DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 08/07/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) has/have been considered by the examiner.
Claim Objections
Claims 10, 11, 13, 17 and 19 objected to because of the following informalities:
Claims 10, 11 and 19 lines 2, 1-2, and 6 respectively “the visual saliency prediction map” should be “the first visual saliency prediction map”
Claim 11 “first correlation value” should be “a first correlation value”
Claim 13 line 2“visual graphic rendering” should be “a visual graphics rendering”
Claim 17 line10 “ground-truth map” should be “the ground-truth map”
Claim 17 line11 “negative density map” should be “the negative density map”
Appropriate correction is required.
Examiner’s Note
The claim language of claim 13 is consistent with Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004) decision regarding the claim interpretation of “at least one of x, y, and z.” at pages 15-16 set forth the rational for determining that the term “and” is conjunctive (i.e. at least one of x, at least one of y, and at least one of z). However, the specification of the instant application suggests an interpretation of “at least one of x, y, OR z.” For example ¶41-42 and 142 explain that their intent of “at least one” language includes disjunctive interpretation of and. Therefore the plain and ordinary meaning of the current claim language “at least one of a data compression function, an object detection function, and visual graphics rendering function.” in light of the specification is interpreted to be “at least one data compression function OR at least one object detection function OR visual graphics rendering function”.
Claim Rejections - 35 USC § 101
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.
Claims 1-11, 14-15, and 17-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed
to an abstract idea (a mental process) without significantly more. The flow chart in MPEP 2106,
Subject Matter Eligibility Test For Products and Processes, will be referred to establish ineligible
subject matter.
Regarding claim 18, Step 1: The claim(s) recite(s) “a device” which would be categorized as a product under the 4 statutory categories. See MPEP 2106.03. Step 2A Prong One: However, the claim is further directed to an abstract idea (mathematical processes and mental processes) of: normalize values of the saliency density ground-truth map to be in a range of 0 to 1 to generate a normalized density ground-truth map (mathematical process), compare values of the normalized density ground-truth map to a predefined threshold value to generate an enhanced ground-truth map (mathematical process); subtract the enhanced ground-truth map from the center bias map to generate a negative candidates map (mathematical process), normalize values of the negative candidates map to be in a range of 0 to 1 to generate a normalized candidates map (mathematical process), perform a sampling process on the normalized candidates map to generate a negative point map (mental process), and apply a filter function to the negative point map to generate a negative density map (mathematical process). See MPEP 2106.04 subsection II and 2106.04(a)(2) subsection III. Step 2A Prong Two: Additional elements include computer elements (a controller and a memory), and receive a center bias map and a saliency density ground-truth map for an image. With regards to the computer elements MPEP 2106.04 (A2) III. Metal Process establishes that the addition of a generic computer-implemented steps does not integrate the judicial exception into a practical application. With regards to receive a center bias map and a saliency density ground-truth map for an image, this is mere data gathering, which in MPEP 2106.05(g) (3) is insignificant extra-solution activity. See MPEP 2106.04(d) Step 2B: The additional claim elements do not amount to significantly more than the judicial exception. With regards to the computer elements MPEP 2106.04 (A2) III. Metal Process establishes that the addition of a generic computer- implemented steps does not integrate the judicial exception into significantly more. With regards to receive a center bias map and a saliency density ground-truth map for an image, this is mere data gathering, and is claimed in a way that is well understood, routine, and conventional. See MPEP 2106.05. Therefore, the claim is not eligible subject matter. Claims 1 has similar limitations and is not eligible for similar reasons.
Regarding claim 15, Step 1: The claim(s) recite(s) “a method” which would be categorized as a process under the 4 statutory categories. See MPEP 2106.03. Step 2A Prong One: However, the claim is further directed to an abstract idea (mental processes) of: generating, via the processor, a negative candidates map based on a difference between the center bias map and the saliency density ground-truth map; generating, via the processor, a negative density map based on the difference between the center bias map and the saliency density ground-truth map. (both can be performed manually by a human). See MPEP 2106.04 subsection II and 2106.04(a)(2) subsection III. Step 2A Prong Two: Additional elements include computer elements (a processor), and receiving a center bias map and a saliency density ground-truth map for an image. With regards to the computer elements MPEP 2106.04 (A2) III. Metal Process establishes that the addition of a generic computer-implemented steps does not integrate the judicial exception into a practical application. With regards to receiving a center bias map and a saliency density ground-truth map for an image, this is mere data gathering, which in MPEP 2106.05(g) (3) is insignificant extra-solution activity. See MPEP 2106.04(d) Step 2B: The additional claim elements do not amount to significantly more than the judicial exception. With regards to the computer elements MPEP 2106.04 (A2) III. Metal Process establishes that the addition of a generic computer- implemented steps does not integrate the judicial exception into significantly more. With regards to receiving a center bias map and a saliency density ground-truth map for an image, this is mere data gathering, and is claimed in a way that is well understood, routine, and conventional. See MPEP 2106.05. Therefore, the claim is not eligible subject matter.
Regarding claims 2-11, 14, 17, and 19, additional limitations are to abstract ideas (mathematical and mental processes) the addition of judicial exception does not amount to significantly more. Therefore they are not considered eligible subject matter.
Regarding claims 12, 13, 16 and 20, additional limitations integrate the claimed invention into a practical application and are considered eligible subject matter. Specifically, claim 12 recites “selecting one of the plurality of visual saliency prediction models based on a condition; and executing a function or action based on a visual saliency prediction map output by the one of the plurality of visual saliency prediction models”. Claims 16 and 20 recite similar limitations and are considered eligible for similar reasons.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jia (Jia S, Bruce ND. Revisiting saliency metrics: Farthest-neighbor area under curve. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (pp. 2667-2676).- from applicant’s admitted prior art).
Regarding claim 15, Jia discloses A method for controlling a device to manage a visual saliency model, the method comprising: receiving, via a processor in the device, (Although not explicitly stated, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the trained model disclosed in Jia would have been implemented on a processor because training is performed on a computer (Jia Section 4. Experiment – Implementation Details – found on righthand column p. 2672)) a center bias map and a saliency density ground- truth map for an image; ; (Jia table 1 and Section 4. Experiment – Implementation details – found on p. 2672; 4 different saliency datasets are disclosed with using the datasets to train a model. Section 4.2 Spatial Biases ¶1; the center bias map is taken from the MIT benchmark.) generating, via the processor, a negative candidates map based on a difference between the center bias map and the saliency density ground-truth map; (Jia Section 3.2. Farthest-Neighbor AUC -¶4 found on lefthand column of p. 2671; the distance is taken between the center bias and the ground truth distributions (i.e. subtraction is performed). ¶5 then explains that the distance between the positive set and negative set are maximized to obtain the negative set.) generating, via the processor, a negative density map based on the difference between the center bias map and the saliency density ground-truth map. (Jia Section 3.2. Farthest-Neighbor AUC -¶4 found on lefthand column of p. 2671; the distance is taken between the center bias and the ground truth distributions (i.e. subtraction is performed). ¶5 then explains that the distance between the positive set and negative set are maximized to obtain the negative set. Jia Section 3.2. Farthest-Neighbor AUC -Algorithm 1 and ¶5 found the righthand column of p. 2671 to lefthand column of p.2672; the negative set is sampled. Section 1. Introduction -¶8 found on lefthand column of p. 2668; negative set is evaluated using AUC and a Gaussian filter is applied this gives the negative density. Jia Section 3.2. Farthest-Neighbor AUC -¶8 found on lefthand column of p. 2672; negative set in pdf form can be evaluated using AUC.)
Claim(s) 1-10, 14, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jia (Jia S, Bruce ND. Revisiting saliency metrics: Farthest-neighbor area under curve. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (pp. 2667-2676).- from applicant’s admitted prior art) in view of Bylinskii (Bylinskii Z, Judd T, Oliva A, Torralba A, Durand F. What do different evaluation metrics tell us about saliency models?. IEEE transactions on pattern analysis and machine intelligence. 2018 Mar 13;41(3):740-57.).
Regarding claim 18, Jia discloses A device for managing visual saliency models, the device comprising: a memory configured to store one or more saliency density ground-truth maps for one or more images, the one or more saliency density ground-truth maps corresponding to one or more visual saliency prediction models; (Jia table 1 and Section 4. Experiment – Implementation details – found on p. 2672; 4 different saliency datasets are disclosed with using the datasets to train a model.) and A controller configured to: (Although not explicitly stated, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the trained model disclosed in Jia would have been implemented on a controller because training is performed on a computer (Jia Section 4. Experiment – Implementation Details – found on righthand column p. 2672))
receive a center bias map and a saliency density ground-truth map for an image, (Jia table 1 and Section 4. Experiment – Implementation details – found on p. 2672; 4 different saliency datasets are disclosed with using the datasets to train a model. Section 4.2 Spatial Biases ¶1; the center bias map is taken from the MIT benchmark.)subtract the enhanced ground-truth map from the center bias map to generate a negative candidates map, (Jia Section 3.2. Farthest-Neighbor AUC -¶4 found on lefthand column of p. 2671; the distance is taken between the center bias and the ground truth distributions (i.e. subtraction is performed). ¶5 then explains that the distance between the positive set and negative set are maximized to obtain the negative set.) perform a sampling process on the(Jia Section 3.2. Farthest-Neighbor AUC -Algorithm 1 and ¶5 found the righthand column of p. 2671 to lefthand column of p.2672; the negative set is sampled.) apply a filter function to the negative point map to generate a negative density map. (Jia Section 1. Introduction -¶8 found on lefthand column of p. 2668; negative set is evaluated using AUC and a Gaussian filter is applied. Jia Section 3.2. Farthest-Neighbor AUC -¶8 found on lefthand column of p. 2672; negative set in pdf form can be evaluated using AUC.)
Although Jia utilizes ROC curve in its evaluation of ground truth (Jia Section 2.3 and Fig. 3; The method is AUC-based which utilizes an ROC curve. ) and negative set (Jia Section 3.2. Farthest-Neighbor AUC -¶8 found on lefthand column of p. 2672; negative set is evaluated using AUC.), it does not explicitly state normalize values of the saliency density ground-truth map to be in a range of 0 to 1 to generate a normalized density ground-truth map, compare values of the normalized density ground-truth map to a predefined threshold value to generate an enhanced ground-truth map; or normalize values of the negative candidates map to be in a range of 0 to 1 to generate a normalized candidates map.
Bylinskii, however, discloses normalize values of the saliency density ground-truth map to be in a range of 0 to 1 to generate a normalized density ground-truth map, compare values of the normalized density ground-truth map to a predefined threshold value to generate an enhanced ground-truth map; (Bylinskii Section A.2 Metric Computation – Sampling thresholds for the ROC curve: - found on p. 20-21; it is explained that for the ROC curve the saliency map is first normalized between 0 and 1, and then specifical values are used to draw the curve.) normalize values of the negative candidates map to be in a range of 0 to 1 to generate a normalized candidates map, (Bylinskii Section A.2 Metric Computation – Sampling thresholds for the ROC curve: - found on p. 20-21; it is explained that for the ROC curve the saliency map is first normalized between 0 and 1, and then specifical values are used to draw the curve.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the device of Jia with the teachings of Bylinskii by including normalization of the ground-truth map and negative candidates in order to utilize the same standard calculation of other similar methods like AUC-Judd and AUC- Borji (Bylinskii Section A.2 Metric Computation – Sampling thresholds for the ROC curve: - found on p. 20-21).
Regarding claim 19, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 18, as described above. They further disclose wherein the controller is further configured to: receive a first visual saliency prediction map corresponding to a first visual saliency prediction model, (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 / first full ¶ on lefthand column of p. 2672; the negative set is found.) compare the first visual saliency prediction map to the negative density map to generate a first correlation value, (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 / first full ¶ on lefthand column of p. 2672; the negative set is compared to the center bias as β.) compare the visual saliency prediction map to the saliency density ground-truth map to generate a second correlation value, , (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 / first full ¶ on lefthand column of p. 2672; the negative set is compared to the ground truth as ϒ.) and generate an evaluation metric for the first visual saliency prediction model based on the first correlation value and the second correlation value. (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 / first full ¶ on lefthand column of p. 2672; low ϒ/β is an indicator of quality.)
Regarding claim 1, it is the corresponding method claim to claim 18 and is rejected for similar reasons.
Regarding claim 2, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose wherein the center bias map is a 2D Gaussian distribution. (Although not explicitly stated, Jia Section 4.2. Spatial Biases - ¶1 found on p. 2673 the center bias is taken from the MIT benchmark. Fig. 4 appears to be a 2D Gaussian. Bylinskii, however, discloses Section 5.3 Systematic viewing biases – sAUC penalizes models that include center bias – found on p. 14; explains that the center bias is a central gaussian. It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the device of the combination of Jia and Bylinskii with the teachings of Bylinskii by including a central gaussian as the central bias because that is the bias as the number of samples goes to infinity in the estimation (Bylinskii Section 5.3 Systematic viewing biases – sAUC penalizes models that include center bias – found on p. 14).).
Regarding claim 3, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose wherein the enhanced ground-truth map is a binary map having some values set to 0 and remaining values set to 1 based on the predefined threshold value. (Bylinskii Section 4.1.1 Area under ROC curve (AUC): Evaluating saliency as a classifier of fixations – found on p.5 found on bottom of lefthand column to righthand column; AUC is a location based metric. Section A.1 Metric computation – Location -based versus distribution-based metrics – found on righthand column p.20; ground truth is thresholded at a fixed value into 0 or 1. It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to further modify the device of the combination of Jia and Bylinskii with the teachings of Bylinskii by including binary values of the ground truth map in order to draw the ACU curve (Bylinskii Section A.1 Metric computation – Location -based versus distribution-based metrics))
Regarding claim 4, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose wherein generating the enhanced ground-truth map includes: converting some values of the saliency density ground-truth map that are less than the predefined threshold to zero and converting other values of the saliency density ground-truth map that are greater than the predefined threshold to one. (Bylinskii Section 4.1.1 Area under ROC curve (AUC): Evaluating saliency as a classifier of fixations – found on p.5 found on bottom of lefthand column to righthand column; AUC is a location based metric. Section A.1 Metric computation – Location -based versus distribution-based metrics – found on righthand column p.20; ground truth is thresholded at a fixed value into 0 or 1. It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to further modify the device of the combination of Jia and Bylinskii with the teachings of Bylinskii by including binary values of the ground truth map in order to draw the ACU curve (Bylinskii Section A.1 Metric computation – Location -based versus distribution-based metrics))
Regarding claim 5, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose wherein the negative point map indicates an inclusion probability. (Jia Section 3.2. Farthest-Neighbor AUC - ¶4-5 found lefthand to the righthand column of p. 2671 to lefthand column of p.2672; the negative set is a representative of the probability distribution of non-fixated points.)
Regarding claim 6, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose wherein the sampling process includes Poisson sampling to generate a set of negative points included in the negative point map. (Jia Section 3.2. Farthest-Neighbor AUC - ¶3 found righthand column of p. 2670to the lefthand column of p. 2671; sampling can be interpreted as Poisson sampling.)
Regarding claim 7, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose wherein the filter function is gaussian filter configured to blur portions of the negative point map. (Jia Section 1. Introduction -¶8 found on lefthand column of p. 2668; negative set is evaluated using AUC and a Gaussian filter is applied.)
Regarding claim 8, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose wherein the saliency density ground-truth map is a type of heat map indicating actual measurements of where viewers eyes have fixated on the image. (Jia Fig. 9; the 3rd column shows the ground truth, which can be seen as a heat map and is labeled fixation.)
Regarding claim 9, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 1, as described above. They further disclose further comprising receiving, via the processor, a first visual saliency prediction map corresponding to a first visual saliency prediction model; and comparing the first visual saliency prediction map to the negative density map to generate a first correlation value. (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 / first full ¶ on lefthand column of p. 2672; the negative set is compared to the center bias as β.)
Regarding claim 10, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 9, as described above. They further disclose
Regarding claim 14, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 9, as described above. They further disclose further comprising: comparing the visual saliency prediction map to the saliency density ground-truth map to generate a second correlation value; (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 / first full ¶ on lefthand column of p. 2672; the negative set is compared to the ground truth as ϒ.) and generating an evaluation metric for the first visual saliency prediction model based on the first correlation value and the second correlation value. (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 / first full ¶ on lefthand column of p. 2672; low ϒ/β is an indicator of quality.)
Regarding claim 17, Jia discloses the claim limitations with respect to claim 15, as described above. Jia further discloses further comprising performing, via the processor a sampling process on the normalized candidates map to generate a negative point map, (Jia Section 3.2. Farthest-Neighbor AUC -Algorithm 1 and ¶5 found the righthand column of p. 2671 to lefthand column of p.2672; the negative set is sampled.) wherein the negative candidates map is generated based on enhanced ground-truth map, and wherein negative density map is generated based on the negative point map. (Jia Section 3.2. Farthest-Neighbor AUC -¶4 found on lefthand column of p. 2671; the distance is taken between the center bias and the ground truth distributions (i.e. subtraction is performed). ¶5 then explains that the distance between the positive set and negative set are maximized to obtain the negative set.)
Although Jia utilizes ROC curve in its evaluation of ground truth (Jia Section 2.3 and Fig. 3; The method is AUC-based which utilizes an ROC curve. ) and negative set (Jia Section 3.2. Farthest-Neighbor AUC -¶8 found on lefthand column of p. 2672; negative set is evaluated using AUC.), it does not explicitly state : normalizing, via the processor, values of the saliency density ground-truth map to be in a range of 0 to 1 to generate a normalized density ground-truth map; comparing, via the processor, values of the normalized density ground-truth map to a predefined threshold value to generate an enhanced ground-truth map; normalizing, via the processor, values of the negative candidates map to be in a range of 0 to 1 to generate a normalized candidates map.
Bylinskii, however, discloses normalizing, via the processor, values of the saliency density ground-truth map to be in a range of 0 to 1 to generate a normalized density ground-truth map; comparing, via the processor, values of the normalized density ground-truth map to a predefined threshold value to generate an enhanced ground-truth map; (Bylinskii Section A.2 Metric Computation – Sampling thresholds for the ROC curve: - found on p. 20-21; it is explained that for the ROC curve the saliency map is first normalized between 0 and 1, and then specifical values are used to draw the curve.) normalizing, via the processor, values of the negative candidates map to be in a range of 0 to 1 to generate a normalized candidates map; (Bylinskii Section A.2 Metric Computation – Sampling thresholds for the ROC curve: - found on p. 20-21; it is explained that for the ROC curve the saliency map is first normalized between 0 and 1, and then specifical values are used to draw the curve.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the device of Jia with the teachings of Bylinskii by including normalization of the ground-truth map and negative candidates in order to utilize the same standard calculation of other similar methods like AUC-Judd and AUC- Borji (Bylinskii Section A.2 Metric Computation – Sampling thresholds for the ROC curve: - found on p. 20-21).
Claim(s) 12-13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jia (Jia S, Bruce ND. Revisiting saliency metrics: Farthest-neighbor area under curve. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (pp. 2667-2676).- from applicant’s admitted prior art) in view of Bylinskii (Bylinskii Z, Judd T, Oliva A, Torralba A, Durand F. What do different evaluation metrics tell us about saliency models?. IEEE transactions on pattern analysis and machine intelligence. 2018 Mar 13;41(3):740-57.) and Li (Li S, Xu M, Ren Y, Wang Z. Closed-form optimization on saliency-guided image compression for HEVC-MSP. IEEE Transactions on Multimedia. 2017 Jun 29;20(1):155-70.).
Regarding claim 12, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 9, as described above. They further disclose further comprising: comparing evaluation metrics of a plurality of visual saliency prediction models with each other; selecting one of the plurality of visual saliency prediction models based on a condition; (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 -9/ first two full ¶ on lefthand column of p. 2672; low ϒ/β is used to evaluate S-AUC and FN-AUC. Fig. 7 shows the comparison. The authors indicate that FN-AUC performs better than S-AUC.)
The combination of Jia and Bylinskii does not explicitly disclose and executing a function or action based on a visual saliency prediction map output by the one of the plurality of visual saliency prediction models.
Li, however, discloses and executing a function or action based on a visual saliency prediction map output by the one of the plurality of visual saliency prediction models. (Li Section I. Introduction ¶4 – found on lefthand column to righthand column p. 156; Saliency is utilized in image compression.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the method of the combination of Jia and Bylinskii with the teachings of Li by using the saliency map to compress images in order to minimize perceptual distortion in compression (Li Section I. Introduction ¶4 – found on lefthand column p. 156).
Regarding claim 13, The combination of Jia, Bylinskii, and Li disclose the claim limitations with respect to claim 12, as described above. They further disclose wherein the function or action includes at least one of a data compression function, an object detection function, and visual graphics rendering function. (Li Section I. Introduction ¶4 – found on lefthand column to righthand column p. 156; Saliency is utilized in image compression. (see examiner’s note above where “at least one of a data compression function, an object detection function, and visual graphics rendering function” is interpreted to mean “at least one of a data compression function, OR an object detection function, OR visual graphics rendering function.”)
Regarding claim 20, The combination of Jia and Bylinskii disclose the claim limitations with respect to claim 9, as described above. They further disclose wherein the controller is further configured to: compare evaluation metrics of a plurality of visual saliency prediction models with each other, select one of the plurality of visual saliency prediction models based on a condition, (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 -9/ first two full ¶ on lefthand column of p. 2672; low ϒ/β is used to evaluate S-AUC and FN-AUC. Fig. 7 shows the comparison. The authors indicate that FN-AUC performs better than S-AUC.)
The combination of Jia and Bylinskii does not explicitly disclose and execute a function based on a visual saliency prediction map output by the one of the plurality of visual saliency prediction models.
Li, however, discloses and execute a function based on a visual saliency prediction map output by the one of the plurality of visual saliency prediction models. (Li Section I. Introduction ¶4 – found on lefthand column to righthand column p. 156; Saliency is utilized in image compression.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the device of the combination of Jia and Bylinskii with the teachings of Li by using the saliency map to compress images in order to minimize perceptual distortion in compression (Li Section I. Introduction ¶4 – found on lefthand column p. 156).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jia (Jia S, Bruce ND. Revisiting saliency metrics: Farthest-neighbor area under curve. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (pp. 2667-2676).- from applicant’s admitted prior art) in view of Li (Li S, Xu M, Ren Y, Wang Z. Closed-form optimization on saliency-guided image compression for HEVC-MSP. IEEE Transactions on Multimedia. 2017 Jun 29;20(1):155-70.).
Regarding claim 16, Jia discloses the claim limitations with respect to claim 15, as described above. Jia further discloses further comprising: selecting one of a plurality of visual saliency prediction models based metrics indicating relationships between the negative density map and visual saliency prediction maps corresponding to the plurality of visual saliency prediction models; (Jia Section 3.2. Farthest-Neighbor AUC – ¶8 -9/ first two full ¶ on lefthand column of p. 2672; low ϒ/β is used to evaluate S-AUC and FN-AUC. Fig. 7 shows the comparison. The authors indicate that FN-AUC performs better than S-AUC.)
Jia does not explicitly disclose and executing a function based on a visual saliency prediction map output by the one of the plurality of visual saliency prediction models.
Li, however, discloses and executing a function based on a visual saliency prediction map output by the one of the plurality of visual saliency prediction models. (Li Section I. Introduction ¶4 – found on lefthand column to righthand column p. 156; Saliency is utilized in image compression.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the method of Jia with the teachings of Li by using the saliency map to compress images in order to minimize perceptual distortion in compression (Li Section I. Introduction ¶4 – found on lefthand column p. 156).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEREDITH TAYLOR whose telephone number is (571)270-5805. The examiner can normally be reached M-Th 7:30-5.
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, Vincent Rudolph can be reached at (571)272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MEREDITH TAYLOR/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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