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 . Claims 1-20 as originally filed are pending and have been considered as follows.
Priority
1. Acknowledgement is made that this application is a continuation of U.S. Patent Application No. 18/933,716 filed on 10/31/2024.
Information Disclosure Statement
2. The information disclosure statement (IDS) filed on 08/12/2025 is being considered by the examiner.
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
4. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specification is utilized for the description citations below.
Regarding claim 1 (and similarly 14 and 20), the phrase “…according to a trajectory selected based on an aggregate cost computed…” is unclear. It is unclear if this is referring to the same “trajectory” and “aggregate cost” introduced earlier or new a “trajectory” and a new “aggregate cost”. For examination purposes, examiner has interpreted “according to a trajectory selected based on an aggregate cost computed” as “according to the trajectory selected based on the aggregate cost computed””.
In the art rejection above, the claims have been treated as best understood by the examiner. Any claim not explicitly rejected under this heading is rejected as being dependent on an indefinite claim.
Double Patenting
5. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-4, 6, 10-14, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 7-9, 12-17, and 19-20 of U.S. Patent No. 12,415,541. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims cover substantially the same scope.
Refer to the table below to see claim mapping for double patenting:
Current Application: 19/297,891
Approved Patent: 12,415,541
1. A computer-implemented method for controlling an autonomous vehicle, the method comprising:
1. A computer-implemented method for controlling an autonomous vehicle, the method comprising:
obtaining perception data that describes an environment of the autonomous vehicle, wherein the environment comprises a multilane roadway;
obtaining perception data that describes an environment of the autonomous vehicle, wherein the environment comprises a multilane roadway;
sampling, from a map data graph having a plurality of nodes that correspond to a plurality of lanes of the multilane roadway, a plurality of markers that respectively correspond to a plurality of locations in the multilane roadway;
14. The computer-implemented method of claim 13, wherein:
the lane cost is generated by sampling the map data to obtain a plurality of lane markers; and
the decreased precision is based on sampling the map data more sparsely for regions beyond the horizon of the candidate trajectory as compared to regions within the horizon of the candidate trajectory.
3. The computer-implemented method of claim 1, comprising:
obtaining, from the map data, a plurality of lane markers that respectively correspond to a plurality of locations in a roadway; and
obtaining a respective lane cost profile for each of the plurality of lane markers.
generating, using the perception data, a dynamic cost profile associated with an object in the environment;
obtaining map data describing lanes of the multilane roadway, wherein the map data stores data describing the multilane roadway in a graph having a plurality of nodes;
obtaining, for the plurality of markers, a plurality of route costs that penalize positions that increase a difficulty of navigating along a route for the autonomous vehicle to a destination;
4. The computer-implemented method of claim 1, comprising:
determining a plurality of route costs that penalize lane positions that increase a difficulty of navigating along a route for the autonomous vehicle through the environment.
generating a cost profile based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the cost profile;
generating, using the map data, a wicket cost profile that comprises a plurality of basins respectively associated with lane centerlines of the lanes, wherein the wicket cost profile is continuous between adjoining basins, wherein the wicket cost profile is generated based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the wicket cost profile;
generating, using the map data, a boundary cost profile associated with lane boundaries of the lanes, wherein the boundary cost profile is continuous across lane boundaries of adjoining lanes of the multilane roadway;
generating a candidate trajectory for the autonomous vehicle to traverse in the environment, wherein the candidate trajectory corresponds to a path from a first location in a first lane of the multilane roadway to a second location in a second lane of the multilane roadway;
generating a candidate trajectory for the autonomous vehicle to traverse in the environment, wherein the candidate trajectory corresponds to a path from a first location in a first lane of the multilane roadway to a second location in a second lane of the multilane roadway;
evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the cost profile;
evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the lane cost profile and the dynamic cost profile;
determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory; and
determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory; and
controlling the autonomous vehicle according to a trajectory selected based on an aggregate cost computed using the cost profile and the plurality of route costs.
controlling the autonomous vehicle according to the selected trajectory.
2. The computer-implemented method of claim 1, wherein, for a respective lane:
the wicket cost profile is registered to a centerline of the respective lane; and
the boundary cost profile is registered to one or more outer boundaries of the respective lane.
5. The computer-implemented method of claim 4, comprising:
obtaining, from the map data, a plurality of lane markers that respectively correspond to a plurality of locations in a roadway;
determining the plurality of route costs respectively for the plurality of locations, wherein the aggregate cost is computed using one or more of the plurality of route costs.
6. The computer-implemented method of claim 4, wherein generating the lane cost profile comprises:
generating, using the map data, a wicket cost profile that comprises a plurality of basins respectively associated with lane centerlines of the lanes, wherein the plurality of route costs are represented in the wicket cost profile;
generating, using the map data, a boundary cost profile associated with lane boundaries of the lanes.
2. The computer-implemented method of claim 1, wherein a route cost is determined based on a navigation action associated with the route for the autonomous vehicle through the environment.
7. The computer-implemented method of claim 4, wherein the plurality of route costs are determined based on a navigation action associated with the route for the autonomous vehicle through the environment.
3. The computer-implemented method of claim 2, wherein the route cost is determined based on a cost to execute the navigation action.
8. The computer-implemented method of claim 7, wherein the plurality of route costs are determined based on a cost to execute the navigation action.
4. The computer-implemented method of claim 2, wherein the route cost is determined based on a cost of not executing the navigation action.
9. The computer-implemented method of claim 7, wherein the plurality of route costs are determined based on a cost of not executing the navigation action.
10. The computer-implemented method of claim 3, comprising:
for a respective lane marker:
generating a lane cost component that varies over a width of a lane associated with the respective lane marker; and
generating a route cost component that is constant over the width of the lane.
11. The computer-implemented method of claim 1, comprising:
sampling a plurality of candidate trajectories, wherein the plurality of candidate trajectories comprises at least one candidate trajectory associated with each respective lane of the multilane roadway.
5. The computer-implemented method of claim 2, wherein the navigation action comprises a turn from a first roadway.
6. The computer-implemented method of claim 1, wherein the plurality of route costs that penalize lane positions that increase the difficulty of navigating along the route for the autonomous vehicle to the destination.
4. The computer-implemented method of claim 1, comprising:
determining a plurality of route costs that penalize lane positions that increase a difficulty of navigating along a route for the autonomous vehicle through the environment.
7. The computer-implemented method of claim 4, wherein the cost to execute the navigation action is based on at least one of:
traffic density, or a number of lanes to cross to perform the navigation action.
8. The computer-implemented method of claim 4, wherein the cost of not executing the navigation action is based on at least one of: time delay, energy cost to travel, number of alternate routes, or mapping coverage.
9. The computer-implemented method of claim 2, wherein the route cost is discounted over a distance from an evaluation position to a location at which the navigational action is to be performed.
10. The computer-implemented method of claim 1, comprising:
evaluating, using the aggregate cost, a long-horizon trajectory associated with the candidate trajectory, wherein the long-horizon trajectory comprises a coarse representation of a motion plan beyond a horizon of the candidate trajectory.
12. The computer-implemented method of claim 1, comprising:
evaluating, using the aggregate cost, a long-horizon trajectory associated with the candidate trajectory, wherein the long-horizon trajectory comprises a coarse representation of a motion plan beyond a horizon of the candidate trajectory.
11. The computer-implemented method of claim 10, wherein the aggregate cost is computed with decreased precision for evaluations beyond the horizon of the candidate trajectory as compared to evaluations within the horizon of the candidate trajectory.
13. The computer-implemented method of claim 12, wherein the aggregate cost is computed with decreased precision for evaluations beyond the horizon of the candidate trajectory as compared to evaluations within the horizon of the candidate trajectory.
12. The computer-implemented method of claim 11, wherein:
the decreased precision is based on sampling the map data more sparsely for regions beyond the horizon of the candidate trajectory as compared to regions within the horizon of the candidate trajectory.
14. The computer-implemented method of claim 13, wherein:
the lane cost is generated by sampling the map data to obtain a plurality of lane markers; and
the decreased precision is based on sampling the map data more sparsely for regions beyond the horizon of the candidate trajectory as compared to regions within the horizon of the candidate trajectory.
13. The computer-implemented method of claim 11, wherein the decreased precision is based on sampling location data for the long-horizon trajectory more sparsely as compared to location data for the candidate trajectory.
15. The computer-implemented method of claim 13, wherein the decreased precision is based on sampling location data for the long-horizon trajectory more sparsely as compared to location data for the candidate trajectory.
14. An autonomous vehicle control system for controlling an autonomous vehicle, the autonomous vehicle control system comprising:
16. An autonomous vehicle control system for controlling an autonomous vehicle, the autonomous vehicle control system comprising:
one or more processors; and
one or more processors; and
one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the autonomous vehicle control system to perform operations, wherein the operations comprise:
one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the autonomous vehicle control system to perform operations, wherein the operations comprise:
obtaining perception data that describes an environment of the autonomous vehicle, wherein the environment comprises a multilane roadway;
obtaining perception data that describes an environment of the autonomous vehicle, wherein the environment comprises a multilane roadway;
sampling, from a map data graph having a plurality of nodes that correspond to a plurality of lanes of the multilane roadway, a plurality of markers that respectively correspond to a plurality of locations in the multilane roadway;
19. The autonomous vehicle control system of claim 17, the operations comprising:
evaluating, using the aggregate cost, a long-horizon trajectory associated with the candidate trajectory, wherein the long-horizon trajectory comprises a coarse representation of a motion plan beyond a horizon of the candidate trajectory;
wherein the aggregate cost is computed with decreased precision for evaluations beyond the horizon of the candidate trajectory as compared to evaluations within the horizon of the candidate trajectory;
wherein the lane cost is generated by sampling the map data to obtain a plurality of lane markers;
wherein the decreased precision is based on sampling the map data more sparsely for regions beyond the horizon of the candidate trajectory as compared to regions within the horizon of the candidate trajectory.
generating, using the perception data, a dynamic cost profile associated with an object in the environment;
obtaining map data describing lanes of the multilane roadway, wherein the map data stores data describing the multilane roadway in a graph having a plurality of nodes;
obtaining, for the plurality of markers, a plurality of route costs that penalize positions that increase a difficulty of navigating along a route for the autonomous vehicle to a destination;
17. The autonomous vehicle control system of claim 16, wherein the operations comprise:
determining a plurality of route costs that penalize lane positions that increase a difficulty of navigating along a route for the autonomous vehicle through the environment.
generating a cost profile based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the cost profile;
generating, using the map data, a wicket cost profile that comprises a plurality of basins respectively associated with lane centerlines of the lanes, wherein the wicket cost profile is continuous between adjoining basins, wherein the wicket cost profile is generated based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the wicket cost profile;
generating, using the map data, a boundary cost profile associated with lane boundaries of the lanes, wherein the boundary cost profile is continuous across lane boundaries of adjoining lanes of the multilane roadway;
generating a candidate trajectory for the autonomous vehicle to traverse in the environment, wherein the candidate trajectory corresponds to a path from a first location in a first lane of the multilane roadway to a second location in a second lane of the multilane roadway;
generating a candidate trajectory for the autonomous vehicle to traverse in the environment, wherein the candidate trajectory corresponds to a path from a first location in a first lane of the multilane roadway to a second location in a second lane of the multilane roadway;
evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the cost profile;
evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the lane cost profile and the dynamic cost profile;
determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory; and
determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory; and
controlling the autonomous vehicle according to a trajectory selected based on an aggregate cost computed using the cost profile and the route cost.
controlling the autonomous vehicle according to the selected trajectory.
18. The autonomous vehicle control system of claim 17, wherein, for a respective lane:
the lane cost profile is registered to a centerline of the respective lane; and
the boundary cost profile is registered to one or more outer boundaries of the respective lane.
19. The autonomous vehicle control system of claim 17, the operations comprising:
evaluating, using the aggregate cost, a long-horizon trajectory associated with the candidate trajectory, wherein the long-horizon trajectory comprises a coarse representation of a motion plan beyond a horizon of the candidate trajectory;
wherein the aggregate cost is computed with decreased precision for evaluations beyond the horizon of the candidate trajectory as compared to evaluations within the horizon of the candidate trajectory;
wherein the lane cost is generated by sampling the map data to obtain a plurality of lane markers;
wherein the decreased precision is based on sampling the map data more sparsely for regions beyond the horizon of the candidate trajectory as compared to regions within the horizon of the candidate trajectory.
15. The autonomous vehicle control system of claim 14, wherein a route cost is determined based on a navigation action associated with the route for the autonomous vehicle through the environment.
16. The autonomous vehicle control system of claim 15, wherein the route cost is determined based on a cost to execute the navigation action.
17. The autonomous vehicle control system of claim 15, wherein the route cost is determined based on a cost of not executing the navigation action.
18. The autonomous vehicle control system of claim 17, wherein the cost of not executing the navigation action is based on at least one of:
time delay, energy cost to travel, number of alternate routes, or mapping coverage.
19. The autonomous vehicle control system of claim 15, wherein the route cost is discounted over a distance from an evaluation position to a location at which the navigational action is to be performed.
20. One or more non-transitory computer-readable media storing instructions that are executable by one or more processors to cause an autonomous vehicle control system to perform operations for controlling an autonomous vehicle, wherein the operations comprise:
20. One or more non-transitory computer-readable media storing instructions that are executable by one or more processors to cause an autonomous vehicle control system to perform operations for controlling an autonomous vehicle, wherein the operations comprise:
obtaining perception data that describes an environment of the autonomous vehicle, wherein the environment comprises a multilane roadway;
obtaining perception data that describes an environment of the autonomous vehicle, wherein the environment comprises a multilane roadway;
sampling, from a map data graph having a plurality of nodes that correspond to a plurality of lanes of the multilane roadway, a plurality of markers that respectively correspond to a plurality of locations in the multilane roadway;
generating, using the perception data, a dynamic cost profile associated with an object in the environment;
obtaining map data describing lanes of the multilane roadway, wherein the map data stores data describing the multilane roadway in a graph having a plurality of nodes;
obtaining, for the plurality of markers, a plurality of route costs that penalize positions that increase a difficulty of navigating along a route for the autonomous vehicle to a destination;
generating a cost profile based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the cost profile;
generating, using the map data, a wicket cost profile that comprises a plurality of basins respectively associated with lane centerlines of the lanes, wherein the wicket cost profile is continuous between adjoining basins, wherein the wicket cost profile is generated based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the wicket cost profile;
generating, using the map data, a boundary cost profile associated with lane boundaries of the lanes, wherein the boundary cost profile is continuous across lane boundaries of adjoining lanes of the multilane roadway;
generating a candidate trajectory for the autonomous vehicle to traverse in the environment, wherein the candidate trajectory corresponds to a path from a first location in a first lane of the multilane roadway to a second location in a second lane of the multilane roadway;
generating a candidate trajectory for the autonomous vehicle to traverse in the environment, wherein the candidate trajectory corresponds to a path from a first location in a first lane of the multilane roadway to a second location in a second lane of the multilane roadway;
evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the cost profile;
evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the lane cost profile and the dynamic cost profile;
determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory; and
determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory; and
controlling the autonomous vehicle according to a trajectory selected based on an aggregate cost computed using the cost profile and the route cost.
controlling the autonomous vehicle according to the selected trajectory.
Allowable Subject Matter
6. Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112 and upon filing of a terminal disclaimer to overcome the nonstatutory double patenting rejection set forth in this Office action.
7. The following is a statement of reasons for the indication of allowable subject matter:
The available prior art fails to teach or suggest obtaining, for the plurality of markers, a plurality of route costs that penalize positions that increase a difficulty of navigating along a route for the autonomous vehicle to a destination; generating a cost profile based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the cost profile; evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the cost profile; determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory, in combination with the further limitations of the independent claims.
Takabayashi et al. (US 20210163010) teaches an apparatus and method for autonomous vehicle path estimation that obtains perception data of an environment, obtains map data, generates a cost profile, generates a candidate trajectory, evaluates the candidate trajectory to determine a selected trajectory, and controls the autonomous vehicle based on the selected trajectory. Maru et al. (US 20180238697) teaches an apparatus and method for vehicle route search and guidance that determines a plurality of route costs that penalizes lane positions that increase a difficulty of navigating along a route for the autonomous vehicle through the environment. Li et al. (US 20200307589) teaches a system and method for an autonomous vehicle to perform an automatic lane merge that samples a plurality of candidate trajectories. The combination of Takabayashi, Maru, and Li fails to teach obtaining, for the plurality of markers, a plurality of route costs that penalize positions that increase a difficulty of navigating along a route for the autonomous vehicle to a destination; generating a cost profile based on data stored in a node of the plurality of nodes associated with a longitudinal position in the multilane roadway represented by the cost profile; evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the cost profile; and determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory.
Therefore, the combination of features is considered to be allowable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIEN MINH LE whose telephone number is (571)272-3903. The examiner can normally be reached Monday to Friday (8:30am-5:30pm eastern time).
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/T.M.L./Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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