DETAILED ACTION
Status of Claims
Claims 1, 4, and 6-23 are currently pending and have been examined in this application. This action is NON-FINAL.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/06/2025 has been entered.
Response to Arguments
Applicant's arguments filed 10/06/2025 have been fully considered but they are not persuasive.
Applicant argues:
Regarding the 35 USC 103 rejection of claim 1, “Claim 1 as amended herein recites in part to, "based on prior lane selections in the vehicle, vehicle operating data from respective times of the prior lane selections, and vehicle occupant data." In rejecting now-canceled claim 5, the Office Action (page 7) alleged that Liu disclosed "to determine the lane override command further based on vehicle occupant data." However, Liu discloses merely that a vehicle computing system can store data including data about the "state of one or more users (e.g., passengers, operators, etc.) of the vehicle." ¶ 0097. Liu does not teach, and the mere fact that Lou discloses vehicle occupant data is not sufficient to suggest, that data about the state of one or more users is used to determine any lane change operations, much less to determine a lane override command.
At most, Liu discloses using "perception data 175A that is indicative of one or more states (e.g., current and/or past state(s)) of one or more objects that are within a surrounding environment of the vehicle 105," ¶ 0091, to predict object motion, ¶ 0092, and determine a trajectory for an autonomous vehicle. ¶ 0093. Data about the state of the user appears to be mentioned only in Liu's paragraph 0097, and is not mentioned in conjunction with motion planning, much less in conjunction with determining a lane override command.
In short, even if Liu were combined with the other references, and even if the other references disclosed to determine a lane override command, there would be no teaching or suggestion to determine the lane override command based on vehicle occupant data." The rejection of claim 1, and also the rejection of claim 21, should be withdrawn at least for this reason.” (Remarks, pg. 5-6)
Examiner respectfully disagrees.
Regarding point (a), Lui teaches storing data regarding the state of a passenger (Lui, para. [0097] “The vehicle computing system 110 can store other types of data. For example, an indication, record, and/or other data indicative of the state of the vehicle (e.g., its location, motion trajectory, health information, etc.), the state of one or more users (e.g., passengers, operators, etc.) of the vehicle, and/or the state of an environment including one or more objects (e.g., the physical dimensions and/or appearance of the one or more objects, locations, predicted motion, etc.) can be stored locally in one or more memory devices of the vehicle 105.”), which can be included in the sensor data (Lui, para. [0086] “In some implementations, the sensor(s) 135 can include one or more internal sensors. The internal sensor(s) can be configured to acquire sensor data 155 associated with the interior of the vehicle 105. For example, the internal sensor(s) can include one or more cameras, one or more infrared sensors, one or more motion sensors, one or more weight sensors (e.g., in a seat, in a trunk, etc.), and/or other types of sensors.”). Lui further teaches that the sensor data can be used to determine a path for a vehicle to execute a lane change (Lui, para. [0090] “The autonomy computing system 140 can perform various functions for autonomously operating the vehicle 105. For example, the autonomy computing system 140 can perform the following functions: perception 170A, prediction 170B, and motion planning 170C. For example, the autonomy computing system 140 can obtain the sensor data 155 via the sensor(s) 135, process the sensor data 155 (and/or other data) to perceive its surrounding environment, predict the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment.”). In other words, Lui teaches collecting passenger data from interior sensors, storing the state of a passenger as sensor data, and then using the sensor data to determine a lane change maneuver for the vehicle to move into the target lane from the current lane. Since Lui teaches using passenger data to control a lane change of a vehicle into a target lane, Lui discloses using vehicle occupant data to determine a lane override command.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 7, 9-11, 13, 16-17, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Lui et al. (US 20220083065 A1), in view of DeCia et al. (US 20170225711 A1), and in further view of Chen et al. (US 20220080972 A1).
Regarding claim 1,
Lui teaches:
A system comprising a computer including a processor and a memory, the memory storing instructions executable by the processor such that the computer is programmed to:
(Lui – Fig. 10, computing system 1000)
determine that a vehicle is currently operating on a roadway for which a default lane is defined;
(Lui – [0056] “In some examples, the target nominal path can be received from a remote server system associated with the autonomous vehicle. The vehicle computing system can determine a current pose for the autonomous vehicle. The current pose for an autonomous vehicle can include a current location and a current heading. The current location of the autonomous vehicle can be associated with a first lane and the target nominal path can be associated with a second lane. For example, if the autonomous vehicle is changing lanes to make a turn, the current lane and the target lane can be two different lanes.”
Examiner Note: where the first lane is the current lane and corresponds to the default lane.)
based on prior lane selections in the vehicle, vehicle operating data
(Lui – [0027] “As part of this process, the vehicle computing system can determine that the autonomous vehicle will move from the autonomous vehicle's current lane to another lane. This determination can be made based on an instruction from a remote services system and/or based on the analysis of a path planning module/system, associated with the vehicle computing system. The vehicle computing system can access a nominal path associated with the target lane from a map database. The vehicle computing system can identify a lane change region based on the current position, velocity, and pose of the autonomous vehicle. The lane change region can be an area in which the vehicle computing system plans to change from the current lane to the target lane.” [0097] “The vehicle computing system 110 can store other types of data. For example, an indication, record, and/or other data indicative of the state of the vehicle (e.g., its location, motion trajectory, health information, etc.), the state of one or more users (e.g., passengers, operators, etc.) of the vehicle, and/or the state of an environment including one or more objects (e.g., the physical dimensions and/or appearance of the one or more objects, locations, predicted motion, etc.) can be stored locally in one or more memory devices of the vehicle 105.”)
actuate a vehicle component based on the target lane override command.
(Lui – [0025] “In some examples, the vehicle computing system can generate a basis path to change from a first lane to a second lane. To do so, the vehicle computing system can access a target nominal path associated with the target lane.” [0031] “The autonomy system can output data indicative of the generated trajectories and corresponding control signals can be sent to vehicle control system(s) (e.g., acceleration, steering, braking, etc. systems) to enable the autonomous vehicle to autonomously navigate (e.g., to its target destination).”)
Lui does not explicitly teach the following limitation, however, DeCia teaches:
based on prior lane selections in the vehicle, vehicle operating data from respective times of the prior lane selections,
(DeCia – [0043] “Before the driver has reached a particular branch lane, the driver has deviated from the selected route four times, with the active route being recalculated each time prior to reaching the branch lane. The branch lane is included as part of the active route. That is, the navigation engine 56 directs the driver to choose this branch lane. On Tuesdays in the past 60 days (eight or nine in total), the driver has chosen this branch lane a total of 5 times. In all of the same two-hour time windows (7:45 am-9:45 am) in the past 60 days, the driver has chosen this segment at this junction a total of 17 times (of a possible 60). The branch lane is of a combined exit ramp-entrance ramp type. That means other vehicles are entering the route that the navigation engine 56 is recommending exiting from. The shared usage is associated with a Route Geometry Analysis report indicating the likelihood of the branch lane being an empty lane as only 20%.”)
DeCia is considered to be analogous to the claimed invention because it is in the same field of directing a lane change. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Lui and DeCia to include selecting a lane change based on the time of prior lane changes in order to prevent unexpected and confusing maneuvers (DeCia para. [0001]).
The combination of Lui and DeCia does not explicitly teach the following limitation, however, Chen teaches:
wherein the vehicle operating data from respective times of the prior lane selections includes relative distances and/or relative speeds of the vehicle from one or more second vehicles when the respective prior lane selections were made; and
(Chen – [0297] “For example, the four-tuple information at the at least one first historical moment is four-tuple information corresponding to a historical moment at which a target action of the autonomous vehicle at the historical moment is lane change in the four-tuple information at the preset quantity of historical moments.” [0321] “The local neighbor feature of the autonomous vehicle at any historical moment in this embodiment of this application is used to represent motion status information (for example, a relative distance and a relative speed) of a specific neighboring vehicles (for example, a front/back neighboring obstacle of the autonomous vehicle in a lane in which the autonomous vehicle is located, a front/back neighboring obstacle of the autonomous vehicle in a left lane adjacent to the lane in which the autonomous vehicle is located, and a front/back neighboring obstacle of the autonomous vehicle in a right lane adjacent to the lane in which the autonomous vehicle is located) of the autonomous vehicle at the historical moment relative to the autonomous vehicle.”)
Chen is considered to be analogous to the claimed invention because it is in the same field of determining a traveling lane for an autonomous vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui and DeCia with Chen to include monitoring the relative distance and speed from a past lane change in order to consider the global flow situation and generate an action decision that is globally optimal (Chen para. [0006]).
Regarding claim 4,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
Lui further teaches:
wherein determining the target lane includes determining the target lane based on a landmark along the current vehicle route.
(Lui – [0041] “The out-of-cycle steps can include generating lane geometry for a plurality of potential lanes including, but not limited to lane boundaries for one or more lanes, determining a nominal path or centerline for each lane, and/or determining any other relevant factors for a particular area. In some examples, generating lane geometry may be accomplished or assisted by a geometry planner. In addition to generating lane geometry, the vehicle computing system can, as another out of cycle step, generate a list of static objects in the relevant geographic area, including, but not limited to, buildings, signs, mailboxes, other semi-permanent fixtures, etc.”)
Regarding claim 7,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
Lui further teaches:
wherein the vehicle occupant data include a detected occupant activity.
(Lui – [0097] “The vehicle computing system 110 can store other types of data. For example, an indication, record, and/or other data indicative of the state of the vehicle (e.g., its location, motion trajectory, health information, etc.), the state of one or more users (e.g., passengers, operators, etc.) of the vehicle, and/or the state of an environment including one or more objects (e.g., the physical dimensions and/or appearance of the one or more objects, locations, predicted motion, etc.) can be stored locally in one or more memory devices of the vehicle 105.”)
Regarding claim 9,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
Lui further teaches:
wherein the instructions include instructions to determine the lane override command further based on data about a second vehicle detected by a sensor in the first vehicle.
(Lui – [0035] “The vehicle computing system can utilize the sensor data to identify one or more objects in the local environment of the autonomous vehicle. Using this sensor data, the vehicle computing system can generate perception data that describes one or more object(s) in the vicinity of the autonomous vehicle (e.g., current location, speed, heading, shape/size, etc.).” [0093] “The vehicle computing system 110 can determine a motion plan for the vehicle 105 based at least in part on the perception data 175A, the prediction data 175B, and/or other data. For example, the vehicle computing system 110 can generate motion planning data 175C indicative of a motion plan… The motion plan can include one or more vehicle motion trajectories that indicate a path for the vehicle 105 to follow.”)
Regarding claim 10,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 9.
Lui further teaches:
wherein the data about the second vehicle include a relative distance and/or a relative speed of the vehicle from the second vehicle.
(Lui – [0091] “The vehicle computing system 110 can generate perception data 175A that is indicative of one or more states (e.g., current and/or past state(s)) of one or more objects that are within a surrounding environment of the vehicle 105. For example, the perception data 175A for each object can describe (e.g., for a given time, time period) an estimate of the object's: current and/or past location (also referred to as position); current and/or past speed/velocity; current and/or past acceleration; current and/or past heading; current and/or past orientation…” [0093] “The vehicle computing system 110 can determine a motion plan for the vehicle 105 based at least in part on the perception data 175A, the prediction data 175B, and/or other data. For example, the vehicle computing system 110 can generate motion planning data 175C indicative of a motion plan. The motion plan can include vehicle actions (e.g., speed(s), acceleration(s), other actions, etc.) with respect to one or more of the objects within the surrounding environment of the vehicle 105 as well as the objects' predicted movements. The motion plan can include one or more vehicle motion trajectories that indicate a path for the vehicle 105 to follow.”)
Regarding claim 11,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 9.
Lui further teaches:
wherein the data about the second vehicle include a type of the second vehicle.
(Lui – [0091] “The vehicle computing system 110 can generate perception data 175A that is indicative of one or more states (e.g., current and/or past state(s)) of one or more objects that are within a surrounding environment of the vehicle 105. For example, the perception data 175A for each object can describe (e.g., for a given time, time period) an estimate of the object's… size/footprint (e.g., as represented by a bounding shape, object highlighting, etc.); class (e.g., pedestrian class vs. vehicle class vs. bicycle class, etc.), the uncertainties associated therewith, and/or other state information.”)
Regarding claim 13,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 9.
Lui further teaches:
wherein the instructions include instructions to determine the lane override command further based on a detected traffic density.
(Lui – [0027] “The lane change region can be an area in which the vehicle computing system plans to change from the current lane to the target lane. The lane change region's distance from the vehicle and total size can be determined based on a plurality of factors including, but not limited to, the current speed of the autonomous vehicle, the density of other objects in the travel way (e.g., a dense road will result in a larger lane change region to allow more flexibility to navigate around other actors/objects)…” )
Regarding claim 16,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
Lui further teaches:
wherein the instructions to actuate the vehicle component include instructions to actuate one or more of propulsion, braking, steering, or a human machine interface.
(Lui – [0031] “The autonomy system can output data indicative of the generated trajectories and corresponding control signals can be sent to vehicle control system(s) (e.g., acceleration, steering, braking, etc. systems) to enable the autonomous vehicle to autonomously navigate (e.g., to its target destination).”)
Regarding claim 17,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
Lui further teaches:
wherein the lane override command is based on output from a machine learning program that was trained with the prior lane selections and the vehicle operating data from the respective times of the prior lane selections.
(Lui – [0032] “To accomplish these operations, the autonomy computing system can include, for example, a perception system, a prediction system, and a motion planning system. Many of the functions performed by the perception system, prediction system, and motion planning system can be performed, in whole or in part, by one or more machine-learning models.”)
DeCia further teaches:
wherein the lane override command is based on output from a machine learning program that was trained with the prior lane selections and the vehicle operating data from the respective times of the prior lane selections.
(DeCia – [0021] “The exemplary drive history learning engine 32 also includes programming to, based on a frequency of occurrence of lane changes associated with certain routes, learn as a function of signals of data such as the immediately preceding route, and factors considered by the navigation and routing engine 30 such as destination and traffic conditions, and which routes and lane changes are most likely to be made. The exemplary learning engine 32 includes programming to associate lane changes with particular branch lanes and accumulate a history of lane changes for particular branch lanes, storing the day of the week and time of day of all such events.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Lui and DeCia to include selecting a lane change based on the time of prior lane changes in order to prevent unexpected and confusing maneuvers (DeCia para. [0001]).
Regarding claim 20,
Claim 20 recites a method comprising substantially the same limitation as claim 1 above, therefore it is rejected for the same reasons.
Regarding claim 21,
Claim 21 recites a method comprising substantially the same limitation as claim 4 above, therefore it is rejected for the same reasons.
Claims 6, 8, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Lui et al. (US 20220083065 A1), in view of DeCia et al. (US 20170225711 A1), in further view of Chen et al. (US 20220080972 A1), and in further view of Hu (US 20180143033 A1).
Regarding claim 6,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Hu teaches:
wherein the vehicle occupant data include a number of vehicle occupants.
(Hu – [0045] “In Step 350, one or more components of system 11 may determine a recommended lane of the route based on the determined vehicle occupant information, e.g., the determined number of occupants.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include occupant data as taught by Hu in order to allow the navigation technologies to distinguish lanes on the same roadway, and therefore, navigate vehicles correctly in certain situations (Hu para. [0014]).
Regarding claim 8,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Hu teaches:
wherein the vehicle occupant data include a detected occupant identity.
(Hu – [0036] “In Step 310, one or more components of system 11 may determine vehicle occupant information, such as a number of the occupants or identities of the occupants.” [0046] “In some embodiments, the processing unit 104 may determine the recommended lane based on the determined profiles described above with respect to step 310.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include occupant data as taught by Hu in order to allow the navigation technologies to distinguish lanes on the same roadway, and therefore, navigate vehicles correctly in certain situations (Hu para. [0014]).
Regarding claim 22,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Hu teaches:
wherein the target lane and/or the default lane is specified based on a current vehicle route that includes a vehicle turn or exit from the roadway within a predetermined distance.
(Hu – [0044] “In Step 340, one or more components of system 11 may determine a route from the current position to the destination… In some embodiments, the route may comprise one or more lanes, and processing unit 104 may further determine one or more possible lane-specific routes from the current position to the destination. For example, processing unit 104 may determine three lane-specific routes from a current position to restaurant XYZ: (1) staying on the leftmost HOV (2+) (or carpool) lane of route 66 for 10 miles, then taking a left exit ramp to route 1, and staying on the rightmost lane of route 1 for 20 miles to reach restaurant XYZ; (2) staying on the local lane of route 66 for 10 miles, then taking a right exit ramp to route 1, and staying on the rightmost lane of route 1 for 20 miles to reach restaurant XYZ; and (3) staying on the leftmost HOV lane of route 66 for 10 miles, then taking a left exit ramp to route 1, staying on the rightmost lane of route 1 for 10 miles, taking a right exit ramp to country road 88, and staying on country road 88 for 15 miles to reach restaurant XYZ.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include a route with an exit from the roadway as taught by Hu in order to allow the navigation technologies to distinguish lanes on the same roadway, and therefore, navigate vehicles correctly in certain situations (Hu para. [0014]).
Regarding claim 23,
Claim 23 recites a method comprising substantially the same limitation as claim 22 above, therefore it is rejected for the same reasons.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lui et al. (US 20220083065 A1), in view of DeCia et al. (US 20170225711 A1), in further view of Chen et al. (US 20220080972 A1), and in further view of Alvarez Rodriguez et al. (US 20190011916 A1).
Regarding claim 12,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Alvarez Rodriguez teaches:
wherein the instructions include instructions to determine the lane override command further based on a detected light intensity.
(Alvarez Rodriguez – [0061] “At block 710, the lane controller 114 determines the light intensities of the headlights 208 of the trailing vehicle 206 within the images (e.g., the image 300 of FIG. 3) captured by the rearview camera 108. For example, the lane controller 114 determines the light intensities of the headlights 208 within the images to enable the lane controller 114 to determine whether the trailing vehicle 206 is providing a message to the vehicle 100 to change lanes from the passing lane 204.”)
Alvarez Rodriguez is considered to be analogous to the claimed invention because it is in the same field of controlling lane changes of a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include detected line intensity as taught by Alvarez Rodriguez to ensure that a slower vehicle receives a message from a vehicle moving faster so that the faster vehicle can pass the slower vehicle (Alvarez Rodriguez para. [0019]).
Claims 14-15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lui et al. (US 20220083065 A1), in view of DeCia et al. (US 20170225711 A1), in further view of Chen et al. (US 20220080972 A1), and in further view of Abad et al. (US 20240017726 A1).
Regarding claim 14,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Abad teaches:
wherein the instructions include instructions to determine the lane override command based on a trailer being towed by the vehicle.
(Abad – [0023] Another strategy that an autonomous vehicle may implement in response to detecting a slow lead agent involves automatically seeking to change lanes if the speed of the detected slow lead agent drops below a predefined speed threshold. This strategy, however, fails to account for situations where the lead agent temporarily decreases speed for a short period, which may make the lane change unnecessary for the autonomous vehicle. For instance, if the autonomous vehicle is a semi-truck pulling a trailer, the autonomous truck may be programmed to travel in the slowest lane during navigation to a destination (e.g., the right most lane) to reduce collision risks.”)
Abad is considered to be analogous to the claimed invention because it is in the same field of controlling the lane change of a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include monitoring the trailer and cargo of a vehicle as taught by Abad so the vehicle can safely transport passengers or objects between locations while avoiding obstacles, obeying traffic requirements, and performing other actions that are typically conducted by the driver (Abad para. [0001]).
Regarding claim 15,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 1.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Abad teaches:
wherein the instructions include instructions to determine the lane override command based on a cargo load of the vehicle.
(Abad – [0030] “In some examples, the speed threshold used to detect slow lead agents can further depend on vehicle parameters, such as the weight and acceleration capability of the vehicle. Similarly, other factors can also influence the speed threshold, such as the type of cargo being carried, the condition of tires, the condition of the roadway, and the proximity of the next exit for the vehicle, among others. As an example result, vehicle systems located on an autonomous semi-truck pulling a trailer may use a speed threshold for judging a slow lead agent that differs from the speed threshold used when the autonomous semi-truck is not pulling the trailer.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include monitoring the trailer and cargo of a vehicle as taught by Abad so the vehicle can safely transport passengers or objects between locations while avoiding obstacles, obeying traffic requirements, and performing other actions that are typically conducted by the driver (Abad para. [0001]).
Regarding claim 18,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 17.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Abad teaches:
wherein the machine learning program is trained based on user input overriding a driver assistance feature.
(Abad – [0039] “Lane change decision making for slow lead agents can be learned using supervised learning using a training dataset comprising real-world and/or simulated scenarios. In some cases, a neural network can be trained based on a loss function that measures travel time, a disengage probability (likelihood that the driving mode will switch to manual), and so forth.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include training the model with overriding input as taught by Abad so the vehicle can improve the autonomous driving strategy (Abad para. [0039]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Lui et al. (US 20220083065 A1), in view of DeCia et al. (US 20170225711 A1), in further view of Chen et al. (US 20220080972 A1), and in further view of Pfeifle et al. (US 20200293815 A1).
Regarding claim 19,
The combination of Lui, DeCia, and Chen teaches the limitations of claim 17.
The combination of Lui, DeCia, and Chen does not explicitly teach the following limitation, however, Pfeifle teaches:
wherein the machine learning program is trained based on data collected while the vehicle is manually operated on the roadway.
(Pfeifle – [0058] As outlined above, the machine-learned predictor 16 may comprise or may be an artificial neural network 30, notably a convolutional neural network. The machine-learned predictor 16 may have been trained using training data comprising a plurality of training data sets captured during manual driving performed by a human driver 20.”)
Pfeifle is considered to be analogous to the claimed invention because it is in the same field of training a machine learning model for use in a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Lui, DeCia, and Chen to include training a machine learning program as taught by Pfeifle in order to increase the sensing capability of a vehicle in an efficient and reliable manner (Pfeifle para. [0059]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Churay et al. (US 20240025450 A1) discloses identifying the visual attention state involves identifying visual attention of the driver directed to a region associated with a side of the current lane of travel in the visual attention direction, determining the driver lane preference involves identifying the adjacent lane on the side of the current lane of travel in the visual attention direction as a preferred lane, adjusting the priority involves increasing the priority associated with the adjacent lane, and autonomously operating the one or more actuators involves autonomously operating the one or more actuators to initiate a lane change from the current lane to the adjacent lane in accordance with the increased priority.
Carlson et al. (US 12110026 B2) discloses a driver intent determination system configured to determine a driver intent for which of two different lanes the driver intends the vehicle to follow during a lane split scenario and a controller configured to operate the vehicle according to an autonomous driving feature whereby the controller automatically controls steering of the vehicle, determine which of the two different lanes are supported for the autonomous driving feature to obtain a target lane, and automatically control at least the steering system of the vehicle to follow the target lane.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELANIE HUBER whose telephone number is (703)756-1765. The examiner can normally be reached M-F 7:30am-4pm.
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/M.G.H./Examiner, Art Unit 3668 /JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668