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 . Claims 1-2,4-10,12-18 and 20 are pending and examined below. This action is in response to the claims filed 2/16/26.
Continued Examination Under 37 CFR 1.114
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/16/26 has been entered.
Response to Amendment
Applicant’s arguments, see Applicant Remarks 35 U.S.C. § 103 filed on 2/16/26, regarding 35 U.S.C. § 103 rejections are persuasive in view of amendments filed 2/16/26.
However, upon further consideration, new grounds of rejection is made in view of Orlov (US 2021/0331713) below.
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 1-2, 4-6, 8-10, 12-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tatourian (US 2020/0189583) in view of Orlov (US 2021/0331713).
Regarding claims 1, 9, and 17, Tatourian discloses a an anti-rutting system/method comprising a processor and a memory, the processor configured to (¶33-34 and ¶62):
a server processor in communication with the one or more autonomous vehicles (Abstract and ¶61-64 – computer system may be a server),
receive, from one or more autonomous vehicles, sensor data indicating conditions of roads traveled by the one or more autonomous vehicles (¶19-21 - The vehicle control system 102 may obtain sensor data from onboard sensors, such as a radar system, and determine where there is road wear that may indicate ruts or other road deterioration);
generate, based on the sensor data, a model of a surface of the roads traveled by the one or more autonomous vehicles (¶19-21 – gathered information about road surface and road conditions corresponding to the recited generate a model of a surface of the roads);
identify one or more road deterioration features from the model (¶19-21 – identification of potential obstacles or road conditions along the road such as a rut corresponding to the recited identification of one or more road deterioration features);
generate a map indicating locations of the road deterioration features (¶34-35 – road condition database corresponding to the recited map indicating locations of the road deterioration features); and
transmit the map to the one or more autonomous vehicles, wherein the one or more autonomous vehicles are configured to generate constraints for a planned path of the autonomous vehicle to avoid contact with the locations of the road deterioration features identified in the map while operating (¶34-43 – autonomous vehicle receives the road condition database information from a cloud service corresponding to the recited transmit the map to the one or more autonomous vehicles configured to calculate travel path offset corresponding to the recited constraints for a planned path based on the geographical positions of road condition information corresponding to the recited avoid contact with the locations of the road deterioration features identified in the map while operating),
wherein the one or more autonomous vehicles control driving operation based on the generated constraints (¶19-20 - the autonomous vehicle 104 gathers information about the road surface ... to determine potential obstacles in the road and initiate mitigation operations, such as braking [or] steering).
While Tatourian does disclose utilizing GPS coordinate based repository of road condition data including the severity of the conditions (¶22), it does not explicitly disclose this data is an array of road depth values.
However, Orlov discloses a system for detecting the presence of ruts on current terrain including the model including an array of road depth values at respective locations on the surface (¶87, ¶110-113, and ¶137-142 – birds eye view of the current road profile which is a representation of a height variation of the surface of the current road 404 along its width extended in front of the vehicle based on LIDAR-based sensor point-cloud data indicative of at least the surface of the current road 404 corresponding to the recited array of road depth values at respective locations on the surface)
comparing the array of road depth values with a nominal road depth value (¶163-174 and Fig. 7 – element 706 compares the current terrain profile with a sampled terrain profile corresponding to the recited nominal road depth value); and
identifying the one or more road deterioration features including a rut in response to at least one depth value in the array of road depth values being greater than the nominal road depth value by a threshold amount (¶152, ¶163-174 and Fig. 7 – element 708 utilizes the comparison data to determine the presence of a rut by utilizing a height threshold value to determine the similarity between the current terrain profile and the sampled terrain profile)
The combination of the lane motion randomization system of Tatourian with the detailed terrain profile comparison of Orlov fully discloses the elements as claimed.
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the lane motion randomization system of Tatourian with the detailed terrain profile comparison of Orlov in order to increase safety of SDC passengers and/or to reduce the risk of collision with other objects on the current terrain and/or to reduce the risk of losing control of the SDC on the current terrain during operation (Orlov - ¶18).
Regarding claims 2, 10, and 18, Tatourian further discloses wherein at least some of the sensor data is generated by one or more of ground-facing LiDAR sensors, ground-penetrating RADAR sensors, acoustic sensors, or cameras of the one or more autonomous vehicles (¶14 – sensor array including cameras, radar, LIDAR, ultrasonic, very high-frequency (VHF) radar, or the like which identify road conditions corresponding to the recited ground facing sensors. The claim element “one or more of” only requires one of the following to be included to disclose the invention as claimed).
Regarding claims 4, 12, and 20, Tatourian further discloses wherein to identify the one or more road deterioration features, the processor is configured to apply an identification model configured to identify the one or more road deterioration features based on an input of the model (¶33 – road condition detection corresponding to the recited road deterioration feature identification for determining structural imperfection is a road rut or other worn portion of the road including further processing to verify or confirm the existence, severity, or identification of the road condition corresponding to the recited identify the road deterioration features utilizing gathered road data corresponding to the recited based on input of the model).
Regarding claims 5 and 13, Tatourian further discloses wherein at least one of the one or more road deterioration features is a rut (¶33 – confirmation that the imperfection is a rut).
Regarding claims 6 and 14, Tatourian further discloses wherein the one or more autonomous vehicles are configured to apply a routing model that determines the constraints to the planned path of the autonomous vehicle based on the locations of the road deterioration features (¶19 and ¶26-28 - acquire data about potential obstacles or road conditions along the route of the autonomous vehicle 104 corresponding to the recited apply routing model based on road condition database utilizing path planning including identified potential obstacles or road conditions to determine the potential offsets corresponding to the recited constraints to the planned path).
Regarding claims 8 and 16, Tatourian further discloses wherein the map further includes information relating to one or more of road materials, rut depths, road sub-surface conditions, or lateral road distortion (¶22 and ¶ 33-34 – road condition database corresponding to the recited map includes the location, type, severity, or other characteristics of road conditions in the vehicle's path corresponding to the recited type corresponding to the recited road material, severity corresponding to the recited depth, and uneven pavement corresponding to the recited subsurface conditions and lateral road distortions. The claim element “one or more of” only requires one of the following to be included to disclose the invention as claimed).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tatourian (US 2020/0189583) and Orlov (US 2021/0331713), as applied to claims 6 and 14 above, in view of Lacaze et al. (US 2020/0150656).
Regarding claims 7 and 15, while Tatourian does disclose the identification of ruts and other road imperfections as well as the type and severity of such imperfections, it does not explicitly utilize a machine learning model, however Lacaze discloses an autonomous truck system with rut depth detection including wherein the routing model is a machine learning module trained to output the constraints based on an input of the map (¶22-24 and ¶32 – sensor data is processed using a Deep Learning training set corresponding to the recited machine learning module to classify objects on a path to be avoided).
The combination of the road imperfection mapping and avoidance system of Tatourian in view of Orlov with machine learning hazard identification of Lacaze fully discloses the elements as claimed.
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the road imperfection mapping and avoidance system of Tatourian in view of Orlov with machine learning hazard identification of Lacaze in order to increase the efficiency and safety in autonomous vehicle control systems (Lacaze - ¶6).
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sridhar et al. (US 2019/0079539) discloses a road surface based vehicle control system which identifies the presence of an extrusion/depression/obstacle by comparing height/depth values detected (¶13-14).
Kundu et al. (US 2020/0250984) discloses a pothole detection system which compares potential pothole candidate depth values to surrounding information to confirm the presence of a pothole (¶67-71).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew J Reda whose telephone number is (408)918-7573. The examiner can normally be reached on Monday - Friday 7-4 ET.
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/MATTHEW J. REDA/Primary Examiner, Art Unit 3665
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