DEAILED 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/14/2026 has been entered.
Status of Claims
This action is in response to the claims filed on 09/14/2026. Wherein, claims 1, 10, and 18 have been amended and claim 21 is cancelled. Claims 1-20 are rejected.
Response to Arguments
Applicant’s arguments, see REMARKS, filed 09/14/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC §103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Carlson et al.
With respect to the rejection of claim 1, the Applicant argues:
Applicant respectfully submits that Lacaze, Purdy, and Rachkov, alone or in combination, do not disclose, teach, or suggest the combination of "a non-transitory computer- readable medium containing instructions that when executed by the at least one processor causes the at least one processor to," "process the sensor data to identify recorded movement of the refuse vehicle in a forward direction," and "autonomously adjust one or more operating parameters of the refuse vehicle to operate the refuse vehicle along the reverse-direction trajectory by reversing the recorded movement," as recited in amended claim 1.
With reference to previous claim 21, the Office Action acknowledges that Lacaze and Purdy do not teach instructions that cause at least one processor to “process the sensor data to identify recorded movement of the refuse vehicle in a forward direction” or "reverse the recorded movement to generate the reverse of at least a portion of the sensor data." However, the Office Action alleges that a portion of Rachkov describing measuring landmarks on board a vehicle through a stereo block of cameras that is placed on a vehicle, and that is controlled by an operator by means of a radio channel, corresponds to the claimed instructions that cause the at least one processor to "process the sensor data to identify recorded movement of the refuse vehicle in a forward direction.” The Office Action also alleges that a portion of Rachkov describing that in reverse motion of the vehicle, machine relative map (MRM) parameters are extracted from a stack computer memory in reverse order and in accordance with stop point numbers of the vehicle, corresponds to the claimed instruction that cause the at least one processor to "autonomously adjust one or more operating parameters of the refuse vehicle to operate the refuse vehicle along the reverse-direction trajectory by reversing the recorded movement."
Applicant respectfully disagrees and submits that Rachkov does not teach that the vehicle is operated by reversing recorded movement. Instead, Rachkov teaches that automatic reverse motion of the vehicle is based on the MRM parameters including a length of a traversed path l, an angle θ of trajectory rotation, a rotation angle of the stereo block of cameras β, and an error estimate based on position of the landmarks captured by the stereo block of cameras relative to the vehicle. Rachkov, p. 2 (stating that the method provides "automatic return by reverse motion with the help of the stored point plans and newly obtained point plans for corrections of the trajectory"
During the automatic reverse motion of the vehicle in Rachkov, none of the MRM parameters are generated as a reverse of the MRM parameters that were generated during forward movement of the vehicle. In particular, Rachkov does not teach generating a reverse length of the traversed path l, a reverse angle θ of trajectory rotation, or a reverse rotation angle of the stereo block of cameras β during the automatic reverse motion of the vehicle. Instead, Rachkov explicitly teaches that “[i]n the reverse motion, the MRM parameters are extracted from the stack computer memory in the reverse order, in accordance with the stop point number of the vehicle.” Rachkov, p. 3. Thus, Rachkov merely retrieves stored parameters in reverse order-it does not reverse the values of those parameters. Furthermore, the error estimate in Rachkov is calculated only during the reverse motion based on alignment of stored point plans with newly obtained point plans. Rachkov, p. 3 ("As a result of the alignment of the plan P'k with the plan Pk, the deviations from the trajectory ... are determined"). The error estimate cannot be a reverse of any data generated during the forward motion of the vehicle because Rachkov does not describe calculating any error during forward motion of the vehicle. Accordingly, amended claim 1 is patentable over Lacaze, Purdy, and Rachkov, taken alone or in combination.
Examiner cordially disagrees. As provided in the Conclusion section, “The principle of autonomous navigation in automatic reverse motion is based on the use of an operational machine relative map automatically formed during the forward movement of the vehicle at the node and reference points.” At its core, Rachkov is performing a reverse function based on a previously determined forward movement. The path length, angle of trajectory rotation, and landmarks determined from the stereo-cameras are to aid in any errors that may occur by reversing the forward movement.
However, to further prosecution, the Examiner has provided a new rejection below in view of Carlson et al. Additionally, Kendall (US 5,247,442, dated: Sep. 21, 1993) is prior art made of record and not relied upon and is considered pertinent to applicant's disclosure.
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.
Claim(s) 1- 3, 5, 6, 10-12, 15, 16, and 18- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lacaze et al. (US 2021/0024068 A1, “Lacaze”) in view of Purdy et al. (US 12,026,956 B1, “Purdy”) and in further view of Carlson et al. (US 2019/0016384 A1, “Carlson”).
Regarding claims 1, 10, and 18, Lacaze discloses an autonomous waste collection truck and teaches:
A refuse vehicle comprising: (The invention is directed towards a self-driving waste collection truck, e.g., vehicle 200 – See at least ¶ [0054] and Fig. 2)
a chassis; (The vehicle has a chassis as show in Fig. 5. Examiner further notes that the disclosure discusses driverless trucks having a chassis in general – See at least ¶ [0009])
a user input device; (The human uses an interface such as a switch, verbal gesture, or hand gesture to indicate to the controller in the truck to move to the next location or to wait in the same location – See at least ¶ [0039])
at least one tractive element; (The allocation of waste removal equipment has been improved by the use of large trucks having compaction capabilities, i.e., a tractive element, extending their effective range and capacity between unloadings – See at least ¶ [0004])
at least one sensor configured to receive sensor data associated with an environment proximate the refuse vehicle; (The vehicle uses a plurality of sensors, e.g., GPS, ranging radios, LIDAR, and optical sensors such as cameras – See at least ¶ [0031])
at least one processor; and (FIG. 1 shows an overall schematic of the basic autonomous waste collection system. It consists of a drive by wire kit that is connected to actuators (101) which controls the steering speed and other types of conditions and is connected to the autonomous driver. The autonomous driver is aware of the sensors (100) and the collection routes and this leads to the human detection of the waste – See at least ¶ [0052])
receive the sensor data from the at least one sensor; (The controller of the autonomous waste collections truck system is aware of the rules of the road and the truck automatically obeys the rules of the road. The location of the waste collector's (human/s) is sensed using GPS or ranging radios, or LIDAR, or stereo vision, or is detected using an EO or IR camera – See at least ¶ [0031])
process the sensor data to identify a non-drivable [] area; (The autonomous waste collections truck system is also equipped with a bin detector which automatically stops as to align the back of the truck with some extra clearance with the detected bins. The trucks are also equipped with sensors that detect if an obstacle is on the route and a controller that automatically stops if the a-priori routes are blocked – See at least ¶ [0030]; For example, if there are no vehicles parked on the road, then, the controller will drive the route close to the curb. If there are vehicles parked by the side of the road, the controller will drive the autonomous waste collections truck with sufficient space for safety and taking under consideration that the human and the bin will need to walk between the truck and parked car – See at least ¶ [0040])
receive an indication from the user input device to autonomously operate the refuse vehicle (The human uses an interface such as a switch, verbal gesture, or hand gesture to indicate to the controller in the truck to move to the next location or to wait in the same location – See at least ¶ [0039]) in a reverse direction; and (The human has an interface that can command the autonomous waste collections truck to automatically move forwards and backwards along the route – See at least ¶ [0041])
responsive to receiving the indication from the user input device to autonomously operate the refuse vehicle in the reverse direction, (The human has an interface that can command the autonomous waste collections truck to automatically move forwards and backwards along the route – See at least ¶ [0041]) autonomously adjust one or more operating parameters of the refuse vehicle to operate the refuse vehicle in the reverse direction [] (The controller in the autonomous waste collections truck system automatically avoids contact with stationary obstacles that are present on the route. The controller also has some freedom to adjust the side separation with respect to the route of the autonomous waste collections truck depending on if the vehicles are parked on the road. For example, if there are no vehicles parked on the road, then, the controller will drive the route close to the curb. If there are vehicles parked by the side of the road, the controller will drive the autonomous waste collections truck with sufficient space for safety and taking under consideration that the human and the bin will need to walk between the truck and parked car – See at least ¶ [0040])
Lacaze does not explicitly teach the use of a non-transitory computer-readable medium or the use of “bounding areas” or “generate a reverse-direction trajectory from a reverse of at least a portion of the sensor data, the reverse-direction trajectory configured to avoid the non-drivable bounding areas” and “operate the refuse vehicle along the reverse-direction trajectory”. However, Purdy discloses object bounding contours based on image data and teaches:
a non-transitory computer-readable medium containing instructions (Memory 718 and 740 are examples of non-transitory computer-readable media. The memory 718 and 740 can store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems – See at least Col. 26, ln. 56-61) that when executed by the at least one processor causes the at least one processor to: (The processor(s) 716 of the vehicle 702 and the processor(s) 738 of the computing device(s) 736 can be any suitable processor capable of executing instructions to process data and perform operations as described herein – See at least Col. 26, ln. 42-45)
process the sensor data to identify a non-drivable bounding area; (While navigating driving environments, autonomous vehicles may use various sensors to capture sensor data associated with the environment. Sensor data, such as image data, radar data, lidar data, etc., may be associated with and may identify various objects within the environment. The objects encountered within an environment can include dynamic objects that are moving or capable of movement (e.g., vehicles, motorcycles, bicycles, pedestrians, animals, etc.), and/or static objects (e.g., buildings, road surfaces, trees, signs, barriers, parked vehicles, etc.). In some instances, the autonomous vehicles may include components configured to determine information about the objects in the environment, such as components to identify objects and determine bounding boxes, perform object classifications, determine segmentation information, and the like. For example, a segmentation component or operation may identify a portion of sensor data as being attributable to a particular object, and a bounding box component or operation may generate a bounding box associated with the particular object – See at least Col. 2, ln. 16-35)
generate a reverse-direction trajectory from [] at least a portion of the sensor data, the reverse-direction trajectory configured to avoid the non-drivable bounding area; (FIG. 3 shows an example technique 300 in which an autonomous vehicle detects a dynamic object (e.g., another vehicle) and determines a vehicle trajectory based on the bounding contours and the trajectory of the dynamic object. As noted above, an autonomous vehicle 102 may use the bounding contours determined for objects in the environment to control the operation of the autonomous vehicle in various ways – See at least Col. 12, ln. 55-60)
autonomously adjust one or more operating parameters of the [] vehicle to operate the [] vehicle in the reverse direction along the reverse-direction trajectory. (the autonomous vehicle 102 encounters driving scenario 100, it may use the contour generator 108 to determine bounding contours associated with the objects 110-120 in the environment. In this example, a number of two-dimensional, top-down bounding contours are shown in the top-down bounding contours map 124. As this example shows, by generating top-down bounding contours associated with the objects 110-120, the autonomous vehicle 102 may be able to use the bounding contours to predict various object behaviors and trajectories, and for planning a trajectory and route for the autonomous vehicle 102 to traverse the environment – See at least ¶ [Col. 6, ln. 59-68 and Col. 7, ln. 1-2] Examiner notes that the autonomous vehicle may be a level 5 classification. This would allow the vehicle to travel in forward and reverse directions without the input of a driver – See at least Col. 5, ln. 1-7)
In summary, Lacaze discloses identifying spaces that it should not travel and what objects are in the environment. Lacaze does not explicitly teach the use of bounding areas. However, Purdy discloses object bounding contours based on image data and teaches utilizing bounding contours to identify objects and non-drivable areas in the environment.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze to provide for the object bounding contours based on image data, as taught in Purdy, to provide technical advantages that improve vehicle safety and efficiency of vehicle navigation in real-world driving environments. (At Purdy Col. 4 ln. 57-59)
The combination of Lacaze and Purdy does not explicitly teach process the sensor data to identify recorded movement of the refuse vehicle in a forward direction and; responsive to receiving the indication from the user input device to autonomously operate the refuse vehicle in the reverse direction, autonomously adjust one or more operating parameters of the refuse vehicle to operate the refuse vehicle along the reverse-direction trajectory by reversing the recorded movement. However, Carlson discloses a method for reversing a vehicle and teaches:
process the sensor data to identify recorded movement of the [] vehicle (Independent parking controller 156 may “learn” characteristics, such as vehicular drive parameters, associated with traversing a path of travel 130, whereby independent parking controller 156 may reuse the learned characteristics to automatically guide the transit of autonomous vehicle 120 via the same (or substantially the same) path of travel 130 to park at a customized position and orientation Independent parking controller 156 may be configured to detect and store vehicular drive parameters (or values thereof) as autonomous vehicle 120 transits over via a path segment 130. Examples of vehicular drive parameters include parameter data representing steering data (e.g., degree(s) of wheel angle to effect a turn), acceleration data (e.g., an amount of throttle or power to apply to a drive train or the like), deceleration data (e.g., an amount of pressure to apply to brakes to reduce velocity), transmission data (e.g., a state of a transmission subsystem to effect forward motion and reverse motion in one or more states of speed and torque), and the like – See at least ¶ [0022]; Examiner notes that the data comes from sensors – See at least ¶ [0027]) in a forward direction and; (As autonomous vehicle 520 drives along path of travel 530a, under human control, data associated with waypoints 580 may be captured during motion from position 520b (e.g., in forward gear) to position 520b. In some examples, autonomy controller 550 may generate a macro that sequences waypoint data 580 in a reverse sequence. Thus, when executed, autonomous vehicle 520 can traverse in a forward gear via path of travel 532a from position 520a to position 520b. Note that path of travel 532a may be the same as path of travel 530a (but in an opposite direction). Thus, a paths of travel 530a and 532a may be formed, optionally, in single pass, with the same path of travel being used for traversing the same path in both directions as paths of travel 530a and 532a, according at least to some examples – See at least ¶ [0060])
responsive to receiving the indication from the user input device to autonomously operate the [] vehicle in the reverse direction, autonomously adjust one or more operating parameters of the [] vehicle to operate the [] vehicle along the reverse-direction trajectory by reversing the recorded movement. (Additionally, independent parking controller 156 may be configured to form a modified macro application to generate executable instructions to facilitate transit over the path of travel 130 in a reverse manner than was initially captured. For example, if a macro application is based on a preprogrammed path of travel originating at waypoint 180a and terminating at waypoint 180z, then independent parking controller 156 may be configured to generate executable instructions to facilitate driverless transit originating at waypoint 180z and passing through waypoint 180a as autonomous vehicle 120 exits boundary 110. Thus, independent parking controller 156 may apply predetermined vehicular drive parameters in a reverse sequence to enable autonomous vehicle to travel in a reverse direction travel over path of travel 130. Note that the reverse direction of travel may be either in a forward gear or a reverse gear relative to a transmission – See at least ¶ [0039])
In summary, Lacaze discloses generating trajectories for a refuse vehicle based on sensor and environmental data. The combination of Lacaze and Purdy does not explicitly teach process the sensor data to identify recorded movement of the refuse vehicle in a forward direction and; responsive to receiving the indication from the user input device to autonomously operate the refuse vehicle in the reverse direction, autonomously adjust one or more operating parameters of the refuse vehicle to operate the refuse vehicle along the reverse-direction trajectory by reversing the recorded movement. However, Carlson discloses a method for reversing a vehicle and teaches recording a vehicle characteristics and forward movement between way points, under the guide of a human driver, and storing that data for future use. Carlson also teaches that the software can reverse the vehicle characteristics and movement data to achieve a reverse movement between the way points without having to record data in that specific movement direction, i.e., forward and reverse paths of travel being formed in a single pass.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze and Purdy to provide for the method of reversing a vehicle, as taught in Carlson, so paths of travel may be formed in single pass, with the same path of travel being used for traversing the same path in both directions as paths of travel. (At Carlson ¶ [0060])
Regarding claims 2 and 15, Lacaze further teaches:
responsive to receiving a second indication from the user input device, end the autonomous adjustment of the one or more operating parameters. (The humans may also signal the truck by pressing a switch (701) using hand signals, giving voice commands, or some combination. The commands that are given could be for the truck to go or for the truck to wait – See at least ¶ [0059]; Examiner notes that the command to wait would end the movement of the vehicle, i.e., end the autonomous adjustment.)
Regarding claims 3 and 16, the combination of Lacaze and Carlson does not explicitly teach, but Purdy further teaches:
wherein the non-drivable bounding area is associated with an overhead obstacle. (Although the object is a vehicle 306 in this example, in other examples bounding contours may be generated for any type of static object or dynamic object detected within the driving environment 304. For example, static objects may include trees, buildings, signs, traffic signals, and the like – See at least Col. 13, ln. 11-16; Examiner notes that trees are overhead obstacles)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze and Carlson to provide for the object bounding contours based on image data, as taught in Purdy, to provide technical advantages that improve vehicle safety and efficiency of vehicle navigation in real-world driving environments. (At Purdy Col. 4 ln. 57-59)
Regarding claims 5, 11, and 19, Lacaze discloses an autonomous refuse vehicle. The combination of Lacaze and Carlson does not explicitly teach, but Purdy further teaches:
process the sensor data to identify a bounded drivable area and a safety threshold associated with the bounded drivable area; (Distance 322 is a distance between the vehicle and the bounded area. The distance 322 is compared to a safety threshold – See at least Col. 14, ln. 9-22)
receive a second indication from the at least one sensor that the [] vehicle is exceeding the safety threshold; and (For a potential vehicle trajectory 312, the autonomous vehicle 102 may determine, at any point on the trajectory, a distance between the autonomous vehicle 102 and the bounding contour associated with vehicle 306 at that time. For instance, when determining and evaluating the trajectory 312 as a potential trajectory, the autonomous vehicle 102 may calculate the distance 322 at the second time (e.g., Time=T2) between the autonomous vehicle 102 and the bounding contour 320 at their nearest edges. As shown in this example, the nearest edge of the bounding contour at Time=T, may be the driver-side mirror. If the distance 322 meets or exceeds a minimum safe distance threshold, then the autonomous vehicle 102 may determine that the potential trajectory 312 is sufficiently safe at Time=T₂ - See at least Col. 14, ln. 9-22)
responsive to receiving the indication that the [] vehicle is exceeding the safety threshold, execute a correction action. (As discussed herein, the bounding contour 308 may be used to plan routes/trajectories for the autonomous vehicle that may avoid collisions and maintain a safe distance from vehicle 306. – Col. 13, ln. 36-41; Examiner notes that the system maintains a safe distance, i.e., will only execute a trajectory that is within the safety threshold. Therefore, when a trajectory is created that does not stay within the safety threshold, then a new trajectory is created, i.e., a corrective action is executed.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze and Carlson to provide for the object bounding contours based on image data, as taught in Purdy, to provide technical advantages that improve vehicle safety and efficiency of vehicle navigation in real-world driving environments. (At Purdy Col. 4 ln. 57-59)
Regarding claims 6, 12, and 20, Lacaze further teaches:
wherein the correction action includes autonomously adjusting, by the at least one processor, the one or more operating parameters to adjust a position of the refuse vehicle to be within the safety threshold. (If there are vehicles parked by the side of the road, the controller will drive the autonomous waste collections truck with sufficient space for safety and taking under consideration that the human and the bin will need to will need to walk between the tuck and parked car – See at least ¶ [0041])
Claim(s) 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lacaze in view of Purdy and Carlson, as applied to claims 1 and 10, and in further view of Koga et al. (US 2021/0373560 A1, “Koga”).
Regarding claims 4 and 17, the combination of Lacaze, Purdy, and Carlson does not explicitly teach wherein the indication is associated with an instruction to engage a refuse container. However, Koga discloses automated alignment and dumping of refuse cans and teaches:
wherein the indication is associated with an instruction to engage a refuse container. (In some embodiments, when a human being is detected within a danger zone (e.g., within a predefined zone and/or distance of refuse vehicle 10 and/or actuator assembly 436), control module 424 may initiate safety actions. The safety actions may include, for example, preventing refuse vehicle 10 and/or actuator assembly 436 from moving to and/or engaging the refuse can while the human being is detected within the danger zone – See at least ¶ [0062])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze, Purdy, Carlson to provide for the automated alignment and dumping of refuse cans, as taught in Koga, to restrict movement of a refuse vehicle and/or an actuator assembly, such that the vehicle and/or the actuator assembly cannot move to engage a refuse can if a human being is detected within a danger zone. (At Koga ¶ [0070])
Claim(s) 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lacaze in view of Purdy and Carlson, as applied to claims 1 and 10, and in further view of Irrgang et al. (US 2012/0262284 A1, “Irrgang”)
Regarding claims 7 and 13, the combination or Lacaze, Purdy, and Carlson does not explicitly teach wherein the correction action includes autonomously transmitting, by the at least one processor, a notification of the exceeding of the safety threshold. However, Irrgang discloses method and system for warning a driver of a vehicle about potential obstacles behind the vehicle and teaches:
wherein the correction action includes autonomously transmitting, by the at least one processor, a notification of the exceeding of the safety threshold. (For example, this involves determining a probability value that characterizes the probability of a collision or intersection of the potential obstacle 8 with the moving critical zone 2. If this probability value exceeds a predefined threshold, then a warning is triggered and provided to the driver of the subject vehicle 1 – See at least ¶ [0018])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze, Purdy, and Carlson to provide for the method and system for warning a driver of a vehicle about potential obstacles behind the vehicle, as taught in Irrgang, to accurately predict whether a collision or time-critical intersection of the subject vehicle or its warning zone with the trajectory of the potential obstacle vehicle will occur, especially when the obstacle vehicle is approaching at an oblique angle relative to the longitudinal axis of the subject vehicle. (At Irrgang ¶ [0004])
Regarding claims 8 and 14, the combination of Lacaze, Carlson, Irrgang does not explicitly teach, but Purdy further teaches:
wherein the notification is one of a visual notification, haptic notification, or audio notification. (The vehicle 702 can also include one or more emitters 708 for emitting light and/or sound, as described above. The emitters 708 in this example include interior audio and visual emitters to communicate with passengers of the vehicle 702. By way of example and not limitation, interior emitters can include speakers, lights, signs, display screens, touch screens, haptic emitters (e.g., vibration and/or force feedback), mechanical actuators (e.g., seatbelt tensioners, seat positioners, headrest positioners, etc.), and the like. The emitters 708 in this example also include exterior emitters. By way of example and not limitation, the exterior emitters in this example include lights to signal a direction of travel or other indicator of vehicle action (e.g., indicator lights, signs, light arrays, etc.), and one or more audio emitters (e.g., speakers, speaker arrays, horns, etc.) to audibly communicate with pedestrians or other nearby vehicles, one or more of which comprising acoustic beam steering technology – See at least Col. 24, ln. 13-30)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze, Carlson, and Irrgang to provide for the object bounding contours based on image data, as taught in Purdy, to provide technical advantages that improve vehicle safety and efficiency of vehicle navigation in real-world driving environments. (At Purdy Col. 4 ln. 57-59)
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lacaze in view of Purdy and Carlson, as applied to claim 1, and in further view of Koga.
Regarding claim 9, the combination of Lacaze, Purdy, and Carlson does not explicitly teach, but Koga further teaches:
wherein an engagement assembly of the refuse vehicle is exceeding the safety threshold. (In some embodiments, when a human being is detected within a danger zone (e.g., within a predefined zone and/or distance of refuse vehicle 10 and/or actuator assembly 436), control module 424 may initiate safety actions. The safety actions may include, for example, preventing refuse vehicle 10 and/or actuator assembly 436 from moving to and/or engaging the refuse can while the human being is detected within the danger zone – See at least ¶ [0062])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the autonomous waste collection truck of Lacaze, Purdy, and Carlson to provide for the automated alignment and dumping of refuse cans, as taught in Koga, to restrict movement of a refuse vehicle and/or an actuator assembly, such that the vehicle and/or the actuator assembly cannot move to engage a refuse can if a human being is detected within a danger zone. (At Koga ¶ [0070])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kendall (US 5,247,442 A) discloses vehicular steering system for reverse paths and teaches that “a desired backing path could be stored during forward travel. Appropriate software could thus convert the forward path into reverse path information. With such a system, the driver could drive forward through the path that was desired for subsequent backing. Such path information could be permanently stored for frequently needed backing paths, such as one's driveway.” (Col. 13, ln. 12-19)
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