DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a Final Office action in response to the communication received 04/09/2026. Claims 1, 3, 4, 7, 8, 16 and 19 are amended, claims 9, 17 and 18 are canceled without prejudice or disclaimer, and new claims 21 and 22 are added. Accordingly, claims 1, 3-8, 10, 11, 14- 16 and 19-22 are now pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 06/04/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Response to Arguments/Rejections
Applicant’s arguments, see Remarks, filed 02/11/2026, with respect to the rejection(s) of claim(s) 1, 3, 4, 7, 8, 16 and 19 under 35 USC § 103 and newly added claims 20-21 have been fully considered. Applicant has amended claims to overcome rejections under 35 USC § 103.
Claim Rejections under 35 U.S.C. § 103
Per remarks, independent claim 1 is amended to include the features of claim 9, and additional features that are based on, for example, Figure 1 and the description beginning in paragraph 0034 of the present application. That is, independent claim 1 is amended to recite that the processing circuitry is configured to acquire a command rudder angle relative to the ship, generate an updated turning signal based on the command rudder angle and the estimated rudder angle; and control a rudder of the ship based on the updated turning signal. Thus, this clarifies that the rudder is not merely controlled by the turning signal, or by the command rudder angle, but rather the rudder is controlled by an "updated" version of the turning signal that is generated based on the command rudder angle and the estimated rudder angle.
Independent claim 19 is amended in a similar manner.
Applicant further states, “Pease and Ma both rely on a measured rudder angle, and neither estimates a rudder angle. That is, in Pease, the rudder angle is directly measured by rudder position sensor 117, which is part of the ship's motion sensors as described, for example, in paragraph 0049 of Pease. The system simply uses this measured rudder angle in the autopilot control equation, and there is no suggestion of reconstructing rudder angle from other signals.”. And, "estimated rudder angle" is clearly a state variable output from the estimator, not a sensor value nor a simple command. Pease and Ma do not disclose this concept and Pease and Ma fail to teach or suggest such a separation or this type of feedback loop based on an estimated rudder angle.
Examiner respectfully disagrees. The Applicant is reminded that the claims are given their broadest reasonable interpretation. First, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As Pease Figure 3 Generating/outputting Rudder Command 110 using autopilot App 134 based on Pilot Line Course and Pilot Line Turn Rate inputs. This is disclosing the rudder command estimated for input pilot course line and pilot line turn rate and the Ship Course & Ship Turn Rate input to Autopilot App 134. It is noted this is the actual detected response to the rudder estimate and associated command. Additionally, in combination Ma teaches in paragraphs Ma [0048]-[0049], [0050]-[0057] disclosing that rudder angle can be determined using inputs to a deep neural network.
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. Therefore, Pease in view of Ma is maintained to teach the subject matter of claim.
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 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, 5-7, 10-11, and 19-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pease (Pub. No.: US 2012/0130570, previously recorded), hereinafter, referred to as “Pease” in view of Ma et al. (Pub. No.: US 2021/0116922; previously recorded), hereinafter, referred to as “Ma”.
Regarding [claim 1], Pease discloses a ship state estimation device (Pease Fig. 2-3), comprising: processing circuitry configured to: (see, Paragraph [0049]: “Referring to FIG. 2, the steering system 60 of this invention includes a computing system 130 and a navigation command unit 102. The computing system 130 includes a central processing unit (CPU) 132, a navigation application 65, an autopilot application 134 and a database 136”);
receive a direction signal indicating a direction of a ship (see, “Fig. 3 Pilot line Course, Pilot Line Turn Rate input into Autopilot App 134”: and Paragraph [0052]: “Next, the offset 85, distance 86 and angular direction 87 of the ship's current location from the track line 81 are determined (156). Next, a track keeping application (track keeping App) uses the determined offset and a piecewise continuous navigation algorithm to compute the pilot line direction (Pc) and the pilot line turning rate (dP.sub.c/dt)”);
estimate at least an estimated rudder angle of the ship by inputting the direction signal into one of:
a Kalman filter which is a linear stochastic system; an extended Kalman filter which is a nonlinear stochastic system; an Unscented Kalman filter; a particle filter; or artificial intelligence (see, Pease Fig. 3 Generating/outputting Rudder Command 110 using autopilot App 134 based on Pilot Line Course and Pilot Line Turn Rate inputs (Note: This is the rudder command estimated for input pilot course line and pilot line turn rate) and
inputting a turning signal for the ship into one of:
the Kalman filter which is a linear stochastic system; the extended Kalman filter which is a nonlinear stochastic system;
the Unscented Kalman filter;
the particle filter; or
the artificial intelligence
such that at least the estimated rudder angle is output when the direction signal and the turning signal are input (see, “Fig. 3 Ship Course & Ship Turn Rate input to Autopilot App 134” (Examiner Note: This is the actual detected response to the rudder estimate and associated command);
acquire a command rudder angle relative to the ship (see, Paragraph [0055]: “The difference (.SIGMA.1) between the pilot line course (P.sub.c) 63 and the ship's course line (S.sub.c) 64 is multiplied by the Kmax constant. Similarly, the difference (.SIGMA.2) between the pilot line turning rate (dP.sub.c/dt) 61 and the ship's turning rate dS.sub.c/dt 56 is multiplied by the Kvel constant. The sum (.SIGMA.) of the two differences (.SIGMA.1) and (.SIGMA.2) (partial rudder commands) results in the rudder command 110 according to which the servo-motor 32 in the rudder servomechanism 30 is directed. The ship responds to the rudder motion and takes a new position (S.sub.c') and turning rate (dS.sub.c/dt'). This process repeats itself until the ship's position 98 is equal to the pilot line course (P.sub.c) 63 and the ship's turning rate dS.sub.c/dt 56 is equal to the pilot line turning rate (dP.sub.c/dt) 61. The ship's course line (S.sub.c) 64 is provided by the ship angular position sensor 116 (i.e., a compass). The ship's turning rate dS.sub.c/dt 56 is provided by the ship turn rate sensor 120.”; See also Pease [0082]; and [0084]: “disclosing the effects of rough sea (wind waves) on ship drift, changing Pilot Line and associated rudder adjustment”);
generate an updated turning signal based on the command rudder angle and the estimated rudder angle (see, Fig. 3 – Updated Rudder Commands 110; and Paragraphs [0052]; and [0055] ***process is repeated until a waypoint is reached***), see also [0082]; and [0084]: “where repeated tracked drift causes change in the Pilot Line that is fed into the autopilot system”); and
control a rudder of the ship based on the updated turning signal (see, “Fig. 3 Ship Response 30”; and Paragraph [0052]: “the servomechanism 30 rotates the rudder according to the calculated rudder commands and the ship's angular position is adjusted in order to reduce the offset to zero”; See also [0055];[0082]; and [0084]:”for disclosure of the cyclical nature of control based on the rudder angle estimated to produce the anticipated response and the updated inputs that effect updates to the rudder commands”).
Pease does not explicitly disclose the applied models as claimed.
However, Ma teaches
estimate at least a rudder angle of the ship by inputting the direction signal into one of: a Kalman filter which is a linear stochastic system; an extended Kalman filter which is a nonlinear stochastic system; an Unscented Kalman filter; a particle filter; or artificial intelligence that outputs at least the rudder angle of the ship when the direction signal is input (see, Paragraph [0048]: “Specifically, the decision-making module fuses data perceived by all the sensors, analyzes current navigational environment of the MASS, makes a decision on the state of motion of the MASS according to the current navigational status of the MASS, and sends operating instructions of the propeller and the rudder to the execution module”. [0049]: “The decision-making module inputs the navigational environment information of the MASS and the navigational state of the MASS into a trained deep neural network based on a deep deterministic policy gradient (DDPG) algorithm, and the deep neural network outputs vessel control instructions including vessel thrust information and rudder angle information.”; and (See [0050]-[0057] for computation/analysis disclosing that rudder angle can be determined using inputs to a deep neural network).
…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to estimate at least a rudder angle of the ship as taught by Ma. One would be motivated to make this modification in order to convey an efficient perception control method is designed based on the deep deterministic policy gradient (DDPG) algorithm, to improve utilization efficiency of the data perceived by the perception module, reduce exploration frequency of the perception device and reduce generation of junk data (See, Ma Paragraph [0062]).
Regarding claim 5, Pease in view of Ma teaches the ship state estimation device of claim 1. Pease further teaches wherein the processing circuitry is further configured to: estimate a heading of the ship (Pease [0048]: “The ship 90 is shown moving along track line direction 98. A steering device or rudder 92 is attached to the stern 95 and is used to steer the ship. A steering system 60 transfers steering commands to the rudder servomechanism in order to rotate the vessel 90 around the turning point 94 by an angle 89 and thereby to change the current ship's direction 98 toward the direction of a pilot line 96 in order to approach a new track line 81 as part of a voyage plan 80” and [0049]: “The CPU 132 calculates the rudder commands 110 based on the autopilot application 134 instructions and based on input from the ship's motion sensors and the pilot-line parameters. The ship's motion sensors provide the ship's position and velocity and include a GPS 112, ship velocity sensor 114, ship angular position sensor 116, rudder position sensor 117, ship turn rate sensor 120”; [0050] –[0052] FIG. 5, the process flow diagram 150 of the "pilot-line" steering methodology includes the following steps. First, a waypoint triplet including points 82, 83, 84 is extracted from the voyage plan database 136 (151). The ship's current location 64 is obtained periodically from the GPS (152). In one example, the GPS reading provides ship's location every three minutes. Next, the offset 85, distance 86 and angular direction 87 of the ship's current location from the track line 81 are determined (156). Next, a track keeping application (track keeping App) uses the determined offset and a piecewise continuous navigation algorithm to compute the pilot line direction (Pc) and the pilot line turning rate (dP.sub.c/dt) and then to calculate rudder commands according to equation 1, as will be described below (155); see also [0051]-[0055]).
Regarding claim 6, the combination of Pease and Ma discloses the ship state estimation device of claim 1. Pease further discloses wherein the processing circuitry is further configured to: estimate a turnrate of the ship (see at least Paragraph [0048]: “The ship 90 is shown moving along track line direction 98. A steering device or rudder 92 is attached to the stern 95 and is used to steer the ship. A steering system 60 transfers steering commands to the rudder servomechanism in order to rotate the vessel 90 around the turning point 94 by an angle 89 and thereby to change the current ship's direction 98 toward the direction of a pilot line 96 in order to approach a new track line 81 as part of a voyage plan 80” and [0049]: “The CPU 132 calculates the rudder commands 110 based on the autopilot application 134 instructions and based on input from the ship's motion sensors and the pilot-line parameters. The ship's motion sensors provide the ship's position and velocity and include a GPS 112, ship velocity sensor 114, ship angular position sensor 116, rudder position sensor 117, ship turn rate sensor 120”; [0050] –[0052] FIG. 5, the process flow diagram 150 of the "pilot-line" steering methodology includes the following steps. First, a waypoint triplet including points 82, 83, 84 is extracted from the voyage plan database 136 (151). The ship's current location 64 is obtained periodically from the GPS (152). In one example, the GPS reading provides ship's location every three minutes. Next, the offset 85, distance 86 and angular direction 87 of the ship's current location from the track line 81 are determined (156). Next, a track keeping application (track keeping App) uses the determined offset and a piecewise continuous navigation algorithm to compute the pilot line direction (Pc) and the pilot line turning rate (dP.sub.c/dt) and then to calculate rudder commands according to equation 1, as will be described below (155).); see also [0051]-[0055]).
Regarding claim 7, Pease in view of Ma teaches the ship state estimation device of claim 1. Pease in view of Ma discloses wherein an initial estimation value of the when the estimated rudder angle is based on a value of the estimated rudder angle is based on a value when the ship is stopped or going straight. (Pease [0051] The steering system 60 of the present invention utilizes a "pilot-line" methodology for providing directional guidance of the ship. A "pilot-line" is a mathematical vector 96 that has its origin attached to a point 94 on the ship's center line 90 and points to a desired instantaneous direction, as shown in FIG. 4, FIG. 7A, and FIG. 7B. Referring to FIG. 3 and FIG. 4, for each of the periodic GPS readings, the offset 85 between the ship's location and the track line 81, the distance 86 on the track line 81 to reach the next waypoint 83, and the angular direction 87 of the desired track line 81 are determined by the navigation application 65; [0050] –[0052] FIG. 5, the process flow diagram 150 of the "pilot-line" steering methodology includes the following steps. First, a waypoint triplet including points 82, 83, 84 is extracted from the voyage plan database 136 (151). The ship's current location 64 is obtained periodically from the GPS (152). In one example, the GPS reading provides ship's location every three minutes. Next, the offset 85, distance 86 and angular direction 87 of the ship's current location from the track line 81 are determined (156). Next, a track keeping application (track keeping App) uses the determined offset and a piecewise continuous navigation algorithm to compute the pilot line direction (Pc) and the pilot line turning rate (dP.sub.c/dt) and then to calculate rudder commands according to equation 1, as will be described below (155).).)
Regarding claim 10, Pease in view of Ma discloses a ship state estimation system of claim 1. Pease in view of Ma further discloses wherein the processing circuitry is further configured to:
acquire a parameter value relating to a characteristic of the ship (Pease [0049] Database 136 includes voyage plan 80 information, ship dimensions, ship constants, maximum ship turn rate, and maximum rudder turn rate, among other; Ma, Fig. & 3, Perception Module); and
set an estimation model corresponding to a cruising state of the ship (Pease [0054] application of database information to rudder control calculation) to the one of: the Kalman filter which is a linear stochastic system; the extended Kalman filter which is a nonlinear stochastic system; the Unscented Kalman filter; the particle filter; or the artificial intelligence based on the parameter value (Ma [0049] The decision-making module inputs the navigational environment information of the MASS and the navigational state of the MASS into a trained deep neural network based on a deep deterministic policy gradient (DDPG) algorithm, and the deep neural network outputs vessel control instructions including vessel thrust information and rudder angle information. [0059] The state observation function corresponds to information obtained by the sensors in the vessel state perception submodule and the external natural condition perception submodule as shown in FIG. 3, the information is subjected to normalization operations firstly, and then is combined to form a one-dimensional vector as the input of the decision-making network, see also claim 4).
Regarding claim 11 , Pease in view of Ma discloses a ship state estimation system of claim 1. Pease in view of Ma further discloses further comprising: a sensor configured to detect the direction of the ship and generate the direction signal (Pease [0049] The autopilot application 134 calculates the rudder commands 110 for all various specific maneuvers required in a voyage plan. The CPU 132 calculates the rudder commands 110 based on the autopilot application 134 instructions and based on input from the ship's motion sensors and the pilot-line parameters. The ship's motion sensors provide the ship's position and velocity and include a GPS 112, ship velocity sensor 114, ship angular position sensor 116, rudder position sensor 117, ship turn rate sensor 120, compass, speedlog, among others, see also Pease see also [0051]-[0055]; Ma [0048]-[0057]).
Regarding claim 19, the claim recites limitations analogous to those in claim 1. See the rejection of claim 1 above.
Regarding claim 20, the claim recites limitations are analogous to those in claim 1. See the rejection of claim 1 above.
As to [claim 21], Pease in view of Ma teaches the ship state estimation device of claim 1. As Pease discloses wherein the updated turning signal is fed back as the turning signal for the ship that is input (see, Pease Fig. 3 Pilot line Course, Pilot Line Turn Rate input into Autopilot App 134 [0052] Next, the offset 85, distance 86 and angular direction 87 of the ship's current location from the track line 81 are determined (156). Next, a track keeping application (track keeping App) uses the determined offset and a piecewise continuous navigation algorithm to compute the pilot line direction (Pc) and the pilot line turning rate (dP.sub.c/dt)”; and Pease Fig. 3 Generating/outputting Rudder Command 110 using autopilot App 134 based on Pilot Line Course and Pilot Line Turn Rate inputs) …
Ma further teaches … into one of: the Kalman filter which is a linear stochastic system; the extended Kalman filter which is a nonlinear stochastic system; the Unscented Kalman filter; the particle filter (see, Ma [0048]-[0049], [0050]-[0057] disclosing that rudder angle can be determined using inputs to a deep neural network) ; or the artificial intelligence.
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to estimate at least a rudder angle of the ship as taught by Ma. One would be motivated to make this modification in order to convey an efficient perception control method is designed based on the deep deterministic policy gradient (DDPG) algorithm, to improve utilization efficiency of the data perceived by the perception module, reduce exploration frequency of the perception device and reduce generation of junk data (See, Ma Paragraph [0062]).
Regarding claim 22, the claim recites limitations are analogous to those in claim 21. See the rejection of claim 22 above.
Claim(s) 3-4, 8, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pease in view of Ma, in view of Syed et al. (Pub. No.: US 2012/0245839; previously recorded), hereinafter, referred to as “Syed”.
Regarding claim 3, Pease in view of Ma teaches the ship state estimation device of claim 1. The combination of Pease and Ma further discloses wherein the processing circuitry is further configured to:
estimate the estimated rudder angle based…a state equation that estimates the estimated rudder angle based on that was estimated previously (Pease, Fig. 2-3, disclosing turnrate feedback re-applied to the autopilot app 134; Ma, see Fig. 1, including providing the output of the decision making model is fed back into the perceptron module; see, Ma Paragraph [0048]: "Specifically, the decision-making module fuses data perceived by all the sensors, analyzes current navigational environment of the MASS, makes a decision on the state of motion of the MASS according to the current navigational status of the MASS, and sends operating instructions of the propeller and the rudder to the execution module. [0049] The decision-making module inputs the navigational environment information of the MASS and the navigational state of the MASS into a trained deep neural network based on a deep deterministic policy gradient (DDPG) algorithm, and the deep neural network outputs vessel control instructions including vessel thrust information and rudder angle information."; and (See [0050]-[0057] for computation/analysis)
The combination of Pease and Ma does not explicitly disclose estimate the at least a rudder angle based on the Kalman filter including a state equation. However, Syed teaches methods for enhancing navigation in which multiple techniques can be applied, including both Kalman filter using a state equation and neural network (Syed [0173] The state estimation technique can be linear, nonlinear or a combination thereof. Different examples of techniques used in the navigation solution may rely on a Kalman filter, an Extended Kalman filter, a non-linear filter such as a particle filter, or an artificial intelligence technique such as Neural Network or Fuzzy systems. The state estimation technique used in the navigation solution can use any type of system and/or measurement models. The navigation solution may follow any scheme for integrating the different sensors and systems, such as for example loosely coupled integration scheme or tightly coupled integration scheme among others. The navigation solution may utilize modeling (whether with linear or nonlinear, short memory length or long memory length) and/or automatic calibration for the errors of inertial sensors and/or the other sensors used; [0191] disclosing a 21 states Kalman filter).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the Neural Network of Pease and Ma for the Kalman Filter of Syed. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 4, Pease in view of Ma and Syed teaches the ship state estimation device of claim 3. The combination of Pease, Ma and Syed further discloses wherein the processing circuitry is further configured to: estimate the at least a rudder angle based on … the state equation and a measurement equation which uses the direction signal (Pease, Fig. 2-3, disclosing turnrate feedback re-applied to the autopilot app 134; Ma, see Fig. 1, including providing the output of the decision making model is fed back into the perceptron module; see, Ma Paragraph [0048]: "Specifically, the decision-making module fuses data perceived by all the sensors, analyzes current navigational environment of the MASS, makes a decision on the state of motion of the MASS according to the current navigational status of the MASS, and sends operating instructions of the propeller and the rudder to the execution module. [0049] The decision-making module inputs the navigational environment information of the MASS and the navigational state of the MASS into a trained deep neural network based on a deep deterministic policy gradient (DDPG) algorithm, and the deep neural network outputs vessel control instructions including vessel thrust information and rudder angle information."; and (See [0050]-[0057] for computation/analysis,).
Syed further, as shown above with respect to claim 3, further teaches methods for enhancing navigation in which multiple techniques can be applied, including both a Kalman filter using a state equation and neural network (Syed [0173] The state estimation technique can be linear, nonlinear or a combination thereof. Different examples of techniques used in the navigation solution may rely on a Kalman filter, an Extended Kalman filter, a non-linear filter such as a particle filter, or an artificial intelligence technique such as Neural Network or Fuzzy systems. The state estimation technique used in the navigation solution can use any type of system and/or measurement models. The navigation solution may follow any scheme for integrating the different sensors and systems, such as for example loosely coupled integration scheme or tightly coupled integration scheme among others. The navigation solution may utilize modeling (whether with linear or nonlinear, short memory length or long memory length) and/or automatic calibration for the errors of inertial sensors and/or the other sensors used; [0191] disclosing a 21 states Kalman filter).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the Neural Network of Pease and Ma for the Kalman Filter of Syed. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 8, Pease in view of Ma discloses the ship state estimation device of claim 1. Pease in view of Ma discloses estimate the at least a rudder angle (see claim 1, Pease teachings, Fig. 2/3, [0048]-[0052]; Ma teachings, [0048]-[0057]). However, the combination of does not explicitly disclose making said estimate based on the linear stochastic system.
However, Syed teaches making an estimate based on the linear stochastic system (Syed [0173] The state estimation technique can be linear, nonlinear or a combination thereof. Different examples of techniques used in the navigation solution may rely on a Kalman filter, an Extended Kalman filter, a non-linear filter such as a particle filter, or an artificial intelligence technique such as Neural Network or Fuzzy systems. The state estimation technique used in the navigation solution can use any type of system and/or measurement models. The navigation solution may follow any scheme for integrating the different sensors and systems, such as for example loosely coupled integration scheme or tightly coupled integration scheme among others. The navigation solution may utilize modeling (whether with linear or nonlinear, short memory length or long memory length) and/or automatic calibration for the errors of inertial sensors and/or the other sensors used; [0174] As mentioned earlier, the embodiments of the present method can be combined with a mode of conveyance algorithm or a mode detection algorithm to establish the mode of conveyance.
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the Neural Network of Pease and Ma for the linear stochastic system of Syed. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 14, Pease in view of Ma and Syed teaches the ship state
estimation device of claim 13. Pease further discloses wherein the processing
circuitry is further configured to: estimate a heading of the ship (Pease [0048]:
“The ship 90 is shown moving along track line direction 98. A steering device or rudder
92 is attached to the stern 95 and is used to steer the ship. A steering system 60
transfers steering commands to the rudder servomechanism in order to rotate the
vessel 90 around the turning point 94 by an angle 89 and thereby to change the current
ship's direction 98 toward the direction of a pilot line 96 in order to approach a new track
line 81 as part of a voyage plan 80” and [0049]: “The CPU 132 calculates the rudder
commands 110 based on the autopilot application 134 instructions and based on input
from the ship's motion sensors and the pilot-line parameters. The ship's motion sensors
provide the ship's position and velocity and include a GPS 112, ship velocity sensor
114, ship angular position sensor 116, rudder position sensor 117, ship turn rate sensor
120”, see also [0051]-[0055]).
Regarding claim 15, the combination of Pease, Ma, and Syed teaches the ship state estimation device of claim 14. Pease further discloses wherein the processing circuitry is further configured to: estimate a turnrate of the ship (see at least Paragraph [0048]: “The ship 90 is shown moving along track line direction 98. A steering device or rudder 92 is attached to the stern 95 and is used to steer the ship. A steering system 60 transfers steering commands to the rudder servomechanism in order to rotate the vessel 90 around the turning point 94 by an angle 89 and thereby to change the current ship's direction 98 toward the direction of a pilot line 96 in order to approach a new track line 81 as part of a voyage plan 80” and [0049]: “The CPU 132 calculates the rudder commands 110 based on the autopilot application 134 instructions and based on input from the ship's motion sensors and the pilot-line parameters. The ship's motion sensors provide the ship's position and velocity and include a GPS 112, ship velocity sensor 114, ship angular position sensor 116, rudder position sensor 117, ship turn rate sensor 120”; see also [0051]-[0055]).
Regarding claim 16, the combination of Pease, Ma, and Syed disclose the ship state estimation device of claim 15. Pease in view of Ma and Syed further discloses (see claim 1, Pease Fig. 2-3, [0048]-[0055]; Ma teachings, [0048]-[0057]) estimated rudder angle based on the linear stochastic system (Syed [0173] The state estimation technique can be linear, nonlinear or a combination thereof. Different examples of techniques used in the navigation solution may rely on a Kalman filter, an Extended Kalman filter, a non-linear filter such as a particle filter, or an artificial intelligence technique such as Neural Network or Fuzzy systems. The state estimation technique used in the navigation solution can use any type of system and/or measurement models. The navigation solution may follow any scheme for integrating the different sensors and systems, such as for example loosely coupled integration scheme or tightly coupled integration scheme among others. The navigation solution may utilize modeling (whether with linear or nonlinear, short memory length or long memory length) and/or automatic calibration for the errors of inertial sensors and/or the other sensors used; [0174] As mentioned earlier, the embodiments of the present method can be combined with a mode of conveyance algorithm or a mode detection algorithm to establish the mode of conveyance.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.U./Examiner, Art Unit 3663
/JAMES M MCPHERSON/Examiner, Art Unit 3663
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