CTNF 18/844,923 CTNF 101698 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. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Claim 29 recites, in part, “An apparatus for image processing, comprising: means for downsampling”, “means for detecting”, “means for configuring”, “means for adjusting”, and “means for tuning”. The broadest reasonable interpretation of these claim elements is limited by the description in the specification, specifically paragraph [0100], which recites in part “In configurations, a method or an apparatus for graphics processing is provided. The apparatus may be a GPU, a graphics processor, or some other processor that may perform graphics processing. In aspects, the apparatus may be the processing unit 120 within the device 104, or may be some other hardware within the device 104 or another device. The apparatus, e.g., processing unit 120, may include means for downsampling … means for detecting … means for configuring … means for adjusting … means for tuning … means for dividing … means for analyzing … means for obtaining … means for transmitting … and means for storing”. Therefore, “means for” as recited in claim 29 will be interpreted as “a GPU, a graphics processor, or some other processor that may perform graphics processing” and equivalents thereof. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 30 is rejected under 35 U.S.C. 101 as it recites an article of manufacture comprising one or more computer readable media. The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. 101 as covering non-statutory subject matter. The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. 101 as covering both non-statutory subject matter and statutory subject matter. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. 101 by adding the limitation "non-transitory" to the claim. Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The applicant’s specification in paragraph [0107] states “Computer-readable media may include computer data storage media or communication media including any medium that facilitates transfer of a computer program from one place to another. In this manner, computer-readable media generally may correspond to (1) tangible computer- readable storage media, which is non-transitory or (2) a communication medium such as a signal or carrier wave.”. Since the applicant’s disclosure states that “computer-readable media generally may correspond to … a communication medium such as a signal or carrier wave”, it could be a signal. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1-5, 7-11, 13-20, 22-24, and 26-30 are rejected under 35 U.S.C. 103 as being unpatentable over Pao et al (US 12591950 B2, hereinafter Pao) as modified by Hui et al ( Learning Image-Adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-Time , hereinafter Hui) . Regarding claim 1, Pao teaches an apparatus for image processing, comprising: a memory; and at least one processor coupled to the memory (Col 24 Line 24-27 “the components of the salient object segmentation system 106 can include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices”) and configured to: downsample an input image including a plurality of pixels, the downsampled input image corresponding to a downsampled image including a plurality of downsampled pixels (Col 3 Line 55-59 “the salient object segmentation system generates the reduced-resolution digital image by down-sampling the input digital image to have a lower pixel resolution than the input digital image.”) ; detect at least one salient object in the downsampled image, the at least one salient object including one or more downsampled pixels of the plurality of downsampled pixels (Col 11 Line 44-49 “For instance, the salient object segmentation system 106 can apply a convolutional neural network trained to detect or otherwise identify a salient object portrayed therein and further determine which pixels of the reduced-resolution digital image correspond to the salient object”) ; configure a saliency map based on the at least one salient object in the downsampled image (Col 9 Line 41-45 “Specifically, the salient object segmentation system 106 can generate a segmentation map that includes a mapping of pixels corresponding to one or more salient objects identified within the reduced-resolution digital image 208”) ; adjust, based on the saliency map, a color value of at least one downsampled pixel of the one or more downsampled pixels in the at least one salient object (Col 19 Line 50-53 “Moreover, the salient object segmentation system 106 can modify the appearance (e.g., color, hue, contrast, or brightness), size, orientation, or alignment of the salient object”) Pao teaches adjusting the color of a salient object (Col 9 Line 41-45 “Specifically, the salient object segmentation system 106 can generate a segmentation map that includes a mapping of pixels corresponding to one or more salient objects identified within the reduced-resolution digital image 208”, Col 19 Line 50-53 “Moreover, the salient object segmentation system 106 can modify the appearance (e.g., color, hue, contrast, or brightness), size, orientation, or alignment of the salient object”), but fails to explicitly teach tune the input image based on the adjusted color value of the at least one downsampled pixel. In related field of endeavor, Hui teaches tune the input image based on the adjusted color value of the at least one downsampled pixel (Hui Fig. 1, Page 2509, Paragraph 3 “The basis 3D LUTs ensure that the color transform space from input to output images can be well covered, while the CNN model predicts content dependent weights based on the down-sampled input image. The predicted weights are used to fuse the basis 3D LUTs into an image-adaptive one, which is then used to transform the source image.”) PNG media_image1.png 285 710 media_image1.png Greyscale It would have been obvious to one of ordinary skill in the art to have modified Pao to include tune the input image based on the adjusted color value of the at least one downsampled pixel as taught by Hui. Doing so would provide a fast means of photo enhancement (Page 2 Paragraph 3 “LUT based photo enhancement is very fast because only the trilinear interpolation is involved and this operation can be easily parallelized using GPU”). Regarding claim 2, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the at least one processor is further configured to: divide the at least one salient object into a plurality of salient object portions based on a mask (Col 4 Line 65 – Col 5 Line 4“the salient object segmentation system can iteratively apply the neural network to reduced-resolution digital images corresponding to any number of identified regions within the input digital image. As an example, in one or more embodiments, the salient object segmentation system can identify sub-regions of the digital image”, where sub-regions correspond to plurality of salient object portions) , wherein the mask is associated with the saliency map (Col 7 Line 9-12 “In particular, a segmentation map includes a representation of a digital image (or reduced-resolution digital image) in which pixels that reflect a salient object are indicated (e.g., a heat map, an object mask)”) , and the at least one salient object is divided to adjust the color value of the at least one downsampled pixel (Col 19 Line 50-53 “Moreover, the salient object segmentation system 106 can modify the appearance (e.g., color, hue, contrast, or brightness), size, orientation, or alignment of the salient object”) . Regarding claim 3, Pao as modified by Hui teaches the apparatus of claim 2 . Pao further teaches wherein the at least one salient object is divided into the plurality of salient object portions further based on a neural network (NN) or a machine learning (ML) model (Col 5 Line 3-9 “the salient object segmentation system can identify sub-regions of the digital image (e.g., sub-regions with a lower confidence, accuracy, or granularity). The salient object segmentation system can generate refined segmentation maps by applying the neural network to these sub-regions (i.e., reduced-resolution digital images portraying the sub-regions).”) . Regarding claim 4, Pao as modified by Hui teaches the apparatus of claim 1. Hui further teaches wherein the color value of the at least one downsampled pixel is adjusted based on one or more of: at least one three dimensional (3D) lookup table (LUT) or one or more weights for the color value of the plurality of downsampled pixels (Hui figure 1 includes 3 basis LUTs, 3 weights, and an image-adaptive LUT, Page 2509, Paragraph 3 “The basis 3D LUTs ensure that the color transform space from input to output images can be well covered, while the CNN model predicts content dependent weights based on the down-sampled input image. The predicted weights are used to fuse the basis 3D LUTs into an image-adaptive one, which is then used to transform the source image.”) . PNG media_image1.png 285 710 media_image1.png Greyscale It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Pao and Hui to include wherein the color value of the at least one downsampled pixel is adjusted based on one or more of: at least one three dimensional (3D) lookup table (LUT) or one or more weights for the color value of the plurality of downsampled pixels as taught by Hui. Doing so would provide a fast means of photo enhancement (Page 2 Paragraph 3 “LUT based photo enhancement is very fast because only the trilinear interpolation is involved and this operation can be easily parallelized using GPU”). Regarding claim 5, Pao as modified by Hui teaches the apparatus of claim 4. Hui further teaches wherein the at least one 3D LUT includes a plurality of 3D LUTs and the one or more weights include a plurality of weights, and wherein the color value of the at least one downsampled pixel is adjusted based on a product of at least one of the plurality of 3D LUTs and at least one of the plurality of weights (Fig. 1, Page 2509, Paragraph 3 “The basis 3D LUTs ensure that the color transform space from input to output images can be well covered, while the CNN model predicts content dependent weights based on the down-sampled input image. The predicted weights are used to fuse the basis 3D LUTs into an image-adaptive one, which is then used to transform the source image.”) It would have been obvious to one of ordinary skill in the art to have further modified Pao and Hui to include wherein the at least one 3D LUT includes a plurality of 3D LUTs and the one or more weights include a plurality of weights, and wherein the color value of the at least one downsampled pixel is adjusted based on a product of at least one of the plurality of 3D LUTs and at least one of the plurality of weights. Doing so would provide a fast means of photo enhancement (Page 2 Paragraph 3 “LUT based photo enhancement is very fast because only the trilinear interpolation is involved and this operation can be easily parallelized using GPU”). Regarding claim 7, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the at least one salient object is detected with a saliency detection algorithm (Col 11 Line 44-49 “For instance, the salient object segmentation system 106 can apply a convolutional neural network trained to detect or otherwise identify a salient object portrayed therein and further determine which pixels of the reduced-resolution digital image correspond to the salient object”, Col 6 Line 54-55 “the term “neural network” includes one or more machine learning algorithms”) . Regarding claim 8, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the at least one salient object is detected based on at least one saliency dataset of a ground truth of pixels (Col 11 Line 50-53 “the salient object segmentation system 106 trains the convolutional neural network to identify pixels corresponding to a salient object in a digital image”, Col 12 Line 4-18 “the object segmentation system 106 can train the convolutional neural network based on a plurality of training images and associated segmentation maps (or identification of pixels) for objects portrayed therein. Indeed, by analyzing training images and associated segmentation maps that are known to include accurate identifications of pixels corresponding to objects portrayed therein, the object segmentation system 106 can fine-tune the convolutional neural network to accurately identify objects and identify pixels therein. In particular, based the training images and segmentation maps, the object segmentation system 106 can train the convolutional neural network to identify objects, identify pixels within the object, identify pixels around a boundary of objects and further determine which pixels correspond to edges of identified objects” where the training images and associated identification of pixels correspond to a saliency dataset of a ground truth of pixels.) Regarding claim 9, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein to detect the at least one salient object in the downsampled image, the at least one processor is configured to: process the plurality of downsampled pixels in the downsampled image (Col 5 Line 41-45 “by applying the convolutional network to the reduced-resolution digital images and further processing (e.g., post-processing) the resulting segmentation map, the salient object segmentation system facilitates identifying pixels of a salient object”). Regarding claim 10, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the input image is a high resolution image and the downsampled image is a low resolution image. (Col 9 Line 26-29 “the salient object segmentation system 106 generates a reduced-resolution digital image 208 having a lower resolution (e.g., 320×320 pixels) than the input digital image 204 .”, Col 9 Line 66 – Col 10 Line 2 “where the region of the input digital image 204 may include 1000×1000 pixels, the reduced-resolution digital image of the region 212 may include 320×320 pixels”) Regarding claim 11, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches that a reduced-resolution digital image has a lower resolution than the input image, but fails to explicitly teach wherein the high resolution image includes greater than 300 pixels per inch (ppi) and the low resolution image includes less than 300 ppi , but it would be obvious to one of ordinary skill in the art that a high resolution includes greater than 300 ppi, and a low resolution includes less than 300 ppi. Regarding claim 13, Pao as modified by Hui teaches the apparatus of claim 1. Hui further teaches wherein to tune the input image based on the adjusted color value of the at least one downsampled pixel, the at least one processor is configured to: enhance the input image based on the adjusted color value of the at least one downsampled pixel (Hui Fig. 1, Page 2509, Paragraph 3 “The basis 3D LUTs ensure that the color transform space from input to output images can be well covered, while the CNN model predicts content dependent weights based on the down-sampled input image. The predicted weights are used to fuse the basis 3D LUTs into an image-adaptive one, which is then used to transform the source image.”) PNG media_image1.png 285 710 media_image1.png Greyscale It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Pao and Hui to include wherein to tune the input image based on the adjusted color value of the at least one downsampled pixel, the at least one processor is configured to: enhance the input image based on the adjusted color value of the at least one downsampled pixel as taught by Hui. Doing so would provide a fast means of photo enhancement (Page 2 Paragraph 3 “LUT based photo enhancement is very fast because only the trilinear interpolation is involved and this operation can be easily parallelized using GPU”). Regarding claim 14, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the saliency map is configured based on a portion of the at least one salient object, such that the saliency map is configured based on less than all of the at least one salient object (Claim 1 “determine whether to perform additional refinement to the segmentation map of the region of the digital image comprising the single salient object; generate, from a sub-region of the region of the digital image and based on determining to perform additional refinement to the segmentation map, an additional segmentation map of the sub-region of the region having the second resolution”, where a segmentation map of a sub region corresponds to a saliency map based on less than all of the salient object.) Regarding claim 15, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the at least one processor is further configured to: obtain the input image including the plurality of pixels prior to downsampling the input image (Fig 3A, Col 3 Line 55-58 “the salient object segmentation system generates the reduced-resolution digital image by down-sampling the input digital image to have a lower pixel resolution than the input digital image”, Col 11 Line 8-10 “the salient object segmentation system 106 performs an act 302 of receiving an input digital image”, Col 11 Line 18-20 “the salient object segmentation system 106 can perform an act 304 of generating a reduced-resolution digital image”) , wherein the input image is associated with the image processing or display processing (Col 10 Line 61-63 “edit or otherwise modify a display of the input image on a graphical user interface of the client device 108”) . PNG media_image2.png 711 382 media_image2.png Greyscale Regarding claim 16, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the at least one processor is further configured to: transmit the tuned input image to a display or a panel (Col 10 Line 61-63 “edit or otherwise modify a display of the input image on a graphical user interface of the client device 108”) ; or store the tuned input image in a first memory or a buffer (Col 9 Line 16-19 “The input digital image 204 may refer to an original digital image or copy (e.g., a high-resolution copy) of an original digital image stored on the client device 108 and/or server device(s) 102 .”) . Regarding claim 17, Pao as modified by Hui teaches the apparatus of claim 1. Pao further teaches wherein the apparatus is a wireless communication device, further comprising at least one of an antenna or a transceiver coupled to the at least one processor (Col 7 Line 53-55 “the client device 108 may include a mobile device such as a mobile telephone, a smartphone, a PDA, a tablet, or a laptop”) . Regarding claim 18, the method claim 18 is similar in scope to the apparatus claim 1, and is rejected under similar rationale. Regarding claim 19, the method claim 19 is similar in scope to the apparatus claim 3, and is rejected under similar rationale. Regarding claim 20, the method claim 20 is similar in scope to the apparatus claim 5, and is rejected under similar rationale. Regarding claim 22, Pao as modified by Hui teach the method of claim 18. Hui further teaches wherein the at least one salient object is detected with a saliency detection algorithm (Col 11 Line 44-49 “For instance, the salient object segmentation system 106 can apply a convolutional neural network trained to detect or otherwise identify a salient object portrayed therein and further determine which pixels of the reduced-resolution digital image correspond to the salient object”, Col 6 Line 54-55 “the term “neural network” includes one or more machine learning algorithms”) or the at least one salient object is detected based on at least one saliency dataset of a ground truth of pixels (Col 11 Line 50-53 “the salient object segmentation system 106 trains the convolutional neural network to identify pixels corresponding to a salient object in a digital image”, Col 12 Line 4-18 “the object segmentation system 106 can train the convolutional neural network based on a plurality of training images and associated segmentation maps (or identification of pixels) for objects portrayed therein. Indeed, by analyzing training images and associated segmentation maps that are known to include accurate identifications of pixels corresponding to objects portrayed therein, the object segmentation system 106 can fine-tune the convolutional neural network to accurately identify objects and identify pixels therein. In particular, based the training images and segmentation maps, the object segmentation system 106 can train the convolutional neural network to identify objects, identify pixels within the object, identify pixels around a boundary of objects and further determine which pixels correspond to edges of identified objects.” Where the training images and associated identification of pixels correspond to a saliency dataset of a ground truth of pixels.) . Regarding claim 23, the method claim 23 is similar in scope to the apparatus claim 9, and is rejected under similar rationale. Regarding claim 24, the method claim 24 is similar in scope to the apparatus claim 11, and is rejected under similar rationale. Regarding claim 26, the method claim 26 is similar in scope to the apparatus claim 13, and is rejected under similar rationale. Regarding claim 27, the method claim 27 is similar in scope to the apparatus claim 15, and is rejected under similar rationale. Regarding claim 28, the method claim 28 is similar in scope to the apparatus claim 16, and is rejected under similar rationale. Regarding claim 29, the apparatus claim 29 is similar in scope to the apparatus claim 1, and is rejected under similar rationale. Regarding claim 30, the computer-readable medium claim 30 is similar in scope to the apparatus claim 1, and is rejected under similar rationale . 07-22-aia AIA Claim s 6, 12, 21 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Pao and Hui as applied to claim s 1 and 18 above, and further in view of Alakuijala et al (US 20250124539 A1, hereinafter Alakuijala) . Regarding claim 6, Pao and Hui teach the apparatus of claim 1, but fail to explicitly teach wherein the at least one processor is further configured to: analyze the downsampled image with an eye-tracking device, wherein the at least one salient object is detected based on the analyzed downsampled image. In related field of endeavor, Alakuijala teaches analyze the downsampled image with an eye-tracking device, wherein the at least one salient object is detected based on the analyzed downsampled image ([0064] “ For example, when a user is previewing an image via a camera preview feature, a machine learning model may detect one or more candidate salient portions in the previewed image and a user can be provided an option to select a preferred salient portion from the one or more candidate salient portions. The camera can then encode the captured image based on the selected salient portion. In some embodiments, the selection may be made automatically by taking a front facing camera and tracking eye or pupil movement during the capture of the image. Such eye tracking may indicate significant and/or relevant objects in the image (e.g., relevant to the user), and therefore indicate a salient portion.”) It would have been obvious to one of ordinary skill in the art to have further modified Pao and Hui to include analyze the downsampled image with an eye-tracking device, wherein the at least one salient object is detected based on the analyzed downsampled image as taught by Alakuijala. Doing so would provide a way of detecting salient objects in an image ([0063] “the detecting of the one or more objects in the preview of the image may involve identifying the candidate salient portions by tracking a pupil movement of a user of the camera”) Regarding claim 12, Pao and Hui teach The apparatus of claim 1, but fail to explicitly teach wherein the at least one salient object is more noticeable or conspicuous to a human eye compared to one or more other objects in the downsampled image. In related field of endeavor, Alakuijala teaches wherein the at least one salient object is more noticeable or conspicuous to a human eye compared to one or more other objects in the downsampled image ([0064] “ For example, when a user is previewing an image via a camera preview feature, a machine learning model may detect one or more candidate salient portions in the previewed image and a user can be provided an option to select a preferred salient portion from the one or more candidate salient portions. The camera can then encode the captured image based on the selected salient portion. In some embodiments, the selection may be made automatically by taking a front facing camera and tracking eye or pupil movement during the capture of the image. Such eye tracking may indicate significant and/or relevant objects in the image (e.g., relevant to the user), and therefore indicate a salient portion.”) It would have been obvious to one of ordinary skill in the art to have modified Pao and Hui to include wherein the at least one salient object is more noticeable or conspicuous to a human eye compared to one or more other objects in the downsampled image as taught by Alakuijala. Doing so would provide a way of detecting salient objects in an image ([0063] “the detecting of the one or more objects in the preview of the image may involve identifying the candidate salient portions by tracking a pupil movement of a user of the camera”). Regarding claim 21, the method claim 21 is similar in scope to the apparatus claim 6, and is rejected under similar rationale. Regarding claim 25, Pao and Hui teach the method of claim 18. Pao further teaches wherein the saliency map is configured based on a portion of the at least one salient object, such that the saliency map is configured based on less than all of the at least one salient object (Claim 1 “determine whether to perform additional refinement to the segmentation map of the region of the digital image comprising the single salient object; generate, from a sub-region of the region of the digital image and based on determining to perform additional refinement to the segmentation map, an additional segmentation map of the sub-region of the region having the second resolution”, where a segmentation map of a sub region corresponds to a saliency map based on less than all of the salient object), but fails to explicitly teach wherein the at least one salient object is more noticeable or conspicuous to a human eye compared to one or more other objects in the downsampled image . In related field of endeavor, Alakuijala teaches wherein the at least one salient object is more noticeable or conspicuous to a human eye compared to one or more other objects in the downsampled image ([0064] “ For example, when a user is previewing an image via a camera preview feature, a machine learning model may detect one or more candidate salient portions in the previewed image and a user can be provided an option to select a preferred salient portion from the one or more candidate salient portions. The camera can then encode the captured image based on the selected salient portion. In some embodiments, the selection may be made automatically by taking a front facing camera and tracking eye or pupil movement during the capture of the image. Such eye tracking may indicate significant and/or relevant objects in the image (e.g., relevant to the user), and therefore indicate a salient portion.”) It would have been obvious to one of ordinary skill in the art to have modified Pao and Hui to include wherein the at least one salient object is more noticeable or conspicuous to a human eye compared to one or more other objects in the downsampled image as taught by Alakuijala. Doing so would provide a way of detecting salient objects in an image ([0063] “the detecting of the one or more objects in the preview of the image may involve identifying the candidate salient portions by tracking a pupil movement of a user of the camera”) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Galor et al (US 20220100054 A1, hereinafter Galor) . Galor teaches downsampling an image, detecting salient objects in the downsampled image, and generating saliency maps corresponding to salient objects in the downsampled images similar to what is described in the applicant’s disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN PATRICK GOCO whose telephone number is (571)272-5872. The examiner can normally be reached M-Th, 7:00 am - 5:00 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, Kee Tung can be reached at (571)272-7794. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611 /J.P.G./ Examiner, Art Unit 2611 Application/Control Number: 18/844,923 Page 2 Art Unit: 2611 Application/Control Number: 18/844,923 Page 3 Art Unit: 2611 Application/Control Number: 18/844,923 Page 4 Art Unit: 2611 Application/Control Number: 18/844,923 Page 5 Art Unit: 2611 Application/Control Number: 18/844,923 Page 6 Art Unit: 2611 Application/Control Number: 18/844,923 Page 7 Art Unit: 2611 Application/Control Number: 18/844,923 Page 8 Art Unit: 2611 Application/Control Number: 18/844,923 Page 9 Art Unit: 2611 Application/Control Number: 18/844,923 Page 10 Art Unit: 2611 Application/Control Number: 18/844,923 Page 11 Art Unit: 2611 Application/Control Number: 18/844,923 Page 12 Art Unit: 2611 Application/Control Number: 18/844,923 Page 13 Art Unit: 2611 Application/Control Number: 18/844,923 Page 14 Art Unit: 2611 Application/Control Number: 18/844,923 Page 15 Art Unit: 2611 Application/Control Number: 18/844,923 Page 16 Art Unit: 2611 Application/Control Number: 18/844,923 Page 17 Art Unit: 2611 Application/Control Number: 18/844,923 Page 18 Art Unit: 2611 Application/Control Number: 18/844,923 Page 19 Art Unit: 2611 Application/Control Number: 18/844,923 Page 20 Art Unit: 2611 Application/Control Number: 18/844,923 Page 21 Art Unit: 2611 Application/Control Number: 18/844,923 Page 22 Art Unit: 2611
