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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a computer implemented method for determining a fuel consumption of a driveline in a marine vessel and claim 9 is directed to a computer implemented method for monitoring an output of a physical fuel consumption sensor system configured to monitor a fuel consumption of a driveline in a marine vessel (i.e., process). Therefore, claims 1 and 9 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong 1
Regarding Prong I 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. Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
“a computer implemented method for determining a fuel consumption of a driveline in a marine vessel, the method comprising
generating a library of fuel consumption models, where each fuel consumption model in the library of models is configured to predict fuel consumption of a driveline having a respective specification, where each fuel consumption model in the library of models is configured to operate based on a respective set of sensor input signals,
selecting one or more fuel consumption models from the library based on a specification of the driveline in the marine vessel and on the available sensor input signals of the marine vessel, obtaining real-time physical sensor input signal data related to the driveline in the marine vessel, and
determining the fuel consumption of the driveline in the marine vessel based on the selected one or more fuel consumption models from the library and on the obtained real-time physical sensor input signal data related to the driveline in the marine vessel.”
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. The claimed “computer” is being interpreted to be equivalent in function to the human mind. For example, “a computer implemented method for determining a fuel consumption of a driveline in a marine vessel, the method comprising generating a library of fuel consumption models, where each fuel consumption model in the library of models is configured to predict fuel consumption of a driveline having a respective specification, obtaining real-time physical sensor input signal data related to the driveline in the marine vessel, and determining the fuel consumption of the driveline in the marine vessel based on the selected one or more fuel consumption models from the library and on the obtained real-time physical sensor input signal data related to the driveline in the marine vessel.” in the context of this claim encompasses that the operator may manually observe the fuel gauge and driveline (e.g., throttle commands via levers, engine speed) readings and reference a fuel consumption look-up table with associated predicted fuel consumption values and as a results, set the driveline according to the observed values and referenced look-up table values. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II 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 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.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows: “…where each fuel consumption model in the library of models is configured to operate based on a respective set of sensor input signals, selecting one or more fuel consumption models from the library based on a specification of the driveline in the marine vessel and on the available sensor input signals of the marine vessel,...”
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of using a computer to process where each fuel consumption model in the library of models is configured to operate based on a respective set of sensor input signals, selecting one or more fuel consumption models from the library based on a specification of the driveline in the marine vessel and on the available sensor input signals of the marine vessel, the examiner submits that these limitations are mere instructions to apply the above-noted abstract idea by merely using a computer to perform the process (MPEP § 2106.05). In particular, the processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of general data gathering) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to where each fuel consumption model in the library of models is configured to operate based on a respective set of sensor input signals, selecting one or more fuel consumption models from the library based on a specification of the driveline in the marine vessel and on the available sensor input signals of the marine vessel, amounts to nothing more than mere instructions to apply the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Claim 9 was analyzed and rejected for the same reasons as claim 1.
Dependent claim(s) 2 - 8 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of 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. Therefore, dependent claims 2 - 8 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1 and 9. Therefore, claim(s) 1 – 9 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 – 9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Josselyn (Patent No.: US 11,598,282 B1).
Regarding claim 1, Josselyn discloses a computer implemented method for determining a fuel consumption of a driveline in a marine vessel (total predicted fuel consumption of seafaring vessel, See Abstract), the method comprising
generating a library of fuel consumption models (Similarly, fuel consumption of the plurality of thrust engines and adjusting engine specific fuel consumptions model for particular engine, col. 3, lines 16-21 and “For each thrust engine, the engine-specific fuel consumption model can be defined by: generating a plurality of candidate fuel consumption models…” col. 3, lines 53-55), where each fuel consumption model in the library of models is configured to predict fuel consumption of a driveline having a respective specification (“…wherein each fuel consumption model includes a machine learning model configured to receive a power output level for the corresponding thrust engine as an input and to generate an engine-specific predicted fuel consumption by the corresponding thrust engine as an output…” col. 4, lines 20-25), where each fuel consumption model in the library of models is configured to operate based on a respective set of sensor input signals (Receiving vessel operation data via sensors 120a-120c, FIG. 1 and also see step 305, 315; FIG. 3),
selecting one or more fuel consumption models from the library based on a specification of the driveline in the marine vessel and on the available sensor input signals of the marine vessel (col. 4, lines 20-25 and Receiving vessel operation data via sensors 120a-120c, FIG. 1), obtaining real-time physical sensor input signal data related to the driveline in the marine vessel (optimum engine configuration based on real-time data collected by sensors 120, col. 21, lines 6-9), and
determining the fuel consumption of the driveline in the marine vessel based on the selected one or more fuel consumption models from the library and on the obtained real- time physical sensor input signal data related to the driveline in the marine vessel (305, 315, 320, 325, 330; FIG. 3).
Regarding claim 2, Josselyn discloses the method, where the real-time physical sensor input signal data comprises any of; engine speed, engine torque, engine temperature, engine combustion pressure, engine vibration, and engine sound (discloses engine power in FIG. 6 which is known to be calculated by engine speed/RPM and displacement).
Regarding claim 3, Josselyn discloses the method according to-any-previous claim where the real-time physical sensor input signal data comprises any of; vessel speed through water, STW, vessel pitch roll and/or yaw, vessel longitudinal acceleration, and vessel lateral acceleration (vessel speed, FIG. 5).
Regarding claim 4, Josselyn discloses the method, comprising adjusting the determined fuel consumption of the driveline in the marine vessel based on long-term fuel consumption data of the vessel (Comparing monthly actual vs optimal fuel consumption by crew, FIG. 7).
Regarding claim 5, Josselyn discloses the method, where the adjusting is based on long term tank level measurement and/or operating time between refueling of the vessel (“For example, the vessel may include one or more liquid storage tanks. Bulk liquids (for example, fuel, ballast, pot water etc.) may be transferable between the storage tanks to adjust the trim of the vessel. The level of adjustment possible for a given vessel can vary depending on the nature, size and locations of the storage tanks relative to the vessel stern and vessel bow, and the quantity of bulk liquids that may be pumped into or out of the tanks.” col. 26, lines 10-17).
Regarding claim 6, Josselyn discloses the method, where the fuel consumption of the driveline in the marine vessel is determined as a weighted combination of respective outputs from a plurality of fuel consumption models from the library and on the obtained real-time physical sensor input signal data related to the driveline in the marine vessel (“Alternatively, model generation engine 226 may use a combination of multiple criteria while selecting among the candidate models. For example, model generation engine 226 may use a minimum threshold accuracy to perform an initial selection among the candidate models and then use model complexity to perform the final selection. The model generation engine 226 may also use a weighted selection process in which the various criteria are weighted in order to select a desired candidate model.” col. 20, lines 1-9 and col. 34, lines 32-38).
Regarding claim 7, Josselyn discloses a control system (100, FIG. 1), comprising:
processing circuitry (208, FIG. 2);
an interface (214, FIG. 2) coupled to the processing circuitry (208, FIG. 2); and
a memory (210, FIG. 2) coupled to the processing circuitry (208, FIG. 2), wherein the memory comprises machine readable computer program instructions that, when executed by the processing circuitry, causes the control system to perform a method according to claim 1 (col. 6, lines 13-15).
Regarding claim 8, Josselyn discloses a marine vessel comprising a control system according to claim 7 (100, FIG. 1).
Regarding claim 9, Josselyn discloses a computer implemented method for monitoring an output of a physical fuel consumption sensor system configured to monitor a fuel consumption of a driveline in a marine vessel (Current engine fuel consumption 605, FIG. 6), the method comprises
generating a library of fuel consumption models (Similarly, fuel consumption of the plurality of thrust engines and adjusting engine specific fuel consumptions model for particular engine, col. 3, lines 16-21 and “For each thrust engine, the engine-specific fuel consumption model can be defined by: generating a plurality of candidate fuel consumption models…” col. 3, lines 53-55), where each fuel consumption model in the library of models is configured to predict fuel consumption of a driveline having a respective specification (“…wherein each fuel consumption model includes a machine learning model configured to receive a power output level for the corresponding thrust engine as an input and to generate an engine-specific predicted fuel consumption by the corresponding thrust engine as an output…” col. 4, lines 20-25), where each fuel consumption model in the library of models is configured to operate based on a respective set of sensor input signals (Receiving vessel operation data via sensors 120a-120c, FIG. 1 and also see step 305, 315; FIG. 3),
selecting one or more fuel consumption models from the library based on a specification of the driveline in the marine vessel and on the available sensor input signals of the marine vessel (col. 4, lines 20-25 and Receiving vessel operation data via sensors 120a-120c, FIG. 1), obtaining real-time physical sensor input signal data related to the driveline in the marine vessel (optimum engine configuration based on real-time data collected by sensors 120, col. 21, lines 6-9),
determining the fuel consumption of the driveline in the marine vessel based on the selected one or more fuel consumption models from the library and on the obtained real- time physical sensor input signal data related to the driveline in the marine vessel (305, 315, 320, 325, 330; FIG. 3), and
monitoring an output of the physical fuel consumption sensor system (Total current fuel consumption, FIG. 6) by comparing its output to the fuel consumption of the driveline determined based on the selected one or more fuel consumption models from the library and on the obtained real-time physical sensor input signal data related to the driveline in the marine vessel (Current engine power, current engine fuel consumption, optimal engine configuration, FIG. 6 and “The current engine fuel consumption may be based on data received from sensors measuring marine diesel oil (MDO) consumption of the engine. Alternatively, the current engine fuel consumption may be generated based on the output power level of the engine and the engine-specific fuel consumption model. (197) Portion 610 can display a predicted optimum engine configuration based on the total current engine power. For example, control unit 110 may generate candidate engine configurations where the sum of power output from each of the thrust engines is at least equal to the total current engine power (instead of the predicted required power determined at 310 of process 300). Control unit 110 may then determine total predicted fuel consumption amounts for all the candidate engine configurations and select a predicted optimal engine configuration in a manner analogous to that described above herein corresponding to 315-330 of process 300. Portion 610 can also display the optimal number of engines running corresponding to the predicted optimum engine configuration and the corresponding optimal total predicted fuel consumption amount.” Col. 28, lines 63-67 and col. 29, lines 1-10).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mikalsen et al (Pub. No.: US 2021/0027225 A1). Mikalsen teaches a marine vessel to show fuel consumption savings based on adjustment of vessel speed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYLER J LEE whose telephone number is (571)272-9727. The examiner can normally be reached M-F 7:30-5:00.
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, Abby Flynn can be reached at 571-272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TYLER J LEE/Primary Examiner, Art Unit 3663
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