CTFR 18/909,235 CTFR 95898 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims Pending 1-3, 5-7 Cancelled 4, 8 35 U.S.C. 103 1-3, 5-7 Response to Amendment This office action is in response to applicant’s arguments and amendments filed 03/26/2026, which are in response to USPTO Office Action mailed 12/08/2025. Applicant’s arguments and amendments have been considered with the results that follow: THIS ACTION IS MADE FINAL . Information Disclosure Statement The information disclosure statement(s) (IDS(s)) submitted on 02/09/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claim interpretations from the USPTO Office Action mailed 12/08/2025 remain invoked in regards to an input interface device in claim 1 and the corresponding structure recited in FIG. 10 and paragraph [0095] (“a computer system 1300 may include at least one of a processor 1310, memory 1330, an input interface device 1350”) of applicant’s specification. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. 07-21-aia AIA Claim (s) 1-3 and 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez (US 2023/0063368 A1, “Lopez”) and further in view of Kim et al. (US 2024/0375642 A1, “Kim”) . Regarding claim 1: Lopez teaches: A system for managing a driving negotiation target for a minimal risk maneuver, the system comprising ([0021]; [0023]; [0062]): an input interface device configured to receive surrounding vehicle information from surrounding vehicles of an ego vehicle through V2X communication and to receive map information from a high precision map management unit ([0041]; [0023]; [0031]; [0036]; [0039]; [0040]; [0074]; [0075]; [0100]); memory in which a program that selects a driving negotiation target based on the surrounding vehicle information and the map information has been stored ([0023]; [0039]); and a processor configured to execute the program ([0023]; [0062]), wherein the processor selects the driving negotiation target, among the surrounding vehicles, by considering a minimal risk maneuver mode ([0023]; [0039]; [0099]; [0108]; [0134]). wherein the processor selects, as the driving negotiation target, the surrounding vehicle that is expected to influence or to be influenced by a driving path of the ego vehicle when performing a minimal risk maneuver by considering the minimal risk maneuver mode, ([0023] generate future state for P1s/P2s, select MRM, determine MRM reward, update maneuver based on reward, operate vehicle with updated maneuver; [0031] P1 is current and predicted future states of vehicle; P2 is current and predicted future states of object; continuously receiving P1s/P2s; [0035] systems, methods, computer program products, techniques for selecting optimal MRM used by vehicle during operation; [0099] FIGS. 5A-5B, FIG. 6, process for selecting optimal MRM by vehicle; [0108] provides various MRM trajectories (stop in lane, pull over) to system safety controller, provides signals associated MRM to operate vehicle; [0119] assigns positive reward for correctly selected MRM; negative reward assigned for incorrectly selected/used MRM; infinitely negative reward for MRM that results in accident, injury, damage to vehicle, damage to environment; [0134] transmit signal to operate vehicle using selected MRM) wherein the minimal risk maneuver mode includes [various maneuver trajectories], and ([0108] provides various MRM trajectories (stop in lane, pull over) to system safety controller, provides signals associated MRM to operate vehicle) wherein the minimal risk maneuver mode is repeatedly determined until the ego vehicle is fully stopped ([0039] routes include precise state sequences along high level action sequence with limited lookahead horizon to reach intermediate goals, where combination of successive iterations of limited horizon state sequences cumulatively correspond to trajectories that collectively form high level route to terminate at final goal state. [0121]-[0125] Once the MRM has been selected, determines reward value associated with selected MRM. reward values based on safety rules (stop at a stop sign). Once the reward value has been assigned to selected MRM, determines whether the selected MRM is the correct MRM in view of parameter data from sensors and determined through modeling. if selected MRM has negative reward or infinitely negative reward, another MRM should be selected, as the currently selected MRM may be unfeasible under vehicle’s/environment’s health or state. controller updates MRM using assigned reward determination or received/modeled data relating to vehicle’s or environment’s health or state. provides the updated MRM to drive by wire component, for operating the vehicle using updated MRM. [0136] New MRMs based on parameter data that it continuously receives and trained MRM model. update existing MRMs based on such continuous receipt of parameter data and the trained MRM model; [0108] provides various MRM trajectories (stop in lane, pull over) to system safety controller, provides signals associated MRM to operate vehicle). However, Lopez does not explicitly teach: wherein the minimal risk maneuver mode includes an emergency stop, a going- straight stop, an ego vehicle lane stop, a right lane stop, shoulder parking, and safety zone parking. Kim teaches: wherein the minimal risk maneuver mode includes an emergency stop, a going- straight stop, an ego vehicle lane stop, a right lane stop, shoulder parking, and safety zone parking ([0076]-[0080] select one of a plurality of MRM strategies once minimal risk operation is initiated. The MRM strategies may include four types as shown in FIG. 3. lane stop strategy include straight stop and in-lane stop. road shoulder stop strategy include half-shoulder stop and full-shoulder stop. straight stop: using only the deceleration control, without involving lateral control. in-lane stop: stopping within boundaries of lane vehicle was traveling in. half-shoulder stop: stopping with portion of vehicle positioned on shoulder of road; full-shoulder stop: stopping when entire vehicle positioned on should of road. Fig. 3: various kinds of minimal risk maneuvers). Lopez and Kim are analogous art to the claimed invention since they are from the similar field of vehicle controls for minimal risk maneuvers. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Lopez with the aspects of Kim to create, with a reasonable expectation for success, a system for managing a driving negotiation target for a minimal risk maneuver, wherein the minimal risk maneuver includes an emergency stop, a going- straight stop, an ego vehicle lane stop, a right lane stop, shoulder parking, and safety zone parking. The motivation for modification would have been to minimize known risk of collision with surrounding vehicles in order to reach a minimum risk state, thus increasing overall vehicle and occupant safety (Kim, [0052]). Regarding claim 2: Lopez-Kim further teach: The system of claim 1, wherein the surrounding vehicle information comprises vehicle ID information, absolute location information, speed information, and acceleration information (Lopez: [0041]; [0026]; [0107]; [0113]; [0118]; [0120]; [0127]). Regarding claim 3: Lopez-Kim further teach: The system of claim 1, wherein the processor generates base information for selecting the driving negotiation target based on the surrounding vehicle information and the map information (Lopez: [0023]; [0035]; [0039]; [0120]). Regarding claim 5: Lopez teaches: A method of managing a driving negotiation target for a minimal risk maneuver, the method performed by a system for managing a driving negotiation target and comprising steps of ([0035]; [0021]; [0023]; [0062]): (a) receiving surrounding vehicle information and high precision map information ([0041]; [0023]; [0031]; [0036]; [0039]; [0040]; [0074]; [0075]; [0100]); and (b) selecting a driving negotiation target in a minimal risk maneuver mode based on the surrounding vehicle information and the high precision map information ([0023]; [0039]; [0099]; [0108]; [0134] transmit signal to operate vehicle using selected MRM) wherein the step (b) comprises selecting, as the driving negotiation target, a surrounding vehicle that is expected to influence or to be influenced by a driving path of the ego vehicle when performing a minimal risk maneuver by considering the minimal risk maneuver mode, ([0023] generate future state for P1s/P2s, select MRM, determine MRM reward, update maneuver based on reward, operate vehicle with updated maneuver; [0031] P1 is current and predicted future states of vehicle; P2 is current and predicted future states of object; continuously receiving P1s/P2s; [0035] systems, methods, computer program products, techniques for selecting optimal MRM used by vehicle during operation; [0099] FIGS. 5A-5B, FIG. 6, process for selecting optimal MRM by vehicle; [0108] provides various MRM trajectories (stop in lane, pull over) to system safety controller, provides signals associated MRM to operate vehicle; [0119] assigns positive reward for correctly selected MRM; negative reward assigned for incorrectly selected/used MRM; infinitely negative reward for MRM that results in accident, injury, damage to vehicle, damage to environment; [0134] transmit signal to operate vehicle using selected MRM) wherein the minimal risk maneuver mode includes [various maneuver trajectories], and ([0108] provides various MRM trajectories (stop in lane, pull over) to system safety controller, provides signals associated MRM to operate vehicle) wherein the minimal risk maneuver mode is repeatedly determined until the ego vehicle is fully stopped ([0039] routes include precise state sequences along high level action sequence with limited lookahead horizon to reach intermediate goals, where combination of successive iterations of limited horizon state sequences cumulatively correspond to trajectories that collectively form high level route to terminate at final goal state. [0121]-[0125] Once the MRM has been selected, determines reward value associated with selected MRM. reward values based on safety rules (stop at a stop sign). Once the reward value has been assigned to selected MRM, determines whether the selected MRM is the correct MRM in view of parameter data from sensors and determined through modeling. if selected MRM has negative reward or infinitely negative reward, another MRM should be selected, as the currently selected MRM may be unfeasible under vehicle’s/environment’s health or state. controller updates MRM using assigned reward determination or received/modeled data relating to vehicle’s or environment’s health or state. provides the updated MRM to drive by wire component, for operating the vehicle using updated MRM. [0136] New MRMs based on parameter data that it continuously receives and trained MRM model. update existing MRMs based on such continuous receipt of parameter data and the trained MRM model; [0108] provides various MRM trajectories (stop in lane, pull over) to system safety controller, provides signals associated MRM to operate vehicle). However, Lopez does not explicitly teach: wherein the minimal risk maneuver mode includes an emergency stop, a going- straight stop, an ego vehicle lane stop, a right lane stop, shoulder parking, and safety zone parking. Kim teaches: wherein the minimal risk maneuver mode includes an emergency stop, a going- straight stop, an ego vehicle lane stop, a right lane stop, shoulder parking, and safety zone parking ([0076]-[0080] select one of a plurality of MRM strategies once minimal risk operation is initiated. The MRM strategies may include four types as shown in FIG. 3. lane stop strategy include straight stop and in-lane stop. road shoulder stop strategy include half-shoulder stop and full-shoulder stop. straight stop: using only the deceleration control, without involving lateral control. in-lane stop: stopping within boundaries of lane vehicle was traveling in. half-shoulder stop: stopping with portion of vehicle positioned on shoulder of road; full-shoulder stop: stopping when entire vehicle positioned on should of road. Fig. 3: various kinds of minimal risk maneuvers). Lopez and Kim are analogous art to the claimed invention since they are from the similar field of vehicle controls for minimal risk maneuvers. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Lopez with the aspects of Kim to create, with a reasonable expectation for success, a system for managing a driving negotiation target for a minimal risk maneuver, wherein the minimal risk maneuver includes an emergency stop, a going- straight stop, an ego vehicle lane stop, a right lane stop, shoulder parking, and safety zone parking. The motivation for modification would have been to minimize known risk of collision with surrounding vehicles in order to reach a minimum risk state, thus increasing overall vehicle and occupant safety (Kim, [0052]). Regarding claim 6: Lopez-Kim further teach : The method of claim 5, wherein the step (a) comprises receiving the surrounding vehicle information comprising vehicle ID information, absolute location information, speed information, and acceleration information (Lopez: [0041]; [0026]; [0107]; [0113]; [0118]; [0120]; [0127]). Regarding claim 7: Lopez-Kim further teach : The method of claim 5, wherein the step (b) comprises generating base information for selecting the driving negotiation target, based on the surrounding vehicle information and the high precision map information (Lopez: [0023]; [0035]; [0039]; [0120]). Response to Arguments Applicant’s arguments and amendments filed 03/26/2026 with respect to the 35 U.S.C. 101 rejections of the claims have been fully considered and are persuasive. The subject matter eligibility rejections of claims 1-3 and 5-7 have been withdrawn. Applicant's arguments filed 03/26/2026 regarding the prior art rejections have been fully considered. Applicant argues: Applicant respectfully submits that the reference relied upon in the rejection under 35 U.S.C. 102 does not disclose such a combination of features. Lopez is directed to selecting an appropriate minimal risk maneuver to be executed by an autonomous vehicle based on predicted conditions and evaluation criteria. In other words, Lopez focuses on determining which maneuver the vehicle itself should perform in response to a particular situation. By contrast, the presently claimed embodiment is not concerned with selecting the maneuver to be performed, but rather with managing surrounding vehicles during execution of a minimal risk maneuver that has already been determined. The amended claims specifically recite selecting, as a driving negotiation target, a surrounding vehicle that is expected to influence or be influenced by the driving path of the ego vehicle while performing the maneuver. Thus, the presently claimed embodiment addresses cooperative interaction with other vehicles, not the internal selection of a maneuver. Lopez contains no disclosure of identifying surrounding vehicles as negotiation targets based on predicted interaction with the ego vehicle's path during execution of a maneuver. Additionally, the presently claimed embodiment requires that the negotiation target be a surrounding vehicle whose behavior is expected to affect, or be affected by, the driving path of the ego vehicle during execution of the maneuver. This path-based interaction criterion distinguishes the presently claimed embodiment from systems that merely evaluate environmental conditions in a generalized manner. Lopez evaluates predicted outcomes of candidate maneuvers but does not identify specific surrounding vehicles as cooperative participants based on anticipated path interaction. Examiner response : Examiner respectfully disagrees with Applicant. The claim(s) recites: “wherein the processor selects, as the driving negotiation target, the surrounding vehicle that is expected to influence or to be influenced by a driving path of the ego vehicle when performing a minimal risk maneuver by considering the minimal risk maneuver mode”. The following paragraphs of Lopez were/are used by Examiner for the rejection of the claim limitation. Citation from Lopez Examiner interpretation [0023] In some embodiments, one or more processors (e.g., arbitration unit, system controller, etc.) receive at least one first parameter associated with a characteristic of a vehicle and at least one second parameter associated with at least one object (e.g., other vehicles, pedestrians, etc. that can be external to the vehicle. The processor(s) generate at least one future state for at least one of the first and second parameters and select at least one maneuver (e.g., MRM) from a plurality of such maneuvers based on the generated future state. The processor(s) determine at least one reward value associated with the selected maneuver. The processor(s) update the selected maneuver based on the determined reward value to generate an updated maneuver. The vehicle is then operated based on the updated maneuver. Ego vehicle considers the current and future states of surrounding objects and vehicles when determining which MRM to perform. [0031] As stated above, the first parameter includes at least one of a current state and a predicted future state associated with the vehicle, and the second parameter includes at least one of a current state and a predicted future state associated with the object. Further, the receiving of parameters includes the vehicle’s processor(s) continuously receiving at least one of the first and/or second parameters. The training includes the vehicle’s processor(s) continuously training the model using continuously received first and/or second parameters. Ego vehicle determines current state of itself and predicts a future state of itself. (first parameters) Ego vehicle determines current state of object/vehicle and predicts future state of object/vehicle. (second parameters) Ego vehicle continuously receives the first and second parameters. [0035] . . . techniques for selecting an optimal MRM as well as training an MRM model that is used by the vehicle to select such optimal MRM during driving/operation. In particular, the current subject matter allows dynamic selection of MRMs based on vehicle’s parameters and/or environment (that may closely resemble what an actual driver may do). It also avoids selection/use of unintended/unnecessary MRMs that can result in detrimental consequences (e.g., accidents, etc.). Select an optimal MRM for the vehicle to perform based on vehicle and environment parameters. [0099] Referring now to FIGS. 5A-5B, . . . selecting an optimal MRM (e.g., from a finite number of MRMs that may be stored by the vehicle) as well as training an MRM model for use in selecting an optimal MRM during vehicle driving/operation. FIG. 6 . . . process for selecting an optimal MRM by a vehicle. FIG. 7 . . . process for training a MRM model for the purposes of selection of the optimal MRM during driving/operating of the vehicle. Shows the details of how an MRM is selected. [0108] As discussed above, the AV stack 506 controls the vehicle during driving/operation. Additionally, the AV stack 506 provides various MRM trajectories (e.g., stop in lane, pull over, etc.) to the system safety controller 510, at 509, and provides one or more signals (including signals associated with execution of a selected MRM) 507 to the drive by wire component 514. The drive by wire component 514 uses these signals to operate the vehicle. Operate the vehicle based on the selected MRM. Can include stopping in the lane or pulling over. [0119] In some embodiments, at 604, to generate and/or determine any future or predicted states of one or more vehicle/environment health/state parameters, the system 500, 599 performs modeling of the parameters. . . reward function can be designed to dis-incentivize any unnecessary MRM transitions. . . assigns a positive reward (e.g., motivation) for the correctly selected MRM. A negative reward is assigned for an incorrectly or unnecessarily selected/used MRM. The system 500, 599 assigns an infinitely negative reward for an MRM that results in an accident, injury, damage to the vehicle, damage to the environment, etc. The system 500, 599 assigns rewards using rules 524. The system safety controller 510 uses assigned rewards to determine whether or not a specific situation warrants use of the MRM with such a reward assigned. Vehicle assigns positive reward for correctly selected MRM, negative reward for incorrectly selected/used MRM, infinitely negative for MRM that results in accident, injury, damage to vehicle or environment. Vehicle considers potential effects of its action on its surroundings prior to performing an MRM. [0134] Based on the received information, the reward function component 522 outputs an indication (e.g., a flag signal, a trigger signal, etc.) of whether an MRM should be used and if so, which MRM should be used, and/or whether selected MRM is an optimal MRM. Upon the trigger signal indicating that the selected MRM can be used to operate the vehicle, the controller 510 can transmit a signal to the drive by wire component 514 to instruct it to operate the vehicle using the selected MRM. Alternatively, if the trigger signal indicates that the selected MRM cannot be used to operate the vehicle, the controller 510 can prevent operation of the vehicle using the selected MRM and select another MRM from the plurality of MRMs. In some embodiments, the trigger signal can indicate which MRM to select for a particular scenario. Vehicle is operated using the selected MRM. In order to perform an MRM, the vehicle of Lopez is required to consider its surroundings and predict how actions will affect the situation. This process includes monitoring current states of the vehicle and the environment, and managing surrounding vehicles during execution of an MRM that has been determined. Performing a maneuver requires cooperative interaction based on the fact that the maneuver is performed within an environment with obstacles, other vehicles, and the like. Selection of the maneuver requires consideration of the cooperative interaction in order for the selection to be useful in any way. As such, Lopez discloses the above limitation. Applicant argues: Furthermore, the amended claims define the minimal risk maneuver mode as including specific stopping strategies such as emergency stop, going-straight stop, ego vehicle lane stop, right lane stop, shoulder parking, and safety zone parking. These are concrete operational modes corresponding to real-world stopping scenarios, each of which may require different interaction with surrounding traffic. The claimed system selects negotiation targets in consideration of the particular maneuver mode being executed. Lopez does not disclose or suggest managing surrounding vehicles differently depending on specific types of stopping maneuvers, nor does it describe any framework for coordinating vehicle behavior with neighboring vehicles during such maneuvers. Examiner response: Applicant’s arguments with respect to this limitations have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Lopez is modified by Kim in order to teach “wherein the minimal risk maneuver mode includes an emergency stop, a going- straight stop, an ego vehicle lane stop, a right lane stop, shoulder parking, and safety zone parking”. Applicant argues: A particularly significant distinction is that the amended claims require that the minimal risk maneuver mode be repeatedly determined until the ego vehicle is fully stopped. This limitation reflects a dynamic control process in which the vehicle continuously reassesses conditions during the emergency maneuver and updates its operational decisions accordingly. In practical terms, this enables the system to respond to changes such as worsening vehicle conditions or evolving traffic situations while the vehicle is decelerating. Lopez, however, describes selecting a maneuver based on predicted parameters and then operating the vehicle according to that selection. It does not disclose repeatedly re-determining the maneuver mode throughout execution of the maneuver, nor does it describe updating surrounding-vehicle management decisions in response to such re-determination. Examiner response: Examiner respectfully disagrees with Applicant. The claim(s) recites: “wherein the minimal risk maneuver mode is repeatedly determined until the ego vehicle is fully stopped”. The following paragraphs of Lopez were/are used by Examiner for the rejection of the claim limitation. Citation from Lopez Examiner interpretation [0039] Routes 106a-106n (referred to individually as route 106 and collectively as routes 106) are each associated with (e.g., prescribe) a sequence of actions (also known as a trajectory) connecting states along which an AV can navigate. . . In an example, routes 106 include a plurality of precise state sequences along the at least one high level action sequence with a limited lookahead horizon to reach intermediate goals, where the combination of successive iterations of limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form the high level route to terminate at the final goal state or region. Successive iterations of limited horizon state sequences cumulatively create trajectories to make the final goal state. [0108] As discussed above, the AV stack 506 controls the vehicle during driving/operation. Additionally, the AV stack 506 provides various MRM trajectories (e.g., stop in lane, pull over, etc.) to the system safety controller 510, at 509, and provides one or more signals (including signals associated with execution of a selected MRM) 507 to the drive by wire component 514. The drive by wire component 514 uses these signals to operate the vehicle. Operate the vehicle based on the selected MRM. Can include stopping in the lane or pulling over. [0121] . . . The reward values can be based on one or more safety rules 524 that are stored by the system 500 (e.g., stop at a stop sign, etc.). The controller 510 assigns a positive reward for a correctly selected MRM, a negative reward for an incorrectly selected MRM (e.g., operation of the vehicle in an unnecessary manner), and an infinitely or maximum negative reward for a clear violation of stored rules 524. Once the MRM has been selected, determines reward value associated with selected MRM. Stopping is an example of an MRM. [0122] Once the reward value has been assigned to the selected MRM, the controller 510 determines, whether the selected MRM is the correct MRM in view of the parameter data it received from the sensors and/or determined through modeling. For example, if a positive reward was assigned to the initially selected MRM, the controller 510 can determine that the MRM should be executed by the vehicle’s operating systems and provide it to the drive by wire component 514 to execute. Beginning of the iterative process for deciding if the MRM is executable. [0123] Otherwise, if the selected MRM has been assigned a negative reward, the controller 510 can determine that the selected MRM should still be executed in view of the parameter data it has received/determined. Alternatively, or in addition to, the controller 510 can determine that another MRM should be selected, as the currently selected MRM may be unfeasible under the vehicle’s/environment’s health and/or state. Vehicle iterates MRM selection until finding a proper MRM. [0124] Further, if an infinitely negative reward has been assigned to the selected MRM, the controller 510 can determine that another MRM should be selected. The controller 510 can also determine that because selection of such MRM caused assignment of an infinitely or maximum negative reward, any future selections of this MRM, in view of the vehicle’s/environment’s health and/or state data, should be and/or must be avoided. Vehicle iterates MRM selection until finding a proper MRM. [0125] The controller 510 also updates, at 610, the MRM 520 (whether the selected MRM and/or any other MRMs 520) using the assigned reward determination and/or received/modeled data relating to vehicle’s/environment’s health/state. For example, the selected MRM may be updated by adjusting speed of movement of the vehicle, turn radius, etc. The controller 610 then provides the updated MRM to the drive by wire component 514, at 612, for operating the vehicle using the updated MRM. Vehicle iterates MRM selection until finding a proper MRM. Updated selection of MRM is provided. [0136] In some embodiments, as a result of the training method 700, the controller 510 can generate new MRMs and store them for future use. New MRMs can be based on the parameter data that it continuously receives and the trained MRM model. The controller 510 can also update existing MRMs based on such continuous receipt of parameter data and the trained MRM model. Since the parameter data is continuously supplied, the controller 510 can also perform continuous training of the MRM model. In some embodiments, the controller 510 can generate MRMs and/or select MRMs (e.g., using the trained MRM model) while the vehicle is operating. Parameter data is continuously received and used to update MRMs and for training MRMs. The system iterates the MRM selection process until finding the proper maneuver. The maneuver can be stopping in a lane or pulling over. The state of the vehicle is changing as the MRM is performed, so the decision making process is also repeated to correctly perform the selected MRM. Thus, if the vehicle is being controlled to pull over to a stop, the process to select and execute the maneuver is repeated until the vehicle has reached the final MRM state, which would be in a fully stopped state. As such, Lopez teaches this limitation. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MADISON B EMMETT whose telephone number is (303)297-4231. The examiner can normally be reached Monday - Friday 9:00 - 5:00 ET. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MADISON B EMMETT/Examiner, Art Unit 3658 /JASON HOLLOWAY/Primary Examiner, Art Unit 3658 Application/Control Number: 18/909,235 Page 2 Art Unit: 3658 Application/Control Number: 18/909,235 Page 3 Art Unit: 3658 Application/Control Number: 18/909,235 Page 4 Art Unit: 3658 Application/Control Number: 18/909,235 Page 5 Art Unit: 3658 Application/Control Number: 18/909,235 Page 6 Art Unit: 3658 Application/Control Number: 18/909,235 Page 7 Art Unit: 3658 Application/Control Number: 18/909,235 Page 8 Art Unit: 3658 Application/Control Number: 18/909,235 Page 9 Art Unit: 3658 Application/Control Number: 18/909,235 Page 10 Art Unit: 3658 Application/Control Number: 18/909,235 Page 11 Art Unit: 3658 Application/Control Number: 18/909,235 Page 12 Art Unit: 3658 Application/Control Number: 18/909,235 Page 13 Art Unit: 3658 Application/Control Number: 18/909,235 Page 14 Art Unit: 3658 Application/Control Number: 18/909,235 Page 15 Art Unit: 3658 Application/Control Number: 18/909,235 Page 16 Art Unit: 3658
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