DETAILED ACTION
Remarks
This final office action is in response to the arguments filled on 04/08/2026. Claims 1-15 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 .
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 and 5-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0163021 (“Frazzoli”), and further in view of US 2024/0054826 (“Maddock”).
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]), 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 limitation:
the first type differs from the second type.
However, Maddock discloses a system wherein the first type differs from the second type (see at least [0062], where “The in-vehicle information capture device 12 preferably includes a number of different sensors to capture input information in relation to measurable parameters relating to the vehicle. Typically, the device includes a number of sensors configured to capture different types of information. The different types of information will typically be captured from the sensors contemporaneously. The advantage of capture of different types of information contemporaneously is that analysis of different types of information captured contemporaneously may reveal more than analysis of a single type of information.”).
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 Maddock 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 6, Frazzoli further discloses a computer system wherein the first quality metric comprises any one or more out of:
-a first accuracy of the first sensor data (see at least [0350], where “driving conditions such as fast speeds, weather conditions, and road conditions such rough or unpaved roads may provide less accurate sensor readings or more variance among samples.”), and
-a first irregularity associated with the first set of sensors.
Regarding claim 7, Frazzoli further discloses a computer system wherein the processing circuitry is configured to detect the first irregularity by being configured to detect any one or more of:
-a sensor fault of one or more sensors out of the first set of sensors (see at least [0184] and [0366]),
-an absence of expected sensor data out of the first sensor data,
-that the first sensor data comprises sensor data indicative of values outside an operational driving domain of the vehicle, and
-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.
Regarding claim 8, Frazzoli further discloses a computer system wherein the one or more common reference entities are arranged to comprise one or more parts of the vehicle (see at least [0019], [0123], [0172] and [0365]), wherein the one or more parts of the vehicles comprises any one or more out of:
-one or more wheels and/or tires of the vehicle (see at least [0160]),
-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.
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 14, Frazzoli further discloses a computer program product comprising program code for performing, when executed by the processing circuitry, the method of claim 13 ( see at least [0137] and Refer at least to claim 1 for reasoning and rationale).
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 2024/0054826 (“Maddock”), as applied to claim 3 above, and further in view of US 10,108,192 (“LaForge”).
Regarding claim 4, Frazzoli in view of Maddock 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 Maddock 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 filled on 04/08/2026, with respect to claims 1-15, have been considered but they are not persuasive.
The Applicant contends that:
“Any "environmental inputs external to the autonomous vehicle" cannot be regarded as vehicle motion information as recited by claim 1. Thus, [0011]-[0012], as well as [0019], of Frazzoli fail to disclose any determination of vehicle motion information.
For the sake of completeness, it is noted that the examiner refers to [0123], [0172] and [0365] for examples of vehicle motion information. However, there is nothing in [0123], [0172], [0365] or any other passage of Frazzoli which in any way teaches that any information that could possibly be equated to vehicle motion information is determined using a first and a second sensor data in the manner recited by claim 1.
Turning to Maddock, Maddock also fails to disclose the above features in bold of claim 1. At the very best, Maddock could possibly be regarded as disclosing a procedure for assessing whether or not a sensor is correctly mounted to a vehicle, see e.g. claim 1 of Maddock, and there is nothing in Maddock indicating that a first quality metric may be used for determining "whether to use the first sensor data, the second sensor data, or a combination thereof as a basis for determining said vehicle motion information" in the manner recited by claim 1.”
The Examiner disagrees:
The claim limitation that is argued is copied and pasted here “sensor data for estimating vehicle motion”.
Frazzoli discloses a system wherein vehicle velocity, acceleration and heading are measured or estimated using sensors installed on the vehicle, see at least [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).”. Vehicle velocity, acceleration and heading are vehicle motion information. [0123] was also cited on previous OC. Vehicle velocity, acceleration and heading are indicative of how the vehicle moves. [0012] of Frazzoli describe first sensor and second sensor produce environmental information. [0232] of describe first sensor, 121. [0123] of Frazzoli describe sensor, 121 measuring velocity, acceleration and heading.
The Applicant also contends that:
“As regards to claim 4, it should be noted that LaForge relates to "pose data" but there is nothing in LaForge teaching that such "pose data" may be in accordance with the following: "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" as recited by claim 1 and consequently also by a combination of claims 1 and 4 presently on file. Thus, claim 4 presently on file is non-obvious over Frazzoli in view of Maddock and further in view of LaForge.”
The Examiner also disagrees:
Frazzoli discloses a system wherein first sensor data or second sensor data is estimating vehicle motion, see citation above. LaForge discloses a system wherein vehicle data e.g., pose information is compared for quality, see at citation above. So, it would be obvious to compare sensor data collected by Frazzoli utilizing data comparison disclosed by LaForge for identifying better data set.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOHANA TANJU KHAYER whose telephone number is (408)918-7597. The examiner can normally be reached on Monday - Thursday, 7 am-5.30 pm, PT.
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/SOHANA TANJU KHAYER/Primary Examiner, Art Unit 3657
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