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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 10-12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0368629 A1 to Fekete et al. hereinafter, “Fekete” in view of US 2017/0076572 A1 to Rao and KR 102123248 B1 to Jin et al., hereinafter, “Jin”.
Claim 1. Fekete teaches A method comprising: [Abstract] a method, apparatus and computer program for privacy masking video surveillance data
obtaining video data captured from a video camera having a field of view;
[0011] FIG. 2 illustrates the field of view of a camera
initiating a privacy override for the video data for a time duration, [0026] …The privacy mask data may also include specific time restraints for applying the privacy mask. a privacy mask may be applied only between certain times of the day…information indicating certain user permissions that allow some users to remove the masking (privacy override).
the privacy override applicable to time-dependent, [0026] …The privacy mask data may also include specific time restraints for applying the privacy mask.
spatial locations consistent with a tracking trajectory of a target object; [0026] The privacy mask data will include coordinates defining the area of each frame which will be masked.
applying object detection analytics to detect a plurality of objects in the field of view of the video camera; [0002] analyse video surveillance data and detect specific objects or activity. These will typically attach metadata to the video stream indicating a time and position in the frame where the objects or activity have been detected
[0017] analytics server can also run analytics software for image analysis, for example motion or object detection, facial recognition, event detection.
Fekete fails to explicitly teach applying the privacy override to enable object recognition analytics to be applied to the video data over the time duration of the privacy override, Roa, in the similar field of masking an object in image data, teaches and [0003] …overriding the privacy masking operations such that at least one second captured image or second captured video is not encoded with second privacy masking features during a certain period of time.
[0026] certain sensors or video analytics events in the enterprises can inhibit the masking function in the IP camera 112. For instance, occurrence of an EAS alarm or recognizing a face of a shoplifter by video analytics can be used to inhibit the masking function…
at the locations of the privacy override, [0022] The following discussion assumes that detected faces or other configured ROI in video frames are masked by appropriate blur filters.
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fekete with the teachings of Rao [0003] to selectively control privacy masking of image and/or video content.
Fekete fails to explicitly teach applying the privacy override to enable object recognition analytics. Jin, in the field of applying the privacy override to enable object recognition analytics teaches [page 1]… the face recognition based on the process of de-identifying the face of the person
to selectively identify the target object from the plurality of objects [page 1] A monitoring device that displays real-time video data and displays a screen for monitoring, a database that stores a list of shooting consents, which is a list of faces of users who have agreed on preset shooting, and sends real-time video data generated by the shooting device To provide a streaming broadcast, but recognizes a face from real-time image data using an artificial intelligence algorithm, compares the recognized face with the shooting consent list, and displays the remaining face areas except the same face as the face stored in the shooting consent list. And an image processing server that performs de-identification processing and tracks the corresponding face region object to transmit real-time image data.
[page 1] The rest of the face area excepted may be de-identified from the corresponding image data, and the de-identified image data may be encoded
and inhibiting the object recognition analytics for a remainder of the plurality of objects. [page 1] recognizes a face from real-time image data using an artificial intelligence algorithm, compares the recognized face and displays the remaining face areas except the same face as the face stored in the shooting consent list. And an image processing server that performs de-identification processing and tracks the corresponding face region object to transmit real-time image data.
[page 1] de-identifying the remaining face regions except for the same face as the face stored in the photographing consent list from the corresponding image data and encoding the completed de-identification image data.
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fekete with the teachings of Jin [Abstract] for real-time deidentification function, such as mosaic, blurring, and the like, is supported in real-time streaming broadcasting, thereby providing preventing portrait rights of people who do not agree to photographing.
Claim 2. Rao teaches wherein the privacy override is initiated in response to detection of an event in the video data. [0003] The alarm condition may be associated with an intrusion into a facility, unauthorized removal of an item from the facility, or an actual or potential crime being committed within or in proximity to the facility. In response to the arming of an intrusion detection system or the detection of the alarm condition, a first message is communicated to the privacy masking component for overriding the privacy masking operations such that at least one second captured image or second captured video is not encoded with second privacy masking features during a certain period of time.
[0024] there could be certain conditions (as in case of some incidents such as theft) when it will be required to unmask and view the original video content in order to investigate the case.
[0026] occurrence of an EAS alarm or recognizing a face of a shoplifter by video analytics can be used to inhibit the masking function
Claim 10. Rao teaches further comprising: when the target object is no longer detected after the time duration, revoking the privacy override. [0003] When the intrusion detection system is disarmed or the alarm condition is terminated, a second message is communicated to the privacy masking component for causing the privacy masking component to once again perform the privacy masking operations such that at least one third captured image or third captured video is encoded with third privacy masking features
Claim 11. Reviewed and analyzed in the same way as claim 1. See the above analysis and rationale.
Claim 12. Reviewed and analyzed in the same way as claim 2. See the above analysis and rationale.
Claim 20. Reviewed and analyzed in the same way as claim 10. See the above analysis and rationale.
Claim(s) 3, 5, 13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0368629 A1 to Fekete et al. hereinafter, “Fekete” in view of US 2017/0076572 A1 to Rao and KR 102123248 B1 to Jin et al., hereinafter, “Jin” and in further view of US 2022/0172586 A1 to San Pedro et al., hereinafter, “San Pedro”.
Claim 3. Fekete fails to explicitly teach the detection of the event in the video data is performed by an artificial intelligence algorithm or statistical modelling. San Pedro, in the field of targeted video surveillance teaches wherein the detection of the event in the video data is performed by an artificial intelligence algorithm or statistical modelling. [0063] video analytics system 206 can be configured to implement one or more object detection algorithms to detect one or more events of interest including a presence of a class of objects or specific objects within video frames of video footage 204….an object detection algorithm can include one or more machine learning algorithms such as Convolution Neural Networks (CNNs), Region-based CNN (R-CNN), Fast R-CNN, Faster R-CNN, You Only Look Once (YOLO), etc. [0078-0079]
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fekete with the teachings of San Pedro [0005] it is desirable to significantly reduce the amount of video footage collected as well as retained for subsequent review. It is likewise desirable to significantly reduce the labor required to review collected video footage timely following events of interest.
Claim 5. Fekete fails to explicitly teach the detection analytics comprise detection of one or more faces in the field of view. San Pedro, in the field of targeted video surveillance teaches wherein the detection analytics comprise detection of one or more faces in the field of view [0013] a presence of one or more persons within a field of view
[0058] watchlist 224 can include one or more specific objects or activities within a field of view.
and wherein the recognition analytics comprise facial recognition of the target object to identify the target object from the one or more faces. [0064] to implement one or more facial recognition algorithms to detect a presence of one or more persons or to detect one or more specific persons of interest within video frames of video footage 204.
[0067] a first indicator of presence of one or more persons detected in the video segment, a second indicator of a presence of a specific person of interest detected in the video segment
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fekete with the teachings of San Pedro [0005] it is desirable to significantly reduce the amount of video footage collected as well as retained for subsequent review. It is likewise desirable to significantly reduce the labor required to review collected video footage timely following events of interest.
Claim 13. Reviewed and analyzed in the same way as claim 3. See the above analysis and rationale.
Claim 15. Reviewed and analyzed in the same way as claim 5. See the above analysis and rationale.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0368629 A1 to Fekete et al. hereinafter, “Fekete” in view of US 2017/0076572 A1 to Rao and KR 102123248 B1 to Jin et al., hereinafter, “Jin” and in further view of US 2024/0331161 A1 to Keskikangas et al., hereinafter, “Keskikangas”.
Claim 6. Fekete fails to explicitly teach the privacy override is applicable to corresponding portions of the field of view of the video camera corresponding to the time-dependent spatial locations. Keskikangas, in the field of unmasking an object in video data teaches wherein the privacy override is applicable to corresponding portions of the field of view of the video camera corresponding to the time-dependent spatial locations. [0029] FIG. 1 shows …a camera 100 for masking a detected object in a video stream captured by the camera 100….a security camera or a surveillance camera, which may be arranged in a camera system including the camera 100 and at least a further device, wherein a location and a field of view is known for the camera 100 and a location and field of view is known for said further device…The camera 100 captures a sequence of image frames, e.g., in the form of a video, wherein each image frame depicts a scene defined by the field of view of the camera 100., [Claim 1]
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fekete with the teachings of Keskikangas [0003] to allow revealing private information for the objects being involved in the incident, but alleviate the reveal of the private information regarding other objects which are not involved in the incident.
Claim 16. Reviewed and analyzed in the same way as claim 6. See the above analysis and rationale.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0368629 A1 to Fekete et al. hereinafter, “Fekete” in view of US 2017/0076572 A1 to Rao and KR 102123248 B1 to Jin et al., hereinafter, “Jin” and in further view of US 2019/0306408 A1 to Hofer et al., hereinafter, “Hofer”.
Claim 7. Fekete fails to explicitly teach forwarding metadata of the recognition analytics to at least one of a server. Hofer, in the field of video surveillance teaches further comprising forwarding metadata of the recognition analytics to at least one of a server [0016] The predetermined distance (and other information pertaining to the tracking) may also be stored on a server local to or remote from the at least one first camera device and the at least one second camera device in embodiments.
and one or more adjacent video cameras to update the tracking trajectory. [0014] the at least one second camera device is configured to follow (or track) a first actionable motion object. Examiner interprets “follow” to be the updated tracking trajectory.
[0015] the multi-camera tracking allows the at least one first camera device to communicate and provide analytic information to multiple camera devices so that the at least one second camera device (e.g., camera 2, . . . camera N) will track objects in the scene that are detected by the at least one first camera device. Examiner interprets “communicate and provide analytic information” to be tracking trajectory.
[Claim 1]… processing a first video stream, which is captured by at least one first camera device, to identify a first actionable motion object (AMO); generating first metadata associated with the first AMO transmitting the first metadata to at least one second camera device
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fekete with the teachings of Hofer [0002] for calibration of cameras used in multi-camera tracking.
Claim 17. Reviewed and analyzed in the same way as claim 7. See the above analysis and rationale.
Allowable Subject Matter
Claims 4 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 4. The method of claim 2, further comprising: determining a confidence level of detection of the event; San Pedro [0013] generating a confidence score indicating a likelihood that the first event of interest is accurately detected in the first video segment
Prior art fails to explicitly teach
Claim 14. Reviewed and analyzed in the same way as claim 4. See the above analysis and rationale.
Claims 8 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art fails to explicitly teach “updating the tracking trajectory of the target object based on the metadata of the recognition analytics; and updating the privacy override according to the updated tracking trajectory.”
Claims 9 and 19 are respectively depended on claims 8 and 18 and therefore, would be allowable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DELOMIA L GILLIARD whose telephone number is (571)272-1681. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DELOMIA L GILLIARD/Primary Examiner, Art Unit 2661
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