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. Election/Restrictions Claim s 10-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected species II and III , there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 02/23/2026 . As states in Requirement of Restriction/Election filed on 12/23/2025. There is an examination and search burden for these patentably distinct species I, II, and III due to their mutually exclusive characteristics. The species require a different field of search (e.g., searching different classes/subclasses or electronic resources, or employing different search queries); and/or the prior art applicable to one species would not likely applicable to another species; and/or the species are likely to raise different non-prior art issues under U.S.C.101 and/or 35 U.S.C. 112 first paragraph. 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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Chinen et al. (Chinen) (US 2023/0099526 A1) in view of Brailovskiy et al. (Brailovskiy) (US 10,269,155 B1) . Regarding claim 1, Chinen discloses a method (e.g., the present disclosure are directed to systems and methods for determining a perceptual quality of video content, paragraph 20 ) comprising: determining at least one distortion associated with a content item (e.g., a perceptual quality may correlate to an opinion of a consumer of video content about the experience with the video content item, paragraph 24 ) ; determining, based on the content item comprising a source content item, that the at least one distortion (e.g., The perceptual quality may correlate to, for example, the experience of viewing the video content, the presence of discernible defects or distort ions in the video content, paragraph 24 ) . Chinen, in one embodiment, does not specifically disclose an intentional artifact and determining a quality score associated with the content item, wherein the at least one intentional artifact has no effect on the quality score. Brailovskiy discloses an intentional artifact and determining a quality score associated with the content item, wherein the at least one intentional artifact has no effect on the quality score (e.g., Examples of masks include, for example, transition effects typically used in video editing applications, graphical elements such as images of objects, animations, text, etc. that are placed over the artifact s. Such masks may also be applied to areas with no or small artifact s to make the addition of the masks to the image appear more natural , paragraph 17 ). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Chinen to include an intentional artifact and determining a quality score associated with the content item, wherein the at least one intentional artifact has no effect on the quality score as taught by Brailovskiy. It would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Chinen by the teaching of Brailovskiy to make image looking better. Regarding claim 2, Chinen discloses further comprising determining that the content item comprises a source content item, wherein determining that the content item comprises a source content item comprises at least one of determining that the content item has not been edited, determining that the content item has not been compressed, or determining that the content item has not been de-compressed (e.g., the present disclosure are directed to systems and methods for determining a perceptual quality of video content. More particularly, example aspects are directed to determining a perceptual quality score using one or more features based on the video content and generated by a machine-learned model. For instance, a perceptual quality score may correlate to an opinion of consumers of the video content relating to their experience with the video content. Example embodiments may output a perceptual quality score for a video content item that may be used to evaluate and deliver higher quality video content to users (which has not been edited), paragraph 20 ) . Regarding claim 3, Chinen discloses further comprising determining that the content item comprises a source content item using a machine learning model, wherein the machine learning model is trained to determine whether content has been edited, compressed, or de-compressed (e.g., The encoding can be decoded (e.g., with or without being transmitted and/or subjected to a simulated transmission) according to the codec to produce a subject video content item. In some embodiments, the codec comprises a compress ion algorithm for decreasing the data used or required to store, transmit, or otherwise represent the video content and/or a processing cost of rendering the video content. In some embodiments, distortion or other degradation effects can result from compress ion of the video content, such that the compress e d video content item is a distorted version of the original , paragraph 48 ) . Regarding claim 4, Chinen discloses further comprising determining that the content item comprises a source content item using data associated with the content item, wherein the data associated with the content item is indicative of at least one of frame rate, resolution, audio bitrate, video bitrate, subtitle formats, container format, codec identifier, duration, width, height, color space, resolution, chroma subsampling, bit depth, scan type, compression mode, or stream size (e.g., The training data may also include, for example, data indicating one or more characteristics of the device used to display the content to the user (e.g., manufacturer, model, screen size, screen technology, screen brightness, screen color temperature, screen gamut, screen contrast ratio, screen refresh rate, screen directionality pixel density, maximum frame rendering rate, etc.). The training data may include, for example, data indicating one or more characteristics of the context in which the device was used (e.g., room brightness, room lighting temperature, viewing angle, viewing distance, viewing duration, solo vs. group viewing, etc.), paragraph 44 ) . Regarding claim 5 , Brailovskiy discloses wherein determining the quality score associated with the content item comprises determining at least one parameter of a content quality algorithm, wherein the at least one parameter is associated with the at least one intentional artifact (e.g., The remote device 202 may then insert a mask overlaying the identified artifact s, illustrated as block 222. In one example, the insertion of the mask may include identifying pixel values that correspond to an error or artifact (such as a first pixel value corresponding to the first artifact ), and replace the pixel values corresponding to the error or artifact with new pixel values associated with the mask. Thus, for purposes of displaying the image data on a display, the pixels corresponding to the error or artifact are changed to display the pixels corresponding to the mask to prevent display of the error or artifact , paragraph 25 ) . Regarding claim 6, Chinen discloses wherein determining the quality score associated with the content item further comprises at least one of disabling the at least one parameter or decreasing a weight associated with the at least one parameter (e.g., the model trainer 460 can train the machine-learned models 420 and/or 440 based on a set of training data 462 . The training data 462 can include, for example, data associating video content and/or image content with viewer feedback (e.g., a score for content input by a viewer while or after viewing the content) regarding the perceptual quality of the content , paragraph 73 ) . Regarding claim 7 Chinen discloses wherein the at least one distortion associated with the content item comprises at least one of a basis pattern, blockiness, blurriness, color distortion, mosquito noise, white noise, graininess, ringing, flickering, floating, or jerkiness (e.g., In some embodiments, distortion or other degradation effects can result from compression of the video content, such that the compressed video content item is a distorted version of the original, paragraph 48 ) . Regarding claim 8, Chinen discloses wherein the content item comprises at least one of an image or a video (e.g., in one embodiment of the example computing system, the score correlates to a perceptual similarity between the subject video content item and the reference video content item, paragraph 7 ) . Regarding claim 9, Chinen discloses wherein the source content item comprises a content item that has not been edited, compressed, or de-compressed (e.g., the present disclosure are directed to systems and methods for determining a perceptual quality of video content. More particularly, example aspects are directed to determining a perceptual quality score using one or more features based on the video content and generated by a machine-learned model (which has not been edited) , paragraph 20 ) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT QUANG N VO whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1121 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday, 7AM-4PM, EST . 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, Abderrahim Merouan can be reached at 571-270-5254 . 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. /QUANG N VO/ Primary Examiner, Art Unit 2683