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 .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS’s) submitted on 9/23/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 - 4, 11, 18, & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Kikani (US 2022/0412051 A1).
Regarding Claim 1:
Austen discloses: An autonomous vehicle steering system using a blade tool attachment, comprising: (Austen discloses in at least Paragraphs 0005 & 0030 a earth shaping vehicle operating and navigating autonomously in a dig site [i.e. an autonomous vehicle steering system], the earth shaping vehicle including a bulldozer blade as disclosed in at least Paragraph 0203 [i.e. using a blade tool attachment])
a bulldozer vehicle with a chassis, tracks, a blade tool attachment on a front of the chassis, hydraulic arms between the chassis and the blade tool attachment, one or more first controls for manipulating movement of the tracks, and one or more second controls for manipulating the blade tool attachment via the hydraulic arms; (Austen discloses in at least Paragraphs 0034 & 0203 wherein the earth shaping vehicle may include a bulldozer, the earth shaping vehicle including a chassis, drive system, and earth shaping tool as disclosed in at least Paragraph 0041 [i.e. a bulldozer vehicle with a chassis, tracks (see annotated Figure 2A, below], and a blade tool attachment on a front of the chassis]. At least Paragraphs 0035, 0042, & 0050 of Austen further disclose wherein arms, actuated by hydraulics, may be used to control the position of the tool [i.e. hydraulic arms between the chassis and the blade tool attachment]. At least Paragraphs 0057 & 0059 of Austen discloses wherein the earth shaping vehicle may include manual input devices, such as joysticks, for controlling the drive system and earth shaping tool [i.e. one or more first controls for manipulating movement of the tracks, and one or more second controls for manipulating the blade tool attachment via the hydraulic arms]. Figure 2A of Austen, annotated by the Examiner, is presented below which depicts many of the above features)
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a microcontroller unit on the bulldozer vehicle that is capable of effecting movement of the first and second controls via piston displacement mechanisms; and a control system on the bulldozer vehicle that is configured to be in communication with the microcontroller unit and to perform automated operations including: (Austen discloses in at least Paragraphs 0104, 0128, & 0132 wherein a hydraulic distribution engine [i.e. a microcontroller unit on the bulldozer vehicle] may be provided to monitor and adjust hydraulic pressure provided to actuate the hydraulic tool actuators [i.e. effecting movement of the first and second controls via piston displacement mechanisms]. At least Paragraphs 0031, 0045, & 0123 of Austen further disclose wherein the vehicle may further include a controller to perform analysis of sensor data and issue control instructions [i.e. a control system on the bulldozer vehicle that is configured to be in communication with the microcontroller unit and to perform automated operations])
determining, while the bulldozer vehicle is in motion and is using the blade tool attachment to push ground material in at least one of a pushing mode or a cutting mode or a loading mode, and by a… model using multiple data readings from a plurality of sensors on the bulldozer vehicle, at least one of a predicted degree or a predicted amount of how full the blade tool attachment is with the ground material; (Austen discloses in at least Paragraphs 0105 & 0117 wherein a fill estimate engine is configured to determine an estimate of the tool fill level as the tool is moved over a target path, such as when the tool is actuated below the earth surface and the vehicle is driven to fill the tool with earth as disclosed in at least Paragraph 0083 [i.e. determining, while the bulldozer vehicle is in motion and is using the blade tool attachment to push ground material in at least one of a pushing mode or a loading mode a predicted amount of how full the blade tool attachment is with the ground material]. At least Paragraphs 0118 & 0119 of Austen further disclose wherein the fill estimate engine may estimate the volume of earth in the tool using a previously trained prediction model and measurements from sensors, such as the measured force of the earth acting on the tool beneath the surface, visual/spatial sensors to detect the volume of earth, the distance travelled, and the like [i.e. by a model using multiple data readings from a plurality of sensors on the bulldozer vehicle])
determining, based at least in part on the determined at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material satisfying a defined threshold, to initiate an end to the at least one of the pushing mode or the cutting mode or the loading mode; and (Austen discloses in at least Paragraphs 0120 – 0122 wherein the fill estimate is compared to a threshold volume, which may be defined as the maximum available volume of the tool, or a volume manually set by a human operator [i.e. determining if the predicted amount of how full the blade tool attachment is with the ground material satisfies a defined threshold] to determine if the threshold volume has been met. If the estimated quantity is less than the threshold volume, the digging routine may resume, however if the estimated volume is greater than the threshold volume, the excavation vehicle is configured to raise the tool above the ground surface and perform a dump routine as disclosed in at least Paragraphs 0122 & 0123 of Austen [i.e. based at least in part on the defined threshold being satisfied, initiating an end to the at least one of the pushing mode or the loading mode])
initiating, in response to the determining to initiate the end to the at least one of the pushing mode or the cutting mode or the loading mode, autonomous operations of the bulldozer vehicle to at least one of raise the blade tool attachment to end contact with the ground material by using the second controls to manipulate the hydraulic arms via at least one of the piston displacement mechanisms, or stop the motion of the bulldozer vehicle by using the first controls to stop the movement of the tracks via at least one of the piston displacement mechanisms. (Austen discloses in at least Paragraphs 0122 & 0123 wherein when the measured fill volume exceeds the threshold quantity, the controller of the vehicle is configured to raise the tool above the ground surface and perform a dump routine, in which the vehicle moves the excavated earth to a dump pile location, deposits the earth, and returns to the target excavation location to continue excavating as disclosed in at least Paragraphs 0198 – 0200 of Austen [i.e. initiating, in response to the determining to initiate the end to the at least one of the pushing mode or the loading mode, autonomous operations of the bulldozer vehicle to at least one of raise the blade tool attachment to end contact with the ground material by using the second controls to manipulate the hydraulic arms via at least one of the piston displacement mechanisms])
Austen however appears to be silent regarding:
Wherein the predicted amount of how full the blade tool attachment is determined by a trained machine learning model
However Kikani teaches wherein a machine learning model is utilized to determine the fill level of an excavator bucket or similar tool as the excavator traverses the field.
Wherein the predicted amount of how full the blade tool attachment is determined by a trained machine learning model (However Kikani teaches in at least Paragraph 0081 wherein a fill level of an excavator bucket or other tool may be estimated mathematically using a trained machine learning model, based on the depth of the leading edge of the tool beneath the ground surface, imagery, sensor data, and/or through measured kinetic force on the tool [i.e. a trained machine learning model is used to predict the amount of how full the blade tool attachment is])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the determination of excavator tool fill level based on a trained machine learning model as taught by Kikani.
The motivation to do so is that, as acknowledged by Kikani in at least Paragraph 0081, the fill level of the tool may be better estimated through a plurality of factors, improving the determination of if the tool is filled or not during an earth-manipulation operation.
Regarding Claim 2:
The autonomous vehicle steering system of claim 1 wherein the determining to initiate the end to the at least one of the pushing mode or the cutting mode or the loading mode includes determining to switch to a carrying mode to move the ground material in the blade tool attachment to a different location, and wherein the initiated autonomous operations include using the second controls to raise the blade tool attachment to end contact with the ground material, and using the first controls to cause the movement of the tracks toward the different location.
Austen discloses in at least Paragraphs 0122 & 0123 wherein when the measured fill volume exceeds the threshold quantity, the controller of the vehicle is configured to raise the tool above the ground surface and perform a dump routine [i.e. determining to switch to a carrying mode to move the ground material in the blade tool attachment to a different location], in which the vehicle moves the excavated earth to a dump pile location, deposits the earth, and returns to the target excavation location to continue excavating as disclosed in at least Paragraphs 0198 – 0200 of Austen [i.e. wherein the initiated autonomous operations include using the second controls to raise the blade tool attachment to end contact with the ground material, and using the first controls to cause the movement of the tracks toward the different location].
Regarding Claim 3:
The autonomous vehicle steering system of claim 1 wherein the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material includes generating a binary output of the blade tool attachment being fully loaded and the defined threshold is that generated binary output, or wherein the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material includes generating a degree of loading of the blade tool attachment relative to being fully loaded and the defined threshold is an indicated degree of loading that is exceeded by the generated degree of loading, or wherein the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material includes generating a predicted estimate of the quantity of the ground material loaded in the blade tool attachment and the defined threshold is an indicated quantity that is exceeded by the generated estimate of the quantity.
Austen discloses in at least Paragraphs 0120 – 0122 wherein the fill estimate is compared to a threshold volume, which may be defined as the maximum available volume of the tool, or a volume manually set by a human operator [i.e. wherein the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material includes generating a predicted estimate of the quantity of the ground material loaded in the blade tool attachment and the defined threshold is an indicated quantity that is exceeded by the generated estimate of the quantity] to determine if the threshold volume has been met. If the estimated quantity is greater than the threshold volume, the excavation vehicle is configured to raise the tool above the ground surface and perform a dump routine as disclosed in at least Paragraphs 0122 & 0123 of Austen.
Regarding Claim 4:
The autonomous vehicle steering system of claim 1 wherein the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material is performed repeatedly during the motion of the bulldozer vehicle using the blade tool attachment to push the ground material in the at least one of the pushing mode or the cutting mode or the loading mode, and wherein the automated operations include determining, before the determining to initiate the end to the at least one of the pushing mode or the cutting mode or the loading mode, to continue the at least one of the pushing mode or the cutting mode or the loading mode in response to one or more prior determinations of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material being below the defined threshold.
Austen discloses in at least Paragraphs 0120 – 0122 wherein the fill estimate is compared to a threshold volume, which may be defined as the maximum available volume of the tool, or a volume manually set by a human operator [i.e. determining if the predicted amount of how full the blade tool attachment is with the ground material satisfies a defined threshold] to determine if the threshold volume has been met. If the estimated quantity is less than the threshold volume, the digging routine may resume [i.e. determining to continue the at least one of the pushing mode or the cutting mode or the loading mode in response to one or more prior determinations of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material being below the defined threshold]. Austen further discloses in at least Paragraph 0112 wherein the tool fill level is updated periodically in an automatic manner [i.e. wherein the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material is performed repeatedly during the motion of the bulldozer vehicle using the blade tool attachment to push the ground material].
Regarding Claim 11:
The autonomous vehicle steering system of claim 1 wherein the control system is configured to implement at least some automated operations of an earth-moving vehicle autonomous operations control system by executing software instructions of the earth-moving vehicle autonomous operations control system, and wherein the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material and the determining to initiate the end to the at least one of the pushing mode or the cutting mode or the loading mode and the initiating of the autonomous operations are performed autonomously without receiving human input and without receiving external signals other than GPS signals and real-time kinematic (RTK) correction signals.
Austen discloses in at least Paragraph 0077 wherein the control logic disclosed is implemented via software [i.e. the control system is configured to implement at least some automated operations of an earth-moving vehicle autonomous operations control system by executing software instructions of the earth-moving vehicle autonomous operations control system]. Austen further discloses in at least Paragraph 0112 wherein the tool fill level is updated periodically in an automatic manner [i.e. without receiving human input and without receiving external signals], said tool fill level evaluation being compared to a threshold to trigger ending the pushing mode as set forth above.
Regarding Claim 18:
Austen discloses: A non-transitory computer-readable medium having stored contents that cause one or more hardware processors to perform automated operations including at least: (Austen discloses in at least Paragraphs 0005, 0030, & 0031 a earth shaping vehicle operating and navigating autonomously in a dig site using an on-unit computer, which at least Paragraph 0070 of Austen discloses may include a non-transitory computer-readable storage medium coupled to a processor)
receiving, by the one or more hardware processors, data for a powered earth-moving vehicle with a blade tool attachment, wherein the powered earth-moving vehicle is moving at least one of tracks or wheels and using the blade tool attachment to perform pushing of material in an environment of the powered earth-moving vehicle, (Austen discloses in at least Paragraph 0061 wherein the on-unit computer may receive data from a sensor assembly [i.e. receiving, by the one or more hardware processors, data for a powered earth-moving vehicle with a blade tool attachment]. Austen discloses in at least Paragraphs 0034 & 0203 wherein the earth shaping vehicle may include a bulldozer, the earth shaping vehicle including a chassis, drive system, and earth shaping tool as disclosed in at least Paragraph 0041 [i.e. a powered earth-moving vehicle with a blade tool attachment, wherein the powered earth-moving vehicle is moving at least one of tracks or wheels and using the blade tool attachment]. At least Paragraph 0083 of Austen discloses wherein the tool is actuated below the earth surface and the vehicle is driven to fill the tool with earth [i.e. perform pushing of material in an environment of the powered earth-moving vehicle])
the data indicating at least one of engine revolutions per minute, fuel consumption, engine torque, engine load, speed of the at least one of the tracks or wheels, or a transmission gear ratio; (Austen discloses in at least Paragraph 0208 wherein an engine RPM may be monitored as a vehicle performance metric [i.e. the data indicating at least one of engine revolutions per minute]. At least Paragraph 0050 of Austen further discloses wherein measurement sensors may measure the vehicle speed [i.e. the data indicating at least one of speed of the at least one of the tracks or wheels])
applying, by the one or more hardware processors, a …model to the received data to generate a predicted estimated degree of loading of the blade tool attachment with the material during the pushing of the material; (Austen discloses in at least Paragraphs 0105 & 0117 wherein a fill estimate engine is configured to determine an estimate of the tool fill level as the tool is moved over a target path, such as when the tool is actuated below the earth surface and the vehicle is driven to fill the tool with earth as disclosed in at least Paragraph 0083 [i.e. a predicted estimated degree of loading of the blade tool attachment with the material during the pushing of the material]. At least Paragraphs 0118 & 0119 of Austen further disclose wherein the fill estimate engine may estimate the volume of earth in the tool using a previously trained prediction model and measurements from sensors, such as the measured force of the earth acting on the tool beneath the surface, visual/spatial sensors to detect the volume of earth, the distance travelled, and the like [i.e. applying, by the one or more hardware processors, a …model to the received data])
determining, by the one or more hardware processors and based at least in part on the predicted estimated degree of loading of the blade tool attachment satisfying a defined threshold level, an output indicating at least one of the blade tool attachment being fully loaded or the powered earth-moving vehicle experiencing slippage from the pushing of the material; and (Austen discloses in at least Paragraphs 0120 – 0122 wherein the fill estimate is compared to a threshold volume, which may be defined as the maximum available volume of the tool, or a volume manually set by a human operator [i.e. determining if the predicted amount of how full the blade tool attachment is with the ground material satisfies a defined threshold] to determine if the threshold volume has been met. If the estimated quantity is less than the threshold volume, the digging routine may resume, however if the estimated volume is greater than the threshold volume, the excavation vehicle is configured to raise the tool above the ground surface and perform a dump routine as disclosed in at least Paragraphs 0122 & 0123 of Austen [i.e. an output indicating the blade tool attachment being fully loaded])
initiating, by the one or more hardware processors and in response to the determining of the output, an automated remedial action. (Austen discloses in at least Paragraphs 0122 & 0123 wherein when the measured fill volume exceeds the threshold quantity, the controller of the vehicle is configured to raise the tool above the ground surface and perform a dump routine, in which the vehicle moves the excavated earth to a dump pile location, deposits the earth, and returns to the target excavation location to continue excavating as disclosed in at least Paragraphs 0198 – 0200 of Austen [i.e. initiating, in response to the determining of the output, an automated remedial action])
Austen however appears to be silent regarding:
Wherein the predicted amount of how full the blade tool attachment is determined by a trained machine learning model
However Kikani teaches wherein a machine learning model is utilized to determine the fill level of an excavator bucket or similar tool as the excavator traverses the field.
Wherein the predicted amount of how full the blade tool attachment is determined by a trained machine learning model (However Kikani teaches in at least Paragraph 0081 wherein a fill level of an excavator bucket or other tool may be estimated mathematically using a trained machine learning model, based on the depth of the leading edge of the tool beneath the ground surface, imagery, sensor data, and/or through measured kinetic force on the tool [i.e. a trained machine learning model is used to predict the amount of how full the blade tool attachment is])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the determination of excavator tool fill level based on a trained machine learning model as taught by Kikani.
The motivation to do so is that, as acknowledged by Kikani in at least Paragraph 0081, the fill level of the tool may be better estimated through a plurality of factors, improving the determination of if the tool is filled or not during an earth-manipulation operation.
Regarding Claim 19:
The non-transitory computer-readable medium of claim 18 wherein the determined output includes that the blade tool attachment is fully loaded, and wherein the automated remedial action includes at least one of: switching the powered earth-moving vehicle to a carrying mode that includes carrying the material in the fully-loaded blade tool attachment; or ending the pushing of the material by at least one of raising the blade tool attachment or stopping the moving of the at least one of the tracks or wheels.
Austen discloses in at least Paragraphs 0122 & 0123 wherein when the measured fill volume exceeds the threshold quantity [i.e. wherein the determined output includes that the blade tool attachment is fully loaded], the controller of the vehicle is configured to raise the tool above the ground surface and perform a dump routine [i.e. switching the powered earth-moving vehicle to a carrying mode that includes carrying the material in the fully-loaded blade tool attachment], in which the vehicle moves the excavated earth to a dump pile location, deposits the earth, and returns to the target excavation location to continue excavating as disclosed in at least Paragraphs 0198 – 0200 of Austen.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Kikani (US 2022/0412051 A1) as applied to claim 4 above, and further in view of Maeda (US 2025/0277351 A1).
Regarding Claim 5:
The autonomous vehicle steering system of claim 4 wherein the automated operations further include, as part of one of the one or more prior determinations of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material being below the defined threshold: determining, while the bulldozer vehicle is in motion and is using the blade tool attachment to push the ground material in the at least one of the pushing mode or the cutting mode or the loading mode, and by a trained machine learning model using multiple data readings from the plurality of sensors on the bulldozer vehicle, predicted vehicle slippage of the bulldozer vehicle from a reduction in traction based at least in part on the using of the blade tool attachment; and initiating, in response to the determining of the vehicle slippage, autonomous operations of the bulldozer vehicle to use the second controls to manipulate the hydraulic arms via at least one of the piston displacement mechanisms to raise the blade tool attachment while maintaining contact with the ground material.
Austen does not appear to specifically disclose wherein slippage is predicted by a machine learning model and initiating an adjustment of the blade tool based on such, however discloses in at least Paragraphs 0104, 0128, & 0132 wherein a hydraulic distribution engine [i.e. a microcontroller unit on the bulldozer vehicle] may be provided to monitor and adjust hydraulic pressure provided to actuate the hydraulic tool actuators [i.e. effecting movement of the first and second controls via piston displacement mechanisms].
However Maeda teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein a reinforcement learning based approach [i.e. by a trained machine learning model] may be used to determine if a slippage of the work vehicle is predicted to occur along a movement path based on if the predicted slip of the vehicle exceeds the shoe slip limit of the vehicle [i.e. a predicted reduction in traction of the bulldozer vehicle that is causing vehicle slippage]. At least Paragraphs 0040, 0043, & 0083 of Maeda teach wherein this may be based on vehicle operation parameters, which may be measured from sensors provided in the work vehicle [i.e. using data readings from a plurality of sensors on the bulldozer vehicle]. Maeda further teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein based on slip being determined to occur, another candidate movement path may be assessed [i.e. determining, based at least in part on the determined predicted reduction in traction of the bulldozer vehicle, to initiate a change to the at least one of the pushing mode or the cutting mode or the loading mode] such that the slip of the work vehicle does not occur, which may include the adjustment of excavation depth as taught in at least Paragraph 0046 of Maeda [i.e. raising the blade tool attachment while maintaining contact with the ground material]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the adjustment of travel path, including implement depth, responsive to the prediction of slippage as taught by Maeda.
The motivation to do so is that, as acknowledged by Maeda in at least Paragraph 0077, the path and work operations may be set such that the slip of the work vehicle does not occur and the work equipment path plan and the travel path plan having a high excavation efficiency can be generated, improving the work machine operations.
Claim(s) 6 - 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Kikani (US 2022/0412051 A1) as applied to claim 1 above, and further in view of Milton (US 2021/0312725 A1).
Regarding Claim 6:
The autonomous vehicle steering system of claim 1 wherein the trained machine learning model is an LSTM (long short-term memory) recurrent neural network with a classifier output, and wherein the multiple data readings from the plurality of sensors on the bulldozer vehicle provide vehicle status data that includes multiple data types from a group including RPMs (revolutions per minute) of an engine of the bulldozer vehicle, and fuel consumption of the bulldozer vehicle, and engine torque of the bulldozer vehicle, and engine load of the bulldozer vehicle, and speed of the tracks of the bulldozer vehicle, and speed of the chassis of the bulldozer vehicle, and pitch pressure for the blade tool attachment, and a transmission gear ratio in use by the bulldozer vehicle.
Austen discloses in at least Paragraph 0208 wherein an engine RPM may be monitored as a vehicle performance metric [i.e. wherein the multiple data readings from the plurality of sensors on the bulldozer vehicle provide vehicle status data that includes engine revolutions per minute]. At least Paragraph 0050 of Austen further discloses wherein measurement sensors may measure the vehicle speed [i.e. wherein the multiple data readings from the plurality of sensors on the bulldozer vehicle provide vehicle status data that includes speed of the at least one of the tracks or wheels/chassis of the vehicle]. Austen however does not appear to specifically disclose wherein the trained machine learning model is an LSTM (long short-term memory) recurrent neural network with a classifier output.
While Kikani teaches in at least Paragraph 0054 wherein the machine learning model may be a neural network model, Kikani appears to be silent regarding wherein the trained machine learning model is an LSTM (long short-term memory) recurrent neural network with a classifier output.
However Milton teaches in at least Paragraphs 0093 & 0094 wherein a machine learning model implemented on the vehicle and configured to determine and label vehicle movement patterns may be embodied as a convolutional long-short-term memory (LSTM) neural network configured to classify time series data [i.e. wherein the trained machine learning model is an LSTM (long short-term memory) recurrent neural network with a classifier output].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the use of an LSTM (long short-term memory) recurrent neural network with a classifier output in the vehicle system as taught by Milton.
The motivation to do so is that, as acknowledged by Milton in at least Paragraphs 0093 & 0094, the interpretation of time series data by the learning model may be improved, improving the assessment of vehicle fill degree.
Regarding Claim 7:
The autonomous vehicle steering system of claim 6 wherein the multiple data readings further include at least one of a measure of an amount of the ground material that is spilling over at least one of a side or a top of the blade tool attachment, or a degree of tilt in a pitch of the bulldozer vehicle.
Austen discloses in at least Paragraph 0118 wherein the fill estimate may be based in part on the depth of the leading edge of the tool beneath the ground surface, which may be based in part on the angle of the implement with respect to the ground as disclosed in at least Paragraph 0127 [i.e. the data readings include a measure of a degree of tilt in a pitch of the bulldozer vehicle].
Regarding Claim 8:
The autonomous vehicle steering system of claim 6 wherein the multiple data readings further include one or more data readings from one or more additional sensors external to the bulldozer vehicle, the one or more additional sensors including at least one of an image sensor of a camera or a sensor of a LiDAR component.
Austen discloses in at least Paragraphs 0133 & 0134 wherein the fill estimate engine may acquire information from an imaging sensor, such as a point cloud or direct image, which may be used to determine a fill state of the tool by comparing the image or point cloud to an empty representation of the tool as disclosed in at least Paragraphs 0136 – 0138 of Austen [i.e. wherein the multiple data readings further include one or more data readings from an image sensor or LiDAR component].
Regarding Claim 9:
The autonomous vehicle steering system of claim 6 wherein the automated operations further include training the machine learning model before using the trained machine learning model for the determining of the at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material, including, for each of one or more operational bulldozer vehicles each having a respective blade tool attachment, obtaining multiple camera-based estimates of how full that blade tool attachment is at multiple time points based at least in part on images of that blade tool attachment captured at the multiple time points that are input to a camera-based blade load predictor machine learning model, and supplying the multiple camera-based estimates of how full the blade tool attachment is as training data to the machine learning model along with additional values for the vehicle status data that is captured at the multiple time points for that operational bulldozer vehicle.
Austen discloses in at least Paragraphs 0133 & 0134 wherein the fill estimate engine may acquire information from an imaging sensor, such as a point cloud or direct image, which may be used to determine a fill state of the tool by comparing the image or point cloud to an empty representation of the tool, which may be acquired for multiple height and angle measurements captured [i.e. obtaining multiple camera-based estimates of how full that blade tool attachment is at multiple time points based at least in part on images of that blade tool attachment captured at the multiple time points that are input to a camera-based blade load… model] and stored in a lookup table as disclosed in at least Paragraphs 0136 – 0138 of Austen [i.e. training the… model before using the trained machine learning model by supplying the multiple camera-based estimates of how full the blade tool attachment is as training data to the machine learning model along with additional values for the vehicle status data that is captured at the multiple time points for that operational bulldozer vehicle]. Austen however appears to be silent regarding wherein the model is a machine learning model.
However Kikani teaches in at least Paragraph 0081 wherein a fill level of an excavator bucket or other tool may be estimated mathematically using a trained machine learning model, based on the depth of the leading edge of the tool beneath the ground surface, imagery, sensor data, and/or through measured kinetic force on the tool [i.e. a trained machine learning model is used to predict the amount of how full the blade tool attachment is])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the determination of excavator tool fill level based on a trained machine learning model as taught by Kikani.
The motivation to do so is that, as acknowledged by Kikani in at least Paragraph 0081, the fill level of the tool may be better estimated through a plurality of factors, improving the determination of if the tool is filled or not during an earth-manipulation operation.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Kikani (US 2022/0412051 A1) as applied to claim 1 above, and further in view of Oasa (US 2023/0304255 A1).
Regarding Claim 10:
The autonomous vehicle steering system of claim 1 further comprising: a LiDAR component that is mounted on the bulldozer vehicle and configured to obtain LiDAR data indicating a plurality of three-dimensional (“3D”) points on surfaces of at least some of a job site on which the bulldozer vehicle is located; one or more GPS antennas mounted at one or more positions on the chassis and capable of receiving GPS signals for use in determining GPS coordinates of at least some of the chassis; one or more inertial navigation system units mounted at one or more positions on the chassis and capable of determining a current direction of the bulldozer vehicle; and one or more first position sensors mounted on the hydraulic arms and configured to detect one or more first angles between the chassis and the hydraulic arms, and one or more second position sensors mounted on the blade tool attachment and configured to detect one or more second angles between the blade tool attachment and at least one of the hydraulic arms, and wherein the automated operations include providing information for display about the determined at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material.
Austen discloses in at least Paragraphs 0048 & 0049 wherein the earth-moving vehicle may include spatial and imaging sensors such as LIDAR sensors [i.e. a LiDAR component that is mounted on the bulldozer vehicle and configured to obtain LiDAR data indicating a plurality of three-dimensional (“3D”) points on surfaces of at least some of a job site on which the bulldozer vehicle is located]. At least Paragraph 0045 of Austen further discloses wherein the vehicle may include a GPS system as a position sensor for determining he position of the earth shaping vehicle [i.e. one or more GPS antennas mounted at one or more positions on the chassis and capable of receiving GPS signals for use in determining GPS coordinates of at least some of the chassis]. At least Paragraph 0050 of Austen discloses wherein a plurality of measurement sensors may be disposed on the vehicle, including inertial measurement sensors [i.e. one or more inertial navigation system units mounted at one or more positions on the chassis and capable of determining a current direction of the bulldozer vehicle] and end-effector sensors positioned at each joint of the earth shaping tool, the latter measuring a change in angle of the tool relative to each joint [i.e. one or more first position sensors mounted on the hydraulic arms and configured to detect one or more first angles between the chassis and the hydraulic arms, and one or more second position sensors mounted on the blade tool attachment and configured to detect one or more second angles between the blade tool attachment and at least one of the hydraulic arms]. Austen however appears to be silent regarding wherein the automated operations include providing information for display about the determined at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material.
However Oasa teaches in at least Paragraphs 0213 & 0214 wherein a vehicle, such as a loading machine with associated bucket as taught in at least Paragraph 0006, may be provided with an output device, such as an LCD screen, through which the estimated excavated weight is output [i.e. providing information for display about the determined at least one of the predicted degree or the predicted amount of how full the blade tool attachment is with the ground material].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the output of estimated excavated weight through a display as taught by Oasa.
The motivation to do so is that, as acknowledged by Oasa in at least Paragraphs 0213 & 0214, the operator may be made aware of the predicted weight excavated, such that control to excavate a target weight may take place, improving the operator awareness of the excavation process.
Claim(s) 12 & 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Maeda (US 2025/0277351 A1).
Regarding Claim 12:
Austen discloses: An autonomous vehicle steering system using a blade tool attachment, comprising: (Austen discloses in at least Paragraphs 0005 & 0030 a earth shaping vehicle operating and navigating autonomously in a dig site [i.e. an autonomous vehicle steering system], the earth shaping vehicle including a bulldozer blade as disclosed in at least Paragraph 0203 [i.e. using a blade tool attachment])
a bulldozer vehicle with a chassis, tracks, a blade tool attachment on a front of the chassis, hydraulic arms between the chassis and the blade tool attachment, one or more first controls for manipulating movement of the tracks, and one or more second controls for manipulating the blade tool attachment via the hydraulic arms; (Austen discloses in at least Paragraphs 0034 & 0203 wherein the earth shaping vehicle may include a bulldozer, the earth shaping vehicle including a chassis, drive system, and earth shaping tool as disclosed in at least Paragraph 0041 [i.e. a bulldozer vehicle with a chassis, tracks (see annotated Figure 2A, above], and a blade tool attachment on a front of the chassis]. At least Paragraphs 0035, 0042, & 0050 of Austen further discloses wherein arms, actuated by hydraulics, may be used to control the position of the tool [i.e. hydraulic arms between the chassis and the blade tool attachment]. At least Paragraphs 0057 & 0059 of Austen discloses wherein the earth shaping vehicle may include manual input devices, such as joysticks, for controlling the drive system and earth shaping tool [i.e. one or more first controls for manipulating movement of the tracks, and one or more second controls for manipulating the blade tool attachment via the hydraulic arms]. Figure 2A of Austen, annotated by the Examiner, is presented above which depicts many of the above features)
a microcontroller unit on the bulldozer vehicle that is capable of effecting movement of the first and second controls via piston displacement mechanisms; and a control system on the bulldozer vehicle that is configured to be in communication with the microcontroller unit and to perform automated operations including: (Austen discloses in at least Paragraphs 0104, 0128, & 0132 wherein a hydraulic distribution engine [i.e. a microcontroller unit on the bulldozer vehicle] may be provided to monitor and adjust hydraulic pressure provided to actuate the hydraulic tool actuators [i.e. effecting movement of the first and second controls via piston displacement mechanisms]. At least Paragraphs 0031, 0045, & 0123 of Austen further disclose wherein the vehicle may further include a controller to perform analysis of sensor data and issue control instructions [i.e. a control system on the bulldozer vehicle that is configured to be in communication with the microcontroller unit and to perform automated operations])
raising the blade tool attachment while maintaining contact with the ground material by using the second controls to manipulate the hydraulic arms via at least one of the piston displacement mechanisms. (Austen discloses in at least Paragraphs 0104, 0128, & 0132 wherein a hydraulic distribution engine [i.e. a microcontroller unit on the bulldozer vehicle] may be provided to monitor and adjust hydraulic pressure provided to actuate the hydraulic tool actuators [i.e. raising the blade tool attachment while maintaining contact with the ground material by using the second controls to manipulate the hydraulic arms via at least one of the piston displacement mechanisms])
Austen however appears to be silent regarding:
determining, while the bulldozer vehicle is in motion and is using the blade tool attachment to push ground material in at least one of a pushing mode or a cutting mode or a loading mode, and by a trained machine learning model using data readings from a plurality of sensors on the bulldozer vehicle, a predicted reduction in traction of the bulldozer vehicle that is causing vehicle slippage;
determining, based at least in part on the determined predicted reduction in traction of the bulldozer vehicle, to initiate a change to the at least one of the pushing mode or the cutting mode or the loading mode; and
initiating, in response to the determining to initiate the change to the at least one of the pushing mode or the cutting mode or the loading mode, autonomous operations of the bulldozer vehicle that include raising the blade tool attachment while maintaining contact with the ground material
However Maeda teaches wherein a reinforcement learning model may be utilized to predict work vehicle slippage along a route, and the route parameters, such as excavation depth, may be modified in order to mitigate the predicted slippage.
determining, while the bulldozer vehicle is in motion and is using the blade tool attachment to push ground material in at least one of a pushing mode or a cutting mode or a loading mode, and by a trained machine learning model using data readings from a plurality of sensors on the bulldozer vehicle, a predicted reduction in traction of the bulldozer vehicle that is causing vehicle slippage; (However Maeda teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein a reinforcement learning based approach [i.e. by a trained machine learning model] may be used to determine if a slippage of the work vehicle is predicted to occur along a movement path based on if the predicted slip of the vehicle exceeds the shoe slip limit of the vehicle [i.e. a predicted reduction in traction of the bulldozer vehicle that is causing vehicle slippage]. At least Paragraphs 0040, 0043, & 0083 of Maeda teach wherein this may be based on vehicle operation parameters, which may be measured from sensors provided in the work vehicle [i.e. using data readings from a plurality of sensors on the bulldozer vehicle])
determining, based at least in part on the determined predicted reduction in traction of the bulldozer vehicle, to initiate a change to the at least one of the pushing mode or the cutting mode or the loading mode; and initiating, in response to the determining to initiate the change to the at least one of the pushing mode or the cutting mode or the loading mode, autonomous operations of the bulldozer vehicle that include raising the blade tool attachment while maintaining contact with the ground material (However Maeda teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein based on slip being determined to occur, another candidate movement path may be assessed [i.e. determining, based at least in part on the determined predicted reduction in traction of the bulldozer vehicle, to initiate a change to the at least one of the pushing mode or the cutting mode or the loading mode] such that the slip of the work vehicle does not occur, which may include the adjustment of excavation depth as taught in at least Paragraph 0046 of Maeda [i.e. raising the blade tool attachment while maintaining contact with the ground material])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the determination and response to predicted slippage in the work vehicle as taught by Maeda.
The motivation to do so is that, as acknowledged by Maeda in at least Paragraph 0077, the path and work operations may be set such that the slip of the work vehicle does not occur and the work equipment path plan and the travel path plan having a high excavation efficiency can be generated, improving the work machine operations.
Regarding Claim 13:
The autonomous vehicle steering system of claim 12 wherein determining of whether the predicted reduction in traction of the bulldozer vehicle is occurring is performed repeatedly during the motion of the bulldozer vehicle using the blade tool attachment to push the ground material in the at least one of the pushing mode or the cutting mode or the loading mode, and wherein the automated operations include, in response to each of multiple determinations of the predicted reduction in traction of the bulldozer vehicle that is causing vehicle slippage, performing the raising of the blade tool attachment relative to a prior raising of the blade tool attachment and while maintaining the contact with the ground material.
Austen discloses in at least Paragraphs 0104, 0128, & 0132 wherein a hydraulic distribution engine may be provided to monitor and adjust hydraulic pressure provided to actuate the hydraulic tool actuators. Austen however appears to be silent regarding wherein the work vehicle responds in such a manner to predicted slippage.
However Maeda teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein a reinforcement learning based approach may be used in a repeated manner to determine if a slippage of the work vehicle is predicted to occur along a movement path based on if the predicted slip of the vehicle exceeds the shoe slip limit of the vehicle [i.e. determining of whether the predicted reduction in traction of the bulldozer vehicle is occurring is performed repeatedly during the motion of the bulldozer vehicle using the blade tool attachment to push the ground material in the at least one of the pushing mode or the cutting mode or the loading mode]. Based on slip being determined to occur, another candidate movement path may be assessed such that the slip of the work vehicle does not occur, which may include the adjustment of excavation depth as taught in at least Paragraph 0046 of Maeda [i.e. performing the raising of the blade tool attachment relative to a prior raising of the blade tool attachment and while maintaining the contact with the ground material].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the adjustment of travel path, including implement depth, responsive to the prediction of slippage as taught by Maeda.
The motivation to do so is that, as acknowledged by Maeda in at least Paragraph 0077, the path and work operations may be set such that the slip of the work vehicle does not occur and the work equipment path plan and the travel path plan having a high excavation efficiency can be generated, improving the work machine operations.
Claim(s) 14 - 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Kikani (US 2022/0412051 A1) and Maeda (US 2025/0277351 A1).
Regarding Claim 14:
Austen discloses: A computer-implemented method, comprising: (Austen discloses in at least Paragraphs 0002, 0005, 0006, & 0030 a method implemented using an earth shaping vehicle operating and navigating autonomously in a dig site with an associated computer [i.e. a computer-implemented method], the earth shaping vehicle including a bulldozer blade as disclosed in at least Paragraph 0203)
determining, by one or more configured hardware processors on a powered earth-moving vehicle while the powered earth-moving vehicle is in motion and is performing autonomous operations that include positioning at least some of a blade tool attachment of the powered earth-moving vehicle below a ground surface to move ground material in a cutting mode, and using a …model and data readings from a plurality of sensors on the powered earth-moving vehicle, at least one of that the powered earth-moving vehicle is predicted to be experiencing vehicle slippage from the vehicle operations, or that a predicted degree of how full the blade tool attachment is with the ground material is above a defined threshold, (Austen discloses in at least Paragraphs 0105 & 0117 wherein a fill estimate engine, implemented using hardware processors as disclosed in at least Paragraphs 0069 & 0070, is configured to determine an estimate of the tool fill level as the tool is moved over a target path, such as when the tool is actuated below the earth surface and the vehicle is driven to fill the tool with earth as disclosed in at least Paragraph 0083 [i.e. determining, while the powered earth-moving vehicle is in motion and is performing autonomous operations that include positioning at least some of a blade tool attachment of the powered earth-moving vehicle below a ground surface to move ground material in a cutting mode, and using a trained machine learning model and data readings from a plurality of sensors on the powered earth-moving vehicle a predicted degree of how full the blade tool attachment is with the ground material]. At least Paragraphs 0118 & 0119 of Austen further disclose wherein the fill estimate engine may estimate the volume of earth in the tool using a previously trained prediction model and measurements from sensors, such as the measured force of the earth acting on the tool beneath the surface, visual/spatial sensors to detect the volume of earth, the distance travelled, and the like [i.e. by a model using multiple data readings from a plurality of sensors on the bulldozer vehicle]. Austen further discloses in at least Paragraphs 0120 – 0122 wherein the fill estimate is compared to a threshold volume, which may be defined as the maximum available volume of the tool, or a volume manually set by a human operator [i.e. determining if the predicted amount of how full the blade tool attachment is with the ground material satisfies a defined threshold] to determine if the threshold volume has been met [i.e. determining that a predicted degree of how full the blade tool attachment is with the ground material is above a defined threshold])
wherein the powered earth-moving vehicle has a chassis and has at least one of tracks or wheels and has one or more first controls for manipulating movement of the at least one of the tracks or wheels and has one or more second controls for manipulating the blade tool attachment via one or more intervening hydraulic arms; and (Austen discloses in at least Paragraphs 0034 & 0203 wherein an earth shaping vehicle includes a chassis, drive system, and earth shaping tool as disclosed in at least Paragraph 0041 [i.e. a bulldozer vehicle with a chassis, tracks (see annotated Figure 2A, above], and a blade tool attachment on a front of the chassis]. At least Paragraphs 0035, 0042, & 0050 of Austen further disclose wherein arms, actuated by hydraulics, may be used to control the position of the tool [i.e. hydraulic arms between the chassis and the blade tool attachment]. At least Paragraphs 0057 & 0059 of Austen discloses wherein the earth shaping vehicle may include manual input devices, such as joysticks, for controlling the drive system and earth shaping tool [i.e. one or more first controls for manipulating movement of the tracks, and one or more second controls for manipulating the blade tool attachment via the hydraulic arms]. Figure 2A of Austen, annotated by the Examiner, is presented above which depicts man of the above features)
wherein the initiated change is to initiate an end to the cutting mode if it is determined that the predicted degree of how full the blade tool attachment is with the ground material is above the defined threshold, and (Austen discloses in at least Paragraphs 0120 – 0122 wherein the fill estimate is compared to a threshold volume, which may be defined as the maximum available volume of the tool, or a volume manually set by a human operator [i.e. determining if the predicted amount of how full the blade tool attachment is with the ground material satisfies a defined threshold] to determine if the threshold volume has been met. If the estimated quantity is less than the threshold volume, the digging routine may resume, however if the estimated volume is greater than the threshold volume, the excavation vehicle is configured to raise the tool above the ground surface and perform a dump routine as disclosed in at least Paragraphs 0122 & 0123 of Austen [i.e. based at least in part on the defined threshold being satisfied, initiating an end to the at least one of the pushing mode or the loading mode])
wherein the further autonomous operations include at least one of using at least one of the second controls to manipulate the blade tool attachment via the one or more intervening hydraulic arms, or using at least one of the first controls to manipulate the at least one of the tracks or wheels. (Austen discloses in at least Paragraphs 0122 & 0123 wherein when the measured fill volume exceeds the threshold quantity, the controller of the vehicle is configured to raise the tool above the ground surface and perform a dump routine, in which the vehicle moves the excavated earth to a dump pile location, deposits the earth, and returns to the target excavation location to continue excavating as disclosed in at least Paragraphs 0198 – 0200 of Austen [i.e. initiating, in response to the determining to initiate the end to the at least one of the pushing mode or the loading mode, autonomous operations of the bulldozer vehicle to at least one of raise the blade tool attachment to end contact with the ground material by using the second controls to manipulate the hydraulic arms via at least one of the piston displacement mechanisms])
Austen however appears to be silent regarding:
Wherein the predicted amount of how full the blade tool attachment is determined by a trained machine learning model
initiating, by the one or more configured hardware processors and based at least in part on the determining, further autonomous operations of the powered earth-moving vehicle to implement a change to the cutting mode, wherein the initiated change includes raising the blade tool attachment while maintaining contact with the ground surface and continuing the cutting mode if it is determined that the powered earth-moving vehicle is predicted to be experiencing slipping, and
However Kikani teaches wherein a machine learning model is utilized to determine the fill level of an excavator bucket or similar tool as the excavator traverses the field.
Wherein the predicted amount of how full the blade tool attachment is determined by a trained machine learning model (However Kikani teaches in at least Paragraph 0081 wherein a fill level of an excavator bucket or other tool may be estimated mathematically using a trained machine learning model, based on the depth of the leading edge of the tool beneath the ground surface, imagery, sensor data, and/or through measured kinetic force on the tool [i.e. a trained machine learning model is used to predict the amount of how full the blade tool attachment is])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the determination of excavator tool fill level based on a trained machine learning model as taught by Kikani.
The motivation to do so is that, as acknowledged by Kikani in at least Paragraph 0081, the fill level of the tool may be better estimated through a plurality of factors, improving the determination of if the tool is filled or not during an earth-manipulation operation.
However Maeda teaches wherein a work machine may predict vehicle slip along a movement path, and adjust the vehicle path, including the depth of excavation, to avoid said slip.
initiating, by the one or more configured hardware processors and based at least in part on the determining, further autonomous operations of the powered earth-moving vehicle to implement a change to the cutting mode, wherein the initiated change includes raising the blade tool attachment while maintaining contact with the ground surface and continuing the cutting mode if it is determined that the powered earth-moving vehicle is predicted to be experiencing slipping, and (However Maeda teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein a reinforcement learning based approach may be used to determine if a slippage of the work vehicle is predicted to occur along a movement path based on if the predicted slip of the vehicle exceeds the shoe slip limit of the vehicle [i.e. if it is determined that the powered earth-moving vehicle is predicted to be experiencing slipping]. Based on slip being determined to occur, another candidate movement path may be assessed such that the slip of the work vehicle does not occur, which may include the adjustment of excavation depth as taught in at least Paragraph 0046 of Maeda [i.e. wherein the initiated change includes raising the blade tool attachment while maintaining contact with the ground surface and continuing the cutting mode])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the adjustment of travel path, including implement depth, responsive to the prediction of slippage as taught by Maeda.
The motivation to do so is that, as acknowledged by Maeda in at least Paragraph 0077, the path and work operations may be set such that the slip of the work vehicle does not occur and the work equipment path plan and the travel path plan having a high excavation efficiency can be generated, improving the work machine operations.
Regarding Claim 15:
The computer-implemented method of claim 14 wherein the powered earth-moving vehicle is a bulldozer vehicle, wherein the determining includes predicting the degree of how full the blade tool attachment is with the ground material and determining that the predicted degree exceeds the defined threshold, and wherein the initiating of the further autonomous operations includes at least one of raising the blade tool attachment to end contact with the ground material by using the second controls to manipulate the hydraulic arms via at least one piston displacement mechanism, or stopping the motion of the bulldozer vehicle by using the first controls to stop the movement of the at least one of the tracks or the wheels via at least one piston displacement mechanism.
Austen discloses in at least Paragraphs 0122 & 0123 wherein when the measured fill volume exceeds the threshold quantity, the controller of the vehicle is configured to raise the tool above the ground surface and perform a dump routine [i.e. determining to switch to a carrying mode to move the ground material in the blade tool attachment to a different location], in which the vehicle moves the excavated earth to a dump pile location, deposits the earth, and returns to the target excavation location to continue excavating as disclosed in at least Paragraphs 0198 – 0200 of Austen [i.e. wherein the initiated autonomous operations include using the second controls to raise the blade tool attachment to end contact with the ground material, and using the first controls to cause the movement of the tracks toward the different location].
Regarding Claim 16:
The computer-implemented method of claim 14 wherein the powered earth-moving vehicle is a bulldozer vehicle, wherein the determining includes predicting that the powered earth-moving vehicle is experiencing vehicle slippage from the vehicle operations, and wherein the initiating of the autonomous operations includes raising the blade tool attachment while maintaining contact with the ground material and continuing cutting mode by using the second controls to manipulate the hydraulic arms via at least one piston displacement mechanism.
Austen discloses in at least Paragraphs 0104, 0128, & 0132 wherein a hydraulic distribution engine may be provided to monitor and adjust hydraulic pressure provided to actuate the hydraulic tool actuators [i.e. raising the blade tool attachment while maintaining contact with the ground material by using the second controls to manipulate the hydraulic arms via at least one of the piston displacement mechanisms]. Austen however appears to be silent regarding wherein the work vehicle responds in such a manner to predicted slippage.
However Maeda teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein a reinforcement learning based approach may be used to determine if a slippage of the work vehicle is predicted to occur along a movement path based on if the predicted slip of the vehicle exceeds the shoe slip limit of the vehicle [i.e. wherein the determining includes predicting that the powered earth-moving vehicle is experiencing vehicle slippage from the vehicle operations]. Based on slip being determined to occur, another candidate movement path may be assessed such that the slip of the work vehicle does not occur, which may include the adjustment of excavation depth as taught in at least Paragraph 0046 of Maeda [i.e. wherein the initiating of the autonomous operations includes raising the blade tool attachment while maintaining contact with the ground material].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the adjustment of travel path, including implement depth, responsive to the prediction of slippage as taught by Maeda.
The motivation to do so is that, as acknowledged by Maeda in at least Paragraph 0077, the path and work operations may be set such that the slip of the work vehicle does not occur and the work equipment path plan and the travel path plan having a high excavation efficiency can be generated, improving the work machine operations.
Regarding Claim 17:
The computer-implemented method of claim 14 wherein the powered earth-moving vehicle is one of a bulldozer vehicle or a motorized grader vehicle or a plowing vehicle, wherein at least one of the one or more hardware processors is a low-voltage microcontroller that is located on the powered earth-moving vehicle and is configured to implement at least some automated operations of an earth-moving vehicle autonomous operations control system by executing software instructions of the earth-moving vehicle autonomous operations control system, and wherein the determining and the initiating of the further autonomous operations are performed autonomously without receiving human input and without receiving external signals other than GPS signals and real-time kinematic (RTK) correction signals.
Austen discloses in at least Paragraph 0077 wherein the control logic disclosed is implemented via software [i.e. the control system is configured to implement at least some automated operations of an earth-moving vehicle autonomous operations control system by executing software instructions of the earth-moving vehicle autonomous operations control system]. Austen further discloses in at least Paragraph 0112 wherein the tool fill level is updated periodically in an automatic manner [i.e. without receiving human input and without receiving external signals], said tool fill level evaluation being compared to a threshold to trigger ending the pushing mode as set forth above.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Kikani (US 2022/0412051 A1) as applied to claim 18 above, and further in view of Maeda (US 2025/0277351 A1).
Regarding Claim 20:
The non-transitory computer-readable medium of claim 18 wherein the determined output includes that the powered earth-moving vehicle is experiencing slippage from the pushing of the material, and wherein the automated remedial action includes raising the blade tool attachment to reduce an amount of an amount of resistance to the powered earth-moving vehicle from the pushing of the material.
Austen discloses in at least Paragraphs 0104, 0128, & 0132 wherein a hydraulic distribution engine may be provided to monitor and adjust hydraulic pressure provided to actuate the hydraulic tool actuators, however appears to be silent regarding wherein the work vehicle responds in such a manner to predicted slippage.
However Maeda teaches in at least Paragraphs 0060 – 0061 & 0075 – 0077 wherein a reinforcement learning based approach may be used to determine if a slippage of the work vehicle is predicted to occur along a movement path based on if the predicted slip of the vehicle exceeds the shoe slip limit of the vehicle [i.e. wherein the determined output includes that the powered earth-moving vehicle is experiencing slippage from the pushing of the material]. Based on slip being determined to occur, another candidate movement path may be assessed such that the slip of the work vehicle does not occur, which may include the adjustment of excavation depth as taught in at least Paragraph 0046 of Maeda [i.e. wherein the automated remedial action includes raising the blade tool attachment to reduce an amount of an amount of resistance to the powered earth-moving vehicle from the pushing of the material].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the adjustment of travel path, including implement depth, responsive to the prediction of slippage as taught by Maeda.
The motivation to do so is that, as acknowledged by Maeda in at least Paragraph 0077, the path and work operations may be set such that the slip of the work vehicle does not occur and the work equipment path plan and the travel path plan having a high excavation efficiency can be generated, improving the work machine operations.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Austen (US 2022/0120060 A1) in view of Kikani (US 2022/0412051 A1) as applied to claim 18 above, and further in view of Oasa (US 2023/0304255 A1).
Regarding Claim 21:
The non-transitory computer-readable medium of claim 18 wherein the determined output includes that the blade tool attachment is fully loaded and that the powered earth-moving vehicle is experiencing slippage from the pushing of the material, and wherein the automated operations include informing an operator of the powered earth-moving vehicle of at least one of the blade tool attachment being fully loaded, or the powered earth-moving vehicle experiencing slippage from the pushing of the material.
However Oasa teaches in at least Paragraphs 0213 & 0214 wherein a vehicle, such as a loading machine with associated bucket as taught in at least Paragraph 0006, may be provided with an output device, such as an LCD screen, through which the estimated excavated weight is output. At least Paragraphs 0223 & 0224 of Oasa further teach wherein upon the difference between the estimated and target weight being below a specified amount [i.e. when the attachment is fully loaded] the output device is controlled to output notification data indicating such to the operator [i.e. the automated operations include informing an operator of the powered earth-moving vehicle of at least one of the blade tool attachment being fully loaded].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Austen by incorporating the output of information indicating that the estimated weight has reached a specified level through a display as taught by Oasa.
The motivation to do so is that, as acknowledged by Oasa in at least Paragraphs 0223 & 0224, the operator may be made aware that the target weight has been reached, enabling the operator to control the blade accordingly, improving the operator awareness and control of the excavation process.
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Wei (US 9,487,929 B2): Wei recites a method for controlling an earthmoving machine, including the determination of the profile of a work surface, such as bumps or raised portions of a field being worked, and determining a depth adjustment of the work implement based on such.
Verho (US 2023/0175232 A1): Verho recites a method of controlling a work machine, specifically including the boom of said work machine, on the basis of received driveline parameters. The vehicle may include load sensing and traction control systems, in order to determine if the boom/bucket should be actuated or not.
Shinkai (US 2019/0104675 A1): Shinkai recites a work vehicle system for working the ground upon which the vehicle passes, including the determination of slippage of the work vehicle relative to the ground surface. The vehicle state is then changed based on the engine load and slip determined.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER RYAN CARDIMINO whose telephone number is (571)272-2759. The examiner can normally be reached M-Th 8:30-5:00.
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/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661