CTFR 18/146,328 CTFR 99677 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. Response to Arguments Applicant’s arguments, see Remarks pages 10-16, filed 05/05/2026, with respect to the rejections of claims 1-20 under 35 U.S.C. 101 have been fully considered and are persuasive. The rejections of claims 1-20 have been withdrawn. Applicant’s arguments, see Remarks pages 16-18, filed 05/05/2026, with respect to the rejection of amended claim(s) 1, 9, and 17 under 35 U.S.C. 102(a)(2), in regards to the Applicant’s statement of the 35 U.S.C. § 102(b)(2)(c) exception, have been fully considered and are moot in view of the new grounds of rejection (detailed in the rejections below) necessitated by Applicant’s amendment to the claim(s). 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) 1-5, 9-13, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jing et al. (GOOGLE IMAGE SWIRL, A LARGE-SCALE CONTENT-BASED IMAGE BROWSING SYSTEM) hereinafter referenced as Jing, in view of Hahn (imgcompare), Xiong et al. (US2023196831A1) hereinafter referenced as Xiong, and Yan et al. (Recolored Image Detection via a Deep Discriminative Model) hereinafter referenced as Yan . Regarding claim 1, Jing discloses: A computer-implemented method within a computer hardware system including a comparison engine (Jing: Abstract) , the computer-implemented method comprising: comparing, using the comparison engine, each image of a plurality of images in a dataset to remaining images of the plurality of images (Jing: Abstract: “the system extracts image content features such as color, shape, local features, face signatures and metadata from up to 1000 image results, and hierarchically clusters them to form an exemplar tree.”) , wherein each image of the plurality of images has a percentage of pixels identical to pixels of at least one image of the remaining images (Jing: Section: 1. Exemplar Tree: “This work organizes image search results into an exemplar tree. We begin organizing up to 1000 results of an image search query by building a pairwise similarity matrix among these images . The similarity computation is based on a combination of image features such as color , texture, local features, face signatures, and the metadata associated with the images.”; Wherein the similarity computation includes determination of pixel similarities.) ; generating, based on the comparing, a comparison score for each unique image-image pair of a plurality of unique image-image pairs of the plurality of images (Jing: Section: 1. Exemplar Tree: “This work organizes image search results into an exemplar tree. We begin organizing up to 1000 results of an image search query by building a pairwise similarity matrix among these images . The similarity computation is based on a combination of image features such as color, texture, local features, face signatures, and the metadata associated with the images .”) ; clustering the plurality of images into a plurality of image clusters based upon the comparison score of each unique image-image pair of the plurality of unique image-image pairs scores (Jing: Section: 1. Exemplar Tree: “We then perform clustering to partition the search results into hierarchical clusters, each associated with a representative, or exemplar, image.”) . Jing does not disclose expressly: generating, based on the comparing, a difference image for each unique image-image pair of a plurality of unique image-image pairs of the plurality of images; converting the difference image of each unique image-image pair of the plurality of unique image-image pairs into a grayscale difference image and a black-and-white difference image ; generating a comparison score for each unique image-image pair of the plurality of unique image-image pairs based on the black-and-white difference image and the grayscale difference image . Hahn discloses: generating, a difference image for a unique image-image pair; converting the difference image into a grayscale difference image and a black-and-white difference image; and generating a comparison score for the unique image-image pair based on the black-and-white difference image and the grayscale difference image (Hahn: “Algorithm Get diff image using Pillow's ImageChops.difference Convert the diff image to greyscale Sum up all diff pixels by summing up their histogram values Calculate a percentage based on a black and white image of the same size”) . Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the comparison score taught by Hahn into the similarity computation for each unique image pair disclosed by Jing . The suggestion/motivation for doing so would have been “Calculates the difference between images in percent, checks equality with optional fuzzyness” (Hahn; Wherein the algorithm allows for flexibility/tolerance in difference calculation.) . Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Jing in view of Hahn does not disclose expressly: selecting an image cluster of the plurality of image clusters; and performing a data processing operation on the dataset based upon the selecting of the image cluster. Xiong discloses: selecting an image cluster of a plurality of image clusters; and performing a data processing operation on the dataset based upon the selecting of the image cluster (Xiong: 0048: “group selection sub-stage 222 may begin with group analysis of the initial groupings from initial grouping sub-stage 212 . For example, a set of group selection criteria may be applied to the initial groupings and related metrics to determine the most relevant groups from among the initial groups …In some embodiments, user inputs may be received to assist in group analysis . For example, users may be provided with an interface that displays one or more images from a group and allows the user to tag the group with a name, relationship (child, parent, friend, etc.), social group (family, work, soccer team, etc.), priority, and/or other metadata tags to assist in group selection.”; 0051: “At block 230 , a machine learning-based classifier algorithm is selected and trained using the images selected at block 228 for each group selected at block 226 . For example, an image classifier based on a machine learning algorithm (including neural network models), such as support vector machine (SVM), naive Bayes, or K-nearest neighbours (KNN) algorithms targeting the embeddings and/or other metadata related to the face images in the set of training images for the group”) . Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate the machine learning-based classifier algorithm training based on image group selection taught by Xiong after the clustering disclosed by Jing in view of Hahn . The suggestion/motivation for doing so would have been “user input may be used at various stages to assist with selection, identification, and prioritization of groups and/or image content or features for making grouping and ranking decisions. Similarly, image groups, image group classifiers, grouping decisions, and ranking decisions may be validated to enable tuning and retraining as needed.” (Xiong: 0044) . Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Jing in view of Hahn and Xiong does not disclose expressly: selecting an image cluster of the plurality of image clusters as representing an original image . Yan discloses: a method for classifying an image as either recolored or natural (Yan: Abstract: “In this paper, we propose a trainable end-to-end system for distinguishing recolored images from natural images. The proposed network takes the original image and two derived inputs based on illumination consistency and inter-channel correlation of the original input into consideration and outputs the probability that it is recolored.”; Wherein a natural classification constitutes an unedited/original image classification.) . Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the CNN model for detection of recolored images taught by Yan into the group selection criteria disclosed by Jing in view of Hahn and Xiong by classifying and selecting groups based on the one or more group representative images . The suggestion/motivation for doing so would have been “Unfortunately, with the development of low-cost and high-resolution digital cameras and sophisticated photo editing softwares, it is simple to perform image manipulations and the detection of forged images is much difficult through human vision.” (Yan: Section: I. INTRODUCTION; Wherein the model serves to assist users in detecting manipulated images) . Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Jing in view of Hahn and Xiong with Yan to obtain the invention as specified in claim 1. Regarding claim 2 , Jing in view of Hahn, Xiong, and Yan discloses: The computer-implemented method of claim 1, wherein the selecting of the image cluster is performed by a machine learning engine (Yan: Abstract: “The proposed network takes the original image and two derived inputs based on illumination consistency and inter-channel correlation of the original input into consideration and outputs the probability that it is recolored.”; Wherein the groups are selected based on the one or more group representative images disclosed by Xiong) . Regarding claim 3 , Jing in view of Hahn, Xiong, and Yan discloses: The computer-implemented method of claim 1, wherein the selecting of the image cluster includes providing a graphical user interface configured to: visually display a set of images respectively representing each image cluster of the plurality of image clusters, wherein the plurality of images includes the set of images (Jing: Abstract: “A dynamic web-based user interface allows the user to navigate this hierarchy, allowing fast and interactive browsing. The exemplars of each cluster provide a comprehensive visual overview of the query results, and allow the user to quickly navigate to the images of interest.”) ; and receive a selection indicating the image cluster of the plurality of image clusters as representing the original image (Xiong: 0048: “user inputs may be received to assist in group analysis. For example, users may be provided with an interface that displays one or more images from a group and allows the user to tag the group with a name, relationship (child, parent, friend, etc.), social group (family, work, soccer team, etc.), priority, and/or other metadata tags to assist in group selection .”; Wherein the user’s input is used for identification of original images in connection with the machine learning model disclosed by Yan) . Regarding claim 4 , Jing in view of Hahn, Xiong, and Yan discloses: The computer-implemented method of claim 3, wherein the graphical user interface is further configured to present the set of images as a radial cluster that includes the plurality of image clusters (Jing: Section: 2. USER INTERFACE: “ After hierarchical clustering has been performed , the results of an image search query are organized in the structure of a tree. A number of options exist for how to present such a tree to the user… we used radial layout in which each layer of the tree is arranged radially around its parent , see Figure 1. When the user selects a branch of the tree to explore, it is separated from the parent and expanded, while the parent is shrunk to make space, see Figures 2.”) . Regarding claim 5 , Jing in view of Hahn, Xiong, and Yan discloses: The computer-implemented method of claim 3, wherein the graphical user interface is further configured to display one or more differences between respective images associated with a pair of selected image clusters of the plurality of image clusters (Jing: Abstract: “A dynamic web-based user interface allows the user to navigate this hierarchy, allowing fast and interactive browsing. The exemplars of each cluster provide a comprehensive visual overview of the query results, and allow the user to quickly navigate to the images of interest .”; Section: 1. EXEMPLAR TREE: “We then perform clustering to partition the search results into hierarchical clusters, each associated with a representative, or exemplar, image.”; Wherein the exemplars of each cluster allows for a user to navigate the tree by comparing the displayed exemplar images and their feature differences.) . As per claim(s) 9, arguments made in rejecting claim(s) 1 are analogous. In addition, the Abstract of Jing discloses “We demonstrate the first large-scale image browsing system applied to 200,000 popular queries which utilizes image content to organize image search results. Given a query, the system extracts image content features such as color, shape, local features, face signatures and metadata from up to 1000 image results, and hierarchically clusters them to form an exemplar tree” , wherein the image browsing system implies the claim 9 limitations: “A computer hardware system, comprising: a hardware processor including a comparison engine.” As per claim(s) 10, arguments made in rejecting claim(s) 2 are analogous. As per claim(s) 11, arguments made in rejecting claim(s) 3 are analogous. As per claim(s) 12, arguments made in rejecting claim(s) 4 are analogous. As per claim(s) 13, arguments made in rejecting claim(s) 5 are analogous. As per claim(s) 17, arguments made in rejecting claim(s) 1 are analogous. In addition, the Abstract of Jing discloses “We demonstrate the first large-scale image browsing system applied to 200,000 popular queries which utilizes image content to organize image search results. Given a query, the system extracts image content features such as color, shape, local features, face signatures and metadata from up to 1000 image results, and hierarchically clusters them to form an exemplar tree” , wherein the image browsing system implies the claim 17 limitations: “A computer program product, comprising: a computer readable storage medium having stored therein program code, the program code, which when executed by a computer hardware system including a comparison engine, cause the computer hardware system to perform” . As per claim(s) 18, arguments made in rejecting claim(s) 3 are analogous . 07-21-aia AIA Claim (s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jing in view of Hahn, Xiong, and Yan, and further in view of Woo (KR-102354826-B1) . Regarding claim 6, Jing in view of Hahn, Xiong, and Yan discloses: The computer-implemented method of claim 1. Jing in view of Hahn, Xiong, and Yan does not disclose expressly: wherein the data processing operation includes deleting one or more images of the plurality of images in the dataset, the one or more images not corresponding to the selected image cluster one of the plurality of image clusters as representing the original image. Woo discloses: a method for searching and displaying registered dental clinical images (Woo: 0072: “Referring to FIGS. 1 and 8, the processing procedure for searching and displaying the registered dental clinical image is described as follows.”) . Wherein a database is queried based on search keywords in order to retrieved the dental clinical images (Woo: 0074: “In steps S210 and S215, the cloud storage device (150) searches for previously stored dental clinical images in response to the search keyword, and searches the clinical photo management DB (50) as illustrated in FIG. 4 using the search keyword, and transmits the result to the clinical image search device (170).”) . The retrieved dental clinical images are then analyzed in order to determine whether the images have been forged or altered (Woo: 0077: “and in steps S225 and S230, the blockchain server (160) verifies whether the specific dental clinical image is forged or altered and then transmits the result.”) , wherein if a retrieved dental clinical image has been determined to have been forged or altered, the image is deleted and replaced by an image corresponding to the original (Woo: 0081-0082: “the image correction device (120) responds by deleting the falsified/modified dental clinical image and regenerating the dental clinical image using the corresponding dental original photograph . [And, in step S260, the image correction device (120) re-registers the regenerated image in the cloud storage device (150),”) . Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the known technique of deleting and replacing stored falsified/modified images taught by Woo by deleting the stored non-training data corresponding to tampered/recolored images disclosed by Jing in view of Hahn, Xiong, and Yan in order to retrieve images assigned as original . The suggestion/motivation for doing so would have been “By doing so, the present invention… can improve the reliability of dental clinical images by deleting and regenerating them using a normal method if a registered dental clinical image is falsified/modified” (Woo: 0084). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Jing in view of Hahn, Xiong, and Yan with Woo to obtain the invention as specified in claim 6. As per claim(s) 14, arguments made in rejecting claim(s) 6 are analogous . 07-21-aia AIA Claim (s) 7-8, 15-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jing in view of Hahn, Xiong, and Yan, and further in view of Bharati et al. (Transformation-Aware Embeddings for Image Provenance) hereinafter referenced as Bharati . Regarding claim 7, Jing in view of Hahn, Xiong, and Yan discloses: The computer-implemented method of claim 1. Jing in view of Hahn, Xiong, and Yan does not disclose expressly:, wherein the data processing operation includes tagging one or more images of the plurality of images in the dataset, the one or more images not corresponding to the selected image cluster of the plurality of image clusters as representing the original image. Bharati discloses: tagging one or more images of the plurality of images in the dataset, the one or more images not corresponding to a selected image as representing an original image (Bharati: Fig. 3: “Proposed deep learning framework to compute provenance chains. Given, as Input, a set of near-duplicate images where each one is obtained from a transformation on top of the other , the proposed approach learns an image embedding space where less transformed (positive) images lie closer to the original source (anchor) than more transformed versions (weak positive). Unrelated images (negative) must lie in the space farther to the anchor than the positive and weak positive ones. Function d(., .) expresses the distance between two images in the learned space. We use a novel Edit Sequence Loss (ESL) in the learning process. The desired Output is an ordered set that represents the process of transforming one image into the other .”; Wherein the ordered set, which is associated to all transformed images, corresponds to metadata tagging/linking transformed images to its original image.) . Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the image based clustering system taught by Jing in view of Hahn, Xiong, and Yan by integrating the deep learning framework taught by Bharati in order to link tampered/recolored images to the original images they are associated to . The suggestion/motivation for doing so would have been “Given multiple variants for any given image, an important question is: can we find the original? Not necessarily just for maliciously manipulated images, this question is valid for a number of circumstances where image manipulation is present. We propose a framework that helps to determine the sequence of image transformations to trace the provenance of the content .” (Bharati: Fig. 1.; Wherein the determination of the image provenance allows for greater understanding of the composition of each image and allows for a tracing of an image based on its provenance relationships) . Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Jing in view of Hahn, Xiong, and Yan with Bharati to obtain the invention as specified in claim 7. Regarding claim 8, Jing in view of Hahn, Xiong, Yan, and Bharati discloses: The computer-implemented method of claim 7, wherein the tagging of the one or more images includes adding a link to the original image within metadata associated with each image of the one or more images in the dataset (Bharati: Fig. 3: “Proposed deep learning framework to compute provenance chains. Given, as Input, a set of near-duplicate images where each one is obtained from a transformation on top of the other , the proposed approach learns an image embedding space where less transformed (positive) images lie closer to the original source (anchor) than more transformed versions (weak positive). Unrelated images (negative) must lie in the space farther to the anchor than the positive and weak positive ones. Function d(., .) expresses the distance between two images in the learned space. We use a novel Edit Sequence Loss (ESL) in the learning process. The desired Output is an ordered set that represents the process of transforming one image into the other .”; Wherein the ordered set, which is associated to all transformed images, corresponds to metadata tagging/linking transformed images to its original image.) . As per claim(s) 15, arguments made in rejecting claim(s) 7 are analogous. As per claim(s) 16, arguments made in rejecting claim(s) 8 are analogous. As per claim(s) 19, arguments made in rejecting claim(s) 7 are analogous. As per claim(s) 20, arguments made in rejecting claim(s) 8 are analogous. Conclusion 07-40 AIA 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 ANTHONY J RODRIGUEZ whose telephone number is (703)756-5821. The examiner can normally be reached Monday-Friday 10am-7pm. 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, Sumati Lefkowitz can be reached at (571) 272-3638. 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. /ANTHONY J RODRIGUEZ/Examiner, Art Unit 2672 /SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672 Application/Control Number: 18/146,328 Page 2 Art Unit: 2672 Application/Control Number: 18/146,328 Page 3 Art Unit: 2672 Application/Control Number: 18/146,328 Page 4 Art Unit: 2672 Application/Control Number: 18/146,328 Page 5 Art Unit: 2672 Application/Control Number: 18/146,328 Page 6 Art Unit: 2672 Application/Control Number: 18/146,328 Page 7 Art Unit: 2672 Application/Control Number: 18/146,328 Page 8 Art Unit: 2672 Application/Control Number: 18/146,328 Page 9 Art Unit: 2672 Application/Control Number: 18/146,328 Page 10 Art Unit: 2672 Application/Control Number: 18/146,328 Page 11 Art Unit: 2672 Application/Control Number: 18/146,328 Page 12 Art Unit: 2672 Application/Control Number: 18/146,328 Page 13 Art Unit: 2672 Application/Control Number: 18/146,328 Page 14 Art Unit: 2672
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