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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 17 April, 2026 has been entered.
Response to Amendment
Applicant’s amendment filed 17 April, 2026 is acknowledged and has been entered.
Claim objection(s) regarding claim(s) 12 have been overcome in view of the amendment to the claim(s).
Claim rejection(s) with respect to 35 USC 112(b) regarding claim(s) 7-12 have been overcome in view of the amendment to the claim(s).
Response to Arguments
Applicant’s remarks filed 17 April, 2026 has been fully considered and is moot in view of a new ground of rejection.
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) 7 and 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 11,518,395 B1 “ZHU”), in view of Su et al. (US 2020/0280429 A1 “SU”).
Regarding claim 7, ZHU discloses (Examiner’s note: What ZHU does not disclose is ) a method for validating a plurality of environment detection sensors (an autonomous vehicle 102 configured to cross-validate one or more of its sensors [col. 5, lines 45-46 & FIG. 1]) that are rigidly connected to a vehicle at different locations of the vehicle (the autonomous vehicle 102 may also include one or more sensors 146 for detecting objects external to it. The sensors 146 may include lasers, sonar, radar, cameras or any other detection devices. For example, the small passenger car may include a laser mounted on the roof or other convenient location [col. 8, lines 48-52]), the method comprising:
for each sensor of the plurality of sensors, detect via the sensor a relative speed of an object determine state information from the one or more common objects appearing in the one or more images captured by the reference sensor and determine state information for the same objects appearing in the one or more images captured by the sensor being cross-validated (block 1210) [col. 22, lines 65-67 & col. 23, lines 1-2]); (first speed value; second speed value [claim 1])
and defining at least one parameter of a movement model of the vehicle from the relative speeds (the data 112 may include vehicle data 116 that defines one or more parameters for classifying a vehicle. Classifications of vehicle may include such classifications as “passenger car,” “bicycle,” “motorcycle,” and other such classifications. The parameters defined by the vehicle data 116 may inform the autonomous driving computer system 144 as to the type of vehicle detected by a given sensor [col. 11, lines 41-48]); (the processor 106 may classify vehicles based on various characteristics, such as the size of the vehicle (bicycles are larger than a breadbox and smaller than a car), the speed of the vehicle (bicycles do not tend to go faster than 40 miles per hour or slower than 0.1 miles per hour), and other such characteristics [col. 11, lines 54-58])
and assigning a decalibrated state to the plurality of sensors when the object speeds deviate from each other and/or relative to the movement model by more than a predetermined amount (the autonomous driving computer system 144 may then compare the state information determined from the determined sensor data with the state information determined from the sensor data from the first sensor selected to cross-validate the determined sensor [col. 18, lines 44-48]); (deviations (i.e., differences) in the state information may indicate that a sensor (i.e., the laser 304) is experiencing a problem. To determine whether there are differences, the autonomous driving computer system 144 may employ one or more state information thresholds, for example, a speed threshold (e.g., 0.5 km/hr) [col. 19, lines 9-19])
In a same or similar field of endeavor, SU teaches that each of the sensors can have its own sensor coordinate system, i.e., each of the sensors is associated with a respective one of the sensor coordinate systems. For example, a camera may be associated with a projection coordinate system, while RADAR and/or LiDAR sensors may be associated with a Cartesian or Polar coordinate system [0014]. In steps 22 a, 22 b, the feature maps 18 a and 18 b are transformed into a unified coordinate system, i.e. the data of the feature maps 18 a and 18 b is represented in the same coordinate system after the transformation. The unified coordinate system is preferably defined independently from the sensor coordinate systems of sensors 10 a, 10 b. Instead, the unified coordinate system is defined in dependence of a predetermined reference point at an object, for example a predetermined position on a vehicle [0050].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of ZHU to include the teachings of SU, because doing so would enhance detection results and data reliability and decrease computational complexity, as recognized by SU. In addition, both of the prior art references, ZHU and SU, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, detection of objects in a vicinity of a vehicle using sensor network.
Regarding claim 10, ZHU/ SU discloses the method of claim 7, wherein a reliability of the assignment of the decalibrated state and/or a reliability of the assignment of a complementary calibrated state is statistically identified from the plurality of relative speeds (comparing the state information from the various sensors. Deviations (i.e., differences) in the state information may indicate that a sensor (i.e., the laser 304) is experiencing a problem. To determine whether there are differences, and the extent of those differences, the autonomous driving computer system 144 may employ one or more state information thresholds. For example, there may be a speed threshold (e.g., 0.5 km/hr) [ZHU col. 19, lines 9-19], cited and incorporated in the rejection of claim 7). It is further noted that the limitation is in alternative form; and therefore, only one alternative was given patentable weight. In this case, the citations as disclosed by ZHU corresponds to the claimed alternative “a reliability of the assignment of the decalibrated state is statistically identified from the plurality of relative speeds”.
Regarding claim 11, ZHU discloses a vehicle (an autonomous vehicle 102 [col. 5, line 45 & FIG. 1]), comprising:
a computing unit (processor 106 [col. 5, line 65])
and a plurality of environment detection sensors rigidly connected to the vehicle at different locations of the vehicle (the autonomous vehicle 102 may also include one or more sensors 146 for detecting objects external to it. The sensors 146 may include lasers, sonar, radar, cameras or any other detection devices. For example, the small passenger car may include a laser mounted on the roof or other convenient location [col. 8, lines 48-52]), wherein each sensor is configured to detect a relative speed of an object determine state information from the one or more common objects appearing in the one or more images captured by the reference sensor and determine state information for the same objects appearing in the one or more images captured by the sensor being cross-validated (block 1210) [col. 22, lines 65-67 & col. 23, lines 1-2]); (first speed value; second speed value [claim 1]); wherein the plurality of sensors and the computing unit are configured to:
for each sensor of the plurality of sensors, detect via the sensor the relative speed of the object determine state information from the one or more common objects appearing in the one or more images captured by the reference sensor and determine state information for the same objects appearing in the one or more images captured by the sensor being cross-validated (block 1210) [col. 22, lines 65-67 & col. 23, lines 1-2])
speeds (the data 112 may include vehicle data 116 that defines one or more parameters for classifying a vehicle. Classifications of vehicle may include such classifications as “passenger car,” “bicycle,” “motorcycle,” and other such classifications. The parameters defined by the vehicle data 116 may inform the autonomous driving computer system 144 as to the type of vehicle detected by a given sensor [col. 11, lines 41-48]); (the processor 106 may classify vehicles based on various characteristics, such as the size of the vehicle (bicycles are larger than a breadbox and smaller than a car), the speed of the vehicle (bicycles do not tend to go faster than 40 miles per hour or slower than 0.1 miles per hour), and other such characteristics [col. 11, lines 54-58])
and assign a decalibrated state to the plurality of sensors when the object speeds deviate from each other and/or relative to the movement model by more than a predetermined amount (the autonomous driving computer system 144 may then compare the state information determined from the determined sensor data with the state information determined from the sensor data from the first sensor selected to cross-validate the determined sensor [col. 18, lines 44-48]); (deviations (i.e., differences) in the state information may indicate that a sensor (i.e., the laser 304) is experiencing a problem. To determine whether there are differences, the autonomous driving computer system 144 may employ one or more state information thresholds, for example, a speed threshold (e.g., 0.5 km/hr) [col. 19, lines 9-19])
In a same or similar field of endeavor, SU teaches that each of the sensors can have its own sensor coordinate system, i.e., each of the sensors is associated with a respective one of the sensor coordinate systems. For example, a camera may be associated with a projection coordinate system, while RADAR and/or LiDAR sensors may be associated with a Cartesian or Polar coordinate system [0014]. In steps 22 a, 22 b, the feature maps 18 a and 18 b are transformed into a unified coordinate system, i.e. the data of the feature maps 18 a and 18 b is represented in the same coordinate system after the transformation. The unified coordinate system is preferably defined independently from the sensor coordinate systems of sensors 10 a, 10 b. Instead, the unified coordinate system is defined in dependence of a predetermined reference point at an object, for example a predetermined position on a vehicle [0050].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of ZHU to include the teachings of SU, because doing so would enhance detection results and data reliability and decrease computational complexity, as recognized by SU.
Regarding claim 12, ZHU/ SU discloses the vehicle of claim 11, wherein the computing unit is the control unit (processor 106 [ZHU col. 5, line 65], cited and incorporated in the rejection of claim 11).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHU, in view of SU, and further in view of Johnson-Roberson et al. (US 2021/0024081 A1 “JOHNSON-ROBERSON”).
Regarding claim 8, ZHU/ SU discloses the method of claim 7,
In a same or similar field of endeavor, JOHNSON-ROBERSON teaches that the evaluation sensor poses are determined based on: the refined sensor poses from S200, transformed into a pairwise transformation matrix; the prior evaluation sensor pose (or prior evaluation matrix); and the prior estimate covariance [0139]. Determining evaluation matrix convergence over time (e.g., in relative sensor poses, in biases, etc.) functions to determine that the shifted sensor poses (represented by the evaluation matrix) are stable and not substantially changing over time (e.g., changing less than a predetermined threshold) [0151].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of ZHU to include the teachings of JOHNSON-ROBERSON, because doing so would provide an improved and dynamic re-calibration of the vehicle localization system, as recognized by JOHNSON-ROBERSON. In addition, both of the prior art references, ZHU and JOHNSON-ROBERSON, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, autonomous vehicle sensor system.
Allowable Subject Matter
Claim(s) 9 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
ZHU, as modified by SU, discloses the method of claim 7; however, Applicant’s claim 9 also encompasses an invention that the prior art does not disclose, teach, or otherwise render obvious. Specifically, there is nothing in the prior art that would suggest modifying ZHU to have the missing elements without the improper use of hindsight. Specifically, nothing in the prior art would suggest that “as the movement model of the vehicle, a vehicle speed based on the vehicle coordinate system is defined according to magnitude and direction; a target relative speed is identified from the vehicle speed according to the respective associated uniform coordinate transformation for every sensor coordinate system; and at least one target relative speed is compared with the relative speed detected for the respective sensor coordinate system according to magnitude and/or direction”.
Within the context of Applicant’s claimed invention as a whole, the prior arts made of record individually or in any combination, failed to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed feature(s) of claim(s) 9.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Diederichs et al. (US 2021/0192788 A1) discloses embodiments for validating the calibration use a simple validation target. The accuracy of a calibration is evaluated qualitatively by projecting the LiDAR points containing the validation target onto the camera image using the camera-to-LiDAR coordinate transformation, and observing mutual features of the validation targets, captured by both sensors, across the camera's field of view. A calibration is classified as acceptable if the LiDAR points corresponding to a specific common validation target come to rest on the same validation target in the camera image.
Nemati et al. (US 2020/0200870 A1) discloses a system and method for detecting a level of misalignment of a vehicle sensor, such as a radar sensor, compared to a reference sensor also associated with the vehicle. The reference sensor may comprise a different type of sensor, such as an optical sensor, motion sensor, or position sensor. One or more reference sensors may be utilized in detecting a level of misalignment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAILEY R LE whose telephone number is (571)272-4910. The examiner can normally be reached 9:00 AM - 5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, WILLIAM J KELLEHER can be reached at (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Hailey R Le/Examiner, Art Unit 3648 April 29, 2026
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