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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. FR2005227, filed on 05/20/2020.
Response to Amendment
This action is in response to amendments and remarks filed on 04/24/2026. The examiner notes the following adjustments to the claims by the applicant:
Claims 10 and 11 are amended;
No additional claims are cancelled (Claims 1-9 were previously cancelled);
No new claims added.
Therefore, Claims 10-29 are pending examination, in which Claims 10 and 11 are independent claims.
In light of the instant amendments and arguments:
The objection to Claims 10-11 for minor informalities is withdrawn.
The objection to the Specifications for minor informalities is withdrawn.
Regarding the rejection of Claims 10-29 under 35 U.S.C. § 101, the applicant’s arguments have been considered but found unpersuasive. The rejection of Claims 10-29 under 35 U.S.C. § 101 is maintained.
Claims 16-17 and 21-22 remain interpreted under 35 U.S.C. § 112(f), as the applicant’s remarks and amendments were mute on this issue.
Further examination resulted in a new rejection of Claims 10-29 under 35 U.S.C. § 103, as detailed below.
THIS ACTION IS MADE FINAL. Necessitated by amendment.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
[A.] 101 Rejection: “the Examiner characterizes the claims as reciting a mathematical concept due to the use of a statistical model, Desjardins confirms that such a characterization at Step 2A, Prong One does not end the inquiry. Rather, the critical question is whether the claims reflect a technological improvement. Desjardins, Appeal No. 2024-000567, at 8-9. The claims here are not directed to generic data processing, but instead recite a structured method for generating a database that maps operational parameters of a physical LNG tank system, specifically, fill level and sea state, across an entirety of an operational range, to predicted sloshing responses derived from experimentally obtained training data. This approach enables rapid evaluation of sloshing behavior across operating conditions without requiring additional physical testing, thereby reducing computational and experimental burden while improving operational decision-making. This constitutes a specific implementation tied to real-world physical constraints and measured phenomena, and therefore represents a technological solution to a technical problem in the field of maritime engineering.” [The examiner respectfully disagrees, and continues to maintain the claim broadly reflect inputting data into a machine learning algorithm and generating an output};
[B.] 101 Rejection: “The USPTO memorandum further instructs that examiners must evaluate the specification to determine whether it discloses an improvement to a technical field, and then assess whether the claims reflect that improvement…The present specification describes, and the claims embody, a particular improvement in how sloshing response data is generated and utilized, namely by constructing a database that systematically covers the operational envelope of the system using model-based estimation informed by experimental data. This is analogous to the improvement recognized in Desjardins, where eligibility was found because the claims reflected enhancements to how a machine learning model operates, including improved performance and efficiency… when properly analyzed under the framework clarified by the USPTO memorandum and Ex Parte Desjardins, the claims integrate any alleged abstract idea into a practical application that improves a technical field, and therefore are not directed to a judicial exception under Step 2A." [The examiner respectfully disagrees. The examiner maintains that claim does not include “specific mathematical calculations” associated with implementing an artificial neural network, which is deemed a consistent reading of the August 4, 2025 Memorandum. In addition, the examiner contends the abstract idea is simply linked to a particular technological field.]
[C.] “Independent claims 10 and 11 are directed to methods for obtaining a database usable to estimate a sloshing response of at least one sealed, thermally insulating tank of a liquefied gas transport ship. Each claim recites a specific sequence of steps including training a statistical model by supervised machine learning on experimentally obtained test data, generating a plurality of input data vectors defined by operational parameters of the tank and vessel, and, critically, ensuring that the generated input data vectors cover the entirety of a range of operation of the ship. The claims further require, for each generated input data vector, obtaining an estimated sloshing response using the trained model and storing that estimated response in association with the corresponding input data vector in a database. Thus, the claims are not directed merely to training or using a model for isolated prediction, but to the systematic construction of a database of model- generated estimations spanning a defined operational domain, thereby decoupling the computationally intensive training phase from subsequent use and enabling efficient downstream estimation.”;
[D.] “Ahn's disclosure is limited to the use of experimental data as an input to model training and, at most, to applying the trained model to predict outcomes for particular conditions. Ahn does not disclose or suggest any post-training phase in which a plurality of input data vectors are generated independently of the experimental dataset, nor does it disclose systematically applying the trained model to such generated vectors. To the contrary, Ahn emphasizes that experimental conditions are selected and constrained by practical testing limitations, and the database it relies upon is a collection of measured data rather than model-generated outputs.”;
[E.] “Lee discloses the use of hydrodynamic testing to generate lookup tables of experimentally measured responses of marine structures under applied forces, which are then stored and used to guide control or operational decisions…. Lee does not disclose or suggest the use of a trained machine learning model to generate predicted responses, nor does it disclose generating a plurality of input data vectors covering an operational domain and populating a database with model-derived estimations.”.
Applicant's arguments C., D., and E. appear to be directed to the instantly amended subject matter. Accordingly, they have been addressed in the rejections below.
Claim Rejections - 35 USC § 101
Claims 10-29 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
As described in MPEP § 2106, the analyses as to whether a claim qualifies as eligible subject matter under 35 U.S.C. § 101 includes the following determinations:
(1) Whether the claim is to a statutory category, i.e. to a process, machine, manufacture or composition of matter ("Step 1")- see MPEP §§ 2106, subsection III, and 2106.03.
(2) If the claim is to a statutory category, whether the claim recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes) ("Step 2A, Prong One") - see MPEP §§ 2106, subsection III, and 2106.04.
(3) If the claim recites a judicial exception, whether the claim recites additional elements that integrate the judicial exception into a practical application ("Step 2A, Prong Two") - see MPEP §§ 2106, subsection III, and 2106.04.
(4) If the claim does not recite additional elements that integrate the judicial exception into a practical application, whether the claim recites additional elements that amount to significantly more than the judicial exception ("Step 2B") – see MPEP §§ 2106, subsection III, and 2106.05.
Step 1: Claims 10-15, 18-20 and 24-29 are a method, and Claims 16-17 and 21-23 are a system. Thus, each independent claim, on its face, is directed to one of the four statutory categories of 35 U.S.C. §101 (MPEP 2106.03).
Claim 10 is considered a representative independent claim. The examiner has determined, the following analysis is applicable to each independent claim. With regard to Claim 10:
A method (300) for obtaining a database (150) usable to estimate a sloshing response of at least one sealed thermally insulating tank in a ship for the transport of liquefied gas, the method comprising the steps consisting of: training (302) a statistical model by a supervised machine learning method on a set of test data, the statistical model being able to estimate a sloshing response of the sealed thermally insulating tank as a function of a level of filling of the sealed thermally insulating tank and of a current sea state, and the set of test data being obtained from results of a plurality of tests each consisting of subjecting a test tank (1010) having a given level of filling to movements and measuring at least one of a pressure at, at least, one point on a wall (1010a) of the test tank (1010) and a number of impacts on at least one wall of the test tank (1010); generating (303) a plurality of input data vectors each comprising a level of filling of the sealed thermally insulating tank and a current sea state, wherein the plurality of input data vectors cover the entirety of a range of operation of the ship for the transport of liquefied gas; and for each input data vector generated in this way: obtaining (303) an estimated sloshing response of the sealed thermally insulating tank with the aid of the statistical model, and storing (303) in a database the estimated sloshing response of the sealed thermally insulating tank in association with the input data vector.
A method for obtaining a database usable to estimate a sloshing response of at least one sealed thermally insulating tank in a ship for the transport of liquefied gas, the method comprising the steps consisting of: training a statistical model by a supervised machine learning method on a set of test data, the statistical model being able to estimate a sloshing response of the sealed thermally insulating tank as a function of a level of filling of the sealed thermally insulating tank and of a current sea state, and the set of test data being obtained from results of a plurality of tests each consisting of subjecting a test tank having a given level of filling to movements and measuring at least one of a pressure at, at least, one point on a wall of the test tank and a number of impacts on at least one wall of the test tank ; generating a plurality of input data vectors each comprising a level of filling of the sealed thermally insulating tank and a current sea state, wherein the plurality of input data vectors cover the entirety of a range of operation of the ship for the transport of liquefied gas; and for each input data vector generated in this way: obtaining an estimated sloshing response of the sealed thermally insulating tank with the aid of the statistical model, and storing in a database the estimated sloshing response of the sealed thermally insulating tank in association with the input data vector.
Step 2A, Prong 1:
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. [See MPEP 2106.04(a)-2106.04(a)(2)]
The examiner submits that the foregoing bolded limitations constitute a “mathematical concept”, under the broadest reasonable interpretation.
Specifically, Claim 1 recites the general idea of inputting gathered data (“set of test data being obtained from results of a plurality of tests each consisting of subjecting a test tank (1010) having a given level of filling to movements and measuring at least one of a pressure at at least one point on a wall (1010a) of the test tank (1010) and a number of impacts on at least one wall of the test tank (1010)”) into a machine-learning algorithm (“a statistical model by a supervised machine learning method”, corresponding to a computer-implemented process) and generating output data (“obtaining a database (150) usable to estimate a sloshing response obtaining (303)…an estimated sloshing response of the tank with the aid of the statistical model”).
In addition, the courts have deemed that implementation of an abstract idea by a generic computer is equivalent to human performing the abstract idea:
Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257-59, 113 USPQ2d 1097, 1105-07 (Fed. Cir. 2014).
Thus, the claim recites, under the broadest reasonable interpretation, recites an abstract ideas implemented by a computer based process. (See MPEP § 2106.04(a)(2)).
Step 2A, Prong 2:
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer or processor to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The examiner submits that the foregoing underlined additional limitation does not integrate the above-noted abstract idea into a practical application. The examiner contends that the additional limitation of “storing (303) in a database the estimated sloshing response of the tank in association with the input data vector” constitutes an insignificant extra-solution activity that merely compiles the outputted data into a database [MPEP 2106.05(g)]; and the additional limitations of “sealed thermally insulating tank for the transport of liquefied gas” and “wherein the plurality of input data vectors cover the entirety of a range of operation of the ship for the transport of liquefied gas” merely links the judicial exception, in a general manner, to a particular technological field of use [MPEP 2106.05(h)]. Thus, the additional limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The examiner further submits that the aforementioned additional element in Claim 1 is not sufficient to amount to significantly more than the judicial exception for the same reason discussed above for Step 2A, Prong 2. Storing the outputted data in a database constitutes an insignificant post-solution activity [MPEP 2106.05(g)]. And applying a judicial exception to a “sealed thermally insulating tank for the transport of liquefied gas” merely links the judicial exception, in a general manner, to a particular technological field of use [MPEP 2106.05(h)].
Hence, the claim is not patent eligible.
The examiner finds that independent Claim 11 includes the same limitations as Claim 10 associated with “obtaining a database (150) usable to estimate a sloshing response obtaining (303)…an estimated sloshing response of the tank with the aid of the statistical model” (discussed above under Step 2A, Prong 1). Thus, Claim 11, under its broadest reasonable interpretation, constitute an abstract idea constituting “mathematical concepts”.
Dependent: Claims 12-29 do not recite any further limitations that cause the claims to be patent eligible. Rather, the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. For example, with regard to Claims 12-29, the claimed invention is directed to additional abstract ideas associated with “mathematical concepts”:
Insignificant pre-solution activity in the form of mere data gathering:
“determining a current level of filling of the tank (Claims 12-13 & 17-18); determining a current sea state” (Claims 12-13)
“measuring a current state of filling of the tank” (Claims 16-17 and 21-22)
Additional application of mathematical concept (i.e., “statistical model by a supervised machine learning”):
“estimating a sloshing response of the tank from the input data vector” (Claims 13 and 15-19)
“estimate a future sloshing response” (Claims 21-22)
“estimate sloshing response” [and may apply post-solution condition or restriction] (Claims 12-13 and 14-29)
Insignificant post-solution activities in the form of data generation:
“determining at least one of a course of the ship and a modification of the level of filling of the tank” (Cl.20)
“determine a course of the ship” (Claims 20 and 23)
Additionally, the “plurality of tanks”, in Claim 14, represents well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. And, the “processing means”, in Claims 16-17 and 21-22 represents a generic computer component, and its implementation in these claims falls under the category of “merely using a computer to implement an abstract idea”, and thus, does not integrate the judicial exceptions into practical applications (Step 2A). Nor does the use of a generic computer component to perform determinations and estimates of parametric data provide an inventive concept in Step 2B.
Therefore, Claims 10-29 are ineligible under 35 USC §101.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 10-13, 24-25 and 27-28 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Ahn et al. ("Database of model-scale sloshing experiment for LNG tank and application of artificial neural network for sloshing load prediction", henceforth Ahn) and Kim et al. (KR 2021/0123437 A, henceforth Kim).
Regarding Claim 10, Ahn discloses the limitations: a method for obtaining a database usable to estimate a sloshing response {generating a database related to sloshing load severity, Abstract} of at least one sealed thermally insulating tank in a ship {engineering scale-model testing of a tank representative of the tanks on liquefied natural gas/LNG ship, Abstract; green tank in Fig. 1} for the transport of liquefied gas {tank within an LNG ship, dealt with below with additional prior art}, the method comprising the steps consisting of: training a statistical model by a supervised machine learning method {multi-layer perceptron, supervised machine learning algorithms, Pg. 75, Section 4.1, to predict sloshing severity} on a set of test data {training using experimental data, Pg. 75, Section 4.1, including the sloshing model test database of Seoul National University, ¶3, Pg. 67}, the statistical model being able to estimate a sloshing response of the sealed thermally insulating tank {output of supervised machine learning algorithms predicts sloshing severity in a tank, Pg. 75, Section 4.1} as a function of a level of filling of the sealed thermally insulating tank {the loading conditions in Table 3 (Pg. 76) are reflective of filling heights, fourth full paragraph on Pg. 76} and of a current {input parameters based on real-time data is dealt with below via additional prior art} sea state {Wave environment and operation parameters summarized in Table 3, Pg. 76}, and the set of test data being obtained from results of a plurality of tests each consisting of subjecting a test tank {Fig. 1} having a given level of filling to movements {within Section 5.2, the last paragraph on Pg. 77 describes varying the fluid level in the tank} and measuring at least one of a pressure at, at least, one point on a wall of the test tank and a number of impacts on at least one wall of the test tank {pressure sensors installed on or within the tank wall are common components for a typical sloshing experiment (see Fig. 1, Pg. 68) and Figs. 9a-9d show peak pressures on the wall at various times, eight paragraph on Pg. 78, representative of tracking peak pressures over time}; generating a plurality of input data vectors each comprising a level of filling of the sealed thermally insulating tank and a current sea state {the examiner interprets this as applying a new set of data to a now trained neural network; such a new set of data is described in Section 5.2 (Verification and Application, Pg. 77) to estimate sloshing that is completely unrelated to the training data}, wherein the plurality of input data vectors cover the entirety of a range of operation of the ship for the transport of liquefied gas {per Section 5.2 (Verification and Application, Pg. 77), the aim is to estimate the sloshing severity in real-world sized tank (i.e., “138,00 LNG carrier”), with the model taking into account a wide range parameters as detailed in Table 3 (Pg. 76), and the particular experiments varying the fill level and wave heading condition}; and for each input data vector generated in this way: obtaining an estimated sloshing response of the sealed thermally insulating tank with the aid of the statistical model {applying the trained neural network to data unrelated to the training data set to predict “sloshing load severity”, Section 5.2 (Verification and Application, Pg. 77}, and storing in a database the estimated sloshing response of the sealed thermally insulating tank in association with the input data vector {storing results of multiple experiments: “all the statistical results of the experiments are being stored, a distribution of the sloshing loads can be illustrated to show their severities”, Section 6 (Pg. 80); as will be appreciated by one skilled in the art, storing the experimental results will include storing all input parameters, in this case, corresponding to all the ship, tank and wave parameters in Table 3 (Pg. 76)}.
Ahn does not appear explicitly recites the limitation: at least one sealed thermally insulating tank in a ship for the transport of liquefied gas; and input data vectors comprising a current sea state.
However, Kim explicitly recites the limitations: at least one sealed thermally insulating tank in a ship for the transport of liquefied gas {a ship’s LNG cargo tank 10, Fig. 1; one skilled in the art will appreciate all LNG tanks on ocean-going vessels are highly insulated, owing to basic heat transfer principles}; input data vectors comprising a current sea state {a sloshing prediction system 100 (Fig. 1) for a ship’s LNG tank 10, is mated with a digital twin 20 that monitors sloshing occurring in the tank (Pg. 1, Lns. 13-15) in real-time (Pg. 5, Lns. 15-18), and includes a signal processing unit 30 that stores data on tank operating conditions and environmental factors/forces that is fed to simulation unit 41 (Pg. 5, Lns. 7-12), that receives input data from the digital twin (which monitors tank sloshing) along with ship operating and environmental conditions (“the signal processing unit may receive sensing data and environmental external force information to simulate real-time vessel status”), with the output from the simulation unit fed into the sloshing prediction unit 42 (Pg. 5, Lns. 14-15) to affect navigation in order to minimize sloshing (Pg. 8, Lns. 21-23}.
Ahn and Kim are analogous art because they both deal with prediction sloshing conditions in a tank representative a liquid natural gas tank.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Ahn and Kim before them, to modify the teachings of Ahn to include the teachings of Kim to predict sloshing in the LNG cargo tank of an actual ship {10, Fig. 1} using current tank sloshing data and environmental conditions affecting the ship {Pg. 5, Lns. 7-12}.
Regarding Claim 11, Ahn discloses the limitations: a method for obtaining a database usable to estimate a sloshing response {generating a database related to sloshing load severity, Abstract} of at least one sealed thermally insulating tank in a ship {engineering scale-model testing of a tank representative of the tanks on liquefied natural gas/LNG ship, Abstract; green tank in Fig. 1} for the transport of liquefied gas {tank within an LNG ship, dealt with below with additional prior art}, the method comprising the steps consisting of: training a statistical model by a supervised machine learning method {multi-layer perceptron, supervised machine learning algorithms, Pg. 75, Section 4.1, to predict sloshing severity} on a set of test data {training using experimental data, Pg. 75, Section 4.1, including the sloshing model test database of Seoul National University, ¶3, Pg. 67}, the statistical model being able to estimate a sloshing response of the sealed thermally insulating tank {output of supervised machine learning algorithms predicts sloshing severity in a tank, Pg. 75, Section 4.1} as a function of a level of filling of the sealed thermally insulating tank {the loading conditions in Table 3 (Pg. 76) are reflective of filling heights, fourth full paragraph on Pg. 76} and a current {input parameters based on real-time data is dealt with below via additional prior art} state of movement of the ship {operation speed parameter in Table 3, Pg. 76}, and the set of test data being obtained from results of a plurality of tests each consisting of subjecting a test tank {Fig. 1} having a given level of filling to movements {within Section 5.2, the last paragraph on Pg. 77 describes varying the fluid level in the tank} and measuring at least one of a pressure at, at least, one point on a wall of the test tank and a number of impacts on at least one wall of the test tank {pressure sensors installed on or within the tank wall are common components for a typical sloshing experiment (see Fig. 1, Pg. 68) and Figs. 9a-9d show peak pressures on the wall at various times, eight paragraph on Pg. 78, representative of tracking peak pressures over time}; generating a plurality of input data vectors each comprising a level of filling of the sealed thermally insulating tank and a current state of movement of the ship {the examiner interprets this as applying a new set of data to a now trained neural network; such a new set of data is described in Section 5.2 (Verification and Application, Pg. 77) to estimate sloshing that is completely unrelated to the training data}, wherein the plurality of input data vectors cover the entirety of a range of operation of the ship {per Section 5.2 (Verification and Application, Pg. 77), the aim is to estimate the sloshing severity in real-world sized tank (i.e., “138,00 LNG carrier”), with the model taking into account a wide range parameters as detailed in Table 3 (Pg. 76), and the particular experiments varying the fill level and wave heading condition}; and for each input data vector generated in this way: obtaining an estimated sloshing response of the sealed thermally insulating tank with the aid of the statistical model {applying the trained neural network to data unrelated to the training data set to predict “sloshing load severity”, Section 5.2 (Verification and Application, Pg. 77}, and storing in a database the estimated sloshing response of the sealed thermally insulating tank in association with the input data vector {storing results of multiple experiments: “all the statistical results of the experiments are being stored, a distribution of the sloshing loads can be illustrated to show their severities”, Section 6 (Pg. 80); as will be appreciated by one skilled in the art, storing the experimental results will include storing all input parameters, in this case, corresponding to all the ship, tank and wave parameters in Table 3 (Pg. 76); and under the broadest reasonable interpretation of a database, any compilation of data available to be referenced in the future can be considered a database}.
Ahn does not appear explicitly recites the limitation: at least one sealed thermally insulating tank in a ship for the transport of liquefied gas; and input data vectors comprising a current state of movement of the ship.
However, Kim explicitly recites the limitations: at least one sealed thermally insulating tank in a ship for the transport of liquefied gas {a ship’s LNG cargo tank 10, Fig. 1; one skilled in the art will appreciate all LNG tanks on ocean-going vessels are highly insulated, owing to basic heat transfer principles}; input data vectors comprising a current state of movement of the ship {a sloshing prediction system 100 (Fig. 1) for a ship’s LNG tank 10, is mated with a digital twin 20 that monitors sloshing occurring in the tank (Pg. 1, Lns. 13-15) in real-time (Pg. 5, Lns. 15-18), and includes a signal processing unit 30 that stores data on tank operating conditions and environmental factors/forces that is fed to simulation unit 41 (Pg. 5, Lns. 7-12), that receives input data from the digital twin (which monitors tank sloshing) along with ship operating and environmental conditions (“the signal processing unit may receive sensing data and environmental external force information to simulate real-time vessel status”), with the output from the simulation unit fed into the sloshing prediction unit 42 (Pg. 5, Lns. 14-15) to affect navigation in order to minimize sloshing (Pg. 8, Lns. 21-23}.
Regarding Claim 12, the combination of Ahn and Kim discloses all the limitations of the method of Claim 10, as discussed supra. In addition, Ahn explicitly recites the limitations: for estimating a sloshing response of at least one sealed and thermally insulating tank for the transport of liquefied gas onboard a ship, the method comprising the steps consisting in: determining a current level of filling of the tank {“loading conditions, which would be the filling heights of the cargo hold”, Table 3, Pg. 76; one skilled in the art will appreciate that use of filling level/height data for sloshing related calculations (i.e., “The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract) can existing or newly obtained data}; determining a current sea state {“Wave environment δw” and “Operation δo”, which includes operation speed, Table 3, Pg. 76}; generating an input data vector comprising the current level of filling of the tank and the current sea state determined in this way {in Table 3, Pg. 76, under the heading “Experimental Parameters” are the plurality of parameters – or the data - input into the neural network to predict the severity of loading on a tank during sloshing: “The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract; one skilled in the art appreciates that neural networks involves calculations involving matrixes, and that each line of matrix can be considered a vector}; and estimating a sloshing response of the tank from the input data vector generated in this way and from the database obtained by the method {“The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract} according to Claim 10 {see Claim 10 above}.
Regarding Claim 13, the combination of Ahn and Kim discloses all the limitations of the method of Claim 11, as discussed supra. In addition, Ahn explicitly recites the limitations: the method for estimating a sloshing response of at least one sealed and thermally insulating tank for the transport of liquefied gas onboard a ship, the method comprising the steps consisting of: determining a current level of filling of the tank {“loading conditions, which would be the filling heights of the cargo hold”, Table 3, Pg. 76; one skilled in the art will appreciate that use of filling level/height data for sloshing related calculations (i.e., “The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract) can be existing or newly obtained data}; determining a current state of movement of the ship {“Wave environment δw” and “Operation δo”, which includes operation speed, Table 3, Pg. 76}; generating an input data vector comprising the current level of filling of the tank and the current state of movement of the ship determined in this way {in Table 3, Pg. 76, under the heading “Experimental Parameters” are the plurality of parameters – or the data - input into the neural network to predict the severity of loading on a tank during sloshing: “The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract; one skilled in the art appreciates that neural networks involves calculations involving matrixes, and that each line of matrix can be considered a vector}; and estimating a sloshing response of the tank from the input data vector generated in this way and from the database obtained by the method {“The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract} according to Claim 11 {see Claim 11 above}.
Regarding Claim 24, the combination of Ahn and Kim discloses all the limitations of the method of Claim 10, as discussed supra. In addition, Ahn explicitly recites the limitation: in which the sloshing response comprises at least one of a number of impacts of fluid on the walls of the tank, a maximum pressure on the walls of the tank {“The most frequently used experimental approach is to measure the sloshing impact peak pressure under operating simulation of the vessel, and statistically analyze the most probable maximum of the peak pressure”, Pg. 66, second paragraph in Section 1, and “Measuring impact peak pressures is a main concern for the sloshing model test [38–40]. In the experiments of SNU, integrated circuit piezoelectric (ICP) type sensors 211B5 made by KISTLER are used. The pressure sensor is used to obtain an impulse peak pressure within very short event duration.”, Pg. 67, second paragraph in Section 2}, and a probability of damage to the tank.
Regarding Claim 25, the combination of Ahn and Kim discloses all the limitations of the method of Claim 10, as discussed supra. In addition, Ahn explicitly recites the limitation: further comprising a step (301) consisting of excluding from the set of test data test results featuring a sloshing response below a threshold before the step (302) of training the statistical model
{pressure data must satisfy a threshold constraint: “This unified post-processing method follows those of Grazyk and Moan, and Kim et al. (2014) [36,37]. A peak-over-threshold method was applied with a 0.2 s time window and a 2.5 kPa threshold pressure [33,37].”, last paragraph on Pg. 68}.
Regarding Claim 27, the combination of Ahn and Kim discloses all the limitations of the method of Claim 11, as discussed supra. In addition, Ahn explicitly recites the limitation: in which the sloshing response comprises at least one of a number of impacts of fluid on the walls of the tank, a maximum pressure on the walls of the tank {“The most frequently used experimental approach is to measure the sloshing impact peak pressure under operating simulation of the vessel, and statistically analyze the most probable maximum of the peak pressure”, Pg. 66, second paragraph in section 1, and “Measuring impact peak pressures is a main concern for the sloshing model test [38–40]. In the experiments of SNU, integrated circuit piezoelectric (ICP) type sensors 211B5 made by KISTLER are used. The pressure sensor is used to obtain an impulse peak pressure within very short event duration.”, Pg. 67, second paragraph in section 2}, and a probability of damage to the tank.
Regarding Claim 28, the combination of Ahn and Kim discloses all the limitations of the method of Claim 11, as discussed supra. In addition, Ahn explicitly recites the limitation: further comprising a step (301) consisting of excluding from the set of test data test results featuring a sloshing response below a threshold before the step (302) of training the statistical model
{pressure data must satisfy a threshold constraint: “This unified post-processing method follows those of Grazyk and Moan, and Kim et al. (2014) [36,37]. A peak-over-threshold method was applied with a 0.2 s time window and a 2.5 kPa threshold pressure [33,37].”, last paragraph on Pg. 68}.
Claims 14-23, 26 and 29 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Ahn, Kim and Chen et al. (US 8,643,509 B1), henceforth Chen.
Regarding Claim 14, the combination of Ahn and Kim discloses all the limitations of the method of Claim 12, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: in which a plurality of tanks are considered and the method comprises a definition step to define the position of each of the tanks of the ship.
However, Chen explicitly recites limitation: in which a plurality of tanks are considered {tanks 12, 14, 16, and 18, Figs. 1} and the method comprises a definition step to define the position of each of the tanks of the ship {the ship includes multiple tanks that are individually tracked for sloshing issues based on wave theory algorithms: “computer 102 receives a tank fill level 120 from each of the tanks on the ship.”, Col. 4, Lns. 28-29, and “The degree of sloshing is in part affected by the amount of liquid in the tanks 12, 14, 16, and 18.”, Col. 3, Lns. 46-47, and “through user input, changing the percentage that each cargo tank is filled will cause the estimated natural periods for roll and pitch, based on the tank dimensions and fill level, to be calculated and displayed. In one embodiment, the natural periods are calculated using linear wave theory algorithms stored in SAAS 100 (shown in FIG. 3 as 132).”, Col. 9, Lns. 44-49}.
The combination of Ahn and Kim along with Chen are analogous art because they all deal with monitoring or determining sloshing in liquid natural gas tanks aboard a ship.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Ahn, Kim and Chen before them, to modify the teachings of the combination of Ahn and Kim to include the teachings of Chen to account for differing levels of sloshing in different tanks of a multiple tank ocean-going ship: {Col. 4, Lns. 28-29 and Col. 9, Lns. 44-49}.
Regarding Claim 15, the combination of Ahn and Kim discloses all the limitations of the method of Claim 12, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: further comprising a step consisting of furnishing an alarm to a user if the estimated sloshing response of the tank exceeds an alert threshold, and p a step of assisting the decision intended to reduce the sloshing.
However, Chen explicitly recites the limitations: comprising a step consisting of furnishing an alarm to a user if the estimated sloshing response of the tank exceeds an alert threshold {“Alerts 142 include audio and/or visual warnings and any other alarms necessary to alert a ship's crew as to a current sloshing condition.”, Col. 4, Lns. 65-67, and “When an amplitude and number of consecutive ship motion periods meet certain thresholds, the SAAS 100 indicates a sloshing warning or sloshing alarm (alert 142) visually and/or audibly.”, Col. 7, Lns. 43-46}, and a step of assisting the decision intended to reduce the sloshing {“FIG. 11 is a polar diagram display selected from the screen of FIG. 5 illustrating headings and speeds likely to reduce sloshing of a load.”, Col. 3, Lns. 3-5, and “polar diagram 1160 in one embodiment passively displays all the possible headings and speeds one could use to minimize sloshing providing all the information needed for a captain to choose one heading and speed to reduce sloshing.”, Col. 10, Lns. 30-33}.
Regarding Claim 16, the combination of Ahn and Kim discloses all the limitations of the method of Claim 10, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: a management system for a ship including at least one sealed and thermally insulating tank for transporting liquefied gas, the system comprising: at least one filling level sensor for measuring a current state of filling of the tank; a device for evaluation of the sea state able to evaluate a current sea state; and a processing means configured to generate an input data vector comprising a current level of filling of the tank and a current sea state evaluated by the sea state evaluation device and to estimate a sloshing response of the tank from the input data vector generated in this way and from the database obtained by the method according to Claim 10.
However, Chen explicitly recites the limitations: a management system {Figs. 3, 10-11} for a ship {Fig. 1} including at least one sealed and thermally insulating tank for transporting liquefied gas {tanks 12, 14, 16, and 18, Fig. 1}, the system comprising: at least one filling level sensor for measuring a current state of filling of the tank {“computer 102 may also receive vibration, and/or fluid level data from one or more sensors 126 within or external to the tanks”, Col. 4, Lns. 42-44; tank fill levels in Fig. 11}; a device for evaluation of the sea state able to evaluate a current sea state {Fig. 11 shows the displaying of data for wave height, wind speed, speed of the current, and wave forecast: “Based on the location and time, the SAAS 100 automatically extracts the forecast environmental conditions from a weather file downloaded from an external source. The wave forecast is depicted by three wave trains 1120, 1122, 1124 or sea and swells. The height 1130, period 1132, and direction 1134”, Col. 9, Ln. 64 to Col. 10, Ln. 1}; and a processing means {102, Fig. 3} configured to generate an input data vector comprising a current level of filling of the tank {120, Fig. 3} and a current sea state evaluated by the sea state evaluation device {sea keeping guidance 140, Fig. 3} and to estimate a sloshing response of the tank from the input data vector generated in this way and from the database {a sloshing evaluation and management system is described in Col. 4, Lns. 1-22, is based on a sloshing modeling algorithm that incorporates real-time and past/predictive data to evaluate the potential for problems related to sloshing: “SAAS 100 incorporates predictive and real-time motion analysis onboard the ship that can be used by the ship operator to reduce ship motions that may lead to sloshing damage…SAAS motion and sloshing modeling algorithms running within SAAS 100 can be improved and modified over time to match actual ship behavior.”; also, “To further facilitate the investigation of past events and developing better algorithms to predict sloshing induced damage, SAAS 100 provides the capabilities to convert the binary records into comma delimited text files for export into spreadsheets…and save under user specified folders as shown in FIG. 9.”, Col. 9, Lns. 27-33} obtained by the method according to Claim 10 {see Claim 10 above}.
Regarding Claim 17, the combination of Ahn and Kim discloses all the limitations of the method of Claim 11, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: a management system for a ship including at least one sealed and thermally insulating tank for transporting liquefied gas, the system comprising: at least one filling level sensor for measuring a current state of filling of the tank; a device for evaluation of the current state of movement of the ship able to evaluate a current state of movement of the ship; and a processing means configured to generate an input data vector comprising a current level of filling of the tank and a current state of movement of the ship and to estimate a sloshing response of the tank from the input data vector generated in this way and from the database obtained by the method according to Claim 11.
However, Chen explicitly recites the limitations: a management system {Figs. 3, 10-11} for a ship {Fig. 1} including at least one sealed and thermally insulating tank for transporting liquefied gas {tanks 12, 14, 16, and 18, Fig. 1}, the system comprising: at least one filling level sensor {120, Fig. 3} for measuring a current state of filling of the tank {“computer 102 may also receive vibration, and/or fluid level data from one or more sensors 126 within or external to the tanks”, Col. 4, Lns. 42-44; tank fill levels in Fig. 11}; a device for evaluation of the current state of movement of the ship able to evaluate a current state of movement of the ship {Fig. 11 shows the displaying of data for wave height, wind speed, speed of the current, and wave forecast: “Based on the location and time, the SAAS 100 automatically extracts the forecast environmental conditions from a weather file downloaded from an external source. The wave forecast is depicted by three wave trains 1120, 1122, 1124 or sea and swells. The height 1130, period 1132, and direction 1134”, Col. 9, Ln. 64 to Col. 10, Ln. 1}; and a processing means {102, Fig. 3} configured to generate an input data vector comprising a current level of filling of the tank {120, Fig. 3} and a current state of movement of the ship {sea keeping guidance 140, Fig. 3} and to estimate a sloshing response of the tank from the input data vector generated in this way and from the database {a sloshing evaluation and management system is described in Col. 4, Lns. 1-22, is based on a sloshing modeling algorithm that incorporates real-time and past/predictive data to evaluate the potential for problems related to sloshing: “SAAS 100 incorporates predictive and real-time motion analysis onboard the ship that can be used by the ship operator to reduce ship motions that may lead to sloshing damage…SAAS motion and sloshing modeling algorithms running within SAAS 100 can be improved and modified over time to match actual ship behavior.”; also, “To further facilitate the investigation of past events and developing better algorithms to predict sloshing induced damage, SAAS 100 provides the capabilities to convert the binary records into comma delimited text files for export into spreadsheets…and save under user specified folders as shown in FIG. 9.”, Col. 9, Lns. 27-33} obtained by the method according to Claim 11 {see Claim 11 above}.
Regarding Claim 18, the combination of Ahn and Kim discloses all the limitations of the method of Claim 10, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: the method of estimating a sloshing response of a sealed and thermally insulating tank for the transport of liquefied gas onboard a ship, the method comprising the steps consisting of: determining a current level of filling of the tank; estimating future sea states from meteorological information and a course of the ship; generating a plurality of input data vectors each comprising a current level of filling of the tank and an estimated future sea state; and estimating a future sloshing response of the tank from the input data vectors generated in this way and from the database obtained by the method according to Claim 10.
However, Chen explicitly recites the limitations: a method comprising the steps consisting of: determining a current level of filling of the tank {“computer 102 may also receive vibration, and/or fluid level data from one or more sensors 126 within or external to the tanks”, Col. 4, Lns. 42-44; tank fill levels in Fig. 11}; estimating future sea states from meteorological information {Fig. 11 shows the displaying of data for wave height, wind speed, speed of the current, and wave forecast: “Based on the location and time, the SAAS 100 automatically extracts the forecast environmental conditions from a weather file downloaded from an external source. The wave forecast is depicted by three wave trains 1120, 1122, 1124 or sea and swells. The height 1130, period 1132, and direction 1134”, Col. 9, Ln. 64 to Col. 10, Ln. 1} and a course of the ship {“SAAS 100 provides users with advisories on how to change the conditions, including, but not limited to, ship heading, speed, route, draft and trim, and schedule so that sloshing is minimized. Further, SAAS 100 provides users anticipated results that would result from such changes.”, Col. 4, Lns. 17-22}; generating a plurality of input data vectors each comprising a current level of filling of the tank and an estimated future sea state {“SAAS 100 incorporates a computer 102 which receives a plurality of inputs 104 from shipboard systems external to SAAS 100. The computer 102 uses the data received as well as programs stored and running therein to generate a plurality of outputs 106. Particularly, computer 102 receives a tank fill level 120 from each of the tanks on the ship”, Col. 4, Lns. 23-29 and Fig. 3}; and estimating a future sloshing response of the tank from the input data vectors generated in this way and from the database {a sloshing evaluation and management system is described in Col. 4, Lns. 1-22, is based on a sloshing modeling algorithm that incorporates real-time and past/predictive data to evaluate the potential for problems related to sloshing: “SAAS 100 incorporates predictive and real-time motion analysis onboard the ship that can be used by the ship operator to reduce ship motions that may lead to sloshing damage…SAAS motion and sloshing modeling algorithms running within SAAS 100 can be improved and modified over time to match actual ship behavior.”; also, “To further facilitate the investigation of past events and developing better algorithms to predict sloshing induced damage, SAAS 100 provides the capabilities to convert the binary records into comma delimited text files for export into spreadsheets…and save under user specified folders as shown in FIG. 9.”, Col. 9, Lns. 27-33} obtained by the method according to Claim 10 {see Claim 10 above}.
Regarding Claim 19, the combination of Ahn and Kim discloses all the limitations of the method of Claim 11, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: the method of estimating a sloshing response of a sealed and thermally insulating tank (2) for the transport of liquefied gas onboard a ship, the method comprising the steps consisting of: determining a current level of filling of the tank; estimating future states of movement of the ship from meteorological information and a course of the ship; generating a plurality of input data vectors each comprising a current level of filling of the tank and an estimated future state of movement of the ship; and estimating a future sloshing response of the tank from the input data vectors generated in this way and from the database obtained by the method according to Claim 11.
However, Chen explicitly recites the limitations: a method comprising the steps consisting of: determining a current level of filling of the tank {“computer 102 may also receive vibration, and/or fluid level data from one or more sensors 126 within or external to the tanks”, Col. 4, Lns. 42-44; tank fill levels in Fig. 11}; estimating future states of movement of the ship from meteorological information {Fig. 11 shows the displaying of data for wave height, wind speed, speed of the current, and wave forecast (i.e., “Based on the location and time, the SAAS 100 automatically extracts the forecast environmental conditions from a weather file downloaded from an external source. The wave forecast is depicted by three wave trains 1120, 1122, 1124 or sea and swells. The height 1130, period 1132, and direction 1134”, Col. 9, Ln. 64 to Col. 10, Ln. 1} and a course of the ship {“SAAS 100 provides users with advisories on how to change the conditions, including, but not limited to, ship heading, speed, route, draft and trim, and schedule so that sloshing is minimized. Further, SAAS 100 provides users anticipated results that would result from such changes.”, Col. 4, Lns. 17-22}; generating a plurality of input data vectors each comprising a current level of filling of the tank and an estimated future state of movement of the ship {“SAAS 100 incorporates a computer 102 which receives a plurality of inputs 104 from shipboard systems external to SAAS 100. The computer 102 uses the data received as well as programs stored and running therein to generate a plurality of outputs 106. Particularly, computer 102 receives a tank fill level 120 from each of the tanks on the ship”, Col. 4, Lns. 23-29 and Fig. 3}; and estimating a future sloshing response of the tank from the input data vectors generated in this way and from the database {a sloshing evaluation and management system is described in Col. 4, Lns. 1-22, is based on a sloshing modeling algorithm that incorporates real-time and past/predictive data to evaluate the potential for problems related to sloshing: “SAAS 100 incorporates predictive and real-time motion analysis onboard the ship that can be used by the ship operator to reduce ship motions that may lead to sloshing damage…SAAS motion and sloshing modeling algorithms running within SAAS 100 can be improved and modified over time to match actual ship behavior.”; also, “To further facilitate the investigation of past events and developing better algorithms to predict sloshing induced damage, SAAS 100 provides the capabilities to convert the binary records into comma delimited text files for export into spreadsheets…and save under user specified folders as shown in FIG. 9.”, Col. 9, Lns. 27-33} obtained by the method according to Claim 11 {see Claim 11 above}.
Regarding Claim 20, the combination of Ahn and Kim discloses all the limitations of the method of Claim 18, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitation: further comprising a step consisting of determining at least one of a course of the ship and a modification of the level of filling of the tank enabling reduction of the future sloshing response of the tank.
However, Chen explicitly recites the limitation: a method comprising the step consisting of: determining at least one of a course of the ship and a modification of the level of filling of the tank enabling reduction of the future sloshing response of the tank {“display to the ship operator on how to reduce or avoid risk of sloshing damage to tanks from the liquid being transported. Such damage avoidance measures include one or more of a change in heading, speed, route, draft and trim, and travel schedule for the ship. More particularly and as further described below, SAAS 100 incorporates predictive and real-time motion analysis onboard the ship that can be used by the ship operator to reduce ship motions that may lead to sloshing damage…SAAS 100 provides users with advisories on how to change the conditions, including, but not limited to, ship heading, speed, route, draft and trim, and schedule so that sloshing is minimized. Further, SAAS 100 provides users anticipated results that would result from such changes.”, Col. 4, Lns. 1-22}.
Regarding Claim 21, the combination of Ahn and Kim discloses all the limitations of Claim 10, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: the management system for a ship including at least one sealed and thermally insulating tank for transporting liquefied gas, the system comprising: at least one level of filling sensor for measuring a current level of filling of the tank; a sea state estimation device able to estimate future sea states from meteorological information and from a course of the ship; and a processing means configured to a generate a plurality of input data vectors each comprising a current level of filling of the tank and a future sea state estimated by the sea state estimation device, and to estimate a future sloshing response of the tank from the input data vectors generated in this way and from the database obtained by the method according to Claim 10.
However, Chen explicitly recites the limitations: a management system {Figs. 3, 10-11} for a ship {Fig. 1} including at least one sealed and thermally insulating tank for transporting liquefied gas {tanks 12, 14, 16, and 18, Fig. 1}, the system comprising: at least one filling level sensor for measuring a current state of filling of the tank {“computer 102 may also receive vibration, and/or fluid level data from one or more sensors 126 within or external to the tanks”, Col. 4, Lns. 42-44; tank fill levels in Fig. 11}; a sea state estimation device {102, Fig. 3, running a predictive algorithm: “SAAS 100 indicates the possibility of tank sloshing in forecast or user-specified weather conditions. In the predictive mode, SAAS 100 utilizes an algorithm to predict vessel motion amplitudes and periods under various sea conditions, headings and speeds utilizing proven ship motion theory. Advice on heading and speed changes to mitigate the risk of structural damage due to sloshing are depicted, in one embodiment, through the use of a polar diagram indicating the relative risks of tank resonance with ship motions.”, Col. 7, Lns. 47-56} able to estimate future sea states from meteorological information {Fig. 11 shows the displaying of data for wave height, wind speed, speed of the current, and wave forecast: “Based on the location and time, the SAAS 100 automatically extracts the forecast environmental conditions from a weather file downloaded from an external source. The wave forecast is depicted by three wave trains 1120, 1122, 1124 or sea and swells. The height 1130, period 1132, and direction 1134”, Col. 9, Ln. 64 to Col. 10, Ln. 1} and a course of the ship {“SAAS 100 provides users with advisories on how to change the conditions, including, but not limited to, ship heading, speed, route, draft and trim, and schedule so that sloshing is minimized. Further, SAAS 100 provides users anticipated results that would result from such changes.”, Col. 4, Lns. 17-22}; and a processing means {102, Fig. 3} configured to generate an input data vector comprising a current level of filling of the tank {120, Fig. 3} and a future sea state estimated by the sea state estimation device {computer 102 combined with sea keeping guidance 140 and wave theory/modeling 132, Fig. 3, providing the wave forecasting described in Col. 9, Ln. 64 to Col. 10, Ln. 1}; and to estimate a future sloshing response of the tank from the input data vectors generated in this way and from the database {a sloshing evaluation and management system is described in Col. 4, Lns. 1-22, is based on a sloshing modeling algorithm that incorporates real-time and past/predictive data to evaluate the potential for problems related to sloshing: “SAAS 100 incorporates predictive and real-time motion analysis onboard the ship that can be used by the ship operator to reduce ship motions that may lead to sloshing damage…SAAS motion and sloshing modeling algorithms running within SAAS 100 can be improved and modified over time to match actual ship behavior.”; also, “To further facilitate the investigation of past events and developing better algorithms to predict sloshing induced damage, SAAS 100 provides the capabilities to convert the binary records into comma delimited text files for export into spreadsheets…and save under user specified folders as shown in FIG. 9.”, Col. 9, Lns. 27-33} obtained by the method according to Claim 10 {see Claim 10 above}.
Regarding Claim 22, the combination of Ahn and Kim discloses all the limitations of Claim 11, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: the management system for a ship including at least one sealed and thermally insulating tank for transporting liquefied gas, the system comprising: at least one level of filling sensor for measuring a current level of filling of the tank; a state of movement estimation device able to estimate future sea states from meteorological information and from a course of the ship; and a processing means configured to a generate a plurality of input data vectors each comprising a current level of filling of the tank and a future state of movement of the ship estimated by the state of movement of the ship estimation device, and to estimate a future sloshing response of the tank from the input data vectors generated in this way and from the database obtained by the method according to Claim 11.
However, Chen explicitly recites the limitations: a management system {Figs. 3, 10-11} for a ship {Fig. 1} including at least one sealed and thermally insulating tank for transporting liquefied gas {tanks 12, 14, 16, and 18, Fig. 1}, the system comprising: at least one filling level sensor for measuring a current state of filling of the tank {“computer 102 may also receive vibration, and/or fluid level data from one or more sensors 126 within or external to the tanks”, Col. 4, Lns. 42-44; tank fill levels in Fig. 11}; a state of movement estimation device {102, Fig. 3, running a predictive algorithm: “SAAS 100 indicates the possibility of tank sloshing in forecast or user-specified weather conditions. In the predictive mode, SAAS 100 utilizes an algorithm to predict vessel motion amplitudes and periods under various sea conditions, headings and speeds utilizing proven ship motion theory. Advice on heading and speed changes to mitigate the risk of structural damage due to sloshing are depicted, in one embodiment, through the use of a polar diagram indicating the relative risks of tank resonance with ship motions.”, Col. 7, Lns. 47-56} able to estimate future sea states from meteorological information {Fig. 11 shows the displaying of data for wave height, wind speed, speed of the current, and wave forecast: “Based on the location and time, the SAAS 100 automatically extracts the forecast environmental conditions from a weather file downloaded from an external source. The wave forecast is depicted by three wave trains 1120, 1122, 1124 or sea and swells. The height 1130, period 1132, and direction 1134”, Col. 9, Ln. 64 to Col. 10, Ln. 1} and a course of the ship {“SAAS 100 provides users with advisories on how to change the conditions, including, but not limited to, ship heading, speed, route, draft and trim, and schedule so that sloshing is minimized. Further, SAAS 100 provides users anticipated results that would result from such changes.”, Col. 4, Lns. 17-22}; and a processing means {102, Fig. 3} configured to generate an input data vector comprising a current level of filling of the tank {120, Fig. 3} and a future state of movement of the ship estimated by the state of movement of the ship estimation device {computer 102 combined with sea keeping guidance 140 and wave theory/modeling 132, Fig. 3, providing the wave forecasting described in Col. 9, Ln. 64 to Col. 10, Ln. 1}, and to estimate a future sloshing response of the tank from the input data vectors generated in this way and from the database {a sloshing evaluation and management system is described in Col. 4, Lns. 1-22, is based on a sloshing modeling algorithm that incorporates real-time and past/predictive data to evaluate the potential for problems related to sloshing: “SAAS 100 incorporates predictive and real-time motion analysis onboard the ship that can be used by the ship operator to reduce ship motions that may lead to sloshing damage…SAAS motion and sloshing modeling algorithms running within SAAS 100 can be improved and modified over time to match actual ship behavior.”; also, “To further facilitate the investigation of past events and developing better algorithms to predict sloshing induced damage, SAAS 100 provides the capabilities to convert the binary records into comma delimited text files for export into spreadsheets…and save under user specified folders as shown in FIG. 9.”, Col. 9, Lns. 27-33} obtained by the method according to Claim 11 {see Claim 11 above}.
Regarding Claim 23, the combination of Ahn, Kim and Chen discloses all the limitations of Claim 21, as discussed supra. The combination of Ahn and Kim does not appear explicitly recite the limitations: in which the processing means is further configured to determine a course of the ship enabling reduction of the future sloshing response of the tank.
However, Chen explicitly recites the limitation: in which the processing means {102, Fig. 3} is further configured to determine a course of the ship enabling reduction of the future sloshing response of the tank {“FIG. 11 is a polar diagram display selected from the screen of FIG. 5 illustrating headings and speeds likely to reduce sloshing of a load.”, Col. 3, Lns. 3-5, and “polar diagram 1160 in one embodiment passively displays all the possible headings and speeds one could use to minimize sloshing providing all the information needed for a captain to choose one heading and speed to reduce sloshing.”, Col. 10, Lns. 30-33}.
Regarding Claim 26, the combination of Ahn and Kim discloses all the limitations of the method of Claim 10, as discussed supra. Ahn does not appear explicitly recite the limitation: in which the statistical model considers a plurality of tanks, the statistical model being able to estimate a sloshing response of each tank as a function of its position in the ship.
However, Chen explicitly recites limitation: in which the statistical model considers a plurality of tanks {tanks 12, 14, 16, and 18, Fig. 1}, the statistical model being able to estimate a sloshing response of each tank as a function of its position in the ship {the ship includes multiple tanks that are individually tracked for sloshing issues based on wave theory algorithms: “computer 102 receives a tank fill level 120 from each of the tanks on the ship.”, Col. 4, Lns. 28-29, and “The degree of sloshing is in part affected by the amount of liquid in the tanks 12, 14, 16, and 18.”, Col. 3, Lns. 46-47, and “through user input, changing the percentage that each cargo tank is filled will cause the estimated natural periods for roll and pitch, based on the tank dimensions and fill level, to be calculated and displayed. In one embodiment, the natural periods are calculated using linear wave theory algorithms stored in SAAS 100 (shown in FIG. 3 as 132).”, Col. 9, Lns. 44-49}.
Regarding Claim 29, the combination of Ahn and Kim discloses all the limitations of the method of Claim 11, as discussed supra. Ahn does not appear explicitly recite the limitation: in which the statistical model considers a plurality of tanks, the statistical model being able to estimate a sloshing response of each tank as a function of its position in the ship.
However, Chen explicitly recites limitation: in which the statistical model considers a plurality of tanks {tanks 12, 14, 16, and 18, Figs. 1}, the statistical model being able to estimate a sloshing response of each tank as a function of its position in the ship {the ship includes multiple tanks that are individually tracked for sloshing issues based on wave theory algorithms: “computer 102 receives a tank fill level 120 from each of the tanks on the ship.”, Col. 4, Lns. 28-29, and “The degree of sloshing is in part affected by the amount of liquid in the tanks 12, 14, 16, and 18.”, Col. 3, Lns. 46-47, and “through user input, changing the percentage that each cargo tank is filled will cause the estimated natural periods for roll and pitch, based on the tank dimensions and fill level, to be calculated and displayed. In one embodiment, the natural periods are calculated using linear wave theory algorithms stored in SAAS 100 (shown in FIG. 3 as 132).”, Col. 9, Lns. 44-49}.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 10,515,426 B2 – Teaches of generating a database of data on liquified gas tank(s) on seagoing ships, using a variety of sensors, in which the data is accumulated over considerable length of time and includes recorded data about the tank contents, and the corresponding sea conditions.
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/R.E.G./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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