CTNF 18/809,990 CTNF 87845 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. Information Disclosure Statement The IDS(s) has/have been considered and placed in the application file. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-15 are rejected under 35 U.S.C. § 101 as directed to a judicial exception without significantly more. Claim 4 is rejected under 35 U.S.C. § 112(b) as indefinite for inconsistency with the specification. Claims 1-6 and 10-14 are rejected under 35 U.S.C. § 102(a)(1) as anticipated by Geng et al. , "Face recognition based on the multi-scale local image structures," ("GENG"). Claims 1, 2, 11, 12, 14, and 15 are rejected, in the alternative, under 35 U.S.C. § 102(a)(1) as anticipated by Suzuki et al., US 2009/0041340 ("SONY"). Claims 1, 3, 5, 10, 14, and 15 are rejected, in the alternative, under 35 U.S.C. § 102(a)(1) as anticipated by Magai et al., US 2010/0074530 ("CANON"). Claims 7-9, 13, and 15 are rejected, in the alternative, under 35 U.S.C. § 103 as unpatentable over GENG in view of Hong et al., US 2020/0250807 (“HONG”). 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. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “feature point extraction unit,” “feature comparison unit,” “registration unit,” “feature calculation unit,” and “output unit” in claim(s) 1-13 and 15 . Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. For computer implemented limitations the corresponding structure must include the algorithm that performs the function. Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1349 (Fed. Cir. 2015). MPEP 2181(II)(B). The comparison unit is disclosed by the DSP/FPGA in ¶43. The algorithm is shown in Fig. 3 and ¶¶42-45 which discloses computing an inner-vector distance among feature vectors (¶¶52-54) and applying an optional threshold. This description is sufficient. The registration unit is disclosed by the by the CPU in ¶42. The algorithm reads feature points from memory M2, select based on signal from communication interface, transmit to drawing unit, store selected feature point data in feature point memory M3 (¶43). This description is sufficient. The extraction, calculation, and output units hardware is disclosed as DSP/FPGA and CPU (¶¶39-40). Their associated functions are disclosed in ¶48 and Fig. 3 describing extraction feature point positions from each conversion processing step (Step St4, Fig. 3), computing features vectors as numerical data sequences for each feature point (Step St5, Fig. 3) and generating a feature point image by superimposing the extracted feature points onto a conversion processing image and outputs a screen containing that image to the display device (¶44 and Fig.’s 5, 6, or 9), respectively. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 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. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mathematical concepts) without significantly more. The claim(s) recite(s) the mathematical steps of extracting feature vectors from images, computing inner-vector distances across images, and selecting feature points based on a threshold comparison. This judicial exception is not integrated into a practical application because the claims recite data processing steps performed by generic DSP/FPGA/CPU hardware, and the industrial automation context is described in the specification but not recited in the claims. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of generic processors, memory, and camera(s) are well-understood, routine, and conventional components that perform their ordinary functions. Step 1. Claims 1 and 15 recite a machine. Claim 14 recites a process. All are within statutory categories. Step 2A, Prong 1. The independent claims recite three mathematical operations: extracting feature points (SIFT, ¶39); computing inner-vector distances among feature vectors (¶¶53-54); selecting points whose computed value satisfies a threshold (¶¶54, 81). These are mathematical concepts. MPEP 2106.04(a)(2)(I). Step 2A, Prong 2 Not integrated into a practical application. The recited “units” are DSP/FPGA/CPU executing software (¶¶39-40 and 42-44). These are generical computer applying the abstract idea (MPEP 2106.05(f). Dependent claims add further math, conventional preprocessing, generic display, conventional storage, and routine user input. The claims stop at registering the features points. The spec mentions downstream robot control (¶¶30, 33), but no claim recites it. The improvement is to data quality, not to computer operation or any particular technology (MPEP 2106.05(a)). Any technical effect applicant may assert – industrial component variation, factory automation – is unclaimed. Claim 15 adds a generic camera providing one-directional input, which is conventional data gathering (MPEP 2106.05(g)); see Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55 (Fed. Cir. 2016); Yu v. Apple 1 F.4th 1040 (Fed. Cir. 2021). Step 2B. Individually and as an ordered combination, the additional elements are well-understood, routine, and conventional (WURC). SIFT is admitted to be “well-known” (¶39); Euclidean distance, threshold selection, and flash/HDD/SDD storage are ubiquitous in computer v ision. The full pipeline (filter → extract → compute features → compare across images → threshold select → store) was published in Geng (October 2011) more than a decade before the effective filling date and is applied in the §§ 102/103 rejections below. Claim Eligible? Additional Limitation(s) Reasoning 1 No Independent- extract feature points from multiple images, compare among images, register subset based on comparison result Generic hardware performing mathematical operations (extraction, distance computation, threshold selection); terminates at data storage with no downstream physical effect 2 No Adds feature calculation unit that computes features of each feature point; comparison unit uses computed features to calculate similarity Adds more math (feature vector computation, similarity calculation) on a generic processor (DSP/FPGA). Further mathematical detail of the same abstract idea. MPEP 2106.05(f) 3 No Adds image filter performing smoothing/sharpness with a predetermined parameter; extraction operates on filtered images Conventional image preprocessing (Gaussian filtering) on a generic processor. Insignificant extra-solution activity- data conditioning before the abstract analysis. MPEP 2106.05(g) 4 No Registers feature points when similarity ≤ threshold Refines the mathematical selection criterion - specifying the threshold condition is additional mathematical detail, not a practical application 5 No Adds feature calculation unit + comparison uses computed features on conversion processing (filtered) images Combines claim 3's filtering with claim 2's feature calculation . More math on preprocessed data on generic hardware. Same analysis as claims 2 and 3 6 No Comparison occurs after number of conversion processing images reaches a predetermined number Specifies when to trigger the mathematical comparison (batch size condition). This is a computational workflow parameter, not a meaningful limitation beyond the abstract idea 7 No Adds output unit generating a feature point image (feature points superimposed on an input image) displayed on a display device Outputting results of abstract analysis to a generic display. Insignificant extra- solution activity- mere data output. MPEP 2106.05(g). Display device is generic (LCD/OLEO) 8 No Output unit displays input images and feature point image in a comparable manner Refines display layout (side-by-side presentation). Still data output to a generic display- no meaningful limit beyond visualization of the abstract analysis results 9 No Adds feature calculation+ similarity computation+ output unit color-codes similarity results on the feature point image Color-coding computed similarity values is data visualization – presenting mathematical results graphically. Additional math (similarity computation)+ insignificant extra-solution activity (display). MPEP 2106.05(g) 10 No Image conversion processing is smoothing or sharpness processing Narrows the type of conventional preprocessing. Smoothing and sharpness are standard image filters - specifying which filter type does not impose a meaningful limit. WURC 11 No Adds image memory storing images from a camera configured to capture the object Generic storage (HDD/SSD/flash) receiving data from a conventional camera. WURC data gathering+ WURC storage. MPEP 2106.05(g) 12 No Adds feature memory storing computed features; comparison unit uses stored features Generic memory (flash/HDD/SSD) storing intermediate math results. Conventional data management, WURC storage supporting the mathematical workflow 13 No Adds input interface receiving parameter adjustment for the image filter Generic user input device (mouse/keyboard/touch panel) allowing parameter tuning. Conventional human-computer interaction. Does not alter the abstract nature of the underlying analysis 14 No Independent- method: input images, extract feature points, compare among images, register subset based on comparison Same mathematical workflow as claim 1 without any hardware. Less basis for practical application than the device Claims. 15 No Independent- system: camera capturing object+ registration device communicably connected to camera Camera adds a physical data source but only supplies one-directional input. No feedback loop, no downstream actuation, no physical effect from registration output. Diehr distinguishable (closed-loop control). Stronger Prong 2 candidate than claims 1/14, but still insufficient Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 4 registers feature points when similarity "is equal to or less than a threshold," which contradicts , ¶53 ("select only the features C in which the inter-vector distance … is within the predetermined threshold"). In the spec, similarity and distance are inversely related – short distance means high similarity, meaning the feature point appears consistently across images and should be registered. Therefore, the metes and bounds of claim 4 cannot be ascertained. Applicant may amend to recite that the inter-vector distance is at or below the threshold (consistent with ¶53). Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-6 and 10-12, 14 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Geng et al., “Face recognition based on the multi-scale local image structures,” ( “GENG” ) . Claim 1. GENG discloses a feature point registration device (GENG: "we propose three schemes: template selection, unstable keypoint removal and template synthesis." (Sect. 2, p. 2566). Geng’s “keypoints” are feature points under the BRI – both are characteristic positions extracted from images based on scale/rotation invariance.) , comprising: a feature point extraction unit that extracts a plurality of feature points related to an object from each of a plurality of different input images in which the object appears (GENG: “If we have multiple training images per subject, we can check the repeatability of a keypoint in different images of the same subject. A keypoint with low repeatability is unstable and hence can be removed." (Sect. 2.2, p. 2566).) ; a feature comparison unit that compares the feature points extracted from each of the plurality of different input images among the input images (GENG: "the descriptor of this probe keypoint is compared with all the keypoints of the other images of the same subject." (Sect. 2.2, p. 2567) .; and a registration unit that registers a part of feature points among the plurality of feature points extracted from at least one of the input images in association with the object based on a comparison result of the feature points ((GENG: "The stable candidate keypoints will be integrated into the template I t ." (Sect. 2.3, p. 2567)..) . Claim 2. GENG further discloses the feature point registration device according to claim 1, further comprising: a feature calculation unit that calculates respective features of the plurality of feature points extracted from the input images (GENG: "we adopt Lowe's descriptor, which is a set of histograms consisting of oriented gradients." (Sect. 3.2, p. 2569).) , wherein the feature comparison unit calculates and compares a similarity of the feature points among the input images by using calculation results of the features corresponding to the feature points extracted from the input images (GENG: "the descriptor of this probe keypoint is compared with all the keypoints of the other images of the same subject." (Sect. 2.2, p. 2567).) . Claim 3. GENG further discloses the feature point registration device according to claim 1, further comprising: an image filter that performs image conversion processing on the input images by using at least one predetermined parameter (GENG: "Lowe [24] proposed to use Difference-of Gaussian (DoG) of nearby scales at k σ and σ to approximate the normalized Laplacian-of Gaussian." (Sect. 3.1, pp. 2567-2568).) , wherein the feature point extraction unit extracts the plurality of feature points based on conversion processing images that are the input images after the image conversion processing by the image filter (GENG: "Lowe in his SIFT framework proposes to compare each sample point to its eight neighbors in the current DoG image (spatial space) and nine neighbors in the scale above and below (scale space)." (Sect. 3.1, p. 2568). In SIFT, the DoG filtering happens first, and feature point detection operates on the resulting DoG images.) . Claim 4. GENG further discloses the feature point registration device according to claim 2, wherein the registration unit registers the part of feature points used for the comparison among the input images when it is determined that a calculation result of the similarity of the feature points between the input images is equal to or less than a threshold (Subject to the §112(b) rejection at ¶11 above. Read in light of ¶53 (inter-vector distance ≤ threshold). GENG: "We can set a threshold T 1 to select keypoints with high repeatability. If the value of γ of a keypoint is smaller than T 1 , it will be removed." (Sect. 2.2, p. 2567).) ) . Claim 5. GENG further discloses the feature point registration device according to claim 3, further comprising: a feature calculation unit that calculates respective features of the plurality of feature points extracted from the input images (GENG: "we adopt Lowe's descriptor, which is a set of histograms consisting of oriented gradients." (Sect. 3.2, p. 2569).) , wherein the feature comparison unit calculates and compares a similarity of the feature points among the conversion processing images by using calculation results of the features corresponding to the feature points extracted from the conversion processing images ((GENG: "the descriptor of this probe keypoint is compared with all the keypoints of the other images of the same subject." (Sect. 2.2, p. 2567).)) . Claim 6. GENG further discloses the feature point registration device according to claim 5, wherein after a number of the conversion processing images in which the features corresponding to the feature points are calculated reaches a predetermined number, the feature comparison unit calculates and compares the similarity of the feature points among the predetermined number of the conversion processing images (GENG: "Take a training image I t from the training image set S of a subject" (Sect. 2.2, pp. 2566-2567).) . Claim 10. GENG further discloses the feature point registration device according to claim 3, wherein the image conversion processing is smoothing processing or sharpness processing (GENG: "Lowe [24] proposed to use Difference-of Gaussian (DoG) of nearby scales at k σ and σ to approximate the normalized Laplacian-of Gaussian." (Sect. 3.1, pp. 2567-2568)) . Claim 11. GENG further discloses the feature point registration device according to claim 1, further comprising: an image memory that stores the plurality of different input images input from a camera configured to capture the object (GENG's algorithm operates on the stored training image set S of plural images of the same subject (face), captured by a camera. GENG: "Take a training image I t from the training image set S of a subject" (Sect. 2.2, pp. 2566-2567).) . Claim 12. GENG further discloses the feature point registration device according to claim 2, further comprising: a feature memory that stores the calculation results of the features corresponding to the feature points extracted from the input images, wherein the feature comparison unit calculates the similarity of the feature points among the input images by using the calculation results of the features stored in the feature memory (GENG's cross-image comparison requires retrieval of previously calculated descriptors of all keypoints across all images, requiring a feature memory. GENG: "the descriptor of this probe keypoint is compared with all the keypoints of the other images of the same subject." (Sect. 2.2, p. 2567)) . Claim 14. GENG discloses the corresponding method steps for the same reasons discussed with respect to claim 1, mutatis mutandis . 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. 07-23-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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 7-9, 13, and 15 are rejected under 35 U.S.C. 103 as unpatentable over GENG in view of US 2020/0250807 to Hong et al. ("HONG"). Claim 7. GENG further discloses the feature point registration device according to claim 1, further comprising: an output unit that generates a feature point image in which the plurality of feature points extracted by the feature point extraction unit are superimposed on one of the plurality of different input images, and outputs a screen including at least the feature point image (GENG: "the blue points in Fig. 5(a) show the initially detected keypoints by Lowe's approach." (Sect. 3.3 / Fig. 5, p. 2569).) to a display device . GENG illustrates feature points overlaid on the input image but does not expressly recite an output unit that outputs the visualization to a display device. HONG teaches this (HONG: "a display device can be used to display key-points of an image" (¶125).). Deploying GENG's keypoint-stability framework as an operator-usable system would predictably involve adopting HONG's display device, GUI, manual filter-parameter input, and explicit camera input – all features HONG describes for the same type of keypoint detection pipeline. HONG expressly identifies the purpose: "to improve the performance of the key-point detector " (¶135) – directly aligned with GENG's goal of obtaining a stable keypoint set for template synthesis. The combinations yield predictable results from known elements applied for their established functions. KSR . Claim 8. GENG in view of HONG further discloses the feature point registration device according to claim 7, wherein the output unit outputs the screen including the plurality of different input images and the feature point image in a comparable manner (GENG: "Initial keypoints detected by the original method (in blue) and extra keypoints detected by the proposed approach (in red)." (Fig. 5 caption, p. 2569).) to the display device (HONG: "a display device can be used to display key-points of an image" (¶125).) . The motivation to combine for claim 7 applies to claim 8. Claim 9. GENG further discloses the feature point registration device according to claim 8, further comprising: a feature calculation unit that calculates respective features of the plurality of feature points extracted from the input images, wherein the feature comparison unit calculates and compares a similarity of the feature points among the input images by using calculation results of the features corresponding to the feature points extracted from the input images (GENG: "the descriptor of this probe keypoint is compared with all the keypoints of the other images of the same subject." (Sect. 2.2, p. 2567).) , and the output unit color-codes (GENG: Fig. 5’s description color-codes feature points overlaid on the image – red and blue) a calculation result of the similarity of the feature points to draw the feature points on the feature point image (HONG: "a display device can be used to display key-points of an image" (¶125); "detected key-point strengths are calculated based on a Strength Measure (SM)." (¶157).) . Rendering GENG's per-keypoint similarity values using a color scale on HONG's GUI would have been obvious before the EFD. Color-coding a per-keypoint computed value is the conventional way to make such values legible on a GUI of HONG's type and assists the operator in distinguishing stable from unstable points – GENG's stated goal. Claim 13. GENG further discloses the feature point registration device according to claim 3, further comprising an input interface that receives an adjustment operation of the predetermined parameter, wherein the image filter performs the image conversion processing on the input images by using the predetermined parameter after the adjustment operation . GENG discloses the device of claim 3 but not an input interface that receives an adjustment operation of the predetermined parameter, wherein the image filter performs the image conversion processing using the adjusted parameter. HONG teaches manual adjustment of the filter parameter (HONG: "the amount of filtering can be adjusted in order to improve the performance of the key-point detector 120. In such cases, the filter parameters can be adjusted manually." (¶135).), with key-point detection performed on the filtered image (HONG: "potential key-points in the input image data are detected, based on which image blocks have a complexity measure that satisfy a specific range." (¶152)). Making GENG's DoG scale parameter user-adjustable per HONG would have been obvious before the EFD. HONG states the motivation: "to improve the performance of the key-point detector" – aligned with GENG's goal of obtaining a stable keypoint set. Claim 15. GENG discloses the corresponding image processing system for the same reasons discussed with respect to claim 1, mutatis mutandis . The additionally recited camera is believed to be inherent in Geng’s imaging setup used to capture the gallery of face images. See Sect. 4, p. 2569. Arguendo, a camera is also disclosed by Hong’s ¶131 which teaches an “image/video capture device 136.” Deploying GENG's keypoint-stability framework as an operator-usable system would predictably involve adopting HONG's explicit camera input – all features HONG describes for the same type of keypoint detection pipeline. HONG expressly identifies the purpose: "to improve the performance of the key-point detector " (¶135) – directly aligned with GENG's goal of obtaining a stable keypoint set for template synthesis. The combinations yield predictable results from known elements applied for their established functions. KSR . Claims 1, 2, 11 , 12, 14, and 15 are rejected, in the alternative, under 35 U.S.C. 102(a)(1) as anticipated by Suzuki et al., US 2009/0041340 to (hereinafter " SONY "). Claim 1. SONY discloses a feature point registration device, comprising a feature point extraction unit (SONY: "The feature point extraction section 24 extracts feature points from a learning use input image" (¶51, p. 15).) that extracts feature points from each of plural input images of the same object (SONY: "multiple images photographed from various viewpoints are prepared as learning-use input images" (¶65, p. 16).); a feature comparison unit (SONY: "the registered feature amounts are compared with feature amounts extracted from an image prepared also as an image used for learning. A result of this comparison is used to set feature amounts used in an actual recognition process." (¶28, p. 15).); and a registration unit that registers a part of feature points based on the comparison result (SONY: "A feature amount that has formed a pair the greatest number of times as a result of the comparison is registered in the model dictionary registration section" (Abstract, p. 1).). Claims 2, 11, 12. SONY further discloses calculating respective features (feature amounts) of the feature points (SONY: "The feature point extraction section 24 extracts feature points from a learning use input image" (¶51, p. 15).), comparing similarity of features among the input images (SONY: "the registered feature amounts are compared with feature amounts extracted from an image prepared also as an image used for learning. A result of this comparison is used to set feature amounts used in an actual recognition process." (¶28, p. 15).), and storing the input images and feature amounts in the disclosed memory devices used by SONY's registration sections (SONY: "A feature amount that has formed a pair the greatest number of times as a result of the comparison is registered in the model dictionary registration section" (Abstract, p. 1).). Claims 14, 15. SONY discloses the corresponding method (claim 14) and the system including a learning-use camera and the registration device ( claim 15) (SONY: "A feature amount that has formed a pair the greatest number of times as a result of the comparison is registered in the model dictionary registration section" (Summary, p. 1).) (SONY: "multiple images photographed from various viewpoints are prepared as learning-use input images" (1 [0065], p. 16).) . Claims 1, 3, 5, 10, 14, and 15 are rejected, in the alternative, under 35 U.S.C. 102(a)(1) as anticipated by Magai et al., US 2010/0074530 (hereinafter " CANON "). Claim 1. CANON discloses a feature point registration device comprising a feature point extraction unit that extracts feature points from each of plural input images (CANON: "extracts a feature point from a group of images including the input image and one or more of the reduced images" (Abstract, p. 1).) (CANON: "the feature point extraction unit 104 performs a Harris operator on the input image" (¶46, p. 10).) ; a feature comparison unit that compares feature points among the images (CANON: "determines as a matched feature point the feature point extracted from a matching position in each of two or more images in the group of images" (Abstract, p. 1).) ; and a registration unit that registers a part of feature points (the stable subset) in association with the input image based on the comparison result (CANON: "determines the feature points having the high reproducibility of extraction from among the candidates of the extracted feature points by the feature point extraction unit 104 and narrows down to a stable feature point." (¶25, p. 9).) (CANON: "register the calculated local feature quantity as a local feature quantity of the input image" (Independent claim 1).) . Claim 3. CANON discloses an image filter that performs image conversion processing on the input images using a predetermined parameter (CANON: "performs reduction processing on an input image to acquire a reduced image" (Abstract, p. 1).) , and that feature point extraction operates on the conversion-processing images (CANON: "extracts a feature point from a group of images including the input image and one or more of the reduced images" (Abstract, p. 1).) . Claim 5. CANON discloses, in combination with claim 3, comparison among the conversion-processing images (CANON: "determines as a matched feature point the feature point extracted from a matching position in each of two or more images in the group of images" (Abstract, p. 1).) . Claim 10. CANON's reduction processing applies low-pass smoothing - the "smoothing processing" alternative (CANON: "performs reduction processing on an input image to acquire a reduced image" (Abstract, p. 1).). Claims 14, 15. CANON discloses the corresponding method (claim 14) and a registration apparatus including the foregoing components (claim 15) (CANON: "extracts a feature point from a group of images including the input image and one or more of the reduced images" (Abstract, p. 1).) (CANON: "determines as a matched feature point the feature point extracted from a matching position in each of two or more images in the group of images" (Abstract, p. 1).) (CANON: "determines the feature points having the high reproducibility of extraction from among the candidates of the extracted feature points by the feature point extraction unit 104 and narrows down to a stable feature point." (¶25, p. 9).). Conclusion Examiner notes co-pending Application No. 18/809,538 (Panasonic IP Management; common inventors Fuchikami, Imura, and Hu; filed Aug. 20, 2024; pub. US 2024/0412391 A1; non-final action mailed Apr. 29, 2026), which claims a related but distinct invention directed to a graphical user interface for adjusting an image-filter parameter with live feature-point preview. The two applications carve out separate inventions from a shared disclosure and no double patenting rejection is warranted at this time. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ross Varndell whose telephone number is (571)270-1922. The examiner can normally be reached M-F, 9-5 EST. 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, O’Neal Mistry can be reached at (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Ross Varndell/Primary Examiner, Art Unit 2674 Application/Control Number: 18/809,990 Page 2 Art Unit: 2674 Application/Control Number: 18/809,990 Page 3 Art Unit: 2674 Application/Control Number: 18/809,990 Page 4 Art Unit: 2674 Application/Control Number: 18/809,990 Page 5 Art Unit: 2674 Application/Control Number: 18/809,990 Page 6 Art Unit: 2674 Application/Control Number: 18/809,990 Page 7 Art Unit: 2674 Application/Control Number: 18/809,990 Page 8 Art Unit: 2674 Application/Control Number: 18/809,990 Page 9 Art Unit: 2674 Application/Control Number: 18/809,990 Page 10 Art Unit: 2674 Application/Control Number: 18/809,990 Page 11 Art Unit: 2674 Application/Control Number: 18/809,990 Page 12 Art Unit: 2674 Application/Control Number: 18/809,990 Page 13 Art Unit: 2674 Application/Control Number: 18/809,990 Page 14 Art Unit: 2674 Application/Control Number: 18/809,990 Page 15 Art Unit: 2674 Application/Control Number: 18/809,990 Page 16 Art Unit: 2674 Application/Control Number: 18/809,990 Page 17 Art Unit: 2674 Application/Control Number: 18/809,990 Page 18 Art Unit: 2674 Application/Control Number: 18/809,990 Page 19 Art Unit: 2674 Application/Control Number: 18/809,990 Page 20 Art Unit: 2674 Application/Control Number: 18/809,990 Page 21 Art Unit: 2674 Application/Control Number: 18/809,990 Page 22 Art Unit: 2674 Application/Control Number: 18/809,990 Page 23 Art Unit: 2674 Application/Control Number: 18/809,990 Page 24 Art Unit: 2674 Application/Control Number: 18/809,990 Page 25 Art Unit: 2674
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