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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-8,10-15 , 17-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over the combination of claims 1-8 of Rao et al. (US Patent. No.: US 12,348, 859 B2).
Regarding claims 1-8 of the instant application, although the claims are not identical to claims 1-8 of Rao et al , they are not patentably distinct from each other because claims 1-8 of the instant application are an obvious variant of the combination of claims 1-8 of Rao et al. respectively.
Regarding claims 10-15 of the instant application, although the claims are not identical to claims 9-14 of Rao et al, they are not patentably distinct from each other because claims 10-15 of the instant application are an obvious variant of the combination of claims 9-14 of Rao et al. respectively.
Regarding claims 17-22 of the instant application, although the claims are not identical to claims 15-20 of Rao et al, they are not patentably distinct from each other because claims 17-22 of the instant application are an obvious variant of the combination of claims 15-20 of Rao et al. respectively.
Claim 1-8, 10-15 ,17-22 of the instant application are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-8 of copending Application No. [ 19/223,218] (reference application).
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-8, 10-15, 17-22 of the instant application are an obvious variant of the combination of claims 1-8,10-15,17-22 of copending Application No. [ 19/223,218] respectively.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1-8, 10-15 ,17-22 of the instant application are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-8 of copending Application No. [ 19/223,214] (reference application).
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-8, 10-15, 17-22 of the instant application are an obvious variant of the combination of claims 1-8,10-15,17-22 of copending Application No. [ 19/223,214] respectively.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Allowable subject matter
Claims 1-8,10-15,17-22 would be allowable if rewritten to overcome the double patenting rejection
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1, prior art on record Ding et al. (US Pub. No.: US 2022/0377239 A1) discloses a method for dynamically tuning camera parameters in a video analytics system (VAS) to optimize analytics accuracy (Para 26; method for limiting motion blur in the visual tracking system includes accessing a first image generated by an optical sensor of the visual tracking system, identifying camera operating parameters of the optical sensor for the first image, determining a motion of the optical sensor for the first image, determining a motion blur level of the first image based on the camera operating parameters of the optical sensor and the motion of the optical sensor, and adjusting the camera operating parameters of the optical sensor based on the motion blur level.), comprising:
capturing a current scene (Para 107; visual tracking system comprising: accessing a first image generated by an optical sensor of the visual tracking system ) using a video-capturing camera (Para 89 ; the image data may be video data ) ;
learning optimal camera parameter settings for the current scene using a Reinforcement Learning (RL) engine ( Para 49; The motion blur estimation engine 402 retrieves camera operating parameters of the optical sensor 212 from the optical sensor module 304 ) by defining a state within the RL engine as a tuple of a first vector representing current camera parameter values ( Para 41; The optical sensor module 304 accesses optical sensor data (e.g., image, camera settings/operating parameters) from the optical sensor 212. Examples of camera operating parameters include, but are not limited to, exposure time of the optical sensor 212, a field of view of the optical sensor 212, an ISO value of the optical sensor 212, and an image resolution of the optical sensor 212.) and a second vector representing measured values of captured frames of the current scene ( Para 50; the motion blur estimation engine 402 determines an angular velocity of the optical sensor 212 based on IMU sensor data from the inertial sensor 210.), and defining sets of actions for modifying parameter values and maintaining the current parameter values (Fig. 6; Para 65; In block 610, the camera parameters adjustment engine 404 adjusts camera operating parameters of the optical sensor 212. In block 612, the camera parameters adjustment engine 404 keeps the current camera operating parameters.);
estimating a quality of the captured frames using a perceptual no-reference quality estimator (Para 65; the motion blur estimation engine 402 determines an angular velocity during exposure time of the current image. In block 606, the motion blur estimation engine 402 estimates a motion blur level based on angular velocity. Wherein motion blur level affects a quality of the image frame), and tuning the camera parameter settings based on the quality estimator and the RL engine to optimize analytics accuracy of the VAS (Fig. 6; Para 65; In block 606, the motion blur estimation engine 402 estimates a motion blur level based on angular velocity. In decision block 608, the motion blur estimation engine 402 determines whether the motion blur level exceeds a preset motion blur threshold. In block 610, the camera parameters adjustment engine 404 adjusts camera operating parameters of the optical sensor 212. In block 612,the camera parameters adjustment engine 404 keeps the current camera operating parameters).
However, the prior art does not disclose evaluating an effectiveness of the tuning by perceptual Image Quality Assessment (IQA) to quantify a quality measure);
iteratively adaptively tuning the camera parameter settings in real-time using the RL engine, responsive to environmental condition changes affecting the scene, based on the learned optimal camera parameter settings, the state, the quality measure, and the set of actions, to further optimize the analytics accuracy until a threshold condition is reached.
Claims 2-8 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and when all of its intervening claims are rewritten to overcome the Double Patenting rejection as being dependent from claim 1.
Regarding claim 10, prior art on record Ding et al. (US Pub. No.: US 2022/0377239 A1) discloses a system for optimizing analytics accuracy in a Video Analytics System (VAS) by dynamically tuning camera parameters (Fig. 2; Para 24; display including a visual tracking system with blur reduction function), comprising:
a video-capturing camera (Para 29, 89; displays images captured with a camera; the image data may be video data) configured to capture a current scene;
a processor ( Para 81; The various memories (e.g., memory 904, main memory 912, static memory 914, and/or memory of the Processors 902) and/or storage unit 916 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 908), when executed by Processors 902, cause various operations to implement the disclosed embodiments.) operatively coupled to a computer-readable storage medium, the processor being configured for:
learning optimal camera parameter settings for the current scene using a Reinforcement Learning (RL) engine (Para 49; The motion blur estimation engine 402 retrieves camera operating parameters of the optical sensor 212 from the optical sensor module 304 ) by defining a state within the RL engine as a tuple of a first vector representing current camera parameter values (Para 41; The optical sensor module 304 accesses optical sensor data (e.g., image, camera settings/operating parameters) from the optical sensor 212. Examples of camera operating parameters include, but are not limited to, exposure time of the optical sensor 212, a field of view of the optical sensor 212, an ISO value of the optical sensor 212, and an image resolution of the optical sensor 212.) and a second vector representing measured values of captured frames of the current scene, and defining sets of actions for modifying parameter values and maintaining the current parameter values (Para 50; the motion blur estimation engine 402 determines an angular velocity of the optical sensor 212 based on IMU sensor data from the inertial sensor 210.), and defining sets of actions for modifying parameter values and maintaining the current parameter values ( Fig. 6; Para 65; In block 610, the camera parameters adjustment engine 404 adjusts camera operating parameters of the optical sensor 212. In block 612, the camera parameters adjustment engine 404 keeps the current camera operating parameters);
estimating a quality of the captured frames using a perceptual no-reference quality estimator, and tuning the camera parameter settings based on the quality estimator and the RL engine to optimize analytics accuracy of the VAS (Para 65; the motion blur estimation engine 402 determines an angular velocity during exposure time of the current image. In block 606, the motion blur estimation engine 402 estimates a motion blur level based on angular velocity. Wherein motion blur level affects a quality of the image frame), and tuning the camera parameter settings based on the quality estimator and the RL engine to optimize analytics accuracy of the VAS (Fig. 6; Para 65; In block 606, the motion blur estimation engine 402 estimates a motion blur level based on angular velocity. In decision block 608, the motion blur estimation engine 402 determines whether the motion blur level exceeds a preset motion blur threshold. In block 610, the camera parameters adjustment engine 404 adjusts camera operating parameters of the optical sensor 212. In block 612,the camera parameters adjustment engine 404 keeps the current camera operating parameters);
However, prior art does not disclose evaluating an effectiveness of the tuning by perceptual Image Quality Assessment (IQA) to quantify a quality measure; iteratively adaptively tuning the camera parameter settings in real-time using the RL engine, responsive to environmental condition changes affecting the scene, based on the learned optimal camera parameter settings, the state, the quality measure, and the set of actions, to further optimize the analytics accuracy until a threshold condition is reached.
Claims 11-15 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and when all of its intervening claims are rewritten to overcome the Double Patenting rejection as being dependent from claim 10.
Regarding claim 17, the subject matter disclosed in claim 17 is similar to the subject matter disclosed in claim 10, therefore, claim 17 would be allowable if rewritten to overcome the double patenting rejection
Claims 18-22 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and when all of its intervening claims are rewritten to overcome the Double Patenting rejection as being dependent from claim 17.
Claims 9,16,23 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and when all of its intervening claims are rewritten to overcome the Double Patenting rejection.
Regarding claim 9, Rao et al (Claim 1) disclose iteratively adaptively tuning the camera parameter settings.
However, none of the prior art discloses wherein the iteratively adaptively tuning the camera parameter settings includes using the quality measure to inform automated decision making for selecting optimal parameter adjustments.
Regarding claim 16, Rao et al (Claim 1) disclose iteratively adaptively tuning the camera parameter settings.
However, none of the prior art discloses wherein the iteratively adaptively tuning the camera parameter settings includes using the quality measure to inform automated decision making for selecting optimal parameter adjustments.
Regarding claim 23, Rao et al (Claim 1) disclose iteratively adaptively tuning the camera parameter settings.
However, none of the prior art discloses “the iteratively adaptively tuning
the camera parameter settings includes using the quality measure to inform automated
decision making for selecting optimal parameter adjustments” in combination of other limitation in the claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XI WANG whose telephone number is (469)295-9155. The examiner can normally be reached on 9:00 am-5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SINH TRAN can be reached on 571-272-7564. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/XI WANG/ Primary Examiner, Art Unit 2637
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