DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The 03/12/2026 Amendments to claims are entered. Claims 1, 8, 11, and 18 are amended. No claims are canceled. No claims are newly added. Claims 1-20 remain pending.
The Claim Objections
The objections to claims 8 and 18 over informalities are withdrawn in light of the amendments made. The Examiner thanks Applicant for their cooperation in correcting these informalities.
Response to Arguments
The 03/12/2026 Remarks (“the Remarks”) have been fully considered by the Examiner but are found unconvincing for the reasons below.
The § 103 Rejections
Applicant argues on p. 7 of the Remarks that Fu is distinct from amended claims 1 and 11 because that reference teaches a system that outputs track point heights that represent the surface upon which a vehicle travels; applicant contends that these track point heights are not “a predicted distance of vertical displacement of the wheel,” nor could a predicted vertical displacement of the wheel be readily and apparently derived by the track point heights of Fu.
This argument is unconvincing. One of ordinary skill in the art would have understood that the track point heights upon which the vehicle drives would have affected the vertical displacement of the wheel relative to whatever baseline height the track point heights refer to because the vehicle drives along the surface with the wheels contacting the surface. In other words, the claim is completely silent as to what baseline is being used to measure the vertical displacement distance of the wheel. Furthermore, the claim is silent as to what point of the wheel is being used to measure the displacement.
In the interest of compact prosecution, the Examiner agrees that there are some scenarios where wheel vertical displacement may not be easily derivable from road height. For example, in FIG. 3 of Applicant’s disclosure, the depicted wheel is not shown resting on the road surface as it typically would on a smooth road. Such a case would require some calculation to convert the width and depth of the anomaly to wheel vertical displacement using the wheel’s diameter, the vehicle’s speed, and other relevant factors. However, the claims do not require such a scenario to exist, nor do they require any calculation like that to exist either- they merely require some situation where the wheel vertical displacement is able to be predicted. In the case of a smooth road and without further narrowing language, road height is enough to predict a broadly-recited distance of wheel vertical displacement.
The prior art rejections of claims 1-20 stand.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 10486485 B1 to Levinson, Jesse et al. (“Levinson”), in view of US 20230311582 A1 to Haronian, Dan (“Haronian”), further in view of US 20250308052 A1 to Fu, Tao et al. (“Fu”).
Regarding claim 1, Levinson teaches a system, comprising a computer including a processor and a memory, the memory storing instructions executable by the processor such that the processor is programmed to:
Levinson does not appear to expressly teach based on inputting collected data of a host vehicle to a machine learning program, determine a predicted load on a wheel of the host vehicle.
However, Haronian teaches based on inputting collected data of a host vehicle to a machine learning program, determine a predicted load on a wheel of the host vehicle (Haronian [0047]: “The load on the tire depends on the patch length, on the tire pressure and to some extent on the tire temperature, age, usage time and manufacturer. The load may be calculated either using for example an empirical equation, a lookup table or a machine learning software that considers different properties of the tire and of the environment.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that uses machine learning to predict force exerted on a tire by the roadway based on data collected by a vehicle taught by Haronian with the system that adjusts suspension settings based on anticipated forces on the suspension taught by Levinson. Doing so would have given the system the ability to accurately estimate tire wear, improving the safety of the vehicle ADAS by allowing it to adjust parameters based on the wear or alert the driver of the tire state as taught in [0066]. The ability to accurately estimate tire wear would have also improved range estimation by allowing the system to factor in weight measurements as taught in [0005].
The above combination of Levinson and Haronian does not appear to expressly teach based on inputting collected data of a host vehicle to a machine learning program, determine a predicted distance of vertical displacement of the wheel via output from the machine learning program.
However, Fu teaches based on inputting collected data of a host vehicle to a machine learning program, determine a predicted distance of vertical displacement of the wheel via output from the machine learning program (Fu [0090]: “The application system 1250 can operate or deploy a model 1260 to generate the output response 1270 to input data 1252.”; Fu [0091]: “The model 1260 can be or be received as the neural network for implementing disparity estimation 110, a portion thereof, or a representation thereof.”; Fu [0094]: “The model 1260 can generate an output response 1270 responsive to receiving the model-compliant input from the dataset generator 1256. The output response 1270 can include values corresponding to the disparity image 115, which can be provided to height estimation 130 to generate the track point heights 135 in the manner described.”; Fu [0036]: “This allows the road profile generated using the track point heights 135 to be consistent . . . ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that estimates a height map of the road using sensor data taught by the above combination of Levinson and Haronian with the system that estimates heights of various points on the road using sensor data and a neural network taught by Fu. Doing so would have “improved accuracy and robustness over conventional systems” of estimating road height profiles as taught by Fu at [0025].
One of ordinary skill in the art would have recognized that the above combination of Levinson, Haronian, and Fu further teaches the system configured to identify a road disturbance traversed by the host vehicle (Levinson [12:47-49]: “In various examples, the computing device and/or the global map server may use machine learning techniques to identify the deformation(s) 202.”) based on at least one of the predicted load and the predicted vertical displacement (Levinson [4:33-41]: “. . . the local height map data may also include a map constructed from the raw sensor data from multiple sensors on the vehicle and fused together into a local height map. . . . the computing device may process the raw sensor data into a 2.5D or 3D local height map, which may include location data, a timestamp, and deformations on a road surface proximate to the vehicle.” Height map taken as predicted vertical displacement of the wheel.); and
update map data to include the road disturbance (Levinson [19:49]-[20:2]: “After an initial build of the global height map, the mapping module 424 may be configured to update based on real-time sensor data (e.g., data recently captured by perception and/or location sensors). . .. The mapping module 424 may compare deformations in the local height map to those on the global height map in the same locations, and may determine if differences exist between the two height maps. The differences may include a new deformation (e.g., new pothole), a removed deformation (e.g., filled in pothole), changes to the shape and/or size of deformations (e.g., bigger pothole), and the like.” See also Claim 13 of Levinson. The disclosed system compares local and global height maps, then, based on the comparison, updates the global height map (take as updating map data).).
Regarding claim 3, the above combination of Levinson, Haronian, and Fu teaches the system of claim 1, wherein the processor is further programmed to provide the updated map data to a remote computer, the computer being included in the host vehicle and the remote computer being a server (Levinson FIG. 3: Global height map 316 depicted as stored in the global map server. Local height map 304 depicted as stored in the vehicle 302.; Levinson [11:16-24]: “In such examples, the local height map may be compared to the global height map to determine differences in deformation shape and/or road size. In some examples, the computing device can determine a change to the size and/or shape of the deformation based on feedback input from one or more of the feedback sensors. In such examples, the computing device may determine that the unanticipated movement corresponds to a change in size and/or shape of the deformation. The computing device may send the data (e.g. update data) associated with the deformation differences to the global map server.”).
Regarding claim 4, the above combination of Levinson, Haronian, and Fu teaches the system of claim 3, further comprising the remote computer, including a second processor and a second memory storing instructions executable by the second processor such that the remote computer is programmed to:
update a map based on aggregated data including updated map data from a plurality of vehicles (Levinson [13:10-17]: “FIG. 3 is a schematic diagram of a system 300 to generate a global height map. For the purpose of illustration, the vehicles 302, such as vehicle 102, may comprise a fleet (e.g., a platoon) of multiple vehicles 302, each of which being configured to communicate the local height map data 304 to the global map server 306. The vehicles 302 may thus provide crowd sourced data to the global map server 306 for building and/or updating a global height map.”); and
provide the updated map to the computer and to a second computer (Levinson [14:41-43]: “The global map server 306 may provide the global height map 316 to the vehicles 302 for use by the suspension control systems.” Understood that all involved vehicles send local height map data to the server and receive global height map data from the server using identical computers 308 as depicted by FIG. 3. Thus, any operation performable by one vehicle appears to be performable by each of the others as well.).
Regarding claim 5, the above combination of Levinson, Haronian, and Fu teaches the system of claim 4, further comprising the second computer, including a third processor and a third memory storing instructions executable by the third processor such that the second computer is programmed to:
upon detecting the road disturbance via the updated map, adjust a component parameter of a second vehicle based on the road disturbance (Levinson [9:47-62]: “The computing device may evaluate the global height map, and determine the dimensions (i.e., height, width, depth) of the deformation . . . The computing device may determine . . . an amount of the tire track that will encounter the deformation . . . Based on the dimensions of the deformation 108 and/or a percentage of the track 104 that will encounter the deformation 108, the computing device may calculate one or more adjustments to make to one or more components of the suspension system to negate the effects of the deformation 108.”); and
operate the second vehicle based on the adjusted component parameter while traversing the road disturbance (Levinson [10:11-13]: “The computing device may send a signal to the respective suspension systems to cause the one or more components to adjust for the deformation 108.”).
Regarding claim 6, the above combination of Levinson, Haronian, and Fu teaches the system of claim 5, wherein the second computer is included in the second vehicle (Levinson FIG. 3: Each vehicle 302 is equipped with the suspension control module 310. Understood that each vehicle can perform the same kinds of suspension controls based on the height map data. ).
Regarding claim 7, the above combination of Levinson, Haronian, and Fu teaches the system of claim 1, wherein the processor is further programmed to, upon detecting, via a map, a second road disturbance, determine a planned path based on the second road disturbance (Levinson [10:39-46]: “For example, the computing device may determine that with a deviation in trajectory to the left, the track 104(2) may avoid the deformation 108(1). Based on a determination that the deformation 108 may be avoidable, the computing device can provide deformation avoidance data to a vehicle control system. The vehicle control system may thus adjust the course . . ..”).
Regarding claim 8, the above combination of Levinson, Haronian, and Fu teaches the system of claim 7, wherein the second road disturbance is identified (Levinson [12:47-49]: “In various examples, the computing device . . . may use machine learning techniques to identify the deformation(s) 202.”) based on at least one of a second predicted load on a wheel of a second vehicle and a second predicted vertical displacement of the wheel, wherein (Levinson [4:33-41]: “. . . the local height map data may also include a map constructed from the raw sensor data from multiple sensors on the vehicle and fused together into a local height map. . . . the computing device may process the raw sensor data into a 2.5D or 3D local height map, which may include location data, a timestamp, and deformations on a road surface proximate to the vehicle.” Height map taken as predicted vertical displacement of the wheel.), based on inputting collected data of the second vehicle to the machine learning program, the second predicted load (Haronian [0047]: “The load on the tire depends on the patch length, on the tire pressure and to some extent on the tire temperature, age, usage time and manufacturer. The load may be calculated either using for example an empirical equation, a lookup table or a machine learning software that considers different properties of the tire and of the environment.”) and the second predicted vertical displacement (Fu [0090]: “The application system 1250 can operate or deploy a model 1260 to generate the output response 1270 to input data 1252.”; Fu [0091]: “The model 1260 can be or be received as the neural network for implementing disparity estimation 110, a portion thereof, or a representation thereof.”; Fu [0094]: “The model 1260 can generate an output response 1270 responsive to receiving the model-compliant input from the dataset generator 1256. The output response 1270 can include values corresponding to the disparity image 115, which can be provided to height estimation 130 to generate the track point heights 135 in the manner described.”; Fu [0036]: “This allows the road profile generated using the track point heights 135 to be consistent . . . ”) are determined via output from the machine learning program (See FIG. 3. APOSITA would have understood that the suspension control system of Levinson as modified by Haronian and Fu would have been present in each of the plurality of vehicles, which each carry the same suspension control system.).
Regarding claim 9, the above combination of Levinson, Haronian, and Fu teaches the system of claim 7, wherein the processor is further programmed to, upon determining the planned path extends around the second road disturbance, operate the host vehicle along the planned path (Levinson [10:42-48]: “ Based on a determination that the deformation 108 may be avoidable, the computing device can provide deformation avoidance data to a vehicle control system. The vehicle control system may thus adjust the course (e.g., series of trajectories) of the vehicle to avoid the deformation, and reestablish on an original course or a course substantially similar to the original course . . ..”).
Regarding claim 10, the above combination of Levinson, Haronian, and Fu teaches the system of claim 7, wherein the processor is further programmed to:
upon determining the planned path traverses the second road disturbance, adjust a component parameter of the host vehicle based on the second road disturbance (Levinson [9:47-62]: “The computing device may evaluate the global height map, and determine the dimensions (i.e., height, width, depth) of the deformation . . . The computing device may determine . . . an amount of the tire track that will encounter the deformation . . . Based on the dimensions of the deformation 108 and/or a percentage of the track 104 that will encounter the deformation 108, the computing device may calculate one or more adjustments to make to one or more components of the suspension system to negate the effects of the deformation 108.”); and
operate the host vehicle based on the adjusted component parameter while traversing the second road disturbance (Levinson [10:11-13]: “The computing device may send a signal to the respective suspension systems to cause the one or more components to adjust for the deformation 108.”).
Claim 11 is rejected over similar reasons to claim 1 as applied to a method.
Claim 13 is rejected over similar reasons to claim 3 as applied to a method.
Claim 14 is rejected over similar reasons to claim 4 as applied to a method.
Claim 15 is rejected over similar reasons to claim 5 as applied to a method.
Claim 16 is rejected over similar reasons to claim 6 as applied to a method.
Claim 17 is rejected over similar reasons to claim 7 as applied to a method.
Claim 18 is rejected over similar reasons to claim 8 as applied to a method.
Claim 19 is rejected over similar reasons to claim 9 as applied to a method.
Claim 20 is rejected over similar reasons to claim 10 as applied to a method.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over US 10486485 B1 to Levinson, Jesse et al. (“Levinson”), in view of US 20230311582 A1 to Haronian, Dan (“Haronian”), and US 20250308052 A1 to Fu, Tao et al. (“Fu”), further in view of US 20240316992 A1 to Cho, Young Gul (“Cho”).
Regarding claim 2, the above combination of Levinson, Haronian, and Fu teaches the system of claim 1.
While Haronian teaches keeping the driver appraised of tire wear via a signal in [0066], the above combination of prior art does not appear to expressly teach wherein the processor is further programmed to:
determine a classification of a vehicle component based on at least one of the predicted load and the predicted vertical displacement, wherein the classification is one of healthy and unhealthy; and
output a message based on the vehicle component being unhealthy.
However, Cho teaches wherein the processor is further programmed to:
determine a classification of a vehicle component based on at least one of the predicted load and the predicted vertical displacement, wherein the classification is one of healthy and unhealthy; and
output a message based on the vehicle component being unhealthy (Cho [0054]: “The output device 30 may visually or audibly provide the wear amount of the tire of the vehicle to the driver, and when the wear amount of the tire exceeds a threshold value, notify the driver visually or audibly of tire replacement.” Wear amount exceeding the threshold taken as the tire being unhealthy.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that predicts tire wear using a machine learning model taught by the above combination of Levinson, Haronian, and Fu with the system that visually notifies the driver when tire wear exceeds a threshold taught by Cho. Doing so would have improved the safety of the vehicle by notifying the driver when a tire must be replaced.
Claim 12 is rejected over similar reasons to claim 2 as applied to a method.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Furuta, Hiroki. US 20210276566 A1. PREVIEW DAMPING CONTROL APPARATUS FOR VEHICLE AND PREVIEW DAMPING CONTROL METHOD FOR VEHICLE.
Chen, Wei-Huan et al.. CN 116663434 A. Vehicle Load Decomposition Method Based On LSTM Deep Neural Network.
Chen, Wei-Huan et al.. CN 116702628 A. Automobile Wheel Centre Vertical Displacement Solving Method Based On Convolution Depth Neural Network.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY RICHARD HINTON whose telephone number is (703)756-1051. The examiner can normally be reached Monday-Friday 7:30-4:30.
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/HENRY R HINTON/Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
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