CTNF 18/302,670 CTNF 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. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/22/2026 has been entered. Response to Arguments Applicant’s arguments, see Remarks page 7, filed 01/22/2026, with respect to the Objections of Claims 1 & 9 have been fully considered and are persuasive. The Objections of Claims 1 & 9 have been withdrawn. Applicant’s arguments, see Remarks pages 7-8, filed 01/22/2026, with respect to the Rejections of Claims 1, 3, and 9-18 under 35 U.S.C. 101 have been fully considered and are persuasive. The Rejections of Claims 1, 3, and 9-18 have been withdrawn. Applicant's arguments, see Remarks pages 8-10, filed 01/22/2026, with respect to the Rejections of amended Claims 1, 17, and 18 under 35 U.S.C. 103 have been fully considered but they are not persuasive. On pages 8-9 of Remarks, Applicant argues: PNG media_image1.png 717 598 media_image1.png Greyscale Examiner respectfully disagrees. 07-37-13 AIA In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller , 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Paragraph 0070-0071 of Sumi disclose: ““Unnecessary region presence evaluation” evaluates the degree of presence of an unnecessary region in an image actually arranged in a slot that is an image arrangement region…“Unnecessary region ” is an obstructive region that makes the image poor when displayed , for example, a region of an unrelated person who is passing by or a region of a user's finger unintentionally placed on the camera lens and shot ,” wherein the unnecessary region is an obstructive region present in the image, including, for example a finger placed on the camera lens. Paragraph 0223 of Sumi further discloses “…when only the weight of the unnecessary region presence evaluation is set to 1.0 or the like, and the weights of all the remaining items are set to 0, a layout result specialized to the unnecessary region presence evaluation can be obtained ,” wherein the weights of the image selection are adjusted in order to remove, or not select, images containing an unnecessary region, thus constituting the sorting of an image as “other”. However, Sumi does not disclose expressly: “ detect a first subject including a child in the cropped image; wherein an image in which a region of the second subject including the blocking object hides a part of a region of the first subject including the child is sorted as "other".” Paragraph 0185 of Steinberg discloses “ Based on the knowledge of the face and its pertinent features such as eyes, lips nose and ears, the software can either automatically or via a user interface that would recommend the next action to the user, crop portions of the image to reach such composition.” Wherein images may be cropped based on a detection of a face. However, Sumi in view of Steinberg does not disclose expressly “detect a first subject including a child in the cropped image; wherein an image in which a region of the second subject including the blocking object hides a part of a region of the first subject including the child is sorted as "other".” Section 1. Introduction of Das discloses “we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment targeted to jointly classify gender, age and race”; Section 4.1 Datasets: “BEFA challenge dataset2 is the official dataset of the related challenge. It contains 13431 test images. It has been annotated for age ( baby, child , teenagers, young, adults, middle age and senior), gender (male and female), and ethnicity (white, black, Asian and Indian),” wherein the MTCNN is able to classify images based on age, such as distinguishing baby and child subjects from adults or seniors. Thus, Sumi in view of Steinberg and Das, as further disclosed in the rejection of claim 1 under 35 U.S.C. 103, discloses the limitations “detect a first subject including a child in the cropped image; in a case where the first subject including the child is detected in the cropped image, detect a second subject including a blocking object in the cropped image, wherein the second subject including the blocking object includes either (a) an arm or a back of a person other than the child or (b) an object located in front of the child, or both (a) and (b); wherein an image in which a region of the second subject including the blocking object hides a part of a region of the first subject including the child is sorted as "other".” Therefore, the rejection of claim 1 under 35 U.S.C. 103 is maintained. As per claim(s) 17 and 18, arguments made in rejecting claim(s) 1 are analogous. 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, 3, 9-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sumi et al. (US-20140010459-A1) hereinafter referenced as Sumi, in view of Steinberg et al. (US-20090003708-A1) hereinafter referenced as Steinberg, and Das et al. (Mitigating Bias in Gender, Age and Ethnicity Classification: a Multi-Task Convolution Neural Network Approach) hereinafter referenced as Das . Regarding claim 1, Sumi discloses: An information processing apparatus comprising: one or more memories; and one or more processors in communication with the one or more memories (Sumi: 0048: “the CPU (Central Processing Unit) 100 executes an information processing method to be described in this embodiment in accordance with programs such as an application. The ROM 101 stores the programs to be executed by the CPU 100 . The RAM 102 provides a memory to temporarily store various kinds of information when the CPU 100 executes the programs .”) , wherein the one or more processors and the one or more memories are configured to: detect a first subject in an image (Sumi: 0210: “The person matching represents the matching ratio of a person designated for a slot to a person who exists in the image actually arranged in the slot. For example, assume that “father” and “son” are designated for a slot by the PersonGroup tag designated by XML. At this time, when the two persons are included in the image assigned to the slot, the person matching of the slot scores 100 . If only one of the persons is included, the matching scores 50. If neither person is included, the person matching scores 0 .”) ; in a case where the first subject is detected in the image, detect a second subject including a blocking object in the image, wherein the second subject including the blocking object includes either (a) an arm or a back of a person other than the subject or (b) an object located in front of the subject , or both (a) and (b) (Sumi: 0070-0071: ““Unnecessary region presence evaluation” evaluates the degree of presence of an unnecessary region in an image actually arranged in a slot that is an image arrangement region…“Unnecessary region ” is an obstructive region that makes the image poor when displayed , for example, a region of an unrelated person who is passing by or a region of a user's finger unintentionally placed on the camera lens and shot .”; 0209: “the evaluation value of the matching between an image and a slot is calculated using the above-described unnecessary region presence evaluation .”) ; and sort the cropped image as "recommended" (Sumi: 0204 & 0206: “based on the above-described image selection/arrangement/trimming criterion, temporary layouts are generated as many as possible… the L temporary layouts created above are evaluated using predetermined layout evaluation amounts. Table 3 shows a list of layout evaluation amounts according to this embodiment. As shown in Table 3, the layout evaluation amounts used in this embodiment can mainly be divided into three categories .”; 0208: “The second evaluation category is evaluation of matching between an image and a slot in a template (image/slot matching evaluation). This scores the degree of matching between an image and a slot.”; 0210: “Another example of the image/slot matching evaluation value is person matching . The person matching represents the matching ratio of a person designated for a slot to a person who exists in the image actually arranged in the slot. For example, assume that “father” and “son” are designated for a slot by the PersonGroup tag designated by XML . At this time, when the two persons are included in the image assigned to the slot, the person matching of the slot scores 100 . If only one of the persons is included, the matching scores 50 . If neither person is included, the person matching scores 0. That is, trimming that makes the designated target region (person) exists in the slot is evaluated highly.”; Wherein images are ranked and selected/sorted into a layout based on the presence of a particular subject within the image.) or "other", wherein an image in which a region of the second subject including the blocking object hides a part of a region of the first subject is sorted as "other" (Sumi: 0107: “the following method is used to obtain the unnecessary region presence evaluation value. It is determined whether the unnecessary region is included in the trimming region as indicated by 3409. If at least part of the unnecessary region is included, the evaluation value is set to 0. If the unnecessary region is not included at all, the evaluation value is set to 100.”; 0223: “…when only the weight of the unnecessary region presence evaluation is set to 1.0 or the like, and the weights of all the remaining items are set to 0, a layout result specialized to the unnecessary region presence evaluation can be obtained .”; Wherein images not containing the unnecessary region are selected) . Sumi does not disclose expressly: automatically capture a plurality of images in accordance with predetermined conditions; detect a face region of a person in each of the plurality of images; generate a cropped image by trimming around the face region, based on coordinates and a size of the face region of the person detected in each of the plurality of images; detect a first subject in the cropped image; in a case where the first subject is detected in the cropped image, detect a second subject including a blocking object in the cropped image; and sort the cropped image as "recommended" or "other". Steinberg discloses: automatically capture a plurality of images in accordance with predetermined conditions (Steinberg: 0140: “The method may be performed within any digital image capture device, which as, but not limited to digital still camera or digital video camera.”) ; detect a face region of a person in each of the plurality of images (Steinberg: 0209: “For example, such technology may be used for identification of faces in video sequences, particularly when the detection is to be performed in real-time.”; 0216: “ Faces may be detected in complex visual scenes and/or in a neural network based face detection system , particularly for digital image processing in accordance with preferred or alternative embodiments herein”) ; generate a cropped image by trimming around the face region, based on coordinates and a size of the face region of the person detected in each of the plurality of images (Steinberg: Figure 1a; 0161: “Block 150 describes the proposed composition such as cropping and zooming of an image to create a more pleasing composition.”; 0185: “ Based on the knowledge of the face and its pertinent features such as eyes, lips nose and ears, the software can either automatically or via a user interface that would recommend the next action to the user, crop portions of the image to reach such composition. For this specific image, the software will eliminate the bottom region 370 and the right portion 380. The process of re-compositioning a picture is subjective. In such case this invention will act as guidance or assistance to the user in determining the most pleasing option out of potentially a few. In such a case a plurality of proposed compositions can be displayed and offered to the user and the user will select one of them.”; Wherein the images are cropped in order to extract facial regions based on the locations and sizes of the facial regions). Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute the image databases used to retrieve the images disclosed in Sumi with the face detection and image enhancement algorithm processed digital video camera frames taught by Steinberg . The suggestion/motivation for doing so would have been “Most conventional techniques concentrate on face recognition, assuming that a region of an image containing a single face has already been extracted and will be provided as an input. Such techniques are unable to detect faces against complex backgrounds or when there are multiple occurrences in an image. For all of the image enhancement techniques introduced below and others as may be described herein or understood by those skilled in the art, it is desired to make use of the data obtained from face detection processes for… automatically improving or enhancing quality of digital images ” (Steinberg: 0019; Wherein the algorithms focus in on areas of interest and enhance their quality) . Further, one skilled in the art could have substituted the elements as described above by known methods with no change in their respective functions, and the substitution would have yielded nothing more than predictable results. Sumi in view of Steinberg does not disclose expressly: detect a first subject including a child in the cropped image; and sort the cropped image as "recommended" or "other", wherein an image in which a region of the second subject including the blocking object hides a part of a region of the first subject including the child is sorted as "other". Thus, Sumi in view of Steinberg does not disclose expressly: the detection of a first subject including a child in the cropped image. Das discloses: a Convolutional Neural Network able to classify face images based on gender, age, and race (Das: Section 1. Introduction: “we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment targeted to jointly classify gender, age and race”; Section 4.1 Datasets: “BEFA challenge dataset2 is the official dataset of the related challenge. It contains 13431 test images. It has been annotated for age ( baby, child , teenagers, young, adults, middle age and senior), gender (male and female), and ethnicity (white, black, Asian and Indian).”) . Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute the algorithms used to classify the target region disclosed in Sumi in view of Steinberg with the MTCNN taught by Das . The suggestion/motivation for doing so would have been “we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment towards classification of named soft biometrics, as well as towards mitigation of soft biometrics related bias .” (Das: Abstract; Wherein the MTCNN is configured towards preventing model bias) . Further, one skilled in the art could have substituted the elements as described above by known methods with no change in their respective functions, and the substitution would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Sumi in view of Steinberg with Das to obtain the invention as specified in claim 1. Regarding claim 3, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1, wherein in a case where the second subject including the blocking object is detected in the cropped image, the cropped image is sorted based on an area of the region of the second subject including the blocking object (Sumi: 0107: “As another embodiment, the ratio of an area (display area ratio) where the unnecessary region is displayed in the slot may be calculated , and the evaluation value may be decided in accordance with the display area ratio. The lower the display area ratio is, the higher the evaluation value is set… A threshold may be provided for the display area ratio , and the evaluation value may be set to 100 if the display area ratio is lower than the threshold or 0 if the display area ratio is equal to or higher than the threshold .”; Wherein if the image contains an unnecessary region greater than a threshold, the image is not selected) . Regarding claim 9, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1, wherein the cropped image is stored in a folder corresponding to a result of sorting the cropped image as "recommended" or "other" (Sumi: 0227: “…the layout identifier stored in LayoutList[0] is read out, and the temporary layout result corresponding to the identifier is read out from the secondary storage device 103 or RAM 102 . In the layout result , as described above, template information and image names and trimming information assigned to the respective slots existing in the template are set . In step S 605, the layout result is rendered based on these pieces of information using the rendering function of the OS operating on the information processing apparatus 115 and displayed, as indicated by a layout 2902 in FIG. 25.”; Wherein the information for the selected images being stored in the layout information constitutes the image being stored in a folder corresponding to its sorting result) . Regarding claim 10, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1, wherein an image in which the region of the face of a person is not detected (Steinberg: 0185: “Based on the knowledge of the face and its pertinent features such as eyes, lips nose and ears , the software can either automatically or via a user interface that would recommend the next action to the user, crop portions of the image to reach such composition ”; Wherein the face detection algorithm of Steinberg crops face images, therefore “sorting” edited images not containing faces as “other”.) , and an image in which the region of the face of a person is detected and the first subject including the child is not detected are sorted as "other" (Sumi: 0210: “The person matching represents the matching ratio of a person designated for a slot to a person who exists in the image actually arranged in the slot. For example, assume that “father” and “son” are designated for a slot by the PersonGroup tag designated by XML . At this time, when the two persons are included in the image assigned to the slot, the person matching of the slot scores 100 . If only one of the persons is included, the matching scores 50 . If neither person is included, the person matching scores 0. That is, trimming that makes the designated target region (person) exists in the slot is evaluated highly.”; 0223: “ when only the weight of the unnecessary region presence evaluation is set to 1.0 or the like, and the weights of all the remaining items are set to 0 , a layout result specialized to the unnecessary region presence evaluation can be obtained ”; Wherein the weights of the image evaluation may be adjusted to reject images not containing a first subject including a child.) . Regarding claim 11, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1, wherein at least one of an image in which the second subject including the blocking object is present (Sumi: 0107: “If at least part of the unnecessary region is included , the evaluation value is set to 0 . If the unnecessary region is not included at all, the evaluation value is set to 100.”; 0223: “ when only the weight of the unnecessary region presence evaluation is set to 1.0 or the like, and the weights of all the remaining items are set to 0, a layout result specialized to the unnecessary region presence evaluation can be obtained ”; Wherein the weights of the image evaluation may be adjusted to reject images containing an unnecessary region which constitutes the sorting of the edited images as “other”) , and an image in which the second subject including the blocking object is present in a state where an area of the region of the second subject including the blocking object is greater than or equal to a threshold, is sorted as "other" (Sumi: 0107-0108: “As another embodiment, the ratio of an area (display area ratio) where the unnecessary region is displayed in the slot may be calculated, and the evaluation value may be decided in accordance with the display area ratio…A threshold may be provided for the display area ratio, and the evaluation value may be set to 100 if the display area ratio is lower than the threshold or 0 if the display area ratio is equal to or higher than the threshold .”; Wherein the image is rejected if the area of the unnecessary region is greater than or equal to a threshold) . Regarding claim 12, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1, wherein the one or more processors and the one or more memories are further configured to classify the cropped image sorted as "recommended" into a group (Sumi: Figure 19; 0191: “Two weeks before the first birthday of “son”, theme decision, template selection, and image selection are performed .”; 0227: “…the layout identifier stored in LayoutList[0] is read out, and the temporary layout result corresponding to the identifier is read out from the secondary storage device 103 or RAM 102 . In the layout result , as described above, template information and image names and trimming information assigned to the respective slots existing in the template are set . In step S 605, the layout result is rendered based on these pieces of information using the rendering function of the OS operating on the information processing apparatus 115 and displayed, as indicated by a layout 2902 in FIG. 25.”; Wherein the sorted/selected images are grouped together by the layouts, which constitutes the classification of the cropped image sorted as "recommended" into a group) . Regarding claim 13, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1,wherein coordinates and a size of the region of the face of a person are output based on input of the image to a neural network model (Steinberg: 0216: “ Faces may be detected in complex visual scenes and/or in a neural network based face detection system ”; Wherein the face detection system determines the location and size of the face, which constitutes the output of the coordinates and size of the region) , and wherein the image is cropped based on the coordinates and the size of the region of the face of a person (Steinberg: 0185: “Based on the knowledge of the face and its pertinent features such as eyes, lips nose and ears , the software can either automatically or via a user interface that would recommend the next action to the user, crop portions of the image to reach such composition .”; Wherein the output of the face detection system is used to crop the face) . Regarding claim 14, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 13, wherein the image is cropped by being converted by contrast adjustment and color tone correction (Steinberg: 0190-0191: “By applying exposure correction to the face regions as illustrated in FIG. 4-e, such as passing the image through a lookup table 4-f, the effect is similar to the one of a fill flash that illuminated the foreground, but did not affect the background…Although exposure, or tone reproduction , may be the most preferred enhancement to simulate fill flash, other corrections may apply such as sharpening of the selected region , contrast enhancement, of even color correction .”) . Regarding claim 15, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1, wherein the first subject including the child or the second subject including the blocking object is detected based on input of the cropped image to a neural network model (Das: Section 1. Introduction: “we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment targeted to jointly classify gender, age and race”; Section 4.1 Datasets: “BEFA challenge dataset2 is the official dataset of the related challenge. It contains 13431 test images. It has been annotated for age (baby, child, teenagers, young, adults, middle age and senior), gender (male and female), and ethnicity (white, black, Asian and Indian).”; Wherein the first subject including a child is detected based on the MTCNN.) . Regarding claim 16, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1, wherein the image includes an image automatically captured based on a predetermined condition (Steinberg: 0140: “The method may be performed within any digital image capture device, which as, but not limited to digital still camera or digital video camera.”; 0209: “For example, such technology may be used for identification of faces in video sequences, particularly when the detection is to be performed in real-time.”) . As per claim(s) 17, arguments made in rejecting claim(s) 1 are analogous. As per claim(s) 18, arguments made in rejecting claim(s) 1 are analogous. In addition, paragraph 0008 of Sumi discloses a non-transitory computer-readable storage medium storing a program for causing a computer to perform a method for controlling an information processing apparatus 07-21-aia AIA Claim (s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sumi in view of Steinberg and Das, and further in view of Chen et al. (CN-114297428-A) hereinafter referenced as Chen . Regarding claim 19, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1. Sumi in view of Steinberg and Das does not disclose expressly: wherein a number of the cropped images in which the first subject including the child is detected is counted, and, in a case where the counted number is less than a predetermined number, the second subject including the blocking object is not detected in the cropped image. Thus, Sumi in view of Steinberg and Das does not disclose expressly: The counting of the cropped images based on the detection of a subject, wherein if the number of counted cropped images is less than a predetermined number, the blocking object, used to filter the cropped images based on a threshold, is not detected in any of the subject’s cropped image. Chen discloses: A method of generating a photo album from a video based on a classification of infant and toddler images (Chen: 0004: embodiments of the present invention provide a method, apparatus, device and medium for optimizing electronic photo albums for the classification of infant and toddler images, in order to solve the technical problem that the use of automatically generated electronic photo albums cannot meet the different aesthetic needs of users, resulting in reduced user stickiness) . Wherein once a set of target images is extracted from a processed video, based on classification of each individual image (Chen: 0106-0107: “Obtain the number of target images; Specifically, the number of target images includes all images in the electronic album corresponding to the target image group and the action category of the target image group.”) , the size of the target image set is compared to image thresholds. Wherein if the set size is greater than a first threshold, the target images in the set are deleted based on an image similarity threshold (Chen: 0111: “ a first and a second threshold are set for the number of images in the electronic photo album. When the number of images in the electronic photo album is between the first and second thresholds, images that meet the similarity requirements are deleted. It should be noted that when deleting images that meet the similarity requirements from the album, the number of deleted images is not less than the first threshold… When the number of images exceeds the second threshold , a combination of direct deletion and deletion based on similarity is used for image deletion .”) . However, if the set size is less than the first threshold, then a target electronic photo album is generated without performing similarity filtering (Chen: 0112: “It should be noted that when the number of images is less than the first threshold, the target electronic album is generated directly without similarity filtering, thus avoiding the problem of too few images in the electronic album affecting the user's viewing experience”). 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 performing image deletion methods on images in target image sets based on an image set size threshold taught by Chen on the cropped images disclosed by Sumi in view of Steinberg and Das . The suggestion/motivation for doing so would have been “It should be noted that when the number of images is less than the first threshold, the target electronic album is generated directly without similarity filtering, thus avoiding the problem of too few images in the electronic album affecting the user's viewing experience ” (Chen: 0112; Wherein the filtering based on the set size threshold serves to maximize the number of relevant images within a range deemed reasonable for users.) . 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 Sumi in view of Steinberg and Das with Chen to obtain the invention as specified in claim 19. Regarding claim 20, Sumi in view of Steinberg and Das discloses: The information processing apparatus according to claim 1. Sumi in view of Steinberg and Das does not disclose expressly: wherein a number of the cropped images in which the first subject including the child is detected is counted, and, in a case where the counted number is less than a predetermined number, the cropped image is not sorted as the "other". Thus, Sumi in view of Steinberg and Das does not disclose expressly: The counting of the cropped images based on the detection of a subject, wherein if the number of counted cropped images is less than a predetermined number, the cropped images are not sorted, or filtered, based on the blocking object. Chen discloses: A method of generating a photo album from a video based on a classification of infant and toddler images (Chen: 0004: embodiments of the present invention provide a method, apparatus, device and medium for optimizing electronic photo albums for the classification of infant and toddler images, in order to solve the technical problem that the use of automatically generated electronic photo albums cannot meet the different aesthetic needs of users, resulting in reduced user stickiness) . Wherein once a set of target images is extracted from a processed video, based on classification of each individual image (Chen: 0106-0107: “Obtain the number of target images; Specifically, the number of target images includes all images in the electronic album corresponding to the target image group and the action category of the target image group.”) , the size of the target image set is compared to image thresholds. Wherein if the set size is greater than a first threshold, the target images in the set are deleted based on an image similarity threshold (Chen: 0111: “ a first and a second threshold are set for the number of images in the electronic photo album. When the number of images in the electronic photo album is between the first and second thresholds, images that meet the similarity requirements are deleted. It should be noted that when deleting images that meet the similarity requirements from the album, the number of deleted images is not less than the first threshold… When the number of images exceeds the second threshold , a combination of direct deletion and deletion based on similarity is used for image deletion .”) . However, if the set size is less than the first threshold, then a target electronic photo album is generated without performing similarity filtering (Chen: 0112: “It should be noted that when the number of images is less than the first threshold, the target electronic album is generated directly without similarity filtering, thus avoiding the problem of too few images in the electronic album affecting the user's viewing experience”). 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 performing image deletion methods on images in target image sets based on an image set size threshold taught by Chen on the cropped images disclosed by Sumi in view of Steinberg and Das . The suggestion/motivation for doing so would have been “It should be noted that when the number of images is less than the first threshold, the target electronic album is generated directly without similarity filtering, thus avoiding the problem of too few images in the electronic album affecting the user's viewing experience ” (Chen: 0112; Wherein the filtering based on the set size threshold serves to maximize the number of relevant images within a range deemed reasonable for users.) . 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 Sumi in view of Steinberg and Das with Chen to obtain the invention as specified in claim 20. Conclusion 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: 18/302,670 Page 2 Art Unit: 2672 Application/Control Number: 18/302,670 Page 3 Art Unit: 2672 Application/Control Number: 18/302,670 Page 4 Art Unit: 2672 Application/Control Number: 18/302,670 Page 5 Art Unit: 2672 Application/Control Number: 18/302,670 Page 6 Art Unit: 2672 Application/Control Number: 18/302,670 Page 7 Art Unit: 2672 Application/Control Number: 18/302,670 Page 8 Art Unit: 2672 Application/Control Number: 18/302,670 Page 9 Art Unit: 2672 Application/Control Number: 18/302,670 Page 10 Art Unit: 2672 Application/Control Number: 18/302,670 Page 11 Art Unit: 2672 Application/Control Number: 18/302,670 Page 12 Art Unit: 2672 Application/Control Number: 18/302,670 Page 13 Art Unit: 2672 Application/Control Number: 18/302,670 Page 14 Art Unit: 2672 Application/Control Number: 18/302,670 Page 15 Art Unit: 2672 Application/Control Number: 18/302,670 Page 16 Art Unit: 2672 Application/Control Number: 18/302,670 Page 17 Art Unit: 2672 Application/Control Number: 18/302,670 Page 18 Art Unit: 2672 Application/Control Number: 18/302,670 Page 19 Art Unit: 2672
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