Prosecution Insights
Last updated: April 19, 2026
Application No. 18/917,451

MOTION ESTIMATION OF A VEHICLE

Non-Final OA §103
Filed
Oct 16, 2024
Examiner
KHAYER, SOHANA T
Art Unit
3657
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Volvo Truck Corporation
OA Round
1 (Non-Final)
82%
Grant Probability
Favorable
1-2
OA Rounds
2y 11m
To Grant
99%
With Interview

Examiner Intelligence

Grants 82% — above average
82%
Career Allow Rate
241 granted / 292 resolved
+30.5% vs TC avg
Strong +22% interview lift
Without
With
+21.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
35 currently pending
Career history
327
Total Applications
across all art units

Statute-Specific Performance

§101
4.5%
-35.5% vs TC avg
§103
47.7%
+7.7% vs TC avg
§102
12.3%
-27.7% vs TC avg
§112
28.8%
-11.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 292 resolved cases

Office Action

§103
DETAILED ACTION Remarks This non-final office action is in response to the application filled on 10/16/2024. 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 . Priority Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a) ‐ (d). The certified copy has been filed in parent Application No. EP 23204373.7, filed on 10/18/2023. Information Disclosure Statement As of date of this action, IDS filled has been annotated and considered. Drawings The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. The submitted figures (fig 2, fig 4 and fig 6) include blocks with numbering only not text description. Therefore, the figures are not clear. Examiner recommend to add description on the blocks. No new matter should be entered. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. 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. Conclusion 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Lin can be reached on 571-270-3976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SOHANA TANJU KHAYER/Primary Examiner, Art Unit 3657
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Prosecution Timeline

Oct 16, 2024
Application Filed
Jan 04, 2026
Non-Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
82%
Grant Probability
99%
With Interview (+21.9%)
2y 11m
Median Time to Grant
Low
PTA Risk
Based on 292 resolved cases by this examiner. Grant probability derived from career allow rate.

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