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 .
Response to Amendment
The amendment filed on 02/09/2026 has been entered. Claims 14-22 and 24-26 remain pending in the application. Claim 23 has been canceled.
Claim Interpretation
Examiner notes the amended limitation added to claim 1, “wherein the chronological and/or the spatial change in the characteristic feature and/or the sensor data associated with the characteristic feature is characterized by a stretching, a compression, and/or a curvature.” Particular emphasis is placed on the “is characterized by” term as its meaning is not explicitly defined in applicant’s disclosure, and thus is given its broadest reasonable interpretation. The term “Characterized” is best understood to mean that something is representative of a quality. Therefore, under a broadest reasonable interpretation, the change in the characteristic feature and/or sensor data need only be indicative, representative, or a portrayal of a stretching/compression/curvature to satisfy the claim language. The claimed limitation that reads “wherein the chronological and/or the spatial change in the characteristic feature and/or the sensor data.., is characterized by a stretching, a compression, and/or a curvature” is not interpreted the same, nor is it given the same patentable weight, as a hypothetical limitation that would read “wherein the chronological and/or the spatial change in the characteristic feature and/or the sensor data… is a stretching, a compression, and/or a curvature of the characteristic feature and/or sensor data”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 14-22 and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Meng et al. (US 20200353939 A1) in view of Pohl (US 11802947 B1).
Regarding claim 14, Meng teaches a method for determining a correction factor for a correction of a design- related aberration of an automobile lidar sensor during intended operation of the lidar sensor, the method comprising:
continuously receiving sensor data from the lidar sensor, wherein the sensor data describe surroundings of a vehicle by way of a point cloud ([0069-0070], where the rotatable laser scanning systems (e.g., lidar systems), function by interpreting reflected pulses of points “to generate 3D representations of the regions surrounding the autonomous vehicle”),
detecting a characteristic feature in the sensor data, wherein the characteristic feature has a predetermined characteristic at a first point in time ([0043] and [0073], where the sensor data is processed into a parameter, i.e. characteristic feature, based on the detected object; [0079] and [0081], where parameters are analyzed based on predetermined characteristics including shape and size),
detecting the design-related aberration by way of a renewed check of the characteristic feature for the predetermined characteristic at a second point in time ([0072-0074] and [0076], where the first data and historical data are taken at different times and analyzed together, wherein the first data is deemed uncalibrated if a difference, i.e. aberration, is detected between the parameters),
and determining the recalibration method, with aid of which the design-related aberration in the sensor data is correctable such that the characteristic feature, when detected in the sensor data corrected using the recalibration method, has the predetermined characteristic in context of the renewed check ([0060] and [0125], wherein a recalibration method is determined and performed on the sensor system, and the sensor is again analyzed for accurate detection of an object, and is subsequently approved if successfully verified),
wherein the design-related aberration is detected in the context of the renewed check in that the characteristic feature and/or the sensor data associated with the characteristic feature are subject to a recalibration ([0125], where the recalibrated sensor is again tested, and any differences are analyzed compared to the historical data in order to determine if the sensor reconfiguration was successful).
Meng discloses that a recalibration is performed on the sensor if it’s determined to be uncalibrated. This is determined by analyzing parameters of the data, which includes an angle of view ([0073]). It does not disclose the specifics of this recalibration method and doesn’t explicitly teach that it comprises determining a correction factor, wherein the characteristic feature and/or the sensor data associated with the characteristic feature are subject to a chronological and/or a spatial change contrary to the predetermined characteristic, wherein the chronological and/or the spatial change in the characteristic feature and/or the sensor data associated with the characteristic feature is characterized by a stretching, a compression, and/or a curvature.
In the same field of endeavor, Pohl teaches a method of improving the calibration of lidar sensors in vehicles, wherein the method comprises determining a correction factor (Col. 4, lines 3-8, where the correction factor is the angle to correct the lidar offset) and applying this correction factor to the lidar data so that it is subject to a chronological and/or a spatial change contrary to the predetermined characteristic (Col. 4, lines 8-15, where the offset data of the lidar sensor is recalibrated with a spatial change in the form of a change of the offset angle so that the produced data reflects a correct angular offset), and wherein the chronological and/or the spatial change in the characteristic feature and/or the sensor data associated with the characteristic feature is characterized by a stretching, a compression, and/or a curvature (Col. 4, lines 1-15, where the change of the offset angle of the lidar sensor results in a curvature of the data detected by the lidar sensor. The spatial change in the characteristic feature is characterized by an offset angle, which is shown in Fig. 1 to be a curvature).
A skilled artisan would have been able to give this explicit functionality to the recalibration methods of Meng. This would predictably fix the angle of view of the parameter of the first data so that any validation performed subsequent the recalibration will have a corrected angle of view in light of the angle correction factor.
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the recalibration methods of Meng based on a reasonable expectation of success and motivation of ensuring that LIDAR sensors detect objects at the proper angles, thus ensuring they are functioning normally and are safely operating. This also optimizes the recalibration process by avoiding the need to fully recalibrate the sensor when a simple angle change is all that is necessary, thereby increasing efficiency.
Regarding claim 15, the prior art remains as applied in claim 14. Meng teaches wherein:
the characteristic feature in the point cloud of the sensor data from the lidar sensor describes an infrastructure feature and the predetermined characteristic comprises at least one minimum dimension of the infrastructure feature ([0079] and [0081], where the size of buildings is analyzed for discrepancies, including a minimum distance in the form of “a relative spacing between the one or more features”).
Regarding claim 16, the prior art remains as applied in claim 14. Meng teaches wherein:
the characteristic feature describes a specific geometric parameter of an object recognized in the point cloud of the sensor data from the lidar sensor ([0081-0082]).
Regarding claim 17, the prior art remains as applied in claim 16. Meng teaches wherein:
additional image data are received from a camera, which image data describe the surroundings of the vehicle and by way of which image data the recognized object is verified ([0069], [0073], and [0089], where a second validation on second data compared to the first data is performed to validate the sensor system, wherein both the first and second data describe the surroundings of the vehicle to be recognized).
Regarding claim 18, the prior art remains as applied in claim 14. Meng teaches wherein:
additional image data are received from a camera, wherein the additional image data describe an object in the surroundings of the vehicle by way of an object area and the characteristic feature is detected in the sensor data associated with the object area ([0069], [0073], and [0089], where a second validation on second data compared to the first data is performed to validate the sensor system, wherein both the first and second data describe the surroundings of the vehicle to be recognized).
Regarding claim 19, the prior art remains as applied in claim 17. Meng does not explicitly teach wherein: the characteristic feature is describable by a straight visual object edge in the image data. However, it does teach that characteristic features such as the road and grass, are extracted from the sensor data ([0043]). It is implicit that these objects are describable by a straight visual object edge in the image data or else it would not be possible to extract them from the image.
Regarding claim 20, the prior art remains as applied in claim 18. Meng does not explicitly teach wherein: the characteristic feature is describable by a straight visual object edge in the image data. However, it does teach that characteristic features such as the road and grass, are extracted from the sensor data ([0043]). It is implicit that these objects are describable by a straight visual object edge in the image data or else it would not be possible to extract them from the image.
Regarding claim 21, the prior art remains as applied in claim 14. Meng does not explicitly teach wherein: the characteristic feature is describable by multiple reflection points of the point cloud arranged along a straight line and the reflection points arranged along the straight line have similar reflection properties. However, Meng teaches that a rotatable laser scanning system (e.g., lidar system) of the sensor system functions by measuring the reflections of pulses of objects around the vehicle ([0070]). It also teaches that this sensing system is configured to identify objects including traffic lights (see Fig. 11). When the characteristic feature is determined based on traffic lights detected by a lidar sensor configured to generate a 3D point cloud, it is implicit that the characteristic feature is describable by multiple reflection points of the point cloud arranged along a straight line and the reflection points arranged along the straight line have similar reflection properties as traffic lights are essentially comprised of a plurality of lights arranged along a straight line that have similar reflection properties.
Regarding claim 22, the prior art remains as applied in claim 14. Meng teaches wherein:
system status data are received, which system status data describe an operating status of the vehicle, the operating status comprises at least one driving status of the vehicle and/or an external surroundings status, and the determination of the correction factor only takes place when the system status data describe an operating status to be corrected ([0108-0109], where the recalibration engine performs a recalibration upon such a recalibration being deemed necessary by the determination engine and error handling system, wherein the recalibration include examining the external landmarks around the vehicle so as to recalibrate the sensors).
Regarding claim 24, the prior art remains as applied in claim 14. Meng teaches a computing device for a vehicle, wherein the computing device is configured to carry out the method according to claim 14 ([0128] and [0130], where the device performs the method previously disclosed).
Regarding claim 25, the prior art remains as applied in claim 24. Meng teaches a lidar sensor system for a vehicle (see Fig. 1), the lidar sensor system comprising:
a lidar sensor, which comprises multiple lidar subsensors and/or a design cover ([0043]),
and the computing device for the vehicle according to claim 24 ([0128] and [0130]).
Regarding claim 26, the prior art remains as applied in claim 14. Meng teaches a computer product comprising a non-transitory computer-readable medium having stored thereon program code, which when executed on a computing device, carries out the method according to claim 14 ([0131]).
Response to Arguments
Applicant's arguments filed 02/09/2026 have been fully considered.
Regarding the rejection of the claims under 35 U.S.C 103, applicant argues that the rejection over Meng and Pohl should be removed as “Pohl does not disclose that changing the offset angle results in a change in the curvature of the data. Further, neither Meng nor Pohl discloses that "the chronological and/or the spatial change in the characteristic feature and/or the sensor data associated with the characteristic feature is characterized by a stretching, a compression, and/or a curvature," as recited in amended claim 14.” This argument is unpersuasive.
Examiner notes the interpretation of this limitation as defined in the ‘Claim Interpretation’ section above. Specifically, Pohl teaches the spatial change in the characteristic feature and/or sensor data associated with the characteristic feature, i.e. the change between the Lidar reference 125 and the vehicle reference 120, is indicative of an offset angle between the two. As seen in Fig. 1 of Pohl, this offset angle is a curvature. Therefore, Pohl teaches the limitation amended to the independent claim, and the previous rejection remains as applied.
It is further noted that the features upon which applicant relies (i.e., “a change in the curvature of the data”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). As stated above, the limitation as claimed and as interpreted under a broadest reasonable interpretation merely requires that the spatial change be indicative or a portrayal of a curvature. As this curvature is not claimed to be “of the data”, but rather is only required to be present in order to meet the metes and bounds of the claim language. Applicant is recommended to amend the claim to specify that data itself is directly subjected to this stretching/compression/curvature, rather than the change in the data being just indicative of some generic stretching/compression/curvature.
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.
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/JACK ROBERT BREWER/Examiner, Art Unit 3663
/ADAM D TISSOT/Primary Examiner, Art Unit 3663
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