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 .
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 11-12 are rejected under 35 U.S.C. § 103 as being unpatentable over Hoye et al. (US 2017/0243370 A1) in view of RoyChowdhury et al. (US 2022/0044034 A1).
Regarding claim 1, Hoye teaches “a system comprising: a first vehicle configured to” perform pothole detection, including a vehicle event recorder mounted in a vehicle and communicating with a vehicle data server through a network. Hoye discloses that “vehicle event recorder 102 comprises a vehicle event recorder mounted in a vehicle” and that the vehicle event recorder “communicates with vehicle data server 104 via network 100” (para 0019; Fig. 1).
Hoye teaches the first vehicle is configured to “perform a primary determination of whether there is a pothole in a captured road image” because Hoye discloses that the vehicle event recorder receives sensor data, including camera/video data, and determines whether the sensor data indicates a pothole. Hoye states that sensor data may include “camera data” and “video recorder data,” and that “in 506, it is determined whether the sensor data indicates a potential pothole” (paras 0017, 0025; Fig. 5A). Hoye further teaches using a forward camera video taken while the pothole was visible through the windshield, stating that “a pothole video comprises a video from a forward facing video camera” and “a video taken during a period of time when the pothole was visible through the front windshield” (para 0020).
Hoye teaches the first vehicle is configured to “provide, based on the primary determination indicating that the pothole is in the captured road image, the captured road image and pothole information” because Hoye discloses storing and providing potential pothole data after determining that sensor data indicates a pothole. Hoye states that “in the event sensor data indicates a pothole,” “the potential pothole data is stored associated with the pothole,” and “the potential pothole data is provided” (para 0025; Fig. 5A). Hoye further teaches that the pothole data may include “a pothole video, location data, vehicle type data, vehicle speed data, pothole classification, pothole severity, pothole visibility, pothole difficulty to avoid,” and other pothole data (paras 0017, 0020).
Hoye teaches “a server configured to receive the captured road image and the pothole information provided by the first vehicle” because Hoye discloses that the potential pothole data is provided to a vehicle data server and that the server receives the potential pothole data. Hoye states that “the pothole data is provided to a vehicle data server” (para 0025), and that “potential pothole data is received” by the vehicle data server, including “accelerometer data, interior video data, forward camera video data, audio data, braking data, etc.” (para 0026; Fig. 5B).
Hoye teaches the server is configured to “perform a secondary determination of whether the pothole is in the captured road image” because Hoye discloses that the server further analyzes the potential pothole data using a model and confirms whether the data is caused by an actual pothole. Hoye states that “the potential pothole data is transmitted to a server and further analyzed using a model that determines a likelihood of the potential pothole data being from an actual pothole” (para 0018). Hoye further states that the pothole data is analyzed “using a model to determine the likelihood that the pothole data is caused by an actual pothole,” and that “a forward video and/or an interior video is/are reviewed to confirm that the pothole data is actually caused by the vehicle hitting an actual pothole” (para 0026).
Hoye teaches the server is configured to “store, based on the secondary determination indicating that the pothole is in the captured road image, the pothole information” because Hoye discloses that, when the new pothole is confirmed, review data is associated with the pothole data and the pothole data is added to a pothole map. Hoye states that “in the event that the new pothole is confirmed,” “review data is associated with the pothole data,” and “pothole data is added to a pothole map” (para 0026; Fig. 5B).
Hoye teaches the server is configured to “generate an indication of the pothole information associated with a location of the pothole corresponding to the pothole information” because Hoye discloses generating a pothole map having pothole indicators at pothole locations. Hoye states that “pothole map 400 comprises roads … and pothole indicators,” where the pothole indicators are “marks … indicating detection of a pothole at the indicated location” (para 0024; Fig. 4). Hoye further teaches that “an icon associated with the pothole data is placed on a map,” and that “the size or color of the icon is related to avoidability rating, severity rating, visibility rating, date first recorded, etc.” (para 0026).
Hoye teaches transmitting pothole data/indications, but does not expressly teach “transmit, to a plurality of vehicles, the indication.” RoyChowdhury teaches this limitation. RoyChowdhury discloses that a cloud-based vehicle safety network “pushes warning notifications to subscriber vehicles that are within a vicinity,” and that “if a first subscriber vehicle encounters a pothole and communicates that pothole’s location information to cloud 106, a second subscriber vehicle driving on a same road and heading in same direction receives a warning notification for display on HMI 105” (para 0031). RoyChowdhury also teaches that by identifying locations of road damage, the vehicle safety system may alert “nearby vehicle drivers connected to the network service” (para 0055).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoye’s pothole detection and server-based pothole map/indication system to transmit pothole indications to subscriber vehicles, as taught by RoyChowdhury, because doing so would provide nearby vehicles with advance warning of confirmed potholes so that drivers or vehicle control systems may avoid or mitigate damage from the potholes.
Regarding claim 2, Hoye further teaches “wherein the pothole information indicates: a road that the first vehicle has entered, and the location of the pothole” because Hoye discloses that pothole data includes location data and that a pothole map includes roads and pothole indicators at pothole locations. Hoye states that pothole data may include “location data” and other pothole-related data (paras 0017, 0020). Hoye further states that “pothole map 400 comprises roads … and pothole indicators,” and that the pothole indicators are “marks … indicating detection of a pothole at the indicated location” (para 0024; Fig. 4).
Hoye further teaches “wherein the server is further configured to store an association between the road and the location” because Hoye discloses a vehicle data server that receives pothole data from vehicle event recorders, stores the pothole data, and adds pothole data to a pothole map at the pothole location. Hoye states that “potential pothole data is stored,” “the pothole data is stored in a pothole database,” and “pothole data is added to a pothole map” (para 0026; Fig. 5B). Hoye also teaches adding a “map indicator” to the map “at the pothole location,” which associates the pothole location with the corresponding road/map position (para 0028; Fig. 7).
Regarding claim 3, Hoye further teaches “wherein the first vehicle is configured to provide the captured road image and the pothole information by: determining a reliability value of the primary determination” because Hoye discloses that the vehicle event recorder locally determines whether sensor data indicates a potential pothole using a pothole definition/signature and/or trained model. Hoye states that “in 506, it is determined whether the sensor data indicates a potential pothole,” and that the determination may use “a pothole definition or signature” including threshold conditions (para 0025; Fig. 5A). Hoye further teaches that extracted waveform features are used to train a model, and that “the distance between the trained model and the example is used as a proxy to the likelihood of a pothole event” (para 0023).
Hoye further teaches “sending, based on the reliability value being greater than a threshold value, the captured road image and the pothole information to the server” because Hoye discloses identifying an event as a pothole when the likelihood/model-distance satisfies a threshold, and then providing the potential pothole data to the vehicle data server. Hoye states that “if this distance is less than a threshold, then the example is identified as a pothole” (para 0023). Hoye further states that “in the event sensor data indicates a pothole,” the “potential pothole data is stored” and “provided,” including being “provided to a vehicle data server” (para 0025). The pothole data includes video/camera information and location/pothole information, including “a pothole video, location data, vehicle type data, vehicle speed data, pothole classification, pothole severity,” and related data (paras 0017, 0020).
Regarding claim 4, RoyChowdhury further teaches “further comprising a second vehicle configured to: send, to the server, a request for information regarding a road section of a road on which the second vehicle is traveling” because RoyChowdhury teaches subscriber vehicles connected to a cloud-based vehicle safety network, where the cloud/network service provides road-damage information to vehicles based on the road and vicinity in which the vehicles are traveling. RoyChowdhury states that cloud 106 is “a cloud-based network” and that “cloud 106 connects to a vehicle safety network operative to push notifications to subscriber vehicles currently in motion.” RoyChowdhury further teaches that the vehicle safety network pushes warning notifications “to subscriber vehicles that are within a vicinity” (para 0031).
RoyChowdhury further teaches “receive, from the server, pothole location information that is associated with the road section, wherein the pothole location information indicates the location of the pothole corresponding to the pothole information” because RoyChowdhury teaches that pothole location information is communicated to the cloud and used to warn another vehicle on the same road. RoyChowdhury states that “the information may be used to populate map data … with locations of unreported/new instances of road damage and their corresponding road damage classifications,” and that “if a first subscriber vehicle encounters a pothole and communicates that pothole’s location information to cloud 106, a second subscriber vehicle driving on a same road and heading in same direction receives a warning notification for display on HMI 105” (para 0031). RoyChowdhury also teaches that identifying locations of road damage allows the system to alert “nearby vehicle drivers connected to the network service” (para 0055).
Regarding claim 11, Hoye teaches “performing, by a first vehicle, a primary determination of whether there is a pothole in an image captured by the first vehicle” because Hoye discloses a vehicle event recorder mounted in a vehicle that receives sensor data, including camera/video data, and determines whether the sensor data indicates a potential pothole. Hoye states that the vehicle event recorder may include or communicate with sensors including “cameras” and “video recorders,” and that the vehicle event recorder comprises “a system for processing sensor data and detecting events” and “a system for detecting potholes” (para 0019). Hoye further teaches that “in 506, it is determined whether the sensor data indicates a potential pothole,” and that the sensor data may include “camera data” and “video recorder data” (para 0025; Fig. 5A).
Hoye teaches “providing, based on the primary determination indicating that the pothole is in the image, the image and pothole information to a server” because Hoye discloses storing and providing potential pothole data when the sensor data indicates a pothole. Hoye states that “in the event sensor data indicates a pothole,” “the potential pothole data is stored associated with the pothole,” and “the potential pothole data is provided,” including being provided “to a vehicle data server” (para 0025; Fig. 5A). Hoye further teaches that pothole data may include “a pothole video, location data, vehicle type data, vehicle speed data, pothole classification, pothole severity, pothole visibility, pothole difficulty to avoid,” and other pothole data (paras 0017, 0020).
Hoye teaches “performing, by the server, a secondary determination of whether the pothole is in the image” because Hoye discloses that the potential pothole data is transmitted to a server and further analyzed to determine whether it corresponds to an actual pothole. Hoye states that “the potential pothole data is transmitted to a server and further analyzed using a model that determines a likelihood of the potential pothole data being from an actual pothole,” and that, when the likelihood exceeds a threshold, “the potential pothole is confirmed by review of a video” (para 0018). Hoye further teaches that the server analyzes the pothole data “using a model to determine the likelihood that the pothole data is caused by an actual pothole,” and that “a forward video and/or an interior video is/are reviewed to confirm that the pothole data is actually caused by the vehicle hitting an actual pothole” (para 0026).
Hoye teaches “storing, based on the secondary determination indicating that the pothole is in the image, the pothole information” because Hoye discloses that, when the new pothole is confirmed, review data is associated with the pothole data and pothole data is added to a pothole map. Hoye states that “in the event that the new pothole is confirmed,” “review data is associated with the pothole data,” and “pothole data is added to a pothole map” (para 0026; Fig. 5B).
Hoye teaches “generating an indication of the pothole information associated with a location of the pothole corresponding to the pothole information” because Hoye discloses generating pothole indicators on a pothole map at the pothole location. Hoye states that “pothole map 400 comprises roads … and pothole indicators,” where the pothole indicators are “marks … indicating detection of a pothole at the indicated location” (para 0024; Fig. 4). Hoye further teaches placing “an icon associated with the pothole data” on a map, where the icon may be modified based on pothole information such as severity, avoidability, visibility, and date first recorded (para 0026).
Hoye teaches transmitting pothole data/indications, but does not expressly teach “transmitting, to a plurality of vehicles, the indication.” RoyChowdhury teaches this limitation. RoyChowdhury discloses that cloud 106 connects to a vehicle safety network that “pushes warning notifications to subscriber vehicles that are within a vicinity,” and that “if a first subscriber vehicle encounters a pothole and communicates that pothole’s location information to cloud 106, a second subscriber vehicle driving on a same road and heading in same direction receives a warning notification for display on HMI 105” (para 0031). RoyChowdhury further teaches outputting pothole warnings such as “POTHOLE DETECTED IN 20 METERS” (Fig. 5A).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoye’s pothole detection and server-based pothole map/indication method to transmit pothole indications to subscriber vehicles, as taught by RoyChowdhury, because doing so would provide nearby vehicles with advance warning of confirmed potholes so that drivers or vehicle control systems may avoid or mitigate damage from the potholes.
Regarding claim 12, see rejection of claim 3.
Claims 5-10 and 13-16 are rejected under 35 U.S.C. § 103 as being unpatentable over Hoye et al. (US 2017/0243370 A1) in view of RoyChowdhury et al. (US 2022/0044034 A1) and further in view of Kundu et al. (US 2020/0250984 A1).
As discussed above, Hoye in view of RoyChowdhury teaches the system of claim 4.
Regarding claim 5, Kundu further teaches “wherein the second vehicle comprises a navigation vision service processor configured to process an image of the road captured by a camera” because Kundu discloses a vehicle system including one or more cameras and a processor for detecting potholes/depressions from roadway images. Kundu states that “example implementations described herein involve a camera system … mounted in the vehicle,” where “the camera system captures the images continuously” (para 0047). Kundu further states that “the camera system captures images,” “the roadway depression candidates such as potholes are extracted,” and “using depth classifier … and intensity classifiers … depressions are detected from extracted candidates” (para 0051; Fig. 3). Kundu also discloses that the vehicle system includes a processor configured to “determine a difference image for images of a roadway received from one or more cameras of a vehicle,” “identify one or more candidate depressions on the roadway,” and classify the depressions (paras 0010, 0158).
Kundu further teaches “wherein the navigation vision service processor is activated by the receiving of the pothole location information” because Kundu discloses that a vehicle receives known depression/pothole information from a cloud system or other vehicles via a map positioning unit, and uses the received information to assist pothole detection and/or control of the vehicle. Kundu states that the ECU receives from the map positioning unit signals representing “map data,” “the position of the vehicle on a map,” “the direction of the vehicle,” “lane information,” and “one or more known depressions for a given roadway as received from a cloud system or other vehicles” (para 0136). Kundu further states that a management apparatus/cloud system can “provide information regarding previously detected instances of depressions and their locations on a roadway corresponding to a particular vehicle system,” and that the vehicle system “utilizes the previously detected instances of depressions from the management apparatus … to be incorporated to assist in the detection and/or control the vehicle” (para 0146). Kundu also teaches that, when the vehicle system fails to identify candidate depressions on the roadway, the processor obtains from a cloud system “locations of depressions on the roadway and the types of depressions for each of the obtained depressions” (paras 0162; claim 18).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoye’s pothole detection and server-based pothole information system, as modified by RoyChowdhury to provide pothole location information to a second vehicle, to further include Kundu’s camera-based vehicle processor that uses received known pothole/depression location information to assist detection and/or vehicle control, because doing so would improve the second vehicle’s ability to detect, confirm, and respond to known potholes on the roadway, including under conditions where the pothole may be difficult to detect visually.
Regarding claim 6, RoyChowdhury teaches “wherein the second vehicle is further configured to: determine, based on a location of the second vehicle and the pothole location information, a distance between the second vehicle and the pothole” because RoyChowdhury discloses that when a first subscriber vehicle communicates pothole location information to cloud 106, “a second subscriber vehicle driving on a same road and heading in same direction receives a warning notification for display on HMI 105” (para 0031). RoyChowdhury further teaches displaying pothole distance information, including the warning message “POTHOLE DETECTED IN 20 METERS” (para 0082; Fig. 5A).
Kundu further teaches “activate, based on the determined distance being within a threshold distance, a pothole detection operation to detect potholes” because Kundu discloses that the vehicle uses camera-based image processing to detect potholes/depressions and uses distance/range information to select the detection region. Kundu states that “the camera system captures images,” “the roadway depression candidates such as potholes are extracted,” and “depressions are detected from extracted candidates” (para 0051; Fig. 3). Kundu further teaches ROI detection using “distance information selection: to select range of detection” and selecting upper/lower ROI boundaries “depending upon the range of detection” (paras 0065-0067; Fig. 7). Kundu also teaches classifying depressions based on distance, including “near” and “far” depressions, and controlling the vehicle based on the detected depression type and distance (paras 0133-0135; Fig. 33).
Kundu also supports that the vehicle receives known pothole/depression locations from a cloud system and uses those locations to assist detection/control. Kundu states that the ECU receives “one or more known depressions for a given roadway as received from a cloud system or other vehicles,” and that the vehicle system “utilizes the previously detected instances of depressions from the management apparatus … to be incorporated to assist in the detection and/or control the vehicle” (paras 0136, 0146).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoye’s pothole detection and server-based pothole information system, as modified by RoyChowdhury and Kundu, so that the second vehicle determines a distance to the received pothole location and activates the vehicle’s pothole detection operation when the pothole is within a threshold detection range, because doing so would focus camera-based pothole detection on relevant upcoming roadway regions and provide timely detection/control before the vehicle reaches the pothole.
Regarding claim 7, Kundu further teaches “wherein the second vehicle is further configured to, based on a road image that is captured after the pothole detection operation is activated, detect the pothole on the road” because Kundu discloses a vehicle camera system that captures roadway images and detects potholes/depressions from those captured images. Kundu states that “the camera system captures the images continuously” and that “once the two stereo images are captured, the images are processed by pothole detection system” (paras 0047, 0050). Kundu further teaches that “the camera system captures images,” “the roadway depression candidates such as potholes are extracted,” and “depressions are detected from extracted candidates” using depth and intensity classifiers (para 0051; Fig. 3). Kundu also discloses detecting pothole candidates by processing right/left images, disparity images, edge images, regions of interest, and classifying extracted candidates as potholes or non-potholes (paras 0065-0073; Figs. 4, 7, 11).
Regarding claim 8, Kundu further teaches “wherein the second vehicle is further configured to output, based on detecting at least one pothole, a pothole warning” because Kundu discloses that the vehicle camera-based pothole detection system detects potholes/depressions and outputs information or warning signals based on the detected potholes/depressions. Kundu states that after the camera system captures roadway images, “the roadway depression candidates such as potholes are extracted,” “depressions are detected from extracted candidates,” and “the depression locations are output” (para 0051; Fig. 3). Kundu further states that “warning signals can be provided by the vehicle when such depressions are detected and classified” (para 0135). Kundu also teaches an information output unit that “displays images, generates sounds and turns on warning lights” representing information about supporting operations (para 0142).
RoyChowdhury also teaches outputting a pothole warning in the vehicle. RoyChowdhury discloses warnings through an HMI, including textual alerts, visual alerts, and auditory alerts, and expressly shows/display messages such as “POTHOLE DETECTED IN 20 METERS” and “POTHOLE DETECTION ENGAGED” (paras 0030, 0082; Fig. 5A).
Regarding claim 9, Hoye teaches “a system comprising … a server” configured to store and provide pothole information because Hoye discloses a vehicle data server that receives potential pothole data from a vehicle event recorder, stores the pothole data, confirms whether the pothole data corresponds to an actual pothole, adds pothole data to a pothole map, and transmits pothole data. Hoye states that “potential pothole data is received,” “potential pothole data is stored,” the pothole data is analyzed “using a model to determine the likelihood that the pothole data is caused by an actual pothole,” and, if confirmed, “pothole data is added to a pothole map” and “pothole data is transmitted” (para 0026; Fig. 5B). Hoye further teaches that the pothole data includes “location data,” “pothole classification,” “pothole severity,” and related pothole information (paras 0017, 0020).
Kundu teaches “a vehicle configured to send a request for road hazard information on a road section” because Kundu discloses a vehicle system having a map positioning unit and a cloud/management apparatus that provides known pothole/depression information for a given roadway to the vehicle system. Kundu states that the ECU receives signals from the map positioning unit representing “a set route, map data, the position of the vehicle on a map, the direction of the vehicle, lane information,” and “one or more known depressions for a given roadway as received from a cloud system or other vehicles” (para 0136). Kundu further teaches that the management apparatus/cloud system provides information regarding “previously detected instances of depressions and their locations on a roadway corresponding to a particular vehicle system” (para 0146).
Hoye and Kundu teach “a server configured to: in response to the request and based on determining that there is a pothole on the road section, provide, to the vehicle, pothole location information associated with the pothole” because Hoye teaches a server determining that pothole data corresponds to an actual pothole and storing/transmitting the pothole data, including location data (paras 0017, 0020, 0026), and Kundu teaches a management apparatus/cloud system that provides previously detected depression locations on a roadway to a corresponding vehicle system (paras 0145-0146). Kundu further teaches obtaining from a cloud system “locations of depressions on the roadway and the types of depressions for each of the obtained depressions” (para 0162; claim 18).
RoyChowdhury further teaches providing pothole location information to another vehicle based on the road being traveled. RoyChowdhury states that cloud 106 connects to a vehicle safety network that pushes warning notifications to subscriber vehicles in a vicinity, and that “if a first subscriber vehicle encounters a pothole and communicates that pothole’s location information to cloud 106, a second subscriber vehicle driving on a same road and heading in same direction receives a warning notification for display on HMI 105” (para 0031).
RoyChowdhury and Kundu teach “wherein the vehicle is further configured to: determine, based on the pothole location information and a location of the vehicle, a distance between the vehicle and the pothole” because RoyChowdhury teaches providing a warning to a second vehicle on the same road using pothole location information and displaying distance information such as “POTHOLE DETECTED IN 20 METERS” (paras 0031, 0082; Fig. 5A). Kundu further teaches that the vehicle receives its position, direction, route, lane information, and known depressions for a given roadway from the map positioning/cloud system (para 0136), and classifies depressions based on distance, including “near” and “far” depressions (para 0134; Fig. 33).
RoyChowdhury and Kundu teach “output, based on the distance being within a threshold distance, a pothole warning” because RoyChowdhury teaches outputting warning notifications through an HMI, including visual, textual, and auditory alerts, and displaying “POTHOLE DETECTED IN 20 METERS” (paras 0030, 0082; Fig. 5A). Kundu also teaches that “warning signals can be provided by the vehicle when such depressions are detected and classified,” and that an information output unit “displays images, generates sounds and turns on warning lights” (paras 0135, 0142).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoye’s server-based pothole map/information system to provide pothole location information to vehicles traveling on the relevant road section, as taught by RoyChowdhury and Kundu, because doing so would allow vehicles to receive advance notice of known potholes on their route and output timely warnings so that drivers or vehicle control systems may avoid or mitigate damage from the potholes.
Regarding claim 10, Kundu further teaches “wherein the request comprises road information of a road on which the vehicle is traveling” because Kundu discloses that a vehicle system receives and uses road/map information for the vehicle’s current route. Kundu states that the ECU receives signals from a map positioning unit representing “a set route, map data, the position of the vehicle on a map, the direction of the vehicle, lane information,” and road/location information including “types of roads/vehicle locations” (para 0136). Kundu further teaches that the management apparatus provides previously detected depression information for “a roadway corresponding to a particular vehicle system” (para 0146).
Regarding claim 13, Kundu further teaches “sending, to the server, a request for a location for activating a road pothole detection operation to detect potholes” because Kundu discloses a vehicle system that communicates with a cloud/management apparatus to obtain known pothole/depression locations for a roadway and uses such locations to assist pothole detection and/or vehicle control. Kundu states that the ECU receives signals from a map positioning unit representing “a set route, map data, the position of the vehicle on a map, the direction of the vehicle, lane information,” and “one or more known depressions for a given roadway as received from a cloud system or other vehicles” (para 0136). Kundu further teaches that a management apparatus/cloud system “provides information regarding previously detected instances of depressions and their locations on a roadway corresponding to a particular vehicle system” (para 0146).
Regarding claim 14, see rejection of claim 6.
Regarding claim 15, see rejection of claim 7.
Regarding claim 16, Hoye teaches “after sending the pothole information” because Hoye discloses that, when sensor data indicates a pothole, “the potential pothole data is stored” and “the potential pothole data is provided,” including to a vehicle data server (para 0025; Fig. 5A). Hoye further teaches that the pothole data includes “a pothole video, location data, vehicle type data, vehicle speed data, pothole classification, pothole severity,” and related pothole information (paras 0017, 0020).
Kundu teaches “sending, to the server, a request to provide pothole location information based on the vehicle entering a new road” because Kundu discloses a vehicle system that communicates with a cloud/management apparatus and receives roadway-specific known depression/pothole information based on the vehicle’s route, map position, direction, lane information, and roadway location. Kundu states that the ECU receives signals from a map positioning unit representing “a set route, map data, the position of the vehicle on a map, the direction of the vehicle, lane information,” and “one or more known depressions for a given roadway as received from a cloud system or other vehicles” (para 0136). Kundu further teaches that the management apparatus/cloud system provides information regarding “previously detected instances of depressions and their locations on a roadway corresponding to a particular vehicle system” (para 0146).
Kundu teaches “receiving, from the server, the pothole location information” because Kundu discloses that a management apparatus functions as a cloud system that records roadway depression instances detected by vehicle systems and provides previously detected depression locations to vehicle systems. Kundu states that management apparatus 102 “functions as a cloud system” that records detected roadway depressions (para 0145), and that the management apparatus can “provide information regarding previously detected instances of depressions and their locations on a roadway corresponding to a particular vehicle system” (para 0146). Kundu also teaches obtaining, from a cloud system, “locations of depressions on the roadway and the types of depressions for each of the obtained depressions” (para 0162; claim 18).
RoyChowdhury and Kundu teach “determining, based on the pothole location information and vehicle location information, a distance between the vehicle and the pothole” because RoyChowdhury teaches providing a pothole warning to a vehicle based on pothole location information, including displaying distance information such as “POTHOLE DETECTED IN 20 METERS” (paras 0031, 0082; Fig. 5A). Kundu further teaches that the vehicle receives map data, vehicle position, direction, lane information, and known depressions for a roadway, and classifies depressions based on distance, including “near” and “far” depressions (paras 0134, 0136; Fig. 33).
RoyChowdhury and Kundu teach “outputting, based on the determined distance being within a threshold distance, a pothole warning” because RoyChowdhury teaches outputting warning notifications through an HMI, including textual, visual, and auditory alerts, and displaying “POTHOLE DETECTED IN 20 METERS” (paras 0030, 0082; Fig. 5A). Kundu also teaches that “warning signals can be provided by the vehicle when such depressions are detected and classified,” and that an information output unit “displays images, generates sounds and turns on warning lights” (paras 0135, 0142).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoye’s vehicle/server pothole information method, as modified by RoyChowdhury, to further include Kundu’s feature of providing roadway-specific known pothole/depression location information to a vehicle based on the vehicle’s map position/route/roadway, because doing so would allow the vehicle to receive updated pothole information for a newly entered road and output timely warnings based on the vehicle’s distance to known potholes.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Barrera et al (US 2024/0247944) abstract and Fig. 1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMEED ALIZADA whose telephone number is (571)270-5907. The examiner can normally be reached Monday-Friday, 9:30 am until 5:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Zimmerman can be reached at 571-272-3059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OMEED ALIZADA/Primary Examiner, Art Unit 2686
Prosecution Insights