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 .
Response to Amendment
This action is in response to amendments and remarks filed on 11/24/2025. The examiner notes the following adjustments to the claims by the applicant: (i) Claims 1-9 are cancelled; (ii) Claims 10-15 and 18-20 are amended; and (iii) Claims 23-29 are new. 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The examiner’s objection to the Abstract is withdrawn.
The examiner’s objection to the Specifications for minor informalities is withdrawn.
The examiner’s objection to the Claims for minor informalities is withdrawn.
The objection to the Specifications, due to informalities, is withdrawn.
Further examination resulted in a new rejection of Claims 10-29 under 35 U.S.C. § 103, as detailed below.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
“Claims 1 to 3, 5, 6, 8 and 9 stand rejected as unpatentable under 35 U.S.C. §102 as anticipated by Ahn. Claims 1 to 3, 5, 6, 8 and 9 are canceled by this amendment rendering this rejection moot.”;
“Ahn does not disclose or suggest the core steps of [amended] claim 10... Training does not involve producing estimated sloshing responses for newly generated input vectors, nor does it involve storing any such model-generated responses in a database. In other words, Ahn describes only the input-side use of experimental data to train a model; it does not disclose the output-side step of applying the trained model to a set of generated input data vectors to obtain predicted sloshing responses, much less storing each such predicted response in association with its generated input vector.";
“Lee fails to remedy these deficiencies. Lee concerns storing experimental measurements and measured response data in a look-up table generated from hydrodynamic tests in a water tank or wind tunnel….Lee's teaching about using a look-up table to avoid repeating physical experiments might motivate the avoidance of duplicative testing facilities, but it provides no motivation for the skilled person to generate and store model-based predictions for each of a plurality of input vectors.”;
“Because neither Ahn nor Lee discloses or suggests obtaining, for each generated input vector, an estimated sloshing response using the trained statistical model and storing each estimated response in association with the corresponding input vector, the central steps of claim 10 remain absent from the cited art. For at least these reasons, the subject matter of amended claim 10 is not rendered obvious by Ahn and Lee.”.
Applicant's arguments A., B., C. and D. appear to be directed to the instantly amended subject matter. Accordingly, they have been addressed in the rejections below.
Specification
The disclosure is objected to because of the following informalities:
Improper formatting of paragraph numbers, in accordance with MPEP Rule 1.52(b)(6), (i.e., “The number should consist of at least four numerals enclosed in square brackets, including leading zeros (e.g., [0001])”.
Regarding Paragraphs [0010-0012, 0028 and 0030]: The phrase “a pressure at at least one point on a wall” is grammatically incorrect, and should include appropriate punctuation: “a pressure at, at least, one point on a wall”.
Appropriate correction is required.
Claim Objections
A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim.
A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n).
Claims 10-11 are objected to because of the following informalities:
The phrase “a pressure at at least one point on a wall” is grammatically incorrect, and should include appropriate punctuation: “a pressure at, at least, one point on a wall”.
Appropriate correction is required.
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 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 tank as a function of a level of filling of the 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 tank and a current sea state; and for each input data vector generated in this way: obtaining (303) an estimated sloshing response of the tank with the aid of the statistical model, and storing (303) in a database the estimated sloshing response of the 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 limitation of “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)]. 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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations is/are for the terms:
• “processing means”, in Claims 16-17 and 21-22.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f)
or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the
corresponding structure described in the specification as performing the claimed
function, and equivalents thereof. The specification states:
“a processing means configured to train a statistical model by a supervised machine learning method on a set of test data” ¶[0029]
“A supervised machine learning method is typically executed by a computer; thus the step consisting in training the statistical model is typically executed by a computer.” ¶[0013]
If applicant does not intend to have this/these limitation(s) interpreted under
35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1)
amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C.
112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient
structure to perform the claimed function); or (2) present a sufficient showing that
the claim limitation(s) recite(s) sufficient structure to perform the claimed function
so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35
U.S.C. 112, sixth paragraph.
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 Lee (US 2017/0183062 A1).
Regarding Claim 10, Ahn discloses the limitations: a method for obtaining a database (150) usable to estimate a sloshing response {“An experimental database has been created to provide information of sloshing load severity,” Abstract} of at least one sealed thermally insulating tank for the transport of liquefied gas {“small-scale sloshing model tests, 1/70–1/25 scales, particularly focusing on the tanks of liquefied natural gas (LNG) carriers”, Abstract; one skilled in the art will appreciate all LNG tanks on ocean-going vessels are highly insulated}, the method comprising the steps consisting of: training a statistical model by a supervised machine learning method on a set of test data {“The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract, wherein the database has been compiled by Seoul National University/SNU: “SNU has studied to predict the sloshing load severity based on the SNU sloshing model test database (SNU DB) that SNU has accumulated since the test facility was built. SNU DB contains more than 20,000 h of small-scale model tests for copious projects of many different floating units.”, third paragraph on Pg. 67}, the statistical model being able to estimate a sloshing response of the tank {“an artificial neural network (ANN) has been developed…a supervised neural network system…Using the SNU DB [database], the ANN can be applied to predict the sloshing load severity from the experimental results…Statistical experiment results are Froude scaled and used to train the ANN.”, Pg. 67, ¶4} as a function of a level of filling of the tank {“loading conditions, which would be the filling heights of the cargo hold”, Table 3, Pg. 76} and of a current sea state {“Wave environment δw”, 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 {“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 [8–11]. The model test simulates the actual operation of six degree-of-freedom (6DoF) irregular motions for at least 5 h for each environmental and operational condition [9,10], and the 3-h most probable maximum of sloshing impact pressure for the test condition is usually used as a representative pressure based on the extreme wave statistics [12,13]”, Abstract} 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 are common components to a typical sloshing experiment (see Fig. 1, Pg. 68): “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…While sensor arrangement of each project is different with respect to the client and the purpose of the project, as many pressure sensors as possible are installed to cover the wetted surface of the cargo hold models”, eight paragraph on Pg. 67}; generating a plurality of input data vectors {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} each comprising a level of filling of the tank {“loading conditions, which would be the filling heights of the cargo hold”, Table 3, Pg. 76} and a current sea state {“Wave environment δw”, Table 3, Pg. 76}; and for each input data vector generated in this way: obtaining an estimated sloshing response of the tank {“The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract} with the aid of the statistical model {“an artificial neural network (ANN) has been developed…a supervised neural network system…Using the SNU DB [database], the ANN can be applied to predict the sloshing load severity from the experimental results…Statistical experiment results are Froude scaled and used to train the ANN.”, Pg. 67, ¶4}.
Ahn does not appear explicitly recites the limitation: storing in a database the estimated sloshing response of the tank in association with the input data vector.
However, Lee explicitly recites the limitations: storing in a database the estimated sloshing response of the tank in association with the input data vector {creation and storage of a lookup table: “(1) accumulating data about an internal or external force applied to a marine structure by a gas flow out of the marine structure by means of a linear test (e.g., hull form test) in a water tank or a wind tunnel and data about a response of the marine structure according to the internal or external force to generate a look-up table, and storing the look-up table in a database; (2) measuring the internal or external force by using a time-of-flight method in an actual voyage of the marine structure and storing the internal or external force in the database; (3) comparing the measurement data of the internal or external force obtained in the step (2) with the data about the internal or external force accumulated in the look-up table in the step (1) to predict data about a response of the marine structure; and (4) controlling a posture or navigation path of the marine structure in real time by using the predicted data about a response of the marine structure.”, ¶[0055]}.
Ahn and Lee are analogous art because they both deal with hydrodynamic forces on marine structures.
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 Lee before them, to modify the teachings of Ahn to include the teachings of Lee to provide a lookup table to guide future actions to minimize potential damage to a marine structure {“as a look-up table to control a posture of the marine structure and minimize a damage”, ¶[0281]}.
Regarding Claim 11, Ahn discloses the limitations: a method for obtaining a database usable to estimate a sloshing response {“An experimental database has been created to provide information of sloshing load severity,” Abstract} of at least one sealed thermally insulating tank for the transport of liquefied gas {“small-scale sloshing model tests, 1/70–1/25 scales, particularly focusing on the tanks of liquefied natural gas (LNG) carriers”, Abstract; one skilled in the art will appreciate all LNG tanks on ocean-going vessels are highly insulated}, the method comprising the steps consisting of: training a statistical model by a supervised machine learning method on a set of test data {“The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract, wherein the database has been compiled by Seoul National University/SNU: “SNU has studied to predict the sloshing load severity based on the SNU sloshing model test database (SNU DB) that SNU has accumulated since the test facility was built. SNU DB contains more than 20,000 h of small-scale model tests for copious projects of many different floating units.”, third paragraph on Pg. 67}, the statistical model being able to estimate a sloshing response of the tank {“an artificial neural network (ANN) has been developed…a supervised neural network system…Using the SNU DB [database], the ANN can be applied to predict the sloshing load severity from the experimental results…Statistical experiment results are Froude scaled and used to train the ANN.”, Pg. 67, ¶4} as a function of a level of filling of the tank {“loading conditions, which would be the filling heights of the cargo hold”, Table 3, Pg. 76}, a current sea state {“Wave environment δw”, 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 {“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 [8–11]. The model test simulates the actual operation of six degree-of-freedom (6DoF) irregular motions for at least 5 h for each environmental and operational condition [9,10], and the 3-h most probable maximum of sloshing impact pressure for the test condition is usually used as a representative pressure based on the extreme wave statistics [12,13]”, Abstract} 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 are common components to a typical sloshing experiment (see Fig. 1, Pg. 68): “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…While sensor arrangement of each project is different with respect to the client and the purpose of the project, as many pressure sensors as possible are installed to cover the wetted surface of the cargo hold models”, eight paragraph on Pg. 67}; generating a plurality of input data vectors {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} each comprising a level of filling of the tank {“loading conditions, which would be the filling heights of the cargo hold”, Table 3, Pg. 76} and a current sea state of movement of the ship {“Wave environment δw”, Table 3, Pg. 76}; and for each input data vector generated in this way: obtaining an estimated sloshing response of the tank {“The artificial neural network is trained based on the database to predict sloshing load severity.”, Abstract} with the aid of the statistical model {“an artificial neural network (ANN) has been developed…a supervised neural network system…Using the SNU DB [database], the ANN can be applied to predict the sloshing load severity from the experimental results…Statistical experiment results are Froude scaled and used to train the ANN.”, Pg. 67, ¶4}.
Ahn does not appear explicitly recites the limitation: storing in a database the estimated sloshing response of the tank in association with the input data vector.
However, Lee explicitly recites the limitations: storing in a database the estimated sloshing response of the tank in association with the input data vector {creation and storage of a lookup table: “(1) accumulating data about an internal or external force applied to a marine structure by a gas flow out of the marine structure by means of a linear test (e.g., hull form test) in a water tank or a wind tunnel and data about a response of the marine structure according to the internal or external force to generate a look-up table, and storing the look-up table in a database; (2) measuring the internal or external force by using a time-of-flight method in an actual voyage of the marine structure and storing the internal or external force in the database; (3) comparing the measurement data of the internal or external force obtained in the step (2) with the data about the internal or external force accumulated in the look-up table in the step (1) to predict data about a response of the marine structure; and (4) controlling a posture or navigation path of the marine structure in real time by using the predicted data about a response of the marine structure.”, ¶[0055]}.
Regarding Claim 12, the combination of Ahn and Lee 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 Lee 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 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 Lee 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 Lee 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 Lee 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 Lee discloses all the limitations of the method of Claim 11, as discussed supra. In addition, Ahn explicitly recites the limitation: further comprising a step consisting of excluding from the set of test data test results featuring a sloshing response below a threshold before the step 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, Lee and Chen et al. (US 8,643,509 B1), henceforth Chen.
Regarding Claim 14, the combination of Ahn and Lee discloses all the limitations of the method of Claim 12, as discussed supra. The combination of Ahn and Lee 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, Fig. 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 Lee 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, Lee and Chen before them, to modify the teachings of the combination of Ahn and Lee to include the teachings of Chen to account for differing levels of sloshing in different tanks of a multiple tank ocean-going ship.
Regarding Claim 15, the combination of Ahn and Lee discloses all the limitations of the method of Claim 12, as discussed supra. The combination of Ahn and Lee 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 Lee discloses all the limitations of the method of Claim 10. The combination of Ahn and Lee 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 and 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 Lee discloses all the limitations of the method of Claim 11. The combination of Ahn and Lee 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 and 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 10 above}.
Regarding Claim 18, the combination of Ahn and Lee discloses all the limitations of the method of Claim 10. The combination of Ahn and Lee 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 Lee discloses all the limitations of the method of Claim 11. The combination of Ahn and Lee 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, Lee and Chen discloses all the limitations of the method of Claim 18. The combination of Ahn and Lee 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 Lee discloses all the limitations of the method of Claim 10. The combination of Ahn and Lee 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 and 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 Lee discloses all the limitations of the method of Claim 11. The combination of Ahn and Lee 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 and 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, Lee and Chen discloses all the limitations of Claim 21, as discussed supra. The combination of Ahn and Lee 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 Lee 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 Lee 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, 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}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2020225353 A1 – Teaches of a pre-compiled database of tank sloshing responses for comparison to current onboard conditions to anticipate or identify sloshing levels that may lead to degradation of the tank insulation {“The sloshing determination step can be carried out in different ways. In one embodiment, the sloshing data is determined as a function of unimodal excitation by consulting a pre-established database containing data representing sloshing as a function of unimodal excitation. The database may include sloshing levels obtained experimentally in the laboratory or during onboard measurement campaigns at sea as a function of unimodal excitation”, Pg. 2, Lns. 12-16}.
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/R.E.G./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665