DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is in response to amendments and remarks filed on 05/11/2026. Claims 1-20 are pending. Claims 1-3, 6, 9-11, 14, and 17-19 have been amended. This action is made final, as necessitated by amendment.
Response to Arguments
Applicant’s arguments appear to be directed solely to the amended subject matter which have been considered and addressed as detailed below under Claim Rejections.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-10, 12-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao (WO 2020113187) in view of Chase (US 20200066159) and Stein (US 20190294179).
Regarding claim 1, Rao teaches a method comprising:
detecting, by a processor, an animal in proximity to an autonomous vehicle (par. 78, "animals 212 other moveable objects may be identified by the autonomous vehicle.”):
receiving, by the processor, an indication that a (par. 23, "A plurality of probabilistic outcomes can be created including the various trajectories of objects. In each scenario a probability associated with each object moving can be varied within a variance threshold. This scenario can be run a plurality of times to generate a conflict free collision avoidance path through various objects.");
determining, by the processor, whether a physical attribute of the animal satisfies a threshold (par. 78, “In other instances, animals 212 other moveable objects may be identified by the autonomous vehicle. Each of these objects may be classified by a neural network or recognition system to be in a certain category”—the animal is categorized in order to determine which response the system should take. The “threshold” is be the likelihood that the animal is a specific species),
when the physical attribute satisfies the threshold, causing further actions comprising: determining, by the processor, at least one lighting apparatus associated with the autonomous vehicle is configured to emit light in a direction of the animal (par. 79, “Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.”—Rao would need to be able to determine which headlights are shining on the deer in order to avoid shining on it);
disabling, by the processor, the at least one lighting apparatus (par. 79, "Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.");
and enabling, by the processor, a sound-generating device associated with the autonomous vehicle (par. 74, "The autonomous vehicle may further contain an influencer module 118 which may attempt to influence an environment. The influencer module of the environment may enable the autonomous vehicle to alert other object as to its presence. As an example, the AV may alert other vehicles by sound, light, messaging, and alerts to mobile devices");
Although Rao does not explicitly teach Rao receiving, by the processor, an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal, Rao does teach identifying an animal and then determining a plurality of possible trajectories (scenarios) that would result in no collision. Therefore, Rao would teach recognizing the animal as a possible source of collision, and then determining a trajectory that would avoid it. Rao also only teaches decreasing the intensity or changing the direction of the headlights instead of fully disabling it, though fully disabling the light instead of decreasing it would have been a trivial change.
However, Chase teaches receiving, by the processor, an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal (par. 53, “If the speed and direction (or velocity) of the encroaching animal (e.g. deer) results in a calculated vector of the animal's estimated future position that is in conflict with the vehicle's 100 estimated future position, then the system proceeds to Block 226 to adjust vehicle operation to avoid contact with the animal”); and disabling, by the processor, the at least one lighting apparatus (par. 36, "If responsive action is identified as being needed, the system 10 triggers such responsive action in the vehicle's operation, such as flashing the vehicle's headlights").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao to incorporate the teachings of Chase in order to adjust operation of the vehicle to prevent danger from the animal (par. 18). These modifications to Rao would have been well-known in the field or trivial to one of ordinary skill in the art.
Rao and Chase fail to teach when the physical attribute does not satisfy the threshold, controlling the autonomous vehicle to proceed with the current trajectory without causing the further actions.
However, Stein teaches detecting, by a processor, an animal in proximity to an autonomous vehicle (par. 142, “Processing unit 110 may also execute monocular image analysis module 402 to detect various road hazards at step 520, such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like”);
receiving, by the processor, an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal (par. 234, “determine whether the object is on a possible collision course with vehicle 200, and act accordingly”);
determining, by the processor, whether a physical attribute of the animal satisfies a threshold (abstract, “the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold”);
when the physical attribute does not satisfy the threshold, controlling the autonomous vehicle to proceed with the current trajectory without causing the further actions (par. 217, “if the height of object 1202 is less than the roadway clearance height of vehicle 200c, action module 1330 may determine that the safest course of action is to simply do nothing, and allow vehicle 200c to drive over object 1202 with the object oriented between the wheels of vehicle 200c”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Rao in view of Chase to incorporate the teachings of Stein to add using the height of the animal as a threshold to determine whether or not mitigating actions should be done. Stein teaches a method for detecting low-height objects in a roadway, the objects including small animals. Stein further teaches that if the height of the obstacle is short enough, it is safer to drive over the object rather than try and change the course of the vehicle to avoid it (par. 217).
Regarding claim 2, the combination of Rao in view of Chase and Stein teaches the method of claim 1. Rao further teaches identifying, by the processor, a plurality of alternative trajectories for the autonomous vehicle (par. 51 line 1, “A plurality of trajectories, speeds, acceleration, and headings may be calculated for the AV and objects nearby to the AV”).
Regarding claim 4, the combination of Rao in view of Chase and Stein teaches the method of claim 1 Rao teaches the attribute corresponds to a mass, density, or volume of the animal (par. 38, “A plurality of cameras may classify a plurality of objects on a roadway of a certain type.”; par. 79, “if an autonomous vehicle identifies an animal such as a deer”—Rao teaches identifying the object using physical attributes).
Stein also teaches the attribute corresponds to a mass, density, or volume of the animal (abstract, “the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold”).
Regarding claim 5, the combination of Rao in view of Chase and Stein teaches the method of claim 1. Rao further teaches the attribute of the animal is identified by executing an artificial intelligence model configured to ingest data from at least one sensor (par. 38, cameras) of the autonomous vehicle and predict the attribute of the animal (par. 78, “In other instances, animals 212 other moveable objects may be identified by the autonomous vehicle. Each of these objects may be classified by a neural network or recognition system to be in a certain category”).
Regarding claim 6, the combination of Rao in view of Chase and Stein teaches the method of claim 1. Rao further teaches reducing, by the processor, a velocity of the autonomous vehicle (par. 74, "Alternatively or in addition, the AV may influence an environment by speed, trajectory, acceleration, velocity, and lane switching."; par. 23, “This scenario can be run a plurality of times to generate a conflict free collision avoidance path through various objects.”; par. 30, the system will determine the best course of action, which may include slowing the vehicle down or stopping).
Regarding claim 7, the combination of Rao in view of Chase and Stein teaches the method of claim 1. Rao further teaches enabling, by the processor, the at least one lighting apparatus when the likelihood of the collision no longer satisfies the collision threshold (par. 79, " Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.").
Although Rao does not explicitly state the lighting apparatus is re-enabled, it would have been obvious to one of ordinary skill in the art that the lights would return to the normal operating state after the deer is no longer a risk. It would not make sense for Rao to continuously drive with decreased or disabled lights.
Regarding claim 8, the combination of Rao in view of Chase and Stein teaches the method of claim 1. Rao further teaches disabling, by the processor, the sound-generating device when the likelihood of the collision no longer satisfies the collision threshold (par. 74, "The autonomous vehicle may further contain an influencer module 118 which may attempt to influence an environment. The influencer module of the environment may enable the autonomous vehicle to alert other object as to its presence. As an example, the AV may alert other vehicles by sound, light, messaging, and alerts to mobile devices").
Although Rao does not explicitly state the lighting apparatus is disabled, it would have been obvious to one of ordinary skill in the art that the speakers would return to the normal operating state after the deer is no longer a risk. It would not make sense for Rao to continuously drive while causing sounds.
Regarding claim 9, Rao teaches a system comprising:
a computer readable medium having a set of instructions (par. 93, computer readable medium), that when executed, cause a processor (Fig. 8, processors 802) to:
detect an animal in proximity to an autonomous vehicle (par. 78, "animals 212 other moveable objects may be identified by the autonomous vehicle.”);
receive an indication that a (par. 23, "A plurality of probabilistic outcomes can be created including the various trajectories of objects. In each scenario a probability associated with each object moving can be varied within a variance threshold. This scenario can be run a plurality of times to generate a conflict free collision avoidance path through various objects.");
determine whether a physical attribute of the animal satisfies a threshold (par. 78, “In other instances, animals 212 other moveable objects may be identified by the autonomous vehicle. Each of these objects may be classified by a neural network or recognition system to be in a certain category”—the animal is categorized in order to determine which response the system should take. The “threshold” is be the likelihood that the animal is a specific species);
when the physical attribute satisfies the threshold, cause further actions comprising: determining at least one lighting apparatus associated with the autonomous vehicle is configured to emit light in a direction of the animal (par. 79, “Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.”—Rao would need to be able to determine which headlights are shining on the deer in order to avoid shining on it);
disabling the at least one lighting apparatus (par. 79, "Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.");
and enabling a sound-generating device associated with the autonomous vehicle (par. 74, "The autonomous vehicle may further contain an influencer module 118 which may attempt to influence an environment. The influencer module of the environment may enable the autonomous vehicle to alert other object as to its presence. As an example, the AV may alert other vehicles by sound, light, messaging, and alerts to mobile devices");
Although Rao does not explicitly teach Rao receiving, by the processor, an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal, Rao does teach identifying an animal and then determining a plurality of possible trajectories (scenarios) that would result in no collision. Therefore, Rao would teach recognizing the animal as a possible source of collision, and then determining a trajectory that would avoid it. Rao also only teaches decreasing the intensity or changing the direction of the headlights instead of fully disabling it, though fully disabling the light instead of decreasing it would have been a trivial change.
However, Chase teaches receive an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal (par. 53, “If the speed and direction (or velocity) of the encroaching animal (e.g. deer) results in a calculated vector of the animal's estimated future position that is in conflict with the vehicle's 100 estimated future position, then the system proceeds to Block 226 to adjust vehicle operation to avoid contact with the animal”), and disable the at least one lighting apparatus (par. 36, "If responsive action is identified as being needed, the system 10 triggers such responsive action in the vehicle's operation, such as flashing the vehicle's headlights").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao to incorporate the teachings of Chase in order to adjust operation of the vehicle to prevent danger from the animal (par. 18). These modifications to Rao would have been well-known in the field or trivial to one of ordinary skill in the art.
Rao and Chase fail to teach when the physical attribute does not satisfy the threshold, controlling the autonomous vehicle to proceed with the current trajectory without causing the further actions.
However, Stein teaches detecting, by a processor, an animal in proximity to an autonomous vehicle (par. 142, “Processing unit 110 may also execute monocular image analysis module 402 to detect various road hazards at step 520, such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like”);
receiving, by the processor, an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal (par. 234, “determine whether the object is on a possible collision course with vehicle 200, and act accordingly”);
determining, by the processor, whether a physical attribute of the animal satisfies a threshold (abstract, “the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold”);
when the physical attribute does not satisfy the threshold, controlling the autonomous vehicle to proceed with the current trajectory without causing the further actions (par. 217, “if the height of object 1202 is less than the roadway clearance height of vehicle 200c, action module 1330 may determine that the safest course of action is to simply do nothing, and allow vehicle 200c to drive over object 1202 with the object oriented between the wheels of vehicle 200c”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Rao in view of Chase to incorporate the teachings of Stein to add using the height of the animal as a threshold to determine whether or not mitigating actions should be done. Stein teaches a method for detecting low-height objects in a roadway, the objects including small animals. Stein further teaches that if the height of the obstacle is short enough, it is safer to drive over the object rather than try and change the course of the vehicle to avoid it (par. 217).
Regarding claim 10, the combination of Rao in view of Chase and Stein teaches the system of claim 9. Rao further teaches identifying a plurality of alternative trajectories for the autonomous vehicle (par. 51 line 1, “A plurality of trajectories, speeds, acceleration, and headings may be calculated for the AV and objects nearby to the AV”).
Regarding claim 12, the combination of Rao in view of Chase and Stein teaches the system of claim 9. Rao further teaches the attribute corresponds to a mass, density, or volume of the animal (par. 38, “A plurality of cameras may classify a plurality of objects on a roadway of a certain type.”; par. 79, “if an autonomous vehicle identifies an animal such as a deer”—Rao teaches identifying the object using physical attributes).
Stein also teaches the attribute corresponds to a mass, density, or volume of the animal (abstract, “the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold”)
Regarding claim 13, the combination of Rao in view of Chase and Stein teaches the system of claim 9. Rao further teaches the attribute of the animal is identified by executing an artificial intelligence model configured to ingest data from at least one sensor (par. 38, cameras) of the autonomous vehicle and predict the attribute of the animal (par. 78, “In other instances, animals 212 other moveable objects may be identified by the autonomous vehicle. Each of these objects may be classified by a neural network or recognition system to be in a certain category”).
Regarding claim 14, the combination of Rao in view of Chase and Stein teaches the system of claim 9. Rao further teaches reducing a velocity of the autonomous vehicle (par. 74, "Alternatively or in addition, the AV may influence an environment by speed, trajectory, acceleration, velocity, and lane switching."; par. 23, “This scenario can be run a plurality of times to generate a conflict free collision avoidance path through various objects.”; Par. 30, the system will determine the best course of action, which may include slowing the vehicle down or stopping).
Regarding claim 15, the combination of Rao in view of Chase and Stein teaches the system of claim 9, Rao further teaches the set of instructions further cause the processor to: enable the at least one lighting apparatus when the likelihood of the collision no longer satisfies the collision threshold (par. 79, " Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.").
Although Rao does not explicitly state the lighting apparatus is re-enabled, it would have been obvious to one of ordinary skill in the art that the lights would return to the normal operating state after the deer is no longer a risk. It would not make sense for Rao to continuously drive with decreased or disabled lights.
Regarding claim 16, the combination of Rao in view of Chase and Stein teaches the system of claim 9. Rao further teaches the set of instructions further cause the processor to: disable the sound-generating device when the likelihood of the collision no longer satisfies the collision threshold (par. 74, "The autonomous vehicle may further contain an influencer module 118 which may attempt to influence an environment. The influencer module of the environment may enable the autonomous vehicle to alert other object as to its presence. As an example, the AV may alert other vehicles by sound, light, messaging, and alerts to mobile devices").
Although Rao does not explicitly state the lighting apparatus is disabled, it would have been obvious to one of ordinary skill in the art that the speakers would return to the normal operating state after the deer is no longer a risk. It would not make sense for Rao to continuously drive while causing sounds.
Regarding claim 17, Rao teaches an autonomous vehicle having a processor configured to:
detect an animal in proximity to the autonomous vehicle (par. 78, "animals 212 other moveable objects may be identified by the autonomous vehicle.”);
receive an indication that a (par. 23, "A plurality of probabilistic outcomes can be created including the various trajectories of objects. In each scenario a probability associated with each object moving can be varied within a variance threshold. This scenario can be run a plurality of times to generate a conflict free collision avoidance path through various objects.");
determine whether a physical attribute of the animal satisfies a threshold (par. 78, “In other instances, animals 212 other moveable objects may be identified by the autonomous vehicle. Each of these objects may be classified by a neural network or recognition system to be in a certain category”—the animal is categorized in order to determine which response the system should take. The “threshold” is be the likelihood that the animal is a specific species);
when the physical attribute satisfies the threshold, cause further actions comprising: determining, at least one lighting apparatus associated with the autonomous vehicle is configured to emit light in a direction of the animal (par. 79, “Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.”—Rao would need to be able to determine which headlights are shining on the deer in order to avoid shining on it);
disabling the at least one lighting apparatus (par. 79, "Alternatively, the AV that detects a deer and calculates that there are not other objects in a path can determine to decrease the intensity of the headlights or the direction of the headlights so as to avoid directly shining light in the deer.");
and enabling a sound-generating device associated with the autonomous vehicle (par. 74, "The autonomous vehicle may further contain an influencer module 118 which may attempt to influence an environment. The influencer module of the environment may enable the autonomous vehicle to alert other object as to its presence. As an example, the AV may alert other vehicles by sound, light, messaging, and alerts to mobile devices") ;
Although Rao does not explicitly teach Rao receiving, by the processor, an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal, Rao does teach identifying an animal and then determining a plurality of possible trajectories (scenarios) that would result in no collision. Therefore, Rao would teach recognizing the animal as a possible source of collision, and then determining a trajectory that would avoid it. Rao also only teaches decreasing the intensity or changing the direction of the headlights instead of fully disabling it, though fully disabling the light instead of decreasing it would have been a trivial change.
However, Chase teaches receive an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal (par. 53, “If the speed and direction (or velocity) of the encroaching animal (e.g. deer) results in a calculated vector of the animal's estimated future position that is in conflict with the vehicle's 100 estimated future position, then the system proceeds to Block 226 to adjust vehicle operation to avoid contact with the animal”), and disable the at least one lighting apparatus (par. 36, "If responsive action is identified as being needed, the system 10 triggers such responsive action in the vehicle's operation, such as flashing the vehicle's headlights").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao to incorporate the teachings of Chase in order to adjust operation of the vehicle to prevent danger from the animal (par. 18). These modifications to Rao would have been well-known in the field or trivial to one of ordinary skill in the art.
Rao and Chase fail to teach when the physical attribute does not satisfy the threshold, controlling the autonomous vehicle to proceed with the current trajectory without causing the further actions.
However, Stein teaches detecting, by a processor, an animal in proximity to an autonomous vehicle (par. 142, “Processing unit 110 may also execute monocular image analysis module 402 to detect various road hazards at step 520, such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like”);
receiving, by the processor, an indication that a current trajectory of the autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with the animal (par. 234, “determine whether the object is on a possible collision course with vehicle 200, and act accordingly”);
determining, by the processor, whether a physical attribute of the animal satisfies a threshold (abstract, “the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold”);
when the physical attribute does not satisfy the threshold, controlling the autonomous vehicle to proceed with the current trajectory without causing the further actions (par. 217, “if the height of object 1202 is less than the roadway clearance height of vehicle 200c, action module 1330 may determine that the safest course of action is to simply do nothing, and allow vehicle 200c to drive over object 1202 with the object oriented between the wheels of vehicle 200c”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Rao in view of Chase to incorporate the teachings of Stein to add using the height of the animal as a threshold to determine whether or not mitigating actions should be done. Stein teaches a method for detecting low-height objects in a roadway, the objects including small animals. Stein further teaches that if the height of the obstacle is short enough, it is safer to drive over the object rather than try and change the course of the vehicle to avoid it (par. 217).
Regarding claim 18, the combination of Rao in view of Chase and Stein teaches the autonomous vehicle of claim 17. Rao further teaches identifying a plurality of alternative trajectories for the autonomous vehicle (par. 51 line 1, “A plurality of trajectories, speeds, acceleration, and headings may be calculated for the AV and objects nearby to the AV”).
Regarding claim 20, the combination of Rao in view of Chase and Stein teaches the autonomous vehicle of claim 17. Rao further teaches the attribute corresponds to a mass, density, or volume of the animal (par. 38, “A plurality of cameras may classify a plurality of objects on a roadway of a certain type.”; par. 79, “if an autonomous vehicle identifies an animal such as a deer”—Rao teaches identifying the object using physical attributes).
Stein also teaches the attribute corresponds to a mass, density, or volume of the animal (abstract, “the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold”).
Claim(s) 3, 11, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Chase and Stein, and further in view of Bougdal-Lambert (US 20240109532 A1) and Simon (US 20110137527 A1).
Regarding claim 3, the combination of Rao in view of Chase and Stein teaches the method of claim 2. Rao further teaches calculating, by the processor, (par. 52, “identify the appropriate path to proceed on based on a maximization of one or more criteria that avoids contact with any of the objects”).
The probability of a collision-free path is one such criteria that helps determine which path to take (par. 92 and Figure 7). Rao also describes assigning a complexity score to each navigation route depending on factors such as types of vehicles, speed of roads, number intersections, et cetera (par. 66).
Rao fails to teach calculating, by the processor, a first cost value for the current trajectory and directing, by the processor, the autonomous vehicle on either the current trajectory or one of the alternative trajectories. Rao instead only teaches calculating costs for the alternative trajectories and directing the vehicle on one of the alternative trajectories. This would be due to the assumption that the alternative trajectories would have a lower cost than the current trajectory, since the current trajectory results in a collision.
However, Simon teaches that there are situations in which colliding with some objects could be preferable to performing an evasive maneuver (par. 26, “In this connection, it is advantageous to be able to distinguish between objects worth being protected, such as highway users, and objects less worthy of protection, such as delineators or guard rails. If an imminent, unavoidable collision with a pedestrian is detected, then emergency braking should be carried out. If the detected object is, for example, a delineator or a small tree, then the damage produced by emergency braking or an evasive maneuver, e.g. the danger to the following traffic, may be greater than the benefit. In the case of objects not worth protecting, such unintentional activation of the system acting on the vehicle dynamics may only be prevented with the aid of a classification system”).
Using an alternative trajectory only when the cost is less than a current trajectory is already well known in the field, and would have been an obvious modification to Rao. Bougdal-Lambert teaches calculating, by the processor, a first cost value for the current trajectory and a respective second cost value for each of the alternative trajectories; and directing, by the processor, the autonomous vehicle on either the current trajectory or one of the alternative trajectories that advances the autonomous vehicle away from the animal based on its cost value (par. 88, “the forward planning system 168 or maneuver planning system 169 may only publish a new or updated trajectory if the new or updated trajectory has a cost which is significantly lower (e.g., a difference greater than a threshold value) than the current trajectory in order to avoid unnecessary switches between different trajectories”).
There would be motivation to include comparing the current collision-bound trajectory with the alternative trajectories because the current trajectory could be preferable (and thus have a lower cost) than the alternative trajectories, as Simon teaches. Furthermore, Bougdal-Lambert teaches it would be beneficial to avoid unnecessary switches between different trajectories when not needed. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Rao, Chase, and Stein to incorporate the teachings of Bougdal-Lambert with the reasoning of Simon in order to select the lowest cost trajectory.
Regarding claim 11, the combination of Rao in view of Chase and Stein teaches the system of claim 10. Rao further teaches the set of instructions further cause the processor to: calculate(par. 52, “identify the appropriate path to proceed on based on a maximization of one or more criteria that avoids contact with any of the objects”).
The probability of a collision-free path is one such criteria that helps determine which path to take (par. 92 and Figure 7). Rao also describes assigning a complexity score to each navigation route depending on factors such as types of vehicles, speed of roads, number intersections, et cetera (par. 66).
Rao fails to teach calculate a first cost value for the current trajectory and direct the autonomous vehicle on either the current trajectory or one of the alternative trajectories. Rao instead only teaches calculating costs for the alternative trajectories and directing the vehicle on one of the alternative trajectories. This would be due to the assumption that the alternative trajectories would have a lower cost than the current trajectory, since the current trajectory results in a collision.
However, Simon teaches that there are situations in which colliding with some objects could be preferable to performing an evasive maneuver (par. 26, “In this connection, it is advantageous to be able to distinguish between objects worth being protected, such as highway users, and objects less worthy of protection, such as delineators or guard rails. If an imminent, unavoidable collision with a pedestrian is detected, then emergency braking should be carried out. If the detected object is, for example, a delineator or a small tree, then the damage produced by emergency braking or an evasive maneuver, e.g. the danger to the following traffic, may be greater than the benefit. In the case of objects not worth protecting, such unintentional activation of the system acting on the vehicle dynamics may only be prevented with the aid of a classification system”).
Using an alternative trajectory only when the cost is less than a current trajectory is already well known in the field, and would have been an obvious modification to Rao. Bougdal-Lambert teaches calculating, by the processor, a first cost value for the current trajectory and a respective second cost value for each of the alternative trajectories; and directing, by the processor, the autonomous vehicle on either the current trajectory or one of the alternative trajectories that advances the autonomous vehicle away from the animal based on its cost value (par. 88, “the forward planning system 168 or maneuver planning system 169 may only publish a new or updated trajectory if the new or updated trajectory has a cost which is significantly lower (e.g., a difference greater than a threshold value) than the current trajectory in order to avoid unnecessary switches between different trajectories”).
There would be motivation to include comparing the current collision-bound trajectory with the alternative trajectories because the current trajectory could be preferable (and thus have a lower cost) than the alternative trajectories, as Simon teaches. Furthermore, Bougdal-Lambert teaches it would be beneficial to avoid unnecessary switches between different trajectories when not needed. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Rao, Chase, and Stein to incorporate the teachings of Bougdal-Lambert with the reasoning of Simon in order to select the lowest cost trajectory.
Regarding claim 19, the combination of Rao in view of Chase and Stein teaches the autonomous vehicle of claim 18. Rao further teaches the processor is further configured to: calculate (par. 52, “identify the appropriate path to proceed on based on a maximization of one or more criteria that avoids contact with any of the objects”).
The probability of a collision-free path is one such criteria that helps determine which path to take (par. 92 and Figure 7). Rao also describes assigning a complexity score to each navigation route depending on factors such as types of vehicles, speed of roads, number intersections, et cetera (par. 66).
Rao fails to teach the processor is further configured to: calculate a first cost value for the current trajectory and a respective second cost value for each of the alternative trajectories; and direct the autonomous vehicle on either the current trajectory or one of the alternative trajectories. This would be due to the assumption that the alternative trajectories would have a lower cost than the current trajectory, since the current trajectory results in a collision.
However, Simon teaches that there are situations in which colliding with some objects could be preferable to performing an evasive maneuver (par. 26, “In this connection, it is advantageous to be able to distinguish between objects worth being protected, such as highway users, and objects less worthy of protection, such as delineators or guard rails. If an imminent, unavoidable collision with a pedestrian is detected, then emergency braking should be carried out. If the detected object is, for example, a delineator or a small tree, then the damage produced by emergency braking or an evasive maneuver, e.g. the danger to the following traffic, may be greater than the benefit. In the case of objects not worth protecting, such unintentional activation of the system acting on the vehicle dynamics may only be prevented with the aid of a classification system”).
Using an alternative trajectory only when the cost is less than a current trajectory is already well known in the field, and would have been an obvious modification to Rao. Bougdal-Lambert teaches the processor is further configured to: calculate a first cost value for the current trajectory and a respective second cost value for each of the alternative trajectories; and direct the autonomous vehicle on either the current trajectory or one of the alternative trajectories that advances the autonomous vehicle away from the animals based on the calculated first cost value and the second cost value (par. 88, “the forward planning system 168 or maneuver planning system 169 may only publish a new or updated trajectory if the new or updated trajectory has a cost which is significantly lower (e.g., a difference greater than a threshold value) than the current trajectory in order to avoid unnecessary switches between different trajectories”).
There would be motivation to include comparing the current collision-bound trajectory with the alternative trajectories because the current trajectory could be preferable (and thus have a lower cost) than the alternative trajectories, as Simon teaches. Furthermore, Bougdal-Lambert teaches it would be beneficial to avoid unnecessary switches between different trajectories when not needed. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Rao, Chase, and Stein to incorporate the teachings of Bougdal-Lambert with the reasoning of Simon in order to select the lowest cost trajectory.
Conclusion
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/M.L.H./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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