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 second Non-Final Office action in response to the communication received 11/21/2025. Based on the received Response after Final Action, Examiner has a clearer understanding of intended claim scope. As this understanding effects the determination of allowable subject matter, a second Non-Final Office action has been drafted based on the last set of entered claims, dated 3/3/2025.
Claim(s) 1-20 have been examined and fully considered.
Claim(s) 1-19 have been amended.
Claim(s) 1-20 are pending in Instant Application.
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, 2, 5-7, 9-11, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pease (US 20120130570), 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: (Pease 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; and estimate at least a rudder angle of the ship by inputting the direction signal… that outputs at least the rudder angle of the ship when the direction signal is input (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)
control a rudder of the ship based on the rudder angle (Pease Fig. 2/3, [0049] The CPU 132 calculates the rudder commands 110 based on the autopilot application 134)
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, 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)
…
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 2, Pease in view of Ma discloses the ship state estimation device of claim 1. The combination of Pease and Ma further discloses the processing circuitry is further configured to: input a turning signal for the ship (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 Fig. 2-3) 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; and estimate the ship state at least a rudder angle based on the direction signal and the turning signal (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)
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 ship state 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 9, Pease in view of Ma discloses the ship state estimation device of claim 1. The combination of Pease and Ma further discloses acquire a command rudder angle relative to the ship (Pease [0014]: “a controller configured to control a rudder angle of the ship based on positional information of the ship …. The controller controls the rudder angle of the ship based also on the first output value outputted from the estimator of the ship characteristic estimating device”); and generate a turning signal based on the command rudder angle and the at least a rudder angle that was estimated (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 Ma, Fig. 1 where decisions are fed back into the perception module; [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), EXAMINER NOTE: See also feedback process (ship course and turnrate) in Fig. 3.; see also [0051]-[0055] continuous correction process).
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
Claim(s) 3-4, 8, and 12-18 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 at least a rudder angle based on … a state equation that estimates the at least a rudder angle based on the at least a rudder angle 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 claims 12 and 13, the claims recite limitations analogous to those presented in claims 3 and 4, therefore claims 12 and 13 would be rejected for the same/similar premise presented in conjunction with claims 3 and 4 above.
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 estimate the at least a rudder angle (see claim 1, Pease Fig. 2-3, [0048]-[0055]; Ma teachings, [0048]-[0057]) based on the linear stochastic system linear 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.).
Regarding claim 17, the combination of Pease, Ma, and Syed disclose the ship state estimation device of claim 16. The combination of Pease, Ma, and Syed further disclose acquire a command rudder angle relative to the ship (Pease [0014]: “a controller configured to control a rudder angle of the ship based on positional information of the ship …. The controller controls the rudder angle of the ship based also on the first output value outputted from the estimator of the ship characteristic estimating device”, Fig. 2-3, 6); and generate a turning signal based on the command rudder angle and the at least a rudder angle that was estimated (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 Pease [0051]-[0055]; Ma, Fig. 1 where decisions are fed back into the perception module).
Regarding claim 18, the combination of Kawasaki, Ma, Syed, Pease and Shroff disclose the ship state estimation device of claim 17. Claim 10 recites limitations analogous to those in claim 10. See the rejection of claim 10 above.
Conclusion
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/B.U./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663