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
Pending
1-7, and 9-12
103
1-7, and 9-12
Response to Amendment
This office action is responsive to the amendment filed on 17April2026. As directed by the amendment: claims 1 and 11 has (have) been amended, claims 8 has/have been cancelled, and no new claims has/have been added. Thus, claims 1-7, and 9-12 are presently pending in this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-7, and 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bagheri US 20160161265 in view of Deng US 20180056995.
Bagheri discloses,
Claims 1 and 11; An external environment recognition device(2) including storage configured to store map information therein([0066] The map data 311 and/or the digital map 31 may be comprised in an optional map database 3), the external environment recognition device being configured to estimate a self-position which is a position of a host vehicle on a map([0068] the vehicle 2 furthermore has on-board a positioning system 6, based on which the current position 21 of the vehicle 2 may be determined. The vehicle 2 is currently travelling on an exemplifying road 7, along which the stationary landmark 4 is situated) stored in a map database contained in the storage([0069] Vehicle position 21 corresponds with map data 311 which is stored in the map database 3), the self-position being based on external environment information acquired by an external environment sensor mounted on the host vehicle([0069] Position of the vehicle can be estimated by detecting a landmark 8 around the vehicle and compare it to a mapped digital landmark); recognize targets around the host vehicle based on the external environment information(Fig. 1 shows that the vehicle 2 is detecting different stationary landmarks such as 4 and 8); acquire map information, which includes a map point group which is a set of feature points on the map([0066] The map system uses digital map data from map database 3 which contains mapped digital landmarks 3111 that represent a stationary landmark 4) and feature information including information on a position and a type of a feature([0071] the exemplifying optional landmark position table 312 may furthermore hold additional information associated with mapped digital landmarks 3111, 3112, such as characteristics and/or attributes of the corresponding stationary landmarks 4, 8); acquire a sensor point group around the targets estimate positions of the targets on the map by matching between the sensor point group and the map point group([0066] Optionally, the pre-stored position 31111 may be associated with an accuracy confidence level value, which value may indicate a level of confidence of the correctness of the pre-stored position 31111, as compared to the de facto position of the stationary landmark 4, this indicates that the map has a point where the landmark is, [0073] discloses that the position of the landmark is updated based on the detected position 41 which means the vehicle also assigns a point for the landmark).
Claims 2 and 12; Further configured to select the target satisfying a predetermined condition among the targets recognized based on the self-position estimated, a recognition result of the targets, and the feature information([0069] through [0073] discloses that using the position on the vehicle, detecting landmarks around the vehicle, and characteristics and/or attributes of the corresponding stationary landmarks, a target is selected to determine the accuracy confidence level), acquire a sensor point group around the selected target([0073] discloses updating a value indicating the detected position which means the system would need to identify the location of the selected target).
Claim 3; Further configured to select the target in a case where there is a corresponding feature around the targets(Fig.1 shows that a landmark 4 is detected along with a corresponding feature 8) while referring to an information table in which a type of the target and the type of the feature are associated with each other(Fig. 2).
Claim 4; Further configured to estimate a provisional position of the target on the map by using the self-position and the recognition result of the targets([0072] the landmark position table 312 comprises respective pre-stored positions 31111, 31121 of stationary landmarks 4, 8. The pre-stored position 31111 comprised in the mapped digital landmark representing the stationary landmark 4 of FIG. 1, here has an accuracy confidence level value of exemplifying 85%), and select the target in a case where a distance between the provisional position and a position of a feature corresponding to the target in the information table is less than or equal to a threshold value([0073] in an updated landmark position table 312′, the pre-stored position 31111 comprised in the mapped digital landmark 3111 representing the stationary landmark 4, has been updated with a value indicating the detected position 41 of the stationary landmark 4. The accuracy confidence level value of the detected position 41 here has a value of exemplifying 92%).
Claim 5; Wherein the information table retains a threshold value used for each association of the type of the target with the type of the feature(Fig. 2, and as indicated by [0071] the table can contain characteristics and/or attributes of the corresponding stationary landmarks 4, 8), and the external environment recognition device is further configured to change the threshold value retained in the information table based on at least one of the recognition result of the targets, weather, and brightness(The table and confidence percentage is updated based on the landmarks detected by sensor device 5).
Claim 6; Further configured to determine whether or not point group matching has succeeded, and outputs a position of the target on the map estimated by the matching between the sensor point group and the map point group in a case where the point group matching has succeeded(Fig. 4 shows a flow chart that determines the accuracy of the detected landmarks and updates the pre-stored position when an accuracy is determined to be higher than the stored value).
Claim 7; Further configured to determine whether or not point group matching has succeeded, and outputs a position of the target on the map calculated by using the self-position and a recognition result of the targets in a case where the point group matching has failed(Fig. 4 also shows that if the determined accuracy isn't high enough, the system goes from determining the position of the vehicle along with the landmark straight to updating the stored map without an accuracy factor).
However, Bagheri fails to disclose:
Claims 1 and 11; Predict at least one of a trajectory, a speed, or an intention of the target based on a position of the target on the map and a position of the feature on the map; and control a speed of the host vehicle based on at least one of the trajectory, the speed, or the intention of the target.
Deng teaches a similar device in the same field of Autonomous Vehicle Control.
Deng teaches,
Claims 1 and 11; Predict at least one of a trajectory, a speed, or an intention of the target based on a position of the target on the map and a position of the feature on the map([0020] The host vehicle 30 may interact with the target vehicle 32 while each are traveling. “Target path prediction” means predicting, by the host vehicle 30, a path P along which the target vehicle 32 will travel); and control a speed of the host vehicle based on at least one of the trajectory, the speed, or the intention of the target([0028] If, in the decision block 305, the quality measure Q is above the quality threshold Q.sub.th, the controller 34 performs the exemplary process 600 for operating the host vehicle 30 using target path prediction, as shown in FIG. 6 and described below. After the process 600, the process 300 ends).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to include predicting at least one of a trajectory, a speed, or an intention of the target based on a position of the target on the map and a position of the feature on the map; and control a speed of the host vehicle based on at least one of the trajectory, the speed, or the intention of the target as taught by Deng, for the purpose of compensating for dynamic situations.
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bagheri US 20160161265 in view of Deng US 20180056995 further in view of Shambik US 20220027642.
Regarding claim(s) 9-10, Bagheri and Deng discloses the claimed invention substantially as claimed, as set forth above for claim(s) 1.
However, Bagheri and Deng fails to disclose:
Claim 9; Further configured to generate the map by using relative position and posture of the host vehicle estimated by odometry, a recognition result of the targets, and the sensor point group.
Claim 10; Further configured to store the sensor point group as the map in the map database around the feature described in an information table in which a type of the target and the type of the feature are associated with each other.
Shambik teaches a similar device in the same field of improving map accuracy.
Shambik teaches,
Claim 9; Further configured to generate the map by using relative position and posture of the host vehicle estimated by odometry, a recognition result of the targets, and the sensor point group([0202] Sparse map 800 is generated based on data collected from one or more vehicles which have position sensors 130 that estimate the position of each vehicle, [0269] vehicle transmitting navigation information from inertial sensors (odometry), results of targets from their processors 110, data from monocular image analysis module 402, and [0200] discloses the data can be transmitted/stored/accessed to/from a remote server 1230).
Claim 10; Further configured to store the sensor point group as the map in the map database around the feature described in an information table in which a type of the target and the type of the feature are associated with each other([0299] Server 1230 stores data for sparse map 800 using data from monocular image analysis module 402, [0436] Data table 3600 is stored inside 150 or 160 where 800 is also stored which means 800 has access to read/write data table 3600).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to include generate the map by using relative position and posture of the host vehicle estimated by odometry, a recognition result of the targets, and the sensor point group, and store the sensor point group as the map in the map database around the feature described in an information as taught by Shambik, for the purpose of generating a more accurate map using multiple data points.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to John Merino whose telephone number is (703)756-4721. The examiner can normally be reached Mon - Fri 11am-7pm.
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, Erin Piateski can be reached at (571) 270-7429. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/John C Merino/Patent Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
Prosecution Insights