DETAILED ACTION
Response to Arguments
Applicant's arguments filed 10/20/2025 have been fully considered. With respect to the rejection under 35 U.S.C. 102 and 103, Applicant's arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
With respect to the rejection under 35 U.S.C. 101 Applicants arguments are persuasive. The rejection under 35 U.S.C. 101 regarding claims 1-9 has been withdrawn.
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.
Claims 1, 3-9, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over HIRAI(US20180095103A1) in view of ZHOU(CN114882070A).
Regarding claim 1, HIRAI Discloses
A method for tracking a moving target, executed by a three- dimensional detection radar (EQUs. 7, 8, and “ a travel azimuth “[0011]) and a processor (“ a general-purpose processor” [0115]), […], the method comprising: step 1: constructing by the processor, a state equation (“ state equation” [0067]) and an observation equation based on three-dimensional radar observation data (“ observation equation” [0067]) comprising: for the moving target with a constant velocity, constructing the state equation and the observation equation in a transformation state space; […], A, B, U and H respectively represent the time-varying state-transition matrix (“ state transition of the covariance matrix” [0070]), the time-varying noise-driven matrix (“system noise covariance matrix” [0070]) a process noise (“w.sub.k is a system noise with zero mean “ [0068]), and an observation matrix (“ H.sub.k is an observation model” [0068]); where U(k) respectively represent a process noise in a meridional direction, a process noise in a radial direction, and a process noise in an azimuthal direction (“w.sub.k is a system noise with zero mean “ [0068]); Z(k) represents a radar observation value interfered by noises at the moment k, including a distance, an azimuth, and a pitch angle of the moving target relative to the three-dimensional detection radar under interferences of the noises at the moment k (Equation 8); w(k) represents an observation noise of the three-dimensional detection radar at the moment k where
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respectively represent a pitch angular noise, a distance noise, and an azimuth noise (“v.sub.k is an observation noise” [0068]);
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are all white Gaussian noises with a mean value of zero (“noise with zero mean” [0068]); variances of the pitch angular noise, the distance noise, and the azimuth noise respectively are
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(“FIG. 9 illustrates changes in error variances in the vehicle velocity in a covariance matrix estimate” [0086]) and a noise covariance matrix is expressed as follows:
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(“the system noise covariance matrix Q.sub.k “ [0070]) step 2: initializing by the processor a transforming state of the moving target to obtain an initial state of the moving target when k=1 (FIG.11, S101); step 3: calculating by the processor the time-varying state-transition matrix (“ state transition of the covariance matrix is calculated from a previous covariance matrix P.sub.k-1|k-1 and the time transition model F.sub.k thereof “ [0070]), the time-varying noise-driven matrix, and statistical characteristics of the process noise at a moment k based on the initial state of the moving target when k=2 (“an increase in the system noise is calculated from the system noise covariance matrix Q.sub.k and the time transition model G.sub.k thereof” [0070]); and calculating by the processor time-varying state-transition matrix, the time-varying noise-driven matrix, and statistical characteristics of a process noise at the moment k based on a posteriori estimation of the moving target at a moment k-1 when k>2 (“a subsequent state x̂.sub.k|k-1 is predicted from a previous estimate x̂.sub.k|k-1 with use of the time transition model F.sub.k.” [0070]); step 4: performing, by the processor, based on the state equation at the moment k, one-step prediction on a state of the moving target at the moment k, thereby to obtain a prediction state of the moving target at the moment k (“ The predicted estimate from equation (9) and the predicted error covariance matrix from equation (10) are made into output of the prediction step” [0070); step 5: acquiring, by the three dimensional detection radar, observation data at the moment k of a three-dimensional detection radar (“In step S103, the target data is updated with the target data having higher association treated for the same target and with the target data having lower association treated for other targets.” [0104]), and performing by the processor dimension-expansion processing on the acquired observation data at the moment k of the three-dimensional detection radar, thereby to obtain dimension-expansion data (FIG.11, S101); step 6: performing by the processor fusion filtering on the prediction state of the moving target at the moment k and the dimension-expansion data based on a minimum variance estimation theory, thereby to obtain a posteriori estimation of the moving target at the moment k (“Calculates a state of a moving object through so-called sensor fusion based on target information from a first sensor unit 3 that will be described later and state information from a second sensor unit “ [0021]); and step 7: progressing by the processor and three-dimensional detection radar, the moment k to a moment k+1 for further tracking of the moving target (FIG.11, S105) […] and navigating, monitoring, controlling or attacking, by the processor (“FIG. 11 is a flow chart illustrating processing in an object tracking unit” [0019]).
HIRAI does not appear to explicitly disclose a cartesian position or azimuth as factors in the state equation. ZHOU discloses wherein, wherein the three-dimensional detection radar is stationary and fixed in a preset position (FIG.2), […]the state equation and the observation equation being expressed as follows:
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(EQU. 1 & 8) where
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represents a state vector constructed directly from the three-dimensional radar observation data;
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respectively represent a pitch angle, a pitch angular velocity, a distance, a Doppler velocity, an azimuth, and an azimuth angular velocity of the moving target relative to the three-dimensional detection radar at the moment k (EQU.1 & 7) […] [Observation matrix not reproduced] (EQU.9 & 10) […] step 8: transforming, by the processor, the posteriori estimation of the moving target at the moment kto a three-dimensional Cartesian coordinate system to obtain positions of the moving target in X, Y, and Z axes in the three-dimensional Cartesian coordinate system (FIG.1) and velocities of the moving target in the X, Y, and Z axes in the three-dimensional Cartesian coordinate system (“the three-dimensional coordinates of the target A detected in each frame need to be transformed to the observation coordinate system” [n0010]), […] and based on the positions of the moving target in X, Y, and Z axes in the three-dimensional Cartesian coordinate system and velocities of the moving target in the X, Y, and Z axes in the three-dimensional Cartesian coordinate system, the moving target (EQU.7), wherein the transforming, by the processor, the posteriori estimation of the moving target at the moment k to a three-dimensional Cartesian coordinate system to obtain positions of the moving target in X, Y, and Z axes in the three-dimensional Cartesian coordinate system (“the three-dimensional coordinates, azimuth, and pitch angles of the dynamic target detected in the (k-1)th frame and the kth frame” [n0044]) and velocities of the moving target in the X, Y, and Z axes in the three-dimensional Cartesian coordinate system comprises: [equations not reproduced] (“three-dimensional constant rotational speed and constant velocity model “ [n0012]) where ((k, k) represents the posteriori state estimation of the moving target at the moment k, S" (k, k) represents an element in row n and column m in ((k,k), a represents a variance of the pitch angle, and c- represents a variance of the azimuth; x(k), y(k), and z(k)respectively represent the positions of the moving target in the X, Y, and Z axes in the three-dimensional Cartesian coordinate system(EQUs. 1 & 6); and x'(k), y'(k), and z'(k)respectively represent the velocities of the moving target in X, Y, and Z axes in the three-dimensional Cartesian coordinate system (EQUs. 6 and 7).
ZHOU teaches in the same field of endeavor of radar tracking systems. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify HIRAI with the teachings of ZHOU to incorporate the features using cartesian positions and velocities to track a target so as to gain the advantage of improving motion estimation ([n0002], ZHOU). Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Regarding claim 3, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI discloses the method wherein, the initializing, by the processor the transforming state of the moving target to obtain the initial state of the moving target when k=1 comprises: for the moving target, initializing by the processor a state and a covariance of the moving target based on prior information of the moving target in the three dimensional Cartesian coordinate system assuming [equations not reproduced] (“the object tracking unit 15c converts the target information into a vehicle coordinate system based on the target information obtained from a radar at time k, subject vehicle state estimates obtained from the filter unit 15b at the time k, and radar installation position information” [0103]) where
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are all Gaussian distributions that follow a mean value of zero(“The stationary object curve theoretically refers to a curve along distribution of samples that are observed on the power map of azimuth θ-Doppler velocity V even if the vehicle M travels relative to a stationary object” [0046]); and variances of
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are respectively
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(“The error variances in the vehicle velocity and the yaw rate, the yaw rate ω.sub.V and the travel velocity V.sub.V that are outputted from the travel estimation unit “ [0059]).
Regarding claim 4, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI Discloses the method wherein, the performing, by the processor based on the state equation at the moment k, the one-step prediction on the state of the moving target at the moment k, thereby to obtain the prediction state of the moving target at the moment k comprises: using by the processor the state equation at the moment k in the transformation state space to perform the one-step prediction on the state and a variance at the moment k, and equations of performing the one-prediction (“Estimation based on the Kalman filter includes a prediction step and an observation update step. “ [0069]) being expressed as follows:
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(“In the calculation of the predicted estimate, a subsequent state x̂.sub.k|k-1 is predicted from a previous estimate x̂.sub.k|k-1 with use of the time transition model F.sub.k” [0070]) where
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respectively represent the prediction state of the moving target at the moment k and the prediction variance of the moving target at the moment k, (“(10) represents calculation of a predicted error variance matrix.” [0070]) and D(u(k) represents a process noise covariance matrix at the moment k (“n increase in the system noise is calculated from the system noise covariance matrix Q.sub.k” [0070]).
Regarding claim 5, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI Discloses the method wherein, the acquiring, by the three-dimensional detection radar, the observation data at the moment k of the three-dimensional detection radar, (“Data measured by the first sensor unit 3 and the second sensor unit 5 is used for Z.sub.k and R.sub.k. By substitution of above values into the algorithm of the Kalman filter, x.sub.k (that is, the velocity v and the yaw rate ω) is estimated.” [0078]) and performing by the processor the dimension-expansion processing on the acquired observation data at the moment k of the three-dimensional detection radar (FIG.11, S101), thereby to obtain the dimension-expansion data comprises: acquiring, by the three dimensional detection radar the observation data at the moment k of the three-dimensional detection radar (“The input unit 11 outputs the received state information to the control unit” [0030]), performing by the processor the dimension-expansion processing on a radar observation value interfered by noises and the noise covariance matrix at the moment k (FIG.11, S101), and equations of performing the dimension-expansion processing being expressed as follows:
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Where E represents a dimension-expansion matrix, Ze(k) represents a dimension-expansion vector, and Re(k) represents a covariance matrix of the dimension-expansion vector (“ converts the target information into a vehicle coordinate system based on the target information obtained from a radar at time k, subject vehicle state estimates obtained from the filter unit 15b at the time k, and radar installation position information.” [0103]).
Regarding claim 6, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI Discloses the method wherein, the performing by the processor the fusion filtering on the prediction state at the moment k and the dimension-expansion data based on the minimum variance estimation theory (“the state calculation apparatus 1 calculates a state of a moving object through so-called sensor fusion based on target information from a first sensor unit 3” [0021]), thereby to obtain the posteriori estimation of the moving target at the moment k (“ the error variance P posterior to the weighted averaging is smaller than the error variances P.sub.1 and P.sub.2 that are input values “ [0094])comprises: performing by the processor the fusion filtering on the prediction state of the moving target at the moment k and the dimension-expansion data based on the minimum variance estimation theory (“the state calculation apparatus 1 calculates a state of a moving object through so-called sensor fusion based on target information from a first sensor unit 3” [0021]), thereby to obtain a posteriori state estimation and a posteriori state covariance of the moving target at the moment k, and equations of performing the fusion filtering (“ the error variance P posterior to the weighted averaging is smaller than the error variances P.sub.1 and P.sub.2 that are input values “ [0094])being expressed as follows:
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(“A covariance S.sub.k in the observation residual to be calculated from equation (12) is found from a covariance in a measurement and a covariance in the predicted value.” [0071]).
Regarding claim 7, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI Discloses the method wherein, the calculating by the processor the time-varying state-transition matrix(“ state transition of the covariance matrix is calculated from a previous covariance matrix P.sub.k-1|k-1 and the time transition model F.sub.k thereof “ [0070]), the time-varying noise-driven matrix, and the statistical characteristics of the process noise at the moment k based on the initial state of the moving target when k=2 (“an increase in the system noise is calculated from the system noise covariance matrix Q.sub.k and the time transition model G.sub.k thereof” [0070]); and calculating by the processor the time-varying state-transition matrix, the time-varying noise-driven matrix, and the statistical characteristics of the process noise at the moment k based on the posteriori estimation of the moving target at the moment k-1 when k>2 (“a subsequent state x̂.sub.k|k-1 is predicted from a previous estimate x̂.sub.k|k-1 with use of the time transition model F.sub.k.” [0070]): for the moving target with a constant velocity, using by the processor following equations to calculate parameters of the state equation and the observation equation at the moment k based on the posteriori estimation of the moving target at the moment k-1[equation not reproduced] (“Subsequently, the variables x.sub.k, F.sub.k, and G.sub.k that are used in the Kalman filter will be described.” [0073] & [0074],[0075], [0076] & [0077]) where, T represents a radar sampling interval time (“Δt represents an interval between time k and time k−1 “ [0077]);
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And
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respectively represent posteriori estimations of a distance, a Doppler velocity, an azimuth, and a pitch angle at the moment k-1 (“generate tracking information by following the target information over a plurality of frames, such as positions, distances, travel velocities, and travel direction of the target that are observed by the first sensor unit “ [0101]); q represents a process noise in the three dimensional Cartesian coordinate system (“G.sub.k is a time transition model of system noise, “ [0068]), and q_x, q_y, q_z respectively represent white Gaussian noise variances in X, Y, and Z axes (“noise with zero mean” [0068]); G(k)represents a process noise transition matrix at the moment k (“ w.sub.k is a system noise[0068])”; and D(U(k))represents a process noise covariance matrix at the moment k (“ covariance matrix Q.sub.k” [0068]).
Regarding claim 8, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI Discloses the system wherein, the system comprises: a memory (“ a working memory” [0031]), configured to store a computer program (“a program memory,” [0031]); and the processor (“ a microcomputer” [0031]), configured to execute the computer program to implement the method as claimed in claim 1 (“the microcomputer executes the programs.” [0033]).
Regarding claim 9, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI Discloses,
A non-transitory computer-readable storage medium storing instructions, wherein the method as claimed in claim 1 is executed when the instructions are executed by the processor (“The microcomputer executes the programs by using the working memory “ [0034]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over HIRAI as modified by ZHOU for containing the same subject matter as claims 1, 4, and 6 above, and is therefore rejected for the same teachings.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over HIRAI as modified by ZHOU for the same reasons as claims 1 above.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over HIRAI(US20180095103A1) as modified by ZHOU(CN114882070A) as applied in claim 1 above, and in further view of Mookerjee(US7180443B1)
Regarding claim 10, HIRAI as modified by ZHOU discloses all the limitations of claim 1. HIRAI Discloses the method further comprising, navigating, monitoring, controlling or attacking (FIG.10 , Parts 15c & 15e) […] by the processor (“ a general-purpose processor” [0115]).
HIRAI does not appear to explicitly disclose a cartesian position or azimuth as factors in the state equation. ZHOU discloses wherein, based on the positions of the moving target in X, Y, and Z axes in the three-dimensional Cartesian coordinate system (“the three-dimensional coordinates of the target A detected in each frame need to be transformed to the observation coordinate system” [n0010]and velocities of the moving target in the X, Y, and Z axes in the three-dimensional Cartesian coordinate system (EQUs. 6 & 7).
ZHOU teaches in the same field of endeavor of radar tracking systems. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify HIRAI with the teachings of ZHOU to incorporate the features using cartesian positions and velocities to track a target so as to gain the advantage of improving motion estimation ([n0002], ZHOU). Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Further, HIRAI as modified by ZHOU does not appear to explicitly disclose that the moving target is an aircraft. Mookerjee discloses the State estimation of a system having multidimensional parameters wherein the target of observation is an aircraft ([Col.8, ll.1-5] & FIG.1)
Mookerjee teaches in the same field of endeavor of radar tracking systems. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify HIRAI as modified by ZHOU with the teachings of Mookerjee to incorporate the features of tracking an aircraft so as to gain the advantage of improving motion estimation ([n0002], ZHOU). Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLAYTON PAUL RIDDER whose telephone number is (571)272-2771. The examiner can normally be reached Monday thru Friday ET.
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/C.P.R./Examiner, Art Unit 3646
/JACK W KEITH/Supervisory Patent Examiner, Art Unit 3646