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 .
STATUS OF CLAIMS
This action is in response to the Applicant’s arguments and amendments filed on 1/14/2026. Applicant amended claims 1, 11 and 12; and canceled claims 9, 13-14, 19 and 20. Claims 1, 3, 11-12, 15-18 and 21-22 are pending and are examined below.
RESPONSE TO REMARKS AND ARGUMENTS
In regards to the claim objections, Applicant’s amendments filed on 1/14/2026 obviate the claim objections except for the following in claims 1 and 11: the first instance of “the classified data” still lacks antecedent basis. Also, the claim amendments introduce new grounds of objection — see infra. Accordingly, the claim objections are maintained.
In regards to the claim rejections under §§ 112(a),(b), Applicant’s amendments filed on 1/14/2026 obviate the rejections. However, new grounds of rejection under § 112(b) have been identified — see infra.
In regards to the claim rejections under § 101, Applicant’s arguments and amendments filed on 1/14/2026 have been fully considered but are unpersuasive.
As to amended claims 1 and 11, Applicant argues that the claims now recite a specific configuration that performs interpolation for missing data regions using spatio-temporal analysis and time-series hierarchical clustering techniques, thereby demonstrating that the claimed invention is a specific engineering algorithm structured as a combination of technical modules to monitor the integrated status of an ice-going vessel. Applicant concludes that the foregoing integrates the recited abstract ideas into a practical application. Applicant further argues that the utilization of spatio-temporal analysis using a time-series hierarchical clustering technique also represents an improvement in the field of monitoring ice-going vessels.
Examiner respectfully disagrees. The claims are directed towards performing abstract ideas without significantly more.
First, the limitation of “interpolate missing values using a time-series hierarchical clustering technique” analogizes to an abstract idea as it is a mathematical concept (i.e., algorithm) — mathematical algorithms have been found by the courts to constitute an abstract idea. (See, e.g., Gottschalk v. Benson1; In re Grams2)
Zooming out, the claimed invention uses various generic computing components (i.e., a server, a data collector, a data classifier, a data analyzer, a data learner, a database unit and a performance evaluator) to carry out abstract ideas (e.g., monitoring an integrated condition of a vessel, determining an ice environment, classifying measured data, etc.3) Here, Electric Power Group4 is particularly instructive because the present claims are similar to Electric Power Group’s claimed invention which collects information, analyzes the collected information and outputs the results of the analysis; the Court in Electric Power Group held such to be patent ineligible. Additionally, the claimed invention is not necessarily rooted in computer technology but rather uses a generic computer environment to carry out abstract ideas; such is a version of the Court’s “apply it” guidance in Electric Power Group.
Hence, Examiner respectfully disagrees that the claim integrates the recited mental processes into a practical application. As stated above, the claimed invention merely performs an “apply it” with the recited judicial exceptions — including the “time-series hierarchical clustering technique” — to merely implement the recited mental processes through generic computer components. The Court has held that such does not constitute an integration into a practical application. (See MPEP § 2106.05(f).)
Continuing, Examiner respectfully disagrees that the claim puts forth an improvement in technology. Improving an abstract idea (e.g., providing a “more accurate assessment”) does not constitute an improvement to technology. (See MPEP § 2106.05(a)(II).) Likewise, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. (See ibid.) As the claim merely puts forth mere instructions to perform an abstract idea on generic computer components, the claimed invention does not constitute an improvement in technology.
Building on the above, Examiner notes that Applicant’s specification doesn’t explain how incorporating a “time-series hierarchical clustering technique” sets forth an improvement in the functioning of a computer itself. Additionally, Applicant’s specification doesn’t explain how the “time-series hierarchical clustering technique” solves a unique maritime/polar technical problem that could not be addressed by other data-interpolating techniques. Rather, the specification merely appears to lay out that the claimed technique may be applied when data have a problem or are missing. (See Applicant’s PGPUB, ¶¶ 70-72.) Therefore, Applicant’s specification fails to identify a technical improvement associated with the claimed technique.
Accordingly, the claim rejections under § 101 are maintained.
In regards to the claim rejections under § 103, Applicant’s arguments and amendments filed on 1/14/2026 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
CLAIM OBJECTIONS
Claims 1 and 11 are objected to because of claim informalities.
As to claims 1 and 11:
The first instance of “the classified data” still lacks antecedent basis. Furthermore, in the “database unit” limitation, “classified data” lacks an article.
“Where F is a linear force component” appears to entail a typo as the claim (and the specification) lays out that the claimed equation calculates a global ice load, not a linear force component. And now Fx is undefined. Examiner suggests reverting back to the original language: “where [[F]] Fx is a linear force component”
“base” appears to be a typo of “based”
As to claim 11 in particular: “the bow’s point of impact” still lacks antecedent basis.
Appropriate correction is required.
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.
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 are:
Claim 1: “an imaging unit … configured to image an ice environment and generate ice-environment image data;”
Claim 11:
classifying the acquired data using a server-based data classifier,
identifying abnormal or missing data using a data analyzer,
interpolating missing values … by performing spatio-temporal analysis … and applying a time series hierarchical clustering technique using a data learner,
storing processed data by the data learner,
evaluating local and global ice load and vessel performance using a performance evaluator,
wherein the method is implemented using a hardware-integrated system, and
wherein the data collector analyzes ice thickness … and outputs a trigger signal.
The corresponding structure described in the specification as performing the claimed function at least includes:
image unit: a network camera (¶ 76)
server-based data classifier, data analyzer, data learner, performance evaluator and hardware-integrated system: server 200 (¶ 62; FIG. 3.)
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.
Because these claim limitation(s) 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.
If applicant does not intend to have 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 U.S.C. § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1, 3, 11, 12, 15-18 and 21-22 is/are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. § 112, the applicant), regards as the invention.
As to claim 1, the recitation “an imaging unit installed at a bow, a stern, and left and right sides of the vessel and configured to image an ice environment and generate ice environment image data” is vague and indefinite. Namely, it is unclear how one imaging unit can be installed in multiple, disparate locations of a vessel. The specification does not appear to shed light on this matter. Therefore, it is unclear what is being claimed in light of Applicant’s original disclosure.
As to claim 11, the recitation “capturing ice environment images using a network camera installed at a bow, a stern, and left and right sides of the vessel” is vague and indefinite. Namely, it is unclear how one network camera can be installed in multiple, disparate locations of a vessel. The specification does not appear to shed light on this matter. Therefore, it is unclear what is being claimed in light of Applicant’s original disclosure.
Claim 3 depends on claim 1. Claims 12, 15-18 and 21-22 depend on claim 11.
Therefore, claims 1, 3, 11, 12, 15-18 and 21-22 are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA ), second paragraph.
Appropriate correction is required.
CLAIM REJECTIONS—35 U.S.C. § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1, 3, 11, 12, 15-18 and 21-22 is/are rejected under 35 U.S.C. § 101 because the claims fail to pass the Alice/Mayo test for determining patent eligibility.
The patent eligibility test is performed below for independent claims 1 and 11.
Step 1—Does the claim fall within a statutory category?
Claim 1: Yes, the claim recites a machine or manufacture.
Claim 11: Yes, the claim recites a process.
Step 2A, Prong One—Is a judicial exception recited?
Claims 1 and 11 are provided below with the abstract idea indicated in bold and additional elements without bold.
1. An integrated condition monitoring system for an ice-going vessel, comprising:
an imaging unit installed at a bow, a stern, and left and right sides of the vessel and configured to image an ice environment and generate ice environment image data; and
a server monitoring an integrated condition of the vessel on the basis of images taken by the imaging unit, the server comprising:
a data collector configured to acquire motion characteristic data including yaw, pitch, and acceleration from a 3-axis strain gauge sensor and an angular acceleration sensor mounted on a bow module of the vessel;
a data classifier configured to classify the acquired motion-characteristic data into structured data or unstructured data for data framing;
a data analyzer configured to detect abnormal data or missing data from the classified data;
a data learner configured to perform spatio-temporal analysis on the classified data and interpolate missing values using a time-series hierarchical clustering technique;
a database unit configured to construct and update a database based on the interpolated data and classified data;
a performance evaluator configured to calculate a local ice load, a global ice load, and ice performance metrics by analyzing vessel response data;
wherein the performance evaluator calculates the global ice load based on the motion characteristics of the vessel measured at a point of impact or origin, and the global ice load is expressed as a function including Mz and My, where Mz denotes yaw moment and My denotes pitch moment;
wherein the global ice load is calculated using the following equation:
F
=
(
F
X
)
2
+
(
M
Z
X
a
b
)
2
+
(
M
Y
X
a
b
)
2
where F is a linear force component, Mz and My are moment components acting on the vessel in ice breaking, and Xab represents a distance from the angular acceleration sensor to a point of impact on hull; and
wherein the server integrates each of the above modules to perform real-time monitoring and analysis for safe navigation in a polar environment, and
wherein the data collector analyzes ice thickness base on an image of ice piece generated by the imaging unit, and outputs a trigger signal when both the analyzed ice thickness and ice concentration exceed a preset threshold.
11. A method of monitoring an ice-going vessel, the method comprising:
capturing ice environment images using a network camera installed at a bow, a stern, and left and right sides of the vessel;
acquiring vessel motion characteristic data including yaw, pitch, and acceleration using a data collector that includes a 3-axis strain gauge sensor and an angular acceleration sensor mounted on a bow module of the vessel;
classifying the acquired motion characteristic data into structured data or unstructured data for data framing using a server-based data classifier;
identifying abnormal or missing data using a data analyzer;
interpolating missing values when the classified data have a problem or are missing by performing spatio-temporal analysis on the classified data and applying a time series hierarchical clustering technique using a data learner;
storing the data by the data learner into a database unit;
evaluating local and global ice load and vessel performance using a performance evaluator,
wherein the global ice load is determined based on measurements at the bow's point of impact or ship origin using measured Mz and My,
wherein the global ice load is calculated using the following equation:
F
=
(
F
X
)
2
+
(
M
Z
X
a
b
)
2
+
(
M
Y
X
a
b
)
2
where F is a linear force component, Mz and MY are moment components acting on the vessel in ice breaking, and Xab represents a distance from the angular acceleration sensor to a point of impact on hull; and
wherein the method is implemented using a hardware-integrated system customized for ice-going vessel navigation, and
wherein the data collector analyzes ice thickness base on an image of ice piece generated by the imaging unit, and outputs a trigger signal when both the analyzed ice thickness and ice concentration exceed a preset threshold.
The above shows: yes, a judicial exception is recited.
As to claims 1 and 11, the claim limitation pertaining to “performing real-time monitoring and analysis for safe navigations in a polar environment” and “wherein the method is implemented … for ice-going vessel navigation” are processes which can practically be performed in the human mind with or without the use of a physical aid. Specifically, the broadest reasonable interpretation (BRI) of the claim limitations encompasses analyzing the condition of the vessel though an analysis technique; such follows Applicant’s disclosure at PGPUB para. [0093], stating: “[T]he integrated condition monitoring method for an ice-going vessel according to the present disclosure is characterized in an ice performance analysis technique.” Such is a form of observation, evaluation, judgment, or opinion.
Continuing, the claim limitations pertaining to performing analysis, classification, searching and evaluating are processes which can practically be performed in the human mind with or without the use of a physical aid. That is, these claim limitations are also forms of observation, evaluation, judgment or opinion. The courts have held such forms of observation, evaluation, judgment, or opinion to represent the abstract idea of a mental process. As a result, the bolded limitations respectively represent a mental process. Hence, the claim recites an abstract idea. (See MPEP § 2106.04(a)(2)(C)(III).)
Finally, the claim limitations pertaining to interpolating missing values using a time-series hierarchical clustering technique and calculating a global ice load through an equation are forms of a mathematical concept (i.e., algorithm) — mathematical algorithms have been found by the courts to constitute an abstract idea. (See, e.g., Gottschalk v. Benson; In re Grams; MPEP § 2106.04(a)(2)(I.)). Hence, in addition to the abstract ideas identified above, the claim recites the abstract idea of a mathematical concept.
Step 2A, Prong Two—Is the abstract idea integrated into a practical application?
No. The claims as a whole merely use generic computer components — i.e., a server, an imaging unit, a data collector, a data classifier, a data analyzer, a data learner, a performance evaluator, a server-based data classifier, a data learner and a hardware-integrated system — that are recited at a high level of generality such that they cannot be considered more than mere instructions to apply the judicial exception using generic computer components. Therefore, the abstract idea is not integrated into a practical application.
Step 2B—Does the claim provide an inventive concept?
No. The additional elements of the claims amount to either:
Insignificant pre-solution activity in the form of mere data gathering:
an imaging unit … configured to image an ice environment and generate ice-environment image data
a data collector configured to acquire motion characteristic data including yaw, pitch, and acceleration from a 3-axis strain gauge sensor and an angular acceleration sensor mounted on a bow module of the vessel
Insignificant post-solution activity in the form of well-understood and conventional activity:
a database unit configured to construct and update a database based on the interpolated data and classified data.
outputting a trigger signal when both the analyzed ice thickness and ice concentration exceed a preset threshold
Claim 3 depends from claim 1 but does not render the claimed invention patent eligible because it merely narrows the insignificant extra-solution activity of claim 1.
Claims 12, 15-18 and 21-22 depend from claim 11 but do not render the claimed invention patent eligible because they are directed to:
Additional abstract ideas:
calculating local ice load using an influence coefficient method
calculating ice resistance applied to the vessel
calculating global ice load applied to the vessel through motion analysis
Insignificant extra-solution activity:
extracting information
Furthermore, the claim does not put forth an improvement in technology. Improving an abstract idea (e.g., providing a “more accurate assessment”) does not constitute an improvement to technology. (See MPEP § 2106.05(a)(II).) Likewise, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. (See ibid.) As the claim merely puts forth mere instructions to perform an abstract idea on generic computer components, the claimed invention does not constitute an improvement in technology.
Claims 1, 3, 11, 12, 15-18 and 21-22 do not pass the patent eligibility test. Accordingly, claims 1, 3, 11, 12, 15-18 and 21-22 are rejected under § 101.
CLAIM REJECTIONS—35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 11 and 17 is/are rejected under § 103 as being unpatentable over Lee et al. (Prediction of ice loads on Korean IBRV ARAON with 6-DOF inertial measurement system during trials of Chukchi and East Siberian Seas 5; “Lee”) in view of Cho et al. (KR101349501B1; “Cho ’501”), in view of Kang (US20240046288A1; “Kang”), in view of V et al. (US20210182602A1; “V”), in view of Rath et al. (US20200167355A1; “Rath”), in view of Takimoto et al. (Field Measurements of Local Ice Load on a Ship Hull in Pack Ice of the Southern Sea of Okhotsk6; “Takimoto”) and in view of Matsumoto (JP6838230B1; “Matsumoto”).
As to claim 1, Lee discloses an integrated monitoring system for an ice-going vessel, comprising:
a data collector configured to acquire motion characteristic data including yaw, pitch, and acceleration from a 3-axis strain gauge sensor and an angular acceleration sensor (“The motion data in six degrees of freedom were measured using the inertial measurement system “MotionPak Ⅱ” developed by systron donner inertial, inc., which is shown in Fig. 3. The system consists of three translational accelerometers and three angular rate sensors like the MOTAN sensor. Each sensor measures the total ship accelerations and the angular rates of the ship in the directions of the x-, y-, and z-coordinates, respectively.” p. 28. Continuing, “pitch motions” and “yaw motions in the angular rates” may be obtained – see id; see also FIG. 13.);
a data classifier configured to classify the collected motion characteristic data (Translational and angular motions may be classified in regards to at least roll, pitch and yaw – see at least FIG. 13 and associated discussion in section 4.2 at p. 29.);
a performance evaluator configured to calculate a local ice load, a global ice load, and ice performance metrics by analyzing vessel response data (“Global and local ice loads” may be calculated through analyzing vessel response data – see section 4.3 at p. 30.);
wherein the performance evaluator calculates the global ice load based on the motion characteristics of the vessel measured at a point of impact or origin, and the global ice load is expressed as a function including Mz and My, where Mz denotes yaw moment and My denotes pitch moment; wherein the global ice load is calculated using the following equation:
F
=
(
F
X
)
2
+
(
M
Z
X
a
b
)
2
+
(
M
Y
X
a
b
)
2
where F is a linear force component, Mz and My are moment components acting on the vessel in ice breaking, and Xab represents a distance from the angular acceleration sensor to a point of impact on the hull (“The resultant ice load is calculated as
F
P
O
I
=
F
1
2
+
(
F
6
/
X
a
b
)
2
+
(
F
5
/
X
a
b
)
2
where F6 and F5 are the yaw and pitch moments acting on the center of gravity, and Xab is the longitudinal distance in the direction of the x-coordinate axis from the location where the inertial measurement system is to the point of impact ” – see at least p. 27. See also p. 24 which states that inertial measurement system includes at least an “angular rate sensor[].”); and
integrating the above modules to perform real-time monitoring and analysis for safe navigation in a polar environment (“This study was conducted … to develop an integrated system for ice impact load measurement on the Korean [ship] IBRV ARAON” – see at least p. 24. That is, the disclosed real-time monitoring and analysis is designed to a ship to safely navigate a polar environment.).
Lee fails to explicitly disclose: a 3-axis strain gauge sensor and an angular acceleration sensor mounted on a bow module of the vessel.
Nevertheless, it would have been obvious to one of ordinary skill in the art to arrive at the claimed configuration with a reasonable expectation of success. Namely, Lee discloses that it is known in the art to mount a 3-axis strain gauge sensor and an angular acceleration sensor on the hull of a vessel to acquire desired motion characteristics from a certain area of the vessel due to ice impact. Additionally, it is known in the art that a design incentive exists to measure motion characteristics of a bow module of the vessel as the bow is a vulnerable area to ice impact. Synthesizing the above, one of ordinary skill in the art would have predictably identified a variation of Lee wherein said sensors are mounted at the bow module, wherein such would have a reasonable expectation of success as the same mechanics for mounting said sensors would be carried out given that the bow is part of the hull. Accordingly, the claim limitation is obvious according to KSR rationale: Known Work in One Field of Endeavor May Prompt Variations of It for Use in Either the Same Field or a Different One Based on Design Incentives or Other Market Forces if the Variations Are Predictable to One of Ordinary Skill in the Art. (See MPEP 2143 I. F. )
Lee fails to explicitly disclose: an imaging unit installed at a bow, a stern, and left and right sides of the vessel and configured to image an ice environment and generate ice environment image data; and wherein the data collector analyzes ice thickness based on an image of ice piece generated by the imaging unit.
Nevertheless, Cho teaches:
an imaging unit installed at a bow, a stern, and left and right sides of the vessel and configured to image an ice environment and generate ice environment image data (“[A]n object of the present invention is to provide a real sea ice thickness measuring system using an imaging device for easily measuring the sea ice thickness in the real sea area.” – see at least p. 2.); and
analyzing ice thickness based on an image of ice piece generated by the imaging unit (“[A]n object of the present invention is to provide a real sea ice thickness measuring system using an imaging device for easily measuring the sea ice thickness in the real sea area.” – see at least p. 2.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lee with the feature of: an imaging unit installed at a bow, a stern, and left and right sides of the vessel and configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit, as taught by Cho ’501, with a reasonable expectation of success because this feature is useful for “measur[ing] the thickness of the sea ice in the section where the actual use is expected with high accuracy at a low cost in real time.” (Cho, Abstract.)
The combination of Lee and Cho ’501 fails to explicitly disclose: a data classifier configured to classify the acquired motion-characteristic data into structured data or unstructured data for data framing.
Nevertheless, Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing (“[C]lassifying, by an information classification unit, the data set into first data including structured data and second data including unstructured data” – see at least Abstract.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee and Cho ’501 with the feature of: a data classifier configured to classify measured data into structured data and unstructured data for data framing, as taught by Kang, with a reasonable expectation of success because this feature is a useful feature in the art for performing data processing to facilitate feature extraction, which is a well-known technique in the artificial intelligence (AI) and machine learning (ML) arts.
The combination of Lee, Cho ’501 and Kang fails to explicitly disclose:
a data analyzer configured to detect abnormal data or missing data from the classified data;
a data learner configured to perform spatio-temporal analysis on the classified data and interpolate missing values using a time-series hierarchical clustering technique; and
a database unit configured to construct and update a database based on interpolated data and classified data.
Nevertheless, V teaches:
a data analyzer configured to detect abnormal data or missing data from the classified data (“At 220, a dataset with at least one data missing record is received.” ¶ 44. “At 240, a similarity adjustment of the data missing record is determined. Such a similarity adjustment can be determined via similarity between the data missing record and a similar data complete record.” ¶ 45.);
a data learner configured to perform spatio-temporal analysis on classified data and interpolate missing values using a hierarchical clustering technique (“Imputation of missing data can be provided for both normalized and non-normalized data. Data can be partitioned, and data complete records used to impute values for data missing records. Hierarchical clustering can be used to cluster the data. Imputation can rely on a closed record in a same cluster. Record similarity can be used to adjust an observed value, and an estimated mean can also be incorporated. Useful for augmenting datasets that can be applied to analytics, machine learning, and other scenarios.” Abstract. Note: Imputation of missing data analogizes to the BRI of interpolating missing values because V’s teaching matches Applicant’s disclosure at ¶¶ 70-71 wherein hierarchical clustering technique uses “mean imputation” to perform the described interpolation of missing data. That is, Applicant has effectively defined the claimed interpolation to constitute imputation.); and
a database unit configured to construct and update a database based on interpolated data and classified data (“In the example, the system 100 can include a data imputation engine 150 that receives an original dataset 110 and generates an augmented dataset 190 that includes an adjusted record 115′, which is based on a data missing record 115 of the original data set 110.” ¶ 37.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501 and Kang with the features of: a data analyzer configured to detect abnormal data or missing data from the classified data; a data learner configured to perform spatio-temporal analysis on classified data and interpolate missing values using a hierarchical clustering technique; and a database unit configured to construct and update a database based on interpolated data and classified data, as taught by V, with a reasonable expectation of success because this feature is useful “for augmenting datasets that can be applied to analytics, machine learning, and other scenarios.” (V, Abstract.)
The combination of Lee, Cho ’501, Kang and V fails to explicitly disclose: a time-series hierarchical clustering technique.
Nevertheless, Rath teaches: a time-series hierarchical clustering technique (“FIG. 2 illustrates … a scalable architecture for a distributed time-series database, including hierarchical clustering of ingested time-series data, according to one embodiment.” ¶ 41 and FIG. 2.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang and V with the feature of: a time-series hierarchical clustering technique, as taught by Rath, with a reasonable expectation of success because this feature is useful for accounting data which is in time-series format, which is especially applicable to monitoring sensor data of an ice-going vessel given that sensor data typically is in time-series format.
The combination of Lee, Cho ’501, Kang, V and Rath fails to explicitly disclose: wherein the data collector outputs a trigger signal when both the analyzed ice thickness and ice concentration exceed a preset threshold.
Nevertheless, Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold (FIGS. 6 and 7 respectively show a positive correlation between ice thickness and ice concentration and ice load imparted on a ship hull. The graphs respectively illustrate that values of approximately around 30 cm ice thickness or 60% ice concentration are associated with a significant amount of ice load (e.g., around 200 k*N/m).).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V and Rath with the feature of: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold, as taught by Takimoto, to yield the claim limitation at issue with a reasonable expectation of success because this feature is useful for measuring a local ice load on a ship hull.
While Takimoto does not explicitly teach transmitting a trigger signal when the ice thickness and the concentration exceed a preset threshold as the result of analysis, the claim limitation would have been obvious to one of ordinary skill in the art. Again, Takimoto’s FIGS. 6 and 7 respectively illustrate that values of approximately around 30 cm ice thickness or 60% ice concentration are associated with a significant amount of ice load (e.g., around 200 k*N/m). One of ordinary skill in the art would therefore recognize with a reasonable expectation of success that 30 cm ice thickness or 60% ice concentration are representative of values wherein a significant amount of ice load are imparted on a ship hull, thereby requiring the sending of a trigger signal.
The combination of Lee, Cho ’501, Kang, V, Rath and Takimoto fails to explicitly disclose: a server monitoring an integrated condition of the vessel on the basis of images taken by the imaging unit; and wherein the server integrates each of the above modules to perform real-time monitoring and analysis for safe navigation in a polar environment.
Nevertheless, Matsumoto teaches: a server monitoring an integrated condition of the vessel (“ship condition monitoring server 101” – see at least ¶ 33.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold. Matsumoto teaches: a server monitoring an integrated condition of the vessel.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath and Takimoto with the feature of: a server monitoring an integrated condition of the vessel, as taught by Matsumoto, to yield the claim limitation at issue with a reasonable expectation of success because the utilization of servers to monitor the conditions of vehicles (e.g., ice-going vessels) is well-known in the art due to the utility provided by a server’s greater computational ability.
Furthermore, it would have been obvious to one of ordinary skill in the art to integrate the disclosed functional modules into Matsumoto’s server with a reasonable expectation of success as it is well-known in the art that computational tasks can be offloaded to a server. One of ordinary skill in the art would have recognized that such is useful for taking advantage of a server’s heightened computational capacity and centralizing tasks to one device, thus resulting in the predictable result of a centralized computing station capable of performing the claimed functions. Therefore, arriving at the claimed invention would merely require combining prior art elements according to known methods to yield predictable results.
Independent claim 11 is rejected for at least the same reasons as claim 1 as the claims recite similar subject matter but for minor differences. In a similar manner, claim 17 is rejected for at least the same reasons as claim 11 as the claim does not materially narrow claim 11 upon which claim 17 depends.
Claim(s) 3, 12 and 18 is/are rejected under § 103 as being unpatentable over Lee in view of Cho ’501, in view of Kang, in view of V, in view of Rath, in view of Takimoto and in view of Matsumoto as applied to claim 1 — further in view of Min (Comparison of the 6-DOF Motion Sensor and Stain Gauge Data for Ice Load Estimation on IBRV ARAON7; “Min”)
As to claims 3, 12 and 18, the combination of Lee, Cho ’501, Kang, V, Rath, Takimoto and Matsumoto fails to explicitly disclose: wherein the 3-axis strain gauge sensor is mounted on an internal plate disposed at a bow module of the vessel.
Nevertheless, Min teaches: a 3-axis strain gauge sensor mounted on the hull of the vessel, and a frame and the internal plate of a hull to measure a hull strain (“Strain gauges were attached to the inside of the hull in the area affected by the ice load, as shown in Fig. 2, and the deformation of the hull was measured by the sensor.” See at least p. 530.)
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold. Matsumoto teaches: a server monitoring an integrated condition of the vessel. Min teaches: a 3-axis strain gauge sensor mounted on the hull of the vessel, and a frame and the internal plate of a hull to measure a hull strain.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath, Takimoto and Matsumoto to include the features of: a 3-axis strain gauge sensor mounted on the hull of the vessel, and a frame and the internal plate of a hull to measure a hull strain; and the 3-axis strain gauge sensor measures a hull strain of the vessel, as taught by Min, to arrive at the claim limitation at issue with a reasonable expectation of success because these features are useful for monitoring an integrated condition of a vessel, as a strain gauge indicates when a load (e.g., ice load) is imparted on a ship’s hull. And recall that from above that one of ordinary skill in the art would have found it obvious to install a 3-axis strain gauge sensor at the bow of a vessel; in this regard it is also well-known in the art that the bow, being part of the hull, also comprises an internal plate such as taught by Min.
As to claims 15 and 21, Lee discloses: wherein the performance evaluator calculates the global ice load applied to the vessel through motion analysis using motion characteristic measurement data of the vessel in ice breaking (“Global and local ice loads” may be calculated through analyzing vessel response data – see section 4.3 at p. 30.).
The combination of Lee, Cho ’501, Kang, V, Rath, Takimoto and Matsumoto fails to explicitly disclose: wherein the performance evaluator calculates the local ice load applied in a local area of the hull using an influence coefficient method on the basis of information of a hull strain in the hull in ice breaking.
Nevertheless, Min teaches: wherein a performance evaluator calculates a local ice load applied in a local area of the hull using an influence coefficient method on the basis of information of a hull strain in the hull in ice breaking (“The calculated hull stress is applied to the hull using the influence coefficient matrix.” Emphasis added; see at least p. 531.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath, Takimoto and Matsumoto to include the feature of: wherein a performance evaluator calculates a local ice load applied in a local area of the hull using an influence coefficient method on the basis of information of a hull strain in the hull in ice breaking, as taught by Min, to arrive at the claim limitation at issue with a reasonable expectation of success because the influence coefficient method is a useful and well-known method in structural mechanic analysis for calculating a response to a load at a specific point in a structure.
Claim(s) 16 and 22 is/are rejected under § 103 as being unpatentable over Lee in view of Cho ’501, in view of Kang, in view of V, in view of Rath, in view of Takimoto, in view of Matsumoto and in view of Min as applied to claim 12 — further in view of Dokken (US20090271054A1; “Dokken”), in view of Park (US20190147669A1; “Park”), in view of Brofos et al. (US20240331553A1; “Brofos”) and in view of Cho (KR20200059839A; “Cho ’839”).
As to claims 16 and 22, the combination of Lee, Cho ’501, Kang, V, Rath and Takimoto fails to explicitly disclose: wherein the performance evaluator extracts data for a predetermined period.
Nevertheless, Matsumoto teaches: a performance evaluator extracts data for a predetermined period (“[E]ach measured value data can be constantly acquired, each acquired measured value data can be stored in the ship condition monitoring server, and can be transmitted to the land server at predetermined time intervals.” See at least ¶ 25.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold. Matsumoto teaches: a server monitoring an integrated condition of the vessel; and a performance evaluator extracts data for a predetermined period.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath and Takimoto with the feature of: a performance evaluator extracts data for a predetermined period, as taught by Matsumoto, with a reasonable expectation of success because it is well-known and routine in the art that data may be extracted for predetermined time period; such is useful for data processing.
The combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto and Min fails to explicitly disclose: wherein the performance evaluator extracts data for a predetermined period from one or more of a heading angle, a draft condition, engine power, a propeller revolution, and a propelling system angle on the basis of voyage information acquired from the information of the voyage data recorder (VDR).
Nevertheless, Dokken teaches: wherein a performance evaluator extracts data from a heading angle on the basis of voyage information acquired from the information of the voyage data recorder (VDR) (“The NMEA interface 29 further interfaces with a voyage data recorder (VDR) 33” – see at least ¶ 70. Continuing, data which may be recorded by the VDR includes: “heading, speed, course over ground, speed over ground and the latitude and longitude of the ship.” See at least ¶ 160.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold. Matsumoto teaches: a server monitoring an integrated condition of the vessel; and a performance evaluator extracts data for a predetermined period. Min teaches: a 3-axis strain gauge sensor mounted on the hull of the vessel, and a frame and the internal plate of a hull to measure a hull strain. Dokken teaches: wherein a performance evaluator extracts data from a heading angle on the basis of voyage information acquired from the information of the voyage data recorder (VDR).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto and Min to include the feature of: wherein a performance evaluator extracts data from a heading angle on the basis of voyage information acquired from the information of the voyage data recorder (VDR), as taught by Dokken, to arrive at the claim limitation at issue with a reasonable expectation of success because it is well-known in the art that VDRs are used to acquire voyage information, such as location, speed, and heading of a vessel.
The combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto, Min and Dokken fails to explicitly disclose: the performance evaluator extracts information on the basis of voyage information acquired from the alarm monitoring system (AMS).
Nevertheless, Park teaches: acquiring voyage information through an alarm monitoring system (AMS) of a vessel (“Alarm Monitoring System (AMS)” – see at least ¶ 139. Continuing, AMS “collects various types of vessel data” – see at least ¶ 171.).
Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold. Matsumoto teaches: a server monitoring an integrated condition of the vessel; and a performance evaluator extracts data for a predetermined period. Min teaches: a 3-axis strain gauge sensor mounted on the hull of the vessel, and a frame and the internal plate of a hull to measure a hull strain. Dokken teaches: wherein a performance evaluator extracts data from a heading angle on the basis of voyage information acquired from the information of the voyage data recorder (VDR). Park teaches: acquiring voyage information through an alarm monitoring system (AMS) of a vessel.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto, Min and Dokken to include the feature of: acquiring voyage information through an alarm monitoring system (AMS) of a vessel, as taught by Park, to arrive at the claim limitation at issue with a reasonable expectation of success because it is well-known in the art that AMS is used to acquire voyage information.
The combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto, Min, Dokken and Park fails to explicitly disclose: wherein the performance evaluator extracts data through analysis of coefficient of variation (CV).
Nevertheless, Brofos teaches: extracting data through analysis of coefficient of variation (CV) (“[T]he coefficient of variation can refer to a standardized measure of probability distribution for a given set of data.” See at least ¶ 63.).
Lee discloses: discloses an integrated monitoring system for an ice-going Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold. Matsumoto teaches: a server monitoring an integrated condition of the vessel; and a performance evaluator extracts data for a predetermined period. Min teaches: a 3-axis strain gauge sensor mounted on the hull of the vessel, and a frame and the internal plate of a hull to measure a hull strain. Dokken teaches: wherein a performance evaluator extracts data from a heading angle on the basis of voyage information acquired from the information of the voyage data recorder (VDR). Park teaches: acquiring voyage information through an alarm monitoring system (AMS) of a vessel. Brofos teaches: extracting data through analysis of coefficient of variation (CV).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto, Min, Dokken and Park with the feature of: extracting data through analysis of coefficient of variation (CV), as taught by Brofos, with a reasonable expectation of success because coefficient of variation (CV) is a useful and well-known statistical analysis method for identifying outliers or unusual data in a given dataset; such is useful for enhancing the monitoring of an integrated condition of a vessel.
The combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto, Min, Dokken, Park and Brofos fails to explicitly disclose: calculating ice resistance applied to the vessel in ice breaking.
Nevertheless, Cho ’839 teaches: calculating ice resistance applied to the vessel in ice breaking (“The present invention relates to a system and method for calculating the frictional resistance of floating ice based on linear information, and more specifically, to a system and method for calculating the frictional resistance of floating ice based on linear information, which can accurately calculate the normal force and frictional resistance acting on the hull surface.” See at least ¶ 1; see also ¶¶ 3–108.).
Lee discloses: discloses an integrated monitoring system for an ice-going Lee discloses: an integrated monitoring system for an ice-going vessel, comprising: a data collector configured to acquire motion characteristic data from a 3-axis strain gauge sensor and an angular acceleration sensor; a data classifier; and a performance evaluator which calculates local ice load, global ice load and ice performance metrics by analyzing vessel response data. Cho ’501 teaches: an imaging unit comprising a network camera configured to image an ice environment and generate ice environment image data; and analyzing ice thickness based on an image of ice piece generated by the imaging unit. Kang teaches: a data classifier configured to classify measured data into structured data and unstructured data for data framing. V teaches: interpolate missing values using a hierarchical clustering technique. Rath teaches: a time-series hierarchical clustering technique. Takimoto teaches: determining when both the analyzed ice thickness and ice concentration exceed a preset threshold. Matsumoto teaches: a server monitoring an integrated condition of the vessel; and a performance evaluator extracts data for a predetermined period. Min teaches: a 3-axis strain gauge sensor mounted on the hull of the vessel, and a frame and the internal plate of a hull to measure a hull strain. Dokken teaches: wherein a performance evaluator extracts data from a heading angle on the basis of voyage information acquired from the information of the voyage data recorder (VDR). Park teaches: acquiring voyage information through an alarm monitoring system (AMS) of a vessel. Brofos teaches: extracting data through analysis of coefficient of variation (CV). Cho ’839 teaches: calculating ice resistance applied to the vessel in ice breaking.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lee, Cho ’501, Kang, V, Rath, Takimoto, Matsumoto, Min, Dokken, Park and Brofos with the feature of: calculating ice resistance applied to the vessel in ice breaking, as taught by Cho ’839, with a reasonable expectation of success because this feature is useful for monitoring the condition of a hull of a ship in an ice-breaking environment.
Regarding calculating ice resistance applied to the vessel in ice breaking using Work-Energy Law and Newton’s Second Law, the underlying principles required to calculate ice resistance in Cho ’839 ultimately depend on the foregoing Laws because (1) the concept of resistance requires that Newton’s Second Law (F = ma) is true as friction by definition is a force which opposes motion according to Newton’s Second Law; and (2) the concept of resistance also requires that the work-energy law (W = ΔKE) is true because friction is by definition a force which performs negative work on a moving object. Summarizing, the existence—and, therefore, calculation—of friction (claimed as resistance) requires both Newton’s Second Law and Work-Energy law to both be true. Accordingly, while Cho ’839 does not explicitly disclose calculating ice resistance applied to the vessel in ice breaking using Work-Energy Law and Newton’s Second Law, it would have been obvious to one of ordinary skill in the art to apply the foregoing Laws for the above reasons.
CONCLUSION
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, this action is 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Mario C. Gonzalez whose telephone number is (571) 272-5633. The Examiner can normally be reached M–F, 10:00–6:00 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey S. Jabr, can be reached on (571) 272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.C.G./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
1 Gottschalk v. Benson, 409 U.S. 63 (1972)
2 In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989)
3 See § 101 rejection below for a detailed identification of all the recited mental processes and mathematical concepts in claims 1 and 11.
4 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
5 Lee et al., “Prediction of ice loads on Korean IBRV ARAON with 6-DOF inertial measurement system during trials of Chukchi and East Siberian Seas,” Ocean Engineering, Volume 151, 2018, Pages 23-32, ISSN 0029-8018, https://doi.org/10.1016/j.oceaneng.2018.01.010.
(https://www.sciencedirect.com/science/article/pii/S0029801818300106)
6 T. Takimoto, S. Kanada, H. Shimoda, D. Wako, S. Uto and K. Izumiyama, "Field Measurements of Local Ice Load on a Ship Hull in Pack Ice of the Southern Sea of Okhotsk," OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean, Kobe, Japan, 2008, pp. 1-6, doi: 10.1109/OCEANSKOBE.2008.4530957.
7 MIN, ET AL., “Comparison of the 6-DOF Motion Sensor and Stain Gauge Data for Ice Load Estimation on IBRV ARAON,” Journal of the Society of Naval Architects of Korea, DISSN:1225-1143. Vol 53. No 6 op 529-535 December 2016
N.B. The translated document provided with the Office Action has minor typographical errors wherein the translation software attempted to translate non-Korean text (e.g., equations). Examiner suggests that review of Min is done subsequently with the original document (already present in the file wrapper via Applicant’s IDS) which does not contain the typographical errors.
8 N.B. The translated document for Cho ’839 skips numbering paragraphs 2, 6, and 9.
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