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 .
Status of Claims
This office action is in response to the application filed on April 24, 2025. Claims 1-14 are presently pending and are presented for examination.
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. EP24173231.2, filed om April 30, 2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 24, 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-4, 9-11 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Luo et al., US 20250356624 A1 (Hereinafter, “Luo”), in view of Calleija et al., US 20240286626 A1 (Hereinafter, “Calleija”).
Regarding Claim 1, 9, 10 and 14 Luo discloses a vehicle comprising: a plurality of sensors; an automated driving system including an object perception system configured to ingest sensor data samples generated by one or more sensors out of the plurality of sensors and to output object perception data indicative of one or more detected objects in a surrounding environment of the vehicle and of one or more attributes of the detected objects; See [0026], “systems, methods, and computer program products described herein include and/or implement camera-assisted LiDAR data verification. A vehicle (such as an autonomous vehicle) has multiple sensors mounted at various locations on the vehicle. Data from these sensors can be used for object detection. In object detection, sensor data is analyzed to annotate portions of the sensor data with confidence scores that indicate the presence of a particular object class instance within a respective portion of the data captured by a sensor.” And [0066], “perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system 402 receives image data captured by at least one camera (e.g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system 402 classifies at least one physical object based on one or more groupings of physical objects.”
an apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least: output reference data indicative of one or more detected objects in the surrounding environment of the vehicle and of one or more attributes of the detected objects based on sensor data samples generated by one or more sensors out of the plurality of sensors; compare the object perception data with the reference data; assign one or more confidence values to the object perception data based on the comparison; and See Fig.3 and [0055] for device and Fig.9,11, 12, [0092-0097] and [0105-0114] for process where a detected color (one or more attributes of the detected object based on sensor data samples) is compared to a reference color (reference data) and the confidence value is assigned/adjusted based on this comparison.
control the vehicle, the object perception system, and/or one or more downstream ADS functions configured to ingest the object perception data, based on the assigned one or more confidence values. See [0040-0041], [0041]“…The data generated by the one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located. In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle compute 202f, drive-by-wire (DBW) system 202h, and safety controller 202g.” and [0116-0125], [0119] ”In examples, the present techniques enable accurate confidence scores associated with LiDAR data. Based on the accurate confidence scores, updating or generating LiDAR data enables the generation of precise images that represent the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like. The updated LiDAR information enables better localization, where a localization system (e.g., localization system 406 of FIG. 4) determines the position of the AV in the area based on comparing updated LiDAR information to a map.”, [0124]”… The method includes causing, with the at least one processor, a vehicle to be controlled based on the first spatial location, first detection confidence score, second spatial location and second detection confidence score.”...
Luo discloses a vehicle perception evaluation system, but does not explicitly disclose reference data comparison from prior vehicle sensor data. However, Calleija teaches using reference (baseline) data for determining the current detections/performance of the system with the reference data being determined from prior vehicle sensor data in [0021], “As the perception system detects and classifies different types of objects and surfaces using incoming sensor data from vehicle sensors, the system can analyze detection parameters to evaluate the perception system's performance in real-time. In particular, the system can evaluate how well the perception system is detecting and classifying the different types of objects based on prior baseline detection parameters generated to enhance performance in the current conditions of the environment.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Luo’s device with the reference data comparison limitations disclosed in Calleija with reasonable expectation of success. The motivation for doing so would have been to enable the perception system to more accurately interpret the surrounding environment, see Calleija [0021].
Regarding Claim 3, Luo discloses the following limitation dependent on Claim 1:
wherein the object perception data and the reference data are based on sensor data samples generated by the same one or more vehicle-mounted sensors. See [0108-0109].
Regarding Claim 4, Luo discloses the following limitation dependent on Claim 1:
wherein the object perception data and the reference data are based on sensor data samples generated by different vehicle-mounted sensors. See [0110-0112].
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Luo in view of Calleija, in further view of Sane et al., US 20250206331 A1 (Hereinafter, “Sane”).
Regarding Claims 2 and 11, Luo discloses the following limitation dependent on Claim 1:
wherein the one or more confidence values comprises: one or more per-object confidence values indicative of a confidence level of the one more detected objects of the object perception data, wherein a higher confidence level of a detected object indicates a higher likelihood that the detection is correct; and See [0026-0028] and [0083-0087].
Luo discloses a vehicle perception evaluation system, but does not explicitly disclose sub-section confidence scoring. However, Sane teaches autonomous systems including the following: one or more per-zone confidence values indicative of a confidence level of each zone of a plurality of zones within the surrounding environment of the vehicle, wherein a higher confidence level of a zone indicates a higher likelihood that the object perception data pertaining to that zone is correct. See [0071-0072] and [0081], “in response to a determination that fine-level blockages and/or degradations are present in the sensor data 202 corresponding to the individual sensor, it may be determined whether and/or where the fine-level degradations affect sub-sections of the aggregate field of view corresponding to the visibility confidence model 206. In some embodiments, one or more other data structures and/or techniques may be used to determine whether fine-level blockages and/or degradations intersect with sub-sections of the aggregate field of view represented in the visibility confidence model 206.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Luo’s device with the sub-section confidence limitations disclosed in Sane with reasonable expectation of success. The motivation for doing so would have been to generating a visibility confidence model which may indicate a level of confidence in sensor data that may correspond to individual sub-sections of an aggregate field of view, see Sane [Abstract].
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Luo in view of Calleija, in further view of Gyllenhammar et al., US 20220371614 A1 (Hereinafter, “Gyllenhammar”).
Regarding Claims 5 and 12, Luo discloses a vehicle perception evaluation system, but does not explicitly disclose estimation errors. However, Gyllenhammar teaches a vehicle perception system including the following: further comprising: calculating one or more estimation errors of the object perception data based on the comparison; aggregating the calculated estimation errors over time; and wherein the assigned one or more confidence values are based on the aggregated estimation errors. See [0029].
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Luo’s device with the error estimation limitations disclosed in Gyllenhammar with reasonable expectation of success. The motivation for doing so would have been to evaluate the obtained perception data in reference to the joint world view in order to determine an estimation deviation in an identified match between the perceptive parameter of the perception data and a corresponding perceptive parameter in the joint world view, see Gyllenhammar [0006].
Claims 6-8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Luo in view of Calleija, in further view of Sadek et al., US 20230322259 A1 (Hereinafter, “Sadek”).
Regarding Claim 6, Luo discloses a vehicle perception evaluation system, but does not explicitly disclose data capture time periods. However, Sadek teaches a vehicle perception system including the following: wherein the object perception system is configured to output the object perception data pertaining to a specific moment in time based on sensor data samples captured during a first time period; and wherein the output reference data pertains to the specific moment in time based on sensor data samples captured during a second time period, wherein the first time period is shorter than the second time period. See [0115], “However, some types of objects and features that may be included in map data may be temporary or change over a period time such that the date or age of the associated map data is relevant, and may be included in the safety and/or confidence information. FIG. 4B illustrates a few examples of objects and features that may change over time for which date or age information may be included in confidence information. For example, while the roadway curb 404, driving Lane 406, and dividing lines 408 are likely to remain as defined in the map data for a significant period of time, some roadways structures 430 that may be useful as navigation points for vehicle sensors (e.g., radar, lidar, cameras) may be modified or torn down over time. As another example, potholes 432 are roadway features that are relevant to autonomous driving but likely temporary. The location of potholes may be reported to a computing device that generates the map data by vehicles equipped with an ADS implementing various embodiments. The location of potholes may then be distributed to all autonomous vehicles via and updated map database. Because potholes may be repaired at some point, the date that the pothole was first reported or an age of the pothole location data may be included in the confidence information associated with the pothole map data that is included in the map database or a linked database. Providing this information may enable a vehicle ADS operating in autonomous driving mode to avoid confusion when approaching the location of a pothole 432 that is not observed by vehicle sensors (e.g., cameras) if the age of the pothole location data exceeds a threshold value.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Luo’s device with the time period limitations disclosed in Sadek with reasonable expectation of success. The motivation for doing so would have been to enable a vehicle ADS operating in autonomous driving mode to avoid confusion when approaching a location not observed by vehicle sensors, see Sadek [0115].
Regarding Claims 7 and 13, Luo discloses a vehicle perception evaluation system, but does not explicitly disclose data capture time periods. However, Sadek teaches a vehicle perception system including the following: wherein the first time period encompasses a time prior to the specific moment in time until and including the specific moment in time, and the second time period encompasses a time prior to the specific moment in time and a time after the specific moment in time. See [0115].
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Luo’s device with the time period limitations disclosed in Sadek with reasonable expectation of success. The motivation for doing so would have been to enable a vehicle ADS operating in autonomous driving mode to avoid confusion when approaching a location not observed by vehicle sensors, see Sadek [0115].
Regarding Claim 8, Luo discloses a vehicle perception evaluation system, but does not explicitly disclose data capture time periods. However, Sadek teaches a vehicle perception system including the following: wherein the object perception data is output at a first frequency and wherein the reference data is output at a second frequency lower than the first frequency. Claim 8 uses the same limitation as the time period in Claim 7 just inverse relationship to period of time to calculate frequency in [0115].
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Luo’s device with the time period limitations disclosed in Sadek with reasonable expectation of success. The motivation for doing so would have been to enable a vehicle ADS operating in autonomous driving mode to avoid confusion when approaching a location not observed by vehicle sensors, see Sadek [0115].
Additional Relevant Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and may be found on the accompanying PTO-892 Notice of References Cited:
US Publication US 20250335746 A1 by Zhang et al.
US Publication US 20150266490 A1 by Coelingh et al.
Conclusion
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/B.K.P./Examiner, Art Unit 3669 /KENNETH M DUNNE/Primary Examiner, Art Unit 3669
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