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 the Claims
Applicant filed remarks and amendments on 10/28/2025. Claims 1, 7 and 8 were amended. Claims 1 and 3-8 are presented for examination.
Response to Arguments
Regarding the claim rejections under 35 USC 102: Applicant's arguments filed 10/28/2025 with respect to by Kim (WO 2021157759 A1), have been fully 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
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 and 3-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (WO 2021157759 A1) in view of Wheeler et al. (WO2019079311A1) and further in view of Ress et al. (Electronic Horizon – Providing Digital Map Data for ADAS Applications), hereinafter referred to as Kim, Wheeler and Ress respectively.
Regarding claims 1, 7 and 8, Kim discloses generating, based on surrounding information corresponding to a surrounding area around an own vehicle selected from between the surrounding information and map data, an expected autonomous route along which the own vehicle is to travel, the surrounding information being detected by a vehicle-mounted detector (“the processor 830 may generate visual field information for autonomous driving in which the sensing information is fused with the optimal path” [P. 47] and “the processor 830 may receive at least one of internal sensing information and external sensing information sensed from one or more sensors provided in the vehicle.” [P. 59]). KIM’s “sensing information” from vehicle sensors = surrounding information/detector; fused “visual field information” = expected autonomous route.
acquiring an expected map route along which the own vehicle is to travel based on the map data selected from between the surrounding information and the map data (“the processor 830 may estimate an optimal route for which the movement of the vehicle 100 is expected or planned based on the specified lane on a lane-by-lane basis using map information (S1340).” [P.45]). KIM’s “optimal route … using map information” = expected map route.
determining that a first route is less reliable than a second route based on a result of the information accuracy determination the autonomous route information accuracy and the map route information accuracy, the first route being one of the map route or the autonomous route and the second route being the other one of the map route or the autonomous route (“changing the optimal path based on at least one of the internal sensing information and the external sensing information” [P. 4] and “when changing the optimal route based on at least one of the internal sensing information and the external sensing information, the processor 830 may change the optimal route in different ways based on whether a destination is set.” [P. 59]). KIM’s sensor-driven change (when sensing indicates lower reliability) determines first/less reliable vs. second route.
generating a corrected route by correcting the first route using the autonomous route and the map route (“the processor 830 may generate visual field information for autonomous driving in which the sensing information is fused with the optimal path” [P. 47]). KIM’s fusion of sensing (autonomous) + map (optimal) = corrected route using both.
integrating the second route and the corrected route to generate an integrated route (“the processor 830 fuses the acquired location information of the vehicle and the received location information of the other vehicle with the received map information, and the fused map information and the vehicle sensed through the sensing unit 840 and The vehicle 100 may be controlled based on at least one of related information.” [P. 40]). KIM’s fused output = integrated route.
causing the own vehicle to perform one or more control operations based on the integrated route (“the processor 830 can use only information within the predetermined range from the vehicle, so that the vehicle can be controlled within a range.” [P.41] and “Vehicle control described in this specification may include at least one of autonomously driving the vehicle 100 and outputting a warning message related to driving of the vehicle.” [P.42]).
Kim does not explicitly teach determining a map route information accuracy for the map route based on a freshness of the map data that corresponds to the surrounding area.
However, Wheeler does teach determining a map route information accuracy for the map route based on a freshness of the map data that corresponds to the surrounding area (“freshness of data by ensuring that the map is updated to reflect changes on the road within a reasonable time frame.” [0027].) and (“Embodiments of the invention maintain high definition (HD) maps containing up to date information using high precision.” [0024].). This bases map route accuracy on freshness/up-to-date status for the surrounding area. Both Kim and Wheeler teach methods for generating an expected route along which an own vehicle is to travel. However, Wheeler explicitly teaches determining a map route information accuracy for the map route based on a freshness of the map data that corresponds to the surrounding area.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the route generation method of Kim to also include determining a map route information accuracy for the map route based on a freshness of the map data that corresponds to the surrounding area, as taught by Wheeler, with a reasonable expectation of success. Doing so improves route generation for a navigating vehicle (With regard to this reasoning, see at least [Wheeler, 0024 and 0027]).
Kim does not explicitly teach determining an autonomous route information accuracy for the expected autonomous route based on a visibility of the surrounding area by the vehicle-mounted detector.
However, Ress does teach determining an autonomous route information accuracy for the expected autonomous route based on a visibility of the surrounding area by the vehicle-mounted detector (“Since built-in sensors are limited to a relatively short range, digital map data can be used to ‘look’ much further into the direction of the vehicle’s path.” [Abstract] and “these on-board sensors are limited to a relatively short range of a few hundred meters.” [Page 1]) and “approaching a curve, driving in bad weather, or low light conditions, Electronic Horizon data is taken into account as an additional virtual sensor. This provides information about lane attributes and geometry of the road ahead. In consequence this enables the lane tracker to evaluate more reliably if the vehicle is still within the lane, or whether an action needs to be taken.” [P. 47-48]). This ties autonomous/sensor route accuracy directly to visibility/range of vehicle-mounted detectors. Both Kim and Ress teach methods for generating an expected route along which an own vehicle is to travel. However, Ress explicitly teaches determining an autonomous route information accuracy for the expected autonomous route based on a visibility of the surrounding area by the vehicle-mounted detector.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the route generation method of Kim to also include determining an autonomous route information accuracy for the expected autonomous route based on a visibility of the surrounding area by the vehicle-mounted detector, as taught by Ress, with a reasonable expectation of success. Doing so improves route generation for a navigating vehicle (With regard to this reasoning, see at least [Ress, Abstract and Page 1]).
Regarding claim 3, Kim discloses wherein the operations further comprise fitting the first route to the second route to generate the corrected route (“the processor 830 deletes the mismatch information or corrects the mismatch information based on the LDM data.” [P. 50]). KIM’s correction of mismatch by fusing/aligning routes = fitting the first route to the second for the corrected route.
Regarding claim 4, Kim discloses wherein: the second route is the autonomous route, and the operations further comprise compensating for a missing part of the autonomous route with the corrected route to generate the integrated route (“overcome the conventional technical limitations in which autonomous driving is possible only within a certain range by simply fusion of high-precision map information with vehicle-related information acquired through the sensing unit 840.” [P. 42]). KIM explicitly compensates the “certain range” limitation (missing part) of sensor/autonomous data with map fusion to generate the integrated visual field route; second route = autonomous/sensor-based per the sensing-fusion logic.
Regarding claim 5, Kim does not explicitly teach wherein: the first route is the map route, the operations further comprise weighting the corrected route with respect to the autonomous route in accordance with a reliability of the map route and then generating the integrated route by connecting a part of the corrected route used to compensate for the missing part of the autonomous route and the autonomous route.
However, Ress does teach wherein: the first route is the map route, the operations further comprise weighting the corrected route with respect to the autonomous route in accordance with a reliability of the map route and then generating the integrated route by connecting a part of the corrected route used to compensate for the missing part of the autonomous route and the autonomous route (“The prediction of the Most Likely Path, which the vehicle is expected to take, has a significant influence on the quality and reliability of the Electronic Horizon.”[ Page 40–41] and “That allows a high quality of Electronic Horizon data and improves reliability of applications.” [ Page 48]). Ress’s reliability-weighted “Most Likely Path” (map route) for integration with sensor data (autonomous route) + connection of compensated parts = the claimed weighting and connecting step; POSITA applies to KIM’s first=map / second=autonomous scenario. Both Kim and Ress teach methods for generating an expected route along which an own vehicle is to travel. However, Ress explicitly teaches wherein: the first route is the map route, the operations further comprise weighting the corrected route with respect to the autonomous route in accordance with a reliability of the map route and then generating the integrated route by connecting a part of the corrected route used to compensate for the missing part of the autonomous route and the autonomous route.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the route generation method of Kim to also include wherein: the first route is the map route, the operations further comprise weighting the corrected route with respect to the autonomous route in accordance with a reliability of the map route and then generating the integrated route by connecting a part of the corrected route used to compensate for the missing part of the autonomous route and the autonomous route, as taught by Ress, with a reasonable expectation of success. Doing so improves route generation for a navigating vehicle (With regard to this reasoning, see at least [Ress, Page 40–41 and Page 48]).
Regarding claim 6, Kim discloses wherein the operations further comprise generating the integrated route based on a relationship in reliability between the autonomous route and the map route (“The horizon pass data may include data representing a relative probability of selecting any one road at a decision point (eg, a fork, a junction, an intersection, etc.).” [P.25]). KIM’s relative probability (reliability relationship) between map and sensor-derived paths directly generates the integrated route.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/A.A./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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