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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis-Step 1
Claims 1, 9 are directed to a process and system. Therefore, claims 1, 9 are within at least one of the four statutory categories.
101 Analysis-Step2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
1. A computer-implemented method for determining a trajectory for a vehicle using a motion planning algorithm, the method comprising:
acquiring motion data representative of the vehicle moving in a given road section, the motion data including data of surrounding objects of the vehicle within the given road section;
generating, based on the motion data, a ground-truth simulated environment for modelling the motion of the vehicle,
the ground-truth simulated environment being representative of a respective ground-truth behavior of each surrounding object of the vehicle in the given road section during a plurality of modelling iterations;
executing, during a given modelling iteration of the plurality of modelling iterations: generating, based on the motion data, using a current version of the motion planning algorithm, a respective simulated trajectory of the vehicle in the ground-truth simulated environment during the given modelling iteration;
determining, based on the respective simulated trajectory, a simulated behavior of a given surrounding object;
in response to the respective ground-truth behavior of the given surrounding object during the given modelling iteration being different from the simulated behavior of the given surrounding object:
substituting, in the ground-truth simulated environment, for at least one subsequently following modelling iteration of the plurality of modelling iterations, the respective ground-truth behavior of the given surrounding object with the simulated behavior thereof, thereby generating a modified ground-truth simulated environment;
using a respective instance of the modified ground-truth simulated environment, from one of the plurality of modelling iterations, for determining trajectories for the vehicle using subsequent versions of the motion planning algorithm.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “generating, executing, determining…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
1. A computer-implemented method for determining a trajectory for a vehicle using a motion planning algorithm, the method comprising:
acquiring motion data representative of the vehicle moving in a given road section, the motion data including data of surrounding objects of the vehicle within the given road section;
generating, based on the motion data, a ground-truth simulated environment for modelling the motion of the vehicle,
the ground-truth simulated environment being representative of a respective ground-truth behavior of each surrounding object of the vehicle in the given road section during a plurality of modelling iterations;
executing, during a given modelling iteration of the plurality of modelling iterations: generating, based on the motion data, using a current version of the motion planning algorithm, a respective simulated trajectory of the vehicle in the ground-truth simulated environment during the given modelling iteration;
determining, based on the respective simulated trajectory, a simulated behavior of a given surrounding object;
in response to the respective ground-truth behavior of the given surrounding object during the given modelling iteration being different from the simulated behavior of the given surrounding object:
substituting, in the ground-truth simulated environment, for at least one subsequently following modelling iteration of the plurality of modelling iterations, the respective ground-truth behavior of the given surrounding object with the simulated behavior thereof, thereby generating a modified ground-truth simulated environment;
using a respective instance of the modified ground-truth simulated environment, from one of the plurality of modelling iterations, for determining trajectories for the vehicle using subsequent versions of the motion planning algorithm.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “acquiring…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (controller) to perform the process. In particular, the acquiring steps are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, the “computer” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. The computer is recited at a high level of generality and merely automates the evaluating step.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the executing… amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquiring” the examiner submits that these limitations are insignificant extra-solution activities. Hence, the claim is not patent eligible.
Dependent claims 2-8, 10-16 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-8, 10-16 are not patent eligible under the same rationale as provided for in the rejection of [independent claim].
Therefore, claims 1-16 are ineligible under 35 USC §101.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-6, 9-10, 12-14 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Pronovost (US 2024/0208546)
As to claim 1 Pronovost discloses a computer-implemented method for determining a trajectory for a vehicle using a motion planning algorithm, the method comprising:
acquiring motion data representative of the vehicle moving in a given road section, the motion data including data of surrounding objects of the vehicle within the given road section (Paragraph 14 “When an autonomous vehicle is operating in an environment, the vehicle may use sensors to capture sensor data (e.g., image or video data, radar data, lidar data, sonar data, etc.) of the surrounding environment, and may analyze the sensor data to detect and classify objects within the environment. Objects encountered by the autonomous vehicle may include other dynamic objects (which also may be referred to as agents) that are capable of movement (e.g., vehicles, motorcycles, bicycles, pedestrians, animals, etc.), and/or static objects such as buildings, road surfaces, trees, signs, barriers, parked vehicles, etc. In order to safely traverse the environment, the autonomous vehicle may include components configured to analyze the attributes of the detected objects and predict trajectories for objects”);
generating, based on the motion data, a ground-truth simulated environment for modelling the motion of the vehicle (Paragraph 26 “At operation 104, an ML model training component, such as training component 102, may receive ground truth data representing a driving environment including one or more agents (or dynamic objects). In some examples, the ground truth data received in operation 104 may include log data captured or collected by autonomous or non-autonomous (e.g., human-driven) vehicles operating in real-world physical environments, and/or log data from simulated autonomous vehicles operating in simulated driving environments.”),
the ground-truth simulated environment being representative of a respective ground-truth behavior of each surrounding object of the vehicle in the given road section during a plurality of modelling iterations (Paragraph 19 “By training the prediction model using a combination of a standard loss function (e.g., an L2 loss function) representing the accuracy of the predicted trajectories relative to ground truth trajectories, and the auxiliary loss representing the accuracy (e.g., in terms of realistic behaviors) of the agent-to-agent interactions of the predicted trajectories, the trained prediction model may output improved joint trajectory predictions of highly-accurate trajectories that also reflect more realistic agent-to-agent interactions.”);
executing, during a given modelling iteration of the plurality of modelling iterations: generating, based on the motion data, using a current version of the motion planning algorithm, a respective simulated trajectory of the vehicle in the ground-truth simulated environment during the given modelling iteration (Paragraph 26 “At operation 104, an ML model training component, such as training component 102, may receive ground truth data representing a driving environment including one or more agents (or dynamic objects). In some examples, the ground truth data received in operation 104 may include log data captured or collected by autonomous or non-autonomous (e.g., human-driven) vehicles operating in real-world physical environments, and/or log data from simulated autonomous vehicles operating in simulated driving environments.”);
determining, based on the respective simulated trajectory, a simulated behavior of a given surrounding object (Paragraph 27 “Although the driving scene shown in box 106 depicts the environment at a single point in time, the ground truth environment data received in operation 104 can include data representing the driving scene over a period of time (e.g., 5 seconds, 10 seconds, 15 seconds, etc.), and may include the positions, movements, and/or other states for the various objects in the driving scene at each timestep in a sequence of timesteps. For instance, the ground truth data may include, for each timestep, the complete state of the vehicle 108 (e.g., including observable attributes and/or internal state data), and the perceived states of the other agents in the environment (e.g., vehicle 110, vehicle 112, vehicle 114, and/or any other vehicles, pedestrians, bicycles, animals, etc.).”);
in response to the respective ground-truth behavior of the given surrounding object during the given modelling iteration being different from the simulated behavior of the given surrounding object:
substituting, in the ground-truth simulated environment, for at least one subsequently following modelling iteration of the plurality of modelling iterations, the respective ground-truth behavior of the given surrounding object with the simulated behavior thereof, thereby generating a modified ground-truth simulated environment (Paragraph 34 “At operation 126, the training component 102 may determine a first loss during the current training stage, based on the accuracy of the predicted agent trajectories 120-124, relative to the actual (e.g., observed) trajectories of the vehicles 110-114 within the ground truth data. As shown in box 128, in some examples, determining the first loss may include determining relative position differences between the predicted agent trajectories 120-124 and the actual ground truth trajectories, individually or collectively, for the set of agents (e.g., vehicles 110-114).”);
using a respective instance of the modified ground-truth simulated environment, from one of the plurality of modelling iterations, for determining trajectories for the vehicle using subsequent versions of the motion planning algorithm (Paragraph 39 “t operation 140, the training component 102 may continue the training stage by training the prediction model based on a combination of the first loss and the second loss. As shown in box 142, a prediction model 144 may be trained using backpropagation (and/or other model training techniques) based on the first loss indicating the degree of accuracy of the predicted agent trajectories 120-124 relative to the corresponding ground truth trajectories, and the second indicating the degree of to which the predicted agent trajectories 120-124 represent realistic agent-to-agent interactions. As noted above, by training the prediction model 144 using the combination of the first loss and second loss, the prediction model may be trained to output improved joint trajectory predictions that include both highly accurate trajectories as well as trajectories that reflect more realistic agent-to-agent interactions”, Paragraph 48 “] In general, the perception component 210 may include functionality to determine what is in the environment surrounding the vehicle 202, the prediction component 212 may include functionality to generate predicted information associated with objects in an environment, and the planning component 216 may include functionality to determine how to operate the vehicle 202 according to information received from the perception component 210 and the prediction component 212. Multiple sub-components of the perception component 210, prediction component 212, and/or planning component 216 may be used to determine the trajectory 220 for the vehicle 202 to follow based at least in part on the perception data, predicted agent trajectories output by the prediction model 214, and/or other information such as, for example, one or more maps, localization information (e.g., where the vehicle 202 is in the environment relative to a map and/or features detected by the perception component 210), and/or a path generated by a high-level mission planner of the planning component 216.”).
As to claim 2 Pronovost discloses a method wherein the generating the ground-truth simulated environment comprises determining, for each surrounding object in the given road section, a respective object class (Paragraph 14).
As to claim 4 Pronovost discloses a method wherein:
the motion data includes bounding boxes representative of the surrounding objects(Paragraph 50); and
the determining the respective object class for each surrounding object comprises applying a machine-learning algorithm (MLA) that has been trained to determining the respective object class of the given surrounding object based on a respective bounding box representative thereof(Paragraph 50).
As to claim 5 Pronovost discloses a method wherein the determining the simulated behavior for the given surrounding object comprises applying an MLA that has been trained to determine actual behaviors of surrounding objects based on a current trajectory of the vehicle(Paragraph 29).
As to claim 6 Pronovost discloses a method wherein the substituting comprises substituting until, at a given subsequent modelling iteration of the plurality of modelling iterations, a respective simulated behavior of the given surrounding object corresponds to the respective ground-truth behavior thereof for the given modelling iteration (Paragraph 34).
As to claim 9 the claim is interpreted and rejected as in claim 1.
As to claim 10 the claim is interpreted and rejected as in claim 2.
As to claim 12 the claim is interpreted and rejected as in claim 4.
As to claim 13 the claim is interpreted and rejected as in claim 5.
As to claim 14 the claim is interpreted and rejected as in claim 6.
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 3, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Pronovost (US 2024/0208546) in view of Bagschik (US 2021/0370972)
As to claim 3 Bagschik teaches a method wherein the determining the respective object class for each surrounding object comprises soliciting a respective label therefor from a human assessor (Paragraph 77). It would have been obvious to one of ordinary skill to modify Pronovost to include the teachings of labeling the object for the purpose of providing input of the user of the object classification of the objects in the environment.
As to claim 11 the claim is interpreted and rejected as in claim 3.
Claims 7-8, 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Pronovost (US 2024/0208546) in view of Jiang (US 2023/0192130)
As to claim 7 Jiang teaches a method further comprising, in response to a stopping event during the given modelling iteration:
aborting modelling the motion of the vehicle without executing a subsequent modelling iteration(Paragraph 41); and
removing the current version of the motion planning algorithm from further consideration for determining the trajectories for the vehicle (Paragraph 41). It would have been obvious to one of ordinary skill to abort modeling the motion of a vehicle during a stopping event for the purpose of choosing trajectories that avoid a collision.
As to claim 8 Jiang teaches a method wherein the stopping event comprises an occurrence of an accident associated with the vehicle during the given modelling iteration(Paragraph 41).
As to claim 15 the claim is interpreted and rejected as in claim 7.
As to claim 16 the claim is interpreted and rejected as in claim 8.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMRAN K MUSTAFA whose telephone number is (571)270-1471. The examiner can normally be reached Mon-Fri 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached at 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IMRAN K. MUSTAFA
Primary Examiner
Art Unit 3668
/IMRAN K MUSTAFA/Primary Examiner, Art Unit 3668
1/28/2026