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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/30/2022 and 04/24/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Examiner’s Note
To help the reader, examiner notes in this detailed action claim language is in bold, strikethrough limitations are not explicitly taught and language added to explain a reference mapping are isolated from quotations via square brackets.
Response to Arguments
Applicant's arguments filed 04/24/2026 have been fully considered but they are not persuasive. An explanation is provided below.
Applicant alleges on p.5:
Moustafa does not teach treating a sensor's own output as a ground truth at close range or performing a retrospective comparison of sensor readings using an odometry-based position calculation. Moustafa's system
is concerned with fusing multiple sensor inputs and adjusting their weights via machine learning algorithms; it does not disclose a specific procedure of using one sensor's near-field reading as a ground truth standard to evaluate that same sensor's earlier far-field reading
The Examiner respectfully disagrees. Moustafa teaches in para 0200 “The models relied upon by the autonomous vehicle's systems may also be developed through training on data sets that describe other preceding trips (by the vehicle or other vehicles), whose ground truth may also be based on the perspective of the vehicle and the results it observes or senses through its sensors.”. Furthermore, the claims do not require using ‘near-field reading’. As such, Applicant’s arguments are unpersuasive.
Applicant alleges on p.6:
Klotzbeucher describes what appears to be a one-time (not real-time) radar calibration technique. In greater detail, Klotzbeucher assumes a stationary target and uses the radar's own closest-range reading as the true value for that target's angle, then back-calculates what the radar should have measured at earlier distances based on the vehicle's known path. This is used to adjust the radar's angular calibration. Klotzbeucher does not describe outputting any measured or quantitative "inaccuracy" value.
The Examiner respectfully disagrees. Klotzbeucher describes real-time radar calibration as described in p.4 “The method can be performed on-line while operating the vehicle, which is an advantage since it allows for continuously maintaining vehicle sensor calibration over time.”.
Furthermore, Klotzbeucher described outputting measured inaccuracy values as taught in para 0849 “The sensor model keeps track of the effect of the previous data abstraction actions on the level of blur throughout the data as well. Finally, the filtering action keeps track of the level of noise and blur in its output, throughout the output data. This information may be used during the next time instant, if the filtering action is a time-recursive process, e.g., a type of Kalman filtering. This information may also be used by subsequent processes, such as sensor fusion of the abstracted sensor data, or by the detection stage.” Here, noise is an inaccuracy value and this information is used within the sensor fusion/ADAS system. As such, Applicant’s arguments are unpersuasive.
Claim Objections
Claim(s) 1 is objected to because of the following informalities: There is a missing comma after the phrase ‘based on data from the odometry system’ (missing comma before the amendment). Appropriate correction is required.
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 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) 1, 3, 5-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moustafa et al. (US 20220126864 hereinafter Moustafa) in view of Klotzbeucher et al. (EP 3761054 hereinafter Klotzbeucher).
Regarding claim 1, Moustafa teaches A method for assessing measuring uncertainties of at least one environment detection sensor of an ego vehicle, comprising (0798 “FIG. 124A illustrates an approach for learning weights for sensors under different contexts in accordance with certain embodiments. First, a model that detects objects as accurately as possible may be trained for each individual sensor, e.g., camera, LIDAR, or radar”; 0060 “FIG. 57 depicts a flow for triggering an action based on an accuracy of a linear classifier.”):
- recording an environment of an ego vehicle by means of at least one environment detection sensor of the ego vehicle (0166 “autonomous driving stacks may allow vehicles to self-control or provide driver assistance to detect roadways, navigate from one point to another, detect other vehicles and road actors (e.g., pedestrians (e.g., 135), bicyclists, etc.), detect obstacles and hazards (e.g., 120), and road conditions (e.g., traffic, road conditions, weather conditions, etc.),”);
- detecting at least one object which is located in a region in front of the ego vehicle in a direction of travel (0166 “autonomous driving stacks may allow vehicles to self-control or provide driver assistance to detect roadways, navigate from one point to another, detect other vehicles and road actors (e.g., pedestrians (e.g., 135), bicyclists, etc.), detect obstacles and hazards (e.g., 120), and road conditions (e.g., traffic, road conditions, weather conditions, etc.),”);
- specifying a sensor output of at least one environment detection sensor as a ground truth (0431 “Data scoring trainer 4928 trains models on categories and/or scores. In various embodiments, the instances of the detected objects and their associated scores and/or categories may be used as ground truth by the data scoring trainer 4928”)
- calculating a position of the object in relation to the ego vehicle at an earlier point in time based on data of a system for positioning (Abstract “Sensor data is received from a plurality of sensors”),
- comparing a sensor output at the earlier point in time with the calculated position of the object (0786 “In a particular embodiment, to assist with object tracking, when the ground truth data are available for different contexts and object position at various instants under these different contexts, the fusion weights may be determined from the training data using a combination of a machine learning algorithm that predicts context and a tracking fusion algorithm that facilitates prediction of object position.”); and
- assessing a measuring inaccuracy of the at least one environment detection sensor based on a result of the comparison (0790 “the fusion algorithm 12102 may take data (e.g., sensor data 12104) from various sensors and ground truth context info 12106 as input, fuse the data together using different weights, predict an object position using the fused data, and utilize a cost function (such as a root-mean squared error (RMSE) or the like) that minimizes the error between the predicted position and the ground truth position (e.g., corresponding location of object locations 12108).”; 0060 “FIG. 57 depicts a flow for triggering an action based on an accuracy of a linear classifier.”)
- wherein the system for positioning is an odometry system of the ego vehicle (0384 “FIG. 41 shows a variety of sensor inputs including non-line of sight, line of sight, vehicle state, and positioning.”; 0178 “A localization engine 240 may also be included within an in-vehicle processing system 210 . . . to determine a high confidence location of the vehicle and the space it occupies within a given physical space (or “environment”); 1003 “The autonomous vehicle can then use the new dimensions in its autonomous vehicle algorithms, including for example, the safe distance algorithm.””) such that the calculated position of the object at the earlier point in time is based on data from the odometry system (0846 “combining multiple images or LIDAR scans captured at slightly different times and accounting for the motion of the sensor between the two capture times. This combination of multiple images of the same scene enables improved resolution (super-resolution), noise reduction, and other forms of sensor fusion”; 0848 “In some embodiments, a time-recursive method of filtering may be used. A time-recursive image filter may use the previously filtered image at the previous time instant and combine it with image data sensed at the current time”)
- providing the assessed measuring inaccuracy to at least one of a sensor fusion system or a driver assistance system (0849 “Finally, the filtering action keeps track of the level of noise and blur in its output, throughout the output data. This information may be used during the next time instant, if the filtering action is a time-recursive process, e.g., a type of Kalman filtering. This information may also be used by subsequent processes, such as sensor fusion of the abstracted sensor data, or by the detection stage”; 1868 “training the third machine learning model further based on a loss representing a difference between ground truth labels and hard prediction targets of the third machine learning model.”; 0518 “a computing system of a vehicle determines a signal quality metric based on sensor data and a context of the sensor data. At 6704, a likelihood of safety associated with a handoff of control of the vehicle is determined based on the signal quality metric. At 6706, a handoff is prevented or initiated based on the likelihood of safety.”).
Moustafa does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Klotzbeucher teaches
specifying a sensor output of at least one environment detection sensor as a ground truth when a distance between the ego vehicle and the detected object falls below a specifiable minimum distance between the ego vehicle and the detected object (Abstract “determining a corresponding sequence of ground truth values associated with the stationary target (140), wherein the ground truth values are determined based on a minimum detected distance from the vehicle (100) to the stationary target (140) and on a track of the vehicle”)wherein the calculation is more precise and subject to fewer errors than the sensor output (p.7 “due to calibration error, there is a spread in estimates of bearing to the detected object, which shows up as some deviation in the upper part 215 of the graph 200. It is desired to compensate for this error by calibrating the radar transceiver 110.”)
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the sensor calibration system and method of Klotzbeucher with the autonomous vehicle system and method of Moustafa. One would have been motivated to do so in order to advantageously reduce complexity and improve signal processing resources (Klotzbeucher p.5). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Klotzbeucher merely teaches that it is well-known to incorporate the particular ground truth features. Since both Moustafa and Klotzbeucher disclose similar ADAS technology, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 3, Moustafa in view of Klotzbeucher teach The method according to Claim 1, wherein the object is a further road user (Moustafa fig 1), a feature of the surroundings or a landmark.
Regarding claim 5, Moustafa in view of Klotzbeucher teach The method according to Claim 1, further comprising establishing at least one angle between the object and ego vehicle (Klotzbeucher p. 3 “estimating relative angle to a detected object with respect to, e.g., a boresight direction of the radar transceiver is a difficult problem in general.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the sensor calibration system and method of Klotzbeucher with the autonomous vehicle system and method of Moustafa. One would have been motivated to do so in order to advantageously reduce complexity and improve signal processing resources (Klotzbeucher p.5). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Klotzbeucher merely teaches that it is well-known to incorporate the particular ground truth features. Since both the prior combination and Klotzbeucher disclose similar ADAS technology, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 6, Moustafa in view of Klotzbeucher teach The method according to Claim 1, wherein assessing the measuring inaccuracy further comprises considering further environmental factors (Moustafa 0297 “as shown in the example of FIG. 12, internal system health information 1210 may be provided (e.g., from one or more internal sensors and/or a system diagnostics module) along with data 1215 (from integrated or extraneous sensors) describing conditions of the external environment surrounding the vehicle (e.g., weather information, road conditions, traffic conditions, etc.) or describing environmental conditions along upcoming portions of a determined path plan, among other example inputs. The machine learning model 1205 may determine one or more types of events from these inputs, such as broken or otherwise compromised sensors (e.g., 1220) and weather (e.g., 1225) events, such as discussed above, as well as communication channel characteristics (1230) (e.g., such as areas of no coverage, unreliable signal, or low bandwidth wireless channels, which may force the vehicle to collect rich or higher-fidelity data for future use using event and classification models), and road condition and traffic events (e.g., 1235)”).
Regarding claim 7, Moustafa in view of Klotzbeucher teach The method according to Claim 6, wherein the further environmental factors comprise at least one of one or more current weather conditions or a time of day (Moustafa 0297 “as shown in the example of FIG. 12, internal system health information 1210 may be provided (e.g., from one or more internal sensors and/or a system diagnostics module) along with data 1215 (from integrated or extraneous sensors) describing conditions of the external environment surrounding the vehicle (e.g., weather information, road conditions, traffic conditions, etc.) or describing environmental conditions along upcoming portions of a determined path plan, among other example inputs. The machine learning model 1205 may determine one or more types of events from these inputs, such as broken or otherwise compromised sensors (e.g., 1220) and weather (e.g., 1225) events, such as discussed above, as well as communication channel characteristics (1230) (e.g., such as areas of no coverage, unreliable signal, or low bandwidth wireless channels, which may force the vehicle to collect rich or higher-fidelity data for future use using event and classification models), and road condition and traffic events (e.g., 1235)”).
Regarding claim 8, claim 8 recites substantially the same limitations as claim 1. Therefore, claim 8 is rejected for substantially the same reasons as claim 1. Moustafa further teaches the computing device may be external (0167 “For instance, as shown in the illustrative example of FIG. 1, supporting drones 180 (e.g., ground-based and/or aerial), roadside computing devices (e.g., 140), various external (to the vehicle, or “extraneous”) sensor devices (e.g., 160, 165, 170, 175, etc.), and other devices may be provided as autonomous driving infrastructure separate from the computing systems, sensors, and logic implemented on the vehicles (e.g., 105, 110, 115) to support and improve autonomous driving results provided through the vehicles, among other examples.”).
Claim(s) 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moustafa et al. (US 20220126864 hereinafter Moustafa) in view of Klotzbeucher et al. (EP 3761054 hereinafter Klotzbeucher) as applied to claims 1 and 8, and further in view of Hilligardt et al. (US 20190300007 hereinafter Hilligardt).
Regarding claim 10, the cited prior art teaches The system according to Claim 8,
The cited prior art does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Hilligardt teaches
wherein the odometry system comprises a wheel speed sensor, and the data from the odometry system comprises data from the wheel speed sensor (0040 “Additionally or alternatively, the odometry sensors 316 may include one or more encoders, Hall speed sensors, and/or other measurement sensors/devices configured to measure a wheel speed, rotation, and/or number of revolutions made over time.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the autonomous vehicle system and method of Hilligardtwith with the cited prior art. One would have been motivated to do so in order to advantageously increase the number of available information for an autonomous vehicle system (Hilligardt 0026). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Hilligardt merely teaches that it is well-known to incorporate the particular ground truth features. Since both the prior combination and Hilligardt disclose similar ADAS technology for autonomous vehicles, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 11, the cited prior art teaches The system according to Claim 8,
The cited prior art does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Hilligardt teaches
wherein the data from the odometry system comprises a number of wheel revolutions of the ego vehicle (0040 “Additionally or alternatively, the odometry sensors 316 may include one or more encoders, Hall speed sensors, and/or other measurement sensors/devices configured to measure a wheel speed, rotation, and/or number of revolutions made over time.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the autonomous vehicle system and method of Hilligardtwith with the cited prior art. One would have been motivated to do so in order to advantageously increase the number of available information for an autonomous vehicle system (Hilligardt 0026). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Hilligardt merely teaches that it is well-known to incorporate the particular ground truth features. Since both the prior combination and Hilligardt disclose similar ADAS technology for autonomous vehicles, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 12, the cited prior art teaches The method according to Claim 1,
The cited prior art does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Hilligardt teaches
wherein the odometry system comprises a wheel speed sensor, and the data from the odometry system comprises data from the wheel speed sensor (0040 “Additionally or alternatively, the odometry sensors 316 may include one or more encoders, Hall speed sensors, and/or other measurement sensors/devices configured to measure a wheel speed, rotation, and/or number of revolutions made over time.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the autonomous vehicle system and method of Hilligardtwith with the cited prior art. One would have been motivated to do so in order to advantageously increase the number of available information for an autonomous vehicle system (Hilligardt 0026). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Hilligardt merely teaches that it is well-known to incorporate the particular ground truth features. Since both the prior combination and Hilligardt disclose similar ADAS technology for autonomous vehicles, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 13, the cited prior art teaches The method according to Claim 1,
The cited prior art does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Hilligardt teaches
wherein the data from the odometry system comprises a number of wheel revolutions of the ego vehicle (0040 “Additionally or alternatively, the odometry sensors 316 may include one or more encoders, Hall speed sensors, and/or other measurement sensors/devices configured to measure a wheel speed, rotation, and/or number of revolutions made over time.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the autonomous vehicle system and method of Hilligardtwith with the cited prior art. One would have been motivated to do so in order to advantageously increase the number of available information for an autonomous vehicle system (Hilligardt 0026). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Hilligardt merely teaches that it is well-known to incorporate the particular ground truth features. Since both the prior combination and Hilligardt disclose similar ADAS technology for autonomous vehicles, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to application’s disclosure:
Han et al. (US 12065147) discloses “A method of simultaneously estimating a movement and shape of a target vehicle using a preliminary distribution model of a tracklet may have high reliability in estimation performance even with a change in heading of the target vehicle (See abstract)”
Kim et al. (US 20200189525) discloses “An active vehicle control notification method may include receiving external data from a server by a vehicle, determining whether the external data received from the server and a control driving condition of the vehicle are matched, by the vehicle, when the external data and the control driving condition are not matched, correcting the external data based on the control driving condition inside the vehicle, and uploading the corrected external data to the server (See abstract)”.
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/ISMAAEEL A. SIDDIQUEE/
Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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