CTNF 18/704,993 CTNF 101504 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. Election/Restrictions Applicant's election of Species I (claims 5-6, and 23-24) in the reply filed on 04/10/2026 is acknowledged. Applicant's election is noted with traverse. Although Applicant points to the similarity of classifications between Species I and II and asserts that a single search can cover both species without serious burden, the two species are directed to independent and distinct technical features such that the search is necessarily diverse for each species ( Species I is directed to a method wherein the positioning line for an object to be detected comprises a first positioning sub-line and a second positioning sub-line , each corresponding to a respective sub-region of the object's region, wherein the distance between the object and the reference region is derived from a plurality of individual sub-line-to-reference-line distances. Species II is directed to a method wherein the positioning line is obtained by directly selecting a plurality of second positioning points from within the region of the object to be detected and deriving the positioning line from the coordinate values of those points in the second direction. ) Thus, while both species may be disclosed in the same overall image-based distance-measurement environment, the species are directed to different underlying positioning-line construction techniques, diverge precisely at the step of how the positioning line is constructed. Species I requires a sub-region/sub-line approach and a distance calculation based on multiple distances associated with multiple positioning sub-lines. Species II does not require the first and second sub-regions, the first and second positioning sub-lines, or the plurality of distances between the reference line and the first and second positioning sub-lines. Instead, Species II uses a coordinate-point-based approach in which a plurality of second positioning points are selected in the object region and the positioning line is obtained from coordinate values of those positioning points. These represent fundamentally different technical approaches to positioning-line generation, and Applicant's reliance on claim 1 is not persuasive because claim 1 was already identified as generic. The existence of a generic claim does not require examination of all dependent species before any generic claim is found allowable. The issue is not whether the species share the broad objective of measuring a distance in an image, but whether the species share the same or corresponding special technical features defining the contribution of each claimed species over the prior art. Here, the species-specific features are different : Species I relies on sub-region segmentation, separate positioning sub-lines, and plural sub-line-based distance determinations, whereas Species II relies on selecting multiple positioning points and deriving a positioning line from coordinate values of those points. A search directed to Species I would require investigation into techniques for region segmentation, sub-region partitioning, and multi-line distance aggregation in image-based measurement systems (e.g., CPC classes G06T 7/70, G06T 7/11, G06T 7/62). Such a search would not necessarily be expected to locate the most relevant prior art for Species II, which requires investigation into direct point-sampling and coordinate-averaging methods for deriving a single representative positioning line from a detected object region (e.g., CPC classes G06T 7/70, G06V 10/25, G06T 7/10). Likewise, a search tailored to Species II's point-coordinate averaging approach would not necessarily surface the most relevant prior art directed to multi-sub-region decomposition and sub-line aggregation of Species I. These searches may overlap at a broad level, but they are not coextensive and would require separate consideration of distinct technical approaches. Accordingly, the search for the generic claims would not reasonably encompass the specific subject matter of each dependent subcombination or species, and examination of all groups together would impose a serious search and examination burden. For at least these reasons, and upon reconsideration of Applicant’s traversal, the restriction requirement is still deemed proper and is therefore made FINAL. Examination will proceed on the elected invention only. The application has pending claims 1–20 (non-elected claims 7 and 25 are withdrawn from further consideration). Claim Objections 07-29-01 AIA Claim 3 is objected to because of the following informalities: the phrase “-- obtaining respective a first positioning point-- ” contains an apparent typographical/ grammatical error. It should be “--obtaining a respective first positioning point--” . Appropriate correction is required. 07-29-01 AIA Claim 24 is objected to because of the following informalities: claim 24 depends from claim 23 and recites steps beginning with " the computer program instructions cause the computer to perform: " without an introductory transitional clause connecting the dependent language to the claim body. Compare with claims 22, 23, 26, and 27, each of which properly introduce dependent steps with the phrase " wherein the computer program instructions cause the computer to perform: ". The absence of the " wherein " transitional clause in claim 24 renders the dependent claim body grammatically incomplete and structurally inconsistent with the parallel dependent claims of this application . Appropriate correction is required. Claims 19, 21–24, and 26–27 are objected to because the phrase “ non-transitory readable storage medium ” appears to omit the modifier " computer- " before " readable " from the conventional phrase “ non-transitory computer-readable storage medium ”. Appropriate correction is required. 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 1-3, 5-6, 8-10, 19-24, and 26-29 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without significantly more. This rejection has been made in accordance with the current USPTO subject matter eligibility framework, including MPEP §§ 2103–2106.07, the 2019 Revised Patent Subject Matter Eligibility Guidance, the October 2019 Patent Eligibility Guidance Update, the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the July 2024 AI Subject Matter Eligibility Examples, the August 4, 2025 USPTO memorandum titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. § 101," and the USPTO's guidance concerning Ex parte Desjardins , Appeal No. 2024-000567. The claims have been evaluated under the broadest reasonable interpretation, and the claims have been considered as a whole. Step 1 (Statutory Category) Independent claim 1 is directed to a distance measurement method and therefore falls within the statutory category of a process. Independent claim 19 is directed to a non-transitory readable storage medium having stored thereon computer program instructions and therefore falls within the statutory category of a manufacture. Independent claim 20 is directed to a computer program product stored on a non-transitory computer-readable storage medium and therefore likewise falls within the statutory category of a manufacture. Accordingly, the analysis proceeds to Step 2A. Step 2A, Prong One (Judicial Exception) Independent claim 1 recites, in substance: obtaining an image to be detected including at least one object; obtaining a reference region and a region of the object based on the image; obtaining a reference line based on the reference region, the reference line being used to locate the reference region; obtaining a positioning line based on the region of the object, the positioning line being used to locate the region of the object; and obtaining a distance between the region of the object and the reference region based on the reference line and the positioning line. These limitations recite an abstract idea: collecting image data, identifying regions within the image, deriving representative lines from coordinate values of selected points within those regions, and computing a numerical distance between those lines. The core operations are: selecting points from an identified region, computing the average of their coordinate values to obtain a representative line, and calculating the difference between two such lines. These are mathematical concepts ; specifically, mathematical relationships (the average coordinate value of selected points defines a representative line position) and mathematical calculations (the distance between two lines derived from coordinate averages). See 2019 Revised Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019). The claim does NOT recite an improvement to the way images are captured, encoded, segmented, or rendered. The claim does NOT recite an improvement to how regions are identified or how points are selected from a technical standpoint. The claim does NOT recite an improvement to computer architecture, memory, processing speed, or any other aspect of computer functionality. Rather, the image and regions are used as inputs to an abstract mathematical averaging and distance-computation process. The claim is similar in character to claims courts have found abstract where the focus is collecting information, analyzing the information, and acting on the results. In Electric Power Group, LLC v. Alstom S.A. , 830 F.3d 1350, 1354 (Fed. Cir. 2016), the Federal Circuit recognized claims directed to collecting, analyzing, and displaying information as abstract. The present claims similarly collect image data, identify regions, perform mathematical operations on coordinate values of selected points within those regions, and output a computed distance. The character of claim 1 as a whole is mathematical coordinate processing and distance computation, not an improvement to imaging technology, object detection, or distance measurement hardware. Independent claims 19 and 20 recite substantially the same abstract idea implemented as computer program instructions stored on a non-transitory medium. Merely encoding the same abstract mathematical process as stored program instructions executed on a generic computer does not avoid the judicial exception. Accordingly, claims 1, 19, and 20 recite an abstract idea under Step 2A, Prong One. Step 2A, Prong Two (Practical Application) The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application. The recited " image to be detected ", " reference region ", " region of the object to be detected ", " reference line ", " positioning line ", and " distance " amount to data inputs, intermediate computed values, and a final output of the abstract mathematical process, not additional elements that impose a meaningful practical constraint on the claims. The claims do not recite a particular improvement to image-processing technology . They do not improve how an image is captured, segmented, classified, compressed, enhanced, or transformed. The step of "obtaining an image to be detected" is a mere data-gathering step that adds no meaningful weight to the eligibility analysis. See MPEP § 2106.05(g). The claims do not recite a particular improvement to object detection or region identification technology. Claim 1 recites "obtaining a reference region and a region of an object based on the image" at a purely functional, result-oriented level without specifying any technical mechanism for how regions are identified. The claim covers any and all methods of obtaining regions from any image for any purpose, an overbreadth that forecloses a finding of practical application integration. The claims do not recite a particular improvement to distance measurement technology. The steps of obtaining a reference line, a positioning line, and a distance between them describe the mathematical concept itself, averaging coordinate values and computing the difference, without any specific technical implementation that would confine the claims to a concrete technological improvement. See Electric Power Group , 830 F.3d at 1356 (claims focused on the results of an analysis without specifying how the results are achieved are abstract). This case is distinguishable from Ex parte Desjardins . In Desjardins , the claims were found to reflect an improvement in machine-learning technology itself. Here, the claims do not recite any improvement to a technical process, they recite mathematical operations applied to image data using generic computing, which is precisely the type of result-oriented claiming that fails Prong 2. Nor does limiting the abstract idea to the field of image-based distance measurement make the claims eligible. In Recentive Analytics, Inc. v. Fox Corp. , the Federal Circuit rejected the argument that applying data-processing logic to a new field of use was sufficient for eligibility where the claims did not recite a technical improvement in the underlying process itself. Similarly here, applying coordinate averaging and line-distance computation to regions in an image is a field-of-use limitation, not an integration of the abstract idea into a practical application. Accordingly, the claims do not integrate the judicial exception into a practical application under Step 2A, Prong Two. Step 2B (Inventive Concept) The additional elements, considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. The claims use generic computer components to perform ordinary computing functions, obtaining image data, identifying regions, selecting points, computing averages, and computing distances. These are conventional data-processing operations performed using generic computer technology well-understood, routine, and conventional in the field of image-based detection at the time of filing. See Alice Corp. v. CLS Bank Int'l , 573 U.S. 208, 225–26 (2014). The ordered combination also does not provide an inventive concept. The ordered combination follows the abstract idea itself: obtain an image, identify regions, select points, average their coordinate values to derive lines, compute the distance between the lines. This is no more than the abstract mathematical idea implemented on generic computer components. Dependent claim 2 recites selecting a plurality of first positioning points in the reference region and obtaining the reference line based on the average of their coordinate values in a second direction. This limitation recites the mathematical averaging operation itself and does not add significantly more. Dependent claim 3 recites selecting calibration points on the contour of the reference region at top, bottom, and intermediate positions, drawing lines parallel to the second direction through each calibration point, and taking the midpoint of each such line within the reference region as a positioning point. These additional steps recite further mathematical operations, geometric construction and midpoint computation, applied to the abstract coordinate data and do not add significantly more. Dependent claims 5 and 6 recite subdividing the object region into first and second sub-regions, selecting positioning sub-points within each, computing the average coordinate value of each set to obtain respective sub-lines, computing individual distances between each sub-line and the reference line, and combining those distances. These limitations recite additional mathematical averaging and distance computation operations applied to sub-regions and do not add significantly more. Dependent claim 8 recites performing binarization processing on the image to obtain the reference region. Binarization is a conventional, well-understood image-processing operation and does not add significantly more. See MPEP § 2106.05(d). Dependent claim 9 recites processing the image using a neural network algorithm to obtain the region of the object. The use of a neural network algorithm for object detection in images was well-understood, routine, and conventional in the field of computer vision at the time of filing. The claim recites the use of a neural network at a purely functional level without specifying any improvement to the neural network architecture, training methodology, or operation. This is consistent with Recentive Analytics and the USPTO's 2024 AI guidance, which recognize that applying a generic neural network to a new data type or field of use, without more, does not supply an inventive concept. Dependent claim 10 recites that the object region is located on the same side of the reference region. This is a spatial relationship between data objects, a mathematical relationship and does not add significantly more. Claims 19, 21-24, and 26-27 recite storage medium counterparts using stored computer program instructions to perform substantially the same operations as claims 1-3, 5-6, 8-10 . The recitation of a non-transitory storage medium and generic computer program instructions does not transform the abstract idea into patent-eligible subject matter. Claims 20, and 28-29 recite computer program product counterparts. The recitation of a computer program product stored on a non-transitory computer-readable storage medium is a generic computer implementation that does not add significantly more to the abstract idea. Accordingly, claims 1-3, 5-6, 8-10, 19-24, and 26-29 are directed to a judicial exception without significantly more and are therefore rejected under 35 U.S.C. § 101 . 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 Claims 1-3, 5-6, 8-10, 19-22, and 28-29 are 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 1 recites "the image to be detected including at least one object to be detected;" and then recites "obtaining a reference region and a region of an object to be detected..." . The phrase "an object" make the it unclear if it refers to the recited object or another object. It should be " the object". Claim 19 has the exact same error as Claim 1: "including at least one object ..." followed by "region of an object ..." . Because claims 1-3, 5-6, 8-10, 20 and 28-29 depend from claim 1, they inherit this ambiguity, fail to cure the deficiency. Because claims 21-24 and 26-27 depend from claim 19, they inherit this ambiguity, fail to cure the deficiency. Claim Rejections - 35 USC § 102 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. 07-07-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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1, 10, 19 and 20 are rejected under 35 U.S.C. §102(a)(2) as being anticipated by Lee (Lee et al, US 2024/0029230 A1, 2024) . Regarding claim 1 , Lee teaches a distance measurement method, comprising: obtaining an image to be detected, the image to be detected including at least one object to be detected; ( [0072-0079]: Lee teaches recognizing a reference mark from a captured digital image of the display apparatus obtained by using an image capturing apparatus, wherein the digital image includes at least one object [the substrate boundary, film member/ cutting line region] to be detected. ) obtaining a reference region and a region of an object to be detected based on the image to be detected; ( [0075-0081], [0083-0085], [Fig. 4-5]: Lee teaches identifying, from the captured digital image, the reference region, the first area A1 having the first boundary A1o and second boundary A1i, and designating the measurement area IA as the region of the object to be detected, wherein the measurement area IA is designated based on the reference mark M and contains the cutting line to be detected. The measurement area/ first area including the cutting line is the claimed region of the object to be detected. ) obtaining a reference line based on the reference region, the reference line being used to locate the reference region; ( [0071], [0089-0092]: Lee teaches generating the reference line BL parallel to the boundary of the substrate within the measurement area IA, based on a pixel matrix selected adjacent to the first boundary A1o of the reference region A1, wherein the reference line BL is generated to overlap the column having the largest number of selected pixels [FIG. 6A–6B] or alternatively the column having the lowest average grayscale value [FIG. 7], and is used to locate the reference region A1. Lee also teaches a boundary line ML positioned along a boundary of the reference mark M and passing through the reference point MP of the reference mark M. ) obtaining a positioning line based on the region of the object to be detected, the positioning line being used to locate the region of the object to be detected; and ( [0071], [0099-0105], [0126-0129]: Lee teaches generating detection areas RoI within the measurement area IA based on coordinate values of the first pixel P1 and second pixel P2 adjacent to the first boundary A1o and second boundary A1i of the first area A1 respectively, and positioning each detection area RoI such that its inner side boundary RE1 is apart from the reference line BL by a preset pixel distance. ) obtaining a distance between the region of the object to be detected and the reference region based on the reference line and the positioning line. ( [0071], [0129-0133], [0141]: Lee teaches measuring the distance d between the cutting line CL and the reference point MP / boundary line ML of the reference mark M, wherein whether the film member 200 is cut into a desired shape is confirmed by measuring this distance d. ) Regarding claim 10 , Lee teaches the distance measurement method according to claim 1, wherein a region of the at least one object to be detected is located on a same side of the reference region. ( [0076-0078], [0083-0086], [0104-0105], [0132-0133], [Fig. 5]: Lee teaches that the reference mark M is positioned in the second area A2, while the cutting line CL is positioned in the first area A1. Lee further teaches that the measurement area IA is designated apart from the reference point MP of the reference mark M toward the boundary of the substrate and includes the first area boundaries A1o and A1i. Lee also teaches that the detection areas RoI for detecting the cutting line CL are positioned relative to the reference line BL and that the distance d is measured between the cutting line CL and the reference point MP of the reference mark M. Accordingly, Lee teaches that the cutting-line/ object region is located on the same side of the reference mark/ reference region, namely toward the boundary/ first-area side from the reference mark. ) Regarding claims 19 and 20 . The rationale provided for claim 1 is incorporated herein. In addition, Lee teaches a computing system, including at least one processor and memory [0072], using computer programs [0021] for method of claim 1. Accordingly, the method for distance measurement of claim 1 corresponds to the non-transitory readable storage medium of claim 19, as well as the computer program product of claim 20, and performs the steps disclosed herein. Therefore, the claims are all rejected . 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. 07-21-aia AIA Claim s 2–3, 21–22 and 28–29 are rejected under 35 U.S.C. §103 as being unpatentable over Lee (Lee et al, US 2024/0029230 A1, 2024) in view of Zhang (Zhang et al, CN 112985274 B, 2021), as provided by the applicant in the IDS filed 10/22/2024 . Regarding claim 2 , Lee teaches the distance measurement method according to claim 1, wherein the reference line is parallel to a first direction; ( [0088], [Fig. 6B]: Lee teaches generating a reference line BL parallel to the boundary of the substrate within the measurement area IA, wherein the boundary of the substrate and the reference line BL extend along the y-axis, which corresponds to the first direction. ) obtaining the reference line based on the reference region includes: selecting a plurality of first positioning points in a pixel matrix associated with the reference region; and ( [Fig. 6A], [0083-0087], [0090-0092]: Lee teaches designating the measurement area IA based on the reference mark M, and selecting a pixel matrix of a preset size adjacent to the first boundary A1o between the first area A1 and the background area BG within the measurement area IA. Lee further teaches extracting grayscale values of respective pixels constituting the pixel matrix and selecting a pixel having a value closest to a preset grayscale value from each row of the pixel matrix. The selected pixels correspond to the claimed plurality of first positioning points in a pixel matrix associated with the reference region. ) obtaining the reference line based on coordinate values of the plurality of first positioning points in a second direction, the second direction being perpendicular to the first direction, and a coordinate value of the reference line in the second direction being an average of the coordinate values of the plurality of first positioning points in the second direction largest number of the shared coordinate values of the plurality of first positioning points in the second direction . ( [Fig. 6A-6B], [0090-0094]: Lee teaches that a column of the pixel matrix may be parallel to the first boundary A1o, and that pixels are selected from rows of the pixel matrix. Lee further teaches that the reference line BL is generated to overlap a column in which the largest number of pixels are selected among the columns of the pixel matrix. For example, Lee teaches selected pixels at (1,2), (2,4), (3,3), and (4,3), and generating the reference line BL to overlap the third column because the third column contains the largest number of selected pixels. ) Lee discloses deriving a reference line by selecting multiple discrete pixels from a pixel matrix and using the column positions of those selected pixels to determine where to place the reference line. In his embodiment, Lee uses a mode-like approach: the reference line overlaps the column in which the largest number of selected pixels are located. Lee does not calculate a spatial average of the selected pixels’ coordinate values, which Zhang teaches: obtaining the reference line based on the reference region includes: selecting a plurality of first positioning points in the reference region; and ( [0078-0082]: Zhang teaches that alignment marks may be provided at both ends of the bonding pad distribution direction, and that the mark areas where the alignment marks are located are identified in the bonding area image. Zhang further teaches determining corresponding position points in the two mark areas, for example determining midpoints A1 and A2 of the short sides in the two aligned areas. The identified mark areas correspond to the claimed reference region, and the corresponding position points/ midpoints A1 and A2 correspond to the claimed plurality of first positioning points in the reference region. ) obtaining the reference line based on coordinate values of the plurality of first positioning points in a second direction, the second direction being perpendicular to the first direction, and a coordinate value of the reference line in the second direction being an average of the coordinate values of the plurality of first positioning points in the second direction. ( [0076], [0078-0082], [0088]: Zhang teaches establishing a coordinate system in the bonding area image and determining corresponding position points A1 and A2 in two mark areas, where connecting A1 and A2 obtains the measurement reference line/baseline parallel to the X-axis/ first direction. Zhang further teaches that A1 and A2 may be midpoints of short sides of the aligned mark areas. Because the Y-axis direction is perpendicular to the X-axis/ first direction, the Y-axis corresponds to the claimed second direction. A midpoint of a side is determined from the coordinates of the two endpoint vertices of that side, such that the midpoint coordinate in the Y-axis/ second direction is the average of the Y-axis/ second-direction coordinates of the two endpoint vertices. ) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lee's reference line generation method to derive the reference line by averaging the second-direction coordinate values of the selected positioning points, as taught by Zhang, because Lee's mode-based approach and Zhang's averaging approach represent nothing more than alternative, well-known statistical techniques for estimating a representative position from a set of discrete sampled points, a mode (Lee) versus a mean (Zhang), and substituting one for the other is a routine engineering choice between two known aggregation methods applied in an identical context to achieve the identical result: a stable, repeatable reference line parallel to the first direction. A person of the ordinary skills would further have been motivated to make this substitution because averaging is well-recognized as producing a more noise-tolerant and geometrically balanced reference position than mode-selection, particularly in a small pixel matrix where a single outlier can disproportionately determine the modal column, and the substitution would have yielded no more than predictable results. Regarding claim 3 , Lee [as modified by Zhang] teaches the distance measurement method according to claim 2, wherein selecting the plurality of first positioning points in the reference region includes: selecting a plurality of first calibration points on a contour of the reference region, the plurality of first calibration points being respectively located at top, bottom, and at least one intermediate position between the top and bottom of the contour of the reference region; ( [0065-0066], [0083-0088], [Fig. 6]: Zhang teaches identifying an image region by identifying the outline/contour of the region using an edge detection algorithm, determining vertices of the region, and obtaining a target straight line based on the vertices. Zhang’s Fig. 6 shows vertices B1, B2, B3, and B4 on the contour of a pad region and side segments L3 and L4 of the region. The vertices and side locations teach or at least suggest selecting calibration points on the contour of the identified region, including calibration points at different vertical positions along the contour, such as top, bottom, and intermediate positions. ) obtaining a first straight line parallel to the second direction based on each first calibration point; and ( [0083-0088], [Fig. 6]: Zhang teaches obtaining target straight lines based on vertices and side segments of the identified region. In particular, Zhang teaches that the pad region is a parallelogram having short sides L3 and L4 and that midpoints are determined on those side segments based on the vertices. These side/ segment lines correspond to straight line segments of the identified region extending in a direction transverse to the first direction, which is parallel to the claimed second direction. ) obtaining respective a first positioning point of the plurality of first positioning points based on a line segment of the first straight line in the reference region, the first positioning point being a midpoint of the line segment of the first straight line in the reference region. ( [0088], [Fig. 6]: Zhang teaches that B1, B2, B3, and B4 are vertices of pad area Pad1; L3 and L4 are short sides of the pad area; midpoint B5 of short side L3 is determined based on vertices B1 and B2; midpoint B6 of short side L4 is determined based on vertices B3 and B4; and the line connecting midpoints B5 and B6 is the target straight line of the pad area. ) Regarding claims 21–22 and 28–29 . The rationale provided for claims 2–3 is incorporated herein. In addition, Lee teaches a computing system, including at least one processor and memory [0072], using computer programs [0021] for method of claims 2-3. Accordingly, the method for distance measurement of claims 2 – 3 corresponds to the non-transitory readable storage medium of claims 21–22, as well as the computer program product of claims 28–29, and performs the steps disclosed herein. Therefore, the claims are all rejected . 07-21-aia AIA Claim s 5–6, 9, 23–24 and 27 are rejected under 35 U.S.C. §103 as being unpatentable over Lee in view of Cai (Cai et al, WO 2021/212297 A1, 2021) . Regarding claim 5 , Lee teaches the distance measurement method according to claim 1, wherein the positioning line is parallel to a first direction; ( [0128-0132], [Fig. 14]: Lee teaches detecting the cutting line CL by connecting at least two selected cutting points, wherein the cutting line CL corresponds to the claimed positioning line. Lee further teaches that the cutting line CL may be formed parallel to the boundary line ML of the reference mark M. The direction of the boundary line ML of the reference mark M corresponds to the claimed first direction. ) However, Lee teaches generating a plurality of detection areas RoI but fails to teach the object region includes a first sub-region and a second sub-region , and the positioning line includes a first positioning sub-line and a second positioning sub-line , which Cai teaches: the region of the object to be detected includes a first sub-region and a second sub-region; the positioning line includes a first positioning sub-line and a second positioning sub-line, the first positioning sub-line is used to locate the first sub-region, and the second positioning sub-line is used to locate the second sub-region; ( [Figs. 7A-7E], [Figs. 8A-8F], [0069–0074]: Cai teaches that an object detected in an image has a region associated with it, and that the object's region can be divided into multiple portions for measurement purposes. Specifically, Cai teaches using a bounding box of the detected object and identifying at least two measurement points, including a first measurement point and a second measurement point, associated with different portions of the object's region, wherein each measurement point is used to locate a respective portion of the object region and to identify a corresponding target line for distance determination. Cai further teaches that the two measurement points respectively correspond to distinct sub-portions of the object and are each associated with a separate projected reference line, such that a first target line locates a first portion of the object region and a second target line locates a second portion of the object region. ) obtaining the positioning line based on the region of the object to be detected includes: selecting a plurality of first positioning sub-points in the first sub-region; obtaining the first positioning sub-line based on coordinate values of the plurality of first positioning sub-points in a second direction, the second direction being perpendicular to the first direction, and a coordinate value of the first positioning sub-line in the second direction being an average of the coordinate values of the plurality of first positioning sub-points in the second direction; selecting a plurality of second positioning sub-points in the second sub-region; and obtaining the second positioning sub-line based on coordinate values of the plurality of second positioning sub-points in the second direction, a coordinate value of the second positioning sub-line in the second direction being an average of the coordinate values of the plurality of second positioning sub-points in the second direction. ( [FIGs. 7D-7E], [FIG. 8F], [0068-0074]: Cai teaches selecting a plurality of measurement points from within the region of a detected object. Specifically, selecting multiple random points within a bounding box of the object, and computing a representative positional value from those points by calculating a weighted average of the coordinate values of the plurality of selected points, wherein the weighted average coordinate value serves as a single representative measurement point used to identify a target line that locates the object region for distance determination. Cai further teaches applying this point-selection and coordinate-averaging process independently to different portions of the object's bounding box to obtain respective measurement points, each associated with a corresponding target line, wherein each target line is determined based on the averaged coordinate values of the selected points in the perpendicular direction. ) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lee’s cutting-line detection to use Cai’s multiple measurement-point and multiple target-line technique. Lee already determines a positioning line from selected points in image detection areas for distance measurement. Cai teaches selecting multiple measurement points from different portions of an object region/bounding box, determining representative positions by averaging/weighted averaging, and using corresponding target lines for distance determination. A person of ordinary skill would have been motivated to apply Cai’s technique to Lee to improve robustness against local edge irregularities, image noise, or erroneous point selection in a single region, with the predictable result of a more stable positioning-line determination for distance measurement. Regarding claim 6 , Lee [as modified by Cai] teaches the distance measurement method according to claim 5, wherein obtaining the distance between the region of the object to be detected and the reference region based on the reference line and the positioning line includes: obtaining a distance between the first positioning sub-line and the reference line based on the first positioning sub-line and the reference line; ( [Figs. 8A-8F], [Fig. 11], [0076-0077]: Cai teaches obtaining a distance corresponding to each target line [each positioning sub-line] individually; specifically, for each of the at least one target line, the processing device determines a target distance corresponding to that target line based on a height and a horizontal distance to the camera corresponding to that target line. Cai further teaches that where two measurement points 962-1 and 962-2 are associated with respective target lines, the system identifies individual projected lines corresponding to each measurement point, such that a distance corresponding to the first target line, determined based on the first measurement point and its associated projected reference line, corresponds to the claimed distance between the first positioning sub-line and the reference line. ) obtaining a distance between the second positioning sub-line and the reference line based on the second positioning sub-line and the reference line; and ( [Figs. 8A-8F], [Fig. 11], [Fig. 12A], [0098-0105]: Cai teaches that the distance-determination process is applied to each of the at least one target lines. Cai further teaches, where first and second measurement points 962-1 and 962-2 are used, determining projected/target lines associated with the region between the projected line where the first measurement point is located and the projected line where the second measurement point is located. For each target line, Cai determines a corresponding target distance d i based on the horizontal distance m i and height n i corresponding to that specific target line according to Formula (3). Accordingly, Cai teaches independently obtaining a distance corresponding to a second target line, which corresponds to obtaining a distance between the second positioning sub-line and the reference line . ) obtaining the distance between the region of the object to be detected and the reference region based on the distance between the first positioning sub-line and the reference line and the distance between the second positioning sub-line and the reference line. ( [Fig. 11], [Fig. 12A-12B], [0106-0115]: Cai teaches determining the overall distance between the camera and the object based on at least one target distance corresponding to at least one target line. Cai further teaches determining the overall distance using individual target distances corresponding to respective target lines, including by weighted averaging according to Formula (5) or by weighted summing according to Formula (6). Cai also illustrates determining a first target distance corresponding to a target line associated with reference line 1242, a second target distance corresponding to a target line associated with reference line 1244, and a third target distance corresponding to a target line associated with reference line 1246. ) Regarding claim 9 , Lee teaches the distance measurement method according to claim 1, wherein obtaining the region of the object to be detected based on the image to be detected includes: However, Lee does not teach while Cai expressly does teach: processing the image to be detected based on a neural network algorithm to obtain the region of the object to be detected. ( [0062]: Cai teaches processing an image including an object and determining a bounding box including the object. Cai further teaches that the bounding box may be determined using an object detection algorithm, including R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, or a combination thereof. The detected bounding box corresponds to the claimed region of the object to be detected, and the listed object-detection algorithms correspond to neural-network algorithms. ) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lee’s image-based object/cutting-line region identification to use Cai’s neural-network object-detection technique. Lee already processes a captured image to identify the cutting-line/object region for distance measurement. Cai teaches using neural-network object detection algorithms to obtain an object region/bounding box from an image. A person of ordinary skill would have been motivated to apply Cai’s neural-network detection to Lee to improve automated object-region localization and reduce reliance on manually defined or rule-based image regions, with the predictable result of obtaining the object region from the image for subsequent line and distance measurement. Regarding claims 23–24 and 27 . The rationale provided for claims 5–6 and 9 is incorporated herein. In addition, Lee teaches a computing system, including at least one processor and memory [0072], using computer programs [0021] for method of claims 5–6 and 9 . Accordingly, the method for distance measurement of claims 5–6 and 9 corresponds to the non-transitory readable storage medium of claims 23–24 and 27 , and performs the steps disclosed herein. Therefore, the claims are all rejected . 07-21-aia AIA Claim s 8 and 26 are rejected under 35 U.S.C. §103 as being unpatentable over Lee in view of Nakao (Nakao et al, US 2018/0350060 A1, 2018) . Regarding claim 8 , Lee teaches the distance measurement method according to claim 1, wherein obtaining the reference region based on the image to be detected includes: However, Lee's region identification process is a continuous grayscale analysis ( [0115-0119], [0137-0139] ) , Lee does not convert the image into a binary representation by thresholding all pixels into black/white or 0/1 categories; Nakao teaches this specific binarization: performing a binarization processing on the image to be detected to obtain the reference region. ( [0142-0144], [0255-0257]: Nakao teaches calculating edge position from image/ edge information using binarization in a segment, determining a threshold of the binarization in a narrow region, and calculating a local threshold for edge-position detection by OTSU law. Nakao further teaches forming a segment, binarizing the segment, obtaining edge information, and calculating an edge position from the edge information. Accordingly, Nakao teaches performing binarization processing on image data to obtain edge/ region information used for image inspection, which corresponds to obtaining the claimed reference region by binarization. ) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lee’s grayscale-based region identification to include Nakao’s binarization processing. Lee already processes grayscale image data to identify measurement regions, the reference mark, and the cutting-line region for image-based distance measurement. Nakao teaches that binarization using a threshold, including a local threshold calculated by OTSU law, is a known technique for extracting edge/ region information from inspection images. A person of ordinary skill would have been motivated to apply Nakao’s binarization to Lee’s captured display image to more clearly separate the reference region from surrounding image portions and improve the stability of edge/ region extraction before line and distance measurement. The modification merely applies a known image-segmentation technique to Lee’s known grayscale inspection process to obtain the predictable result of clearer reference-region identification. Regarding claim 26 . The rationale provided for claim 8 is incorporated herein. In addition, Lee teaches a computing system, including at least one processor and memory [0072], using computer programs [0021] for method of claim 8 . Accordingly, the method for distance measurement of claim 8 corresponds to the non-transitory readable storage medium of claim 26 , and performs the steps disclosed herein. Therefore, the claims are all rejected. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEN KUDO whose telephone number is (571)272-4498. The examiner can normally be reached M-F 8am - 5pm. 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. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. KEN KUDO Examiner Art Unit 2671 /KEN KUDO/Examiner, Art Unit 2671 /VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671 Application/Control Number: 18/704,993 Page 2 Art Unit: 2671 Application/Control Number: 18/704,993 Page 3 Art Unit: 2671 Application/Control Number: 18/704,993 Page 4 Art Unit: 2671 Application/Control Number: 18/704,993 Page 5 Art Unit: 2671 Application/Control Number: 18/704,993 Page 6 Art Unit: 2671 Application/Control Number: 18/704,993 Page 7 Art Unit: 2671 Application/Control Number: 18/704,993 Page 8 Art Unit: 2671 Application/Control Number: 18/704,993 Page 9 Art Unit: 2671 Application/Control Number: 18/704,993 Page 10 Art Unit: 2671 Application/Control Number: 18/704,993 Page 11 Art Unit: 2671 Application/Control Number: 18/704,993 Page 12 Art Unit: 2671 Application/Control Number: 18/704,993 Page 13 Art Unit: 2671 Application/Control Number: 18/704,993 Page 14 Art Unit: 2671 Application/Control Number: 18/704,993 Page 15 Art Unit: 2671 Application/Control Number: 18/704,993 Page 16 Art Unit: 2671 Application/Control Number: 18/704,993 Page 17 Art Unit: 2671 Application/Control Number: 18/704,993 Page 18 Art Unit: 2671 Application/Control Number: 18/704,993 Page 19 Art Unit: 2671 Application/Control Number: 18/704,993 Page 20 Art Unit: 2671 Application/Control Number: 18/704,993 Page 21 Art Unit: 2671 Application/Control Number: 18/704,993 Page 22 Art Unit: 2671 Application/Control Number: 18/704,993 Page 23 Art Unit: 2671 Application/Control Number: 18/704,993 Page 24 Art Unit: 2671 Application/Control Number: 18/704,993 Page 25 Art Unit: 2671 Application/Control Number: 18/704,993 Page 26 Art Unit: 2671 Application/Control Number: 18/704,993 Page 27 Art Unit: 2671 Application/Control Number: 18/704,993 Page 28 Art Unit: 2671 Application/Control Number: 18/704,993 Page 29 Art Unit: 2671
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