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. 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 -20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more. Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process (claim 1, a method) or a machine (claim 9 , a system ) or a manufacture (claim 1 6 , a non-transitory machine- readable storage medium), which are statutory categories. However, evaluating claim 1 , under Step 2A , Prong One , the claim is directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship /mental process . The limitations include: cross-correlating each of the plurality of initial seismic phase picks using the plurality of traces as reference traces to generate a set of corrected phase picks for each of the plurality of initial seismic phase picks; calculating a probability density function for each set of corrected phase picks; and selecting a peak of each probability density functions as accurate seismic phase picks. Next , Step 2A , Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application. This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application Therefore, the claims are directed to an abstract idea. At Step 2B , consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B , there are no additional elements that make the claim significantly more than the abstract idea. The additional element s of “ receiving a seismic dataset with a plurality of initial seismic phase picks and a plurality of traces ” is considered insignificant extra-solution activity of collecting data that is not sufficient to integrate the claim into a particular practical application. The act of data gathering is considered insufficient to elevate the claim to a practical application. The additional element of “computer-implemented” is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system (Alice Corp. Pty. Ltd. v. CLS Bank Int’l 573 U.S. __, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014)). The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself. Dependent claim s 2-8 do not add limitations sufficient to integrate the abstract idea into a practical application or provide significantly more that the judicial exception. The additional limitations merely recite conventional data sources or additional mathematical or data-processing operations applied to seismic data. For example: C laim 2 specifies that the seismic dataset comprises distributed acoustic sensing (DAS) data, which only identifies the type of data being analyzed and does not change the nature of the mathematical processing performed on the data. Claims 3, 6 and 8 recite interpolating picks, applying amplitude gain control, and generating a polynomial fit, respectively, which are well-known signal processing or mathematical techniques applied to data. Claim 4 recites generating the initial picks using a machine learning algorithm, which merely introduces another form of mathematical data analysis. The examiner notes that the element “ using a machine learning algorithm ” is considered performing mathematical calculation which falls within the “mathematical concept” grouping of abstract ideas (see Example 47, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). Claim 5 recites determining the number of peaks based on user input, which amounts to insignificant extra-solution activity/mathematical analysis. Claim 7 recites identifying a seismic event location based on the resulting picks, which is simply using results of the mathematical analysis and does not improve the functionality of seismic sensors or any other technology. Accordingly, these additional elements merely limit the abstract mathematical processing to particular types of data or add routine data manipulation steps and therefore do not integrate the abstract idea into a practical application or provide an inventive concept under 35 U.S.C. § 101. Claims 9 and 16 are rejected 35 USC § 101 for the same rationale as in claim 1. The additional element s of “ a processor; a memory , and non-transitory machine-readable storage medium” are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system (Alice Corp. Pty. Ltd. v. CLS Bank Int’l 573 U.S. __, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014)). The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself. Dependent claim s 10-15 and 17-20 do not add limitations sufficient to integrate the abstract idea into a practical application or provide significantly more that the judicial exception. The additional limitations merely recite conventional data sources or additional mathematical or data-processing operations applied to seismic data. C laim 10 specifies that the seismic dataset comprises distributed acoustic sensing (DAS) data, which only identifies the type of data being analyzed and does not change the nature of the mathematical processing performed on the data. Claim 11 recites generating the initial picks using a machine learning algorithm, which merely introduces another form of mathematical data analysis. The examiner notes that the element “ using a machine learning algorithm ” is considered performing mathematical calculation which falls within the “mathematical concept” grouping of abstract ideas (see Example 47, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). Claim 12 recites determining the number of peaks based on user input, which amounts to insignificant extra-solution activity/mathematical analysis. Claims 1 3 and 15 recite applying amplitude gain control, and generating a polynomial fit, respectively, which are well-known signal processing or mathematical techniques applied to data. Claim 14 recites identifying a seismic event location based on the resulting picks, which is simply using results of the mathematical analysis and does not improve the functionality of seismic sensors or any other technology. Accordingly, these additional elements merely limit the abstract mathematical processing to particular types of data or add routine data manipulation steps and therefore do not integrate the abstract idea into a practical application or provide an inventive concept under 35 U.S.C. § 101. Claims 17 and 20 recite interpolating picks and generating a polynomial fit, respectively, which are well-known signal processing or mathematical techniques applied to data. Claim 18 recites determining the number of peaks based on user input, which amounts to insignificant extra-solution activity/mathematical analysis. Claim 19 recites identifying a seismic event location based on the resulting picks, which is simply using results of the mathematical analysis and does not improve the functionality of seismic sensors or any other technology. Accordingly, these additional elements merely limit the abstract mathematical processing to particular types of data or add routine data manipulation steps and therefore do not integrate the abstract idea into a practical application or provide an inventive concept under 35 U.S.C. § 101. 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 , 4 , 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Shearer (NPL: “ Improving local earthquake locations using the L1 norm and waveform cross correlation: Application to the Whittier Narrows, California, aftershock sequence ” (1997) (Applicant’s IDS filed 12/07/23) in view of Zhu et al. (NPL: “ PhaseNet: a deep-neural-network-based seismic arrival-time picking Method ” (2018)) (hereinafter Zhu) . As per claim 1, Shearer teaches receiving a seismic dataset with a plurality of initial seismic phase picks and a plurality of traces, cross-correlating each of the plurality of initial seismic phase picks using the plurality of traces as reference traces to generate a set of corrected phase picks for each of the plurality of initial seismic phase picks (see section Waveform Cross-Correlation: pages 8274-8277 and Figs. 5-6, where Shearer discloses refining seismic arrival times using waveform cross-correlation of seismic traces to obtain improved relative arrival-time measurements derived from waveform similarity between stations. For example, Shearer explains that the time shift/lag corresponding to the maximum of the cross-correlation function between two seismograms provides the differential arrival-time between the signals, thereby enabling highly precise relative arrival-time measurements obtained from similar waveforms recorded at different stations, which describe determining differential arrival times from the maximum of the cross-correlation function applied to seismic waveforms. ( The examiner notes that in seismic analysis, a phase pick corresponds to the estimated arrival-time of a seismic phase on a trace, and therefore under the broadest reasonable interpretation the refined arrival-time estimates obtained by cross-correlation correspond to corrected seismic phase picks and refining seismic phase picks refers to improving the precision of initially estimated phase arrival times by adjusting the pick positions based on analysis of the recorded seismic waveforms, such as aligning similar waveforms using cross-correlation to determine more accurate relative arrival-time measurements ). However, Shearer fails to explicitly teach calculating a probability density function for each set of corrected phase picks and selecting a peak of each probability density functions as accurate seismic phase picks . Zhu, however, teaches generating probabilistic outputs for seismic phase arrival and modeling arrival-time likelihood as a probability distribution over time samples, explaining that the method “generates probability distributions of P arrivals, S arrivals, and noise”, and that the peaks of these probability distributions correspond to the most likely arrival times, which directly corresponds to selecting the peak of the probability density function to determine an accurate phase pick (see last paragraph in page 263 through page 264). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate the probability-density-based-arrival-time estimation of Zhu into the cross-correlation-based arrival-time refinement process of Shearer because Shearer produces multiple candidate arrival-time corrections derived from waveform similarity across traces, while Zhu teaches modeling arrival-time uncertainty using a probability density function and selecting the peak of that distribution as the most probable arrival time, thereby providing a statistically robust mechanism for selecting the most reliable seismic phase pick from a set of candidate corrected picks generated by waveform cross-correlation. As per claim 4, the combination of Shearer and Zhu teaches the system as stated above. Zhu further teaches that the plurality of initial seismic phase picks is generated using a machine learning algorithm (see page 261: Summary ( i.e., “a deep-neural-network-based arrival-time picking method called “PhaseNet” that picks the arrival times of both P and S waves. Deep neural networks have recently made rapid progress in feature learning, and with sufficient training, have achieved super-human performance in many applications” )). As per claim 5, the combination of Shearer and Zhu teaches the system as stated above. While Zhu teaches generating probabilistic outputs representing likelihoods of seismic phase arrivals in the form of probability distributions over time samples and selecting peaks in those distributions corresponding to candidate seismic phase arrival times (see last paragraph in page 263 through page 264), Zhu, however, does not explicitly disclose wherein user input determines how many peaks are selected. However, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to allow user input to determine how many peaks are selected from the probability distributions because providing user-configurable parameters controlling peak detection or selection is a well-known design choice in signal processing and seismic analysis systems, thereby allowing an analyst to control the number of candidate seismic phase picks considered during seismic processing depending on the desired level of detection sensitivity or analysis requirements. As per claim 7, the combination of Shearer and Zhu teaches the system as stated above. Shearer further teaches identifying a seismic event location based on the accurate seismic phase picks (see page 8270, col. 1, i.e., identifying locations to the Catalog of Earthquake based on an iterative least square approach and standard one dime3nsional velocity model and page 8277, Fig. 7 “Reconciling picks and relative times at each station”). Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Cheng et al. (NPL: “ Utilizing distributed acoustic sensing and ocean bottom fiber optic cables for submarine structural characterization ” (2021) (hereinafter Cheng). As per claim 2 , the combination of Shearer and Zhu teaches the system as stated above except that the seismic dataset comprises distributed acoustic sensing (DAS) data. Cheng, however, teaches that distributed acoustic sensing systems use fiber-optic cables as dense arrays of seismic sensors capable of recording seismic wavefields along the cable, thereby generating seismic datasets from DAS measurements suitable for geophysical analysis (see Abstract). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply the seismic phase-picking and waveform cross-correlation techniques of Shearer and Zhu to seismic datasets obtained from DAS systems as taught by Cheng because DAS systems produce seismic waveform measurements recordings, thereby enabling accurate seismic signal processing and phase picking determination using DAS-derived seismic datasets. Claims 3, 9, 11, 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Hauksson et al. (NPL: “ Southern California Hypocenter Relocation with Waveform Cross Correlation, Part 1: Results Using the Double-Difference Method ” (2005) (hereinafter Hauksson). As per claim 3, the combination of Shearer and Zhu teaches the system as stated above except for interpolating the plurality of initial seismic phase picks. Hauksson, however, teaches determining differential seismic arrival times from the peaks of cross-correlation functions and applying interpolation (e.g., spline interpolation) around the correlation peak to achieve higher timing precision (see Abstract). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to interpolate the seismic phase picks generated by the methods of Shearer and Zhu because interpolation of the peak of the cross-correlation function would improve the precision of arrival-time estimates obtained from seismic waveform correlation, thereby improving the accuracy of the seismic phase-pick estimates produced from the seismic traces. As per claim 9, Shearer teaches a processor and a memory (see page 2939, section 3. “L1 Norm, Grid Search Algorithm and Station Terms”, i.e. computer ); receiving a seismic dataset with a plurality of initial seismic phase picks and a plurality of traces, cross-correlating each of the plurality of initial seismic phase picks using the plurality of traces as reference traces to generate a set of corrected phase picks for each of the plurality of initial seismic phase picks (see section Waveform Cross-Correlation: pages 8274-8277 and Figs. 5-6, where Shearer discloses refining seismic arrival times using waveform cross-correlation of seismic traces to obtain improved relative arrival-time measurements derived from waveform similarity between stations. For example, Shearer explains that the time shift/lag corresponding to the maximum of the cross-correlation function between two seismograms provides the differential arrival-time between the signals, thereby enabling highly precise relative arrival-time measurements obtained from similar waveforms recorded at different stations, which describe determining differential arrival times from the maximum of the cross-correlation function applied to seismic waveforms. ( The examiner notes that in seismic analysis, a phase pick corresponds to the estimated arrival-time of a seismic phase on a trace, and therefore under the broadest reasonable interpretation the refined arrival-time estimates obtained by cross-correlation correspond to corrected seismic phase picks and refining seismic phase picks refers to improving the precision of initially estimated phase arrival times by adjusting the pick positions based on analysis of the recorded seismic waveforms, such as aligning similar waveforms using cross-correlation to determine more accurate relative arrival-time measurements ). However, Shearer fails to explicitly teach calculating a probability density function for each set of corrected phase picks and selecting a peak of each probability density functions as accurate seismic phase picks . Zhu, however, teaches generating probabilistic outputs for seismic phase arrival and modeling arrival-time likelihood as a probability distribution over time samples, explaining that the method “generates probability distributions of P arrivals, S arrivals, and noise”, and that the peaks of these probability distributions correspond to the most likely arrival times, which directly corresponds to selecting the peak of the probability density function to determine an accurate phase pick (see last paragraph in page 263 through page 264). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate the probability-density-based-arrival-time estimation of Zhu into the cross-correlation-based arrival-time refinement process of Shearer because Shearer produces multiple candidate arrival-time corrections derived from waveform similarity across traces, while Zhu teaches modeling arrival-time uncertainty using a probability density function and selecting the peak of that distribution as the most probable arrival time, thereby providing a statistically robust mechanism for selecting the most reliable seismic phase pick from a set of candidate corrected picks generated by waveform cross-correlation. The combination of Shearer and Zhu teaches the system as stated above except for interpolating the plurality of initial seismic phase picks. Hauksson, however, teaches determining differential seismic arrival times from the peaks of cross-correlation functions and applying interpolation (e.g., spline interpolation) around the correlation peak to achieve higher timing precision (see Abstract). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to interpolate the seismic phase picks generated by the methods of Shearer and Zhu because interpolation of the peak of the cross-correlation function would improve the precision of arrival-time estimates obtained from seismic waveform correlation, thereby improving the accuracy of the seismic phase-pick estimates produced from the seismic traces. As per claim 11, the combination of Shearer, Zhu and Hauksson teaches the system as stated above. Zhu further teaches that the plurality of initial seismic phase picks is generated using a machine learning algorithm (see page 261: Summary ( i.e., “a deep-neural-network-based arrival-time picking method called “PhaseNet” that picks the arrival times of both P and S waves. Deep neural networks have recently made rapid progress in feature learning, and with sufficient training, have achieved super-human performance in many applications” )). As per claim 12, the combination of Shearer, Zhu and Hauksson teaches the system as stated above. While Zhu teaches generating probabilistic outputs representing likelihoods of seismic phase arrivals in the form of probability distributions over time samples and selecting peaks in those distributions corresponding to candidate seismic phase arrival times (see last paragraph in page 263 through page 264), Zhu, however, does not explicitly disclose wherein user input determines how many peaks are selected. However, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to allow user input to determine how many peaks are selected from the probability distributions because providing user-configurable parameters controlling peak detection or selection is a well-known design choice in signal processing and seismic analysis systems, thereby allowing an analyst to control the number of candidate seismic phase picks considered during seismic processing depending on the desired level of detection sensitivity or analysis requirements. As per claim 14, the combination of Shearer, Zhu and Hauksson teaches the system as stated above. Shearer further teaches identifying a seismic event location based on the accurate seismic phase picks (see page 8270, col. 1, i.e., identifying locations to the Catalog of Earthquake based on an iterative least square approach and standard one dime3nsional velocity model and page 8277, Fig. 7 “Reconciling picks and relative times at each station”). Claims 6 , 16, 18 and 1 9 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Helbig (NPL: “ Fifty years of amplitude control ” (1997). As per claim 6, the combination of Shearer and Zhu teaches the system as stated above except for applying amplitude gain control (AGC) to the seismic dataset. Helbig, however, describes amplitude-control methods used in seismic processing, including automatic gain control (AGC), which scales seismic trace amplitudes (see page 752). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply AGC to the seismic dataset processed by the methods of Shearer and Zhu because AGC is a conventional seismic preprocessing technique used to condition seismic waveform data prior to analysis, thereby, improving the consistency and interpretability of seismic traces used for subsequent seismic signal-processing operations such as phase picking and waveform correlation. As per claim 16, Shearer teaches a non-transitory machine-readable storage medium and a processor (see page 2939, section 3. “L1 Norm, Grid Search Algorithm and Station Terms”, i.e. computer ); receiving a seismic dataset with a plurality of initial seismic phase picks and a plurality of traces, cross-correlating each of the plurality of initial seismic phase picks using the plurality of traces as reference traces to generate a set of corrected phase picks for each of the plurality of initial seismic phase picks (see section Waveform Cross-Correlation: pages 8274-8277 and Figs. 5-6, where Shearer discloses refining seismic arrival times using waveform cross-correlation of seismic traces to obtain improved relative arrival-time measurements derived from waveform similarity between stations. For example, Shearer explains that the time shift/lag corresponding to the maximum of the cross-correlation function between two seismograms provides the differential arrival-time between the signals, thereby enabling highly precise relative arrival-time measurements obtained from similar waveforms recorded at different stations, which describe determining differential arrival times from the maximum of the cross-correlation function applied to seismic waveforms. ( The examiner notes that in seismic analysis, a phase pick corresponds to the estimated arrival-time of a seismic phase on a trace, and therefore under the broadest reasonable interpretation the refined arrival-time estimates obtained by cross-correlation correspond to corrected seismic phase picks and refining seismic phase picks refers to improving the precision of initially estimated phase arrival times by adjusting the pick positions based on analysis of the recorded seismic waveforms, such as aligning similar waveforms using cross-correlation to determine more accurate relative arrival-time measurements ). However, Shearer fails to explicitly teach calculating a probability density function for each set of corrected phase picks and selecting a peak of each probability density functions as accurate seismic phase picks . Zhu, however, teaches generating probabilistic outputs for seismic phase arrival and modeling arrival-time likelihood as a probability distribution over time samples, explaining that the method “generates probability distributions of P arrivals, S arrivals, and noise”, and that the peaks of these probability distributions correspond to the most likely arrival times, which directly corresponds to selecting the peak of the probability density function to determine an accurate phase pick (see last paragraph in page 263 through page 264). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate the probability-density-based-arrival-time estimation of Zhu into the cross-correlation-based arrival-time refinement process of Shearer because Shearer produces multiple candidate arrival-time corrections derived from waveform similarity across traces, while Zhu teaches modeling arrival-time uncertainty using a probability density function and selecting the peak of that distribution as the most probable arrival time, thereby providing a statistically robust mechanism for selecting the most reliable seismic phase pick from a set of candidate corrected picks generated by waveform cross-correlation. The combination of Shearer and Zhu teaches the system as stated above except for applying amplitude gain control (AGC) to the seismic dataset. Helbig, however, describes amplitude-control methods used in seismic processing, including automatic gain control (AGC), which scales seismic trace amplitudes (see page 752). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply AGC to the seismic dataset processed by the methods of Shearer and Zhu because AGC is a conventional seismic preprocessing technique used to condition seismic waveform data prior to analysis, thereby, improving the consistency and interpretability of seismic traces used for subsequent seismic signal-processing operations such as phase picking and waveform correlation. As per claim 18 , the combination of Shearer , Zhu and Helbig teaches the system as stated above. While Zhu teaches generating probabilistic outputs representing likelihoods of seismic phase arrivals in the form of probability distributions over time samples and selecting peaks in those distributions corresponding to candidate seismic phase arrival times (see last paragraph in page 263 through page 264), Zhu, however, does not explicitly disclose wherein user input determines how many peaks are selected. However, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to allow user input to determine how many peaks are selected from the probability distributions because providing user-configurable parameters controlling peak detection or selection is a well-known design choice in signal processing and seismic analysis systems, thereby allowing an analyst to control the number of candidate seismic phase picks considered during seismic processing depending on the desired level of detection sensitivity or analysis requirements. As per claim 19 , the combination of Shearer and Zhu teaches the system as stated above. Shearer further teaches identifying a seismic event location based on the accurate seismic phase picks (see page 8270, col. 1, i.e., identifying locations to the Catalog of Earthquake based on an iterative least square approach and standard one dime3nsional velocity model and page 8277, Fig. 7 “Reconciling picks and relative times at each station”). Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Shi et al. (NPL: “ A Layer-Stripping Method for 3D Near-Surface Velocity Model Building Using Seismic First-Arrival Times ” (2015) (hereinafter Shi). As per claim 8, the combination of Shearer and Zhu teaches the system as stated above except for generating a polynomial fit based on the accurate seismic phase picks. Shi, however, teaches analyzing seismic first-arrival travel-time observations derived from seismic phase arrivals and modeling those travel-time datasets using mathematical fitting techniques to estimate subsurface layer parameters , as described in the Methodology section (see page 503), where first-arrival travel-time data are processed and modeled to determine velocity and thickness parameters of subsurface layers during near-surface velocity model construction. Such modeling of travel-time observations represents curve-fitting of the arrival-time data used to describe the relationship between travel-time and subsurface structure. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to generate a polynomial fit based on the accurate seismic phase picks obtained using the methods of Shearer and Zhu because mathematical fitting of seismic arrival-time datasets is a well-known technique for modeling and interpreting seismic travel-time relationships as taught by Shi, thereby enabling analytical modeling and interpretation of the seismic phase-arrival data derived from seismic traces. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Hauksson and Cheng. As per claim 10, the combination of Shearer, Zhu and Hauksson teaches the system as stated above except that the seismic dataset comprises distributed acoustic sensing (DAS) data. Cheng, however, teaches that distributed acoustic sensing systems use fiber-optic cables as dense arrays of seismic sensors capable of recording seismic wavefields along the cable, thereby generating seismic datasets from DAS measurements suitable for geophysical analysis (see Abstract). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply the seismic phase-picking and waveform cross-correlation techniques of Shearer, Zhu and Hauksson to seismic datasets obtained from DAS systems as taught by Cheng because DAS systems produce seismic waveform measurements recordings, thereby enabling accurate seismic signal processing and phase picking determination using DAS-derived seismic datasets. Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Hauksson and Shi. As per claim 15, the combination of Shearer, Zhu and Hauksson teaches the system as stated above except for generating a polynomial fit based on the accurate seismic phase picks. Shi, however, teaches analyzing seismic first-arrival travel-time observations derived from seismic phase arrivals and modeling those travel-time datasets using mathematical fitting techniques to estimate subsurface layer parameters , as described in the Methodology section (see page 503), where first-arrival travel-time data are processed and modeled to determine velocity and thickness parameters of subsurface layers during near-surface velocity model construction. Such modeling of travel-time observations represents curve-fitting of the arrival-time data used to describe the relationship between travel-time and subsurface structure. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to generate a polynomial fit based on the accurate seismic phase picks obtained using the methods of Shearer, Zhu and Hauksson because mathematical fitting of seismic arrival-time datasets is a well-known technique for modeling and interpreting seismic travel-time relationships as taught by Shi, thereby enabling analytical modeling and interpretation of the seismic phase-arrival data derived from seismic traces. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Hauksson and Helbig. As per claim 13 , the combination of Shearer, Zhu and Hauksson teaches the system as stated above except for applying amplitude gain control (AGC) to the seismic dataset. Helbig, however, describes amplitude-control methogs used in seismic processing, including automatic gain control (AGC), which scales seismic trace amplitudes (see page 752). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply AGC to the seismic dataset processed by the methods of Shearer, Zhu and Hauksson because AGC is a conventional seismic preprocessing technique used to condition seismic waveform data prior to analysis, thereby, improving the consistency and interpretability of seismic traces used for subsequent seismic signal-processing operations such as phase picking and waveform correlation. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Helbig and Hauksson. As per claim 17 , the combination of Shearer , Zhu and Helbig teaches the system as stated above except for interpolating the plurality of initial seismic phase picks. Hauksson, however, teaches determining differential seismic arrival times from the peaks of cross-correlation functions and applying interpolation (e.g., spline interpolation) around the correlation peak to achieve higher timing precision (see Abstract). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to interpolate the seismic phase picks generated by the methods of Shearer , Zhu and Helbig because interpolation of the peak of the cross-correlation function would improve the precision of arrival-time estimates obtained from seismic waveform correlation, thereby improving the accuracy of the seismic phase-pick estimates produced from the seismic traces. Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Shearer in view of Zhu and further in view of Helbig and Shi. As per claim 20 , the combination of Shearer , Zhu and Helbig teaches the system as stated above except for generating a polynomial fit based on the accurate seismic phase picks. Shi, however, teaches analyzing seismic first-arrival travel-time observations derived from seismic phase arrivals and modeling those travel-time datasets using mathematical fitting techniques to estimate subsurface layer parameters , as described in the Methodology section (see page 503), where first-arrival travel-time data are processed and modeled to determine velocity and thickness parameters of subsurface layers during near-surface velocity model construction. Such modeling of travel-time observations represents curve-fitting of the arrival-time data used to describe the relationship between travel-time and subsurface structure. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to generate a polynomial fit based on the accurate seismic phase picks obtained using the methods of Shearer , Zhu and Helbig because mathematical fitting of seismic arrival-time datasets is a well-known technique for modeling and interpreting seismic travel-time relationships as taught by Shi, thereby enabling analytical modeling and interpretation of the seismic phase-arrival data derived from seismic traces. Prior art The prior art made record and not relied upon is considered pertinent to applicant’s disclosure: Kim et al. [‘485] discloses a method of determining an arrival-time of a first seismic event in a seismic data set including, obtaining the seismic data set and an initial seismic velocity model, and determining an updated seismic velocity model based on the seismic data set. Furthermore, the method includes determining a simulated arrival-time of the first seismic event based on the updated seismic velocity model and defining a predicted time-window based on the simulated arrival-time of the first seismic event, and picking the arrival-time of the first seismic event in the seismic data set based on the predicted time-window. Price et al. [‘688] discloses a method or system for detecting a seismic event includes detecting a primary wave of a seismic event using at least one sensor at a measurement location; using at least one parameter of the detected primary wave to determine an estimated peak ground intensity at the measurement location without determining the magnitude of the seismic event; determining an epicenter of the seismic event; and estimating the intensity of the seismic event at a specified location using the determined estimated peak ground intensity and the distance of the specified location from the epicenter. The epicenter can be determined using sensors at a single location. A noise detection system can filter out detected signals that correspond to local vibrations rather than seismic events. Ahn et al. [‘204] discloses a method of detecting an earthquake in an MEMS-based auxiliary seismic observation network, the method including performing detrending of removing a moving average from original acceleration data received from single sensors of an MEMS-based auxiliary seismic observation network to preprocess the acceleration data, calculating a short-term average/long-term average (STA/LTA) value using a filter parameter value specified on the basis of the preprocessed acceleration data, generating an event occurrence message or event end message on the basis of the calculated STA/LTA value and transmitting the event occurrence message or event end message, when the event occurrence message is generated, calculating an earthquake probability through an earthquake detection deep learning model using the preprocessed acceleration data as an input, and analyzing noise by calculating a power spectral density (PSD) from the original acceleration data which is merged at certain intervals. Contact information Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213 . The examiner can normally be reached Monday through Friday, from 9 am to 6 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Mohamed Charioui /MOHAMED CHARIOUI/ Primary Examiner, Art Unit 2857