DETAILED ACTION
Response to Arguments
Applicant's arguments filed 12/11/2025 have been fully considered but they are not persuasive. With respect to the rejection under 35 U.S.C. 102 based on Kanamoto and the rejection of dependent claims under 35 U.S.C 103, the applicant states that the prior art does not disclose or suggest, “determine coefficients representing poles of an auto-regressive model based on the input radar signal and an order size” as recited by independent claim 1. The Examiner respectfully disagrees. Kanamoto discloses the use of the Burg method of autoregression [0176], which is an “all-pole” method. As it is an “all-pole” method, the transfer function contains no zeros. If the transfer function contains no zeros, coefficients in the prediction model must represent poles. The Examiner maintains that Kanamoto discloses the above limitation.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of claims 1-21 has been withdrawn.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-8, 13-15, and 19-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by KANAMOTO(US20120242535A1).
Regarding claim 1, Kanamoto discloses
A system, comprising: at least one processor (“a plurality of devices or processors” [0486]); and at least one non-transitory computer-readable medium storing machine instructions which (“may be recorded on a computer-readable recording medium and the programs recorded on the recording medium may be read and executed by a computer system” [0494]), when executed by the at least one processor, cause the at least one processor to: obtain an input radar signal (“a receiving unit including a plurality of antennas receiving a received wave arriving from a target having reflected a transmitted wave” [0015]); determine coefficients representing poles of an auto-regressive model based on the input radar signal (“The coefficients of an AR filter (AR coefficients) […] are calculated based on the normal equation “ and “an object is modeled using an AR model expressed by a linear equation and a normal equation […] which is a linear equation based on input data is created.” [0081]) and an order size (“In step Sa121, the normal equation creating unit 641 creates an M-th order normal equation to be applied to an AR model for each acquisition based on the complex data acquired in the present detection cycle.” [0258]); and extrapolate the input radar signal based on the auto-regressive model (“the data extending unit may be configured to generate the extended complex data based on the original complex data and the coefficient. “ [0017] & “By applying the original data of the above-mentioned groups to Equation (9), it is possible to acquire backward predicted data.” [0197]) and the determined coefficients to obtain an extrapolated signal (“the coefficient may be calculated based on a linear equation expressing an autoregressive model in a spectrum estimating method using the autoregressive model.” [0020]) determine direction of arrival data from radar signals reflected by one or more objects based on the extrapolated input radar signal (“calculate a direction of arrival of the received wave based on coefficients calculated” [0015]); and provide the direction of arrival data to one or more of a radar-camera-lidar fusion system or an automated driving assistance system of a vehicle (“detect a target using a reflected wave from a target in response to a transmitted wave and which can be suitably used for a vehicle” [0003]).
Regarding claim 5, Kanamoto discloses
The system of claim 1, wherein the machine instructions to determine coefficients for the auto-regressive model comprise machine instructions to use the Burg method to determine coefficients for the auto-regressive model (“it has been verified that it is possible to maintain stable estimation precision even using the method according to the first embodiment in addition to the Burg method based on real data” [0180]).
Regarding claim 6, Kanamoto discloses
The system of claim 1, wherein the at least one processor comprises a digital signal processor and a vector processor (“a plurality of devices or processors (FPGA, DSP, microcomputers) may be made to perform the operation from the data acquiring process.” [0486]), wherein the digital signal processor is configured to (“signal processing unit 20A “ [0094]) execute the machine instructions to determine coefficients for the auto-regressive model (“The coefficients of an AR filter (AR coefficients) and the variance values of input white noise are calculated based on the normal equation “ [0081]), and wherein the vector processor (“first computation processing unit ” [0137]). is configured to execute the machine instructions to extrapolate the input radar signal based on the auto-regressive model and the determined coefficients (“the coefficient may be calculated based on a linear equation expressing an autoregressive model in a spectrum estimating method using the autoregressive model.” [0020]).
Regarding claim 7, Kanamoto discloses
The system of claim 6, wherein the input radar signal comprises a number N of samples, wherein a number of M of extrapolated samples is represented as: M=4Nlog8N2-N2where the operator x2 represents the smallest number that is a power of two and larger than x, wherein the machine instructions to extrapolate the input radar signal based on the auto-regressive model and the determined coefficients comprise machine instructions to determine the number M of extrapolated samples (“Data of an area adjacent to the area of the existing original data is extended from the existing N pieces of original data through the use of a computational expression of Equations (6)” [0174]), wherein each extrapolated sample xn is represented as: xn=-xn-K:n-1⋅a1:Kwhere K is half of the number N of samples, a1:K represents the determined coefficients, and “⋅” indicates a dot product (Equ.6).
Regarding claim 8, Kanamoto discloses
A non-transitory computer-readable medium storing machine instructions which, when executed by at least one processor, cause the at least one processor to (“may be recorded on a computer-readable recording medium and the programs recorded on the recording medium may be read and executed by a computer system” [0494]): obtain an input radar signal (“a receiving unit including a plurality of antennas receiving a received wave arriving from a target having reflected a transmitted wave” [0015]); determine coefficients representing poles of an auto-regressive model based on the input radar signal (“The coefficients of an AR filter (AR coefficients) […] are calculated based on the normal equation “ and “ an object is modeled using an AR model expressed by a linear equation and a normal equation […] which is a linear equation based on input data is created.” [0081]) and an order size (“In step Sa121, the normal equation creating unit 641 creates an M-th order normal equation to be applied to an AR model for each acquisition based on the complex data acquired in the present detection cycle.” [0258]); extrapolate the input radar signal based on the auto-regressive model (“the data extending unit may be configured to generate the extended complex data based on the original complex data and the coefficient. “ [0017] & “By applying the original data of the above-mentioned groups to Equation (9), it is possible to acquire backward predicted data.” [0197]) and the determined coefficients to obtain an extrapolated signal (“the coefficient may be calculated based on a linear equation expressing an autoregressive model in a spectrum estimating method using the autoregressive model.” [0020]) determine direction of arrival data from radar signals reflected by one or more objects based on the extrapolated input radar signal (“calculate a direction of arrival of the received wave based on coefficients calculated” [0015]); and provide the direction of arrival data to one or more of a radar-camera-lidar fusion system or an automated driving assistance system of a vehicle (“detect a target using a reflected wave from a target in response to a transmitted wave and which can be suitably used for a vehicle” [0003]).
Regarding claim 13, Kanamoto discloses
The non-transitory computer-readable medium of claim 8, wherein the machine instructions to determine coefficients for the auto-regressive model (“The coefficients of an AR filter (AR coefficients) and the variance values of input white noise are calculated based on the normal equation “ [0081]) are configured to be executed by a digital signal processor (“signal processing unit 20A “ [0094]), and wherein the machine instructions to extrapolate the input radar signal based on the auto-regressive model and the determined coefficients (“the coefficient may be calculated based on a linear equation expressing an autoregressive model in a spectrum estimating method using the autoregressive model.” [0020]) are configured to be executed by a vector processor (“first computation processing unit ” [0137]).
Regarding claim 14, Kanamoto discloses
The non-transitory computer-readable medium of claim 13, wherein the input radar signal comprises a number N of samples, wherein a number of M of extrapolated samples is represented as: M=4Nlog8N2-N2where the operator x2 represents the smallest number that is a power of two and larger than x, wherein the machine instructions to extrapolate the input radar signal based on the auto-regressive model and the determined coefficients comprise machine instructions to determine the number M of extrapolated samples (“Data of an area adjacent to the area of the existing original data is extended from the existing N pieces of original data through the use of a computational expression of Equations (6)” [0174]), wherein each extrapolated sample xn is represented as: xn=-xn-K:n-1⋅a1:Kwhere K is half of the number N of samples and a1:K represents the determined coefficients (Equ.6).
Regarding claim 15, Kanamoto discloses
A method, comprising: obtaining, by at least one processor, an input radar signal (“a receiving unit including a plurality of antennas receiving a received wave arriving from a target having reflected a transmitted wave” [0015]); determining, by at least one processor, coefficients representing poles of an auto-regressive model based on the input radar signal (“The coefficients of an AR filter (AR coefficients) […] are calculated based on the normal equation “ and “ an object is modeled using an AR model expressed by a linear equation and a normal equation […] which is a linear equation based on input data is created.” [0081]) and an order size (“In step Sa121, the normal equation creating unit 641 creates an M-th order normal equation to be applied to an AR model for each acquisition based on the complex data acquired in the present detection cycle.” [0258]); extrapolating, by at least one processor, the input radar signal based on the auto-regressive model (“the data extending unit may be configured to generate the extended complex data based on the original complex data and the coefficient. “ [0017] & “By applying the original data of the above-mentioned groups to Equation (9), it is possible to acquire backward predicted data.” [0197]) and the determined coefficients to obtain an extrapolated signal (“the coefficient may be calculated based on a linear equation expressing an autoregressive model in a spectrum estimating method using the autoregressive model.” [0020]), and determining direction of arrival data from radar signals reflected by one or more objects based on the extrapolated input radar signal (“calculate a direction of arrival of the received wave based on coefficients calculated” [0015]); and providing the direction of arrival data to one or more of a radar-camera-lidar fusion system or an automated driving assistance system of a vehicle (“detect a target using a reflected wave from a target in response to a transmitted wave and which can be suitably used for a vehicle” [0003]).
Regarding claim 19, Kanamoto discloses
The method of claim 15, wherein the input radar signal comprises a number N of samples and the order size is approximately half the number N (“it has been verified that it is possible to maintain stable estimation precision even using the method according to the first embodiment in addition to the Burg method based on real data” [0180]).
Regarding claim 20, Kanamoto discloses
The method of claim 15, wherein: determining coefficients for the auto-regressive model comprises determining, by a digital signal processor (“signal processing unit 20A “ [0094]), the coefficients for the auto-regressive mode model (“The coefficients of an AR filter (AR coefficients) and the variance values of input white noise are calculated based on the normal equation “ [0081])l; and extrapolating the input radar signal based on the auto-regressive model and the determined coefficients comprises extrapolating (“the coefficient may be calculated based on a linear equation expressing an autoregressive model in a spectrum estimating method using the autoregressive model.” [0020]), by a vector processor (“first computation processing unit ” [0137]), the input radar signal based on the auto-regressive model and the determined coefficients (“the coefficient may be calculated based on a linear equation expressing an autoregressive model in a spectrum estimating method using the autoregressive model.” [0020]).
Regarding claim 21, Kanamoto discloses
The method of claim 20, wherein the input radar signal comprises a number N of samples, wherein a number of M of extrapolated samples is represented as: M=4Nlog8N2-N2where the operator x2 represents the smallest number that is a power of two and larger than x, wherein extrapolating the input radar signal based on the auto-regressive model and the determined coefficients comprises determining the number M of extrapolated samples (“Data of an area adjacent to the area of the existing original data is extended from the existing N pieces of original data through the use of a computational expression of Equations (6)” [0174]), wherein each extrapolated sample xn is represented as: xn=-xn-K:n-1⋅a1:Kwhere K is half of the number N of samples and a1:K represents the determined coefficients (Equ.6).
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 2, 3, 9, 10, 12, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over KANAMOTO(US20120242535A1) in view of KAY(NPL: Spectrum Analysis-A Modern Perspective).
Regarding claim 2, KANAMOTO discloses all of the limitations of claim 1. KANAMOTO fails to set forth the order size being half the sample size. KAY discloses the system wherein, the input radar signal comprises a number N of samples and the order size is approximately half the number N (“due to the spurious peak problem, one should limit the maximum model order to no more than one half the number of data points “ [Pg.19, Par.3]).
KAY teaches in the same field of endeavor of auto-regression. 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 KANAMOTO with the teachings of KAY to incorporate the features of the order size being half the sample size so as to gain the advantage of reducing noise [Pg.9, Col.2, Par.2, KAY]. 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, KANAMOTO as modified by KAY discloses all of the limitations of claim 2. KANAMOTO discloses the system wherein, the machine instructions to determine coefficients for the auto-regressive model comprise machine instructions to determine coefficients for the auto-regressive model based on the number N of samples (“In a DBF or a high-resolution algorithm of estimating an azimuth, it is known that as the number of channels which can be acquired from detected information increases, the order of a matrix or a normal equation as a processing source is increased or the precision of elements of the matrix becomes further improved, thereby enhancing the azimuth estimation precision” [0011]); and the machine instructions to extrapolate the input radar signal comprise machine instructions to: extrapolate a right-side signal based on the auto-regressive model, the determined coefficients, and a second half of the number N of samples (“The principal component AR spectrum estimating unit 620 (FIG. 7) creates a first covariance matrix and a first right-hand vector based on the extended complex data. “ [0210]); extrapolate a left-side signal based on the auto-regressive model, complex conjugates of the determined coefficients, and a first half of the number N of samples (“a*-hat (i) represents a complex conjugate of an AR coefficient” [0195]); and generate the extrapolated signal based on the left-side signal, the radar input signal, and the right-side signal (“By applying the original data of the above-mentioned groups to Equation (9), it is possible to acquire backward predicted data.” [0197]).
Regarding claim 9, KANAMOTO discloses all of the limitations of claim 8. KANAMOTO fails to set forth the order size being half the sample size. KAY discloses the system wherein, the input radar signal comprises a number N of samples and the order size is approximately half the number N (“due to the spurious peak problem, one should limit the maximum model order to no more than one half the number of data points “ [Pg.19, Par.3]).
KAY teaches in the same field of endeavor of spectral analysis. 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 KANAMOTO with the teachings of KAY to incorporate the features of the order size being half the sample size so as to gain the advantage of reducing noise [Pg.9, Col.2, Par.2, KAY]. 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 10, KANAMOTO as modified by KAY discloses all of the limitations of claim 9. KANAMOTO discloses the system wherein, the machine instructions to determine coefficients for the auto-regressive model comprise machine instructions to determine coefficients for the auto-regressive model based on the number N of samples (“In a DBF or a high-resolution algorithm of estimating an azimuth, it is known that as the number of channels which can be acquired from detected information increases, the order of a matrix or a normal equation as a processing source is increased or the precision of elements of the matrix becomes further improved, thereby enhancing the azimuth estimation precision” [0011]); and the machine instructions to extrapolate the input radar signal comprise machine instructions to: extrapolate a right-side signal based on the auto-regressive model, the determined coefficients, and a second half of the number N of samples (“The principal component AR spectrum estimating unit 620 (FIG. 7) creates a first covariance matrix and a first right-hand vector based on the extended complex data. “ [0210]); extrapolate a left-side signal based on the auto-regressive model, complex conjugates of the determined coefficients, and a first half of the number N of samples (“a*-hat (i) represents a complex conjugate of an AR coefficient” [0195]); and generate the extrapolated signal based on the left-side signal, the radar input signal, and the right-side signal (“By applying the original data of the above-mentioned groups to Equation (9), it is possible to acquire backward predicted data.” [0197]).
Regarding claim 12, KANAMOTO as modified by KAY discloses all of the limitations of claim 18. KANAMOTO discloses the method wherein, wherein the machine instructions to determine coefficients for the auto-regressive model comprise machine instructions to use the Burg method to determine coefficients for the auto-regressive model (“it has been verified that it is possible to maintain stable estimation precision even using the method according to the first embodiment in addition to the Burg method based on real data” [0180]).
Regarding claim 16, KANAMOTO discloses all of the limitations of claim 15. KANAMOTO fails to set forth the order size being half the sample size. KAY discloses the method wherein, the input radar signal comprises a number N of samples and the order size is approximately half the number N (“due to the spurious peak problem, one should limit the maximum model order to no more than one half the number of data points “ [Pg.19, Par.3]).
KAY teaches in the same field of endeavor of spectral analysis. 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 KANAMOTO with the teachings of KAY to incorporate the features of the order size being half the sample size so as to gain the advantage of reducing noise [Pg.9, Col.2, Par.2, KAY]. 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 17, KANAMOTO as modified by KAY discloses all of the limitations of claim 16. KANAMOTO discloses the method wherein, determining coefficients for the auto-regressive model comprises determining coefficients for the auto-regressive model based on the number N of samples (“In a DBF or a high-resolution algorithm of estimating an azimuth, it is known that as the number of channels which can be acquired from detected information increases, the order of a matrix or a normal equation as a processing source is increased or the precision of elements of the matrix becomes further improved, thereby enhancing the azimuth estimation precision” [0011]); and extrapolating the input radar signal comprises: extrapolating a right-side signal based on the auto-regressive model, the determined coefficients, and a second half of the number N of samples (“The principal component AR spectrum estimating unit 620 (FIG. 7) creates a first covariance matrix and a first right-hand vector based on the extended complex data. “ [0210]); extrapolating a left-side signal based on the auto-regressive model, complex conjugates of the determined coefficients, and a first half of the number N of samples (“a*-hat (i) represents a complex conjugate of an AR coefficient” [0195]); and generating the extrapolated signal based on the left-side signal, the radar input signal, and the right-side signal (“By applying the original data of the above-mentioned groups to Equation (9), it is possible to acquire backward predicted data.” [0197]).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over KANAMOTO(US20120242535A1) as modified by KAY(NPL: Spectrum Analysis-A Modern Perspective) in view of Helfenstein(US6584486B1).
Regarding claim 4, KANAMOTO as modified by KAY discloses all of the limitations of claim 2. KANAMOTO as modified by KAY fails to set forth IIR filtering. Helfenstein discloses the system wherein, the machine instructions to extrapolate the input radar signal based on the auto-regressive model and the determined coefficients comprise machine instructions to: convolve the determined coefficients and a second half of the number N of samples to obtain an initial condition for an infinite impulse response (IIR) filter (“The feedback path often contains a suitable linear filter, which convolves its input with the filter coefficients”[Col.7, ll.13-15]); load the IIR filter with the initial condition (“the output of which can be written as a convolution of delayed input samples with the coefficients of the impulse response.” [Col.6, ll.37-39]); and use the IIR filter to extrapolate the input radar signal (“the described technique can also be used to implement IIR filters with an infinite recursive impulse response.” [Col.6, ll.39-41]).
Helfenstein teaches in the same field of signal processing. 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 KANAMOTO as modified by KAY with the teachings of Helfenstein to incorporate the features of IIR filtering so as to gain the advantage of improving the error rate [Col.7, ll.8-11, Helfenstein]. 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 11, KANAMOTO as modified by KAY discloses all of the limitations of claim 9. KANAMOTO as modified by KAY fails to set forth the IIR filtering. Helfenstein discloses the system wherein, the machine instructions to extrapolate the input radar signal based on the auto-regressive model and the determined coefficients comprise machine instructions to: convolve the determined coefficients and a second half of the number N of samples to obtain an initial condition for an infinite impulse response (IIR) filter (“The feedback path often contains a suitable linear filter, which convolves its input with the filter coefficients”[Col.7, ll.13-15]); load the IIR filter with the initial condition (“the output of which can be written as a convolution of delayed input samples with the coefficients of the impulse response.” [Col.6, ll.37-39]); and use the IIR filter to extrapolate the input radar signal (“the described technique can also be used to implement IIR filters with an infinite recursive impulse response.” [Col.6, ll.39-41]).
Helfenstein teaches in the same field of signal processing. 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 KANAMOTO as modified by KAY with the teachings of Helfenstein to incorporate the features of IIR filtering so as to gain the advantage of improving the error rate [Col.7, ll.8-11, Helfenstein]. 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 18, KANAMOTO as modified by KAY discloses all of the limitations of claim 16. KANAMOTO as modified by KAY fails to set forth IIR filtering. Helfenstein discloses the system wherein, extrapolating the input radar signal based on the auto-regressive model and the determined coefficients comprises: convolving the determined coefficients and a second half of the number N of samples to obtain an initial condition for an infinite impulse response (IIR) filter (“The feedback path often contains a suitable linear filter, which convolves its input with the filter coefficients”[Col.7, ll.13-15]); loading the IIR filter with the initial condition (“the output of which can be written as a convolution of delayed input samples with the coefficients of the impulse response.” [Col.6, ll.37-39]); and using the IIR filter to extrapolate the input radar signal (“the described technique can also be used to implement IIR filters with an infinite recursive impulse response.” [Col.6, ll.39-41]).
Helfenstein teaches in the same field of signal processing. 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 KANAMOTO as modified by KAY with the teachings of Helfenstein to incorporate the features of IIR filtering so as to gain the advantage of improving the error rate [Col.7, ll.8-11, Helfenstein]. 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