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 .
This is a Non-Final rejection on the merits of this application. Claims 1-16 are currently pending, as discussed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Priority
Acknowledgment is made that the present application is a national stage entry of PCT/EP2021/078772 filed on 10/18/2021 which claims foreign priority to patent application EP20202619.1 filed on 10/19/2020.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 04/17/2023 and 08/17/2023 are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 1-2, Applicant has apparently not described, in the specification, in sufficient details, particularly the recited limitation/step of “selecting either the primary control signal or the secondary control signals”. The recited limitation suggests that an independent decision making mechanism performing the selection between two generated control signals. However, the specification does not disclose such a separate selection mechanism. Instead, the specification (published specification [0053-0056]) describes a system in which the secondary control system is always active, continuously generating its own control signals, and performing a validation or range check of the primary control signals. If the primary control signals are determined/deemed invalid or out of range, the secondary control system outputs its own control signals to the stabilizer system. There is no written description support for an independent selection mechanism/controller as recited in the claim. The claimed selection step appears to be materially broader than the disclosed specific validation logic from the specification. Accordingly, the Examiner believes that Applicant has not demonstrated to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 4-5, Applicant has apparently not described, in the specification, in sufficient details, particularly the recited limitation/step of “the current state is determined by inputting the vessel motion data into an agent being configured to output the current state based on the predetermined parameters, wherein said predetermined parameters are specific vessel type parameters and/or current operational parameters of the marine vessel”. The specification does not provide sufficient details and/or working examples for the recited agents and how such agent would be implemented to derive a “current state” from motion data and any or all “predetermined parameters” to allow a person having ordinary skilled in the art to understand the boundaries/scope of the claim limitation. It is unclear what qualifies as predetermined parameters or how it is selected or applied in the claimed method. While claim 5 attempts to further define “predetermined parameters as specific vessel type parameters and/or current operational parameters” but the specification fails to clearly describe what constitutes these parameters (e.g., hull design, displacement, length, draft, speed, heading, load?) or how they are used by the agent to determine the current state. There are also no disclosed algorithm(s), models or decision rules that map these parameters to the agent’s output. See the 2019 35 U.S.C. 112 Compliance Federal Register Notice (Federal Register, Vol. 84, No. 4, Monday, January 7, 2019, pages 57 to 63). See also http://ptoweb.uspto.gov/patents/exTrain/documents/2019-112-guidance-initiative.pptx . Quoting the FR Notice at pages 61 and 62, "The Federal Circuit emphasized that ‘‘[t]he written description requirement is not met if the specification merely describes a ‘desired result.’ ’’ Vasudevan, 782 F.3d at 682 (quoting Ariad, 598 F.3d at 1349). . . . When examining computer-implemented, software-related claims, examiners should determine whether the specification discloses the computer and the algorithm(s) that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as 'a finite sequence of steps for solving a logical or mathematical problem or performing a task.' Microsoft Computer Dictionary (5th ed., 2002). Applicant may 'express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure.' Finisar, 523 F.3d at 1340 (internal citation omitted). It is not enough that one skilled in the art could theoretically write a program to achieve the claimed function, rather the specification itself must explain how the claimed function is achieved to demonstrate that the applicant had possession of it. See, e.g., Vasudevan, 782 F.3d at 682–83. If the specification does not provide a disclosure of the computer and algorithm(s) in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention that achieves the claimed result, a rejection under 35 U.S.C. 112(a) for lack of written description must be made. See MPEP § 2161.01, subsection I." Accordingly, the Examiner believes that Applicant has not demonstrated to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 9 (similarly claim 11), Applicant has apparently not described, in the specification, in sufficient details, particularly the recited limitation/step of “training the primary non-linear control system by inputting vessel motion data to an agent of a third non-linear control system, outputting an associated state from said agent based on predetermined parameters, determining the performance of the output state, optimizing the agent of the third non-linear control system based on the determined performance and using the agent of the third non-linear control system as an agent of the first non-linear control system”. While the specification (Fig. 4 [0048-0053]) mentions training “third non-linear control system” in a laboratory environment, the existence and operation of a distinct/separate third non-linear control system, as opposed to simply a training phase of the same system, is not clearly described as a separate control architecture. The claim implies a modular architecture involving three distinct control systems, but the specification does not provide sufficient detail to support this structure. The specification further does not describe in sufficient details what the ”associated state” is that the agent outputs because no examples or definitions of “associated state” are provided. While the specification briefly refers to a reinforcement learning mechanism that applies rewards and penalties, it does not adequately describe how performance of a state (and what it is, safety, stability, other evaluation) is measured and what metrics are used and what threshold or criteria are involved. The process by which a trained agent is transfer or used/reused in the first primary control system is only generally stated in the specification (e.g., “the agent …is implemented as the agent of the primary control system”), without technical explanation or structural support for this process but the specification does not describe how compatibility is ensured, how the transfer occurs, or what operational differences (if any) exist between the third system and the first system as recited in limitation of “using the agent of the third control system as an agent of the first”. Because the claim introduces the concepts (e.g., a distinct third control system, outputting a state from an agent, performance evaluation of that state, optimization, and reuse in another control context) that are not sufficiently described in the specification with sufficient details or structural support. Accordingly, the Examiner believes that Applicant has not demonstrated to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The dependent claims that dependent upon independent claims are also rejected under 112 first paragraph by the fact that they are dependent upon the rejected independent claims.
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.
Claims 1-13 and 15-16 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.
Regarding claim 1 (similarly claim 2) recites the limitation “selecting either the primary control signals or the secondary control signals” is indefinite because it is unclear to the Examiner how and under what conditions the selection occurs (e.g., is it user initiated, automated, based on signal comparison, threshold, or fault detection or something else) and further by what/whom this selection is made (that is, is the selection performed by the first/second or another control system, the user, or by a separate module/system, or something else). Hence this limitation renders the claim to be indefinite.
Regarding Claim 4-5, the recited limitation/step of “the current state is determined by inputting the vessel motion data into an agent being configured to output the current state based on the predetermined parameters, wherein said predetermined parameters are specific vessel type parameters and/or current operational parameters of the marine vessel” is indefinite because it is unclear whether the agent is the same as the neural network reference in claim 3, a component of the neural network, or an entirely separate element. The claim does not define the structure or function of the agent with sufficient clarity. Further, it is unclear what qualifies as “predetermined parameters” from the teachings of the specification or how it is selected or applied in the claimed method. Lastly, it is unclear to the Examiner what does or does not fall within “specific vessel type parameters and/or current operational parameters” from the teachings of the specification which does not provide reasonable certainty to what the metes and bounds of “specific vessel type parameters and/or current operational parameters” might be. See Nautilus, Inc. v. Biosig Instruments, Inc. (U.S. Supreme Court, 2014) which held, "A patent is invalid for indefiniteness if its claims, read in light of the patent' s specification and prosecution history, fail to inform, with reasonable certainty, those skilled in the art about the scope of the invention." See also In re Packard, 751 F.3d 1307 (Fed.Cir.2014)(“[A] claim is indefinite when it contains words or phrases whose meaning is unclear,” i.e., “ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention.”) and Ex Parte McAward, Appeal No. 2015-006416 (PTAB, Aug. 25, 2017, Precedential) (“Applying the broadest reasonable interpretation of a claim, then, the Office establishes a prima facie case of indefiniteness with a rejection explaining how the metes and bounds of a pending claim are not clear because the claim contains words or phrases whose meaning is unclear.”)
Regarding Claim 6, the recited limitation/step of “valid pre-set control parameters” is indefinite because the limitation “valid pre-set control parameters” is a subjective term and not clearly defined in the claim such that it fails to inform a personal having ordinary skilled in the art of the scope of the claim with reasonable certainty to as what constitutes as valid or not. It is unclear whether validity is based on safety constraints, specific operational limits or something else. Further, the term “pre-set” implies that the parameters are fixed in advance but it is unclear who/what sets any/all of these parameters, how they are accessed/stored and what parameters are being referenced? Hence, this limitation renders the claim to be indefinite.
Regarding Claim 7, the recited limitation/step of “matched with the valid pre-set control parameters” is indefinite because it is unclear whether “matched” refers to exact equality, a threshold based equivalence, or compliance within a range (but no range is defined). Accordingly, this claim limitation renders the claim to be indefinite.
Regarding Claim 9 (similarly claim 11), the recited limitation(s) of “outputting an associated state from said agent based on predetermined parameters, determining the performance of the output state, optimizing the agent of the third non-linear control system based on the determined performance and using the agent of the third non-linear control system as an agent of the first non-linear control system” is indefinite because of the following reasons: (i) it is unclear what the “associated state” refers to, e.g., internal computational state of the agent, a predicted vessel motion state, a control action of the control system, or something else and how such “associated state” relates to the subsequent steps of performance evaluation and optimization; (ii) the term/phrase “the performance of the output state” is not clearly defined by any measurable criteria/requirements because it is unclear whether the performance refers to (e.g., accuracy, stability, efficiency, safety or any other metrics) and what may or may not constitute as acceptable performance; (iii) it is unclear if the optimization applies to the agent, the neural network of both and further which parameters are adjusted and under what algorithm(s) are any or all of the optimization achieved; and lastly (iv) it is also unclear what “using the agent…as…” means (e.g., copying the trained weights, replacing components, or something else). Hence, these limitation renders the claim to be indefinite.
The dependent claims that dependent upon independent claims are also rejected under 112 second paragraph by the fact that they are dependent upon the rejected independent claims.
Claim Rejections - 35 USC § 101
Claim 15-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 15 (similarly claim 16), the claim(s) does not fall within at least one of the four categories of patent eligible subject matter because 15 (similarly claim 16) is directed toward a computer program which is software per se. Therefore, claim 15 (similarly claim 16) is not within at least one of the four statutory categories (see MPEP 2106.03, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 17S USPQ2d 675 (An "idea" is not patent eligible). Thus, claim 15 (similarly claim 16) does not fall within any statutory category.
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)(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.
(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) 2 and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Steinmann et al. (US 2013/0036958 A1 hereinafter Steinmann).
Regarding claim 2, Steinmann discloses A method of stabilising a marine vessel having a stabiliser system (see at least Abstract), the method comprising:
receiving real-time vessel motion data from at least one vessel motion sensor, (see at least Fig. 1-6 [0020-0070]: A stabiliser control method for stabilising marine vessel motion induced by excitation wave forces, the method comprising the steps of: detecting the availability or otherwise of a precession motion sensor signal and the availability or otherwise of a vessel roll motion sensor signal.)
selecting one of a primary non-linear control system and a secondary linear control system, (see at least Fig. 1-6 [0020-0070]: selecting a first control mode when both the sensor signals are available; selecting a second control mode when only one of the sensor signals is available; and, selecting a third control mode when both sensor signals are unavailable. The first control mode may include PID controller, fuzzy logic controllers, neural network control, robust controller, model predictive control methods, adaptive control methods including the automatic gain control controller, or a combination thereof control processes. The second control mode can be, for example, an adaptive control mode using the signal from the precession motion sensor and its derivatives. The third control mode is a passive or manually operated control model where in the third control mode the control signal for the gyro-actuator can be a pre-set fixed value gain to provide a predetermined precession damping.)
generating one or more primary control signals or one or more secondary control signals based on the vessel motion data, dependent on the selected primary or secondary control system, (see at least Fig. 1-6 [0020-0070]: stabilising control command generating means for generating a stabilising control command in accordance with the selected control mode whereby, in use, the most appropriate control mode in the circumstance is selected to provide vessel stabilization.) and
transmitting the generated primary or secondary control signals to control stabiliser mechanics of the vessel stabiliser system. (see at least Fig. 1-6 [0020-0070]: The stabilising control command generated in the first control mode may provide active control of the precession of the gyrostabiliser using both sensor signals and/or derivatives of the sensor signals as process control variables by either driving the precession or actively resisting the roll induced free precession of the flywheel to produce a desired precession motion. Likewise the stabilising control command generated in the second control mode may provide active control of the precession of the gyrostabiliser using either the gyrostabiliser precession motion sensor signal or the vessel roll motion sensor signal and/or derivatives of the signal as a process control variable by either driving the precession or actively damping the roll induced free precession of the flywheel to produce a desired precession motion. )
Regarding claim 14, Steinmann discloses A controller system for use in a marine vessel stabiliser system (see at least Abstract), said control system being configured to provide control signals to the marine vessel stabiliser system based on data from at least one associated vessel motion sensor(see at least Fig. 1-6 [0020-0070]: A stabiliser control method for stabilising marine vessel motion induced by excitation wave forces, the method comprising the steps of: detecting the availability or otherwise of a precession motion sensor signal and the availability or otherwise of a vessel roll motion sensor signal.), wherein the controller system comprises a primary non-linear control system and a secondary linear control system and means for allowing the primary control system or the secondary control system to generate control signals to control stabiliser mechanics of the associated vessel stabiliser system. (see at least Fig. 1-6 [0020-0070]: selecting a first control mode when both the sensor signals are available; selecting a second control mode when only one of the sensor signals is available; and, selecting a third control mode when both sensor signals are unavailable. The first control mode may include PID controller, fuzzy logic controllers, neural network control, robust controller, model predictive control methods, adaptive control methods including the automatic gain control controller, or a combination thereof control processes. The second control mode can be, for example, an adaptive control mode using the signal from the precession motion sensor and its derivatives. The third control mode is a passive or manually operated control model where in the third control mode the control signal for the gyro-actuator can be a pre-set fixed value gain to provide a predetermined precession damping. The stabilising control command generated in the first control mode may provide active control of the precession of the gyrostabiliser using both sensor signals and/or derivatives of the sensor signals as process control variables by either driving the precession or actively resisting the roll induced free precession of the flywheel to produce a desired precession motion. Likewise the stabilising control command generated in the second control mode may provide active control of the precession of the gyrostabiliser using either the gyrostabiliser precession motion sensor signal or the vessel roll motion sensor signal and/or derivatives of the signal as a process control variable by either driving the precession or actively damping the roll induced free precession of the flywheel to produce a desired precession motion.)
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.
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.
Claim(s) 1, 3, 8, 12,13, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ghassemi et al. (“Neural network-PID controller for roll fin stabilizer” hereinafter Ghassemi) in view of Konar et al. (US 5396415 A hereinafter Konar).
Regarding Claim 1, Ghassemi teaches A method of stabilising a marine vessel having a stabiliser system (see at least Abstract-Introduction: Fin stabilizers are very effective devices for controlling the ship roll motion against external wave-generated moments), the method comprising:
receiving real-time vessel motion data from at least one vessel motion sensor, (see at least entire article: The inputs are surge and sway forces as well as roll and yaw moments)
generating one or more primary control signals based on the vessel motion data by means of a primary non-linear control system, (see at least entire article: neural network is used as a non-linear estimator to model ship dynamics and predicts roll motion.)
generating one or more secondary control signals based on the vessel motion data by means of a secondary linear control system, (see at least entire article: the PID controller is a linear control loop that generates the stabilizer fin control signal)
transmitting the selected control signals to control stabiliser mechanics of the vessel stabiliser system. (see at least entire article: the control output is the fin attack angle that adjusts hydrodynamics forces to counter roll which is the mechanical actuation of the stabilizer.)
it may be alleged that Ghassemi does not explicitly teach selecting either the primary control signals or the secondary control signals, and
Konar is directed to a neuro-PID controller architecture for controlling time varying dynamic processes and systems, Konar teaches selecting either the primary control signals or the secondary control signals,(see at least Fig. 1-6 Col. 3 Line 20 – Col. 8 Line 60: A controller for a closed loop system having a conventional PID controller and a NeuroPID controller which has been trained on the system, wherein the user may select to employ either the output of the NeuroPID controller combined with the conventional PID controller, or the conventional PID controller output alone said user selection being made in a set of ways including, during operation of the controller by a user input switch means, and by automatic means responsive to preselected user criteria.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Ghassemi’s neural network-PID controller for roll fin stabilization system and method to incorporate the technique of selecting either the primary control signals or the secondary control signals as taught by Konar with reasonable expectation of success to improve reliability, safety and fault tolerance in marine control system because neural network controllers while adaptive and powerful for nonlinear dynamics, can sometimes behave unpredictably by producing undesirable outputs under unfamiliar conditions or due to lack of sufficient training, therefore, by including a conventional PID fallback option (e.g. user selectable control mode) would ensure that stable and well defined control is still available when the neural network controller performs outside of permitted operating boundary in order to provide a more robust control system capable of adapting to changing marine environment while maintaining operational safety.
Regarding Claim 3, the combination of Ghassemi in view of Konar teaches The method according to claim 1,
it may be alleged that Ghassemi does not explicitly teach wherein generating one or more primary control signals based on the vessel motion data by means of the primary non-linear control system is performed by a neural network receiving input representing a current state and outputting a selected action, wherein said primary control signal(s) is determined based on the selected action.
Konar is directed to a neuro-PID controller architecture for controlling time varying dynamic processes and systems, Konar teaches wherein generating one or more primary control signals based on the vessel motion data by means of the primary non-linear control system is performed by a neural network receiving input representing a current state and outputting a selected action, wherein said primary control signal(s) is determined based on the selected action. (see at least Fig. 1-6 Col. 3 Line 20 – Col. 8 Line 60: In equations 2-3 with input state variables, the neural network output is a function of the current state, error signal and controller parameters.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Ghassemi’s neural network-PID controller for roll fin stabilization system and method to incorporate the technique of generating non-linear control signals using neural network with input state information to output a control signal as taught by Konar with reasonable expectation of success to improve reliability, safety and fault tolerance in marine control system because neural network controllers while adaptive and powerful for nonlinear dynamics, can sometimes behave unpredictably by producing undesirable outputs under unfamiliar conditions or due to lack of sufficient training, therefore, by including a conventional PID fallback option (e.g. user selectable control mode) would ensure that stable and well defined control is still available when the neural network controller performs outside of permitted operating boundary in order to provide a more robust control system capable of adapting to changing marine environment while maintaining operational safety.
Regarding Claim 8, the combination of Ghassemi in view of Konar teaches The method according to claim 1,
Ghassemi further teaches wherein generating one or more secondary control signals based on the vessel motion data by means of secondary linear control system is performed by means of a PID regulator. (see at least entire article: the PID controller is a linear control loop that generates the stabilizer fin control signal)
Regarding Claim 12, the combination of Ghassemi in view of Konar teaches The method according to claim 1,
Ghassemi further teaches A marine vessel stabiliser system comprising means adapted to execute the steps of the method. (see at least Abstract-Fin-stabilizer modeling)
Regarding Claim 13, the combination of Ghassemi in view of Konar teaches claim 12,
Ghassemi further teaches A marine vessel comprising a stabiliser system (see at least Abstract-Fin-stabilizer modeling)
Regarding Claim 15, the combination of Ghassemi in view of Konar teaches claims 1 and 12,
Ghassemi further teaches A computer programme product comprising instructions to cause the stabiliser system to execute the method (see at least Abstract-Control Loop and Results)
Regarding Claim 16, the combination of Ghassemi in view of Konar teaches Claim 15,
Ghassemi further teaches A computer-readable medium having stored there on the computer programme of claim 15. (see at least Abstract-Control Loop and Results)
Claim(s) 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Ghassemi in view of Konar and Bartlett (US 2021/0124374 A1)
Regarding Claim 4, the combination of Ghassemi in view of Konar teaches The method according to claim 3,
it may be alleged that the combination of Ghassemi in view of Konar does not explicitly teach wherein the current state is determined by inputting the vessel motion data into an agent being configured to outputting the current state based on predetermined parameters.
Bartlett is directed to vessel stability control system using machine learning to optimize resource usage, Bartlett teaches wherein the current state is determined by inputting the vessel motion data into an agent being configured to outputting the current state based on predetermined parameters. (see at least Fig. 3-6 [0043-0053]: In step 402 the stability controller receives vessel operating control inputs, which include engine throttle, heading, rudder angle, trim position, and so on. In step 404 the stability controller also receives input regarding sea and weather conditions. Sea conditions can be determined by an on board system that identifies wave patterns (e.g. up and down motion, rolling motion)and properties such as average wave height, wave period, angle to the vessel, and so on. In step 406 any input constraint conditions and a mode selection can be identified as another input or to select a set of decision coefficients particular to that constraint. In step 408 the machine learning engine applies the inputs to a decision engine that has been properly configured using the decision coefficients to produce an output in the form of a setting for each of the various stability systems on the vessel.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ghassemi and Konar to incorporate the technique of inputting the vessel motion data into an agent being configured to output the current state based on predetermined parameters as taught by Bartlett with reasonable expectation of success to provide the benefit of allowing a vessel operator to select what constraint is important at any given time, thus, a vessel operator can select to optimize resource conservation, passenger comfort, and so on (Bartlett [0053]).
Regarding Claim 5, the combination of Ghassemi in view of Konar and Bartlett teaches The method according to claim 4,
it may be alleged that the combination of Ghassemi in view of Konar does not explicitly teach wherein said predetermined parameters are specific vessel type parameters and/or current operational parameters of the marine vessel.
Bartlett is directed to vessel stability control system using machine learning to optimize resource usage, Bartlett teaches wherein said predetermined parameters are specific vessel type parameters and/or current operational parameters of the marine vessel. (see at least Fig. 3-6 [0043-0053]: In step 402 the stability controller receives vessel operating control inputs, which include engine throttle, heading, rudder angle, trim position, and so on. In step 404 the stability controller also receives input regarding sea and weather conditions. Sea conditions can be determined by an on board system that identifies wave patterns (e.g. up and down motion, rolling motion)and properties such as average wave height, wave period, angle to the vessel, and so on. In step 406 any input constraint conditions and a mode selection can be identified as another input or to select a set of decision coefficients particular to that constraint. In step 408 the machine learning engine applies the inputs to a decision engine that has been properly configured using the decision coefficients to produce an output in the form of a setting for each of the various stability systems on the vessel.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ghassemi and Konar to incorporate the technique of inputting the vessel motion data into an agent being configured to output the current state based on predetermined parameters that are specific vessel type parameters and/or current operational parameters of the marine vessel as taught by Bartlett with reasonable expectation of success to provide the benefit of allowing a vessel operator to select what constraint is important at any given time, thus, a vessel operator can select to optimize resource conservation, passenger comfort, and so on (Bartlett [0053]).
Claim(s) 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ghassemi in view of Konar and Abeloe (US 10,620,631 B1).
Regarding Claim 6, the combination of Ghassemi in view of Konar teaches The method according to claim 1,
it may be alleged that the combination of Ghassemi in view of Konar does not explicitly teach wherein the primary control signals corresponds to specific control parameters for the associated stabiliser system, and wherein the method further comprises comparing the control parameters of the primary control signals with vali pre-set control parameters.
Abeloe is directed to system and method of limiting the operation of neural networks to b within one or more conditions, Abeloe teaches wherein the primary control signals corresponds to specific control parameters for the associated stabiliser system, and wherein the method further comprises comparing the control parameters of the primary control signals with vali pre-set control parameters. (see at least Col. 12 Line 30- Col. 14 Line 56: a set of boundary conditions can be described as allowing the output of a neural network to be only within a certain range—e.g., Region A 351, although the input data can be anywhere within the entire sample space as depicted in FIG. 3a. Referring back to FIG. 3b, if an output from a neural network structured and trained to inference classes located within Region A, the output can be used in a subsequent processing, described below in connection with various embodiments of apex controllers. The expression “apex” in “apex controller” is used for the ease of reference and to connote that an apex controller executes a neural network (or an Implementation Neural Network described below.) However, if output of such a neural network is outside of Region A (e.g., Region B 353 or Region C 355 of FIG. 3b), the output can be discarded and not used. In another simplified depiction of FIG. 4, the decision-making can be illustrated as a function in a one-dimensional space. In this simplified version, the boundary conditions are depicted as a range 401 in which an output from a neural network is checked against.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ghassemi and Konar to incorporate the technique of validating the output control parameters of a neural network to be within a specified control range as taught by Abeloe with reasonable expectation of success to add a valuable safety feature that ensures the neural network outputs are within trusted operational bounds in order to create a hybrid, intelligent control system that is both adaptive and certifiably safe.
Regarding Claim 7, the combination of Ghassemi in view of Konar and Abeloe teaches The method according to claim 6,
it may be alleged that the combination of Ghassemi in view of Konar does not explicitly teach wherein the step of transmitting the primary control signals to the vessel stabiliser system is performed only if the control parameters of the primary control system are matched with the valid pre-set control parameters.
Abeloe is directed to system and method of limiting the operation of neural networks to b within one or more conditions, Abeloe teaches wherein the step of transmitting the primary control signals to the vessel stabiliser system is performed only if the control parameters of the primary control system are matched with the valid pre-set control parameters. (see at least Col. 12 Line 30- Col. 14 Line 56: a set of boundary conditions can be described as allowing the output of a neural network to be only within a certain range—e.g., Region A 351, although the input data can be anywhere within the entire sample space as depicted in FIG. 3a. Referring back to FIG. 3b, if an output from a neural network structured and trained to inference classes located within Region A, the output can be used in a subsequent processing, described below in connection with various embodiments of apex controllers. The expression “apex” in “apex controller” is used for the ease of reference and to connote that an apex controller executes a neural network (or an Implementation Neural Network described below.) However, if output of such a neural network is outside of Region A (e.g., Region B 353 or Region C 355 of FIG. 3b), the output can be discarded and not used. In another simplified depiction of FIG. 4, the decision-making can be illustrated as a function in a one-dimensional space. In this simplified version, the boundary conditions are depicted as a range 401 in which an output from a neural network is checked against.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ghassemi and Konar to incorporate the technique of validating the output control parameters of a neural network to be within a specified control range and only using the neural network’s output when it’s within specified range as taught by Abeloe with reasonable expectation of success to add a valuable safety feature that ensures the neural network outputs are within trusted operational bounds in order to create a hybrid, intelligent control system that is both adaptive and certifiably safe.
Allowable Subject Matter
Claims 9-11 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include 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:
Regarding claim 9, prior arts Ghassemi, Konar, Abeloe and Bartlett taken either individually or in combination with each other or other prior art of records fail to teach or render obvious: training the primary non-linear control system by inputting training vessel motion data to an agent of a third non-linear control system, outputting an associated state from said agent based on predetermined parameters, determining the performance of the output state, optimising the agent of the third non-linear control system based on the determined performance and using the agent of the third non-linear control system as an agent of the first non-linear control system.
Regarding claim 11, prior arts Ghassemi, Konar, Abeloe and Bartlett taken either individually or in combination with each other or other prior art of records fail to teach or render obvious: training the primary non-linear control system by inputting real-time vessel motion data to an agent of a fourth non-linear control system, outputting an associated state from said agent based on predetermined parameters, determining the performance of the output state, optimising the agent of the fourth non-linear control system based on the determined performance, and using the agent of the fourth non-linear control system as an agent of the first non-linear control system.
Claim 10 would be allowable because it is dependent on claim 9.
Conclusion
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/DANA F ARTIMEZ/ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667