DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/04/2026 has been entered.
Response to Amendment
Claims 1 and 16 are amended.
Claims 2 and 17 are canceled.
Claims 1, 3-16, and 18-30 are pending.
Claim Rejections - 35 USC § 112
Claims 1, 3-15 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.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 1 recites the broad recitation “at least one of multifrequency electromagnetic signal data and communication signal data”, and the claim also recites “perform signal filtering and feature extraction on the multifrequency electromagnetic signal data and communication signal data” (emphasis added) which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-16 and 18-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1, claims 1 belongs to the statutory category of a machine.
Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 1 recite(s) “determining and distinguishing buried objects”, “combining at least a portion of the collected data with at least one predefined classifier,” ‘perform signal filtering and feature extraction on the multifrequency electromagnetic signal data and communication signal data using at least one of… principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities, wherein the processed Training Data represents physical electromagnetic behavior of buried utilities, and wherein the processed Training Data is derived from extracted electromagnetic signal features including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features, wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features associated with buried utilities;’ and “classifying the collected data based on a predicted probability to obtain classification data;” which are directed to mathematical concepts and/or mental processes because
Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “a system”, “using Artificial Intelligence (Al)”, “a processor for”, “at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI)” and “a user interface for selecting classification data based on one or more user-defined selection criteria;” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer, and because using generic AI/ML technology to perform data evaluations or calculations, as identified under Prong 1 above without any details regarding how the AI/ML algorithm or model functions or is trained amounts to simply utilizing the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. The elements of “a receiving element comprising at least one of a locator, Sonde, transmitting antenna, receiving antenna, transceiver, inductive clamp, electrical clip, or a satellite system for collecting at least one of multifrequency electromagnetic signal data and communication signal data, wherein the at least one of multifrequency electromagnetic signal data and communication signal data are collected data, wherein the multifrequency electromagnetic signal data comprises physical measurements of electromagnetic fields converted into electronic data suitable for subsequent processing; an input element for allowing a user to input one or more predefined classifiers;”, “wherein the processor outputs Training Data;”, and “an output element configured to output a utility-locating map identifying a location of the buried utility and displaying, on the utility-locating map, a utility type associated with the buried utility” are considered to be data gathering or outputting steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The elements of “wherein the processor is further configured to perform signal filtering and feature extraction on the multifrequency electromagnetic signal data and communication signal data using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities;” are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use.
Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “a system”, “using Artificial Intelligence (Al)”, “a processor for”, “at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI)” and “a user interface for selecting classification data based on one or more user-defined selection criteria;” are considered to amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible see MPEP 2106.05(d) and the use of the AI and Neural Network merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). The elements of “a receiving element comprising at least one of a locator, Sonde, transmitting antenna, receiving antenna, transceiver, inductive clamp, electrical clip, or a satellite system for collecting at least one of multifrequency electromagnetic signal data and communication signal data, wherein the at least one of multifrequency electromagnetic signal data and communication signal data are collected data, wherein the multifrequency electromagnetic signal data comprises physical measurements of electromagnetic fields converted into electronic data suitable for subsequent processing; an input element for allowing a user to input one or more predefined classifiers;”, “wherein the processor outputs Training Data;”, and “an output element configured to present the classification data selected based on the one or more user-defined selection criteria” are considered to be insignificant extra-solution activity per MPEP 2106.05(g) and well-understood, routine, conventional activities previously known to the industry per MPEP 2106.05(d)(see example “i" and prior art of record in addition applicant’s specification Pages 14-27). The elements of “wherein the processor is further configured to perform signal filtering and feature extraction on the multifrequency electromagnetic signal data and communication signal data using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities;” are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) (see prior art of record, in addition applicant’s specification Pages 14-27).
Under step 1, claims 16 belongs to a statutory.
Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 16 recite(s) “method for determining and distinguishing buried objects”, “using the collected data alone or in combination with user predefined classifiers as Training Data;”, ‘signal filtering and feature extraction using at least one of… principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities;’ “classifying the collected data based on a predicted probability to obtain classified data, wherein the processed Training Data represents physical electromagnetic behavior of buried utilities”, “wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics of buried utilities”, “wherein the processed Training Data is derived from extracted electromagnetic signal features including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features, wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features associated with buried utilities;”, “selecting, by a processor, classified data based on one or more user-defined selection criteria;” and “organizing the classified data selected based on the one or more user-defined selection criteria” which are directed to mathematical concepts and/or mental processes because
Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “computer implemented”, “using Artificial Intelligence (Al)”, “using at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI)” and “by a processor” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and because using generic AI/ML technology to perform data evaluations or calculations, as identified under Prong 1 above without any details regarding how the AI/ML algorithm or model functions or is trained amounts to simply utilizing the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. The elements of “collecting at least one of multifrequency electromagnetic signal data and communication signal data comprising receiving the data from at least one of a locator, Sonde, transmitting antenna, receiving antenna, transceiver, inductive clamp, electrical clip, or a satellite system from a plurality of sources, wherein the at least one of multifrequency electromagnetic signal data and communication signal data are collected data, wherein the multifrequency electromagnetic signal data comprises physical measurements of electromagnetic fields converted into electronic data suitable for subsequent processing;”, “providing the Training Data to at least one Neural Network;”, and “outputting a utility-locating map identifying a location of the buried utility and displaying, on the utility-locating map, a utility type associated with the buried utility” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The elements of “performing, by the processor, signal filtering and feature extraction using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde-dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities to obtain processed classification data;” are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use.
Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “computer implemented”, “computer implemented”, “using Artificial Intelligence (Al)”, “using at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI)” and “by a processor” are considered to amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible see MPEP 2106.05(d) and the use of the AI and Neural Network merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). The elements of “collecting at least one of multifrequency electromagnetic signal data and communication signal data comprising receiving the data from at least one of a locator, Sonde, transmitting antenna, receiving antenna, transceiver, inductive clamp, electrical clip, or a satellite system from a plurality of sources, wherein the at least one of multifrequency electromagnetic signal data and communication signal data are collected data;”, “providing the Training Data to at least one Neural Network;” and outputting a utility-locating map identifying a location of the buried utility and displaying, on the utility-locating map, a utility type associated with the buried utility” are considered to be insignificant extra-solution activity per MPEP 2106.05(g) and well-understood, routine, conventional activities previously known to the industry per MPEP 2106.05(d)(see example “i" and prior art of record in addition applicant’s specification Pages 14-27). The elements of “performing, by the processor, signal filtering and feature extraction using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde-dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities to obtain processed classification data;” are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) (see prior art of record, in addition applicant’s specification Pages 14-27).
Claim 1 and 16 recites the additional element(s) of using generic AI/ML technology, i.e. “at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI), and classifying the collected data based on a predicted probability to obtain classification data, wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics”, “providing the Training Data to at least one Neural Network; using at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI) and classifying the collected data based on a predicted probability to obtain classified data” and “wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics of buried utilities” to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI), and classifying the collected data based on a predicted probability to obtain classification data, wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics”, “providing the Training Data to at least one Neural Network; using at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI) and classifying the collected data based on a predicted probability to obtain classified data” and “wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics of buried utilities” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI), and classifying the collected data based on a predicted probability to obtain classification data, wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics”, “providing the Training Data to at least one Neural Network; using at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI) and classifying the collected data based on a predicted probability to obtain classified data” and “wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics of buried utilities” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
Claims 3, 15, 18 and 30 merely describes examples of possible generic receiving/outputting elements that can be use in the claim which does not integrate the claims into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception as they are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity and are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g) (i and ii) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(see prior art of record).
Claims 4-14 and 19-29 further describe the abstract idea and/or further describe the data used by the abstract idea which does not integrate the claims into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-6, 8, 10-16, 18-21, 23-30 are rejected under 35 U.S.C. 103 as being unpatentable over KIM NAMGYU ET AL: "A novel 3D GPR image arrangement for deep learning-based underground object classification” THE INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, (Online vol. 22, no. 6, 2 August 2019 (2019-08-02), pages 740-751, hence forth NPL1 in view of CHIKAYOSHI (JP-2020039841-A) see translation attached for reference, hence forth Chika.
In claim 1, NPL1 discloses a system for determining and distinguishing buried objects using Artificial Intelligence (Al) (see abstract page 740, column 2, lines 17-27) comprising: a receiving element comprising at least one of a locator, Sonde, transmitting antenna, receiving antenna, transceiver, inductive clamp, electrical clip, or a satellite system (figure 2, "30 GPA antenna’ Section 2.1 “two antennas for a transmitter and a receiver”) for collecting at least one of multifrequency electromagnetic signal data and communication signal data (figure 1; paragraph 2.2, Step 1; page 744, column 2, lines 33-45), wherein the at least one of multifrequency electromagnetic signal data and communication signal data are collected data (Introduction: “electromagnet wave propagation” Section 3.1 “frequencies from 500 to 1000 MHz”), wherein the multifrequency electromagnetic signal data comprises physical measurements of electromagnetic fields converted into electronic data suitable for subsequent processing (Section 2.1 “transmitter radiates the electromagnetic wave into the ground. Then, the receiver collects the signal reflected from underground objects or ground layer interfaces”); an input element for allowing a user to input one or more predefined classifiers (page 744, column 2, Step 5: page 744, column 2, line 46 - page 745, column 7, line 6 “users”; page 745, Table 7); a processor (page 747 “NVIDIA TITAN X Pascal”) for combining at least a portion of the collected data with at least one predefined classifier (page 740, column 2, lines 24-26; paragraph 2.2, Step 7 to Step 5; paragraph 3.2), wherein the processor outputs Training Data (paragraph 2.2, Step 5; page 746, Table 2.);
wherein the processor is further configured to perform signal filtering and feature extraction on the multifrequency electromagnetic signal data and communication signal data (Page 745 paragraph 3.2 “extract 2D grid images from 3D GPR data, the dimensions of 3D window box are chosen by considering experimental parameters” examiner considers this to be said signal filtering and feature extraction as it limits the signal data to the data in the specified area and extracts images from the data) to generate processed Training Data representing physical signal characteristics of buried utilities (Page 741 Column 1 “pipeline” Page 743 Column 1, “3D GPR data are reconstructed into 2D grid images to be used as training data”); wherein the processed Training Data represents physical electromagnetic behavior of buried utilities (Page 741 Column 1 “signal reflected from underground objects”);
at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI) (paragraph 2.2, Step 6; page 746, paragraph 3.3), and classifying the collected data based on a predicted probability to obtain classification data (paragraph 2.2, Step 7); a user interface (Page 744 see Figs, examiner notes that “GeoScopeTM Mk IV” has a user interface to display GUI running on external computer with a full 3D real-time data display) for selecting classification data based on one or more user-defined selection criteria (page 744, column 2, Step 5; page 744, column 2, line 46 - page 745, column 2, line 6 notably section 3.1 “enables users to acquire” “selected as training sets”; page 745, Table 1); and an output element configured to
output a utility-locating map (page 744, column 2 “map”) identifying a location of the buried utility (Page 741 Column 1 “pipeline”) and displaying, on the utility-locating map, a utility type associated with the buried utility (figures 19, 20; page 744, first section of column 1 Step 7 “Classification of GPR data” examiner notes that “GeoScopeTM Mk IV” has a display GUI).
NPL1 does not explicitly disclose wherein the processor is further configured to perform signal filtering and feature extraction on the multifrequency electromagnetic signal data and communication signal data using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities, and wherein the processed Training Data is derived from extracted electromagnetic signal features including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features, wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features associated with buried utilities; (Emphasis added).
Chika teaches wherein the processor is further configured to perform signal filtering and feature extraction (Pages 95-96 “Signal (source) separation and feature analysis”, “extraction of feature”) on the multifrequency electromagnetic signal data and communication signal data (Page 13 “electromagnetic waves”) using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques (Page 95-96 “PCA”) to generate processed Training Data representing physical signal characteristics of buried utilities (Page 95 “train” and Pages 87, 162 “ground radar” and “rubber pipes”), and wherein the processed Training Data is derived from extracted electromagnetic signal features (Page 13 “electromagnetic waves” Page 95 “train”) including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features (Page 95-96 “PCA”), wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features (Pages 94-95 and 130 “harmonic”) associated with buried utilities (Pages 87, 162 “ground radar” and “rubber pipes”);
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed that wherein the processor is further configured to perform signal filtering and feature extraction on the multifrequency electromagnetic signal data and communication signal data using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities, and wherein the processed Training Data is derived from extracted electromagnetic signal features including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features, wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features associated with buried utilities based on the teachings of Chika in combination with NPL1 for the benefit of increased accuracy (Chika Page 95).
In claim 3, NPL1 discloses wherein Training Data further includes imaging data collected from a camera or imaging element (page 741 section 2.1 “imaging”; page 743 section 2.2).
In claim 4, NPL1 discloses wherein Training Data further includes sensor data (page 741 section 2.1, page 743 section 2.2).
In claim 5, NPL1 discloses wherein Training Data further includes mapping data (Page 744 column 2 lines 1-30).
In claim 6, NPL1 discloses all of claim 5. NPL1 further discloses wherein mapping data includes at least one of depth or orientation data (page 743 section 2.2 “longitudinal, and depth directions”).
In claim 8, NPL1 discloses wherein Training Data further includes one or more of image data, current and/or voltage data, even and odd harmonics data, active and/or passive signal data, and spatial relationship data (page 741 section 2.1 “image”).
In claim 10, NPL1 discloses wherein Training Data further includes other data (page 743 section 2.2).
In claim 11, NPL1 discloses all of claim 10. NPL1 further discloses wherein other data comprises one or more of observed data, user classification data, and ground truth data (page 741-743 section 2.1, “observed”).
In claim 12, NPL1 discloses all of claim 11. NPL1 further discloses wherein ground truth data comprises one or more of ownership data, manufacturer data, connection data, utility box or junction data, and obstacle data (page 750 “cavity, pipe, manhole and subsoil background”).
In claim 13, NPL1 discloses wherein Training Data may be processed and classified in real time, or stored and post-processed in the Cloud (page 741 section 2.1 page 747 column 2 Lines 1-20, examiner considered the data processed in real time).
In claim 14, NPL1 discloses wherein classifying the collected data comprises determining at least one of a utility type, electrical characteristics, connection type, asset type, manufacturer type, ownership type, location type, direction type, right of way type, or damaged asset type (page 750 “cavity, pipe, manhole and subsoil background”).
In claim 15, NPL1 discloses wherein the output element comprises one or more of a visual display, a speaker or other sound producing element, and a vibration or other tactile producing element (Page 744 see Figs, examiner notes that “GeoScopeTM Mk IV” has a user interface to display GUI running on external computer with a full 3D real-time data display).
In claim 16, NPL1 discloses a computer implemented method for determining and distinguishing buried objects using Artificial Intelligence (Al) (see abstract page 740, column 2, lines 17-27) comprising: collecting at least one of multifrequency electromagnetic signal data and communication signal data (figure 1; paragraph 2.2, Step 1; page 744, column 2, lines 33-45) comprising receiving the data from at least one of a locator, Sonde, transmitting antenna, receiving antenna, transceiver, inductive clamp, electrical clip, or a satellite system (Section 2.1 “two antennas for a transmitter and a receiver”) from a plurality of sources (figure 2, "30 GPA antenna’), wherein the at least one of multifrequency electromagnetic signal data and communication signal data are collected data (Introduction: “electromagnet wave propagation” Section 3.1 “frequencies from 500 to 1000 MHz”), wherein the multifrequency electromagnetic signal data comprises physical measurements of electromagnetic fields converted into electronic data suitable for subsequent processing (Section 2.1 “transmitter radiates the electromagnetic wave into the ground. Then, the receiver collects the signal reflected from underground objects or ground layer interfaces”); performing, by the processor, signal filtering and feature extraction (Page 745 paragraph 3.2 “extract 2D grid images from 3D GPR data, the dimensions of 3D window box are chosen by considering experimental parameters” examiner considers this to be said signal filtering and feature extraction as it limits the signal data to the data in the specified area and extracts images from the data) to generate processed Training Data representing physical signal characteristics of buried utilities to obtain processed classification data (Page 741 Column 1 “signal reflected from underground objects” Column 2 “classification”); using the collected data alone or in combination with user predefined classifiers as Training Data (page 744, column 2, Step 5: page 744, column 2, line 46 - page 745, column 7, line 6 “users”; page 745, Table 7); providing the Training Data to at least one Neural Network (paragraph 2.2, Step 5; page 746, Table 2.); using at least one Neural Network for processing the Training Data using Deep Learning performed by Artificial Intelligence (AI) (paragraph 2.2, Step 6; page 746, paragraph 3.D) and classifying the collected data based on a predicted probability to obtain classified data (paragraph 2.2, Step 7), wherein the processed Training Data represents physical electromagnetic behavior of buried utilities (Page 741 Column 1 “pipeline” Page 743 Column 1, “3D GPR data are reconstructed into 2D grid images to be used as training data”), and wherein classifying the collected data by the at least one Neural Network improves accuracy of determining and distinguishing buried objects based on physical electromagnetic signal characteristics of buried utilities (paragraph 2.1 “increase the classification accuracy”); selecting, by a processor (Page 744 see Figs, examiner notes that “GeoScopeTM Mk IV” has a user interface to display GUI running on external computer i.e. processor with a full 3D real-time data display), classified data based on one or more user-defined selection criteria (page 744, column 2, Step 5; page 744, column 2, line 46 - page 745, column 2, line 6 notably section 3.1 “enables users to acquire” “selected as training sets”; page 745, Table 1); output a utility-locating map (page 744, column 2 “map”) identifying a location of the buried utility (Page 741 Column 1 “pipeline”) and displaying, on the utility-locating map, a utility type associated with the buried utility (figures 19, 20; page 744, first section of column 1 Step 7 “Classification of GPR data” examiner notes that “GeoScopeTM Mk IV” has a display GUI).
NPL1 does not explicitly disclose performing, by the processor, signal filtering and feature extraction using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities, and wherein the processed Training Data is derived from extracted electromagnetic signal features including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features, wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features associated with buried utilities; (Emphasis added).
Chika teaches performing, by the processor, signal filtering and feature extraction (Pages 95-96 “Signal (source) separation and feature analysis”, “extraction of feature”) using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques (Page 95-96 “PCA”) to generate processed Training Data representing physical signal characteristics of buried utilities (Page 95 “train” and Pages 87, 162 “ground radar” and “rubber pipes”), and wherein the processed Training Data is derived from extracted electromagnetic signal features (Page 13 “electromagnetic waves” Page 95 “train”) including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features (Page 95-96 “PCA”), wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features (Pages 94-95 and 130 “harmonic”) associated with buried utilities (Pages 87, 162 “ground radar” and “rubber pipes”);
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed that performing, by the processor, signal filtering and feature extraction using at least one of a particle filter, principal component analysis (PCA) or PCA bandpass, gradient-tensor processing, or Sonde dipole equation techniques to generate processed Training Data representing physical signal characteristics of buried utilities, and wherein the processed Training Data is derived from extracted electromagnetic signal features including at least one of PCA features, gradient tensor features, Sonde dipole features, or filtered electromagnetic signal features, wherein the extracted electromagnetic signal features further comprise at least one of harmonic signal features, phase data features, or phase difference features associated with buried utilities based on the teachings of Chika in combination with NPL1 for the benefit of increased accuracy (Chika Page 95).
In claim 18, NPL1 discloses wherein Training Data further includes imaging data collected from a camera or imaging element (page 741 section 2.1 “imaging”; page 743 section 2.2).
In claim 19, NPL1 discloses wherein Training Data further includes sensor data (page 741 section 2.1, page 743 section 2.2).
In claim 20, NPL1 discloses wherein Training Data further includes mapping data (Page 744 column 2 lines 1-30).
In claim 21, NPL1 discloses all of claim 20. NPL1 further discloses wherein mapping data includes at least one of depth or orientation data (page 743 section 2.2 “longitudinal, and depth directions”).
In claim 23, NPL1 discloses wherein Training Data further includes one or more of image data, current and/or voltage data, even and odd harmonics data, active and/or passive signal data, and spatial relationship data (page 741 section 2.1 “image”).
In claim 24, NPL1 discloses wherein Training Data further includes one or more of phase data, phase difference data, ground penetrating radar (GPR) data, acoustic data, and tomography data (page 743 section 2.2 “GPR”).
In claim 25, NPL1 discloses wherein Training Data further includes other data (page 743 section 2.2).
In claim 26, NPL1 discloses all of claim 25. NPL1 further discloses wherein other data comprises one or more of observed data, user classification data, ground truth data, physics model data, and ground return current data (page 741-743 section 2.1, “observed”).
In claim 27, NPL1 discloses all of claim 26. NPL1 further discloses wherein ground truth data comprises one or more of ownership data, manufacturer data, connection data, utility box or junction data, and obstacle data (page 750 “cavity, pipe, manhole and subsoil background”).
In claim 28, NPL1 discloses wherein Training Data may be processed and classified in real time, or stored and post-processed in the Cloud (page 741 section 2.1 page 747 column 2 Lines 1-20, examiner considered the data processed in real time).
In claim 29, NPL1 discloses wherein classifying the collected data comprises determining at least one of a utility type, electrical characteristics, connection type, asset type, manufacturer type, ownership type, location type, direction type, right of way type, or damaged asset type (page 750 “cavity, pipe, manhole and subsoil background”).
In claim 30, NPL1 discloses wherein the output element comprises one or more of a visual display, a speaker or other sound producing element, and a vibration or other tactile producing element (Page 744 see Figs, examiner notes that “GeoScopeTM Mk IV” has a user interface to display GUI running on external computer with a full 3D real-time data display).
Claim(s) 7 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL1 in view of Chika and in further view of Speasl (US 20190236365 A1).
In claim 7, NPL1 does not explicitly disclose wherein Training Data further includes fiber optic data.
Speasl teaches wherein Training Data further includes fiber optic data (Par. 43).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed that wherein Training Data further includes fiber optic data based on the teachings of Speasl in order to automatically detect such features (Speasl Par. 42) thus leading to an improved system.
In claim 22, NPL1 does not disclose wherein Training Data further includes fiber optic data.
Speasl teaches wherein Training Data further includes fiber optic data (Par. 43).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed that wherein Training Data further includes fiber optic data based on the teachings of Speasl in order to automatically detect such features (Speasl Par. 42) thus leading to an improved system.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL1 in view of Chika and in further view of Salman (US 20190383965 A1).
In claim 9, NPL1 does not explicitly disclose wherein Training Data further includes one or more of phase data and phase difference data.
Salman teaches wherein Training Data further includes one or more of phase data and phase difference data (Par. 210 “vibroseis minimum phase correction” 211 “survey phase match”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed that wherein Training Data further includes one or more of phase data and phase difference data based on the teachings of Salman in order to improve the resolution (Salman Par. 210 “vibroseis minimum phase correction”) thus leading to an improved system.
Response to Arguments
Applicant's arguments filed 06/04/2026 have been fully considered but they are not persuasive. regarding applicant’s 101 arguments on pages 9-11 the examiner respectfully disagrees. Regarding outputting a map of the underground utilities, this amounts to outputting data as cited above and is not considered to be a practical application or an improvement. Specifying the format of the outputted data does not rectify the issue. Further, the improvement is not described as an improvement to the creation process of the map itself, nor the functioning of a computer but rather the analysis of the data from which the map is based on. Per MPEP 2106.05(a)(II) “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Thus, the claims are not considered to be an improvement.
Regarding applicant’s 103 arguments on pages 11-13, the examiner respectfully disagrees. Kim explicitly describes that the images are the electromagnetic waves i.e. signals in section 2.1 which is well within the BRI of “electromagnetic signal data” as claimed. Kim does disclose extracting features from the data, and cited mapping as cited above. The arguments regarding Maier are considered moot.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20200182995 A1 METHOD, APPARATUS, AND SYSTEM FOR OUTDOOR TARGET TRACKING;
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/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
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