Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
The following claims have been rejected or allowed for the following reasons:
Claim(s) 1-20 is rejected under 35 USC § 103
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2024-0182468, filed on 12/10/24.
Information Disclosure Statement
The information disclosure statement/statements (IDS) were filed on 3/6/26. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3, 9-11, 13, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Yoo (US 20170320500 A1), in further view of Stentz (US 20180209801 A1).
Regarding claim 1 Yoo teaches An apparatus of a vehicle, the apparatus comprising: one or more processors; and a memory storing at least one instruction that is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to: (Yoo [0023] reads “Furthermore, control logic of the present invention may be embodied as non-transitory computer readable media on a computer readable medium containing executable program instructions executed by a processor, controller/control unit or the like. Examples of the computer readable mediums include, but are not limited to, ROM, RAM, compact disc (CD)-ROMs, magnetic tapes, floppy disks, flash drives, smart cards and optical data storage devices. The computer readable recording medium can also be distributed in network coupled computer systems so that the computer readable media is stored and executed in a distributed fashion, e.g., by a telematics server or a Controller Area Network (CAN).”);
generate, via a navigation system of the vehicle and based on surrounding object information obtained from a sensor of the vehicle, (Yoo [0029] reads “The surrounding information detector 110 may further be configured to obtain the surrounding information using an image sensor, a distance sensor, a position sensor, and the like. The distance sensor may be any one of an infrared sensor, a radio detection and ranging (RADAR) sensor, ia light detection and ranging (LiDAR) sensor, a laser scanner, and the like, and the position sensor may be a global positioning system (GPS) receiver capable of obtaining position information of the vehicle. One or more image sensors, one or more distance sensors, and one or more position sensors may be mounted within the vehicle.”);
a plurality of candidate routes to merge with a target route from a location of the vehicle; (Yoo [0008] reads “The driving path generator may include: a candidate path generation module configured to generate driving lane candidate paths and target lane candidate paths;”);
Yoo does not teach determine an amount of deviation, from the target route, of a route on which the vehicle is traveling; determine an optimal route, among the plurality of candidate routes, that minimizes the amount of deviation from the target route;
Stentz in analogous art, teaches determine an amount of deviation, from the target route, of a route on which the vehicle is traveling; determine an optimal route, among the plurality of candidate routes, that minimizes the amount of deviation from the target route; (Stentz [0070] reads “For example, the control system 120 can identify one or more alternative routes, and can further calculate or otherwise estimate a time delta for each alternative route, as well as proceeding along the current route 139 (512). In further examples, the control system 120 can determine a risk factor for following each of the alternative routes, as well as the current route 139 (514). In performing the cost analysis, or cost optimization, the control system 120 can determine whether an alternative route is more optimal (e.g., less risky or the least risky option) than the current route 139 (515). If so (517), then the control system 120 can select the optimal alternative route by executing an updated trajectory to follow the alternative route (525).”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo with that of Stentz to include a method that would take into account the total amount of deviation of a candidate route with the currently assigned route. This would allow the system to dynamically optimize and decide which route would be most optimal for the current vehicle. (Stentz abstract reads “A self-driving vehicle (SDV) can dynamically analyze a sensor view of a surrounding area of the SDV, and a current localization map in order to autonomously operate acceleration, braking, and steering systems of the SDV along a current route to a destination. Upon approaching a decision point along the current route, the SDV can perform a cost optimization to determine whether to diverge from the current route.”);
Regarding claim 3 Yoo/Stentz teaches The apparatus of claim 1, wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to generate the plurality of candidate routes by: generating the plurality of candidate routes to connect the location of the vehicle to the target route (Yoo [0037] reads “The candidate path generation module 161 may be configured to generate the candidate paths with respect to the current driving lane of the vehicle and the target lane for lane change, respectively. Particularly, the candidate path generation module 161 may be configured to generate the predetermined number of candidate paths.”);
along a center route of a lane on which the vehicle is traveling. (Yoo [0035] reads “For example, when the maneuver mode of the vehicle is the lane keeping mode, the driving path generator 160 may be configured to generate candidate paths within a current driving lane of the vehicle, and select any one of the candidate paths as a local path.” And figure 2 depicts that one of the given candidate paths may be down the center of the current traveling lane.);
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generate a vehicle control signal to follow the optimal route; And control, based on the vehicle control signal, a driving operation of the vehicle. (Yoo [0044] reads “The driving path generator 160 may be configured to output the selected driving path to a driving control apparatus (not shown). The driving control apparatus may be configured to operate a power plant device, a power transmission device, a driving device, a steering system, a brake device, a suspension system, a speed change device, and the like, to control the vehicle to travel along the selected local path.”);
Regarding claim 9 Yoo/Stentz teaches The apparatus of claim 1, wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by: determining, based on vehicle driving information about the vehicle and the surrounding object information, a collision risk level; (Yoo [0008] reads “The driving path generator may include: a candidate path generation module configured to generate driving lane candidate paths and target lane candidate paths; a collision risk calculation module may be configured to calculate the degree of collision risk with respect to a dynamic obstacle positioned around the vehicle for each of the target lane candidate paths; a velocity profile generation module configured to generate the velocity profile using the surrounding information and the vehicle information; and a path selection module configured to select a local reference path from among the target lane candidate paths based on the degree of collision risk or both the degree of collision risk and the velocity profile.”);
and determining, based on the collision risk level being greater than or equal to a threshold value, one of the plurality of candidate routes as the optimal route. (Yoo [0047] reads “The driving path generator 160 may then be configured to determine whether there is a collision risk in all of the target lane candidate paths based on the calculated degree of collision risk in operation S140. Particularly, the collision risk calculation module 163 may be configured to determine the collision risk when the degree of collision risk is greater than or equal to a threshold value.”);
Regarding claim 10 Yoo/Stentz teaches The apparatus of claim 1, wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to further cause the apparatus to display, via a display device of the vehicle, the plurality of candidate routes and the optimal route. (Yoo [0033] reads “The output 150 may interlock with a navigation system (not shown) to perform a mapping of the selected local reference path on map data and display the mapping results on a screen. The output 150 may be provided as at least one of an audio device, a display device, and a tactile device. The audio device may be configured output a warning sound, a guidance message, and the like, and may be provided as a speaker, a buzzer, and the like. The display device may be provided as at least one of a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light emitting diode (OLED), a flexible display, a three-dimensional (3D) display, a transparent display, a head-up display (HUD), and a touchscreen.“ and [0044] reads “ The driving path generator 160 may be configured to output the selected driving path to a driving control apparatus (not shown).”);
Regarding claim 11 Yoo teaches A method performed by an apparatus of a vehicle, the method comprising: generating, via a navigation system of the vehicle and based on surrounding object information obtained from a sensor of the vehicle, (Yoo [0029] reads “The surrounding information detector 110 may further be configured to obtain the surrounding information using an image sensor, a distance sensor, a position sensor, and the like. The distance sensor may be any one of an infrared sensor, a radio detection and ranging (RADAR) sensor, a light detection and ranging (LiDAR) sensor, a laser scanner, and the like, and the position sensor may be a global positioning system (GPS) receiver capable of obtaining position information of the vehicle. One or more image sensors, one or more distance sensors, and one or more position sensors may be mounted within the vehicle.”);
a plurality of candidate routes to merge with a target route from a location of the vehicle; (Yoo [0008] reads “The driving path generator may include: a candidate path generation module configured to generate driving lane candidate paths and target lane candidate paths;”);
generating a vehicle control signal to follow the optimal route; and controlling, based on the vehicle control signal, a driving operation of the vehicle. (Yoo [0044] reads “The driving path generator 160 may be configured to output the selected driving path to a driving control apparatus (not shown). The driving control apparatus may be configured to operate a power plant device, a power transmission device, a driving device, a steering system, a brake device, a suspension system, a speed change device, and the like, to control the vehicle to travel along the selected local path.”);
Yoo does not teach determining an amount of deviation, from the target route, of a route on which the vehicle is traveling; determining an optimal route, among the plurality of candidate routes, that minimizes the amount of deviation from the target route;
Stentz in analogous art, teaches determining an amount of deviation, from the target route, of a route on which the vehicle is traveling; determining an optimal route, among the plurality of candidate routes, that minimizes the amount of deviation from the target route; (Stentz [0070] reads “For example, the control system 120 can identify one or more alternative routes, and can further calculate or otherwise estimate a time delta for each alternative route, as well as proceeding along the current route 139 (512). In further examples, the control system 120 can determine a risk factor for following each of the alternative routes, as well as the current route 139 (514). In performing the cost analysis, or cost optimization, the control system 120 can determine whether an alternative route is more optimal (e.g., less risky or the least risky option) than the current route 139 (515). If so (517), then the control system 120 can select the optimal alternative route by executing an updated trajectory to follow the alternative route (525).”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo with that of Stentz to include a method that would take into account the total amount of deviation of a candidate route with the currently assigned route. This would allow the system to dynamically optimize and decide which route would be most optimal for the current vehicle. (Stentz abstract reads “A self-driving vehicle (SDV) can dynamically analyze a sensor view of a surrounding area of the SDV, and a current localization map in order to autonomously operate acceleration, braking, and steering systems of the SDV along a current route to a destination. Upon approaching a decision point along the current route, the SDV can perform a cost optimization to determine whether to diverge from the current route.”);
Regarding claim 13 Yoo/Stentz teaches The method of claim 11, wherein the generating of the plurality of candidate routes comprises: generating the plurality of candidate routes to connect the location of the vehicle to the target route (Yoo [0037] reads “The candidate path generation module 161 may be configured to generate the candidate paths with respect to the current driving lane of the vehicle and the target lane for lane change, respectively. Particularly, the candidate path generation module 161 may be configured to generate the predetermined number of candidate paths.”);
along a center route of a lane on which the vehicle is traveling. (Yoo [0035] reads “For example, when the maneuver mode of the vehicle is the lane keeping mode, the driving path generator 160 may be configured to generate candidate paths within a current driving lane of the vehicle, and select any one of the candidate paths as a local path.” And figure 2 depicts that one of the given candidate paths may be down the center of the current traveling lane.);
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Regarding claim 19 Yoo/Stentz teaches The method of claim 11, further comprising, prior to the determining of the optimal route, determining, based on vehicle driving information about the vehicle and the surrounding object information, a collision risk level. (Yoo [0008] reads “The driving path generator may include: a candidate path generation module configured to generate driving lane candidate paths and target lane candidate paths; a collision risk calculation module may be configured to calculate the degree of collision risk with respect to a dynamic obstacle positioned around the vehicle for each of the target lane candidate paths; a velocity profile generation module configured to generate the velocity profile using the surrounding information and the vehicle information; and a path selection module configured to select a local reference path from among the target lane candidate paths based on the degree of collision risk or both the degree of collision risk and the velocity profile.”);
Regarding claim 20 Yoo/Stentz teaches The method of claim 19, wherein the determining of the optimal route comprises: determining, based on the collision risk level being greater than or equal to a threshold value, one of the plurality of candidate routes as the optimal route. (Yoo [0047] reads “The driving path generator 160 may then be configured to determine whether there is a collision risk in all of the target lane candidate paths based on the calculated degree of collision risk in operation S140. Particularly, the collision risk calculation module 163 may be configured to determine the collision risk when the degree of collision risk is greater than or equal to a threshold value.”);
Claim(s) 2, 8, 12, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Yoo/Stentz, in further view of Chan (US 20230059562 A1).
Regarding claim 2 Yoo/Stentz teaches The apparatus of claim 1.
Yoo/Stentz does not teach wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by: determining the optimal route based on a number of times, that the amount of deviation of the route exceeds a threshold deviation amount, being greater than a threshold quantity value.
Chan in analogous art, teaches wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by: determining the optimal route based on a number of times, (Chan [0077] reads “In some non-limiting embodiments, a metric that measures performance of the simulated autonomous vehicle may include a metric associated with (e.g., that represents) … a metric associated with a number of halt events (e.g., an unplanned event that involves the simulated autonomous vehicle decreasing velocity and/or coming to a stop) experienced by the simulated autonomous vehicle during a simulation of operation, such as a number of halt events that exceed a threshold value (e.g., a threshold value of a number of halt events); … a metric associated with a number of times the simulated autonomous vehicle planned to stop during a simulation of operation, such as a number of times the simulated autonomous vehicle planned to stop due to a region of likely intersection (ROLI), and/or a number of times the simulated autonomous vehicle planned to stop due to a contingency; and/or a metric associated with a number of times the simulated autonomous vehicle planned to change a trajectory (e.g., change a trajectory in consecutive cycles) during a simulation of operation.”);
that the amount of deviation of the route exceeds a threshold deviation amount, being greater than a threshold quantity value. (Chan 0079 reads “ In some non-limiting embodiments, the test may measure whether a value of the metric that results from the test scenario satisfies a threshold (e.g., a threshold associated with a maximum value of the metric, a threshold associated with a minimum value of the metric, etc.) while the simulated autonomous vehicle operates along the predetermined route based on the autonomous vehicle control code.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Chan to incorporate a system in which the total number of individual instances of route deviation could be analyzed rather than purely relying on a total distance of deviation. This would allow for improved accuracy of the modeling, simulation and control of real world vehicles. This combination would be considered combining known elements according to known methods to yield predictable results, since different methods of simulating and controlling vehicles are known to one with ordinary skill in the art and their combination would yield a result that would be predictable to one with ordinary skill in the art.
Regarding claim 8 The apparatus of claim 1.
Yoo/Stentz does not teach wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by: determining, based on a number of times that the amount of deviation of the route exceeds a threshold deviation amount, being less than a threshold quantity value, one of the plurality of candidate routes as the optimal route.
Chan in analogous art, teaches wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by: determining, based on a number of times, (Chan 0077] reads “In some non-limiting embodiments, a metric that measures performance of the simulated autonomous vehicle may include a metric associated with (e.g., that represents) … a metric associated with a number of halt events (e.g., an unplanned event that involves the simulated autonomous vehicle decreasing velocity and/or coming to a stop) experienced by the simulated autonomous vehicle during a simulation of operation, such as a number of halt events that exceed a threshold value (e.g., a threshold value of a number of halt events); … a metric associated with a number of times the simulated autonomous vehicle planned to stop during a simulation of operation, such as a number of times the simulated autonomous vehicle planned to stop due to a region of likely intersection (ROLI), and/or a number of times the simulated autonomous vehicle planned to stop due to a contingency; and/or a metric associated with a number of times the simulated autonomous vehicle planned to change a trajectory (e.g., change a trajectory in consecutive cycles) during a simulation of operation.”);
that the amount of deviation of the route exceeds a threshold deviation amount, being less than a threshold quantity value, one of the plurality of candidate routes as the optimal route. (Chan 0079 reads “In some non-limiting embodiments, the test may measure whether a value of the metric that results from the test scenario satisfies a threshold (e.g., a threshold associated with a maximum value of the metric, a threshold associated with a minimum value of the metric, etc.) while the simulated autonomous vehicle operates along the predetermined route based on the autonomous vehicle control code.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Chan to incorporate a system in which the total number of individual instances of route deviation could be analyzed rather than purely relying on a total distance of deviation. This would allow for improved accuracy of the modeling, simulation and control of real world vehicles. This combination would be considered combining known elements according to known methods to yield predictable results, since different methods of simulating and controlling vehicles are known to one with ordinary skill in the art and their combination would yield a result that would be predictable to one with ordinary skill in the art.
Regarding claim 12 Yoo/Stentz teaches The method of claim 11.
Yoo/Stentz does not teach wherein the determining of the optimal route comprises: determining the optimal route based on a number of times, that the amount of deviation of the route exceeds a threshold deviation amount, being greater than a threshold quantity value.
Chan in analogous art, teaches, wherein the determining of the optimal route comprises: determining the optimal route based on a number of times, (Chan [0077] reads “In some non-limiting embodiments, a metric that measures performance of the simulated autonomous vehicle may include a metric associated with (e.g., that represents) … a metric associated with a number of halt events (e.g., an unplanned event that involves the simulated autonomous vehicle decreasing velocity and/or coming to a stop) experienced by the simulated autonomous vehicle during a simulation of operation, such as a number of halt events that exceed a threshold value (e.g., a threshold value of a number of halt events); … a metric associated with a number of times the simulated autonomous vehicle planned to stop during a simulation of operation, such as a number of times the simulated autonomous vehicle planned to stop due to a region of likely intersection (ROLI), and/or a number of times the simulated autonomous vehicle planned to stop due to a contingency; and/or a metric associated with a number of times the simulated autonomous vehicle planned to change a trajectory (e.g., change a trajectory in consecutive cycles) during a simulation of operation.”);
that the amount of deviation of the route exceeds a threshold deviation amount, being greater than a threshold quantity value. (Chan 0079 reads “ In some non-limiting embodiments, the test may measure whether a value of the metric that results from the test scenario satisfies a threshold (e.g., a threshold associated with a maximum value of the metric, a threshold associated with a minimum value of the metric, etc.) while the simulated autonomous vehicle operates along the predetermined route based on the autonomous vehicle control code.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Chan to incorporate a system in which the total number of individual instances of route deviation could be analyzed rather than purely relying on a total distance of deviation. This would allow for improved accuracy of the modeling, simulation and control of real world vehicles. This combination would be considered combining known elements according to known methods to yield predictable results, since different methods of simulating and controlling vehicles are known to one with ordinary skill in the art and their combination would yield a result that would be predictable to one with ordinary skill in the art.
Regarding claim 18 Yoo/Stentz teaches The method of claim 11.
Yoo/Stentz does not teach wherein the determining of the optimal route comprises: determining, based on a number of times, that the amount of deviation of the route exceeds a threshold deviation amount, being less than a threshold quantity value, one of the plurality of candidate routes as the optimal route.
Chan in analogous art, teaches wherein the determining of the optimal route comprises: determining, based on a number of times, (Chan [0077] reads “In some non-limiting embodiments, a metric that measures performance of the simulated autonomous vehicle may include a metric associated with (e.g., that represents) … a metric associated with a number of halt events (e.g., an unplanned event that involves the simulated autonomous vehicle decreasing velocity and/or coming to a stop) experienced by the simulated autonomous vehicle during a simulation of operation, such as a number of halt events that exceed a threshold value (e.g., a threshold value of a number of halt events); … a metric associated with a number of times the simulated autonomous vehicle planned to stop during a simulation of operation, such as a number of times the simulated autonomous vehicle planned to stop due to a region of likely intersection (ROLI), and/or a number of times the simulated autonomous vehicle planned to stop due to a contingency; and/or a metric associated with a number of times the simulated autonomous vehicle planned to change a trajectory (e.g., change a trajectory in consecutive cycles) during a simulation of operation.”);
that the amount of deviation of the route exceeds a threshold deviation amount, being less than a threshold quantity value, one of the plurality of candidate routes as the optimal route. (Chan 0079 reads “ In some non-limiting embodiments, the test may measure whether a value of the metric that results from the test scenario satisfies a threshold (e.g., a threshold associated with a maximum value of the metric, a threshold associated with a minimum value of the metric, etc.) while the simulated autonomous vehicle operates along the predetermined route based on the autonomous vehicle control code.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Chan to incorporate a system in which the total number of individual instances of route deviation could be analyzed rather than purely relying on a total distance of deviation. This would allow for improved accuracy of the modeling, simulation and control of real world vehicles. This combination would be considered combining known elements according to known methods to yield predictable results, since different methods of simulating and controlling vehicles are known to one with ordinary skill in the art and their combination would yield a result that would be predictable to one with ordinary skill in the art.
Claim(s) 4-5 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Yoo/Stentz, in further view of Lee (US 20240400095 A1).
Regarding claim 4 Yoo/Stentz teaches The apparatus of claim 3, for maintaining the center route of the lane. (Yoo [0035] reads “For example, when the maneuver mode of the vehicle is the lane keeping mode, the driving path generator 160 may be configured to generate candidate paths within a current driving lane of the vehicle, and select any one of the candidate paths as a local path.” And figure 2 depicts that one of the given candidate paths may be down the center of the current traveling lane.);
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Yoo/Stentz does not teach wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the amount of deviation of the route by: determining the amount of deviation of the route based on steering angle and acceleration values of the vehicle.
Lee in analogous art, teaches wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the amount of deviation of the route by: determining the amount of deviation of the route based on steering angle and acceleration values of the vehicle (Lee [0042] reads “In this example, candidate trajectories 406-414 are shown representing different trajectories that the vehicle may perform from the vehicle state 404. For instance, candidate trajectories 406-414 may be associated with a vehicle speed, velocity, steering angle, and/or other vehicle trajectory parameters. … The candidate trajectories 406-414 are depicted graphically in this example, each representing a trajectory that the autonomous vehicle 402 may follow from the vehicle state 404. In some examples, candidate trajectories 406-414 may be generated and stored as absolute parameter values (e.g., velocities, steering angles, etc.) while in other cases the generated and stored relative to the parameters of the vehicle state 404 (e.g., a velocity difference, a steering angle difference, etc.).”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Lee to include a method for calculating the amount of deviation from a vehicle based on its steering angle and other on board vehicle metrics. One with ordinary skill in the art would know that a vehicles future trajectory could be calculated by based on a few factors which would include steering angle and current vehicle speed. This combination would also yield predictable results to one with ordinary skill in the art. Therefore it would be obvious to one with ordinary skill in the art to combine these teachings in this manner.
Regarding claim 5 Yoo/Stentz teaches The apparatus of claim 2, wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by: determining the optimal route based on a cost function, (Yoo [0009] reads “The path selection module may further be configured to select, as the local reference path, a candidate path having a minimum cost through a cost function to which the degree of collision risk and the degree of proximity to a target path have been applied.”);
and a target route offset cost. (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
Yoo/Stentz does not teach wherein the cost function comprises a longitudinal and lateral jerk cost, a target speed cost.
Lee in analogous art, teaches wherein the cost function comprises a longitudinal and lateral jerk cost, a target speed cost, (Lee [0021] reads “In some examples, sub-costs may include comfort related costs (e.g., acceleration cost, jerk cost, steering cost, path reference cost, etc.), legality related costs, policy related costs, safety related costs, progress costs, debris cost, an exit cost, an approach cost, a space cost, a payment cost, a yaw cost, and/or any other type of cost.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Lee to include a method for calculating the amount of deviation from a vehicle based on its steering angle and other on board vehicle metrics. One with ordinary skill in the art would know that a vehicles future trajectory could be calculated by based on a few factors which would include steering angle and current vehicle speed. This combination would also yield predictable results to one with ordinary skill in the art. Therefore it would be obvious to one with ordinary skill in the art to combine these teachings in this manner.
Regarding claim 14 Yoo/Stentz teaches The method of claim 13, for maintaining the center route of the lane. (Yoo [0035] reads “For example, when the maneuver mode of the vehicle is the lane keeping mode, the driving path generator 160 may be configured to generate candidate paths within a current driving lane of the vehicle, and select any one of the candidate paths as a local path.” And figure 2 depicts that one of the given candidate paths may be down the center of the current traveling lane.);
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wherein the determining of the amount of deviation of the route comprises: determining the amount of deviation of the route based on steering angle and acceleration values of the vehicle (Lee [0042] reads “In this example, candidate trajectories 406-414 are shown representing different trajectories that the vehicle may perform from the vehicle state 404. For instance, candidate trajectories 406-414 may be associated with a vehicle speed, velocity, steering angle, and/or other vehicle trajectory parameters. … The candidate trajectories 406-414 are depicted graphically in this example, each representing a trajectory that the autonomous vehicle 402 may follow from the vehicle state 404. In some examples, candidate trajectories 406-414 may be generated and stored as absolute parameter values (e.g., velocities, steering angles, etc.) while in other cases the generated and stored relative to the parameters of the vehicle state 404 (e.g., a velocity difference, a steering angle difference, etc.).”);
wherein the determining of the amount of deviation of the route comprises: determining the amount of deviation of the route based on steering angle and acceleration values of the vehicle (Lee [0042] reads “In this example, candidate trajectories 406-414 are shown representing different trajectories that the vehicle may perform from the vehicle state 404. For instance, candidate trajectories 406-414 may be associated with a vehicle speed, velocity, steering angle, and/or other vehicle trajectory parameters. … The candidate trajectories 406-414 are depicted graphically in this example, each representing a trajectory that the autonomous vehicle 402 may follow from the vehicle state 404. In some examples, candidate trajectories 406-414 may be generated and stored as absolute parameter values (e.g., velocities, steering angles, etc.) while in other cases the generated and stored relative to the parameters of the vehicle state 404 (e.g., a velocity difference, a steering angle difference, etc.).”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Lee to include a method for calculating the amount of deviation from a vehicle based on its steering angle and other on board vehicle metrics. One with ordinary skill in the art would know that a vehicles future trajectory could be calculated by based on a few factors which would include steering angle and current vehicle speed. This combination would also yield predictable results to one with ordinary skill in the art. Therefore it would be obvious to one with ordinary skill in the art to combine these teachings in this manner.
Regarding claim 15 Yoo/Stentz teaches The method of claim 12, wherein the determining of the optimal route comprises: determining the optimal route based on a cost function, (Yoo [0009] reads “The path selection module may further be configured to select, as the local reference path, a candidate path having a minimum cost through a cost function to which the degree of collision risk and the degree of proximity to a target path have been applied.”);
and a target route offset cost. (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
Yoo/Stentz does not teach wherein the cost function comprises a longitudinal and lateral jerk cost, a target speed cost.
Lee in analogous art, teaches wherein the cost function comprises a longitudinal and lateral jerk cost, a target speed cost, (Lee [0021] reads “In some examples, sub-costs may include comfort related costs (e.g., acceleration cost, jerk cost, steering cost, path reference cost, etc.), legality related costs, policy related costs, safety related costs, progress costs, debris cost, an exit cost, an approach cost, a space cost, a payment cost, a yaw cost, and/or any other type of cost.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz with that of Lee to include a method for calculating the amount of deviation from a vehicle based on its steering angle and other on board vehicle metrics. One with ordinary skill in the art would know that a vehicles future trajectory could be calculated by based on a few factors which would include steering angle and current vehicle speed. This combination would also yield predictable results to one with ordinary skill in the art. Therefore it would be obvious to one with ordinary skill in the art to combine these teachings in this manner.
Claim(s) 6-7 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Yoo/Stentz/Lee, in further view of Shah (US 11897506 B1).
Regarding claim 6 Yoo/Stentz/Lee teaches The apparatus of claim 5,.
of the target route offset cost. (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
Yoo/Stentz/Lee does not teach wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by adjusting a weight (Shah [0078] reads “The planning component may select a candidate trajectory for implementation by the vehicle based at least in part on determining that the candidate trajectory is associated with a cost that is lowest among the costs associated with the set of candidate trajectories generated by the vehicle. Altering parameter(s) of the cost function may include increasing a weight associated with a sub-cost that is associated with ride comfort; modifying a target optimization metric to increasingly weight comfort or to prioritize weight (e.g., after generating the candidate trajectories and preliminary costs, optimize over costs associated with comfort and safety); altering the cost function such that candidate trajectories associated with lower rates of acceleration, jerk, etc. are lowered further; altering the cost function to decrease costs of trajectories that reduce a likelihood of receiving an additional comfort indication, and/or the like.”);
Shah in analogous art, teaches wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by adjusting a weight (Shah [0078] reads “The planning component may select a candidate trajectory for implementation by the vehicle based at least in part on determining that the candidate trajectory is associated with a cost that is lowest among the costs associated with the set of candidate trajectories generated by the vehicle. Altering parameter(s) of the cost function may include increasing a weight associated with a sub-cost that is associated with ride comfort; modifying a target optimization metric to increasingly weight comfort or to prioritize weight (e.g., after generating the candidate trajectories and preliminary costs, optimize over costs associated with comfort and safety); altering the cost function such that candidate trajectories associated with lower rates of acceleration, jerk, etc. are lowered further; altering the cost function to decrease costs of trajectories that reduce a likelihood of receiving an additional comfort indication, and/or the like.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz/Lee with that of Shah to include a method that would allow the system to change the importance of certain parameters during operation. This would allow the system to better adapt to different situations and user preferences. (Shah abstract reads “A vehicle may include an active ride comfort tuning system that reactively and/or proactively alters a parameter of a system of the autonomous vehicle to mitigate or avoid interruptions to ride smoothness. For example, the comfort tuning system may alter a parameter of a drive system, suspension, and/or a trajectory cost function. The comfort tuning system may alter the parameter based at least in part on detecting and/or receiving a comfort indication, determined based on sensor data, user input, or the like.”);
Regarding claim 7 Yoo/Stentz/Lee/Shah teaches The apparatus of claim 6, wherein the at least one instruction is configured, when executed by the one or more processors communicating with the memory, to cause the apparatus to determine the optimal route by: applying the target route offset cost, (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
with the adjusted weight, in the cost function (Shah [0078] reads “The planning component may select a candidate trajectory for implementation by the vehicle based at least in part on determining that the candidate trajectory is associated with a cost that is lowest among the costs associated with the set of candidate trajectories generated by the vehicle. Altering parameter(s) of the cost function may include increasing a weight associated with a sub-cost that is associated with ride comfort; modifying a target optimization metric to increasingly weight comfort or to prioritize weight (e.g., after generating the candidate trajectories and preliminary costs, optimize over costs associated with comfort and safety); altering the cost function such that candidate trajectories associated with lower rates of acceleration, jerk, etc. are lowered further; altering the cost function to decrease costs of trajectories that reduce a likelihood of receiving an additional comfort indication, and/or the like.”);
and determining the optimal route so that the cost function is minimized. (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
Regarding claim 16 Yoo/Stentz/Lee teaches The method of claim 15, of the target route offset cost. (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
Yoo/Stentz/Lee does not teach wherein the determining of the optimal route comprises adjusting a weight.
Shah in analogous art, teaches wherein the determining of the optimal route comprises adjusting a weight (Shah [0078] reads “The planning component may select a candidate trajectory for implementation by the vehicle based at least in part on determining that the candidate trajectory is associated with a cost that is lowest among the costs associated with the set of candidate trajectories generated by the vehicle. Altering parameter(s) of the cost function may include increasing a weight associated with a sub-cost that is associated with ride comfort; modifying a target optimization metric to increasingly weight comfort or to prioritize weight (e.g., after generating the candidate trajectories and preliminary costs, optimize over costs associated with comfort and safety); altering the cost function such that candidate trajectories associated with lower rates of acceleration, jerk, etc. are lowered further; altering the cost function to decrease costs of trajectories that reduce a likelihood of receiving an additional comfort indication, and/or the like.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Yoo/Stentz/Lee with that of Shah to include a method that would allow the system to change the importance of certain parameters during operation. This would allow the system to better adapt to different situations and user preferences. (Shah abstract reads “A vehicle may include an active ride comfort tuning system that reactively and/or proactively alters a parameter of a system of the autonomous vehicle to mitigate or avoid interruptions to ride smoothness. For example, the comfort tuning system may alter a parameter of a drive system, suspension, and/or a trajectory cost function. The comfort tuning system may alter the parameter based at least in part on detecting and/or receiving a comfort indication, determined based on sensor data, user input, or the like.”);
Regarding claim 17 Yoo/Stentz/Lee/Shah teaches The method of claim 16, wherein the determining of the optimal route comprises: applying the target route offset cost, (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
with the adjusted weight, in the cost function (Shah [0078] reads “The planning component may select a candidate trajectory for implementation by the vehicle based at least in part on determining that the candidate trajectory is associated with a cost that is lowest among the costs associated with the set of candidate trajectories generated by the vehicle. Altering parameter(s) of the cost function may include increasing a weight associated with a sub-cost that is associated with ride comfort; modifying a target optimization metric to increasingly weight comfort or to prioritize weight (e.g., after generating the candidate trajectories and preliminary costs, optimize over costs associated with comfort and safety); altering the cost function such that candidate trajectories associated with lower rates of acceleration, jerk, etc. are lowered further; altering the cost function to decrease costs of trajectories that reduce a likelihood of receiving an additional comfort indication, and/or the like.”);
and determining the optimal route so that the cost function is minimized. (Yoo [0043] reads “The path selection module 167 may be configured to select a local path from among the candidate paths based on the degree of collision risk and the degree of proximity to a target path. In particular, the path selection module 167 may be configured to select, as the local path, a candidate path having a minimum cost MIN(λ) among the candidate paths using a cost function expressed by the following equation 1: λ=α×D toLRP+β×ρmax Equation 1 wherein, α and β indicate weights according to parameters; Dtol.RP indicates a distance from the center of the vehicle to the local path, which is the degree of proximity to the target path; and ρmax indicates the degree of collision risk with respect to the dynamic obstacle.” It would be appreciated by one with ordinary skill in the art that by taking into account the amount of distance that the vehicle is away from the target path, that this would be equivalent to the target route offset cost.);
Other references not Cited
Throughout examination other references were found that could read onto the prior art. Though these references were not used in this examination they could be used in future examination and could read on the contents of the current disclosure. These references are, Liu (US 11858536 B1) and Parks (US 20230406301 A). Liu could be used to teach multiple different embodiments of the cost analysis between different trajectories and parameters. Meanwhile Parks was examined to identify if it could teach multiple aspects including the counting of multiple instances of deviation and the adaption parameters during operation. However, throughout examination it was found that while both sources could teach aspects of the claimed invention it was found that other source either covered more of the claimed invention or better taught the given limitations.
Conclusion
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/JOHN MARTIN O'MALLEY/Examiner, Art Unit 3658
/MICHAEL C ZARROLI/Primary Examiner, Art Unit 3658
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