DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. SG10202203430W, filed on 04/04/2022.
Status of Claims
The following is a Non-Final Office Action.
Claims 1-18 are cancelled. Claims 19-36 are newly added. Claims 19-36 are being considered in this Office Action. Claims 19-36 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/03/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 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.
Claims 31 and 32 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 31 recites the data acquisition sub-modules, which lacks antecedent basis because to prior claim introduces this term to a group set pf data acquisition sub-modules. Claims 20 and 21 recite “a data acquisition module” and individual “data acquisition sub-modules,” but no claim introduces a definite antecedent for the term “data acquisition sub-modules” used in claim 31, which render the claim indefinite.
Claim 32 depends from claim 31 and therefore fail to cure the deficiency noted above, and are therefore rejected based on dependency.
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 19-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 19-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance” (MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 19-32) and method (claims 33-36) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of MPEP 2106, it is next noted that the claims recite an abstract idea of forecast fuel prices and/or fuel indexes and to determine an optimal time, quantity and location to refuel by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “mental process” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106.04 wherein the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. (See MPEP 2106.04(a)(2)). The claims further recite methods of organizing human activity) by reciting concepts of "Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations., which falls into the “Certain Methods of Organizing Human Activity” group within the enumerated groupings of abstract ideas. The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 19, are: a data-driven bunker planner system comprising: a bunker planning system operable in a computing system located in a vessel; wherein the bunker planning system is operable to forecast fuel prices and/or fuel indexes and to determine an optimal time, quantity and location to refuel, to fulfil one or more refuel term contracts based on the forecasted fuel prices and/or fuel indexes. Claims 33 recites substantially the same limitations as claim 19, and therefore subject to the same rationale.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to a data-driven bunker planner system, a bunker planning system operable in a computing system located in a vessel. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification paragraphs [0065] describe high level general purpose computer “The computing system 10 may be a conventional computer”) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: a data-driven bunker planner system and a bunker planning system operable in a computing system located in a vessel to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification paragraphs [0065] describe high-level general-purpose computer “The computing system 10 may be a conventional computer”) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims recite the following additional elements: claim 20 recites the bunker planning system further comprises a data acquisition module, claim 21 recites the data acquisition module further comprises: a refuel term contract data acquisition sub-module; a feature data acquisition sub-module; a fuel price data acquisition sub-module; a route data acquisition sub-module; a fuel consumption data acquisition sub-module; a vessel status data acquisition sub-module; and a port data acquisition sub-module, claim 22 recites the bunker planning system further comprises a fuel price forecasting module, claim 23 recites the fuel price forecasting module further comprises: a long-term price forecasting sub-module; and a short-term price forecasting sub-module, claim 24 recites the bunker planning system further comprises a data optimisation module, claims 25 recites the data optimisation module further comprises: a bunker plan optimisation sub-module; and a nomination date optimisation sub-module. claim 26 recites the bunker planning system further comprises a notification generation module, claim 27 recites the notification generation module generates any one of a combination of notifications that comprise: forecasted fuel prices and/or fuel indexes; refuel term contract nominations; or a bunkering plan, claim 28 recites the fuel price forecasting module receives data from the feature data acquisition sub-module and the fuel price data acquisition sub- module, claim 29/35 recites the fuel price forecasting module employs at least one customized machine learning and optimization model for long-term forecasting and short-term forecasting selected from: Ensemble Lasso, Random Forest, XGBoost, Support Vector Machine, a trained Structural Prescriptive - Empirical Risk Management (SP- ERM) or a trained Truncated Scenario-wise Linear Decision Rule - Empirical Risk Management (TSLDR-ERM), claim 30 recites the data optimisation module receives data from the data acquisition module and the fuel price forecasting module, claim 31 recites the bunker planning system communicates with a server to obtain data for the data acquisition sub-modules, and claim 32 recites the bunker planning system further comprises a human-machine interface system to allow a decision-maker to input information into the bunker planning system and for the bunker planning system to output the bunkering plan. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification paragraphs [0065] describe high-level general-purpose computer “The computing system 10 may be a conventional computer”) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification paragraphs [0065] describe high-level general-purpose computer “The computing system 10 may be a conventional computer”) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. The examiner first notes that the claimed invention does not recite the use of machine learning model. Further, the “machine learning” merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05() &(h)).
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of certain method of organizing human activity and a mental process, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 19-28, 30-34, and 36 rejected under 35 U.S.C. 103 as being unpatentable over Atsushi Yamaguchi (US 2016/0265920 A1, hereinafter “Yamaguchi”) in view of Erica Klampfl (US 2009/0204316 A1, hereinafter “Klampfl”).
Claim 19/33
Yamaguchi teaches:
A data-driven bunker planner system ([0040] Bunkering plan support system 1 ) comprising: a bunker planning system ([0040] Bunkering plan support system ) operable in a computing system located in a vessel([0168] bunkering plan management server device 11 is configured as a device that is different from ship terminal device 12-1, but a configuration in which bunkering plan management server device 11 is integrated with ship terminal device 12-1 to be one device may be adopted. ); wherein the bunker planning system is operable to [...] and to determine an [...] time, quantity and location to refuel, ([0051]the fuel costs for the three alternative bunkering plans (hereafter referred to as “alternative bunkering plans”) with the lowest fuel costs which present optimal alternative, along with the difference between these expenses and the fuel costs according to the present bunkering plan. [0052] the fuel costs signify the cost (estimated value) of fuel to be loaded on ship 9 between now and the completion of voyage. [0053] The detail screen displays details of the present bunkering plan and the alternative bunkering plan double-clicked or the like by the user. Specifically, for each of the ports of call of ship 9 (departure point, ports of call, bunkering ports and destination), the name, date and time of arrival at port, and date and time of departure from port are listed in calling order. For ports at which bunkering takes place from among these ports, bunkering amount, fuel price, fuel costs and fuel quality are displayed. On the detail screen, differences between the alternative bunkering plan and the present bunkering plan are underlined. [0173] describes the optimal bunkering plan with the lowest cost) to fulfil one or more refuel term contracts based on the forecasted fuel prices and/or fuel indexes ([0176] the bunkering plan is limited so as to satisfy conditions related to the amount of fuel remaining indicated by the remaining fuel condition data and conditions related to limits such as maximum draft indicated by the voyage restriction data. Conditions for limiting the bunkering plan are not limited to conditions of these types, and various types of conditions may be adopted. For example, if a monthly minimum bunkering amount is determined by a contract between a ship operation manager and a fuel supplier at a specific bunkering port, an artificially determined condition of setting the bunkering amount at that bunkering port to the maximum amount until the minimum bunkering amount is exceeded may be adopted).
While Yamaguchi teaches [0051] the fuel costs for the three alternative bunkering plans with the lowest fuel costs which present optimal alternative, along with the difference between these expenses and the fuel costs according to the present bunkering plan. [0052] the fuel costs signify the cost (estimated value) of fuel to be loaded on ship 9 between now and the completion of voyage. [0053] The detail screen displays details of the present bunkering plan and the alternative bunkering plan double-clicked or the like by the user. Specifically, for each of the ports of call of ship 9 (departure point, ports of call, bunkering ports and destination), the name, date and time of arrival at port, and date and time of departure from port are listed in calling order. For ports at which bunkering takes place from among these ports, bunkering amount, fuel price, fuel costs and fuel quality are displayed. On the detail screen, differences between the alternative bunkering plan and the present bunkering plan are underlined. [0173] describes the optimal bunkering plan with the lowest cost. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
forecasting fuel prices and/or fuel indexes ([0012] The sever or on-board vehicle computer may forecast expected fuel prices and determine refueling recommendations based on the driver profile as well as current and expected fuel prices. [0025] the refueling optimizer 60 generates fuel purchase recommendations 62 by modeling the problem of determining when, where and how much fuel to buy as a Mixed Integer Program (MIP)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include forecasting fuel prices and/or fuel indexes, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 20
Yamaguchi teaches:
The system according to claim 19, wherein the bunker planning system further comprises a data acquisition module (figure 5 illustrates acquisition unit part of the bunker planning system).
Claim 21
Yamaguchi teaches:
The system according to claim 20, wherein the data acquisition module further comprises: a refuel term contract data acquisition sub-module; a feature data acquisition sub-module; a fuel price data acquisition sub-module; a route data acquisition sub-module; a fuel consumption data acquisition sub-module; a vessel status data acquisition sub-module; and a port data acquisition sub-module(figure 5 illustrates sub-module part of the acquisition model which as described in [008] are : a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, an remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit ).
Claim 22
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The system according to claim 21, wherein the bunker planning system further comprises a fuel price forecasting module([0017] a fuel price forecaster 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include a fuel price forecasting module as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 23
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The system according to claim 22, wherein the fuel price forecasting module further comprises: a long-term price forecasting sub-module; and a short-term price forecasting sub-module([0011] determine a refueling strategy to generally minimize fueling costs based on forecasted (future) fuel prices and expected (future) drive patterns. For example, a refueling strategy may recommend that 2 gallons of fuel be purchased on a given day (short term) and that another 8 gallons of fuel be purchased two days later(long term), when fuel prices are forecasted to be lower).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include the fuel price forecasting module further comprises: a long-term price forecasting sub-module; and a short-term price forecasting sub-module as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 24
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The system according to claim 22, wherein the bunker planning system further comprises a data optimisation module ([0025] refueling optimizer 60).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include a data optimisation module as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 25
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The system according to claim 24, wherein the data optimisation module further comprises: a bunker plan optimisation sub-module; and a nomination date optimisation sub-module ([0025] the refueling optimizer 60 generates fuel purchase recommendations 62 by modeling the problem of determining when, where and how much fuel to buy as a Mixed Integer Program (MIP). Figure 3 further illustrates bunker plan optimisation sub-module; and a nomination date optimisation sub-module).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include the data optimisation module further comprises: a bunker plan optimisation sub-module; and a nomination date optimisation sub-module as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 26
Yamaguchi teaches:
The system according to claim 24, wherein the bunker planning system further comprises a notification generation module ([0154] Bunkering plan management server device 11 is provided with a notification data generation unit 115 that generates notification data that shows a list screen (FIG. 2)).
Claim 27
Yamaguchi teaches:
The system according to claim 26, wherein the notification generation module generates any one of a combination of notifications that comprise: forecasted fuel prices and/or fuel indexes; refuel term contract nominations; or a bunkering plan( [0173]if an alternative bunkering plan with fuel costs that are even slightly lower than the fuel costs when following the present bunkering plan is introduced, the notification data generated by notification data generation unit 115 constantly notifies a user of this through a change in display mode of the icons is adopted. In place thereof, a configuration in which a user is notified only if an alternative bunkering plan with fuel costs in which the cost is reduced by at least a predetermined threshold value compared to the fuel costs when following the present bunkering plan, for example, may be adopted).
Claim 28
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The system according to claim 27, wherein the fuel price forecasting module receives data from the feature data acquisition sub-module and the fuel price data acquisition sub- module ([0017] Referring now to FIG. 2, current (fuel price data) and historical fuel (feature data) prices 36 are fed into a fuel price forecaster 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include the fuel price forecasting module receives data from the feature data acquisition sub-module and the fuel price data acquisition sub- module as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 30
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The system according to claim 24, wherein the data optimisation module receives data from the data acquisition module and the fuel price forecasting module([0025] the current fuel prices 36, expected fuel prices 40 and driver profile 43 are fed into a refueling optimizer 60 and where a driver profile 43 may be created with information collected from the vehicle 10 as described in [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include the data optimisation module receives data from the data acquisition module and the fuel price forecasting module as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 31
Yamaguchi teaches:
The system according to claim 21, wherein the bunker planning system communicates with a server to obtain data for the data acquisition sub-modules(figure 5 and [0079], [0083], and [0086] Bunkering plan management server device 11 is provided with bunkering plan generation unit 113 that specifies various alternative bunkering plans based on different types of data acquired by acquisition unit 112 (fuel consumption data port-to-port distance data, draft trim reference data, voyage plan data, remaining fuel condition data, voyage restriction data, weather and marine element data, current status data).).
Claim 32
Yamaguchi teaches:
The system according to claim 31, wherein the bunker planning system further comprises a human-machine interface system to allow a decision-maker to input information into the bunker planning system and for the bunker planning system to output the bunkering plan (figures 2-3 a screen presented to a user in a bunkering plan support system. [0053]-[0052] describes user interaction with interface. [0058] an acquisition unit that acquires data indicating an amount of fuel remaining and sailing distance measured by a group of sensors mounted on ship 9, for example, and acquiring data indicating load amount, time of arrival at port and time of departure from port input by a user such as a ship navigator, and a transmission unit that transmits data acquired by the acquisition unit to ship terminal device 12 as current status data. [0053] If a user double-clicks or the like on a line indicating any of the alternative bunkering plans on the list in the pop-up screen, then a detail screen as exemplified in FIG. 3 is displayed on the display of bunkering plan management server device 11 or terminal device 12. The detail screen displays details of the present bunkering plan).
Claim 34
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Yamaguchi does not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The method according to claim 33, further comprising: determining an optimal time to place an order in the short term, or determining an optimal combination of contracts in the long-term, with the given refuel term contracts([0011] determine a refueling strategy to generally minimize fueling costs based on forecasted (future) fuel prices and expected (future) drive patterns. For example, a refueling strategy may recommend that 2 gallons of fuel be purchased on a given day (short term) and that another 8 gallons of fuel be purchased two days later(long term), when fuel prices are forecasted to be lower).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi incorporate the teachings of Klampfl to include determining an optimal time to place an order in the short term, or determining an optimal combination of contracts in the long-term, with the given refuel term contracts as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 36
Yamaguchi teaches:
The method according to claim 33, further comprising outputting a bunkering plan based on the forecasted fuel prices and/or fuel indexes and results of data optimisation relating to data on a planned route, fuel consumption, vessel status and a port of call( [0173] configuration in which, if an alternative bunkering plan with fuel costs that are even slightly lower than the fuel costs when following the present bunkering plan is introduced, the notification data generated by notification data generation unit 115 constantly notifies a user of this through a change in display mode of the icons is adopted. In place thereof, a configuration in which a user is notified only if an alternative bunkering plan with fuel costs in which the cost is reduced by at least a predetermined threshold value compared to the fuel costs when following the present bunkering plan, for example, may be adopted. Figure 2. Figure 3 illustrates results of data optimisation relating to data on a planned route, fuel consumption, and a port of call. Figure 14 illustrates vessel status where it is used part of the determination steps as illustrated in figure 2).
Claims 29 and 35 is rejected under 35 U.S.C. 103 as being unpatentable over Yamaguchi in view of Klampfl, as applied in claims 19, 22 and 33, and further in view of James Zarakas (US 2022/0138785 A1, hereinafter “Zaeakas”).
Claim 29
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Klampfl teaches in [0025] the refueling optimizer 60 generates fuel purchase recommendations 62 by modeling the problem of determining when, where and how much fuel to buy as a Mixed Integer Program (MIP). Yamaguchi and Klampfl do not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Zarakas teaches:
The system according to claim 22, wherein the fuel price forecasting module employs at least one customized machine learning and optimization model for long-term forecasting and short-term forecasting selected from: Ensemble Lasso, Random Forest, XGBoost, Support Vector Machine, a trained Structural Prescriptive - Empirical Risk Management (SP- ERM) or a trained Truncated Scenario-wise Linear Decision Rule - Empirical Risk Management (TSLDR-ERM)([0002] The method may include processing the transaction data, the location data, and the user history data, with a machine learning model, to determine fuel prices at the fuel stations [0072] the machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, and/or the like. After training, the machine learning system may store the machine learning model as a trained machine learning model 225 to be used to analyze new observations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi and Klampfl incorporate the teachings of Zarakas to include the fuel price forecasting module employs at least one customized machine learning and optimization model for long-term forecasting and short-term forecasting selected from: Ensemble Lasso, Random Forest, XGBoost, Support Vector Machine, a trained Structural Prescriptive - Empirical Risk Management (SP- ERM) or a trained Truncated Scenario-wise Linear Decision Rule - Empirical Risk Management (TSLDR-ERM) as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Claim 35
While Yamaguchi teaches figure 5 illustrates sub-module part of the acquisition model which as described in [008] are: a fuel consumption acquisition, a port-to-port distance acquisition unit, voyage plan acquisition unit, a remaining fuel condition acquisition unit; a remaining fuel acquisition unit; a fuel price acquisition unit, current status data acquisition unit and figure further illustrates different units/module. Klampfl teaches in [0025] the refueling optimizer 60 generates fuel purchase recommendations 62 by modeling the problem of determining when, where and how much fuel to buy as a Mixed Integer Program (MIP). Yamaguchi and Klampfl do not explicitly teach the following, however, analogous reference in the field of fueling pricing strategy, Klampfl teaches:
The method according to claim 33, wherein the step of forecasting fuel prices and/or fuel indexes employs at least one customized machine learning and optimization model for long-term forecasting and short-term forecasting selected from: Ensemble Lasso, Random Forest, XGBoost, Support Vector Machine, trained Structural Prescriptive - Empirical Risk Management (SP-ERM) or trained Truncated Scenario-wise Linear Decision Rule - Empirical Risk Management (TSLDR-ERM) ([0002] The method may include processing the transaction data, the location data, and the user history data, with a machine learning model, to determine fuel prices at the fuel stations [0072] the machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, and/or the like. After training, the machine learning system may store the machine learning model as a trained machine learning model 225 to be used to analyze new observations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Yamaguchi and Klampfl incorporate the teachings of Zarakas to include the step of forecasting fuel prices and/or fuel indexes employs at least one customized machine learning and optimization model for long-term forecasting and short-term forecasting selected from: Ensemble Lasso, Random Forest, XGBoost, Support Vector Machine, trained Structural Prescriptive - Empirical Risk Management (SP-ERM) or trained Truncated Scenario-wise Linear Decision Rule - Empirical Risk Management (TSLDR-ERM) as part of bunker planning system taught in Yamaguchi, because the references are analogous and compatible since they are both directed fuel pricing strategy. Doing so would accurately predict fuel price using relevant information to generate recommendation bunker plan.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20200401743 A1
Relevant paragraphs: Entire document
Bhattacharyya; Bhaskar et al.
US 20150106204 A1
Relevant paragraphs: [0084]-[0085], [0105], and Abstract
Pudar; Nikola J.
Fuel Bunker Management Strategies Within Sustainable Container Shipping Operation Considering Disruption and Recovery Policies
Relevant paragraphs: Entire document
Arijit De
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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, Brian Epstein can be reached at (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REHAM K ABOUZAHRA/Examiner, Art Unit 3625