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 .
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hu, et al., US 2022/0135068 A1, in view of Xu, et al., US 2022/0018674 A1.
As per Claim 1, Hu teaches a method (¶ 46) comprising:
determining a state of a vehicle in an environment (¶ 52; through sensor system 110 of Figure 1); and
determining, based at least in part on the state and as a tree structure (¶ 87), a plurality of nodes, a node of the plurality of nodes associated with a potential future state and an action of a set of actions (¶¶ 87-89; based on “dynamic nodes” and “adjacent nodes”).
Hu does not expressly teach: determining, based at least in part on a machine learned model, an estimated cost associated with the node; and determining a trajectory for controlling the vehicle based at least in part on the estimated cost. Xu teaches:
determining, based at least in part on a machine learned model, an estimated cost associated with the node (¶¶ 39-40; with cost module 305 of Figure 3 based on machine learning system 123 of Figure 1); and
determining a trajectory for controlling the vehicle based at least in part on the estimated cost (¶¶ 87, 91, 96).
At the time of the invention, a person of skill in the art would have thought it obvious to combine the tree structure process of Hu with the mapping steps of Xu, in order to reduce required travel times for a vehicle from starting point to a destination.
As per Claim 2, Hu does not expressly teach determining, for an additional node of the plurality of nodes, an additional cost using a cost function. Xu teaches determining, for an additional node of the plurality of nodes, an additional cost using a cost function (¶¶ 52, 55). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 3, Hu teaches:
that the set of actions comprises a first maneuver (¶ 75; “to make a right turn”) and a second maneuver (¶ 75; “to stop for a stop sign); and
determining the trajectory comprises determining a trajectory that passes through a first node associated with the first maneuver (¶ 76) and a second node associated with the second maneuver (¶ 79; per “spatial constraint node generator 502” of Figure 5).
As per Claim 4, Hu teaches: determining a total cost that includes the estimated cost with the node and corresponding estimated costs of one or more parent nodes from which the node depends (¶ 71; per a “cost map”), wherein determining the trajectory is further based on the total cost (¶ 94; after calculating a “path cost”).
As per Claim 5, Hu teaches: determining a root node of the tree structure (¶ 87) based at least in part on the state of the vehicle (¶ 89; based on “velocity”).
As per Claim 6, Hu teaches: determining a set of nodes associated with additional future states by sampling a state space that is constrained based least in part on a corresponding state of the vehicle indicated by the node, wherein the additional future states are based on the node (¶¶ 130-131).
As per Claim 7, Hu teaches that the action comprises a control policy for the vehicle (¶¶ 60-61), the control policy including a series of positions for the vehicle to follow, associated with different times (¶ 61; as per path data 228 of Figure 2).
As per Claim 8, Hu does not expressly teach that determining the estimated cost associated with the node is further based on at least one of environment state data or dynamic object data. Xu teaches that determining the estimated cost associated with the node is further based on at least one of environment state data or dynamic object data (¶ 80; based on “traffic affecting events/conditions 501” of Figure 5). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 9, Hu teaches that at least some actions of the set of actions are associated with controlling the vehicle over different time periods, the different time periods having different time lengths (¶ 46; “connecting a sequence of channel segments corresponding to different time intervals”).
As per Claim 10, Hu teaches a non-transitory computer-readable medium storing processor-executable instructions (¶ 122) that, when executed by one or more processors, cause one or more processors to perform operations comprising:
determining a state of a vehicle in an environment (¶ 52; through sensor system 110 of Figure 1); determining, based at least in part on the state and as a tree structure (¶ 87), a plurality of nodes, a node of the plurality of nodes associated with a potential future state and an action of a set of actions (¶¶ 87-89; based on “dynamic nodes” and “adjacent nodes”). Hu does not expressly teach: determining, based at least in part on a machine learned model, an estimated cost associated with the node; and determining a trajectory for controlling the vehicle based at least in part on the estimated cost. Xu teaches:
determining, based at least in part on a machine learned model, an estimated cost associated with the node (¶¶ 39-40; with cost module 305 of Figure 3 based on machine learning system 123 of Figure 1); and
determining a trajectory for controlling the vehicle based at least in part on the estimated cost (¶¶ 87, 91, 96).
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 11, Hu does not expressly teach determining, for an additional node of the plurality of nodes, an additional cost using a cost function. Xu teaches determining, for an additional node of the plurality of nodes, an additional cost using a cost function (¶¶ 52, 55). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 12, Hu teaches:
that the set of actions comprises a first maneuver (¶ 75; “to make a right turn”) and a second maneuver (¶ 75; “to stop for a stop sign); and
determining the trajectory comprises determining a trajectory that passes through a first node associated with the first maneuver (¶ 76) and a second node associated with the second maneuver (¶ 79; per “spatial constraint node generator 502” of Figure 5).
As per Claim 13, Hu teaches: determining a total cost that includes the estimated cost with the node and corresponding estimated costs of one or more parent nodes from which the node depends (¶ 71; per a “cost map”), wherein determining the trajectory is further based on the total cost (¶ 94; after calculating a “path cost”).
As per Claim 14, Hu teaches: determining a root node of the tree structure (¶ 87) based at least in part on the state of the vehicle (¶ 89; based on “velocity”).
As per Claim 15, Hu teaches: determining a set of nodes associated with additional future states by sampling a state space that is constrained based least in part on a corresponding state of the vehicle indicated by the node, wherein the additional future states are based on the node (¶¶ 130-131).
As per Claim 16, Hu teaches that the action comprises a control policy for the vehicle (¶¶ 60-61), the control policy including a series of positions for the vehicle to follow, associated with different times (¶ 61; as per path data 228 of Figure 2).
As per Claim 17, Hu does not expressly teach that determining the estimated cost associated with the node is further based on at least one of environment state data or dynamic object data. Xu teaches that determining the estimated cost associated with the node is further based on at least one of environment state data or dynamic object data (¶ 80; based on “traffic affecting events/conditions 501” of Figure 5). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 18, Hu teaches that at least some actions of the set of actions are associated with controlling the vehicle over different time periods, the different time periods having different time lengths (¶ 46; “connecting a sequence of channel segments corresponding to diffent time intervals”).
As per Claim 19, Hu teaches a system comprising: one or more processors; and memory storing processor-executable instructions that, when executed by the one or more processors (¶ 122), cause one or more processors to perform operations comprising:
receiving sensor data from a sensor associated with a vehicle (¶ 52; through sensor system 110 of Figure 1);
determining a root node of a tree structure based at least in part on the sensor data, the root node indicating at least a current state of the vehicle (¶ 77; “the current location”); and
determining a plurality of candidate future states as prediction nodes for the vehicle in a tree search, a prediction node of the prediction nodes associated with a candidate action of a set of candidate actions (¶¶ 87-89; based on “dynamic nodes” and “adjacent nodes”). Hu does not expressly teach: determining, based at least in part on a machine-learned model, an estimated cost associated with the prediction node; determining, as an optimization of the tree structure and based at least in part on a total cost including the estimated cost, a trajectory from the root node to a final node; and controlling the vehicle based at least in part on the trajectory. Xu teaches:
determining, based at least in part on a machine-learned model, an estimated cost associated with the prediction node (¶¶ 39-40; with cost module 305 of Figure 3 based on machine learning system 123 of Figure 1);
determining, as an optimization of the tree structure and based at least in part on a total cost including the estimated cost, a trajectory from the root node to a final node (¶¶ 87, 91, 96); and
controlling the vehicle based at least in part on the trajectory (¶¶ 113-114, 122-123).
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 20, Hu does not expressly teach determining, based at least in part on the machine-learned model, the estimated cost associated with the prediction node comprises: generating the estimated cost based on at least one of environment state data, dynamic object, or prediction node data that indicates how the prediction node is reached in the tree search. Xu teaches determining, based at least in part on the machine-learned model, the estimated cost associated with the prediction node comprises: generating the estimated cost (¶¶ 30, 32) based on at least one of environment state data (¶¶ 35-36; as collected from vehicle sensors 103a-103n of Figure 1), dynamic object, or prediction node data that indicates how the prediction node is reached in the tree search. See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
Double Patenting
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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent No. 12,311,981 (“the ‘981 patent”). Although the claims at issue are not identical, they are not patentably distinct from each other because the ‘981 patent claims a vehicle with a set of sensors that collect data from an environment surrounding the vehicle, collects, node data, uses machine-learning models to evaluate the positions and statuses of static and dynamic objects in the environment surrounding the vehicle, estimates a travel cost based on the nodes and objects, and determines a trajectory that reduces the estimated travel based on the estimates.
Conclusion
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ATUL TRIVEDI
Primary Examiner
Art Unit 3661
/ATUL TRIVEDI/Primary Examiner, Art Unit 3661
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