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 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 of this title, 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) 1-2, 5-6, 8-9, 12-13, 15-16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bahl et al. (US 20180129892 A1, hereinafter Bahl) in view of Chalom et al. (US 20160284095 A1, hereinafter Chalom).
Regarding 1, Bahl discloses a computer-implemented method (¶0008. Figs. 6-7, ¶0102-0107, ¶0117, claims 1 and dependents) comprising:
receiving, by a computer system (system 102, figs. 1, 3), multiple requests in a request queue (step 602, fig. 6 and/or step 72, fig. 7.
Lag can be defined as a difference between a time of a last-arrived frame and a time of a last-processed frame (e.g., how much time's worth of frames are queued and unprocessed). – ¶0044),
wherein each of the requests is associated with at least one of multiple video clips (step 602, fig. 6 and/or step 72, fig. 7) captured by multiple security cameras (multiple cameras 108-110, figs. 1, 3; security camera ¶0002);
obtaining the video clips from the security cameras (steps 602, 702, figs. 6-7; security camera ¶0002) based on the requests (queries, Abstract), Video streams can be captured by multiple cameras and continuously streamed to a video analytics computing system; the video streams can be received at the video analytics computing system. Multiple video analytics queries can be executed on the video streams. The multiple video analytics queries can be concurrently executed by the video analytics computing system on the video streams as the video streams are continuously streamed to the video analytics computing system. –Abstract
As noted above, the system manager component 122 of the manager computing device 112 can manage resources of the video analytics computing system 102 for processing the video analytics queries 104-106 based on resource-quality tradeoffs with multi-dimensional configurations. Vision algorithms typically include various parameters, also referred to herein as knobs. Examples of knobs are video resolution, frame rate, and internal algorithmic parameters, such as a size of a sliding window to search for objects in object detectors. A combination of knob values is referred to herein as a configuration of a video analytics query. The configuration space grows exponentially with the number of knobs. – ¶0033);
processing the video clips in batches using an analytics system (Video streams can be captured by multiple cameras and continuously streamed to a video analytics computing system; the video streams can be received at the video analytics computing system. Multiple video analytics queries can be executed on the video streams. The multiple video analytics queries can be concurrently executed by the video analytics computing system on the video streams as the video streams are continuously streamed to the video analytics computing system. –Abstract);
analyzing performance metrics of the security cameras based on processing the video clips (The system manager component 122 can generate query resource-quality profiles and allocate resources to the video analytics queries 104-106 to maximize performance on quality and lag; in contrast, various conventional approaches use fair sharing of resources in clusters. The system manager component 122 can consider resource-quality tradeoffs with multi-dimensional configurations and variety in quality and lag goals for the video analytics queries 104-106 when managing resources of the video analytics computing system 102….In view of the foregoing, the output of multiple cameras 108-110 can be efficiently analyzed in real-time. – ¶0032
While simple, such allocation based on resource fairness can be agnostic to query quality and lag, which can detrimentally impact performance of such traditional approaches. In contrast, the video analytics computing system 102 can scale to processing thousands of live video streams from the cameras 108-110 over large clusters. – ¶0037);
generating, using a trained machine learning model, an updated value for the each parameter based on analyzing the performance metrics (While many of the examples set forth herein pertain to processing the video analytics queries 104-106, it is contemplated that the features described herein can be extended to processing other types of analytics queries. For instance, the techniques described herein can be used for various machine learning processing or other data processing, so long as queries being processed have multiple knobs, or parameters, that can be adjusted, which can result in change in quality and/or resource demand. – ¶0101); and
Bahl is not found disclosing expressly the limitation of, wherein each of the security cameras has one or more parameters, and wherein each parameter has a value; and configuring the security cameras with the updated value for the each parameter.
However, Chalom discloses, Methods, apparatuses and systems may provide for operating a machine learning device by obtaining training image data, conducting an offline prediction analysis of the training image data with respect to one or more real-time parameters of an image capture device, and generating one or more parameter detection models based on the offline prediction analysis (Abstract, figs. 1-6, specifically steps 36-38 fig. 2 and steps 50-52, fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the invention of Bahl, with the teaching of Chalom of parameter optimization update thereof in a camera, such that the parameter prediction model for camera of Chatom is used in real-time for updating parameters of the security cameras of Bahl, based on trained data in a machine learning model, to obtain, wherein each of the security cameras has one or more parameters, and wherein each parameter has a value; and configuring the security cameras with the updated value for the each parameter, because, updated parameter based cameras generate images 22 that are optimized depending on the type of scene contained in the images 22 (e.g., optimized for images of a bright sky with a person in the foreground) [see ¶0014 of Chalom].
Regarding claim 2, Bahl in view of Chalom discloses the method of claim 1, comprising:
overlaying extended-reality (XR) data on at least one video clip for display on at least one XR device (Chalom: used for MAR application; ¶0055).
Regarding claim 5, Bahl in view of Chalom discloses the method of claim 1, wherein the computer system is a base station (system 102 is understood as a base station since it receives all camera streams and analytic queries, ¶0023).
Regarding claim 6, Bahl in view of Chalom discloses the method of claim 1, comprising:
training the machine learning model using the video clips (Bahl: ¶0101, Chalom: Title, abstract, figs. 1-5).
Regarding claim 8, Bahl in view of Chalom discloses at least one non-transitory memory storing instructions, which, when executed by at least one hardware processor, cause a computer system to (Bahl: ¶0103, ¶0137-0138; memory 120, fig. 1):
receive multiple requests in a request queue,
wherein each of the requests is associated with at least one of multiple video clips captured by multiple security cameras;
obtain the video clips from the security cameras based on the requests, wherein each of the security cameras has one or more parameters, and wherein each parameter has a value;
process the video clips in batches using an analytics system;
analyze performance metrics of the security cameras based on processing the video clips;
generate, using a trained machine learning model, an updated value for the each parameter based on analyzing the performance metrics; and
configure the security cameras with the updated value for the each parameter (see substantively similar claim 1 rejection above).
Regarding CRM claim(s) 9, 12-13 although wording is different, the material is considered substantively equivalent to the method claim(s) 2, 5-6 as described above.
Regarding claim 15, Bahl in view of Chalom discloses a base station (102, figs. 1, 3) comprising:
one or more processors (118, fig. 1, ¶0027-0028); and
a non-transitory computer-readable storage medium storing instructions, which when executed by the one or more processors cause the base station to (memory 120, fig. 1; Bahl: ¶0103, ¶0137-0138):
receive multiple requests in a request queue,
wherein each of the requests is associated with at least one of multiple video clips captured by multiple security cameras;
obtain the video clips from the security cameras based on the requests, wherein each of the security cameras has one or more parameters, and wherein each parameter has a value;
process the video clips in batches using an analytics system;
analyze performance metrics of the security cameras based on processing the video clips;
generate, using a trained machine learning model, an updated value for the each parameter based on analyzing the performance metrics; and configure the security cameras with the updated value for the each parameter (see substantively similar claim 1 rejection above).
Regarding claim(s) 16, 19 although wording is different, the material is considered substantively equivalent to the method claim(s) 2, 6 as described above.
Claim(s) 3, 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bahl in view of Chalom and further in view of Cona et al. (US 20190333054 A1, hereinafter Cona).
Regarding claim 3, Bahl in view of Chalom discloses the method of claim 1, except, comprising:
receiving a request for access to the computer system (), wherein the request includes a credential stored in a digital wallet.
However, Cona discloses a system for the verification a user's identity and qualifications and authentication of credentials associated with a user's digital identity on a trust network (Abstract), wherein digital wallet stores credential required for access request to a system (¶0038, ¶0085).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the invention of Bahl in view of Chalom, such that the initiation and/or modification of queries followed by parameter updating of the security camera parameters is done by an authenticated person accessing the video analytics computing system 102 of Bahl where Cona’s system provides the verification of a user's identity by authenticating users’ based on credentials stored in a digital wallet, to obtain, receiving a request for access to the computer system, wherein the request includes a credential stored in a digital wallet, because, such combination would enhance the security of the overall surveillance camera system of Bahl during initiating analytics query as well as updating camera parameters using a secured access to the system through established secured credentials.
Regarding claim(s) 10, and 17 although wording is different, the material is considered substantively equivalent to the method claim(s) 3 as described above.
Claim(s) 4, 11, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bahl in view of Chalom and further in view of Bulleit et al. (US 20180060496 A1, hereinafter Bulleit).
Regarding claim 4, Bahl in view of Chalom discloses the method of claim 1, except, comprising:
receiving a request for access to the computer system using self-sovereign identity (SSI).
However, Bulleit discloses receiving a request for access to the computer system using self-sovereign identity (¶0216).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the invention of Bahl in view of Chalom, such that the initiation and/or modification of queries followed by parameter updating of the security camera parameters is done by an authenticated person accessing the video analytics computing system 102 of Bahl where Bulleit’s system provides the verification of a user's identity by receiving a request for access to the computer system using self-sovereign identity, because, such combination would enhance the security of the overall surveillance camera system of Bahl in initiating analytics query as well as updating camera parameters using a secured access to the system through established self-sovereign identity.
Regarding claim(s) 11, and 18 although wording is different, the material is considered substantively equivalent to the method claim(s) 4 as described above.
Claim(s) 7, 14, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bahl in view of Chalom and further in view of Sudo (US 10798293 B2, hereinafter).
Regarding claim 7, Bahl in view of Chalom discloses the method of claim 1, except, wherein the one or more parameters are associated with a type of environment in which the computer system is located.
However, Sudo discloses, parameters are associated with a type of environment in which the computer system is located (Col. 1, lines 40-45 & 50-55; Col. 5, lines 24-28; Col. 11, lines 16-19 & 32-35; Col. 17, lines 47-53).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the invention of Bahl in view of Chalom, with the teaching of Sudo, such that the camera parameters being optimized for performance are associated with a type of environment in which the computer system is located, because, combining prior art elements ready to be improved according to known method to yield predictable results is obvious (see MPEP §2143.I). Furthermore, such combination would enhance the versatility of the overall system by being capable of adapting camera parameters commensurate to surrounding ambiance.
Regarding claim(s) 14 and 20 although wording is different, the material is considered substantively equivalent to the method claim(s) 7 as described above.
Conclusion
The prior and/or pertinent art(s) made of record and not relied upon is considered pertinent to applicant's disclosure, are –McLachlan et al. (US 11430276 B1), Beach et al. (US 11165954 B1), Bataller et al. (US 20160350596 A1) – who disclose different imaging systems of interest performing optimization of imaging parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHBAZ NAZRUL whose telephone number is (571)270-1467. The examiner can normally be reached M-Th: 9.30 am-3 pm, 6.30 pm-9 pm, F: 9.30 am-1.30 pm, 4 pm-8 pm.
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/SHAHBAZ NAZRUL/Primary Examiner, Art Unit 2638
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