DETAILED ACTION
Notice of 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 11, 2025 has been entered.
Status of Claims
Claims 1 and 7 were amended (via Amendment filed 11 December 2025).
Claims 1-8 are pending.
Claims 1-8 are rejected (Non-Final Rejection).
Response to Arguments/Amendments
Applicant’s arguments/remarks referred to below were filed 11 December 2025.
Applicant’s arguments, in conjunction with the filing of the terminal disclaimer on 11 December 2025, are persuasive. Hence, the prior provisional non-statutory double patenting rejections are obviated and withdrawn.
Applicant’s arguments, in conjunction with the claim amendments, including positive recitation of the “validating” step(s), filing of the terminal disclaimer on 11 December 2025, obviate the prior 35 U.S.C. § 101 rejection(s), which are withdrawn.
The arguments regarding the rejections under 35 U.S.C. § 103 challenge certain limitations. These limitations are newly added and were therefore not addressed in the previous rejection; therefore, the arguments are moot. The amendments are newly addressed by the new grounds of rejection under 35 U.S.C. § 103.
Applicant’s arguments regarding the difference between 1D-Wasserstein and Wasserstein are persuasive regarding “anticipation” but, as shown in the action below, it would be obvious to modify a teaching of a Wasserstein metric/distance with a 1-Wasserstein metric/distance. In support of this position, Examiner also notes that the supplied supplemental reference by Abdul Fatir (mentioned in Applicant’s arguments, cited in the attached PTO-892, attached hereto and available at: https://abdulfatir.com/blog/2020/Wasserstein-Distance/) recites “[t]he 1-Wasserstein is the most common variant of the Wasserstein distances” (emphasis added). That is, Applicant’s supplemental reference lends credence to the position that it would have been obvious at the time of the alleged invention to substitute a Wasserstein metric/distance (e.g., QUI’s) with the most common variant (i.e., 1-Wasserstein). That is, Applicant’s Fatir reference, although showing a difference, suggests it would be obvious to modify QUI’s Wasserstein metric/distance to use 1-Wasserstein because 1-Wasserstein is the “most common” Wasserstein metric/distance.
Claim Rejections - 35 U.S.C. § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 5-8 is/are rejected under 35 U.S.C. § 103 as being unpatentable over QIU et al. (U.S. Patent Application Publication No. 2019/0257968) in view of LEE et al. (U.S. Patent No. 11,188,795), and further in view of MILLER et al. (U.S. Patent Application Publication No. 2006/0101402).
As per claim 1, QIU teaches a computer-implemented method for validating simulation data of a simulation model of a technical system (forward modeling is performed at 234 on the starting model, also known as an initial guess, comprises a model containing information gathered using different subsurface imaging methods, such as tomography … inversion, including FWI, is a method to iteratively improve the starting model, with the starting model being a base point to be updated … forward modeling comprises, in at least one embodiment, solving a wave equation to determine synthetic seismic data associated with the starting model and the acquired seismic data … the synthetic seismic data is associated with the acquired seismic data such that it is modeled as a prediction of what the acquired seismic data may be … for instance, during forward modeling, synthetic seismic data can be modeled by solving an acoustic wave equation which governs propagation of acoustic waves through a material medium and describes the evolution of acoustic pressure or particle velocity as a function of position and time … a data misfit between the acquired seismic data and the synthetic seismic data can be determined, Para. [0025] of QIU; [determining whether the acquired [real] seismic data and the modelled synthetic/simulated seismic data are a “misfit”, is interpreted as checking the accuracy of [i.e., validating] the simulation/synthetic/prediction model]), the method comprising the following steps: providing simulation data including a number of simulation signals (synthetic seismic data, in at least one embodiment, comprises seismic data that has been simulated to look like real seismic data acquired in a seismic acquisition process, Para. [0012] of QIU) and providing reference data including a number of reference signals (receivers thereby collect survey data, referred to herein as “seismic data” which can be useful in the discovery and/or extraction of hydrocarbons from subsurface formations, Para. [0010] of QIU), the simulation signals and the reference signals being multidimensional signals and being at least two-dimensional signals (sensing electronics and data-processing facilities that allow receiver readings to be correlated with absolute positions on the sea surface and absolute three-dimensional positions with respect to a three-dimensional coordinate system, Para. [0021] of QIU; See also during forward modeling, synthetic seismic data can be modeled by solving an acoustic wave equation which governs propagation of acoustic waves through a material medium and describes the evolution of acoustic pressure or particle velocity as a function of position and time, Para. [0025] of QIU; [pressure/velocity as a function of position and time is interpreted as being multidimensional/at least two-dimensional]); and determining a metric between a first probability distribution including the simulation data and a second probability distribution including the reference data (the acquired seismic data and the associated synthetic seismic data are transformed into probability-density functions or probability-density function-like data before calculating the Wasserstein distance between them … a probability-density function is a function of a continuous random variable, whose integral across an interval gives the probability that the value of the variable lies within the same interval … for instance, the integral is one … probability-density function-like data comprises data having an integral that is the same … for example, following exponential encoding discussed further herein, acquired seismic data in probability-density function-like data format has the same integral as associated synthetic seismic data in probability-density function-like format … Wasserstein distances between probability-density functions and distances between probability-density function-like data can be determined because of this same integral result, Para. [0028] of QIU) by using a Wasserstein metric (Wasserstein distance is designed to measure the distance between two probability-density functions, Para. [0027] of QIU), wherein: the determining expands an area validation metric to include the multidimensional signals (Wasserstein distance is designed to measure the distance between two probability-density functions, Para. [0027] of QIU; See also in at least one embodiment, a 1D Wasserstein distance is determined, Para. [0016] of QIU; [Examiner’s Note: “determining a [metric/score] between simulation data [that includes multidimensional simulation signals] and reference data [that includes multidimensional reference signals]… using a Wasserstein metric” corresponds to “expanding an area validation metric to include the multidimensional signals”. See Page 3, Lines 26-28 of the Specification of the instant ‘567 application: “Wasserstein metric expands the conventional method of the area validation metric to include multidimensional signals”]).
QIU does not appear to explicitly disclose the Wasserstein (distance) metric is a 1 Wasserstein metric.
However, LEE is in the same field of probability predictions and dimensional probability distributions (Abstract and Col. 1, Lines 50-61, of LEE) and teaches the Wasserstein (distance) metric is a 1 Wasserstein metric (Wasserstein distance is desirable for use in designing loss functions in some of the implementations disclosed herein because of its superiority over other probability measures in this context … some implementations apply the 1-Wasserstein distance, which is also called the earth mover's distance (EMD), to a domain adaptation framework, Col. 6, Lines 60-65, of LEE).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the Wasserstein distance-driven analysis/comparison of simulation/synthetic data with reference/real data as in QIU with the 1 Wasserstein distance of LEE for the purpose of designing superior loss functions (Col. 6, Lines 60-65, of LEE).
QIU as modified by LEE does not appear to explicitly disclose the simulation model is one of a hardware in the loop simulation model or a software in the loop simulation model, and … the multidimensional signals relating to the hardware in the loop simulation model or the software in the loop simulation model.
However, MILLER is in the same field of systems testing/validating during development (Para. [0002] of MILLER) and teaches: “wherein: the simulation model is one of a hardware in the loop simulation model or a software in the loop simulation model (development tools typically support simulation of design models, which enable testing to occur without fully implemented vehicles and supporting systems … development tools with simulation and testing capabilities such as hardware in the loop (HIL) or software in the loop (SIL) are used, Para. [0126] of MILLER), and … the multidimensional signals relating to the hardware in the loop simulation model or the software in the loop simulation model” (projection of a high-dimension sample vector space onto a virtually one or two dimension array that is represented by a set of self-organized nodes, Para. [0159] of MILLER; See also vector contains all the information necessary to determine the system outputs … however, in real applications, this vector usually has a very high dimension … therefore, SOMs is used to regionalize the space spanned by those vectors, because of its excellent capability of visualization of high dimensional data, Para. [0177] of MILLER; See also two-dimensional distribution, Para. [0180] of MILLER).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the 1-Wasserstein distance-driven analysis/comparison of simulation/synthetic data with reference/real data as in QIU (as modified by LEE) with the HIL and/or SIL simulation models of MILLER for the purpose of permitting incremental development of subsystems before a completed system is available (Para. [0126] of MILLER).
As per claim 5, QIU as modified by LEE and MILLER teaches the computer-implemented method as recited in claim 1, further comprising: standardizing the simulation data and/or the reference data (a normalization method is used when performing Wasserstein distance-based inversion. This contrasts with prior approaches that lead to non-differentiable misfit functions and are not compatible with adjoint-state methods or lose information of original data during normalization. Normalization, as used herein, can refer to a method for adjusting values measured on different scales to a notionally common scale, Para. [0027] of QIU; [the originally-filed specification, at Page 7, Lines 15-17, indicate that the standardizing may be related to scaling]; See also initial conditions of the output, including the initial value, and the first and second derivatives, has been normalized, Para. [0199] of MILLER; See also after proper normalization of this feature vector, a SOM has been created based on the training data to regionalize the system dynamics behaviors, Para. [0210] of MILLER; [normalization is interpreted to correspond to standardization]).
As per claim 6, QIU as modified by LEE and MILLER teaches the computer-implemented method as recited in claim 1, wherein the multidimensional signals include two- or multidimensional vectors and/or correlated signals and/or time series signals (sensing electronics and data-processing facilities that allow receiver readings to be correlated with absolute positions on the sea surface and absolute three-dimensional positions with respect to a three-dimensional coordinate system, Para. [0021] of QIU; See also during forward modeling, synthetic seismic data can be modeled by solving an acoustic wave equation which governs propagation of acoustic waves through a material medium and describes the evolution of acoustic pressure or particle velocity as a function of position and time, Para. [0025] of QIU; See also projection of a high-dimension sample vector space onto a virtually one or two dimension array that is represented by a set of self-organized nodes, Para. [0159] of MILLER; See also vector contains all the information necessary to determine the system outputs … however, in real applications, this vector usually has a very high dimension … therefore, SOMs is used to regionalize the space spanned by those vectors, because of its excellent capability of visualization of high dimensional data, Para. [0177] of MILLER; See also two-dimensional distribution, Para. [0180] of MILLER).
As per claim 7, QIU discloses a non-transitory computer-readable medium on which is stored a computer program (hardware, for example, can include processing resources 576 and memory resources 578, such as a machine-readable medium or other non-transitory memory resources 578, Para. [0066]; See also Paras. [0056] and [0061] of QIU discussed below) for validating data of a simulation model (forward modeling is performed at 234 on the starting model … the starting model, also known as an initial guess, comprises a model containing information gathered using different subsurface imaging methods, such as tomography … inversion, including FWI, is a method to iteratively improve the starting model, with the starting model being a base point to be updated … forward modeling comprises, in at least one embodiment, solving a wave equation to determine synthetic seismic data associated with the starting model and the acquired seismic data … the synthetic seismic data is associated with the acquired seismic data such that it is modeled as a prediction of what the acquired seismic data may be … for instance, during forward modeling, synthetic seismic data can be modeled by solving an acoustic wave equation which governs propagation of acoustic waves through a material medium and describes the evolution of acoustic pressure or particle velocity as a function of position and time … a data misfit between the acquired seismic data and the synthetic seismic data can be determined, Para. [0025] of QIU; [determining whether the acquired [real] seismic data and the modelled synthetic/simulated seismic data are a “misfit”, is interpreted as checking the accuracy of [i.e., validating] the simulation/synthetic/prediction model]), the computer program, when executed by a computer, causing the computer to perform the following steps (system can represent program instructions and/or hardware of a machine, Para. [0056] of QIU; See also engines can include a combination of hardware and program instructions that is configured to perform functions described herein … the program instructions, such as software, firmware, etc., can be stored in a memory resource such as a machine-readable medium, etc., as well as hard-wired program such as logic … hard-wired program instructions can be considered as both program instructions and hardware, Para. [0061] of QIU): providing simulation data including a number of simulation signals (synthetic seismic data, in at least one embodiment, comprises seismic data that has been simulated to look like real seismic data acquired in a seismic acquisition process, Para. [0012] of QIU) and providing reference data including a number of reference signals (receivers thereby collect survey data, referred to herein as “seismic data” which can be useful in the discovery and/or extraction of hydrocarbons from subsurface formations, Para. [0010] of QIU), the simulation signals and the reference signals being multidimensional signals and being at least two-dimensional signals (sensing electronics and data-processing facilities that allow receiver readings to be correlated with absolute positions on the sea surface and absolute three-dimensional positions with respect to a three-dimensional coordinate system, Para. [0021] of QIU; See also during forward modeling, synthetic seismic data can be modeled by solving an acoustic wave equation which governs propagation of acoustic waves through a material medium and describes the evolution of acoustic pressure or particle velocity as a function of position and time, Para. [0025] of QIU; [pressure/velocity as a function of position and time is interpreted as being multidimensional/at least two-dimensional]); determining a metric between a first probability distribution including the simulation data and a second probability distribution including the reference data (the acquired seismic data and the associated synthetic seismic data are transformed into probability-density functions or probability-density function-like data before calculating the Wasserstein distance between them … a probability-density function is a function of a continuous random variable, whose integral across an interval gives the probability that the value of the variable lies within the same interval … for instance, the integral is one … probability-density function-like data comprises data having an integral that is the same … for example, following exponential encoding discussed further herein, acquired seismic data in probability-density function-like data format has the same integral as associated synthetic seismic data in probability-density function-like format … Wasserstein distances between probability-density functions and distances between probability-density function-like data can be determined because of this same integral result, Para. [0028] of QIU) by using a Wasserstein metric (Wasserstein distance is designed to measure the distance between two probability-density functions, Para. [0027] of QIU), wherein: the determining expands an area validation metric to include the multidimensional signals (Wasserstein distance is designed to measure the distance between two probability-density functions, Para. [0027] of QIU; See also in at least one embodiment, a 1D Wasserstein distance is determined, Para. [0016] of QIU; [Examiner’s Note: “determining a [metric/score] between simulation data [that includes multidimensional simulation signals] and reference data [that includes multidimensional reference signals]… using a Wasserstein metric” corresponds to “expanding an area validation metric to include the multidimensional signals”. See Page 3, Lines 26-28 of the Specification of the instant ‘567 application: “Wasserstein metric expands the conventional method of the area validation metric to include multidimensional signals”]).
QIU does not appear to explicitly disclose the Wasserstein (distance) metric is a 1 Wasserstein metric.
However, LEE is in the same field of probability predictions and dimensional probability distributions (Abstract and Col. 1, Lines 50-61, of LEE) and teaches the Wasserstein (distance) metric is a 1 Wasserstein metric (Wasserstein distance is desirable for use in designing loss functions in some of the implementations disclosed herein because of its superiority over other probability measures in this context … some implementations apply the 1-Wasserstein distance, which is also called the earth mover's distance (EMD), to a domain adaptation framework, Col. 6, Lines 60-65, of LEE).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the Wasserstein distance-driven analysis/comparison of simulation/synthetic data with reference/real data as in QIU with the 1 Wasserstein distance of LEE for the purpose of designing superior loss functions (Col. 6, Lines 60-65, of LEE).
QIU as modified by LEE does not appear to explicitly disclose the simulation model is one of a hardware in the loop simulation model or a software in the loop simulation model, and … the multidimensional signals relating to the hardware in the loop simulation model or the software in the loop simulation model.
However, MILLER is in the same field of systems testing/validating during development (Para. [0002] of MILLER) and teaches: the simulation model is one of a hardware in the loop simulation model or a software in the loop simulation model (development tools typically support simulation of design models, which enable testing to occur without fully implemented vehicles and supporting systems … development tools with simulation and testing capabilities such as hardware in the loop (HIL) or software in the loop (SIL) are used, Para. [0126] of MILLER), and … the multidimensional signals relating to the hardware in the loop simulation model or the software in the loop simulation model” (projection of a high-dimension sample vector space onto a virtually one or two dimension array that is represented by a set of self-organized nodes, Para. [0159] of MILLER; See also vector contains all the information necessary to determine the system outputs … however, in real applications, this vector usually has a very high dimension … therefore, SOMs is used to regionalize the space spanned by those vectors, because of its excellent capability of visualization of high dimensional data, Para. [0177] of MILLER; See also two-dimensional distribution, Para. [0180] of MILLER).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the 1-Wasserstein distance-driven analysis/comparison of simulation/synthetic data with reference/real data as in QIU (as modified by LEE) with the HIL and/or SIL simulation models of MILLER for the purpose of permitting incremental development of subsystems before a completed system is available (Para. [0126] of MILLER).
As per claim 8, QIU as modified by LEE teaches the method as recited in claim 1, wherein the simulation model is a simulation model of a technical system, the technical system being software, hardware, or an embedded system, during the development of the technical system (development tools typically support simulation of design models, which enable testing to occur without fully implemented vehicles and supporting systems … development tools with simulation and testing capabilities such as hardware in the loop (HIL) or software in the loop (SIL) are used, Para. [0126] of MILLER).
Claim(s) 2-4 is/are rejected under 35 U.S.C. § 103 as being unpatentable over QIU et al. (U.S. Patent Application Publication No. 2019/0257968) in view of LEE et al. (U.S. Patent No. 11,188,795), and further in view of MILLER et al. (U.S. Patent Application Publication No. 2006/0101402) and YE et al. (U.S. Patent Application Publication No. 2017/0083608 A1).
As per claim 2, QIU as modified by LEE and MILLER teaches the computer-implemented method as recited in claim 1, further comprising: deriving an optimal transport plan based on the cost; and computing costs of the optimal transport plan by using the Wasserstein metric/1 Wasserstein metric (at least one embodiment of the present disclosure implements a Wasserstein distance, also known as the W2 norm, based on the optimal transport theory for measuring the data misfit … an explicit solution of the optimal transport is used over the real line resulting in increased efficiency in implementation as compared to prior approaches, with a computational complexity of the adjoint source proportional to the number of actuations, receivers, and the length of recording time. For example, the solution to an optimal transport problem can be evaluated in a finite number of basic operations for a 1D (“real line”) Wasserstein distance, Para. [0016] of QIU; [As shown above, it would be obvious to modify QUI’s Wasserstein metric to use the 1-Wasserstein metric, which is the “most common variant” as discussed above]).
QIU as modified by LEE and MILLER does not appear to explicitly disclose establishing a cost matrix based on the simulation signals and the reference signals and deriving an optimal transport plan based on the cost matrix.
However, YE is in the same field of evaluating distributions using a Wasserstein distance/metric (Para. [0003] of YE) and teaches: establishing a cost matrix based on first (simulation) signals and second (reference) signals (recalculate the distance matrices, Para. [0035]; See also caching the distance matrix per instance, Para. [0040]; [Applicant’s Specification, at Page 8, Lines 6-9, is interpreted to indicate that a cost matrix corresponds to a distance matrix]); deriving an optimal transport plan based on the cost matrix (the transportation metric is useful because it measures the similarity by solving an optimal matching problem known as the transportation problem, Para. [0006] of YE; See also the transportation metric is known as the Kantorovich-Wasserstein metric, which is referred to as the Wasserstein distance, Para. [0006] of YE); and computing costs of the optimal transport plan by using the Wasserstein metric/1 Wasserstein metric (the transportation metric is useful because it measures the similarity by solving an optimal matching problem known as the transportation problem, Para. [0006] of YE; See also the transportation metric is known as the Kantorovich-Wasserstein metric, which is referred to as the Wasserstein distance, Para. [0006] of YE; [As shown above, it would be obvious to modify QUI’s Wasserstein metric to use the 1-Wasserstein metric, which is the “most common variant” as discussed above]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the 1-Wasserstein distance-driven analysis/comparison of simulation/synthetic data with reference/real data as in QIU (as modified by LEE and MILLER) with the cost matrix approach of YE for the purpose of exploiting parallel computing and/or lowering computational complexity (Para. [0010] of YE).
As per claim 3, QIU as modified by LEE, MILLER and YE teaches the computer-implemented method as recited in claim 2, wherein establishing the cost matrix includes computing a distance of a particular simulation signal of the simulation signals to a particular reference signal of the reference signals (the acquired seismic data and the associated synthetic seismic data are transformed into probability-density functions or probability-density function-like data before calculating the Wasserstein distance between them … a probability-density function is a function of a continuous random variable, whose integral across an interval gives the probability that the value of the variable lies within the same interval … for instance, the integral is one … probability-density function-like data comprises data having an integral that is the same … for example, following exponential encoding discussed further herein, acquired seismic data in probability-density function-like data format has the same integral as associated synthetic seismic data in probability-density function-like format … Wasserstein distances between probability-density functions and distances between probability-density function-like data can be determined because of this same integral result, Para. [0028] of QIU; See also recalculate the distance matrices, Para. [0035] of YE; See also caching the distance matrix per instance, Para. [0040] of YE).
As per claim 4, QIU as modified by LEE, MILLER and YE teaches the computer-implemented method as recited in claim 3, wherein for computing the distance, a Euclidian distance is used as a distance measure (use Euclidean distance as the metric, Para. [0183] of YE; See also distance measure could be well-known Euclidean distance, Para. [0155] of MILLER).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
MROUEH et al. (US 20200342361 A1). See, e.g., Para. [0047] teaches Optimal transport (OT) metrics such as Wasserstein-2 have this property since they are built on an explicit cost matrix.
WANG et al. (GB 2584196 A) cited and attached to PTO-892 mailed 3 December 2024. See, e.g., Paras. [0028] & [0029] teaches Wasserstein metric (also known as W2 distance) is computed trace-by-trace for the synthetic data f and real data g, considered to be two density distributions defined on the same domain: (1) where F(t) = ds,C = r and Formula (1) can also be expressed in matrix form.
JIANG et al. (WO 2021228404 A1) cited and attached to PTO-892 mailed 3 December 2024. See, e.g., Page 14, Line 35-36 teaches Correlation Matrix Distance (CMD) can be used to measure the distance between the correlation matrix of real and synthetic data.
ZHANG et al. (US 20210301658 A1). See, e.g., Para. [0005] teaches constructing a distance matrix for the plurality of distributions using a statistical distance metric, which may be a Wasserstein Distance.
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JOHN P. HOCKER
Examiner
Art Unit 4146
/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189