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 Claims
This is the third Office Action on the merits. Claims 1, 4-14, and 16-21 are currently pending. Claims 1, 8, 14, and 19 are currently amended and claims 2-3 and 15 are cancelled. This action is FINAL.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2022-0137473, filed on 10/24/2022.
Response to Amendment
Amendments filed on 12/11/2025 have been entered.
In regards to the Claims, Applicant’s amendments have been acknowledged.
Response to Arguments
Applicant's arguments filed 12/11/2025 with respect to the rejection(s) of claims 1-21 under 35 USC 101 have been fully considered but they are not persuasive. Applicant argues that the present claims do not recite any mental processes deemed to be abstract ideas as the claimed limitations cannot practically be performed in the human mind. Furthermore, Applicant argues that the present claims “improve the technical fields of autonomous driving, path generation, neural networks, and neural network training, as non-limiting examples.” Respectfully, the Examiner does not find these arguments persuasive. The currently provided claim language do not reflect the cited improvements, and regarding the path generation, the currently provided limitations, as claimed, appear to recite abstract ideas that are implemented by generic computing components as set forth in the previous Non-Final Rejection and as updated below, in light of Applicant’s amendments to the claims.
Applicant’s arguments, see Pages 12-15, filed 12/11/2025, with respect to the rejection(s) of claim(s) 1-21 under 35 USC 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Gao et al. (US20210150350A1) in view of Choudhury et al. (The Planner Ensemble: Motion Planning by Executing Diverse Algorithms), hereinafter Gao and Choudhury.
In regards to the 35 USC 102 and 103 rejections, Applicant argues that Gao fails to disclose, teach, or suggest “generating initial information comprising arrival information” and “generating a plurality of paths by inputting the initial information”, and furthermore, that neither Gao, Nister, nor any combination thereof discloses, teaches, or suggest the current amended limitations. Examiner found argument persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Gao in view of Choudhury. Each of the dependent claims, depend directly or indirectly from the independent claims 1, 8, 14 and 19, and by dependency of these independent claims are rejected under 35 USC 103, as discussed below.
Claim Rejections - 35 USC § 112
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.
Claims 5 and 17 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.
Claims 5 and 17 are rejected as being indefinite because claims 5 and 17 depend from cancelled claims 2 and 17, respectively. Since claims 2 and 15 have been cancelled, the scope of claims 5 and 17 cannot be determined.
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, 4-14, and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1. A processor-implemented method, the method comprising:
generating initial information comprising arrival information and either one or both of map information and departure information;
generating a plurality of paths by inputting the initial information to a planner ensemble; and
generating a path distribution corresponding to the plurality of paths by training a path distribution estimation model to output the path distribution corresponding to the plurality of paths,
wherein the planner ensemble comprises a plurality of planners having different characteristics from each other, and
wherein the generating of the plurality of paths comprises generating the plurality of paths corresponding to the plurality of planners, respectively, by inputting the initial information to each of the plurality of planners.
Claim 8. A processor-implemented method, the method comprising:
generating initial information comprising arrival information and either one or both of map information and departure information;
generating a path distribution corresponding to the initial information by inputting the initial information to a path distribution estimation model, wherein the path distribution estimation model is trained using paths obtained from a planner ensemble comprising a plurality of planners having different characteristics from each other; and
determining a final path based on the path distribution.
Claim 14. An electronic device comprising:
a processor configured to:
generate initial information comprising arrival information and either one or both of map information and departure information;
generate a plurality of paths by inputting the initial information to a planner ensemble; and
generate a path distribution corresponding to the plurality of paths by training train a path distribution estimation model to output the path distribution corresponding to the plurality of paths,
wherein the planner ensemble comprises a plurality of planners having different characteristics from each other, and
wherein the processor is configured to generate the plurality of paths corresponding to the plurality of planners, respectively, by inputting the initial information to each of the plurality of planners.
Claim 19. An electronic device comprising:
a processor configured to:
generate initial information comprising arrival information and either one or both of map information and departure information;
generate a path distribution corresponding to the initial information by inputting the initial information to a path distribution estimation model, wherein the path distribution estimation model is trained using paths obtained from a planner ensemble comprising a plurality of planners having different characteristics from each other; and
determine a final path based on the path distribution.
101 Analysis – Step 1: Statutory category – Yes
Claims 1 and 8 recites a method (i.e. process) and claims 14 and 19 recite a device (i.e. machine). These claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis – Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitation can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
Claims 1, 8, 14, and 19 recite the limitations of generate(ing) initial information comprising arrival information and either one or both of map information and departure information. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance in the human mind or with the aid of a pen and paper but for the recitation of by a “processor configured to” or “processor-implemented”. That is, other than reciting the “processor configured to” or “processor-implemented” nothing in the claim elements preclude the step from practically being perform by using a pen and paper. For example, but for the “processor configured to” or “processor-implemented” language, the claim could implicate a person making a judgement related to arrival information and map or departure information in their head. The mere nominal recitation of “a processor configured to” or “processor-implemented” does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Additionally, claims 1 and 14 further recites the limitation generate(ing) a plurality of paths by inputting the initial information to a planner ensemble; generate(ing) a path distribution corresponding to the plurality of paths by training train a path distribution estimation model to output the path distribution corresponding to the plurality of paths; and wherein the planner ensemble comprises a plurality of planners having different characteristics from each other, and wherein the generating of the plurality of paths comprises generating (the processor is configured to) the plurality of paths corresponding to the plurality of planners, respectively, by inputting the initial information to each of the plurality of planners. These limitations, as drafted, are simple process that, under its broadest reasonable interpretation, covers performance in the human mind or with the aid of a pen and paper but for the recitation of by a “processor configured to” or “processor-implemented”. That is, other than reciting the “processor configured to” or “processor-implemented” nothing in the claim elements preclude the step from practically being perform by using a pen and paper. For example, but for the “processor configured to” or “processor-implemented” language, the claim could implicate a person drawing a plurality of paths and making a path distribution with a pen and paper. The mere nominal recitation of “a processor configured to” or “processor-implemented” does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Furthermore, claims 8 and 19 further recites the limitation generate(ing) a path distribution corresponding to the initial information by inputting the initial information to a path distribution estimation model, wherein the path distribution estimation model is trained using paths obtained from a planner ensemble comprising a plurality of planners having different characteristics from each other and determine(ing) a final path based on the path distribution. These limitations, as drafted, are simple process that, under its broadest reasonable interpretation, covers performance in the human mind or with the aid of a pen and paper but for the recitation of by a “processor configured to” or “processor-implemented”. That is, other than reciting the “processor configured to” or “processor-implemented” nothing in the claim elements preclude the step from practically being perform by using a pen and paper. For example, but for the “processor configured to” or “processor-implemented” language, the claim could implicate a person making a path distribution and picking out a “final path” based on judgment and observation. The mere nominal recitation of “a processor configured to” or “processor-implemented” does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
101 Analysis – Step 2A Prong two evaluation: Practical Application – No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: 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.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claims recite the additional elements or steps of processor-implemented or a processor configured to. The processor is recited at a high level of generality (i.e. as a general means of executing instructions), and merely using a computer to implement the abstract idea.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B evaluation: Inventive concept – No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of a “processor configured to” or “processor-implemented” are well-understood, routine, and conventional components because the detailed description of embodiment does not indicate that the processor is anything other than a conventional computer for implementing instructions. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Dependent claims 4-7, 9-13, 16-18, and 20-21 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 4-7, 9-13, 16-18, and 20-21 are not patent eligible under the same rationale as provided for in the rejection of the claims 1, 8, 14, and 19.
Therefore, claims 1, 4-14, and 16-21 are ineligible under 35 USC § 101.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 6, 8-9, 11-14, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gao in view of Choudhury.
Regarding claim 1, Gao teaches of a processor-implemented method ("Embodiments of the subject matter and the functional operations described in this specification can be implemented…as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus", [109], ("The term 'data processing apparatus' refers to data processing hardware and encompasses…a programmable processor, a computer, or multiple processors or computers", [0110]), the method comprising: generating initial information comprising either one or both of map information and departure information ("generates scene data 142…scene data includes…data characterizing map features of a map of the environment", [0030] - [0031], implicit that the departure information is also the current location of the vehicle); generating a plurality of paths ("the data representation system 140 provides the scene data 142 to a trajectory prediction system 150", [0032], "the trajectory prediction output 152 for a given agent can include data characterizing a predicted similarity of the future trajectory of the agent to each plurality of anchor trajectories", [0036]); and generating a path distribution corresponding to the plurality of paths by training a path distribution estimation model to output the path distribution corresponding to the plurality of paths ("the trajectory prediction output 152 for a given agent defines a respective probability distribution over possible future trajectories for the given agent", [0036], "Training the trajectory prediction system 150…Fig. 3", [0048]).
However, Gao does not teach of generating initial information comprising arrival information; inputting the initial information to a planner ensemble; wherein the planner ensemble comprises a plurality of planners having different characteristics from each other, and wherein the generating of the plurality of paths comprises generating the plurality of paths corresponding to the plurality of planners, respectively, by inputting the initial information to each of the plurality of planners.
Choudhury, in the same field of endeavor, teaches of generating initial information comprising arrival information ("the planning problem is to find the shortest dynamically feasible trajectory from start x0 to the goal xf that is collision free", Section II. Problem Statement); inputting the initial information to a planner ensemble ("The central idea is to have an ensemble of planners running in parallel as shown in Fig. 2. These planners work in parallel, often in a complimentary fashion, and bid their plans", Section III. Planner Ensemble, each of the planners are given the same input (such as x0, xf, Xobs)); wherein the planner ensemble comprises a plurality of planners having different characteristics from each other ("In this paper we present the planner ensemble. Each element in the ensemble is a planning algorithm with different parameters", Section I. Introduction), and wherein the generating of the plurality of paths comprises generating the plurality of paths corresponding to the plurality of planners, respectively, by inputting the initial information to each of the plurality of planners ("The central idea is to have an ensemble of planners running in parallel as shown in Fig. 2. These planners work in parallel, often in a complimentary fashion, and bid their plans", Section III. Planner Ensemble, each of the planners are given the same input (such as x0, xf, Xobs)).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of Gao with the teaching of Choudhury to utilize a planning ensemble with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to generate a more diverse set of paths for the model, in order to increase chances of success (Choudhury, Abstract).
Regarding claim 4, modified Gao teaches of all limitations of claim 1 as stated above, additionally, wherein the training comprises training the path distribution estimation model ("Training the trajectory prediction system 150…FIG. 3", [0048]) to minimize a loss function determined based on a difference between the plurality of paths and a test path generated by inputting the initial information to the path distribution estimation model ("the system can compute a loss…that measures errors in the predicted trajectories for the one or more agents relative to the ground truth future trajectories…and use the loss to determine an update to the parameters…through stochastic gradient descent", [0012], implicit that gradient descent means minimizing a loss function).
Regarding claim 6, modified Gao teaches of all limitations of claim 1 as stated above, additionally, wherein the path distribution estimation model comprises a path distribution estimation model based on a generative model ("a trained machine learning model, referred to in this specification as a "trajectory prediction system," to generate a respective trajectory prediction", [0019]).
Regarding claim 8, Gao discloses a processor-implemented method, the method comprising: generating initial information comprising either one or both of map information and departure information ("generates scene data 142…scene data includes…data characterizing map features of a map of the environment", [0030] - [0031], implicit that the departure information is also the current location of the vehicle); generating a path distribution corresponding to the initial information by inputting the initial information to a path distribution estimation model ("the data representation system 140 provides the scene data 142 to a trajectory prediction system 150", [0032], "the trajectory prediction output 152 for a given agent can include data characterizing a predicted similarity of the future trajectory of the agent to each plurality of anchor trajectories", [0036]); and determining a final path based on the path distribution ("When the planning system 160 receives the trajectory prediction outputs 152, the planning system 160 can use the trajectory prediction outputs 152 to generate planning decisions that plan a future trajectory of the vehicle, i.e., to generate a new planned vehicle path", [0042])
However, Gao does not teach of generate initial information comprising arrival information; and wherein the path distribution estimation model is trained using paths obtained from a planner ensemble comprising a plurality of planners having different characteristics from each other.
Choudhury, in the same field of endeavor, teaches of generating initial information comprising arrival information ("the planning problem is to find the shortest dynamically feasible trajectory from start x0 to the goal xf that is collision free", Section II. Problem Statement); and wherein the path distribution estimation model is trained using paths obtained from a planner ensemble comprising a plurality of planners having different characteristics from each other ("In this paper we present the planner ensemble. Each element in the ensemble is a planning algorithm with different parameters", Section I. Introduction).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of Gao with the teaching of Choudhury to utilize a planning ensemble with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to generate a more diverse set of paths for the model, in order to increase chances of success (Choudhury, Abstract).
Regarding claim 9, Gao discloses wherein the determining of the final path comprises determining the final path by performing statistical processing on the path distribution ("The trajectory prediction output 152 for a given agent defines a respective probability distribution over possible future trajectories for the given agent", [0036], defining the probability here is implicit of statistical processing).
Regarding claim 11, Gao discloses wherein the generating of the initial information comprises generating the initial information based on sensor data obtained from one or more sensors ("The data representation system 140, also on-board the vehicle 102, receives the raw sensor data 132 from the sensor system 130 and other data characterizing the environment, e.g., map data that identifies map features in the vicinity of the vehicle, and generates scene data 142. The scene data 142 characterizes the current state of the environment surrounding the vehicle 102 as of the current time point", [0030]).
Regarding claim 12, Gao discloses wherein the generating of the initial information comprises generating the departure information ("The data characterizing the observed trajectories can include data specifying the location of the corresponding surrounding agent", [0031]) based on positioning data obtained from a positioning module ("The data representation system 140, also on-board the vehicle 102, receives the raw sensor data 132 from the sensor system…and generates scene data…the scene data 142 includes at least…data specifying the location of the corresponding surrounding agent", [0030] - [0031], the scene data contains the current location of the agent which implies that there is a modules or sensor that provides the position data in order to generate the scene data).
Regarding claim 13, modified Gao teaches of all limitations of claim 1 as stated above, additionally teaches of a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the method of claim 1 ("Embodiments of the subject matter and the functional operations described in this specification can be implemented…as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus", [109], ("The term 'data processing apparatus' refers to data processing hardware and encompasses…a programmable processor, a computer, or multiple processors or computers", [0110]).
Regarding claim 14, Gao teaches of an electronic device comprising: a processor ("Embodiments of the subject matter and the functional operations described in this specification can be implemented…as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus", [109], ("The term 'data processing apparatus' refers to data processing hardware and encompasses…a programmable processor, a computer, or multiple processors or computers", [0110]) configured to: generate initial information comprising either one or both of map information and departure information ("generates scene data 142…scene data includes…data characterizing map features of a map of the environment", [0030] - [0031], implicit that the departure information is also the current location of the vehicle); generate a plurality of paths ("the data representation system 140 provides the scene data 142 to a trajectory prediction system 150", [0032], "the trajectory prediction output 152 for a given agent can include data characterizing a predicted similarity of the future trajectory of the agent to each plurality of anchor trajectories", [0036]); and generate a path distribution corresponding to the plurality of paths by training a path distribution estimation model to output the path distribution corresponding to the plurality of paths ("the trajectory prediction output 152 for a given agent defines a respective probability distribution over possible future trajectories for the given agent", [0036], "Training the trajectory prediction system 150…Fig. 3", [0048]).
However, Gao does not teach of generating initial information comprising arrival information; inputting the initial information to a planner ensemble; wherein the planner ensemble comprises a plurality of planners having different characteristics from each other, and wherein the processor is configured to generate the plurality of paths corresponding to the plurality of planners, respectively, by inputting the initial information to each of the plurality of planners.
Choudhury, in the same field of endeavor, teaches of generating initial information comprising arrival information ("the planning problem is to find the shortest dynamically feasible trajectory from start x0 to the goal xf that is collision free", Section II. Problem Statement); inputting the initial information to a planner ensemble ("The central idea is to have an ensemble of planners running in parallel as shown in Fig. 2. These planners work in parallel, often in a complimentary fashion, and bid their plans", Section III. Planner Ensemble, each of the planners are given the same input (such as x0, xf, Xobs)); wherein the planner ensemble comprises a plurality of planners having different characteristics from each other ("In this paper we present the planner ensemble. Each element in the ensemble is a planning algorithm with different parameters", Section I. Introduction), and wherein the processor is configured to generate the plurality of paths corresponding to the plurality of planners, respectively, by inputting the initial information to each of the plurality of planners ("The central idea is to have an ensemble of planners running in parallel as shown in Fig. 2. These planners work in parallel, often in a complimentary fashion, and bid their plans", Section III. Planner Ensemble, each of the planners are given the same input (such as x0, xf, Xobs)).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of Gao with the teaching of Choudhury to utilize a planning ensemble with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to generate a more diverse set of paths for the model, in order to increase chances of success (Choudhury, Abstract).
Regarding claim 16, modified Gao teaches of all limitations of claim 14 as stated above, additionally, wherein, for the training, the processor is configured to train the path distribution estimation model ("Training the trajectory prediction system 150…FIG. 3", [0048]) to minimize a loss function determined based on a difference between the plurality of paths and a test path generated by inputting the initial information to the path distribution estimation model ("the system can compute a loss…that measures errors in the predicted trajectories for the one or more agents relative to the ground truth future trajectories…and use the loss to determine an update to the parameters…through stochastic gradient descent", [0012], implicit that gradient descent means minimizing a loss function).
Regarding claim 19, Gao discloses an electronic device comprising: a processor ("Embodiments of the subject matter and the functional operations described in this specification can be implemented…as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus", [109], ("The term 'data processing apparatus' refers to data processing hardware and encompasses…a programmable processor, a computer, or multiple processors or computers", [0110]) configured to: generate initial information comprising either one or both of map information and departure information ("generates scene data 142…scene data includes…data characterizing map features of a map of the environment", [0030] - [0031], implicit that the departure information is also the current location of the vehicle); generate a path distribution corresponding to the initial information by inputting the initial information to a path distribution estimation model ("the data representation system 140 provides the scene data 142 to a trajectory prediction system 150", [0032], "the trajectory prediction output 152 for a given agent can include data characterizing a predicted similarity of the future trajectory of the agent to each plurality of anchor trajectories", [0036]); and determine a final path based on the path distribution ("When the planning system 160 receives the trajectory prediction outputs 152, the planning system 160 can use the trajectory prediction outputs 152 to generate planning decisions that plan a future trajectory of the vehicle, i.e., to generate a new planned vehicle path", [0042]).
However, Gao does not teach of generate initial information comprising arrival information; and wherein the path distribution estimation model is trained using paths obtained from a planner ensemble comprising a plurality of planners having different characteristics from each other.
Choudhury, in the same field of endeavor, teaches of generating initial information comprising arrival information ("the planning problem is to find the shortest dynamically feasible trajectory from start x0 to the goal xf that is collision free", Section II. Problem Statement); and wherein the path distribution estimation model is trained using paths obtained from a planner ensemble comprising a plurality of planners having different characteristics from each other ("In this paper we present the planner ensemble. Each element in the ensemble is a planning algorithm with different parameters", Section I. Introduction).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of Gao with the teaching of Choudhury to utilize a planning ensemble with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to generate a more diverse set of paths for the model, in order to increase chances of success (Choudhury, Abstract).
Regarding claim 20, Gao discloses wherein, for the determining of the final path, the processor is configured to determine the final path by performing statistical processing on the path distribution ("The trajectory prediction output 152 for a given agent defines a respective probability distribution over possible future trajectories for the given agent", [0036], defining the probability here is implicit of statistical processing).
Claims 5, 7, 10, 17-18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Gao in view of Choudhury, and further in view of Nister et al. (US20210253128A1), hereinafter Nister.
Regarding claim 5, modified Gao teaches of all limitations of claim 2 as stated above.
However, modified Gao does not teach of wherein the plurality of planners comprises a sampling-based planner.
Nister, in the same field of endeavor, teaches of wherein the plurality of planners comprises a sampling-based planner ("…the motion planner 172 evaluates given trajectories…for quality very thoroughly, and hypothesis generation 160 provides promising or plausible trajectories is too large to search", [0067], implicit that the planner samples from a large set of possible trajectories).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the processor-implemented method of modified Gao with the sampling-based planner of Nister with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the performance of the system by narrowing the number of trajectories to those that are plausible, as having a large number of trajectories is more difficult to search through (Nister, [0067]).
Regarding claim 7, modified Gao teaches of all limitations of claim 1 as stated above, in addition to, the departure information comprises either one or both of departure location information and departure position information ("…scene data 142 includes at least…data characterizing map features of a map of the environment…can include data specifying the location of the corresponding surrounding agent", [0031]).
However, modified Gao does not teach of wherein the map information comprises an occupancy grid map, and the arrival information comprises either one or both of arrival location information and arrival position information.
Nister, in the same field of endeavor, teaches of wherein the map information comprises an occupancy grid map ("The outputs may include information such as…location of other vehicles (e.g., an occupancy grid)", [0100]), and the arrival information comprises either one or both of arrival location information and arrival position information ("…and a destination point (e.g., a final location)", [0022]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have combined the departure information of modified Gao with the occupancy grid and arrival information of Nister to yield predictable results. One of ordinary skill in the art would have combine the occupancy grid in order to better represent the positions of the objects surrounding the agent, and ultimately improve the accuracy of the system. Additionally, one would have combined the arrival information in order to provide the system with sufficient information to create an optimal path (Nister, [0022]).
Regarding claim 10, modified Gao teaches of all limitations of claim 8 as stated above, specifically, determining the final path ("When the planning system 160 receives the trajectory prediction outputs 152, the planning system 160 can use the trajectory prediction outputs 152 to generate planning decisions that plan a future trajectory of the vehicle, i.e., to generate a new planned vehicle path", [0042]).
However, modified Gao does not teach of by inputting the path distribution to a sampling-based planner.
Nister, in the same field of endeavor, teaches of by inputting the path distribution to a sampling-based planner ("…the motion planner 172 evaluates given trajectories…for quality very thoroughly, and hypothesis generation 160 provides promising or plausible trajectories is too large to search", [0067], implicit that the planner samples from a large set of possible trajectories).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Gao of determining the final path with the sampling-based planner of Nister with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the performance of the system by narrowing the number of trajectories to those that are plausible, as having a large number of trajectories is more difficult to search through (Nister, [0067]).
Regarding claim 17, modified Gao teaches of all limitations of claim 15 as stated above, additionally, wherein the path distribution estimation model comprises a path distribution estimation model based on a generative model ("a trained machine learning model, referred to in this specification as a "trajectory prediction system," to generate a respective trajectory prediction", [0019]).
However, modified Gao does not teach of wherein the plurality of planners comprises a sampling-based planner.
Nister, in the same field of endeavor, teaches of wherein the plurality of planners comprises a sampling-based planner ("…the motion planner 172 evaluates given trajectories…for quality very thoroughly, and hypothesis generation 160 provides promising or plausible trajectories is too large to search", [0067], implicit that the planner samples from a large set of possible trajectories).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified teachings of modified Gao with the sampling-based planner of Nister with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the performance of the system by narrowing the number of trajectories to those that are plausible, as having a large number of trajectories is more difficult to search through (Nister, [0067]).
Regarding claim 18, modified Gao teaches of all limitations of claim 14 as stated above, in addition to, the departure information comprises either one or both of departure location information and departure position information ("…scene data 142 includes at least…data characterizing map features of a map of the environment…can include data specifying the location of the corresponding surrounding agent", [0031]).
However, modified Gao does not teach of wherein the map information comprises an occupancy grid map, and the arrival information comprises either one or both of arrival location information and arrival position information.
Nister, in the same field of endeavor, teaches of wherein the map information comprises an occupancy grid map ("The outputs may include information such as…location of other vehicles (e.g., an occupancy grid)", [0100]), and the arrival information comprises either one or both of arrival location information and arrival position information ("…and a destination point (e.g., a final location)", [0022]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have combined the departure information of modified Gao with the occupancy grid and arrival information of Nister to yield predictable results. One of ordinary skill in the art would have combine the occupancy grid in order to better represent the positions of the objects surrounding the agent, and ultimately improve the accuracy of the system. Additionally, one would have combined the arrival information in order to provide the system with sufficient information to create an optimal path (Nister, [0022]).
Regarding claim 21, modified Gao teaches of all limitations of claim 19 as stated above, additionally, wherein, for the determining of the final path, the processor is configured to determine the final path ("When the planning system 160 receives the trajectory prediction outputs 152, the planning system 160 can use the trajectory prediction outputs 152 to generate planning decisions that plan a future trajectory of the vehicle, i.e., to generate a new planned vehicle path", [0042]).
However, modified Gao does not teach of by inputting the path distribution to a sampling-based planner.
Nister, in the same field of endeavor, teaches of by inputting the path distribution to a sampling-based planner ("…the motion planner 172 evaluates given trajectories…for quality very thoroughly, and hypothesis generation 160 provides promising or plausible trajectories is too large to search", [0067], implicit that the planner samples from a large set of possible trajectories).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Gao of determining the final path with the sampling-based planner of Nister with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the performance of the system by narrowing the number of trajectories to those that are plausible, as having a large number of trajectories is more difficult to search through (Nister, [0067]).
Conclusion
THIS ACTION IS MADE FINAL. 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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ABIGAIL LEE ESPINOZA
Examiner
Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657