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 07/02/2025. Claims 1-20 are pending. Claims 1-2, 9-10, and 17-20 have been amended. The objections to drawings, specification, and claims 7, 15, and 20, the 35 U.S.C. 112 rejections to claims 2-3, 10-11, and 18, and the 35 U.S.C. 102 rejections have been withdrawn in light of the instant amendments. This action is made final, as necessitated by amendment.
Response to Arguments
Applicant's arguments filed 12/22/2025 have been fully considered but they are not persuasive. Applicant provides two main arguments regarding the 35 U.S.C. 103 rejections.
No combination of Chase and Sun describes or suggests "receiving 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, and determining, that the animal has an attribute that satisfies a threshold," as recited in amended claim 1.
There is no motivation to combine Rao with Chase and/or Sung because Chase is explicitly directed to thermal signature animal detection, and sets forth limitations of visual obstacle detection in the background and par. 52.
Regarding Applicant’s argument A, Examiner disagrees the Chase does not teach determining an attribute that satisfies a threshold. Chase states, “the thermal gate is configured to detect a plurality of different animal thermal signatures. The thermal gate may be a dynamic thermal gate and/or be configured to detect a thermal signature centered approximately around 104° F., for example, in a range of 103° F. to 105° F. or 99° F. to 109° F., which is indicative of a deer” (par. 54). Therefore, Chase determines the thermal signature of the animal is within a certain range. Applicant also argues that Rao does not remedy the deficiencies of Chase and Sung with respect to amended claim 1. Examiner disagrees, as detailed below under Claim Rejections.
Regarding Applicant’s argument B, Examiner disagrees that there is no motivation to combine Rao with Chase and/or Sung. Rao teaches using physical attributes (gained via an optical sensor) in order to determine the species of animal. However, this can be done using Chase’s thermal sensor. Chase’s thermal sensor is also in communication with an image processor, which would be able to process the thermal sensor readings and therefore would be able to determine physical characteristics of the animal.
While Examiner disagrees with Applicant’s arguments, a new ground of rejection has been made 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao (WO 2020113187) in view of Chase (US 20200066159).
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, that the animal has an attribute that 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),
in response to the determining, 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.
Regarding claim 2, the combination of Rao in view of Chase teaches the method of claim 1. Rao further teaches calculating, 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 3, the combination of Rao in view of Chase teaches the method of claim 2. Rao further teaches directing, by the processor, the autonomous vehicle on an alternative trajectory away from the animal based on its cost value (paragraph [0052] line 3, “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).
Regarding claim 4, the combination of Rao in view of Chase 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).
Regarding claim 5, the combination of Rao in view of Chase 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 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 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 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 that the animal has an attribute that 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);
in response to the determining, further determine 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);
disable 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 enable 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.
Regarding claim 10, the combination of Rao in view of Chase teaches the system of claim 9. Rao further teaches the set of instructions further cause the processor to: identify 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 11, the combination of Rao in view of Chase teaches the system of claim 10. Rao further teaches the set of instructions further cause the processor to: direct the autonomous vehicle on an alternative trajectory away from the animal based on its cost value (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).
Regarding claim 12, the combination of Rao in view of Chase 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).
Regarding claim 13, the combination of Rao in view of Chase 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 teaches the system of claim 9. Rao further teaches the set of instructions further cause the processor to: reduce 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 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 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 that the animal has an attribute that 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);
in response to the determining, further determine 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);
disable 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 enable 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.
Regarding claim 18, the combination of Rao in view of Chase teaches the autonomous vehicle of claim 17. Rao further teaches the processor is further configured to: identify 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 19, the combination of Rao in view of Chase teaches the autonomous vehicle of claim 18. Rao further teaches the processor is further configured to: direct the autonomous vehicle on an alternative trajectory away from the animal based on its cost value (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).
Regarding claim 20, the combination of Rao in view of Chase 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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Difrancesco (US 20230242154 A1) – Difrancesco teaches a method for mitigating a collision with an animal based on the size of the animal (par. 39).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MINATO LEE HORNER whose telephone number is (571)272-5425. The examiner can normally be reached M-F 8-5.
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/M.L.H./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665