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, 6-10, 12, 14-18, and 20 are pending and examined below. This action is in response to the claims filed 7/7/26.
Response to Amendment
Applicant’s arguments, see Applicant Remarks 35 USC § 103. filed on 7/7/26, regarding 35 USC § 103 rejections are persuasive in view of amendments filed 7/7/26.
However, upon further consideration, new grounds of rejection are made in view of Gawande et al. (US 2020/0148312) below.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-2, 4, 6-10, 12, 14-18, 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) and Gawande et al. (US 2020/0148312).
Regarding claims 1, 9, and 17, Tatourian discloses 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 [group] of autonomous vehicles (Abstract and ¶61-64 – computer system may be a server),
receive, from a [group] of autonomous vehicles, sensor data indicating conditions of roads traveled by the [group] of 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, the one or more road deterioration features including one or more ruts (¶19-21 and ¶33 – 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 an autonomous vehicle of the [group] of autonomous vehicles is configured to generate constraints for a planned path of the autonomous vehicle of the [group] of autonomous vehicles to avoid contact with the locations of the road deterioration features identified in the map while operating, the constraints including costs associated with deviating from or encroaching on the one or more ruts (¶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 autonomous vehicle of the [group] of autonomous vehicles controls driving operation along the planned path that reduces a total cost imposed by the generated constraints (¶19-22 - the autonomous vehicle 104 gathers information about the road surface from the shared road condition repository ... to determine potential obstacles in the road and initiate mitigation operations, such as braking [or] steering based on road condition/severity based offsets corresponding to the recited cost imposed by the generated constraints),
wherein the processor is a server processor, the server processor further configured to compile a map of paths traveled by the [group] of autonomous vehicles, the autonomous vehicle of the [group] of autonomous vehicles configured to identify one or more portions of the roads having reduced use by the [group] of autonomous vehicles, based on the compiled map, and determine the planned path that maximizes usage of the one or more portions, thereby reducing a chance of at least one of developing or deepening the one or more ruts (¶22 and ¶33-35 - repository of road conditions corresponding to the recited compiled map from data shared from other vehicles in a road condition database hosted in a shared server corresponding to the recited server processor for compiling paths traveled by the group of autonomous vehicles in order to gently and subtly change the operation of the autonomous vehicle to mitigate overuse of certain portions of the road lane corresponding to the recited identification of portions of the roads with reduced use and maximize usage of those portions to reduce developing or deepening of the ruts).
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 the one or more ruts 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).
While Tatourian does disclose sharing information including data from specific road conditions identified during travels, it does not explicitly disclose this is shared with an identified fleet of vehicles.
However, Gawande discloses a fleet based system for identifying tire-to-road friction properties for a fleet of vehicles utilizing specific location based estimation for deriving road surface condition estimates between a fleet of vehicles traversing different routes (¶54-55).
The combination of the lane motion randomization system of Tatourian in view of Orlov with the shared fleet based road surface condition assessments of Gawande fully discloses the invention 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 in view of Orlov with the shared fleet based road surface condition assessments of Gawande in order to provide improved autonomous driving and driver assist functions, better driver warnings, and safer road trips may be planned in advance based on a thorough analysis of the drivable conditions (Gawande - ¶55).
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 6 and 14, Tatourian further discloses wherein the [group] of autonomous vehicles are configured to apply a routing model that determines the constraints to the planned path of the autonomous vehicle of the [group] of autonomous vehicles 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).
While Tatourian does disclose sharing information including data from specific road conditions identified during travels, it does not explicitly disclose this is shared with an identified fleet of vehicles.
However, Gawande discloses a fleet based system for identifying tire-to-road friction properties for a fleet of vehicles utilizing specific location based estimation for deriving road surface condition estimates between a fleet of vehicles traversing different routes (¶54-55).
The combination of the lane motion randomization system of Tatourian in view of Orlov with the shared fleet based road surface condition assessments of Gawande fully discloses the invention 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 in view of Orlov with the shared fleet based road surface condition assessments of Gawande in order to provide improved autonomous driving and driver assist functions, better driver warnings, and safer road trips may be planned in advance based on a thorough analysis of the drivable conditions (Gawande - ¶55).
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 Gawande further discloses wherein the routing model is a machine learning module trained to output the constraints based on an input of the map (¶6 - the present disclosure utilizes machine learning (ML) models to detect the road friction or road surface condition beneath a vehicle and predict the road friction or road surface condition ahead of a vehicle—thereby improving vehicle warning and response systems related to other vehicles, obstacles, and the like).
The combination of the lane motion randomization system of Tatourian in view of Orlov with the shared fleet based road surface condition assessments and machine learning based navigation constraints of Gawande fully discloses the invention 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 in view of Orlov with the shared fleet based road surface condition assessments and machine learning based navigation constraints of Gawande in order to provide improved autonomous driving and driver assist functions, better driver warnings, and safer road trips may be planned in advance based on a thorough analysis of the drivable conditions (Gawande - ¶55).
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).
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kenyon (US 2020/0079388) discloses a system for reducing roadway degradation including utilizing metrics indicative of savings from reduced wear and tear of a roadway for applying an offset displacement while driving utilizing shared server based maps (¶32-35).
Nakamura et al. (US 2018/0046195) discloses a system for identifying degradation information of road surface segments including utilizing stored map information for identifying the difficulty of traversing certain routes in a vehicle (¶68-72).
Conclusion
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 Matthew J Reda whose telephone number is (408)918-7573. The examiner can normally be reached Monday - Friday 7-4 ET.
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/MATTHEW J. REDA/ Primary Examiner, Art Unit 3665
