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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/16/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant has amended claims 1, 12, and 16. Claims 1-20 are currently being considered. Applicant’s arguments filed 8/14/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 102(a)(2) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Mohareri et. al. (United States Patent Application Publication US 2025/0204987 A1).
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rittscher et. al. (International Publication WO 2020/201772 A1) in view of Mohareri et. al. (United States Patent Application Publication US 2025/0204987 A1).
Regarding claim 1, Rittscher et. al. discloses a system for monitoring a quality of a video scene, the system comprising: one or more circuits configured to perform operations comprising: providing a pattern comprising an identification of a plurality of regions in the video scene to be evaluated by deep learning models, the deep learning models trained to determine an artifact type present in a region of the video scene and determine a quality score for the region of the video scene (Rittscher et. al. Fig. 1-4, and 6, pg. 8, lines 32-pg. 9, line 11, pg. 10, lines 17-20); evaluating a region of the pattern using a respective deep learning model of the deep learning models to obtain a result comprising a quality score for and artifact types present in the region of the pattern (Rittscher et. al. pg.3, second paragraph); calculating a validation score for the pattern using the result from each region of the pattern (Rittscher et. al. Figs. 3 and 4, pg. 4, lines 6-32); and performing an automated action to affect the quality of the video scene using the validation score (Rittscher et. al. Fig. 1, blocks 24-26).
PNG
media_image1.png
336
698
media_image1.png
Greyscale
However, Rittscher et. al. fails to disclose the validation score related to an uncertainty of a presence of an artifact in at least one region.
Mohareri et. al. teaches the validation score related to an uncertainty of a presence of an artifact in at least one region (Mohareri et. al. Abstract, Fig. 14, [0260]: validation metric and loss are determined with respect to the synthetic video.).
PNG
media_image2.png
482
536
media_image2.png
Greyscale
This is important to the claimed invention because the validation score or metric is a quantitative measure for how well a generated video matches human expectations or quality standards. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Rittscher et. al. and Mohareri et. al. so that the validation score is also included as part of the solution of the claimed invention.
Regarding claim 12, which is a method for monitoring a quality of a video scene, which corresponds to the system of claim 1, which the rejection analysis is incorporated herein.
Regarding claim 16, which is a system for validating neural network determined assessments of a quality of a video scene, which corresponds to the method of claim 12, which the rejection analysis is incorporated herein.
Regarding claim 2, Rittscher et. al. further discloses the system of claim 1, the operations further comprising: providing a configuration comprising a plurality of patterns; and calculating a confidence score using the validation score for each pattern of the configuration (Rittscher et. al. abstract, pg. 3, lines 9-20, the classified regions are used to derive quality score representing image quality of the successive frames, Rittscher et. al. blocks 24-26).
Regarding claim 3, Rittscher et. al. further discloses the system of claim 2, wherein the automated action comprises using a deep learning model to upscale the video scene, and wherein the automated action is performed in response to the confidence score being greater than a threshold (Rittscher et. al. pg. 4, lines 28-34, pg. 13, lines 15-27, The image frames that are restored may be selected on the basis of the quality scores. Image frames that are not selected for restoration may be discarded on the basis that they are not of sufficient quality, even after restoration. The step of restoring may comprise processing the image frames by separate processes for each class to reduce the corresponding type of image artefact).
PNG
media_image3.png
514
968
media_image3.png
Greyscale
Regarding claims 4, 14, and 20, Rittscher et. al. further discloses the system of claims 1 and 16, and method of claim 12, wherein the plurality of regions of the pattern comprise regions related by at least one of: a scale relationship, wherein a first region of the pattern comprises a second region and a third region of the pattern; a spatial relationship, wherein the pattern comprises a first region adjacent to a second region; or a temporal relationship, wherein the pattern comprises a first region of a frame of the video scene and a second region of a subsequent frame related spatially by a speed at which objects are moving in the video scene (Rittscher et. al. Table II, pg. 20, lines 16-21, it was chosen to incorporated YOLOv3 with spatial pyramid pooling (YOLOv3-SPP) for robust detection and improved inference time for endoscopic artefacts detection).
Regarding claim 5, Rittscher et. al. further discloses the system of claim 1, wherein the plurality of regions of the pattern are related by a scale pyramid relationship, wherein a first region comprises a first plurality of equally sized regions and each region of the first plurality of equally sized regions comprise a second plurality of equally sized regions, wherein the first region and each of the first plurality of equally sized regions are downsampled to have the same number of pixels (Rittscher et. al. Table II, pg. 20, lines 16-21, it was chosen to incorporated YOLOv3 with spatial pyramid pooling (YOLOv3-SPP) for robust detection and improved inference time for endoscopic artefacts detection).
PNG
media_image4.png
405
994
media_image4.png
Greyscale
Regarding claims 6, 15, and 18, Rittscher et. al. further discloses the system of claims 1, 12, and 16, wherein evaluating the region of the pattern comprises: generating a plurality of intermediate quality thresholds based on a minimum quality threshold and a local quality threshold of the region of the pattern, the local quality threshold comprising a weighted moving average of quality scores from evaluating the respective deep learning model of the region of the pattern; and comparing the quality score for the region of the pattern to the plurality of intermediate quality thresholds to determine a loss significance value (Rittscher et. al. pg. 4, lines 28-34, pg. 13, lines 15-27, The image frames that are restored may be selected on the basis of the quality scores. Image frames that are not selected for restoration may be discarded on the basis that they are not of sufficient quality, even after restoration. The step of restoring may comprise processing the image frames by separate processes for each class to reduce the corresponding type of image artefact; pg. 10, lines 27-31).
PNG
media_image5.png
396
994
media_image5.png
Greyscale
Regarding claims 7 and 19, Rittscher et. al. further discloses the system of claims 6 and 18, the operations further comprising calculating a loss relevance value using the artifact type present in the region of the pattern and using a mathematical function of the loss significance value and the loss relevance value to determine a validation score element for the region of the pattern (Rittscher et. al. pg. 17, lines 10-15).
PNG
media_image6.png
270
942
media_image6.png
Greyscale
Regarding claim 8, Rittscher et. al. further discloses the system of claim 1, wherein the respective deep learning model is pretrained, stored in the one or more circuits, and selected to evaluate the region based on a criterion of the region (Rittscher et. al. Fig. 3, 4, pg. 12, lines 14-19, examples of two frames which demonstrate how the quality scores are dependent on class, area and location of the classified regions. The bounding boxes and labels identifying regions within those frames that were classified by the CNN block 13 is an example where implemented CNN was YOLOv3-SPP are superimposed on the images).
Regarding claim 9, Rittscher et. al. further discloses the system of claim 1, wherein the pattern is configured based on content of the video scene (Rittscher et. al. pg. 19, lines 27-30, experiments using the analysis apparatus 10 were performed as follows; 7 unique patient videos (gastroesophageal, selected from large cohort of 200 videos) were used for training and 10 different videos were used for extensive validations).
Regarding claim 10, Rittscher et. al. further discloses the system of claim 9, wherein providing the pattern is performed using an additional deep learning model trained to detect and/or segment regions of interest within the video scene (Rittscher et. al. pg. 8, lines 15-20, a CNN may simultaneously predict the regions and the classes of the regions in a single stage. For example, You Only Look Once (YOLO) simplified Faster R-CNNs to predict simultaneously class and bounding box coordinates using a single CNN and a single loss function with good performance and significantly faster inference time).
Regarding claim 11, Rittscher et. al. further discloses the system of claim 1, wherein performing the automated action comprises at least one of: causing the video scene to be provided at a lower resolution; causing the video scene to be provided at a lower bitrate; prompting a customer to upgrade to a different level of service; prioritizing the video scene over other forms of communication traffic; requesting the video scene from a different server; storing a portion of the video scene for further analysis; increasing an amount of buffered video; alerting the customer that they are experiencing intermittent streaming issues; performing a video enhancement technique; alerting a provider of the video scene; or providing quality analytics related to the video scene to the provider of the video scene (Rittscher et. al. pg. 3, lines 21-31, use of the quality score may be used during training of an endoscopist because it provides reliable information on the quality of the video image signal being captured, which can be used to assess clinical utility of a particular image signal or performance of an endoscopist. The broadest reasonable interpretation of the claim language is the upgrade to a different level of service indicates the skill level of the endoscopist or provider and identifying pertinent video in real time).
Regarding claim 13, Rittscher et. al. further discloses the method of claim 12, the method further comprising: providing a configuration comprising a plurality of patterns; and calculating a confidence score by combining the validation score for each pattern of the configuration (Rittscher et. al. blocks 24-26).
Regarding claim 17, Rittscher et. al. further discloses the system of claim 16, the operations further comprising: providing a second plurality of regions within the video scene; evaluating each region of the second plurality of regions using a respective deep learning model of the set of deep learning models to generate a second set of results; combining the second set of results to obtain a second validation score for the second plurality of regions; and calculating a confidence score based on the validation score and the second validation score (Rittscher et. al. Table II, multiple CNN models were used to compare results, YOLOv3, Faster R-CNN, and Retinanet).
Conclusion
Response to Amendment
Examiner has carefully considered the applicant’s amendments and performed an updated search. New prior art was found to reject all claims 1-20 under U.S.C. 103.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSICA YIFANG LIN/Examiner, Art Unit 2668
September 4, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
