Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 0116/2026 has been entered.
Response to Amendment
The amendment filed on 12/16/2025 has been entered and considered by the examiner. By the
amendment, claims 1, 8 and 15 are amended. In view of the amendment made, the previous 101 rejection is still maintained and the prior rejection is modified. See office action for detail.
Applicant 101 arguments
The Applicant respectfully submits that the claims as amended cannot be reasonably considered to be directed to a mental process because the human brain cannot emulate the functionality of an autonomous underwater vehicle nor one or more underwater sensor networks, cannot transmit data, and certainly cannot follow an underwater target based on a propagation trajectory, as the human brain is not equipped to determine a propagation trajectory of an underwater sound, nor track the sound underwater. Furthermore, the human brain is incapable of determining a propagation trajectory of a sound wave in an aquatic medium based on a 3D Gaussian random field in real time as the aquatic environment measurements are collected, which, as one skilled in the art would understand computing operations occurring in real-time to operate, would be executed on the scale of milliseconds, well beyond the limits of human thought.
Examiner response
Examiner respectfully disagrees. Under Alice step 2A, Prong one, a claim is directed to abstract idea even if it cannot practically or literally be performed in the human mind. The Examiner not rely on the mental process category of abstract ideas. Instead, the claims are directed to mathematical concepts, including defining correlation functions, generating 3D Gaussian field and determining a propagation trajectory. Real-time execution is a performance characteristic, not a technical improvement and does not integrate a mathematical concept into a practical application. That such modeling cannot be performed by the human mind, or must be performed in real time using computing devices, does not remove the claims from the realm of abstract ideas.
Applicant arguments
Applicant's claimed embodiment is directed, at least in part, to analyzing sound wave propagation in an aquatic medium by representing the aquatic medium as a 3D Gaussian random field using correlation functions based on aquatic environment measurements. This process is not equivalent to any manual "underwater inspection or mapping" method, and does not exist outside the context of computer implementation. To say otherwise would represent a failure to interpret the claims as a whole; Applicant notes that when evaluating the scope of a claim, every limitation in the claim must be considered. Examiners may not dissect a claimed invention into discrete elements and then evaluate the elements in isolation. Instead, the claim as a whole must be considered. See, e.g., Diamond v. Diehr, 450 U.S. 175, 188-89, 209 USPQ 1, 9 (1981).
Examiner response
Examiner respectfully disagrees. When considered claim as a whole, the claims are directed to mathematical modeling and probabilistic simulation of sound wave propagation in an aquatic medium. The recitation of a specific environment and computing context does not alter the abstract nature of the claimed subject matter. Unlike the Diamond v. Diehr claims, the present claims do not use mathematics to control a physical industrial process or effect a transformation of matter.
Applicant arguments
Furthermore, the Applicant respectfully submits that underwater sensor networks and autonomous underwater vehicles are not generic computer hardware; they constitute special- purpose hardware specially adapted for operation underwater, and cannot be reasonably considered general-purpose or generic computing devices. As such, Applicant's claims cannot be reasonably considered directed to applying a judicial exception on a generic computing device. Applicant's claimed method improves the functioning of a computer. Applicant's claimed method addresses the following problem: the aquatic medium does not allow for efficient electromagnetic propagation. Natural density fluctuations due to factors such as sediments and pollution in the ocean degrade underwater positioning, tracking and communication by autonomous underwater vehicles (AUVs) and underwater sensor networks (USNs) in ways that are difficult to forecast. Because frequency and amplitude attenuations of acoustic waves can be expected over long distances in the underwater environment, underwater long-range communication must occur in the Ultra Low Frequency (ULF) range, 0.3 to 3 kHz (because the attenuation is lower, relative to higher frequencies). As a result, only low rate data streams can be supported. Some recent proposals suggest incorporating real measurement data of sound speed in the ocean to improve tracking; however, such approaches may be limited (particularly for large distances) because measurements of the speed of sound will be sparse, leading to inaccurate estimations. In other words, the physical properties of the aquatic environment, namely poor electromagnetic propagation and variable density, directly impair signal transmission and sensing of electrical devices operating underwater, and sparse measurement of these properties introduces inaccuracies into attempts to compensate for the effects of these properties. This is a technical problem because it represents concrete, physical limitations arising from the behavior of signals and sensors in an underwater medium, and the adverse impact of these physical limitations on positioning, tracking, and communication systems. The Applicant addresses this technical problem using a method for analyzing sound wave propagation in an aquatic medium by taking measurements of the water at different locations using both autonomous underwater vehicles and underwater sensor networks, defining data- driven correlation functions using the measurements, representing the underwater medium as a 3D Gaussian random field, and describing it using the data-driven correlation functions (See, e.g., Applicant's Specification paragraph [0025]). This solution may yield the following advantages: by using realistic correlation functions to characterize random fields for MC simulations, Applicant's claimed method enables real-time identification of the most likely acoustic paths, as well as frequency and amplitude content of the transferred information, and ensures that the random fields represent the real physical environments in which the sound wave travels, thus enabling error bar estimation (Applicant's Specification, paragraph [0018]). Additionally, by treating an aquatic medium (e.g., marine environment) as random, embodiments may be based on an acceptance that there is inherent variability of local pressure values, and the medium may then be approximated as an ensemble of different possible scenarios at different depths. Stochastic three-dimensional (3D) processes may then be used as inputs for a high-throughput simulation of sound wave propagation, enabling the estimation of potential outcomes (sound wave distortions) (Applicant's Specification, paragraph [0019]). In other words, Applicant's claimed method may enable real-time identification of the most likely acoustic paths, enable error bar estimation, enables the use of stochastic three- dimensional processes to perform high-throughput simulation of soundwave propagation, in turn enabling the estimation of potential outcomes; these advantages, in turn, result in improved analysis of sound wave propagation in inhomogeneous media, allowing underwater navigation and communication that is faster (real-time) and more accurate than existing methods. Applicant's claims reflect these improvements; Applicant's claims explicitly recite taking measurements of the water at different locations ("collecting, by one or more autonomous underwater vehicles (AUV) or one or more underwater sensor networks (USN), a plurality of aquatic environment measurements"), defining correlation functions ("defining...one or more correlation functions"), take realizations of a random field as an input describing the aquatic medium ("generating...a model to represent the aquatic medium as a three-dimensional (3D) Gaussian random field, the 3D Gaussian random field being described by the one or more correlation functions"), and thereby more realistically representing the aquatic medium and yielding the aforecited advantages.
Examiner response
Although USNs and AUVs are designed to operate underwater, the claims do not recite any specific improvement to the structure or operation of the USNs and AUVs themselves. Rather, the claimed USNs and AUVs are used in a conventional manner for collecting measurement data and transmit or act upon the results. The specification [0025] and fig 2- Furthermore, the computations may be performed using processing units USNs and/or AUVs, thus reducing a resource burden/cost. Under MPEP 2106.05(f), the use of generic processing unit does not render a claim patent eligible when that equipment is merely used as a tool to perform abstract mathematical analysis.
The focus of the claim is on: defining correlation function, generating a 3D Gaussian radom field, determining a propagation trajectory based on that field. This limitation recites mathematical concepts under MPEP 2106.04(a)(2)(I). Although the claims reference an aquatic medium, the claims do not recite any physical transformation or control of that environment. Instead, the claims analyze data representing the environment and produce informational results.
While Appellant argues that the claim improves the technology or provide a technological solution to a technological problem, this argument is not supported through the additional elements of the claim. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements....” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”. The present claims do not reflect the purported improvements cited by the Applicant, with reference to the sections of the specification cited in the arguments. The present claims do not improve the functioning of the computer as well as any other technology or technical field as presented above. Thus, the arguing of the background of the invention and summary do not transform the additional elements into significantly more. The instant claims do not recite or disclose improvements to a computer or any other technology (only a generic is recited), MPEP 2106.05(a). The claims do not apply or involve a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. The claims do not apply or perform the abstract idea with a particular machine, MPEP 2106.06b. The claims to do transform or reduce a particular article to a processor different state or thing (data remains data when processed by a computer), MPEP 2106.05c. The claims do not apply or use the abstract idea in a meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (i.e. a processor), such that the claims are a drafting effort to monopolize the abstract idea (i.e. the claims do not integrate the abstract idea into a practical application of the abstract idea). Accordingly, the claims are not patent eligible under 35 U.S.C. 101. Thus, the Examiner concludes the claims do not improve the technology.
Applicant argues that poor electromagnetic propagation and density variability in aquatic environments constitute a technical problem. The Examiner agrees that these are real-world physical constraints. However, identifying a real-world problem does not render an abstract solution patent eligible. The claimed solution does not: alter the physical properties of the aquatic medium, improve acoustic hardware or control how acoustic waves are generated or received. Instead, the solution models and predicts behavior using mathematical calculations. While the specification discusses improved underwater tracking in paragraph [0017], [0025] [0050], however, the claims do not recite any specific technological mechanism by which underwater tracking hardware is improved, nor do they recite any particular navigation, sensing or control mechanism that changes how tracking is performed.
Applicant arguments
The Applicant's claimed invention therefore represents an improvement in the field of sound wave propagation analysis, which in turn improves the technical fields of underwater tracking, navigation, and communication. The Applicant further argues that the claims represent a practical application of any alleged abstract idea, for at least the reasons that the claims recite the order of steps, the hardware performing them, and the use of the determined propagation trajectory to perform a tangible, real-world function of following, by one or more AUVs, an underwater target.
Examiner response
As previously mentioned, the limitation “following, by the one or more AUVs, an underwater target based on the propagation trajectory” can also be considered as insignificant post-solution activity of an insignificant application as described in MPEP 2106.05(g). The following limitation is a purely functional, result-oriented statement that merely applies the output of the mathematical model. The claim does not recite how the propagation trajectory is converted into control commands, how the AUV navigated or any feedback/guidance mechanism. Accordingly, the “following” limitation does not limit the abstract idea, but instead merely recites an intended use or result of the mathematical analysis.
Applicant arguments 103 rejection
The cited references fail to teach or suggest Applicant's amended claim limitations of “collecting, by one or more autonomous underwater vehicles (AUV) or one or more underwater sensor networks (USN), a plurality of aquatic environment measurements; defining one or more correlation functions based on the aquatic environment measurements." Khazaie makes no mention of sensors, vehicles, nor collecting measurements of the ocean using autonomous underwater vehicles or underwater sensor networks. To whatever extent Morozs could be said to teach or suggest underwater sensor nodes, such underwater sensor nodes are only mentioned in reference to their ability to propagate acoustic waves between individual nodes, and are nowhere taught or suggested to collect aquatic environment measurements to assist in modeling the aquatic medium. For similar reasons, Khazaie and Morozs additionally fail to teach Applicant's amended claim limitations of "transmitting, by the USN, the propagation trajectory to one or more of the of AUVs," and "following, by the one or more AUVs, an underwater target based on the propagation trajectory."
Examiner response
In view of applicant arguments and amendments, Examiner withdraw the previous Morozs and Sun reference. Examiner added the new reference Ghafoor et al. ("An overview of next-generation underwater target detection and tracking: An integrated underwater architecture." Ieee Access 7 (2019): 98841-98853.) for teaching most of the limitation in view of Khazaie. See office action for detail.
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 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the “a computer program product, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media” encompasses signals per se. The specification does not include a special definition nor does it limit the media to only non-transitory. A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. See MPEP 2106.03(II). Examiner suggest that claim 8 be amended to recite a “non-transitory” computer readable storage media to overcome this rejection. Accordingly, claims 8-14 fails to recite statutory subject matter under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-7 is directed to method or process that falls on one of statutory category.
Claim 8-14 is a computer program product, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media. In view of instant specification [0059] that a computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, thus this fails falls on one of statutory category. Even though this is not a statutory category of invention, in the interest of compact prosecution, the analysis of claim 8-14 will continue below. Therefore claims 1-7 and 15-20 are directed to patent eligible categories of invention and claim 8-14 is not directed to a statutory claimed invention.
Claims:15-20 are directed to apparatus or machine that falls on one of statutory category.
Claim 1, 8 and 15 recites
A computer-implemented method for analyzing sound wave propagation in an aquatic medium, the method comprising:
defining one or more correlation functions based on the aquatic environment measurements; (A correlation function quantifies the statistical (mathematical) relationship between two variables. So, it falls under the mathematical concepts of abstract idea)
generating a model to represent the aquatic medium as a three-dimensional (3D) Gaussian random field, the 3D Gaussian random field being described by one or more correlation functions; (Analyze the nature of model generation and its relation to abstract thinking. This process requires abstract thinking to conceptualize and define the key characteristics of the system. In this case, the aquatic medium is represented as a 3D Gaussian random field, which is an abstract mathematical construct. The process of modeling the aquatic medium using a 3D Gaussian random field is a blend of conceptualizing the problem (abstract mental idea) and applying rigorous mathematical techniques (mathematical concepts) to represent and analyze the spatial variability of the environment. Thus, it is a combination of mental process and mathematical concepts of abstract ideas)
determining in real time as the aquatic environment measurement collected, a propagation trajectory of a sound wave in the aquatic medium based, at least in part, on the 3D Gaussian random field; (Determining a trajectory involves visualizing the sound wave's path as it travels through the water, considering measurement factors like the water's depth, temperature, and any obstacles or changes in the water's properties that could affect the wave's path which is a pure mental process. Also, this process often involves using mathematical models or simulations (3D Gaussian random field model) to predict the wave's path and behavior based on the physical principles and environmental conditions which is mathematical concepts. Thus, it is a combination of mental process and mathematical concepts of abstract ideas))
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, claim recites the additional elements of collecting, by one or more autonomous underwater vehicles (AUV) or one or more underwater sensor networks (USN), a plurality of aquatic environment measurements; and transmitting, by the USN, the propagation trajectory to one or more of the AUVs; are recited at a high level of generality which are mere data gathering and transmitting step and falls under the insignificant extra solution activity. (See MPEP 2106.05(g)) Also, the limitation “following, by the one or more AUVs, an underwater target based on the propagation trajectory” can also be considered as insignificant extra-solution activity of an insignificant application as described in MPEP 2106.05(g). The recited USN and AUVs merely collect data and act upon the results, and do not integrate the abstract idea into a practical application. The additional element of a computer (claim 1), a computer program product for analyzing sound wave propagation in an aquatic medium, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media in claim 8 and a computer system for analyzing sound wave propagation in an aquatic medium, the computer system comprising: one or more computer processors; one or more computer readable storage media; and program instructions stored on at least one of the one or more computer readable storage media for execution by at least one of the one or more computer processors in claim 15 are merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
In view of Step 2B, the claim as a whole does not amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. In particular, claim recites the additional elements of collecting, by one or more autonomous underwater vehicles (AUV) or one or more underwater sensor networks (USN), a plurality of aquatic environment measurements; and transmitting, by the USN, the propagation trajectory to one or more of the AUVs; are recited at a high level of generality which are mere data gathering and transmitting step falls under the insignificant extra solution activity. (See MPEP 2106.05(g)) These elements amount to receiving or transmitting data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Also, the limitation “following, by the one or more AUVs, an underwater target based on the propagation trajectory” can also be considered as insignificant extra-solution activity of an insignificant application: i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); as described in MPEP 2106.05(g). The recited USN and AUVs merely collect data and act upon the results, and do not integrate the abstract idea into a practical application or provide an inventive concept. The additional element of a computer (claim 1), a computer program product for analyzing sound wave propagation in an aquatic medium, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media in claim 8 and a computer system for analyzing sound wave propagation in an aquatic medium, the computer system comprising: one or more computer processors; one or more computer readable storage media; and program instructions stored on at least one of the one or more computer readable storage media for execution by at least one of the one or more computer processors in claim 15 are merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claims 1, 8 and 15 are not patent eligible.
Claim 2, 9 and 16 further recites wherein determining the propagation trajectory of the sound wave comprises: providing the one or more correlation functions to generate realizations of a random field as an input to a Monte Carlo-based simulation to estimate an uncertainty of acoustic propagation in the aquatic medium. This process describes a mathematical concept, specifically involving the application of random fields and Monte Carlo simulations to analyze uncertainty in acoustic propagation. The use of correlation functions to generate random fields is a mathematical tool, and the Monte Carlo simulation is a computational method rooted in probability and statistics. Under the broadest reasonable interpretation, these limitations fall under the mathematical concepts of abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 3, 10 and 17 further recites generating a correlation function for describing a change in the 3D Gaussian random field based on data from one or more underwater sensors. This process describes a mathematical concept, specifically involving the application of random fields and Monte Carlo simulations to analyze uncertainty in acoustic propagation. It involves estimating the covariance structure of the random field, which is a measure of how correlated different points in the field are. This process utilizes generic sensor data to infer the statistical properties of the underlying 3D space. Under the broadest reasonable interpretation, these limitations fall under the mathematical concepts of abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 4, 11 and 18 further recites wherein generating the correlation function comprises optimizing one or more parameters of the correlation function based on the data. It is a process that blends mathematical concepts with the abstract idea of statistical relationship and optimization. Under the broadest reasonable interpretation, these limitations fall under the mathematical concepts of abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 5, 12 and 19 further recites wherein each spatial point in the 3D Gaussian random field represents a variation in at least one property of the aquatic medium, wherein the at least one property of the aquatic medium includes a local pressure. 3D Gaussian random field, when applied to an aquatic medium, is a mathematical construct used to represent the spatial variability of properties. It's an abstract model based on statistical principles that allows for the analysis and simulation of these properties. Under the broadest reasonable interpretation, these limitations fall under the mathematical concepts of abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 6, 13 and 20 further recites determining a first variation in frequency with propagation of the sound wave in the aquatic medium based on the 3D Gaussian random field. It is a mental process involving abstract ideas and mathematical models. The "3D Gaussian random field" and the "variation in frequency" are abstract concepts that need to be translated into a mathematical model to be analyzed. Under the broadest reasonable interpretation, these limitations fall under the combination of mental process and mathematical concepts of abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 7 and 14 recites determining a second variation in amplitude with propagation of the sound wave in the aquatic medium based on the 3D Gaussian random field. It is a mental process involving abstract ideas and mathematical models. The "3D Gaussian random field" and the "variation in amplitude" are abstract concepts that need to be translated into a mathematical model to be analyzed. Under the broadest reasonable interpretation, these limitations fall under the combination of mental process and mathematical concepts of abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
6. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
7. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
8. Claim 1-20 is rejected under 35 U.S.C. 103 as being unpatentable over Khazaie et al. ("Uncertainty quantification for acoustic wave propagation in a shallow water environment." Wave Motion 91 (2019): 102390.) in view of Ghafoor et al. ("An overview of next-generation underwater target detection and tracking: An integrated underwater architecture." Ieee Access 7 (2019): 98841-98853.)
Regarding claim 1, 8 and 15
Khazaie teaches a computer-implemented method for analyzing sound wave propagation in an aquatic medium. (See Abstract-Sound wave propagation in a shallow water environment is complex due to e.g. the uncertainties of sound speed profile being inhomogeneous and imprecisely measured, the bottom reflections, etc. The propagation and influence of several uncertainty parameters are quantified in this paper. See conclusion- The variance-based sensitivity analysis has also shown the importance of the depth of the shallow water and the velocity of the bottom layer on the uncertainties of the recorded sound pressure)
Claim 8-A computer program product for analyzing sound wave propagation in an aquatic medium, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the stored program instructions comprising: (See Abstract-Sound wave propagation in a shallow water environment is complex due to e.g. the uncertainties of sound speed profile being inhomogeneous and imprecisely measured, the bottom reflections, etc. The propagation and influence of several uncertainty parameters are quantified in this paper. For this purpose, the sound field is computed for different realizations of the random variables, when the medium is excited with sources whose frequencies are appropriate, for example, for marine seismic exploration applications. Since classical modeling techniques required a huge sample size to converge, we use three surrogate modeling techniques. See conclusion- The variance-based sensitivity analysis has also shown the importance of the depth of the shallow water and the velocity of the bottom layer on the uncertainties of the recorded sound pressure)
Claim 15-A computer system for analyzing sound wave propagation in an aquatic medium, the computer system comprising: one or more computer processors; one or more computer readable storage media; and program instructions stored on at least one of the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising: (See Abstract-Sound wave propagation in a shallow water environment is complex due to e.g. the uncertainties of sound speed profile being inhomogeneous and imprecisely measured, the bottom reflections, etc. The propagation and influence of several uncertainty parameters are quantified in this paper. For this purpose, the sound field is computed for different realizations of the random variables, when the medium is excited with sources whose frequencies are appropriate, for example, for marine seismic exploration applications. Since classical modeling techniques required a huge sample size to converge, we use three surrogate modeling techniques. See conclusion- The variance-based sensitivity analysis has also shown the importance of the depth of the shallow water and the velocity of the bottom layer on the uncertainties of the recorded sound pressure)
the method comprising:
collecting, a plurality of aquatic environment measurements; (see abstract-Sound wave propagation in a shallow water environment is complex due to e.g. the uncertainties of sound speed profile being inhomogeneous and imprecisely measured, the bottom reflections, etc. See section 4.1 and table 1- 401 measurement stations are assumed at (25(m − 1), 0,−49) m, m = 1, 2, . . . , 401, to record the wave field.)
defining one or more correlation functions based on the aquatic environment measurements; (see section 3.1-The random field Z (x ℓ) is fully described by its auto-correlation function (ACF) R(x, x′ ℓ) = E Z (x, ℓ)Z (x′, ℓ) . The ACF is often considered as a function of the lag distance d = x-x′ and a vector ℓ = ℓ₁, . . . , ℓM ⊤ also called the correlation distance, i.e. R(x, x′ ℓ) = R(d ℓ). The vector ℓ is oftentimes unknown and should be estimated from the pressure measurements. See section 4.2- Following the distributions given in Table 1, 50000 realizations of the random velocity profile are drawn using aquasi Monte Carlo (QMC) sampling method.)
generating, a model to represent the aquatic medium as a three-dimensional (3D) Gaussian random field, the 3D Gaussian random field being described by the one or more correlation functions (see page 2, fig 2 and Introduction-In this work we consider an axisymmetric model which is also known as 2.5-D and therefore it can describe a 3-D inhomogeneous shallow water environment with an underlying semi-infinite ocean layer. See section 3 and 3.1 - In this section, the uncertainty quantification methods used in this paper are introduced. Consider the random pressure field (at any point x) or any other scalar stochastic output quantity of interest (QoI) Y as a function of a vector of M input random variables X = {X₁, X₂, . . . , XM }⊤ ∈ RM , i.e. Y = P(r, X ). Hereinafter the dependence on r is omitted for In this work, Kriging as popular surrogate models will be used. See section 4.3-In the sequel, we use the methods introduced in Section 3 in order to construct meta-models of the random wave field at each recording point. Kriging, PCE and PCE-Kriging methods are thus used to estimate the statistics of the wave field)
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Khazaie does not teach collecting, by one or more autonomous underwater vehicles (AUV) or one or more underwater sensor networks (USN), a plurality of aquatic environment measurements; determining, by the USN in real-time as the aquatic environment measurements are collected, a propagation trajectory of a sound wave in the aquatic medium based, at least in part, on the 3D Gaussian random field; transmitting, by the USN, the propagation trajectory to one or more of the AUVs; and following, by the one or more AUVs, an underwater target based on the propagation trajectory.
In the related field of invention, Ghafoor teaches collecting, by one or more autonomous underwater vehicles (AUV) or one or more underwater sensor networks (USN), a plurality of aquatic environment measurements in real time; (see abstract-Target tracking is one of the important applications of underwater wireless sensor networks (UWSNs). It is a sophisticated process that estimates the state (position, velocity, acceleration) of single or multiple moving targets by conducting the possible measurements that can be available from different types of sensors. See section II-The results are the first successful demonstration at sea to control real-time movement of AUVs in a realistic surveillance scenario.)
determining, by the USN in real-time as the aquatic environment measurements are collected, (see abstract-Target tracking is one of the important applications of underwater wireless sensor networks (UWSNs). It is a sophisticated process that estimates the state (position, velocity, acceleration) of single or multiple moving targets by conducting the possible measurements that can be available from different types of sensors. See section II-The results are the first successful demonstration at sea to control real-time movement of AUVs in a realistic surveillance scenario.) a propagation trajectory of a sound wave in the aquatic medium based, at least in part, on the 3D Gaussian random field. (see section III Ray tracing models WAVE-FRONT QUEUE 3D (WaveQ3D)- WaveQ3D is a 3D ray tracing model especially designed for active sonar simulation systems and distributed as part of Under Sea Modeling Library (USML). It is based on ray theory where other models (e.g., parabolic equation and normal mode) exhibit low performance for frequencies above 1000 Hz. The objective of this model is to generate transmission loss eigenrays accurately only for coastal scenarios. Moreover, it generates other eigenrays products also i.e., multipath travel time, phase, and propagation direction. WaveQ3D enhances Gaussian beam techniques to make it applicable for lower frequencies also. Gaussian beam techniques are based on Gaussian Ray Bundling (GRAB). See also THE BELLHOP RAY TRACING MODEL- Bellhop integrates both ray and dynamic equations. The input files include depth, sound speed, surface type, attenuation, surface shape, directional sources specifications, and geo-acoustic properties. However, in the simplest case, environmental file is the only input file that includes SSP and bottom information. The output files include ray coordinates, travel time, amplitude, eigenrays, acoustic pressure, and transmission loss. Therefore, the model can handle shadows and caustics with the help of Gaussian beam techniques.)
transmitting, by the USN, the propagation trajectory to one or more of the AUVs;(see section IV. NEXT-GENERATION UNDERWATER TARGET DETECTION AND TRACKING SCHME and fig 1- taking advantages form all the novel notions such as SDN, NFV, fog, and cloud computing, we propose novel target detection and tracking scheme. The scheme considers underwater sensor nodes as the fundamental units that sense the presence of the target and report the sensing results to any of the AUV moving around the cluster of the sensor nodes. The AUV collects the sensing results, apply any information gathering algorithm such as decision-based methods or neural networks, and forward the collaborative output to the next-level AUV as shown in Fig. 1 to reach the local controller (any surface buoy).)
and following, by the one or more AUVs, an underwater target based on the propagation trajectory. (See abstract- The utilization of unmanned underwater vehicles for target tracking behavior is gaining great attention due to continuous advancement of underwater vehicular technology. In this paper, we first provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking that answers several questions regarding the current necessities of underwater networks and finally, provides a solution that opens several doors for research community to excel in this area. see section IV. NEXT-GENERATION UNDERWATER TARGET DETECTION AND TRACKING SCHME and fig 1- The integration of these different notions in next-generation underwater target detection and tracking systems allow any node of interest for any other service to collect information from any fog cloud and take full advantage of the whole network. This scheme allows detecting either a single or multi, mobile or fixed target, by collecting the sensing information at nearby AUVs which forwards the collected data to the local/server cloud for stronger processing. The cloud then estimates the position of the target and predicts its trajectory more accurately. The scheme does not involve a single entity; it is a hybrid communication of several underwater devices such as sensors, AUVs, surface buoys, and base stations on land, each performing the tasks in a collaborative manner. Therefore, we can say that, this is an alternative solution to detect and track the mobile target more accurately and precisely.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of acoustic wave propagation in a shallow water environment as disclosed by Khazaie to include collecting, by one or more autonomous underwater vehicles (AUV) or one or more underwater sensor networks (USN), a plurality of aquatic environment measurements; determining, by the USN in real-time as the aquatic environment measurements are collected, a propagation trajectory of a sound wave in the aquatic medium based, at least in part, on the 3D Gaussian random field; transmitting, by the USN, the propagation trajectory to one or more of the AUVs; and following, by the one or more AUVs, an underwater target based on the propagation trajectory as taught by Ghafoor in the system of Khazaie in order to provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking, thus improve underwater communications and thus provide the solution for next generation underwater target detection and tracking.
(See abstract, Ghafoor)
Regarding claim 2, 9 and 16
Khazaie further teaches wherein determining the propagation trajectory of the sound wave comprises: providing the one or more correlation functions to generate realizations of a random field as an input to a Monte Carlo-based simulation to estimate an uncertainty of acoustic propagation in the aquatic medium. (See section introduction-The uncertainty on the aforementioned sound speed profile of the whole system is modeled via six random variables with given lower and upper bounds. These random variables correspond to: (i) two constant sound speeds of the shallow water; (ii) the sound speed of the ocean bottom; and (iii)three-layer depths in the shallow water. One of the objectives of this work is to propagate the input uncertainties through the system and to classify the input parameters in terms of their contribution to the variance (uncertainty) of the wave field via a sensitivity analysis. The relatively high number of random variables involved in this problem forces us to solve deterministic realizations of the problem at least several thousand times to obtain a wave field with converged statistics. On the other hand, the computational cost of the wave field modeling for each particular realization of such a medium is a priori high, particularly in three-dimensional problems. To cope with this problem, we use the fast algorithm proposed in to solve the wave equation. This method is based on the wavenumber integration [1,2,14] and provides a semi-analytical Green’s function for the system that enables drastic reductions of the computational costs. As a result, we can use classical sampling methods such as Quasi Monte Carlo (QMC) to calculate the statistics of the random wave field. see section 4.1-4.2 results of the quasi-Monte Carlo method- [AltContent: textbox (∼)]In this paper, two different scenarios are considered and the influence of the random parameters are discussed for each case. These two cases correspond to large and small values of the wavelength to depth ratio which is the key factor in this work. Indeed, λ/d → ∞ (or λ ≫ d) implies no reflection at the top and bottom boundaries which means a smoothly varying wave field following x direction. On the contrary, the asymptotic limit λ/d→ 0 (or λ ≪ d) means high degrees of reflection at the boundaries which implies in particular a highly fluctuating wave field along the path of the wave propagation. To distinguish these cases, the sound waves are generated via a source located at (x0, y0, z0) = (0, 0,−50)m with two different frequencies of f0 = 10 Hz and f0 = 50 Hz. One of the major applications of this range of frequencies is in marine seismic exploration where an image of the underlying geology (elastic bottom layer of our model which could also be considered as multiple layers) is constructed by emitting the acoustic energy through the water and then recording the energy that is reflected and refracted by the bottom rock layer(s) back to the receivers. In this case, the conventional acquisition systems work with frequencies under 100 Hz. 401 measurement stations are assumed at (25(m − 1), 0,−49) m, m = 1, 2, . . . , 401, to record the wave field. The length of the medium in x direction is thus 10 km. This configuration is depicted in Fig. 2. The simulation of sound wave propagation is realized by the wavenumber integration method implemented by a numerical code developed by the authors, its accuracy)
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Regarding claim 3, 10 and 17
Khazaie further teaches generating a correlation function for describing a change in the 3D Gaussian random field based on data from one or more underwater sensors. (see introduction- The uncertainty on the aforementioned sound speed profile of the whole system is modeled via six random variables with given lower and upper bounds. These random variables correspond to: (i) two constant sound speeds of the shallow water; (ii) the sound speed of the ocean bottom; and (iii)three layer depths in the shallow water. One of the objectives of this work is to propagate the input uncertainties through the system and to classify the input parameters in terms of their contribution to the variance (uncertainty) of the wave field via a sensitivity analysis. see section 3.1 – for generating a correlation function. See fig (2) microphone placed as depth acts as underwater sensor)
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Regarding claim 4, 11 and 18
Khazaie further teaches wherein generating the correlation function comprises optimizing one or more parameters of the correlation function based on the data. (see section 3.1 The function (ACF) R(x, x′ ℓ) E Z (x ℓ)Z (x′ ℓ) . The ACF is often considered as a function of the lag distance d x x′ and a vector ℓ ℓ₁, . . . , ℓM ⊤ also called the correlation distance, i.e. R(x, x′ ℓ) R(d ℓ). The vector ℓ is oftentimes unknown and should be estimated from the pressure measurements. Some frequently used ACF models are introduced. In this study, a hybrid genetic algorithm [40] with upper and lower bounds of [0.001, 10] for each ℓi (1 < i < M) is used to solve the optimization problem via Matlab’s built-in optimization functions. Finally, one should use the optimal values of ℓ to estimate the coefficients of the trend a, the mean µ (x) and the variance σ 2(x) of the Kriging predictors. A leave-one-out (LOO) cross-validation error estimator is frequently used to assess Yˆthe quality of the meta-models. see section 4,.4-The objective of this section is to identify how the uncertain parameters describing the sound source profile along with the depths, i.e. d, d1, d2, v1, v2 and v3 contribute to the variability of the wave field recorded at different distances from the source. Some of the objectives of such a study are: (i) identification of less influential parameters in order to reduce the stochastic dimension of the problem and (ii) reduction of the variance of the recorded wave field (in the forward (direct) problem) by minimizing the variance of the most influential parameters. This variance reduction is crucial for getting better estimations when solving the inverse source localization problem is regarded.)
Regarding claim 5, 12 and 19
Khazaie further teaches wherein each spatial point in the 3D Gaussian random field represents a variation in at least one property of the aquatic medium, wherein the at least one property of the aquatic medium includes a local pressure. (see page 2-Kriging yields a local interpolation of the random pressure and provides a local error estimate. See section 4.3-4.4- we use the methods introduced in Section 3 in order to construct meta-models of the random wave field at each recording point. Kriging, PCE and PCE-Kriging methods are thus used to estimate the statistics of the wave field. Figs. 8 and 9 display for f0 = 10 Hz the variation of the Sobol and total Sobol sensitivity indices in terms of the source station distance obtained by the QMC (black curves), ordinary Kriging (blue), PCE (red) and PCE-Kriging (green) methods. It turns out that the most influential factor is the velocity of the bottom layer v3. Then, the depth of the shallow water d plays the most important role on the variance of the recorded pressures. The other 4 random variables (d1, d2, v1, v2)could therefore be considered as uninfluential. Thus, one should try to decrease the uncertainty on v3 and d in order to reduce the uncertainty on the sound pressure.)
Regarding claim 6, 13 and 20
Khazaie does no teach determining a first variation in frequency with propagation of the sound wave in the aquatic medium based on the 3D Gaussian random field.
However, Ghafoor further teaches determining a first variation in frequency with propagation of the sound wave in the aquatic medium based on the 3D Gaussian random field. (see section II-The technique considers waveguide invariant and Doppler Effect and is known as waveguide invariant Doppler-based localization (WI-DBL) technique)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of acoustic wave propagation in a shallow water environment as disclosed by Khazaie to include determining a first variation in frequency with propagation of the sound wave in the aquatic medium based on the 3D Gaussian random field as taught by Ghafoor in the system of Khazaie in order to provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking, thus improve underwater communications and thus provide the solution for next generation underwater target detection and tracking.
(See abstract, Ghafoor)
Regarding claim 7 and 14
Khazaie further teaches determining a second variation in amplitude with propagation of the sound wave in the aquatic medium based on the 3D Gaussian random field. (see section 4.3- Fig. 4 shows for both frequencies, the mean, standard deviation and coefficient of variation of the wave field in terms of the station position. The black, blue, red and green curves correspond to the QMC (with a sample size of N 50000), Kriging, PCE and PCE-Kriging methods (each with N 500), respectively. As it can be observed from the plots (a,b,c), all surrogate models appropriately estimate the first and second order statistics of the wave field when the source’s central frequency is f0 10 Hz. In this case, the ratio λ/d 1.53 is large enough so that the influence of the reflections and transmissions at the boundaries on the wave field’s fluctuations become very small (see the smooth variation of the mean field in plot (a)). The black curves (QMC results) in plots (c) and (f) show a reduction in the fluctuation amplitudes after some travel distance. This reduction could be justified by the fact that at short travel distances the wave field is reflected by and transmitted into the interface. By contrast, at long travel distances, the energy of the initial waves is attenuated due to the transmissions at the interface and absorption at the level of the PMLs and the wave front is like a wave packet whose shape remains almost unchanged. Fig. 4(d, e and f) shows the same results for f0 50 Hz in which the ratio λ/d 0.31 is much lower compared to the previous case so that the interactions of the waves with the boundaries of the medium become substantial and thus the wave field fluctuates more. In this case, deviations from the QMC results become apparent for the second order statistics. These deviations start from the source-station distances above 4500 m and 2500 m for Kriging and PCE methods, respectively. On the contrary, the PCE-Kriging method appears to yield the results closer to the QMC (green curves in the plots (e,f)).)
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
CHIRAYATH et al. US 20190266712 A1
Discussing a novel high-resolution aquatic remote sensing technique for imaging through ocean waves by harnessing the optical effects of fluid lensing lenslets and caustic bands. As visible light interacts with aquatic surface waves, time-dependent nonlinear optical aberrations appear, forming caustic bands of light on the seafloor, and producing refractive lensing that magnifies and demagnifies underwater objects.
Finette, Steven. "A stochastic representation of environmental uncertainty and its coupling to acoustic wave propagation in ocean waveguides." The journal of the acoustical society of America 120.5 (2006): 2567-2579.
Discussing a method to incorporate environmental uncertainty directly into the computation of acoustic wave propagation in ocean waveguides. In this regard, polynomial chaos expansions are chosen to represent uncertainty in both the environment and acoustic field. The sound-speed distribution and acoustic field are therefore generalized to stochastic processes, where uncertainty in the field is interpreted in terms of its statistical moments.
10. All claims 1-20 are rejected.
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/PURSOTTAM GIRI/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186