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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/02/2026 has been entered.
Status of the Claims
This Office Action is in response to the claims filed on 06/02/2026.
Claims 1-22 have been presented for examination.
Claims 1-22 are currently rejected.
Claims 1-3, 5-10, 12-17, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (U.S. Patent Publication Number 2023/0058169) in view of Isele (U.S. Patent Publication Number 2020/0391738), further in view of Rosman (U.S. Patent Publication Number 2021/0390352).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (U.S. Patent Publication Number 2023/0058169) in view of Isele (U.S. Patent Publication Number 2020/0391738) and Rosman (U.S. Patent Publication Number 2021/0390352), further in view of Qi et al. (U.S. Patent Publication Number 2021/0081787).
Response to Arguments
35 U.S.C. 101
Applicant’s arguments, see Applicant Remarks, filed 06/02/2026, with respect to 35 U.S.C. 101 have been fully considered and are persuasive. See Applicant Remarks at least pages 9-10. Accordingly, the 35 U.S.C. 101 rejection has been withdrawn.
35 U.S.C. 103
The Applicant’s arguments, see Applicant Remarks filed on 06/02/2026, appear to be primarily directed to the amended claim language. The Applicant’s arguments with respect to claim(s) 1-22 have been considered but are moot because amendments shift the scope of claims and necessitate a new ground of rejection, which is made in view of Rosman (U.S. Patent Publication Number 2021/0390352).
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.
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.
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.
Claims 1-3, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (U.S. Patent Publication Number 2023/0058169) in view of Isele (U.S. Patent Publication Number 2020/0391738), further in view of Rosman (U.S. Patent Publication Number 2021/0390352).
Regarding claim 1, Cella discloses a computer-implemented method comprising:
accessing two or more traffic scenes generated by one or more traffic models representing simulated trajectories of multiple vehicles; (Cella ¶ 25 discloses “use of,” therefore accessing, traffic models including “a trigger-response mobile-element-following traffic model” and a “microscopic traffic model,” the traffic model including representation of “an aspect of an environment [i.e., a traffic scene].” The driving environment is modeled using traffic information, see ¶ 487, such that “at least one Internet-of-things device 34150 that captures the data about the vehicle 3410 is disposed external to the vehicle 3410,” see ¶ 298, wherein the data includes “data from multiple vehicles running at different times [i.e., two or more traffic scenes] and under different operating conditions,” see ¶ 511. Therefore, the environment includes a traffic scene.)
accessing preference data stored in a dataset and indicating a preference relationship that indicates relative realism among traffic scenes; (Cella ¶ 208 discloses processing social data sources 22107, thereby accessing the data, and “predicting a high level of attendance [i.e., a realism of a traffic scene] by processing images on many social media feeds that indicate interest in the event by many people [i.e., preference data], prediction of traffic.” Cella ¶ 463 further discloses “a search result display ranking circuit 58210 that orders the search results based on a relevance” such that “in-vehicle results are ranked based on outcomes with respect to in-vehicle searches by other users,” see ¶ 453, wherein the simulations for the vehicle are ranked based on one or more metrics defined by the user, see ¶ 542. Also see ¶ 177 “the vehicle routing system 1492 accounts for the routing preference 14100 of the user 1490 when routing”)
One having ordinary skill in the art would recognize that a realism of a traffic scene, under its broadest reasonable interpretation, refers to the practicality of the traffic scene in reality; therefore, a predicted high level of attendance includes a high practicality or confidence for predicted traffic, thereby indicating a realism of the traffic scene, see Merriam-Webster “realism.”
Cella does not expressly disclose:
calculating, using a reward model, a reward value based, at least in part, on the preference data; and
updating the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective.
However, Isele discloses:
calculating, using a reward model, a reward value based, at least in part, on the preference data; and (Isele ¶ 36 discloses “the intention predictor 154 and model updater 156 [i.e., reward model] ... may be utilized to calculate probabilities and rewards associated with those various possible actions (i.e., of the identified traffic participant and of the autonomous vehicle) to determine the action or operating maneuver to be implemented by the autonomous action selector 158,” wherein the action or maneuver is selected based on “user preference,” see ¶ 53. The intention predictor with the model updater is a reward model in accordance with ¶ 80 of the instant specification defining the reward model as being used to compute a reward score.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the remote control of the autonomous vehicle of Cella with moving the autonomous vehicle based on an updated traffic model, as disclosed in Isele, with reasonable expectation of success to ensure a safe planning strategy (Isele ¶ 52) and to have an updated probability of a successful interaction indicative of a likelihood of success (Isele ¶ 54), rendering the modification to be obvious.
Rosman discloses:
updating the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective. (Rosman ¶ 19 discloses capturing sensor data associated with a new environment of the vehicle, such that a best estimation of the situation is “provided to the trained machine learning model and reward points are assigned to the estimation, based on the accuracy of the estimation. In some examples, reward points are assigned to the estimation when the system declares that the estimation is made with some threshold level of confidence.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the traffic models of Cella, of the combination of Cella and Isele, with updating the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective, as disclosed by Rosman, with reasonable expectation of success, to improve the standard machine learning systems by providing a best estimation of unknown situations using existing trained models (Rosman ¶ 18) and in doing so, the model can help the vehicle system prepare a best estimate of the environment (or aspect(s) thereof) that was/were not identified during the first training phase and ultimately take an appropriate action (Rosman ¶ 18), rendering the limitation to be an obvious modification.
Regarding claim 2, Cella in combination with Isele and Rosman discloses the computer-implemented method of claim 1, wherein:
the preference data is generated based, at least in part, on one or more sets of traffic scenes, (Cella ¶ 208 discloses “predicting a high level of attendance by processing images on many social media feeds that indicate interest in the event by many people [i.e., preference data], prediction of traffic,” wherein parameters for the AI system include “traffic profiles 440 (location, direction, density, and patterns in time),” see ¶ 129.)
where the traffic scenes are ranked to indicate the preference relationship indicating relative realism among the traffic scenes by one or more human labelers. (Cella ¶ 543 “The one or more model interpretability systems may also be used by a human user to improve and guide training of the machine learning model 65102, to help debug the machine learning model 65102, to help recognize bias in the machine learning model 65102,” wherein the model 65102 evaluates simulations and ranks the simulations based on one or more metrics. The Examiner notes that the limitation “to indicate” appears to recite an intended use, which is not expressly required under the broadest reasonable interpretation of the claim.)
Regarding claim 3, Cella in combination with Isele and Rosman discloses the computer-implemented method of claim 1, wherein:
the preference data comprises one or more pairs of traffic scenes (Cella ¶ 156 discloses that “each evaluation in the series of evaluations uses feedback indicative of an effect on at least one of a vehicle operating state 945,” such that “a vehicle routing system 1692 to use the routing preference 16100 of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles 1694.” The corresponding “effect” indicates a pair of traffic scenarios in accordance with the definition provided in ¶ 87 of the instant application which states “Scenario pairs 210 may be used to generate one or more feedback relationships 212.”)
where each pair of traffic scenes in the one or more pairs of traffic scenes comprises a first traffic scene indicated as preferable over a second traffic scene. (Cella ¶ 177 discloses “the vehicle routing system 1492 accounts for the routing preference 14100 of the user 1490 when routing the at least one vehicle 1410 within the set of vehicles 1494,” wherein the route includes a plurality of routes which includes at least a second scenario [i.e., preference of a first traffic scenario over a second]. Also see ¶ 156.)
Regarding claim 5, Cella in combination with Isele and Rosman discloses the computer-implemented method of claim 1, wherein:
the traffic models comprise one or more neural networks. (Cella ¶ 504 discloses “the artificial intelligence system 60112 may train models, such as predictive models (e.g., various types of neural networks”). One having ordinary skill in the art would recognize that a neural network being an example of a model indicates that the model comprises a neural network.)
Regarding claim 6, Cella in combination with Isele and Rosman discloses the computer-implemented method of claim 1, wherein:
the two or more traffic scenes generated by the one or more traffic models comprise traffic simulations indicating a position, direction, and speed of one or more vehicles over an interval of time. (Cella ¶ 129 discloses “Parameters 430 may include parameters of various transportation-relevant profiles, such as traffic profiles 440 (location, direction, density and patterns in time,” such that the parameters are “taken as inputs by an ... AI system,” also see Fig. 4. The driving environment is modeled using traffic information, see ¶ 487, such that “at least one Internet-of-things device 34150 that captures the data about the vehicle 3410 is disposed external to the vehicle 3410,” see ¶ 298, wherein the data includes “data from multiple vehicles running at different times [i.e., two or more traffic scenes] and under different operating conditions,” see ¶ 511. Therefore, the environment includes a traffic scene.)
Regarding claim 7, Cella in combination with Isele and Rosman discloses the computer-implemented method of claim 1, wherein:
moving an autonomous vehicle comprises using the updated one or more traffic models to determine one or more control inputs to the autonomous vehicle to cause the autonomous vehicle to navigate an environment. (Isele ¶ 57 discloses “The autonomous action selector 158 may implement the maneuver based on the updated probability of the successful interaction between the identified traffic participant and the autonomous vehicle,” such that “traffic participants may be modelled according to an intelligent driver model (IDM),” see ¶ 58. See Fig. 5. Also see ¶ 36 “model updater 156 ... may be utilized to calculate probabilities and rewards associated with those various possible actions (i.e., of the identified traffic participant and of the autonomous vehicle) to determine the action or operating maneuver to be implemented by the autonomous action selector 158.” Also see Fig. 4.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the remote control of the autonomous vehicle of Cella with moving the autonomous vehicle based on an updated traffic model, as disclosed in Isele, with reasonable expectation of success to ensure a safe planning strategy (Isele ¶ 52) and to have an updated probability of a successful interaction indicative of a likelihood of success (Isele ¶ 54), rendering the modification to be obvious.
Regarding claim 8, Cella discloses a non-transitory computer readable storage medium storing thereon executable instructions (Cella in at least ¶ 745) that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
access one or more traffic scenes generated by one or more traffic models representing simulated trajectories of multiple vehicles; (Cella ¶ 25 discloses “use of,” therefore accessing, traffic models including “a trigger-response mobile-element-following traffic model” and a “microscopic traffic model,” the traffic model including representation of “an aspect of an environment [i.e., a traffic scene].” The driving environment is modeled using traffic information, see ¶ 487, such that “at least one Internet-of-things device 34150 that captures the data about the vehicle 3410 is disposed external to the vehicle 3410,” see ¶ 298, wherein the data includes “data from multiple vehicles running at different times [i.e., two or more traffic scenes] and under different operating conditions,” see ¶ 511. Therefore, the environment includes a traffic scene.)
access preference data stored in a dataset and indicating a preference relationship indicating relative realism among traffic scenes; (Cella ¶ 208 discloses processing social data sources 22107, thereby accessing the data, and “predicting a high level of attendance [i.e., a realism of a traffic scene] by processing images on many social media feeds that indicate interest in the event by many people [i.e., preference data], prediction of traffic.” Cella ¶ 463 further discloses “a search result display ranking circuit 58210 that orders the search results based on a relevance” such that “in-vehicle results are ranked based on outcomes with respect to in-vehicle searches by other users,” see ¶ 453, wherein the simulations for the vehicle are ranked based on one or more metrics defined by the user, see ¶ 542. Also see ¶ 177 “the vehicle routing system 1492 accounts for the routing preference 14100 of the user 1490 when routing”)
Cella does not expressly disclose:
calculate, using a reward model, a reward value based, at least in part, on the preference data; and
update the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective.
However, Isele discloses:
calculating, using a reward model, a reward value based, at least in part, on the preference data; and (Isele ¶ 36 discloses “the intention predictor 154 and model updater 156 [i.e., reward model] ... may be utilized to calculate probabilities and rewards associated with those various possible actions (i.e., of the identified traffic participant and of the autonomous vehicle) to determine the action or operating maneuver to be implemented by the autonomous action selector 158,” wherein the action or maneuver is selected based on “user preference,” see ¶ 53. The intention predictor with the model updater is a reward model in accordance with ¶ 80 of the instant specification defining the reward model as being used to compute a reward score.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the remote control of the autonomous vehicle of Cella with moving the autonomous vehicle based on an updated traffic model, as disclosed in Isele, with reasonable expectation of success to ensure a safe planning strategy (Isele ¶ 52) and to have an updated probability of a successful interaction indicative of a likelihood of success (Isele ¶ 54), rendering the modification to be obvious.
Rosman discloses:
updating the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective. (Rosman ¶ 19 discloses capturing sensor data associated with a new environment of the vehicle, such that a best estimation of the situation is “provided to the trained machine learning model and reward points are assigned to the estimation, based on the accuracy of the estimation. In some examples, reward points are assigned to the estimation when the system declares that the estimation is made with some threshold level of confidence.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the traffic models of Cella, of the combination of Cella and Isele, with updating the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective, as disclosed by Rosman, with reasonable expectation of success, to improve the standard machine learning systems by providing a best estimation of unknown situations using existing trained models (Rosman ¶ 18) and in doing so, the model can help the vehicle system prepare a best estimate of the environment (or aspect(s) thereof) that was/were not identified during the first training phase and ultimately take an appropriate action (Rosman ¶ 18), rendering the limitation to be an obvious modification.
Regarding claim 9, Cella in combination with Isele and Rosman discloses the parallel limitations contained in parent claim 2 for the reasons discussed above. In addition, the combination of Cella and Isele discloses a non-transitory computer readable storage medium. (See Cella in at least ¶ 745)
Regarding claim 10, Cella in combination with Isele and Rosman discloses the non-transitory computer readable storage medium of claim 8, wherein:
the preference data comprises one or more pair of traffic scenarios where each pair of traffic scenarios in the one or more pairs of traffic scenarios comprises a first traffic scenario pairs of traffic scenes, each pair comprising a first traffic scene indicated as preferable over a second traffic scenario scene. (Cella ¶ 156 discloses that “each evaluation in the series of evaluations uses feedback indicative of an effect on at least one of a vehicle operating state 945,” such that “a vehicle routing system 1692 to use the routing preference 16100 of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles 1694.” The corresponding “effect” indicates a pair of traffic scenarios in accordance with the definition provided in ¶ 87 of the instant application which states “Scenario pairs 210 may be used to generate one or more feedback relationships 212,” wherein the route includes a plurality of routes which includes at least a second scenario [i.e., preference of a first traffic scenario over a second]. Also see ¶ 156.)
Regarding claim 12, Cella in combination with Isele and Rosman discloses the parallel limitations contained in parent claim 5 for the reasons discussed above. In addition, the combination of Cella and Isele discloses a non-transitory computer readable storage medium. (See Cella in at least ¶ 745)
Regarding claim 13, Cella in combination with Isele and Rosman discloses the parallel limitations contained in parent claim 6 for the reasons discussed above. In addition, the combination of Cella and Isele discloses a non-transitory computer readable storage medium. (See Cella in at least ¶ 745)
Regarding claim 14, Cella in combination with Isele and Rosman discloses the non-transitory computer readable storage medium of claim 8, wherein:
the computer system is to further cause an autonomous vehicle to navigate an environment based, at least in part on, updating the traffic model. (Isele ¶ 57 discloses “The autonomous action selector 158 may implement the maneuver based on the updated probability of the successful interaction between the identified traffic participant and the autonomous vehicle,” such that “traffic participants may be modelled according to an intelligent driver model (IDM),” see ¶ 58. See Fig. 5. Also see ¶ 36 “model updater 156 ... may be utilized to calculate probabilities and rewards associated with those various possible actions (i.e., of the identified traffic participant and of the autonomous vehicle) to determine the action or operating maneuver to be implemented by the autonomous action selector 158.” Also see Fig. 4.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the remote control of the autonomous vehicle of Cella with moving the autonomous vehicle based on an updated traffic model, as disclosed in Isele, with reasonable expectation of success to ensure a safe planning strategy (Isele ¶ 52) and to have an updated probability of a successful interaction indicative of a likelihood of success (Isele ¶ 54), rendering the modification to be obvious.
Regarding claim 15, Cella discloses a system comprising:
one or more processors to: access one or more traffic scenes of a traffic model; (Cella ¶ 25 discloses “use of,” therefore accessing, traffic models including “a trigger-response mobile-element-following traffic model” and a “microscopic traffic model,” the traffic model including representation of “an aspect of an environment [i.e., a traffic scene].” The driving environment is modeled using traffic information, see ¶ 487, therefore, the environment includes a traffic scene.)
access preference data indicating a ranking, by one or more users, of a realism of the one or more traffic scenes; (Cella ¶ 208 discloses processing social data sources 22107, thereby accessing the data, and “predicting a high level of attendance [i.e., a realism of a traffic scene] by processing images on many social media feeds that indicate interest in the event by many people [i.e., preference data], prediction of traffic.” Cella ¶ 463 further discloses “a search result display ranking circuit 58210 that orders the search results based on a relevance” such that “in-vehicle results are ranked based on outcomes with respect to in-vehicle searches by other users,” see ¶ 453, wherein the simulations for the vehicle are ranked based on one or more metrics defined by the user, see ¶ 542. Also see ¶ 177 “the vehicle routing system 1492 accounts for the routing preference 14100 of the user 1490 when routing”)
calculate, using a reward model, a reward value, using one or more neural network ... (Cella ¶ 126 discloses that the AI system, which includes “one or more neural networks,” may manage a set of rewards.)
Cella does not expressly disclose:
calculate, using a reward model, a reward value based, at least in part, on the preference data; and
update the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective.
However, Isele discloses:
calculate, using a reward model, a reward value based, at least in part, on the preference data; and (Isele ¶ 36 discloses “the intention predictor 154 and model updater 156 [i.e., reward model] ... may be utilized to calculate probabilities and rewards associated with those various possible actions (i.e., of the identified traffic participant and of the autonomous vehicle) to determine the action or operating maneuver to be implemented by the autonomous action selector 158,” wherein the action or maneuver is selected based on “user preference,” see ¶ 53. The intention predictor with the model updater is a reward model in accordance with ¶ 80 of the instant specification defining the reward model as being used to compute a reward score.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the remote control of the autonomous vehicle of Cella with moving the autonomous vehicle based on an updated traffic model, as disclosed in Isele, with reasonable expectation of success to ensure a safe planning strategy (Isele ¶ 52) and to have an updated probability of a successful interaction indicative of a likelihood of success (Isele ¶ 54), rendering the modification to be obvious.
Rosman discloses:
update the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective. (Rosman ¶ 19 discloses capturing sensor data associated with a new environment of the vehicle, such that a best estimation of the situation is “provided to the trained machine learning model and reward points are assigned to the estimation, based on the accuracy of the estimation. In some examples, reward points are assigned to the estimation when the system declares that the estimation is made with some threshold level of confidence.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the traffic models of Cella, of the combination of Cella and Isele, with updating the one or more traffic models using the reward value at least in part by optimizing parameters of the one or more traffic models according to a training objective, as disclosed by Rosman, with reasonable expectation of success, to improve the standard machine learning systems by providing a best estimation of unknown situations using existing trained models (Rosman ¶ 18) and in doing so, the model can help the vehicle system prepare a best estimate of the environment (or aspect(s) thereof) that was/were not identified during the first training phase and ultimately take an appropriate action (Rosman ¶ 18), rendering the limitation to be an obvious modification.
Regarding claim 16, Cella in combination with Isele and Rosman discloses the parallel limitations contained in parent claim 2 for the reasons discussed above.
Regarding claim 17, Cella in combination with Isele and Rosman discloses the parallel limitations contained in parent claim 3 for the reasons discussed above.
Regarding claim 19, Cella in combination with Isele and Rosman discloses the system of claim 15, wherein:
the one or more processors are further to cause an autonomous machine to navigate an environment based, at least in part on, using the updated traffic model to determine one or more control inputs to the autonomous machine. (Isele ¶ 57 discloses “The autonomous action selector 158 may implement the maneuver based on the updated probability of the successful interaction between the identified traffic participant and the autonomous vehicle,” such that “traffic participants may be modelled according to an intelligent driver model (IDM),” see ¶ 58. See Fig. 5. Also see ¶ 36 “model updater 156 ... may be utilized to calculate probabilities and rewards associated with those various possible actions (i.e., of the identified traffic participant and of the autonomous vehicle) to determine the action or operating maneuver to be implemented [i.e., determine one or more control inputs] by the autonomous action selector 158 [i.e., autonomous machine].” Also see Fig. 4.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the AI system of Cella with updating the traffic model, as disclosed in Isele, with reasonable expectation of success to ensure a safe planning strategy (Isele ¶ 52) and to have an updated probability of a successful interaction indicative of a likelihood of success (Isele ¶ 54), rendering the modification to be obvious.
Regarding claim 20, Cella in combination with Isele and Rosman discloses the system of claim 15, wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a first system for performing simulation operations; a second system for performing deep learning operations; a third system implemented using an edge device; a fourth system implemented using a robot; a fifth system incorporating one or more virtual machines (VMs); a sixth system implemented at least partially in a data center; a seventh system for performing digital twin operations; an eighth system for performing light transport simulation; a nineth system for performing collaborative content creation for 3D assets; a tenth system for performing conversational Artificial Intelligence operations; an eleventh system for generating synthetic data; a twelfth system for implementing a web-hosted service for detecting program workload inefficiencies; an application as an application programming interface ("API"); a thirteenth system implemented at least partially using cloud computing resources; a fourteenth system for presenting one or more of virtual reality content, augmented reality content, or mixed reality content; or a fifteenth system implementing one or more large language models (LLMs). (Cella ¶ 153 discloses “the vehicle comprises an artificial intelligence system 1036, the method further comprising automating at least one control parameter of the vehicle by the artificial intelligence system 1036. In embodiments, the vehicle 1010 is at least a semi-autonomous vehicle”)
Regarding claim 21, Cella in combination with Isele and Rosman disclose the computer-implemented method of claim 1, wherein:
the two or more traffic scenes are generated from a same simulation context and initial conditions. (Cella ¶ 149 discloses that the vehicle 910 has an “artificial intelligence system 936 to execute a genetic algorithm 975 to and generate mutations from an initial vehicle operating state to determine at .least one optimized vehicle operating state.” Also see at least ¶¶ 487 and 511.)
Regarding claim 22, Cella in combination with Isele and Rosman disclose the computer-implemented method of claim 1, wherein:
the preference data is generated by presenting a set of traffic scenes to one or more annotators and receiving a selection of a most realistic traffic scene from the set. (Cella ¶ 543 “The one or more model interpretability systems may also be used by a human user [i.e., annotator] to improve and guide training of the machine learning model 65102, to ... help recognize bias in the machine learning model 65102,” wherein “a training set of human created or human supervised inputs, or the like) may provide a favorable and/or optimized charging plan for a vehicle or a set of vehicles based on the parameters,” see ¶ 142.)
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (U.S. Patent Publication Number 2023/0058169) in view of Isele (U.S. Patent Publication Number 2020/0391738) and Rosman (U.S. Patent Publication Number 2021/0390352), further in view of Qi et al. (U.S. Patent Publication Number 2021/0081787).
Regarding claim 4, Cella in combination with Isele and Rosman discloses the computer-implemented method of claim 1, wherein:
the reward value is calculated by comparing two or more traffic scenarios associated with the preference data ... (Cella ¶ 430 “a preferred outcome of maximum safety. In such a case, the interface 56133 may provide a reward parameter to a model or expert system 5657”)
While Cella in combination with Isele and Rosman does not expressly disclose:
[a reward value] to determine an average loss value over a sequence of the two or more traffic scenarios.
Qi discloses:
[a reward value] to determine an average loss value over a sequence of the two or more traffic scenarios. (Qi ¶ 19 discloses “the loss function is a function for calculating a stun of a first type of loss and a second type of loss; the first type of loss is an average loss,” Also see the loss function provided in ¶ 134 which uses the value derived from the reward function.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have utilized the reward value of Qi in place of the reward value of Cella with reasonable expectation of success because the substitution would result in using the reward value of Cella to determine an average loss value for a sequence of traffic scenarios.
Further, it would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified the combination of Cella and Isele with determining an average loss value, as disclosed in Qi, with reasonable expectation of success, to ensure the processing efficiency of data processing tasks (Qi ¶ 93), and to minimize average execution time of tasks (Qi ¶ 110), rendering the modification to be obvious.
Regarding claim 11, Cella in combination with Isele, Rosman, and Qi discloses the parallel limitations contained in parent claim 4 for the reasons discussed above. In addition, the combination of Cella and Isele discloses a non-transitory computer readable storage medium. (See Cella in at least ¶ 745)
Regarding claim 18, Cella in combination with Isele, Rosman, and Qi discloses the parallel limitations contained in parent claim 4 for the reasons discussed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE T SU whose telephone number is (571)272-5326. The examiner can normally be reached Monday to Friday, 9:30AM - 5:00PM EST.
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/STEPHANIE T SU/Primary Examiner, Art Unit 3662
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