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 .
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/18/2024 was filed and has been considered by the examiner.
Drawings
The drawings that were filed on 12/18/2024 have been 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.
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.
Claim(s) 1-4, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Naserian et al. (US 20230410660 A1), herein after will be referred to as Naserian, in view of Qi et al. (US 20230242152 A1), and herein after will be referred to as Qi.
Regarding Claim 1, Naserian teaches a map generation device, comprising (A system comprising a controller that generates occupancy grid maps; [0004]):
an information acquisition unit that acquires positional information on a specific vehicle and positional information on surrounding vehicles that are positioned around the specific vehicle from on-board devices that are arranged respectively on the specific vehicle and the surrounding vehicle, respectively (Determining the host vehicle’s own location via GPS and receiving the locations of surrounding vehicles via vehicle communication system; [0006-0007]);
a map integration unit that integrates an information being on a surrounding object and having been acquired from the on-board device…(The controller merges (integrates) remote sensor data regarding blind spots (surrounding objects) into the map; [0004]).
Naserian does not explicitly teach determined on the basis of degrees of agreement between the positional information on the specific vehicles detected by the on-board devices mounted in the specific vehicles and the positional information on the specific vehicles detected by the on-board devices mounted in the surrounding vehicles.
However, Qi discloses a misbehavior detection system that validates the vehicle’s data by comparing a vehicle’s self-reported position against location derived from external sensors monitoring that vehicle. Qi teaches determining data validity by comparing the source vehicle location against fusion data coordinates to determine if they match ([0074]). This teaching is equivalent to the claimed limitation because the source vehicle location corresponds to the specific vehicle’s GPS data and the fusion data corresponds to the position of that specific vehicle detected by the sensors of the system to determine the reliability of the data.
Naserian and Qi are considered to be analogous to the claim invention because they are in the same field of vehicle control and communication systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Naserian to incorporate the teachings of validating vehicle positions using fusion data from surrounding vehicles as taught by Qi based on the motivation to detect spoofed GPS data and improve the map generation process by verifying that remote data sources are reporting accurate locations prior to merging the data into the host’s map. This provides the benefit of preventing corruption of the high-definition map with erroneous or malicious data or spoofed vehicles.
Regarding Claim 2, Naserian and Qi remains as applied above in claim 1. Naserian further teaches the map integration unit that integrates positional information with a map, the information being on a surrounding object and having been acquired from the on-board device determined on the basis of the degrees of reliability, the determined on-board device being among the on-board devices (Combining the remote sensor data of surrounding objects after determining that the validity threshold is met; [0010]).
Naserian does not explicitly teach a reliability determination unit that determines a degree of reliability of each of the on-board devices on the basis of degrees of agreement between the positional information on the specific vehicles detected by the on-board devices mounted in the specific vehicles and the positional information on the specific vehicles detected by the on-board devices mounted in the surrounding vehicles.
However, Qi discloses verifying the reliability of a vehicle’s data transmission through the misbehavior detection module that determines a V2V message is legitimate or malicious based on the comparison between the vehicle’s self-reported GPS position and the position detected by the fusion data ([0059] [0074]). This teaching is equivalent to the claimed limitation because the misbehavior module validates the data by comparing the data from the self-reported GPS position of the vehicle and the position detected by the surrounding sensors to agree whether the data is true. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Naserian to incorporate the teachings of the misbehavior detection module to validate the reliability of the data as taught by Qi based on the motivation to improve the map integration process by utilizing the host vehicle as a dynamic calibration target to verify that remote data sources are not miscalibrated or spoofing their locations before merging their data. This provides the benefit of ensuring that the merged data is constructed from verified sources and data and prevents map corruption from erroneous vehicle data.
Regarding Claim 3, Naserian and Qi remains as applied above in claim 2. Naserian further teaches the map integration unit integrates positional information with the map, the positional information being on a surrounding object acquired from the on-board device having the highest degree of reliability (A producer remote system is selected to maximize data quality of the surrounding objects; [0054-0055]).
Regarding Claim 4, Naserian and Qi remains as applied above in claim 2. Naserian further teaches the map integration unit integrates positional information with the map, the positional information being on a surrounding object acquired from the on-board device having the degree of reliability higher than a predetermined threshold (The system combine (integrates) the remote data of overlapping points and if the calculated percentage of agreement (degree of reliability) exceeds a predetermined validity threshold; [0061] [0063]).
Regarding Claim 7, Qi teaches a map generation method, comprising (A system comprising a controller that generates occupancy grid maps; [0004]):
acquiring positional information on a specific vehicle and positional information on surrounding vehicles that are positioned around the specific vehicle from on-board devices that are arranged respectively on the specific vehicle and the surrounding vehicle, respectively (Acquiring the host vehicle’s location via GPS and receiving the locations of surrounding vehicles via vehicle communication system; [0017-0018]);
integrating an information being on a surrounding object and having been acquired from the on-board device……(The controller merges (integrates) remote sensor data regarding blind spots (surrounding objects) into the map; [0004]).
Naserian does not explicitly teach determined on the basis of degrees of agreement between the positional information on the specific vehicles detected by the on-board devices mounted in the specific vehicles and the positional information on the specific vehicles detected by the on-board devices mounted in the surrounding vehicles.
However, Qi discloses a misbehavior detection system that validates the vehicle’s data by comparing a vehicle’s self-reported position against location derived from external sensors monitoring that vehicle. Qi teaches determining data validity by comparing the source vehicle location against fusion data coordinates to determine if they match ([0074]). This teaching is equivalent to the claimed limitation because the source vehicle location corresponds to the specific vehicle’s GPS data and the fusion data corresponds to the position of that specific vehicle detected by the sensors of the system to determine the reliability of the data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Naserian to incorporate the teachings of validating vehicle positions using fusion data from surrounding vehicles as taught by Qi based on the motivation to detect spoofed GPS data and improve the map generation process by verifying that remote data sources are reporting accurate locations prior to merging the data into the host’s map. This provides the benefit of preventing corruption of the high-definition map with erroneous or malicious data or spoofed vehicles.
Claim(s) 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Naserian in view of Qi, as applied in claim 2, and in further view of Kroepfl et al. (US 20230357076 A1), and herein after will be referred to as Kroepfl.
Regarding Claim 5, Naserian and Qi remains as applied above in claim 2. Naserian does not explicitly teach the on-board devices on the basis of degrees of agreement between the positional information on the specific vehicles detected by the on-board devices mounted in the specific vehicles and the positional information on the specific vehicles detected by the on-board devices mounted in the surrounding vehicles.
However, Qi discloses a misbehavior detection system that validates the vehicle’s data by comparing a vehicle’s self-reported position against location derived from external sensors monitoring that vehicle. Qi teaches determining data validity by comparing the source vehicle location against fusion data coordinates to determine if they match ([0074]). This teaching is equivalent to the claimed limitation because the source vehicle location corresponds to the specific vehicle’s GPS data and the fusion data corresponds to the position of that specific vehicle detected by the sensors of the system to determine the reliability of the data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Naserian to incorporate the teachings of validating vehicle positions using fusion data from surrounding vehicles as taught by Qi based on the motivation to detect spoofed GPS data and improve the map generation process by verifying that remote data sources are reporting accurate locations prior to merging the data into the host’s map. This provides the benefit of preventing corruption of the high-definition map with erroneous or malicious data or spoofed vehicles.
Naserian and Qi does not explicitly teach the reliability determination unit determines degrees of reliability for each direction…; and the map integration unit integrates the positional information on the surrounding object with the map on the basis of the degrees of reliability for each direction.
However, Kroepfl discloses a system that processes positional information into directional coordinates of latitude/x, longitude/y, and altitude/z and calculates the covariance for vehicle localization as an ellipsoid ([0069] [0146] [0129]). This teaching is equivalent to the claimed limitation of the reliability determination unit determines degrees of reliability for each direction because the covariance is a metric that defines uncertainty/reliability and is applied for each direction. Kroepfl further teaches that the individual localization with the highest covariance corresponds to the greatest outliers and is weighted less for determining fused localization ([0146]). This teaching is equivalent to the claimed limitation of the map integration unit integrates the positional information on the surrounding object with the map on the basis of the degrees of reliability for each direction because the individual localization with the highest covariance is weighted less (degree of reliability) for fused localization in each direction.
Naserian, Qi, and Kroepfl are considered to be analogous to the claim invention because they are in the same field of autonomous driving systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Naserian and Qi to incorporate the teachings of calculating directional reliability and weighting integrations as taught by Kroepfl based on the motivation to improve the precision of the map generation by accounting for anisotropic errors where a vehicle’s longitudinal position is reliable but the lateral position is not. Applying weighting allows the system to integrate data that is reliable in one direction even if it is less reliable in another which maximizes the precision and utility of the fused map data. This provides the benefit of generating a map that is accurate in all relevant directions.
Regarding Claim 6, Naserian and Qi remains as applied above in claim 1. Naserian does not explicitly teach Naserian the information being on a surrounding object acquires from the on-board devices which is determined on the basis of degrees of agreement between the positional information on the specific vehicles which detected by the on-board devices mounted on the specific vehicles and the positional information on the specific vehicles detected by the on-board devices mounted on the surrounding vehicles.
However, Qi discloses a misbehavior detection system that validates the vehicle’s data by comparing a vehicle’s self-reported position against location derived from external sensors monitoring that vehicle. Qi teaches determining data validity by comparing the source vehicle location against fusion data coordinates to determine if they match ([0074]). This teaching is equivalent to the claimed limitation because the source vehicle location corresponds to the specific vehicle’s GPS data and the fusion data corresponds to the position of that specific vehicle detected by the sensors of the system, as an object surrounding the system, to calculate the agreement between the GPS position and the externally sensed position. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Naserian to incorporate the teachings of validating vehicle positions using fusion data from surrounding vehicles as taught by Qi based on the motivation to detect spoofed GPS data and improve the map generation process by verifying that remote data sources are reporting accurate locations prior to merging the data into the host’s map. This provides the benefit of preventing corruption of the high-definition map with erroneous or malicious data or spoofed vehicles.
Naserian and Qi does not explicitly teach the map integration unit maps the information being on a surrounding object as a dynamic object data.
However, Kroepfl discloses processing sensor data to generate detections of dynamic objects and identifying their locations/types ([0058] [0070]). This teaching is equivalent to the claimed limitation because the system identifies an object as a dynamic actor, tracks the dynamic actor, and encodes it as a dynamic object in the mapstream. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Naserian and Qi to incorporate the teachings of tracking and encoding dynamic object data as taught by Kroepfl based on the motivation to improve the object detection and the map integration by distinguishing dynamic objects from static objects. This provides the benefit of enabling functions, such as predicting the future behavior of the dynamic objects for collision avoidance or filtering them out to create a clean static map layer.
Prior Art
The prior art made of record and not relied upon is considered pertinent, most relevant, to applicant's disclosure.
Shida (US 20130030687 A1)
Ariyoshi (US 20220268594 A1)
Caveney (US 20200241530 A1)
Inaba (US 20210248387 A1)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD ANDREW IZON DIZON whose telephone number is (571)272-4834. The examiner can normally be reached M-F 9AM-5PM.
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/EDWARD ANDREW IZON DIZON/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663