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 .
Claim Objections
Claim 7 is objected to because of the following informalities: the claim appears to contain a typo and recites “an amount of use of equipment installed on the ship”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-7, and 9-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-7, and 9-15 is/are directed to the abstract idea of a mathematical concept and a mental process. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
The claim(s) recite(s) receiving data, and calculating predictions based on the received data. The rejected dependent claims only supply additional steps (mathematical calculations, and mental processes) that a processor must perform. All of these concepts relate to the abstract idea of certain methods of mathematical concepts and mental processes. The concept described in claims 1-7, and 9-15 is/are not meaningfully different than those methods of mathematical concepts and mental processes found by the courts to be abstract ideas. As such, the description in claims 1-7, and 9-15 is an abstract idea.
This judicial exception is not integrated into a practical application because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The hardware is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components that perform the generic functions of [e.g. "transmitting information", "generating information"] common to electronics and computer systems does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves another technology or technical field. Their collective functions merely provide conventional computer implementation (i.e. mere instructions to implement the abstract idea on a generic computing system).
Claims 1-7, and 9-15 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
The Examiner notes that because claim 8 is specifically directed to controlling the ship according to a preset operating mode by using the optimal navigation information it is therefore deemed to be eligible under §101
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, and 7-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Iwasaki et. al. (Machine Translation of KR 20200040829).
Regarding claim 1, Iwasaki discloses a method of optimizing navigation of a ship, the method comprising: (¶1)
generating recommended navigation information about a navigation route of the ship, based on navigation plan information associated with a departure location and an arrival location of the ship; (¶78-80, 83, 85)
predicting a boil-off gas (BOG) generation amount of the ship and a tank pressure value of the ship, based on the recommended navigation information; and (¶86-87)
obtaining optimal navigation information associated with operation control of the ship, based on the BOG generation amount and the tank pressure value. (¶83, 85, 88, 94)
Regarding claim 2, Iwasaki further discloses wherein the navigation plan information comprises a departure time, an arrival time, and location information about locations, and (¶80, 83, 85)
the generating comprises: obtaining environmental information about the navigation route of the ship, based on the navigation plan information; and generating the recommended navigation information based on the navigation plan information and the environmental information, according to a fuel consumption amount and a BOG generation amount for the navigation route of the ship. (¶85-86, 88)
Regarding claim 3, Iwasaki further discloses wherein the recommended navigation information comprises at least one of location information for each navigation section, speed information for each navigation section, and environmental information for each navigation section, for the navigation route. (¶87, 90-91)
Regarding claim 4, Iwasaki further discloses wherein the predicting comprises: predicting the BOG generation amount of the ship, based on at least one of the location information for each navigation section, the speed information for each navigation section, and the environmental information for each navigation section; and (¶87, 89-90)
predicting the tank pressure value of the ship, based on at least one of the location information for each navigation section, the speed information for each navigation section, the environmental information for each navigation section, and a preset liquefied gas consumption amount. (¶86-87)
Regarding claim 7, Iwasaki further discloses wherein the optimal navigation information comprises at least one of a BOG generation amount of the ship, a tank pressure value of the ship, speed information for each navigation section for the ship, a liquefied gas consumption amount of the ship, and an amount of use of equipment installed on the ship. (¶76, 83, 86, 92-94)
Regarding claim 8, Iwasaki further discloses controlling the ship according to a preset operating mode by using the optimal navigation information, wherein the operating mode comprises an operating mode that minimizes a liquefied gas consumption amount of the ship. (¶85, 95)
Regarding claim 9, Iwasaki further discloses a non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute the method of claim 1. (¶42, 46)
Regarding claim 10, Iwasaki further discloses a computing device comprising: at least one memory; and at least one processor, (¶42, 77, 84, 96)
wherein the processor is configured to generate recommended navigation information about a navigation route of a ship, based on navigation plan information associated with a departure location and an arrival location of the ship, predict a boil-off gas (BOG) generation amount of the ship and a tank pressure value of the ship, based on the recommended navigation information, and obtain optimal navigation information associated with operation control of the ship, based on the BOG generation amount and the tank pressure value. (¶80, 85, 86-87, 88)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Iwasaki et. al. (Machine Translation of KR 20200040829) in view of Bordet et. al. (US Patent Publication 2023/0349515).
Regarding claim 11, Iwasaki further discloses a method of predicting a boil-off gas (BOG) generation amount of a ship, the method comprising: (¶86-89)
training the BOG generation amount prediction model comprising a plurality of
predicting the BOG generation amount via the trained BOG generation amount prediction model by using current navigation data of the ship. (¶63, 86)
Iwasaki appears to be silent as to deed learning models.
Bordet however teaches selecting input data for a BOG generation amount prediction model from a portion of pre-stored navigation data, by using an input data selection model; (¶38, 110, 117-118)
training the BOG generation amount prediction model comprising a plurality of deep learning models, by using the input data; and (¶41, 117-119)
predicting the BOG generation amount via the trained BOG generation amount prediction model by using current navigation data of the ship. (¶39, 114, 117, 125)
It would have been obvious to one of ordinary skill in the art at the time of filing to provide the invention of Iwasaki with selecting input data for a BOG generation amount prediction model from a portion of pre-stored navigation data, by using an input data selection model; training the BOG generation amount prediction model comprising a plurality of deep learning models as taught by Bordet with a reasonable expectation of success because the technique for improving a particular class of devices was part of the ordinary capabilities of a person of ordinary skill in the art, in view of the teaching of the technique for improvement in other situations, would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Allowable Subject Matter
Claims 5-6, and 12-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome the applied §101 rejections and in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art fails to disclose or render obvious, in light of the preceding limitations, and, absent impermissible hindsight bias:
5. The method of claim 1, wherein the obtaining of the optimal navigation information comprises: generating n-th intermediate navigation information associated with operation control of the ship, based on the BOG generation amount and the tank pressure value; and confirming (n+1)-th intermediate navigation information as the optimal navigation information, based on a comparison between the n-th intermediate navigation information and the (n+1)-th intermediate navigation information, according to a preset threshold value,the (n+1)-th intermediate navigation information is generated based on an updated value of at least one of speed information for each navigation section and a liquefied gas consumption amount both included in the n-th intermediate navigation information, andn is a natural number greater than or equal to 1.
6. The method of claim 5, wherein the confirming comprises: confirming, in response to a difference value, which is calculated based on the n- th intermediate navigation information and the (n+1)-th intermediate navigation information, being less than or equal to a preset threshold value, the (n+1)-th intermediate navigation information as the optimal navigation information; and generating, in response to the difference value being greater than the preset threshold value, (n+2)-th intermediate navigation information by updating at least one of speed information for each navigation section and a liquefied gas consumption amount both included in the (n+1)-th intermediate navigation information.
12. The method of claim 11, wherein the selecting comprises: calculating a correlation coefficient between the portion of the pre-stored navigation data and the BOG generation amount; training the input data selection model by using the calculated correlation coefficient; and selecting, as the input data, data for which the correlation coefficient is greater than or equal to a predetermined value, by using the trained input data selection model.
13. The method of claim 11, wherein the training comprises: calculating ground-truth data for the training; performing the training by using the input data and the calculated ground-truth data; and validating the BOG generation amount prediction model by using another portion of the pre-stored navigation data.
14. The method of claim 11, wherein the predicting comprises: outputting initial BOG generation amount prediction values from the plurality of deep learning models, respectively; and calculating a final BOG generation amount prediction value by applying different weights to the initial BOG generation amount prediction values, respectively.
15. The method of claim 14, wherein the calculating comprises applying a highest weight to an initial BOG generation amount prediction value that is output from a stacking model, among the initial BOG generation amount prediction values.
Conclusion
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/ALAN D HUTCHINSON/Primary Examiner, Art Unit 3669
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