DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the application filed on March 26, 2025. Claims 1-10 are pending. Claims 1 and 10 are independent.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on March 26, 2025 and May 5, 2026 have been considered. The submission is in compliance with the provisions of 37 CFR 1.97. The Forms PTO-1449 are signed and attached hereto.
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 and 4 are rejected under 35 U.S.C. 102(a)(a)(1) as being anticipated by U.S. 2010/0017128 to Zeng.
Claims 1 and 4 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zeng.
With respect to independent claims 1 and 10, Zeng discloses a vehicle speed acquisition unit configured to acquire a vehicle speed detected by a vehicle speed sensor mounted on the vehicle (see paragraphs [0008] and [0016]: a system and method are disclosed that estimates vehicle dynamics, including vehicle position and velocity, using a stationary object. The system includes an object sensor, such as a radar, lidar or camera, that provides object signals of the stationary object. The processors 16 and 18 also receive signals from in-vehicle sensors 20, such as a steering wheel angle sensor, a yaw-rate sensor, a longitudinal speed sensor, a longitudinal acceleration sensor, a lateral acceleration sensor, etc.,); and
a vehicle speed correction unit configured to correct the vehicle speed based on a detection result of the radar device or the LiDAR (see paragraph [0034]: The longitudinal state estimation processor 16 receives longitudinal position, longitudinal speed, longitudinal acceleration and vehicle speed signals from the sensors, and uses Kalman filtering and an auto-regression noise model to provide a corrected longitudinal acceleration axo, a corrected longitudinal velocity υxo and wheel slippage of the vehicle..),
wherein the vehicle speed correction unit is configured to: acquire information on a plurality of stationary objects estimated to be stationary among the plurality of targets, based on the detection result of the radar device or the LiDAR (see paragraph [0016]: an object detection sensor for detecting stationary objects relative to vehicle motion. The signals from the sensors 12 are sent to an association processor 14 that matches the maps or images from the signals from consecutive views to track the objects. The tracks from the association processor 14 are sent to a longitudinal state estimation processor 16 that estimates vehicle speed in a forward direction and a lateral state estimation processor 18 that estimates vehicle speed in a lateral direction as a result of yaw-rate and side-slip angle.); and
correct the vehicle speed based on relative speeds of the plurality of stationary objects relative to the vehicle detected by the radar device or the LiDAR, in a case where a predetermined condition is satisfied, the predetermined condition includes that a number of the stationary objects is equal to or greater than a predetermined number (see paragraphs [0034] and [0038]: The longitudinal state estimation processor 16 receives longitudinal position, longitudinal speed, longitudinal acceleration and vehicle speed signals from the sensors, and uses Kalman filtering and an auto-regression noise model to provide a corrected longitudinal acceleration axo, a corrected longitudinal velocity υxo and wheel slippage of the vehicle. The lateral state estimation processor 18 receives an object azimuth angle, object lateral offset, steering wheel angle, yaw-rate and lateral acceleration signals from the sensors, and uses Kalman filtering and an auto-regression noise model to provide a yaw-rate correction, a lateral acceleration correction and a lateral velocity signal of the vehicle.).
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.
Claims 2, 3, 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of U.S. Patent Publication No. 2015/0070207 to Millar et al. (hereinafter “Millar”).
With respect to dependent claim 2, Zeng does not explicitly teach wherein the vehicle speed correction unit is configured to calculate a variance or a standard deviation of the relative speeds of the plurality of stationary objects, and the predetermined condition further includes that the variance or the standard deviation is less than a predetermined value.
Millar teaches the one or more digital processors are configured to analyze the clutter ridge of the graph to determine variance values associated with the identified peak values. The one or more digital processors are configured to determine whether to update a tracking filter based, at least in part, on measured variance values. (See paragraphs [0017] and [0018).
It would have been obvious to one skilled in the art before the effective filing date to combine the variance value associated with identified peak values and determining whether to update based on the variance values of Millar with the vehicle speed correction using a Kalman filter of Zeng in order to condition correction on low variance to improve estimation reliability of speed correction.
With respect to dependent claim 3, Zeng does not explicitly teach wherein the predetermined condition further includes that a state where the number of the stationary objects is equal to or greater than the predetermined number continues for a predetermined time or longer.
Millar teaches collecting information on stationary structures in a vicinity of the vehicle using the radar sensor as the vehicle travels past the stationary structures. The calibration procedures described herein may be performed anywhere that the vehicle 12 is able to achieve at least a minimal speed for at least a minimum time duration (e.g., less than 10 seconds in one embodiment). (See paragraphs [0003] and [0046]).
It would have been obvious to one skilled in the art before the effective filing date to combine the temporal data collection for clutter ridge formation of Millar with the accumulate stationary tracking data across multiple frames of Zeng in order to set a minimum duration for sensor data stability before triggering a correction for reliable correction.
With respect to dependent claim 5, Zeng does not explicitly teach wherein the predetermined condition further includes that the plurality of stationary objects that are detected by the radar device or the LiDAR while the vehicle travels at a first speed or higher (see paragraph [0047]: the collection of infrastructure information may be initiated just after vehicle starts up, when the vehicle first reaches a particular speed. Once initiated, the collection of infrastructure information may be rapid. This is because, in some implementations, virtually all collected information is used during the calibration process, regardless of signal strength. Thus, a large amount of data may be rapidly collected.).
It would have been obvious to one skilled in the art before the effective filing date to combine the host velocity for normalized Doppler of Millar with the longitudinal speed sensors of Zeng to input in the Kalman filter that processes vehicle speed in order to set a minimum speed threshold such that the stationary objects’ relative speeds are large enough to provide reliable correction signal.
With respect to dependent claim 6, Zeng does not explicitly teach wherein the predetermined condition further includes that the plurality of stationary objects that are detected by the radar device or the LiDAR while the vehicle travels at a predetermined acceleration or less.
Millar teaches a data collection that may take place when the vehicle of interest is moving within a particular speed range and may involve the receipt of radar return signals 54. Other statistics that may be measured for the calibration values include quality statistics, stability statistics (to detect rain, blockages, etc.), reliability statistics, and/or others. In one possible approach, the calibration values stored in the calibration storage 128 may be updated when new calibration values are available that have a higher quality than those previously stored. (See paragraphs [0049] and [0053]).
It would have been obvious to one skilled in the art before the effective filing date to combine the predictable Doppler pattern across collected radar frames of Millar with the Kalman filtering in low acceleration conditions of Zeng which triggers the correction only in the stable, low acceleration operating regime in order to specify an operational bound on the acceleration variable as a quality gate. It would have been obvious to one skilled in the art before the effective filing date to combine the variance value associated with identified peak values and determining whether to update based on the variance values of Millar with the vehicle speed correction using a Kalman filter of Zeng in order to condition correction on low variance to improve estimation reliability of speed correction.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of U.S. Patent Publication 2018/0203109 to Aoki et al. (hereinafter “Aoki”).
With respect to dependent claim 7, Zeng does not explicitly teach wherein the vehicle speed correction unit is configured to: calculate a correction value based on the relative speeds in the case where the predetermined condition is; and limit the correction value or a corrected vehicle speed, in a case where the calculated correction value is outside a predetermined range or in a case where the corrected vehicle speed is outside a predetermined range with respect to the vehicle speed acquired by the vehicle speed acquisition unit satisfied
Aoki discloses the vehicle velocity correcting unit 76 d may compare the first correction value and the second correction value after performing the second correcting process, and transition from the second correcting process to the first correcting process according to the comparison result. Specifically, the vehicle velocity correcting unit 76 d may calculate the difference between the first correction value and the second correction value, and transition from the second correcting process to the first correcting process if the calculated difference (specifically, the absolute value of the difference) is equal to or greater than a predetermined value. . (See paragraph [0131]).
It would have been obvious to one skilled in the art before the effective filing date of the invention to combine the correction value range-comparison of Aoki with the corrected vehicle speed via the Kalman filter based longitudinal state of Zeng in order to provide limit or switch correction modes for range-limiting and range conditions for correction values.
With respect to dependent claim 8, Zeng does not explicitly teach wherein the predetermined condition further includes that a corrected vehicle speed calculated by the vehicle speed correction unit is within a predetermined range with respect to the vehicle speed acquired by the vehicle speed acquisition unit, or that a correction value calculated by the vehicle speed correction unit is within a predetermined range.
Aoki discloses calculating the difference between the first correction value and the second correction value, and transitions from the second correcting process to the first correcting process if the calculated difference is equal to or larger than a predetermined value. (See paragraph [0152]).
It would have been obvious to one skilled in the art before the effective filing date of the invention to combine the correction value of Aoki with the corrected vehicle speed via the Kalman filter of Zeng in order to down-weights measurements that gates whether the correction are within a threshold range.
Allowable Subject Matter
Claim 9 is 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEMETRA R SMITH-STEWART whose telephone number is (571)270-3965. The examiner can normally be reached 10am - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached at 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEMETRA R SMITH-STEWART/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
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