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 .
DETAILED ACTION
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/11/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive. In regards to the applicant’s arguments, see applicant’s remarks pages 5-7, the applicant argues that the previously presented prior arts do not teach the amended limitations. Specifically, the applicant cites Moshchuk, which teaches “sensors having different data collection rates”, but argues that Moshchuk does not teach the newly introduced limitation “wherein due to the fusion the yaw rate does not need adjusted to the second period duration” (see applicant’s remarks, page 6).
The examiner respectfully disagrees. As acknowledged by the applicant, Moshchuk teaches sensors having different data collection rates, specifically, “one or more vehicle motion sensor(s)”, which obtain “sensed vehicle motion data”, including a “sensed yaw rate” and may be “refreshed every 10 ms”, and a “camera”, which outputs “video-based position data” analyzed to obtain positional data of the vehicle including an “angle of the vehicle” and “may be refreshed every 100 ms , and may have a latency of about 200 ms” (Moshchuk, Para. 0015-0020). Moshchuk further teaches a method of “fusing the latent video-based position data 20 with the positional data estimated from the vehicle motion sensors 16” using “Kalman filtering techniques”, wherein if “new video-based position data 20 is available”, a processor “roll back the dead reckoned position computations”, or predictions, “a number of steps that may be proportional as to the latency and the update speed of the vehicle motion sensors 16” such that “the latent video frame may be synchronized in time with the position calculation at the time the video frame was acquired” and the processor may then “fuse/update the deduced movement/position with the newly acquired video-based position data” and “re-step forward up to the real-time data… until the next video-based position information arrives”, such that the method of Moshchuk accounts for the different refresh rates and latencies by performing “dead reckoning”, or predicting, and corrections accounting for “drift” separately (Moshchuk, Para. 0004-0005, 0018-0026), such that Moshchuk teaches the limitation “wherein due to the fusion the yaw rate does not need adjusted to the second period duration”.
Claim Objections
Claims 1, 8, and 9 are objected to because of the following informalities: The amended limitation appearing in claims 1, 8, and 9 reading “…wherein due to the fusion the yaw rate does not need adjusted to the second period duration” appears to contain a grammatical error, and should instead recite “…wherein due to the fusion the yaw rate does not need to be adjusted to the second period duration”. Appropriate correction is required.
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.
Claim(s) 1-2 and 5-11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhi, et al., hereinafter Zhi (U.S. Patent Application Pub. No. 2016/0290810) in view of Radwan (German Patent App. Pub. DE 10 2018 123 092 A1), and further in view of Moshchuk, et al., hereinafter Moshchuk (U.S. Patent Application Pub. No. 2013/0116854).
Regarding Claim 1, Zhi teaches: A method for calibrating a yaw rate sensor of a vehicle (Zhi, Abstract and Para. 0015 and 0058 – “Adaptive gyroscope bias compensation” for a vehicle navigation module; where the gyroscope is a “yaw gyro”) comprising:
detecting a yaw rate of the vehicle from measurement data from the yaw rate sensor (Zhi, Para. 0018-0019 and 0054 – where the vehicle includes “three gyroscopes”, or sensors, which “measure rotation rates around three orthogonal axes”, i.e. the X, Y, and Z axes; where yaw rate is the rotation rate around the Z axis and where rotation rate is “rate of change of angle with time”),
ascertaining a change in yaw angle from sensor data from at least one optical surroundings sensor unit (Zhi, Para. 0023 and 0027 – obtaining a “reference rotation rate” via “video navigation” by determining “translational and rotational motion of a video camera from apparent motion of objects in successive video images”; where “reference rotation rotate” is obtained by obtaining a “change in heading”, or angle, over a period of time),
ascertaining an offset of the yaw rate sensor, by fusion of the detected yaw rate and the ascertained change in yaw angle (Zhi, Para. 0020-0027 – determining a “gyroscope bias”, or offset of the yaw rate sensor, by “the difference between the rotation rate as measured by the gyroscope and the reference, or actual, rotation rate”, where the reference rotation rate can be measured by “video camera”, or an optical sensor), and
calibrating the yaw rate sensor according to the ascertained offset (Zhi, Para. 0020 – “Rotation rate around an axis may be estimated as the product of rotation rate around the axis as measured by a gyroscope minus gyroscope bias, and a scale factor”); and
While Zhi teaches the detected yaw rate and the ascertained change in yaw angle, Zhi does not teach ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle. Additionally, Zhi does not teach wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration.
However, Radwan teaches ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle (Radwan, Para. 0008-0014 – determining, using “using a fusion model for fusing predicted values and measured values”, “fused values for fused angle-based parameters” and “a fused lateral velocity and a fused yaw rate” by “fusing predicted values and measured values” which include “the measured yaw rate” and measured values for “steering angle”, where additionally yaw rate is the change in yaw angle over time; and where the fusion model is for example a “Kalman filter”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Zhi to include ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle, as taught by Radwan, in order to improve the accuracy of a yaw rate sensor (Radwan, Para. 0015).
Zhi in view of Radwan does not teach wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration.
However, Moshchuk teaches wherein the detecting the yaw rate is performed periodically with a first period duration (Moshchuk, Para. 0017-0020 – where a “positional data obtained from the vehicle motion sensors” is obtained every “10 ms”, where the sensors collect “vehicle motion data”, including a “sensed yaw rate”) and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration (Moshchuk, Para. 0017-0020 – where a “video-based position data”, including “angle of the vehicle” is refreshed on a “periodic basis” every “100 ms” and may have a latency of “200 ms”) and wherein due to the fusion the yaw rate does not need adjusted to the second period duration (Moshchuk, Para. 0004-0005, 0018-0026 – accounting for the different refresh rates and “inherent latency” of the “video-based position data” by performing “dead reckoning”, or predicting, and corrections/updates accounting for “drift” separately; using “Kalman filtering techniques”, wherein if “new video-based position data 20 is available”, a processor may “roll back the dead reckoned position computations”, or predictions, “a number of steps that may be proportional as to the latency and the update speed of the vehicle motion sensors 16” such that “the latent video frame may be synchronized in time with the position calculation at the time the video frame was acquired” and the processor may then “fuse/update the deduced movement/position with the newly acquired video-based position data” and “re-step forward up to the real-time data… until the next video-based position information arrives”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method including the above limitations of Zhi in view of Radwan to include wherein the wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration, as taught by Moshchuk, in order to “enhance the accuracy” of the determined yaw rate by accounting for the different period durations and correct “drift from the true/actual position over time” (Moshchuk, Para. 0018, 0021, 0024).
In regards to Claim 2, Zhi in view of Radwan and Moshchuk teaches the method of Claim 1, and Zhi further teaches further comprising determining a change in orientation of the vehicle by fusion of the ascertained yaw rate and the ascertained change in yaw angle with a Kalman filter (Zhi, Para. 0017 and 0057 – a navigation filter which produces “position and velocity estimates”, where a navigation filter may “implement a Kalman filter to estimate a state vector that includes position, heading, and sensor biases”, using “GNSS, gyroscope, accelerometer and external sensor data”).
In regards to Claim 5, Zhi in view of Radwan and Moshchuk teaches the method of Claim 1, and Zhi further teaches wherein the fusion of the yaw rate and the change in yaw angle is performed periodically with a fusion period duration (Zhi, Para. 0018-0027 and 0051 – where bias estimates are “updated periodically and often”, for example, once per second, or a fusion period duration; where the bias estimates include a gyroscope measuring “rotation rates around three orthogonal axes” and a “video camera” obtaining a “reference rotation rate” which is a heading angle change over time) and the ascertaining the offset (Zhi, Para. 0020-0027 – determining a “gyroscope bias”, or offset of the yaw rate sensor), but Zhi does not teach the ascertaining further comprises ascertaining from a plurality of periodically and successively ascertained measured values relating to the yaw rate and change in yaw angle.
However, Moshchuk teaches the ascertaining further comprises ascertaining from a plurality of periodically and successively ascertained measured values relating to the yaw rate and change in yaw angle (Moshchuk, Para. 0015-0020 – where a “positional data obtained from the vehicle motion sensors” is obtained every “10 ms”, where the sensors collect vehicle yaw data, and where a “video-based position data” is refreshed on a “periodic basis” every “100 ms” and contains a plurality of “still image frames” to determine a heading angle of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method including the above limitations of Zhi in view of Radwan and Moshchuk to further include the ascertaining further comprises ascertaining from a plurality of periodically and successively ascertained measured values relating to the yaw rate and change in yaw angle, as taught by Moshchuk, in order to update vehicle fusion positional data according to the associated refresh rates of the yaw rate sensors and optical sensors, and to account for the latency of the refresh rates.
In regards to Claim 6, Zhi in view of Radwan and Moshchuk teaches the method of Claim 1, and Zhi further teaches further comprising ascertaining a respective image position of at least one image feature in successive frames of an image sequence generated by at least one vehicle camera of the at least one optical surroundings sensor unit (Zhi, Para. 0027 – where the vehicle has “a video camera” which can determine “translational and rotational motion of a video camera from apparent motion of objects in successive video images”), and ascertaining the change in yaw angle on the basis of the change in the image position of the at least one image feature between recording times of the frames (Zhi, Para. 0023 and 0027 – where “a reference rotation rate” may be obtained by observing motion of objects in “successive video images” with an “optical flow” technique; where a rotation rate is change in angle over time).
In regards to Claim 7, Zhi in view of Radwan and Moshchuk teaches the method of Claim 1, and Zhi further teaches wherein an offset compensation takes place at the detected yaw rate (Zhi, Para. 0020 and 0052-0053 – “Rotation rate around an axis may be estimated as the product of rotation rate around the axis as measured by a gyroscope minus gyroscope bias, and a scale factor”, where gyroscope bias is an offset; where a method of “adaptive gyroscope bias compensation” takes place to reliably “estimate position and velocity” when an offset caused by gyroscope bias changes).
Regarding Claim 8, Zhi teaches: A computer program for calibrating a yaw rate sensor of a vehicle, wherein the computer program product comprises instructions (Zhi, Abstract and Para. 0015-0017 and 0058 – “Adaptive gyroscope bias compensation” for a vehicle navigation module, where the gyroscope is a “yaw gyro”; where a navigation filter used for compensation estimation contains “firmware”) comprising:
detecting a yaw rate of the vehicle from measurement data from the yaw rate sensor (Zhi, Para. 0018-0019 and 0054 – where the vehicle includes “three gyroscopes”, or sensors, which “measure rotation rates around three orthogonal axes”, i.e. the X, Y, and Z axes; where yaw rate is the rotation rate around the Z axis and where rotation rate is “rate of change of angle with time”),
ascertaining a change in yaw angle from sensor data from at least one optical surroundings sensor unit (Zhi, Para. 0023 and 0027 – obtaining a “reference rotation rate” via “video navigation” by determining “translational and rotational motion of a video camera from apparent motion of objects in successive video images”; where “reference rotation rotate” is obtained by obtaining a “change in heading”, or angle, over a period of time),
ascertaining an offset of the yaw rate sensor by fusion of the detected yaw rate and the ascertained change in yaw angle (Zhi, Para. 0020-0027 – determining a “gyroscope bias”, or offset of the yaw rate sensor, by “the difference between the rotation rate as measured by the gyroscope and the reference, or actual, rotation rate”, where the reference rotation rate can be measured by “video camera”, or an optical sensor),
calibrating the yaw rate sensor is calibrated according to the ascertained offset (Zhi, Para. 0020 – “Rotation rate around an axis may be estimated as the product of rotation rate around the axis as measured by a gyroscope minus gyroscope bias, and a scale factor”); and
While Zhi teaches the detected yaw rate and the ascertained change in yaw angle, Zhi does not teach ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle. Additionally, Zhi does not teach wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration.
However, Radwan teaches ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle (Radwan, Para. 0008-0014 – determining, using “using a fusion model for fusing predicted values and measured values”, “fused values for fused angle-based parameters” and “a fused lateral velocity and a fused yaw rate” by “fusing predicted values and measured values” which include “the measured yaw rate” and measured values for “steering angle”, where additionally yaw rate is the change in yaw angle over time; and where the fusion model is for example a “Kalman filter”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer program of Zhi to include ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle, as taught by Radwan, in order to improve the accuracy of a yaw rate sensor (Radwan, Para. 0015).
Zhi in view of Radwan does not teach wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration.
However, Moshchuk teaches wherein the detecting the yaw rate is performed periodically with a first period duration (Moshchuk, Para. 0017-0020 – where a “positional data obtained from the vehicle motion sensors” is obtained every “10 ms”, where the sensors collect “vehicle motion data”, including a “sensed yaw rate”) and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration (Moshchuk, Para. 0017-0020 – where a “video-based position data”, including “angle of the vehicle” is refreshed on a “periodic basis” every “100 ms” and may have a latency of “200 ms”) and wherein due to the fusion the yaw rate does not need adjusted to the second period duration (Moshchuk, Para. 0004-0005, 0018-0026 – accounting for the different refresh rates and “inherent latency” of the “video-based position data” by performing “dead reckoning”, or predicting, and corrections/updates accounting for “drift” separately; using “Kalman filtering techniques”, wherein if “new video-based position data 20 is available”, a processor may “roll back the dead reckoned position computations”, or predictions, “a number of steps that may be proportional as to the latency and the update speed of the vehicle motion sensors 16” such that “the latent video frame may be synchronized in time with the position calculation at the time the video frame was acquired” and the processor may then “fuse/update the deduced movement/position with the newly acquired video-based position data” and “re-step forward up to the real-time data… until the next video-based position information arrives”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the computer program including the above limitations of Zhi in view of Radwan to include wherein the wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration, as taught by Moshchuk, in order to “enhance the accuracy” of the determined yaw rate by accounting for the different period durations and correct “drift from the true/actual position over time” (Moshchuk, Para. 0018, 0021, 0024).
Regarding Claim 9, Zhi teaches: A device for calibrating a yaw rate sensor of a vehicle, wherein the device has a processing unit with instructions (Zhi, Abstract and Para. 0015-0017 and 0058 – “Adaptive gyroscope bias compensation” for a vehicle navigation module, or device, where the gyroscope is a “yaw gyro”; where a navigation filter used for compensation estimation contains “firmware”) comprising:
detecting a yaw rate of the vehicle from measurement data from the yaw rate sensor (Zhi, Para. 0018-0019 and 0054 – where the vehicle includes “three gyroscopes”, or sensors, which “measure rotation rates around three orthogonal axes”, i.e. the X, Y, and Z axes; where yaw rate is the rotation rate around the Z axis and where rotation rate is “rate of change of angle with time”),
ascertaining a change in yaw angle from sensor data from at least one optical surroundings sensor unit (Zhi, Para. 0023 and 0027 – obtaining a “reference rotation rate” via “video navigation” by determining “translational and rotational motion of a video camera from apparent motion of objects in successive video images”; where “reference rotation rotate” is obtained by obtaining a “change in heading”, or angle, over a period of time),
ascertaining an offset of the yaw rate sensor by fusion of the detected yaw rate and the ascertained change in yaw angle (Zhi, Para. 0020-0027 – determining a “gyroscope bias”, or offset of the yaw rate sensor, by “the difference between the rotation rate as measured by the gyroscope and the reference, or actual, rotation rate”, where the reference rotation rate can be measured by “video camera”, or an optical sensor),
calibrating the yaw rate sensor is calibrated according to the ascertained offset (Zhi, Para. 0020 – “Rotation rate around an axis may be estimated as the product of rotation rate around the axis as measured by a gyroscope minus gyroscope bias, and a scale factor”); and
While Zhi teaches the detected yaw rate and the ascertained change in yaw angle, Zhi does not teach ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle. Additionally, Zhi does not teach wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration.
However, Radwan teaches ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle (Radwan, Para. 0008-0014 – determining, using “using a fusion model for fusing predicted values and measured values”, “fused values for fused angle-based parameters” and “a fused lateral velocity and a fused yaw rate” by “fusing predicted values and measured values” which include “the measured yaw rate” and measured values for “steering angle”, where additionally yaw rate is the change in yaw angle over time; and where the fusion model is for example a “Kalman filter”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Zhi to include ascertaining a fusioned yaw rate and a fusioned change in yaw angle from the fusion of the detected yaw rate and the ascertained change in yaw angle, as taught by Radwan, in order to improve the accuracy of a yaw rate sensor (Radwan, Para. 0015).
Zhi in view of Radwan does not teach wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration.
However, Moshchuk teaches wherein the detecting the yaw rate is performed periodically with a first period duration (Moshchuk, Para. 0017-0020 – where a “positional data obtained from the vehicle motion sensors” is obtained every “10 ms”, where the sensors collect “vehicle motion data”, including a “sensed yaw rate”) and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration (Moshchuk, Para. 0017-0020 – where a “video-based position data”, including “angle of the vehicle” is refreshed on a “periodic basis” every “100 ms” and may have a latency of “200 ms”) and wherein due to the fusion the yaw rate does not need adjusted to the second period duration (Moshchuk, Para. 0004-0005, 0018-0026 – accounting for the different refresh rates and “inherent latency” of the “video-based position data” by performing “dead reckoning”, or predicting, and corrections/updates accounting for “drift” separately; using “Kalman filtering techniques”, wherein if “new video-based position data 20 is available”, a processor may “roll back the dead reckoned position computations”, or predictions, “a number of steps that may be proportional as to the latency and the update speed of the vehicle motion sensors 16” such that “the latent video frame may be synchronized in time with the position calculation at the time the video frame was acquired” and the processor may then “fuse/update the deduced movement/position with the newly acquired video-based position data” and “re-step forward up to the real-time data… until the next video-based position information arrives”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the device including the above limitations of Zhi in view of Radwan to include wherein the wherein the detecting the yaw rate is performed periodically with a first period duration and the ascertaining the change in yaw angle is performed periodically with a second period duration, which is different from the first period duration and wherein due to the fusion the yaw rate does not need adjusted to the second period duration, as taught by Moshchuk, in order to “enhance the accuracy” of the determined yaw rate by accounting for the different period durations and correct “drift from the true/actual position over time” (Moshchuk, Para. 0018, 0021, 0024).
In regards to Claim 10, Zhi in view of Radwan and Moshchuk teaches the device of Claim 9, and Zhi further teaches wherein the device is in a vehicle (Zhi, Para. 0016 and 0019 – “a vehicle navigation system” having devices for determining a vehicle’s orientation).
In regards to Claim 11, Zhi in view of Radwan and Moshchuk teaches the method of Claim 1, and Zhi further teaches further comprising determining the change in orientation of the vehicle based on fusion of a corrected yaw rate and a visual odometry (Zhi, Para. 0019-0027 and 0058 – determining an orientation of a gyroscope module of a vehicle by a rotation matrix; where a rotation matrix is determined by a “product of rotation rate around the axis as measured by a gyroscope minus gyroscope bias, and a scale factor”, and where the gyroscope bias is determined and compensated for to determine the actual position of the vehicle; where the bias can be determined by a “video camera”).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Zhi in view of Radwan and Moshchuk, and further in view of Tisdale, et al., hereinafter Tisdale (U.S. Patent Application Pub. No. 2019/0033459).
In regards to Claim 12, Zhi in view of Radwan and Moshchuk teaches the method of Claim 11, but Zhi does not teach further comprising carrying out one of semi-automated and fully automated guidance of the vehicle along an ascertained ego trajectory based on the determined change in orientation.
However, Tisdale teaches further comprising carrying out one of semi-automated and fully automated guidance of the vehicle (Tisdale, Para. 0041 – where a vehicle moves in a “semi- or fully-autonomous manner, based, at least in part, on information received from the various sensors and/or reference data”) along an ascertained ego trajectory based on the determined change in orientation (Tisdale, Para. 0041 and 0051-0058 – where the vehicle moves along “a driving path” using “a guidance system” which includes sensors for sensing “position and orientation changes of the vehicle” and where a location of the vehicle is determined by “a pose estimator” which determines the yaw of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method including the above limitations of Zhi in view of Radwan and Moshchuk to further include further comprising carrying out one of semi-automated and fully automated guidance of the vehicle along an ascertained ego trajectory based on the determined change in orientation, as taught by Tisdale, in order to implement yaw sensor calibration within an autonomous vehicle to prevent positioning errors when driving.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Horton, et al. (U.S. Patent No. 7,418,364) teaches a method and system senses the attitude of an accelerating object by measuring acceleration with accelerometers in three orthogonal axes and measuring angular rate with angular rate sensors disposed about each such axis to compute attitude of the object accurately relative to a vertical axis, and a processor which updates a quaternion representation of attitude based upon the angular rate of the object, and a corrective rate signal is determined from level frame acceleration as a reference for a Kalman filter in calculating the attitude of the object.
Chundrlik, et al. (U.S. Patent Application Pub. No. 2013/0231825) teaches control system or method for a vehicle references a camera and sensors to determine when an offset of a yaw rate sensor may be updated.
THIS ACTION IS MADE FINAL. 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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/H.L./Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
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