DETAILED ACTION
Remarks
This non-final office action is in response to the RCE filled on 09/01/2026. Claims 1 and 13 are amended. Claims 6-8 and 14 are canceled. Claims 1-5, 13 and 15 are pending and examined below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/01/2026 has been entered.
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.
Claim(s) 1-3, 5, 9-13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0163021 (“Frazzoli”), and further in view of US 2023/0306722 (“Xu”).
Regarding claim 1 (and similarly claim 13), Frazzoli discloses a computer system comprising processing circuitry configured to determine vehicle motion information of a vehicle (see at least [0022], where “If the autonomous vehicle is operating within its defined operational domain, at least two independent planning modules…generate trajectories for the autonomous vehicle. Each planning module evaluates the trajectory generated by the other planning module for at least one collision with at least one object in a scene description. If one or both trajectories are determined to be unsafe (e.g., due to at least one collision being detected), the autonomous vehicle performs a safe stop maneuver or applies emergency braking using, for example, an autonomous emergency braking (AEB) system.”; see also [0312], where “the control operations 3040 includes affecting the direction of motion of the AV 100.”; see also [0498]), wherein the processing circuitry is configured to:
receive first sensor data from a first set of sensors comprised in the vehicle (see at least [0012], where “an autonomous vehicle includes a first sensor configured to produce a first sensor data stream from one or more environmental inputs external to the autonomous vehicle”),
receive second sensor data from a second set of sensors comprised in the vehicle (see at least [0012], where “a second sensor configured to produce a second sensor data stream from the one or more environmental inputs external to the autonomous vehicle”),
wherein the first sensor data and the second sensor data respectively comprises sensor data for estimating vehicle motion with respect to one or more common reference entities (see at least [0019], where “the first sensor and the second sensor being configured to detect a same type of information. The vehicle includes a processor coupled with the first sensor and the second sensor, the processor being configured to detect an abnormal condition based on a difference between the first sensor data stream and the second sensor data stream.”; see also [0123], where “the AV system 120 includes sensors 121 for measuring or inferring properties of state or condition of the AV 100, such as the AV's position, linear and angular velocity and acceleration, and heading (e.g., an orientation of the leading end of AV 100).”; see also [0172] and [0365]), wherein the one or more common reference entities are arranged to comprise one or more parts of the vehicle, wherein the one or more parts of the vehicle comprises any one or more out of one or more wheels and/or tires of the vehicle, a part and/or area of a vehicle body of the vehicle, and a part and/or area of a trailer attached to the vehicle (see at least [0123], [0160], [0172] and [0365]), and
wherein the first sensor data comprises sensor data of a first type, and wherein the second sensor data comprises sensor data of a second type(see at least [0233], where “the first set of sensors can be different from the second set of sensors”; see also [0042], where “FIG. 13 shows a block diagram of an example of an autonomous vehicle (AV) system that includes two or more synergistically redundant operations subsystems.”),
determine a first quality metric associated with the first sensor data (see at least [0366], where “the abnormal condition is a combination of hardware and software malfunctions. In an embodiment, the abnormal conditions occur as a result of abnormal environmental factors, for example heavy rain or snow, extreme weather conditions, presence of unusually high number of reflective surfaces, traffic jams, accidents etc.”; see also [0344] and [0540]; sensor data quality can be impacted based on environments factors. If the sensor data shows poor quality, then the sensor data will not be used for vehicle status determination. Based on submitted specification, quality metric comprises irregularities associated with the sensors, see at least [0017] of PGPub of submitted specification. irregularities include fault of sensors, absence of sensor data, two measurements obtained during a time period wherein the rate of change of two measurements is above a threshold, see at least [0020-23] of PGPub of submitted specification),
based on the first quality metric associated with the first sensor data, determine whether to use the first sensor data, the second sensor data, or a combination thereof as a basis for determining said vehicle motion information (see at least [0011], where “The first sensor and the second sensor can be configured to detect a same type of information. The technique further includes detecting an abnormal condition based on a difference between the first sensor data stream and the second sensor data stream; and switching among the first sensor, the second sensor, or both as an input to control the autonomous vehicle in response to the detected abnormal condition.”).
Frazzoli does not disclose the following limitations:
wherein sensors of the first set of sensors and sensors of the second set of sensors are configured to measure a same part of the one or more parts of the vehicle;
the first type differs from the second type; and
wherein the first quality metric comprises a first irregularity associated with the first set of sensors, wherein the processing circuitry is configured to determine the first quality metric by assessing the first sensor data independently of the second sensor data, and wherein the processing circuitry is configured to detect the first irregularity by being configured to detect that the first sensor data comprises sensor data indicative of at least two measurements obtained during a time period, and wherein a rate of change of the at least two measurements for the time period is above a rate threshold.
However, Xu discloses a system wherein sensors of the first set of sensors and sensors of the second set of sensors are configured to measure a same part of the one or more parts of the vehicle (see at least [0119], where “a camera image 603 is used, however, it will be understood that different types of images or sensor data can be used”; see also [0076]);
the first type differs from the second type (see at least [0083], where “the sensor data 506 can include different types of sensor data, such as camera data associated with a camera image, radar data associated with a radar image, LiDAR data associated with a lidar image, location data associated with a location sensor, acceleration data associated with an accelerometer, speed data associated with a speed sensor, orientation data associated with a gyroscope, position data associated with a position sensor, weather data associated with a weather sensor, radar data associated with a radar sensor, traffic data associated with a traffic data sensor, etc.”); and
wherein the first quality metric comprises a first irregularity associated with the first set of sensors, wherein the processing circuitry is configured to determine the first quality metric by assessing the first sensor data independently of the second sensor data, and wherein the processing circuitry is configured to detect the first irregularity by being configured to detect that the first sensor data comprises sensor data indicative of at least two measurements obtained during a time period, and wherein a rate of change of the at least two measurements for the time period is above a rate threshold (see at least [0127], where “the category of capture session may identify sensor data that includes a data anomaly.”; [0076], [0087], [0091]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Frazzoli to incorporate the teachings of Xu by including the above feature for providing various types of information for accurately determining the vehicle motion.
Regarding claim 2, Frazzoli further discloses a computer system wherein the processing circuitry is configured to use the second sensor data or a combination of the second sensor data and the first sensor data as a basis for determining said vehicle motion information in response to the first quality metric being outside a predetermined reference range of the first quality metric (see at least [0012]).
Regarding claim 3, Frazzoli further discloses a computer system wherein the processing circuitry is configured to: determine a second quality metric associated with the second sensor data, and
use the first sensor data, the second sensor data, or a combination thereof as a basis for determining vehicle motion information, further based on the second quality metric (see at least [0012]).
Regarding claim 5, Frazzoli further discloses a computer system wherein the processing circuitry is configured to: -based on the first quality metric and the second quality metric, detect that the first sensor data and the second sensor data is unsuitable for use as a basis for determining vehicle motion information, and in response to said detection, trigger the vehicle to enter a safe state (see at least [0022]).
Regarding claim 9, Frazzoli further discloses a computer system wherein the processing circuitry is configured to, based on the first sensor data, the second sensor data, or a combination thereof (see at least [0019]), determine vehicle motion information by being configured to determine any one or more out of the following vehicle motion parameters as part of the vehicle motion information:
a longitudinal speed of at least a portion of the vehicle,
a yaw rate of the vehicle,
one or more wheels speed of wheels of the vehicle (see at least [0123]),
a lateral speed of at least a portion of the vehicle,
a lateral acceleration of at least a portion of the vehicle,
a longitudinal acceleration of at least a portion of the vehicle, and
a turning radius and/or an inverse turning radius of the vehicle.
Regarding claim 10, Frazzoli further discloses a vehicle comprising a first set of sensors and a second set of sensors, wherein the first set of sensors and the second set of sensors respectively comprises sensors configured to measure sensor data for estimating a vehicle motion of one or more common reference entities, wherein said first set of sensors and said second set of sensors comprises different sensor types, and wherein the vehicle comprises the computer system of claim 1 (see at least [0012] and [0019]).
Regarding claim 11, Frazzoli further discloses a vehicle wherein the first set of sensors and/or the second set of sensors respectively comprises any one or more out of:
-one or more wheel speed sensors for measuring wheel rotation (see at least [0123]),
-one or more Tire Monitoring System, TMS, sensors,
-one or more ground speed Radio Detection and Ranging, Radar, sensors,
-one or more accelerometers,
-one or more gyroscope sensors,
-one or more optical devices, and
-an Inertial Measurement Unit, IMU.
Regarding claim 12, Frazzoli further discloses a vehicle wherein the first set of sensors comprises one or more wheel speed sensors for measuring wheel rotation (see at least [0123]).
Regarding claim 15, Frazzoli further discloses a non-transitory computer-readable storage medium comprising instructions, which when executed by the processing circuitry, cause the processing circuitry to perform the method of claim 13 ( see at least [0137] and Refer at least to claim 1 for reasoning and rationale).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0163021 (“Frazzoli”), and in view of US 2023/0306722 (“Xu”), as applied to claim 3 above, and further in view of US 10,108,192 (“LaForge”).
Regarding claim 4, Frazzoli in view of Xu does not disclose claim 4. However, LaForge discloses a computer system wherein the processing circuitry is configured to use the first sensor data or the second sensor data as a basis for determining vehicle motion information, based on a comparison of the first quality metric with the second quality metric (see at least col 1, lines 49-54, where “The vehicle pose data may be set to the first pose data quality when the first pose data is better than, or the same as, the second pose data quality. Alternatively, the vehicle pose data may be changed from the first pose data to the second pose data when the second pose data quality is better than the first pose data quality.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Frazzoli in view of Xu to incorporate the teachings of Frazzoli by including the above feature for determining vehicle motion by comparing sensor data so that the reliability increases.
Response to Arguments
Applicant’s arguments with respect to claim 1-5, 13 and 15 have been considered but are moot because the arguments do not apply to the new combination used in the current rejection that is due to the newly added claim amendments.
Conclusion
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/SOHANA TANJU KHAYER/Primary Examiner, Art Unit 3657
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