DETAILED CORRESPONDENCE
This Office action is in response to the remarks filed 9/17/2025.
Claim Status
Claims 1-11 are canceled.
Claims 12-24 are pending.
Claim 24 is newly added.
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
In light of the amendments, the claim objections have been withdrawn.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show the written descriptive label associated to the references numbers are not properly label. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application.
Response to Arguments
Applicant's arguments filed on 9/17/2025 have been fully considered but they are not persuasive.
On page 8 of the Remarks, Applicant alleges that “Lan et al. describes determining predicted trajectories that may be goal-oriented or interaction-based, such as a pedestrian intending to cross a roadway, and may include consideration of interactions between objects or probabilistic modeling of object behavior. However, Lan et al. do not disclose or suggest the calculation of short-term movement parameters as purely deterministic variables that are derived exclusively from a physical computational model.” The Examiner disagrees.
In response, Lan et al., teaches in at least paragraph [0030] that “the vehicle computing system can determine the one or more predicted interaction trajectories based at least in part on a machine-learned model. For instance, the prediction system can include, employ, and/or otherwise leverage a machine-learned interaction prediction model.” While paragraph [0076] teaches the motion planning system can generate short-term motion plan based on the data. Thus, each new motion plan can describe motion of the vehicle 104 over the next several seconds (e.g., 5 seconds). Taken together these at least cited section reads on this newly amended language of wherein the at least one short-term movement parameter is exclusively calculable via a physical computational model. Thus, the newly added language has necessitated a new ground of rejections.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 12-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lan et al., US 2019/0152490 hereinafter “Lan et al”.
Claims 12 and 22. Lan et al teaches a method for ascertaining at least one of a direction of travel or a future path of travel of a unit movable at least semiautonomously or autonomously in a dynamically changeable surrounding area, the method comprising the following steps:
at least one measuring or ascertaining a plurality of surrounding-area parameters, which may each be assigned to at least one moving, external object in an area surrounding the unit ([0019]-[0021] teaches controlling the motion of the autonomous vehicle in surrounding environment that has “dynamic objects in motion or that will be in motion”);
executing at least one movement prediction algorithm for ascertaining at least one probabilistic movement prediction parameter for each detected external object as a function of the surrounding-area parameters assigned to the detected external object ([0023], [0029] and [0076] reads on this element as such—“[t]he prediction system can be configured to predict the motion of the object(s) within the surrounding environment of the autonomous vehicle. For instance, the prediction system can create prediction data associated with the one or more the objects. The prediction data can be indicative of one or more predicted future locations of each respective object. The prediction data can indicate a predicted path associated with each object. The prediction system can determine a predicted trajectory along which the respective object is predicted to travel over time. The predicted trajectory can be indicative of the predicted path as well as the timing at which the object is predicted to traverse the path. This can be indicative of the intentions of the object.….the prediction system can determine the predicted interaction trajectories based at least in part on a rule(s)-based model. The rule(s)-based model can include an algorithm with heuristics that define the potential trajectories that an object may follow given the type of interaction and the surrounding circumstances. Thus, in some implementations, the motion planning system 128 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 104”);
executing at least one movement determination algorithm for ascertaining at least one short-term movement parameter for each detected external object as a function of the surrounding-area parameter assigned to the detected external object ([0077] reads on this element as such—“The vehicle controller can send one or more control signals to the responsible vehicle control (e.g., braking control system, steering control system, acceleration control system, etc.) to execute the instructions and implement the motion plan 136. This can allow the vehicle control system(s) 116 to control the motion of the vehicle 104 in accordance with planned motion trajectory”) and
wherein the at least one short-term movement parameter is exclusively calculable via a physical computational model ([0030] along with [0076] reads on this element as such—“In some implementations, the vehicle computing system can determine the one or more predicted interaction trajectories based at least in part on a machine-learned model. For instance, the prediction system can include, employ, and/or otherwise leverage a machine-learned interaction prediction model. The machine-learned model interaction prediction model can be or can otherwise include one or more various model(s) such as, for example, neural networks (e.g., deep neural networks), or other multi-layer non-linear models. Neural networks can include convolutional neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), feed-forward neural networks, and/or other forms of neural networks….The motion planning system 128 can be configured to continuously update the vehicle's motion plan 136 and a corresponding planned vehicle motion trajectory. For example, in some implementations, the motion planning system 128 can generate new motion plan(s) 136 for the vehicle 104 (e.g., multiple times per second). Each new motion plan can describe motion of the vehicle 104 over the next several seconds (e.g., 5 seconds). Moreover, a new motion plan may include a new planned vehicle motion trajectory. Thus, in some implementations, the motion planning system 128 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 104.”),
wherein the movement prediction algorithm and the movement determination algorithm are executed at least substantially independently of each other, to ascertain at least of a future direction of travel or a future path of travel of the unit ([0053] reads on this element as such—“The autonomy computing system 114 can include a perception system 124, a prediction system 126, a motion planning system 128, and/or other systems that cooperate to perceive the surrounding environment of the vehicle 104 and determine a motion plan for controlling the motion of the vehicle 104 accordingly.” Here the taught term cooperation implies substantially independently of each other as claimed.).
Claim 13. Lan et al teaches the method as recited in claim 12 and further teaches, wherein the unit is at least of a robot or a vehicle ([0018] teaches an autonomous vehicle).
Claim 14. Lan et al teaches the method as recited in claim 12, further comprising:
subsequently to the executing of the movement determination algorithm, executing at least one emergency collision prevention algorithm is a part of a model predictive control of the unit, wherein emergency control including at least of emergency braking or an evasive movement of the unit or of a future path of travel, is carried out using the emergency collision prevention algorithm, when a virtual spacing of a position of the unit on the future path of travel of the unit and a future position of a detected external object ascertained as a function of an ascertained short-term movement parameter of the at least one short-term movement parameter, falls below a predefined limiting value at at least one instant ([0077] describes emergency braking as “apply a certain magnitude of braking force”. While [0076] reads on this element a such—“Thus, in some implementations, the motion planning system 128 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 104”)).
Claim 15. Lan et al teaches the method as recited in claim 12 and teaches, further comprising: subsequently to the executing of the movement prediction algorithm, executing at least one pathfinding algorithm including a theta* pathfinding algorithm, wherein, using the pathfinding algorithm, a future path of travel of the unit is determined dynamically as a function of the ascertained probabilistic movement prediction parameters of the detected external object ([0036] reads on this element as such—“The motion planning system can determine a motion plan for the autonomous vehicle based at least in part on the one or more predicted interaction trajectories. A motion plan can include vehicle actions (e.g., planned vehicle trajectories, speed(s), acceleration(s), other actions, etc.) with respect to the objects proximate to the vehicle as well as the objects' predicted movements. For instance, the motion planning system can implement an optimization algorithm that considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan.”).
Claim 16. Lan et al teaches the method as recited in claim 12 and teaches further, wherein, to ascertain at least of the future path of travel or direction of travel of the unit as a function of the detected external object, the movement determination algorithm is considered at a higher priority than the movement prediction algorithm ([0075] reads on this element as such—“For instance, the motion planning system 128 can implement an optimization algorithm that considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan 136. The motion planning system 128 can determine that the vehicle 104 can perform a certain action (e.g., pass an object) without increasing the potential risk to the vehicle and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage). For instance, the motion planning system 128 can evaluate each of the predicted interaction trajectories 220A-B (and associated score(s)) during its cost data analysis as it determines an optimized vehicle trajectory through the surrounding environment.”).
Claim 17. Lan et al teaches the method as recited in claim 12 and teaches further, wherein, in the step of executing the movement determination algorithm, a number of short-term movement parameters or of values of a short-term movement parameter is ascertained, inversely proportionally, for each detected external object, as a function of at least of a number or a type of different, measured surrounding-area parameters of the detected external object ([0076] reads on this element a such—“Thus, in some implementations, the motion planning system 128 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 104”).
Claim 18. Lan et al teaches the method as recited in claim 12 and further teaches, wherein, in the step of executing the movement determination algorithm, at least one short-term movement parameter of each detected external object is ascertained as a deterministic variable as a function of measured surrounding-area parameters of the external object exclusively using a stored physical computational model ([0076] reads on this element a such—“Thus, in some implementations, the motion planning system 128 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 104”).
Claim 19. Lan et al teaches the method as recited in claim 12 and further teaches, wherein all of detected external objects are filtered for moving or mobile external objects, wherein, in the executing of the movement determination algorithm, only surrounding-area parameters associated with moving or mobile external objects being taken into consideration for ascertaining the short-term movement parameters ([0019] teaches dynamic object in motion and [0076] teaches—“Thus, in some implementations, the motion planning system 128 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 104”. Taken together the cited section reads on this element).
Claim 20. Lan et al teaches the method as recited in claim 12 and further teaches, wherein the movement determination algorithm is utilized to ascertain a surrounding-area parameter of the external object ([0018] teaches that pedestrian is jaywalking which reads in this element).
Claim 21. Lan et al teaches the method as recited in claim 12 and teaches further, wherein the movement determination algorithm and the movement prediction algorithm are executed in a periodically repeated manner, the movement determination algorithm being executed at a higher frequency than the movement prediction algorithm ([0076] read on this element as such—“The motion planning system 128 can be configured to continuously update the vehicle's motion plan 136 and a corresponding planned vehicle motion trajectory. For example, in some implementations, the motion planning system 128 can generate new motion plan(s) 136 for the vehicle 104 (e.g., multiple times per second). Each new motion plan can describe motion of the vehicle 104 over the next several seconds (e.g., 5 seconds). Moreover, a new motion plan may include a new planned vehicle motion trajectory. Thus, in some implementations, the motion planning system 128 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data”).
Claim 23. Lan et al teaches the device or system as recited in claim 22 and further teaches, wherein the device is a robot movable semiautonomously or autonomously ([0019]-[0021] teaches controlling the motion of the autonomous vehicle in surrounding environment that has “dynamic objects in motion or that will be in motion”).
Claim 24. Lan et al teaches the method as recited in claim 12 and further teaches, wherein input parameters and output parameters of the movement prediction algorithm and the movement determination algorithm are independent from each other, and wherein the movement prediction algorithm and the movement determination algorithm are executed temporally independently ([0067] read on this element as such—“For example, the interaction trajectories can be developed independently. At each iteration, the vehicle computing system 102 can create a graph with vertices that represent trajectories and edges that represent the dependency between two trajectories. For each trajectory, a model (e.g., a classifier) and/or a set of heuristics can be applied to mark a conflicting trajectory as a parent of the current trajectory if the conflicting trajectory should be developed first. An edge can be added to the graph to represent this dependency.”).
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.D.T/Examiner, Art Unit 3661
/RUSSELL FREJD/Primary Examiner, Art Unit 3661