CTFR 18/959,514 CTFR 89414 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-26 AIA Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file. Specification 06-16 AIA Applicant is reminded of the proper language and format for an abstract of the disclosure. The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details. The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided. The abstract of the disclosure is objected to because it contains legal phraseology such as “comprising”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-7, 9-12 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by US 2021/0203997 A1 (“Veselov”) . Regarding claim 1, Veselov discloses an image processing assembly having a transmission device (e.g. see terminal side, paragraphs [0129]-[0134]; also see source device in Fig. 3) comprising: a camera interface for accepting image data from a camera (e.g. see receiving video (uncompressed) as shown in Figs. 5-6, e.g. from a camera, paragraphs [0058]-[0061]); a video encoder device for encoding the image data and for generating encoded image data (e.g. see video encoder, e.g. 100 in Figs. 5-6); a transmitting detector device for detecting image features having a first detection certainty in feature image areas of the image data (e.g. see computing/extracting features at the terminal side, paragraphs [0132]-[0133], 510 in Figs. 5-6) and a cut-out device for cutting out the feature image areas (e.g. see cropping of an image patch, e.g. see Fig. 15, paragraphs [0197]-[0198]), wherein the image features comprise content data with the feature image areas (e.g. see object detection or face detection, along with computing/extracting features at the terminal side, paragraphs [0132]-[0133], [0165]-[0166], [0197]-[0198]), a content encoder device for encoding the content data and generating encoded content data (e.g. see feature encoder, e.g. 520 in Figs. 5-6), a transmission interface for transmitting the encoded image data and the encoded content data (e.g. see output bitstream in Figs. 5-6 transmitted to decoder in Figs. 7-8, e.g. see communication interface 318 in Fig. 3); having a receiving device (e.g. see cloud or server side, paragraphs [0129]-[0134]; also see destination device in Fig. 3) comprising: a receiving interface for receiving the encoded image data and the encoded content data (e.g. see receiving input bitstream in Figs.7-8, e.g. see communication interface 322 in Fig. 3); a video decoder device for decoding the encoded image data and generating decoded image data (e.g. see video decoder, e.g. see 200 in Figs. 7-8); a content decoder device for decoding the encoded content data and generating decoded content data (e.g. see feature decoder, e.g. see 720 in Figs. 7-8), and at least one receiving detector device for detecting and/or verifying the image features with a second detection certainty based on the decoded content data (e.g. see CV analyzer 730 including feature analyzer 732 and feature locator 734, e.g. see Figs. 8-9, paragraphs [0132]-[0133], [0193]-[0203]). Regarding claim 2, Veselov further discloses wherein the transmission device is configured to transfer the feature image areas from a time range in which the image features are active (e.g. see time durations, paragraphs [0139], [0192]). Regarding claim 3, Veselov further discloses wherein the feature image areas are configured as cropped images of the image data (e.g. see cropping of an image patch, e.g. see Fig. 15, paragraphs [0197]-[0198]). Regarding claim 4, Veselov further discloses wherein the feature image areas are transmitted at a higher resolution than the image data, and/or that the image data is compressed more than the feature image areas (e.g. see lossless encoding of features, paragraphs [0153], [0161]-[0162], and/or use of standard encoder for video, e.g. H.265 (which is lossy), paragraphs [0250]-[0251]). Regarding claim 5, Veselov further discloses wherein the transmitting detector device is configured to detect detection objects as the image features in the image data, wherein the feature image areas comprise a position of the detection objects (e.g. see object detection or face detection, along with computing/extracting features at the terminal side, paragraphs [0132]-[0133], [0197]-[0198]). Regarding claim 6, Veselov further discloses wherein the video encoder device and/or the content encoder device operate on the basis of an H.26x encoder (e.g. see at least H.265, paragraphs [0004]-[0005], [0250]-[0251]). Regarding claim 7, Veselov further discloses wherein the receiving device comprises a receiving detector network, wherein the receiving detector network comprises the receiving detector device, wherein the receiving detector network is configured to carry out the detection and/or verification of the image features based on the decoded content data and the decoded image data (e.g. see CV analyzer 730 including feature analyzer 732 and feature locator 734, e.g. see Figs. 8-9, paragraphs [0132]-[0133], [0193]-[0203]). Regarding claim 9, Veselov further discloses wherein the transmitting detector device, receiving detector device and/or the receiving detector network is configured as or comprises a neural network (e.g. see neural network, paragraphs [0128], [0197]). Regarding claim 10, Veselov further discloses wherein the monitoring system is configured for stationary monitoring of monitoring areas and/or monitoring in a vehicle (e.g. see applications such as in video surveillance, driver assistance, autonomous driving, etc., paragraphs [0127]-[0131]). Regarding claims 11-12, the claims recite analogous limitations to the claims above and are therefore rejected on the same premise . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 8, 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Veselov in view of US 2023/0164336 A1 (“Cricri”) . Regarding claim 8, although Veselov discloses the video encoder device and/or the content encoder device, it is noted Veselov differs from the present invention in that it fails to particularly disclose wherein the video encoder device and/or the content encoder device are configured as an autoencoder. Cricri however, teaches wherein the video encoder device and/or the content encoder device are configured as an autoencoder (e.g. see auto-encoder, paragraphs [0064]-[0067], [0093]). Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the references of Veselov and Cricri before him/her, to modify the Hybrid video and feature coding and decoding of Veselov with the teachings of Cricri in order to use learned compression with reduced number of iterations for fine-tuning the coding pipeline for one of the tasks and improve training of data coding pipeline. Regarding claim 13, although Vesselov discloses an image processing assembly according to claim 1 (e.g. see claim 1 mapping), it is noted Vesselov differs from the present invention in that it fails to particularly disclose a method, further comprising training with training data comprising image data and image features, wherein one training target is to reduce/minimize the quantity of encoded content data transmitted. Cricri however, teaches a method, further comprising training end-to-end with training data comprising image data and image features (e.g. see at least training end-to-end based on an auto-encoder structure to encode and decode video data using training data and based on loss, paragraphs [0064]-[0067], [0079], as well as, encode and decode extracted features, e.g. see at least paragraph [0087]), wherein one training target is to reduce/minimize the quantity of encoded content data transmitted (e.g. see at least an encoder transforms input data into a compressed representation suitable for storage or transmission, e.g. see at least paragraph [0002]). The motivation above in the rejection of claim 8 applies here. Regarding claim 14, Vesselov in view of Cricri further teaches the method further comprising reducing/minimizing the quantity of encoded image data transmitted (e.g. see, an encoder transforms input data into a compressed representation suitable for storage or transmission, e.g. see at least paragraph [0002]). The motivation above in the rejection of claim 8 applies here . Response to Arguments 07-37 AIA Applicant's arguments filed 5/4/26 have been fully considered but they are not persuasive. Applicant asserts on pages 9-10 of the Remarks that Vesselov does not disclose "having a first detection certainty" and "with a second detection certainty" because Vesselov, in paragraphs [0133]-[0134], is "silent regarding a first detection certainty (for example, a metric/parameter/threshold (confidence, probability, significance, etc.)) that is applied at a terminal detector and a second detection certainty applied at a receiver detector for feature extraction". However, the examiner respectfully disagrees. Regarding "having a first detection certainty", Vesselov, in at least paragraph [0132], discloses "features are extracted from the original videos at the terminal side before video compression and thus are more accurate", e.g. see Feature extractor 510 in Figs. 5-6. Firstly, it is noted that Vesselov discloses that "features are extracted". This implies that the extraction of features is expected to be performed with success or confidence (or with some accuracy that if a feature is present that the feature will be extracted); thus, the extraction of features has a probability of success from 0 to 1. For example, Fig. 15 of Vesselov discloses that a feature 1540 was successfully extracted; thus, the feature extractor extracts features with probability at least higher than 0 because it is able to extract feature 1540 especially if the extracted feature 1540 matches one of the predetermined ones stored in the database table 1550. Since the claim does not recite a specific certainty value, any certainty value from 0 to 1 meets the limitations in the broadest reasonable sense. Secondly, paragraph [0132] discloses that feature extraction is "more accurate" (and have "high quality", see paragraph [0133]) because they are "extracted from the original videos at the terminal side" (as opposed to extracting them from reconstructed video at the cloud side (server) resulting in less accuracy). Thus, Vesselov discloses that feature extraction in the terminal side has a higher certainty than in the server side. Again, since the claim does not recite a specific certainty value, any certainty value from 0 to 1 meets the limitations in the broadest reasonable sense. Regarding, "with a second detection certainty", Vesselov, in at least paragraph [0199] discloses "the face recognition may use one or more additional features, which may have not been extracted from the image patch by the CNN at the terminal side, in order to make the recognition more accurate… these additional features may be extracted by the CNN from the video frame at the cloud side." Thus, the CV analyzer 730 including feature analyzer 732 and feature locator 734 extracts additional features at the cloud side. This implies that the extraction of features, at the cloud side, is expected to be performed with success; thus, the extraction of features at the cloud side has a certainty value from 0 to 1. Further, since "additional features" are extracted, it also has to detect and/or verify (which is expected to be performed with success) that the features extracted are in fact additional features compared to the features that were already extracted from the terminal side, so when it detects a feature, it verifies whether the feature is additional or not. Vesselov also discloses in paragraph [0201] "[t]he analysis may include in addition or alternatively determining from one frame or multiple frames one or more features, which are different from image feature(s) used to locate the one or multiple frames." Again, it also has to detect and/or verify (which is expected to be performed with success) that the features extracted are additional features compared to the features that were previously extracted . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0343099 A1, Domanski et al., Video coding based on feature extraction and picture synthesis US 2023/0289964 A1, Narukiyo et al., Medical image processing apparatus, method of medical image processing, and non-transitory computer readable medium 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 FRANCIS G GEROLEO whose telephone number is (571)270-7206. The examiner can normally be reached M-F 7:00 am - 3:30 pm. 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, Anna M Momper can be reached at (571) 270-5788. 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. /Francis Geroleo/Primary Examiner, Art Unit 3619 Application/Control Number: 18/959,514 Page 2 Art Unit: 3619 Application/Control Number: 18/959,514 Page 3 Art Unit: 3619 Application/Control Number: 18/959,514 Page 4 Art Unit: 3619 Application/Control Number: 18/959,514 Page 5 Art Unit: 3619 Application/Control Number: 18/959,514 Page 6 Art Unit: 3619 Application/Control Number: 18/959,514 Page 7 Art Unit: 3619 Application/Control Number: 18/959,514 Page 8 Art Unit: 3619
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