CTNF 18/735,368 CTNF 90895 DETAILED ACTION 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. Claims 1-20 are pending. Claim Interpretation - 35 USC § 112(f) 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. 07-30-06 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 preAIA 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: Claims 8 and 15: “a corrosion hotspot recognition module, used to…”; “a corrosion hotspot positioning module, used to…”; “an inspection optimization module, used to…”; “a correlation analysis unit, used to…”; “a correction unit, used to…”; and “a normalization unit, used to…”. 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. 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 preAIA 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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claims 1 and 8 , the following 2-step analysis is applied for analyzing the 35 U.S.C. § 101 subject matter eligibility of the claims. Step 1: The Statutory Categories Claim(s) 1 and 8 recite(s) a process (claim 1) and a machine (claim 8). Step 2A: The Judicial Exceptions Prong 1 : do the claims recite an exception? Claim(s) 1 and 8 is/are directed to an abstract idea, specifically mathematical concepts and mental processes (e.g., reading image information, analyzing correlation information of feature points, correcting location coordinates, and normalizing location information). Prong 2 : is the exception integrated into a practical application? The claim(s) does/do not integrate the abstract idea into a practical application because the recited elements (generic computer, modules, and units) merely provide a generic environment for data gathering and mathematical processing. The claims do not apply the calculated location information to effectuate a specific physical transformation, control action, or tangible outcome. Step 2B: The Inventive Concept Do the claims amount to "significantly more" than the exception? The additional elements in the claims (e.g., computer, corrosion hotspot recognition module, corrosion hotspot positioning module, inspection optimization module) are recited at a high level of generality. When considered individually and as an ordered combination, they represent nothing more than well-understood, routine, and conventional computing components performing standard data-processing functions, which does not amount to significantly more than the abstract idea itself. Conclusion : Claim(s) 1 and 8 is/are directed to an abstract idea and lacks an inventive concept. Claim(s) 1 and 8 is/are rejected as ineligible subject matter under 35 U.S.C. § 101. Regarding dependent claims 2-7 and 9-14 : limitations in these dependent claims have been examined in a similar way as to the above independent claims. It was found that claims 2-7 and 9-14 are ineligible subject matter under 35 U.S.C. § 101: Claims 2 and 9 : Ineligible. Merely adds data classification (recognizing a type), which is a mental process/abstract idea. Claims 3 and 10 : Ineligible. Simply lists specific categories of data (corrosion types), which does not provide an inventive concept. Claims 4 and 11 : Ineligible. Recites a "neural network algorithm," which is treated as a mathematical concept/abstract idea. Claims 5 and 12 : Ineligible. Recites mathematical operations ("Kalman wave"/filter) and mere data gathering from generic sensors (lidar, inertial). Claims 6 and 13 : Ineligible. Recites specific mathematical algorithms (memory neural network, Mutual nearest neighbors) without practical application. Claims 7 and 14 : Ineligible. Recites a purely mathematical step (normalizing line and angular coordinates). Claim Rejections - 35 USC § 103 07-20-fti The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. 07-21-aia AIA Claim (s) 1-2, 4-5, 7-9, 11-12, 14-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (WO2022156192A1) in view of Liu et al (US20160070265A1) . Regarding claims 1, 8 and 15 , Liu teaches a corrosion inspection vehicle, comprising: ( Wang , Fig. 1; "A wall-climbing robot system for rapid non-destructive detection of hidden defects of culverts and gates ... The mobile ultrasonic rapid detection system and the rust detection system are installed at the bottom of the vehicle body", p2; a vehicle configured for detecting rust (corrosion) on structures) a moving platform; ( Wang , Fig. 1; " robot body; The navigation and positioning system and the mobile system are installed on the robot body", p2; a moving platform via the robot body equipped with a mobile system) an image capturing device, disposed on the moving platform, wherein the image capturing device is used to capture an image information; ( Wang , "In this embodiment, the navigation and positioning system includes a multi-eye panoramic camera 21 and a lidar 22, the multi-eye panoramic camera is used to detect surface diseases of the culvert gate", p8; "the miniature multi-eye panoramic camera 21 shoots the surface of the detection area", p8; an image capturing device (multi-eye panoramic camera) disposed on the moving platform (robot body) that captures image information (shoots the surface)) a lidar positioning device, disposed on the moving platform; ( Wang , "In this embodiment, the navigation and positioning system includes a multi-eye panoramic camera 21 and a lidar 22 ... the lidar is used to realize the automatic composition of the culvert gate detection area", p8; a lidar positioning device disposed on the platform) a corrosion positioning system, disposed on the moving platform, wherein the corrosion positioning system includes: ( Wang , "The master controller communicates with the navigation and positioning system, the mobile system, the automatic knock detection system, the mobile ultrasonic rapid detection system, the rust detection system and the dual power system respectively", p2; a positioning system comprising a master controller connected to the navigation, positioning, and rust (corrosion) detection systems on the moving platform) a corrosion hotspot recognition module, used to read the image information to recognize a corrosion hotspot area; ( Wang , “the miniature multi-eye panoramic camera 21 shoots the surface of the detection area to realize a quick census of the defect area. The received information is processed by the general controller to complete the rough identification of defects", p8; using the captured camera images processed by the controller to recognize defect areas (corrosion hotspots)) a corrosion hotspot positioning module, used to obtain a location information of the corrosion hotspot area; and ( Wang , "The received information is processed by the general controller to complete the rough identification of defects and position", p8; "According to the defect positioning information obtained in the quick census mode", p8; obtaining location information (position) of the defect areas) Wang does not expressly disclose but Liu teaches: an inspection optimization module, used to correct the location information of the corrosion hotspot area to obtain a corrected location information, ( Liu , "The estimates provided by the sensors 252, 254, and 256 can be fused using an extended Kalman filter 266 or other suitable Kalman filter type, thereby obtaining a final position and velocity result 268 for the UAV ... The integral of the determined UAV velocity can be fused with the relative position estimate 264 and absolute position estimate 260 in order to determine the UAV position", [0085]; Wang teaches capturing position information but lacks specifically detailing an optimization module that corrects location information via feature point correlation. Liu teaches a processing unit (optimization module) that fuses data to correct/update position estimates to obtain a final, accurate position result) wherein the inspection optimization module includes: ( Liu , "The estimates provided by the sensors 252, 254, and 256 can be fused using an extended Kalman filter 266 or other suitable Kalman filter type, thereby obtaining a final position and velocity result 268 for the UAV.", [0085]; the module containing the following units for position correction) a correlation analysis unit, used to analyze a correlation information of a plurality of feature points in a plurality of frames in the image information; ( Liu , "the difference between keyframes can be determined by identifying and matching feature points present in both keyframes.", [0082]; analyzing the correlation between multiple frames (keyframes) by identifying and matching a plurality of feature points) a correction unit, used to correct the location information of a point to be corrected according to the correlation information; and ( Liu , "Based on the coordinates of the feature points in both keyframes, the similarity transformation matrix for the two keyframes can be determined", [0082]; "image analysis and mathematical modeling can be used to assess the differences between successive keyframes and thereby determine the translational movements", [0084]; correcting the location/movement information based on the transformation matrix and differences calculated from the feature points (correlation information)) a normalization unit, used to normalize the location information to obtain the corrected location information. ( Liu , "the sensing data from each sensor can be combined by converting all the sensing data into a single coordinate system 304.", [0094]; normalizing the location information by converting and combining data from different frames/sensors into a single unified coordinate system) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teaching of Liu into the wall-climbing robot system of Wang in order to improve the accuracy of the defect positioning mapping. While Wang teaches a general controller utilizing lidar and a camera for rough identification and positioning of rust defects, it relies on basic coordinate tracking which is susceptible to sensor noise and drift. Liu provides a robust methodology for correcting and normalizing position estimates by matching image feature points across successive frames. One with ordinary skill in the art would recognize that integrating Liu's feature-matching and coordinate normalization algorithms into Wang's controller would yield highly optimized, corrected location coordinates for the targeted corrosion hotspots, thereby eliminating tracking errors in complex structural environments. The combination of Wang and Liu also teaches other enhanced capabilities. Regarding claims 2 , 9 and 16 , the combination of Wang and Liu also teaches its/their respective base claim(s). The combination further teaches the corrosion positioning method according to claim 1, wherein the corrosion hotspot recognition module further reads the image information to recognize a corrosion hotspot type. ( Wang , "identify the location and category of internal defects", p2; "identifies disease types, locations and contours", p13; processing image and sensor data to recognize the specific type (category or disease type) of the corrosion/defect) Regarding claims 4 , 11 and 17 , the combination of Wang and Liu also teaches its/their respective base claim(s). The combination further teaches the corrosion positioning method according to claim 1, wherein the corrosion hotspot recognition module uses a neural network algorithm to recognize the corrosion hotspot area. ( Wang , "the deep neural network based on deformable convolution rotation area detection is used to identify the location and category of internal defects", p2; using a deep neural network algorithm to detect and recognize the location area of the defects/corrosion) Regarding claims 5 , 12 and 18 , the combination of Wang and Liu also teaches its/their respective base claim(s). The combination further teaches the corrosion positioning method according to claim 1, wherein the step of obtaining, by the corrosion hotspot positioning module, the location information of the corrosion hotspot area includes: obtaining, by the corrosion hotspot positioning module, a spatial coordinate information of an image capturing device and a lidar positioning device via a Kalman wave; ( Liu , "Exemplary relative orientation sensors include vision sensors, lidar", [0080]; "The estimates provided by the sensors 252, 254, and 256 can be fused using an extended Kalman filter 266 or other suitable Kalman filter type", [0085]; obtaining spatial coordinate estimates from vision (image capturing) and lidar devices using a Kalman filter => Kalman wave) removing, by the corrosion hotspot positioning module, noise of the spatial coordinate information via the Kalman wave; ( Liu , “pre-processed to improve data quality by filtering, noise reduction", [0094]; "The absolute and relative estimates provided by the sensors 202, 204, 206 can be fused (e.g., using an extended Kalman filter 216 or other type of Kalman filter) in order to provide a final yaw angle result 218... reduce the extent to which the magnetometer error influences the final result.", [0083]; filtering and removing noise/error from the spatial coordinate information via the Kalman filter (Kalman wave)) obtaining, by the corrosion hotspot positioning module, an inertial estimation coordinate information from an inertial sensing device; and ( Liu , "The scheme 250 utilizes an IMU 252... The IMU 252 can provide an estimation of the UAV acceleration 258.", [0084]; obtaining inertial estimation coordinate information from an IMU (inertial sensing device)) obtaining, by the corrosion hotspot positioning module, the location information of the corrosion hotspot area according to the spatial coordinate information whose noise is removed and the inertial estimation coordinate information. ( Liu , "The integral of the determined UAV velocity can be fused with the relative position estimate 264 and absolute position estimate 260 in order to determine the UAV position.", [0085]; obtaining the final location position by fusing the filtered relative spatial coordinates (from vision/lidar) with the absolute/inertial coordinate estimates) ( Comments : implementing a Kalman filter to fuse spatial data from vision and lidar sensors with inertial estimation data will effectively reduce sensor noise and improve the precision of the location information for the detected corrosion areas) Regarding claims 7 , 14 and 20 , the combination of Wang and Liu also teaches its/their respective base claim(s). The combination further teaches the corrosion positioning method according to claim 1, wherein the step of normalizing, by the inspection optimization module, the location information, the inspection optimization module normalizes a line coordinate and an angular coordinate of each of the feature points. ( Liu , "Rpi^W+T=pi^B, where R represents the rotation matrix between the global and local coordinate systems and T represents the translation vector from the origin of the global coordinate system to the origin of the local coordinate system ... absolute and relative environmental data can be used to estimate the UAV position and orientation using the relation described above”, [0087]; "The correspondence may include information regarding one or more transformations (e.g., translation, rotation, scaling) that can be applied to map the proximity depth images onto the vision depth images", [0103]; normalizing the location information (mapping into a single coordinate system) by applying a translation vector (line/linear coordinate) and a rotation matrix (angular coordinate) to the feature points) 07-21-aia AIA Claim (s) 3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (WO2022156192A1) in view of Liu et al (US20160070265A1) and further in view of Loosararian et al (US20200262261A1) . Regarding claims 3 and 10 , the combination of Wang and Liu also teaches its/their respective base claim(s). The combination does not expressly disclose but Loosararian teaches the corrosion positioning method according to claim 2, wherein the corrosion hotspot type includes flange bolt corrosion, rust bag, floating rust, weld bead corrosion or stainless steel pitting corrosion . ( Loosararian , "In certain embodiments, an industrial surface is ferromagnetic, for example including iron, steel, nickel, cobalt, and alloys thereof.", [0232]; "measuring characteristics of a surface being traversed such as thickness of the surface, curvature of the surface, ultrasound (or ultra-sonic) measurements to test the integrity of the surface and/or the thickness of the material forming the surface, heat transfer, heat profile/mapping, profiles or mapping any other parameters, the presence of rust or other corrosion, surface defects or pitting, the presence of organic matter or mineral deposits on the surface, weld quality and the like.", [0233]; inspecting industrial surfaces, including steel, to detect the presence of rust, other corrosion, pitting, and weld quality, which renders obvious a corrosion hotspot type including stainless steel pitting corrosion, rust, or weld bead corrosion) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teaching of Loosararian into the modified system and method of Wang and Liu in order to expand the system's defect detection capabilities to specifically identify critical structural anomalies such as rust, pitting, and weld quality issues on steel and ferromagnetic surfaces, thereby providing a more comprehensive and accurate diagnosis of surface degradation on the inspected structures. The combination of Wang, Liu and Loosararian also teaches other enhanced capabilities . 07-21-aia AIA Claim (s) 6, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (WO2022156192A1) in view of Liu et al (US20160070265A1) and further in view of Xu et al (US20230252667A1) . Regarding claims 6 , 13 and 19 , the combination of Wang and Liu also teaches its/their respective base claim(s). The combination does not expressly disclose but Xu teaches the corrosion positioning method according to claim 1, wherein the step of analyzing, by the inspection optimization module, the correlation information of the feature points in the frames in the image information, ( Xu , "These techniques compare 2D feature points from the input image 602 to 2D feature points included in the image database 506 to determine a set of images from the image database that include one or more of the extracted 2D feature points.", [0044]; comparing and analyzing the correlation of 2D feature points between an input image and images in a database to match frames) the inspection optimization module uses a memory neural network algorithm to record and encode locations of the feature points, ( Xu , "In an alternative implementation, a convolutional neural network can be trained based on a dataset of acquired reference images to extract 2D feature points from images 502 in the feature point extraction block 504.", [0036]; "Image retrieval is performed first, using techniques such as APGeM, DenseVLAD, NetVLAD, etc. This step determines the set of images from the image database to perform 2D-2D matching between 2D feature points ... NetVLAD is another technique for determining matching images from an image database and is described in “NetVLAD: CNN architecture for weakly supervised place recognition” by Relja Arandjelović, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivic, CVPR 2016.", [0044]; using a Convolutional Neural Network (CNN) trained to extract 2D feature points from images, specifically utilizing CNN architectures designed for place recognition and image retrieval (such as NetVLAD) to record and encode the locations of these feature points. Applying a CNN architecture to encode feature point locations teaches the use of a memory neural network algorithm) and then uses a Mutual nearest neighbors algorithm to match the feature points in the frames. ( Xu , "The extracted 2D feature points are input to 2D-2D matching 606 where the extracted 2D-2D matching can be performed by iteratively matching the locations of the extracted 2D feature points to locations of feature points in the image database to minimize the summed Euclidian distance between the two sets. For example, matches can be determined using mutual nearest neighbors. For an image pair, two features are considered to satisfy mutual nearest neighbors if the minimum Euclidean feature distance point matched in the second image by the first feature has the first feature as it’s corresponding minimum distance point.", [0045]; receiving the extracted feature points and matching them between frames utilizing a mutual nearest neighbors algorithm) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teaching of Xu into the modified system and method of Wang and Liu in order to improve the accuracy and robustness of the robotic system's image-based spatial localization, enabling the system to reliably extract, encode, and match feature points across multiple continuous inspection frames using a convolutional neural network and a mutual nearest neighbors algorithm. The combination of Wang, Liu and Xu also teaches other enhanced capabilities. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time. 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, Amandeep Saini can be reached on (571)272-3382. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272- 1000. /JIANXUN YANG/ Primary Examiner, Art Unit 2662 5/17/2026 Application/Control Number: 18/735,368 Page 2 Art Unit: 2662 Application/Control Number: 18/735,368 Page 3 Art Unit: 2662 Application/Control Number: 18/735,368 Page 4 Art Unit: 2662 Application/Control Number: 18/735,368 Page 5 Art Unit: 2662 Application/Control Number: 18/735,368 Page 6 Art Unit: 2662 Application/Control Number: 18/735,368 Page 7 Art Unit: 2662 Application/Control Number: 18/735,368 Page 8 Art Unit: 2662 Application/Control Number: 18/735,368 Page 9 Art Unit: 2662 Application/Control Number: 18/735,368 Page 10 Art Unit: 2662 Application/Control Number: 18/735,368 Page 11 Art Unit: 2662 Application/Control Number: 18/735,368 Page 12 Art Unit: 2662 Application/Control Number: 18/735,368 Page 13 Art Unit: 2662 Application/Control Number: 18/735,368 Page 15 Art Unit: 2662 Application/Control Number: 18/735,368 Page 16 Art Unit: 2662 Application/Control Number: 18/735,368 Page 17 Art Unit: 2662 Application/Control Number: 18/735,368 Page 18 Art Unit: 2662
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