DETAILED ACTION
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 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) 1-4, 8-11, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Citerin et al (US 20200275016) in view of Zhang et al (US 20210224998).
Regarding claim 1, Citerin discloses a video processing system (Fig. 1 video surveillance system) comprising:
a plurality of recognition models that has learned video learning data corresponding to different video quality parameters, for each of the video quality parameters (¶82 A first phase is a learning phase (reference 300). According to embodiments, it is performed before the installation of the video content analysis module, for example during the development of a software application used for processing the data received from the video content analysis module. It aims at providing the functions denoted f.sub.VCA_accuracy and f.sub.image_quality that estimate the accuracy of the video content analysis module and the quality of an image, respectively, as a function of parameter values as described above. Each of these functions corresponds to a particular video content analysis module; ¶91 a first step (step 405) is directed to analysing images of a training dataset (reference 400) comprising images and the corresponding ground truth (that is to say the expected results of the considered video content analysis module assumed to be perfectly set));
Citerin fails to teach where Zhang teaches a memory configured to store instructions (¶113); and
a processor configured to execute the instructions (¶135) to select a recognition model that performs recognition regarding a target included in video input data to be input, according to a video quality parameter of the video input data (¶77 a target model matching a resolution of the first image (for example, a resolution of the target model is the same as that of the first image, or the resolution of the target model is closest to that of the first image) is selected from the plurality of recognition models, and the first image is segmented into a plurality of first regions by using the target model. When the target region is searched for among the bounding boxes in the first image that use points in the first regions as centers, a plurality of fourth regions found by the target model in all the bounding boxes are obtained, the fourth region being recognized by the target model in the first image as a region in which the abnormal part of the organic tissue of the living body is located).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of a memory configured to store instructions; and a processor configured to execute the instructions to select a recognition model that performs recognition regarding a target included in video input data to be input, according to a video quality parameter of the video input data from Zhang into the video processing system as disclosed by Citerin. The motivation for doing this is to improve the determining accuracy of image recognition.
Regarding claim 2, the combination of Citerin and Zhang disclose the video processing system according to claim 1, wherein the plurality of recognition models learns the video learning data for each range of the video quality parameters (Citerin ¶82 A first phase is a learning phase (reference 300). According to embodiments, it is performed before the installation of the video content analysis module, for example during the development of a software application used for processing the data received from the video content analysis module. It aims at providing the functions denoted f.sub.VCA_accuracy and f.sub.image_quality that estimate the accuracy of the video content analysis module and the quality of an image, respectively, as a function of parameter values as described above. Each of these functions corresponds to a particular video content analysis module), and the processor is further configured to execute the instructions to select the recognition model on the basis of the range corresponding to the video quality parameter of the video input data (Zhang ¶77 a target model matching a resolution of the first image (for example, a resolution of the target model is the same as that of the first image, or the resolution of the target model is closest to that of the first image) is selected from the plurality of recognition models, and the first image is segmented into a plurality of first regions by using the target model). The motivation for doing this is to improve the determining accuracy of image recognition.
Regarding claim 3, the combination of Citerin and Zhang disclose the video processing system according to claim 1, wherein the processor is further configured to execute the instructions to select the recognition model for each region of the video input data on the basis of the video quality parameter of each region of the video input data (Zhang ¶77 a target model matching a resolution of the first image (for example, a resolution of the target model is the same as that of the first image, or the resolution of the target model is closest to that of the first image) is selected from the plurality of recognition models, and the first image is segmented into a plurality of first regions by using the target model. When the target region is searched for among the bounding boxes in the first image that use points in the first regions as centers, a plurality of fourth regions found by the target model in all the bounding boxes are obtained, the fourth region being recognized by the target model in the first image as a region in which the abnormal part of the organic tissue of the living body is located). The motivation to combine the references is discussed above in the rejection for claim 1.
Regarding claim 4, the combination of Citerin and Zhang disclose the video processing system according to claim 3, wherein the processor is further configured to execute the instructions to detect an object included in the video input data, and the region is a region including the object detected by the object detection means (Zhang ¶77 when the first image is segmented into a plurality of first regions by using a target model, a target model matching a resolution of the first image (for example, a resolution of the target model is the same as that of the first image, or the resolution of the target model is closest to that of the first image) is selected from the plurality of recognition models, and the first image is segmented into a plurality of first regions by using the target model. When the target region is searched for among the bounding boxes in the first image that use points in the first regions as centers, a plurality of fourth regions found by the target model in all the bounding boxes are obtained, the fourth region being recognized by the target model in the first image as a region in which the abnormal part of the organic tissue of the living body is located). The motivation to combine the references is discussed above in the rejection for claim 1.
Regarding claim(s) 8-11 (drawn to an apparatus):
The rejection/proposed combination of Citerin and Zhang, explained in the rejection of system claim(s) 1-4, anticipates/renders obvious the steps of the apparatus of claim(s) 8-11 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1-4 is/are equally applicable to claim(s) 8-11.
Regarding claim(s) 15-18 (drawn to a method):
The rejection/proposed combination of Citerin and Zhang, explained in the rejection of system claim(s) 1-4, anticipates/renders obvious the steps of the method of claim(s) 15-18 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1-4 is/are equally applicable to claim(s) 15-18.
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Citerin and Zhang as applied to claim 1, 8, and 15 above, and further in view of Garner (US 20200273013).
Regarding claim 5, the combination of Citerin and Zhang disclose the video processing system according to claim 1, but fail to teach where Garner teaches wherein the video quality parameter includes a frame rate, and the processor is further configured to execute the instructions to select the recognition model on the basis of an increase/decrease tendency of the frame rate of the video input data (¶71 some embodiments utilize mobile-optimized machine learning models and run these models at a rate of several times per second (e.g., at a rate of 5, 10, 20 or more frames per second), which may correspond to a frame rate of video content, which may be implemented in sequence and/or in parallel, so that more results can be produced per second, and can be cooperatively evaluated to determine a consensus identification of a product).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein the video quality parameter includes a frame rate, and the processor is further configured to execute the instructions to select the recognition model on the basis of an increase/decrease tendency of the frame rate of the video input data from Garner into the video processing system as disclosed by the combination of Citerin and Zhang. The motivation for doing this is to improve systems and methods for object recognition.
Regarding claim(s) 12 (drawn to an apparatus):
The rejection/proposed combination of Citerin, Zhang, and Garner, explained in the rejection of system claim(s) 5, anticipates/renders obvious the steps of the apparatus of claim(s) 12 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 5 is/are equally applicable to claim(s) 12.
Regarding claim(s) 19 (drawn to a method):
The rejection/proposed combination Citerin, Zhang, and Garner, explained in the rejection of system claim(s) 5, anticipates/renders obvious the steps of the method of claim(s) 19 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 5 is/are equally applicable to claim(s) 19.
Claim(s) 6, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Citerin, Zhang, and Garner as applied to claim 5, 12 and 19 above, and further in view of Sun et al (US 20230377363).
Regarding claim 6, the combination of Citerin, Zhang, and Garner disclose the video processing system according to claim 5, but fail to teach where Sun teaches wherein the processor is further configured to execute the instructions to change the frame rate of the video input data according to the selected recognition model (¶36 A stream of image frames based on the input video stream 203 is passed to the event detection model 230 as event data 228; the event data 228 may comprise a version of the original input video stream 203 having an adjusted (e.g., reduced) frame rate compared to the frame rate of the original input video stream 203).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein the video quality parameter includes a frame rate, and the processor is further configured to wherein the processor is further configured to execute the instructions to change the frame rate of the video input data according to the selected recognition model from Sun into the video processing system as disclosed by the combination of Citerin, Zhang, and Garner. The motivation for doing this is to improve object detection in video data.
Regarding claim(s) 13 (drawn to an apparatus):
The rejection/proposed combination of Citerin, Zhang, Garner, and Sun, explained in the rejection of system claim(s) 6, anticipates/renders obvious the steps of the apparatus of claim(s) 13 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 6 is/are equally applicable to claim(s) 13.
Regarding claim(s) 20 (drawn to a method):
The rejection/proposed combination Citerin, Zhang, Garner, and Sun, explained in the rejection of system claim(s) 6, anticipates/renders obvious the steps of the method of claim(s) 20 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 6 is/are equally applicable to claim(s) 20.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Citerin and Zhang as applied to claim 1 and 8 above, and further in view of Sethuraman et al (US 20250213216).
Regarding claim 7, the combination of Citerin and Zhang disclose the video processing system according claim 1, but fails to teach where Sethuraman teaches wherein the video input data includes an image of which an image quality is changed (¶126 the set of ultrasound images received the first time the step 1110 is performed may be acquired with a first set of ultrasound parameters or settings. The set of ultrasound images received the second time the step 1110 is performed may be acquired with the second set ultrasound parameters are settings), and the video quality parameter includes an image quality index based on a difference between an image before an image quality change and an image after the image quality change (¶127 In some aspects, after the ultrasound system settings have been adjusted at step 1145, the processor circuit may display a new ultrasound image with new visual indicators corresponding to anatomical features and corresponding quality indices. In some aspects, these new visual indicators may be modified to convey to a user of the system a difference in the quality index before and after the ultrasound system settings were adjusted at the step 1145. For example, differences in pattern, color, shape, size, or any other feature of the visual indicators may correspond to an improvement in quality index.).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein the video input data includes an image of which an image quality is changed and the video quality parameter includes an image quality index based on a difference between an image before an image quality change and an image after the image quality change from Sethurman into the video processing system as disclosed by the combination of Citerin and Zhang. The motivation for doing this is to improve systems and methods for optimizing image acquisition.
Regarding claim(s) 14 (drawn to an apparatus):
The rejection/proposed combination of Citerin, Zhang, and Sethuraman, explained in the rejection of system claim(s) 7, anticipates/renders obvious the steps of the apparatus of claim(s) 14 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 7 is/are equally applicable to claim(s) 14.
Conclusion
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/KEVIN KY/Primary Examiner, Art Unit 2671
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