 13, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 12, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein pattern recognition is employed by the server to detect smoke and to detect fire (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines).
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 14, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 13, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein the pattern recognition is performed using a deep learning algorithm (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines).
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 15, Stoupis teaches A real-time fault monitoring system, comprising:
a server; and a plurality of cameras, each camera positioned to obtain live video of a power system and communicate the live video to the server (see Stoupis paragraphs 13-22 regarding a system of inspecting power distribution elements, including a plurality of devices with cameras where live video data is taken from thermal camera and wirelessly transmitted to server),
However, Stoupis does not explicitly teach a detection protocol as needed for the limitations of claim 15.
Richards, in a similar field of endeavor, teaches a client device (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis);
send an alert to the client device when a hazardous condition is detected (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Stoupis to include the teaching of Richards so that in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis.
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
However, the combination of Stoupis and Richards does not explicitly teach the hazard detection protocol as needed for the limitations of claim 15.
Park, in a similar field of endeavor, teaches wherein the server is configured to monitor the live video for hazardous conditions with the aid of one or more artificial intelligence algorithms (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines) and
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the combination of Stoupis and Richards to include the teaching of Park so that in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines.
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 16, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 15, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein the server is further configured to provide the live video to the client device (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 17, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 15, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein the thermal cameras are bi-spectrum cameras having a thermal imaging sensor and a visible light imaging sensor (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 18, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 15, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein hazardous conditions detected by the server include a temperature above a predetermined threshold (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Regarding claim 19, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 15, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches wherein hazardous conditions detected by the server further include smoke and fire (see Park paragraph 56 and 91 regarding deep learning algorithm for detecting smoke and fire in image- in combination with Stoupis and Richards, the power line detection system may use a deep learning algorithm to detect smoke and fire for power lines).
One would be motivated to combine these teachings in order to enhance a system’s ability to detect hazardous smoke and fire (see Park paragraph 56 and 91).
Regarding claim 20, the combination of Stoupis, Richards, and Park teaches all aforementioned limitations of claim 15, and is analyzed as previously discussed.
Furthermore, the combination of Stoupis, Richards, and Park teaches further comprising a mounting accessory attached to a camera of the plurality of cameras, the mounting accessory configured to allow remote-controlled movement and adjustment of the position and angle of the camera (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92 and figure 10 regarding stored video data from both IR and visible light camera mounted on a pan-tilt remote adjustable mount and a human operator at a remote client device that views the video data as transmitted from the camera, obviously suggesting a real-time viewing as Richards contemplates monitoring the video feed during situations where there is no anomaly, and sending an alert to an operator when a temperature threshold is exceeded- in combination with Stoupis, this camera and viewing method may be incorporated into the power monitoring system of Stoupis using the server storing and processing of Stoupis).
One would be motivated to combine these teachings in order to enhance the operational oversight of a monitoring system for power lines (see Richards paragraphs 23, 25, 41, 45, 59, 67-68, 83-84, 92).
Conclusion
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/MATTHEW DAVID KIM/Primary Examiner, Art Unit 2483