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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 12/19/25, Applicant, on 2/18/26, amended claims. Claims 1-10, 12-13, 18-22 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10, 12-13, 18-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: the claim is amended to oddly put a series of “contingent” limitations in the “based on the need and whether or not…” limitation. As a result, the following step of “analyzing the measured data received from the measurement vehicle” is unclear, because it is no longer required to even have “measured data.” The “based on the need…” limitation includes “whether or not a required a quality level is satisfied, controlling at least one of whether or not to perform measurement of one or more road surface states of the inspection target road by a measurement vehicle… and whether or not to transmit measured data obtained by the measurement, the required quality level being needed for the measured data and being set for the road segments.” The limitations set up multiple scenarios where no measurement is even needed to be collected – 1) “quality level is satisfied”; 2) “not” to perform measurement”; 3) “not” to transmit measured data. Accordingly, it is unclear what is required at the “analyzing the measured data received from the measurement vehicle” with multiple scenarios where no measurement occurs; whether the “analyzing the measured data” is the same as the “quality level”. It is unclear if intervening steps are missing for “analyzing the measured data”, or if this refers to the previous limitation and “required quality level.” Examiner is not sure what to suggest. It appears, as best Examiner understands it, that requiring some measurement is what Applicant desires. Examiner suggests reciting, but warns that it is unclear if this is what Applicant is moving towards: “by a measurement vehicle, based on a need for road inspection and measurement capabilities of the measurement vehicle, , wherein the ;
ting measured data obtained by the measurement to the processor when[[,]] the required quality level being needed for the measured data and being set for the road segments is satisfied by the measurement capabilities;
analyzing the measured data received from the measurement vehicle; and
performing inspection of the road by the analyzed measured data.”
Independent claims 10 and 12 recite similar limitations and are rejected for the same reasons.
Claims 2-9, 13, and 18-22 depend from claims 1, 10, and 12, and are rejected for the same reasons.
Claim 4 is rejected for antecedent basis issues. The 3rd-to-last limitation in claim 1 says “controlling at least one of whether or not to perform measurement of one or more road surface states”, but claim 4 recites the opposite limitation "measuring one or more road surface states” in the 4th limitation. There is insufficient antecedent basis for this limitation in the claim. It is unclear then if the claim 4 limitation of “measuring” negates the earlier one; if this latter “measuring’ is referring to a second measurement. Examiner is not sure what to suggest; but may be possible that portions of claim 4 is overlapping with earlier limitations, and needs many amendments.
Claim 5 depends from claim 4 and is rejected for the same reasons.
Claims 6 and 20 are rejected for antecedent basis issues. The 3rd-to-last limitation in claim 1 says “controlling at least one of whether or not to perform measurement of one or more road surface states”, but claim 6 recites the opposite/same? limitation "based on the inspection need/nonneed information and location information about the measurement, the measurement of the one or more road surface states” in claim 6. There is insufficient antecedent basis for this limitation in the claim. It is unclear then if the claim 6 limitation of “measuring” negates the earlier one; if this latter “measuring’ is referring to a second measurement. Examiner is not sure what to suggest; but may be possible that portions of claim 6 is overlapping with earlier limitations, and needs many amendments.
Claim 7 depends from claim 6 and is rejected for the same reasons.
Claim 8, 21 recite “a required quality level”. Claim 1 also now includes “a required quality level.” There is insufficient antecedent basis for this limitation in the claim. It is believed claim 8 is referring to “the required quality level.”
Claim 9, 22 depends from claim 8, 21 and are rejected for the same reasons.
Claim 10 recites the limitation "measuring the one or more road surface states of the inspection target road” in the 6th limitation but the last limitation says the opposite now of “to control at least one of whether or not to perform measurement of the one or more road surface states”. There is insufficient antecedent basis for this limitation in the claim. It is unclear then if the last limitation of “measuring” negates the earlier one; if this latter “measuring’ is referring to a second measurement. A similar issue with the 7th limitation and “transmitting measured data”, but then the last limitation states that possible there is not transmission. Examiner is not sure what to suggest; but may be possible that the last limitation is overlapping with earlier limitations, and needs many amendments.
Claims 18-22 depend from claim 10, and are rejected for the same reasons.
Claim 12 recites the limitation "transmitting an instruction to the measurement vehicle in response to a query about a need for inspection for each of the road segments from the measurement vehicle, the instruction being used for controlling measurement of one or more road surface states of each of the road segments” in the 4th limitation but the last limitation says the opposite now of “whether or not to perform measurement of the one or more road surface states”. There is insufficient antecedent basis for this limitation in the claim. It is unclear then if the last limitation of “measuring” negates the earlier one; if this latter “measuring’ is referring to a second measurement. Examiner is not sure what to suggest; but may be possible that the last limitation is overlapping with earlier limitations, and needs many amendments.
Claims 13 depend from claim 12, and is rejected for the same reasons.
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 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 1-10, 12-13, and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Shimada (US 2016/0356001) in view of Cox (US 2021/0155248) and Johnson (US 2020/0211385).
Concerning claim 1, Shimada discloses:
A road inspection system (Shimada – See FIG. 1 – patrol vehicle 110, portable terminal 111, road surface condition measurement vehicle 130, server 120; see par 36 – server apparatus 120 obtains information for road sections for which road surface state is to be inspected; the road section whose road surface state is to be inspected is referred to as “inspection target road section”.), comprising:
at least one processor to perform instructions to (Shimada – FIG. 1 has patrol vehicle with portable terminal 111; par 40 - FIG. 2 is a diagram illustrating a hardware configuration of the portable terminal. The portable terminal 111 includes a CPU (Central Processing Unit) 200; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see FIG. 1 – measurement vehicle 130 has road surface condition measurement apparatus 131; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404), and
at least one memory storing the instructions to implement (Shimada - see par 43 - The CPU 200 executes programs stored in the storage 203; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404):
determining, regarding road segments, each of which is a unit obtained by dividing an inspection target road, a need for inspection for each of the road segments (Shimada see par 36 - Further, the server apparatus 120 obtains information related to the kilometer post section, which is included in the road section for which the road surface state is to be inspected and includes the road surface degradation position, and generates measurement target section information. see par 37 - It is noted that the kilometer post is a road post indicating a distance from a predetermined start point, and is disposed every 1 km or 100 m.);
Shimada disclose:
based on the need and … controlling at least one of whether or not to perform a measurement of one or more road surface states of the inspection target road by a measurement vehicle that is capable of measuring the one or more road surface states (Since it is a system claim, it is interpreted as needing to be capable of performing various alternatives here
Shimada discloses the limitations – see par 38 - The road surface condition measurement vehicle 130 travels on the inspection target road section. The road surface condition measurement apparatus 131 performs measurement (referred to as “road surface condition measurement”) such as a step measurement of the road with a laser scan unit, road surface imaging with a camera image capturing part, etc., in order to derive MCI (Maintenance Control Index) values. See par 39 - The road surface condition measurement apparatus 131 performs the road surface condition measurement in the identified kilometer post section. In other words, according to the measurement system 100 of the road surface state, the section in which the road surface condition measurement is to be performed with the road surface condition measurement apparatus 131 is limited within the inspection target road section).
Shimada discloses having kilometer post section with an identified road surface degradation position, for road surface states “to be inspected” (See par 36) and then using road surface condition measurement apparatus 131 to perform a laser scan or road surface imaging, or camera image capturing (See par 38).
To any extent Shimada lacks details on which segments have “needs”, in light of dependent claims, Cox also discloses “based on the need” (Cox – See par 57 - The AV 100 can sometimes be routed over a previously-identified road surface feature (e.g., previously identified by the AV 100 or another AV that shares map data with the AV 100 such as the AVs 402-406 of FIG. 4). When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present… or is not still present; The AV 100 can therefore keep road surface feature data in the map data 130 up-to-date by allowing for the revisiting of road surfaces indicated in the map data 130 as including road surface features, when enough time has passed that the road surface features may no longer exist. When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data) and… controlling at least one of whether or not to perform a measurement of one or more road surface states of the inspection target road… that is capable of measuring the one or more road surface states” (Cox – see par 34 - The road surface analysis component 124 is configured to determine whether a road surface feature is present on or in a roadway on which the AV 100 is traveling based upon lidar data output by the lidar sensor system 102. See par 59 - the perception component 122 can indicate a high probability that a first road surface feature is a pot hole and a high probability that a second road surface feature is motor vehicle accident debris (e.g., based upon output of the neural network component 308). In the example, the perception component 122 updates the map data 130 with indications that the first road surface feature and the second road surface feature are present. The indication that the first road surface feature is present can include an indication that the first road surface feature is likely to be a pot hole).
Shimada discloses vehicle with a road surface condition measurement apparatus 131 (fig. 4) with a laser scan 401 and camera imaging to capture the image of the road (See par 51) and analyzing road surface condition (See par 128). Cox discloses having a perception system (122) with cameras/imaging/Lidar (See par 27) and gives details on lidar in a driving environment for obtaining points 220 representative of surface of roadway (see par 36-37) where training a neural network is used for determining higher probability that road surface has a feature that is present (e.g. pothole – see par 48).
Johnson discloses:
based on the need “and whether or not a required quality level is satisfied” controlling at least whether or not to perform a measurement of one or more road surface states of the inspection target road by a measurement vehicle that is capable of measuring the one or more road surface states (Applicant’s [0050], FIG. 8 give examples of “measurement capabilities”: “resolution (pixels) of camera”, frame rate, bit rate, “object recognition function”, a speed of the measurement vehicle (claim 8; Applicant’s [0052] gives example for this alternative - the image quality deteriorates as the speed increases, and therefore, the speed (range) of the measurement vehicle may be designated as the required quality level.), or an environmental state of the measurement vehicle (claim 8; Applicant’s [0078] as published gives example of this alternative - environmental state of the measurement vehicle (ambient brightness, weather, etc.)
Johnson discloses based on broadest reasonable interpretation in light of the specification the entire limitation – see par 197 - maintenance planning application 710 has received information on the quality of roadway 106 from vehicles 714 as the vehicles 714 moved along the roadway 106. The information may include data elements such as the presence and location of potholes or cracked pavement and may also include a confidence level associated with the quality of the roadway, or the quality of the underlying information. see par 198 - maintenance planning application 710 issues a probe management request to TMC 704 (1100) in response to detecting a problem with roadway 106. The probe management request may, for instance, ask TMC 704 to task vehicles 714 in the vicinity of the sign to capture a higher-resolution image of the pavement, or may ask TMC 704 to ask vehicles 714 in the vicinity of the section of roadway 106 under review for increased resolution in one or more of the image capture or sensor readings).
Shimada, Cox, and Johnson disclose:
and whether or not to transmit measured data obtained by the measurement.. (Shimada- par 51 – road surface condition measurement apparatus 131 includes storage 404 and a communication part 405; par 55 – communication part 405 performs communication with an external apparatus;).
see also Cox – see par 6 - the lidar sensor system can output data indicative of positions of the road surface and any road surface features on or in a portion of the road surface in the travel path of the AV (e.g., potholes, uneven pavement, speed bumps, utility access covers, and the like). see par 54 - In a non-limiting example, the road surface analysis component 124 can output a first probability that a speed bump is present at a location on the roadway and a second probability that a pothole is present at the location on the roadway. see par 55 - In an exemplary embodiment, the perception system 122 of the AV 100 confirms the presence of a road surface feature at a particular location based upon acceleration data output by the IMU 104. Subsequently, the AV 100 can transmit map update data to the server computing device 408, wherein the map update data causes the server computing device 408 to update the map data 414 stored at the data store 412 with an indication that the road surface feature is present at the particular location)
Johnson also discloses:
“and whether or not to transmit measured data obtained by the measurement,” as well as “the required quality level being needed for the measured data and being set for the road segments” (Johnson [as above for the “quality level”] - see par 136 - In some examples, service component 538 may determine that the quality metric is more than one standard deviation below the mean for similar infrastructure articles. In some examples, service component 538 may determine an anomaly in a sensor of a vehicle or an environment of the vehicle. In some examples, service component 538 may send an indication of the quality metric to at least one other vehicle for use to modify an operation of the at least one other vehicle in response to detection of the infrastructure article. see par 137 - For example, the infrastructure data may be the result of pre-processing by the respective vehicle of raw sensor data, wherein the classification comprises less data than the raw data on which the classification is generated. In some examples, infrastructure component 536 may select different sets of infrastructure data from a set of infrastructure data generated by a larger number of vehicles than the set of vehicles. That is, infrastructure component 536 may discard or ignore certain sets of infrastructure data from infrastructure data 532 based on one or more criteria (e.g., anomalous criteria, temporal criteria, locational criteria, or any other suitable criteria) (see par 181-182, 185 – infrastructure examples sensed include potholes or road degradation); see par 152 - Furthermore, in some such approaches, at least some of the information includes a confidence score that can be used with information from other vehicles 714 to better assess the condition of the roadway; see par 197 - information may include data elements such as the presence and location of potholes or cracked pavement and may also include a confidence level associated with the quality of the roadway, or the quality of the underlying information.)
Shimada, Cox, and Johnson disclose:
analyzing the measured data received from the measurement vehicle (Shimada – see par 52 - The laser scan unit 401 emits laser light to the road surface, and measures a distance to a laser spot position to perform the step measurement, etc., of the road. The camera image capturing part 402 captures the image of the road surface to generate images of the road surface. See par 105 - Further, if the determination part 1502 determines that the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323, the determination part 1502 outputs an acquisition instruction to the laser measurement value acquisition part 1503 and the captured image acquisition part 1504;
see also Cox – see par 34- road surface analysis component 124 is configured to determine whether a road surface feature is present on or in a roadway on which the AV 100 is traveling based upon lidar data output by the lidar sensor system 102
see also Johnson – see par 181-185 – quality scoring metrics applied to sensor data; sensed characteristics can be on [0185] – potholes or road degradation); and
performing inspection (Shimada – See par 108 - The storage control part 1505 stores road surface condition measurement information 1410 in which the obtained latitude and longitude, the laser measurement values, and the captured images are associated with date and time of the acquisition thereof in the road surface condition measurement information DB 420. See par 111 - Further, an inspection report document, etc., is made using the road surface condition measurement information of the kilometer post section(s) whose MCI (Maintenance Control index) value is less than or equal to 2, among the derived MCI values).
Shimada and Cox are analogous art as they are directed to analyzing road conditions using imaging such as lasers/Lidar (see Shimada Abstract; See Cox Abstract; Johnson Abstract, par 197). 1) Shimada discloses having kilometer post section with an identified road surface degradation position, for road surface states “to be inspected” (See par 36) and then using road surface condition measurement apparatus 131 to perform a laser scan or road surface imaging, or camera image capturing (See par 38). Cox improves upon Shimada by disclosing revisiting a road surface location after enough time has passed to check if a road surface feature still exists (See par 57) and determining road surface features using road surface analysis and perception components on autonomous vehicle (AV) (See par 34, 59). One of ordinary skill in the art would be motivated to further include revisiting a road surface feature to check if a road surface feature/pothole still exists to efficiently improve upon the road surfaces inspected in Shimada. 2) Shimada discloses vehicle with a road surface condition measurement apparatus 131 (fig. 4) with a laser scan 401 and camera imaging to capture the image of the road (See par 51) and analyzing road surface condition (See par 128). Cox discloses having a perception system (122) with cameras/imaging/Lidar (See par 27) and gives details on lidar in a driving environment for obtaining points 220 representative of surface of roadway (see par 36-37) where training a neural network is used for determining higher probability that road surface has a feature that is present (e.g. pothole – see par 48). Johnson improves upon Shimada and Cox by disclosing discard/ignoring anomalous data (See par 133-137, 181-182, 185 – sensed potholes or road degradation), requesting a higher-resolution image for a particular section of a roadway related to the pavement when detecting a problem with a roadway 106 (see par 198). One of ordinary skill in the art would be motivated to further include having differing resolution images requested to efficiently improve upon the road surfaces inspected in Shimada and the training of a neural network to determine probability of a road surface feature in Cox.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the measuring of a road state using a road surface condition measurement from a vehicle in Shimada to further revisit a road surface feature if enough time has passed to check if still exists as disclosed in Cox, and to further have different resolution images for assessing roadway conditions (e.g. potholes or road degradation) as disclosed in Johnson, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 10, Shimada and Cox and Johnson disclose:
A measurement vehicle (Shimada – See FIG. 1 – patrol vehicle 110, portable terminal 111, road surface condition measurement vehicle 130;
see also Johnson -see par 198 - maintenance planning application 710 issues a probe management request to TMC 704 (1100) in response to detecting a problem with roadway 106. The probe management request may, for instance, ask TMC 704 to task vehicles 714 in the vicinity of the sign to capture a higher-resolution image of the pavement), comprising:
at least one processor to perform instructions to (Shimada – FIG. 1 has patrol vehicle with portable terminal 111; par 40 - FIG. 2 is a diagram illustrating a hardware configuration of the portable terminal. The portable terminal 111 includes a CPU (Central Processing Unit) 200; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see FIG. 1 – measurement vehicle 130 has road surface condition measurement apparatus 131; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404), and
at least one memory storing the instructions to implement (Shimada - see par 43 - The CPU 200 executes programs stored in the storage 203; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404):
transmitting, regarding road segments, each of which is a unit obtained by dividing an inspection target road, a query about a need for inspection for each of the road segments (Shimada see par 36-37 – [as in claim 1 above]);
receiving information indicating whether or not to inspect each of the road segments to which the query is directed from a server, the server determining a need for inspection for each of the road segments, and controlling, based on the need for inspection for each of the road segments (Shimada – see par 36 - Further, the server apparatus 120 obtains information related to the kilometer post section, which is included in the road section for which the road surface state is to be inspected and includes the road surface degradation position, and generates measurement target section information. see par 37 - It is noted that the kilometer post is a road post indicating a distance from a predetermined start point, and is disposed every 1 km or 100 m; see FIG. 7 – server apparatus 120 has “measurement target section information output part 703” and “measurement target section information 323”; see par 105 - determination part 1502 determines whether the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323 supplied from the server apparatus 120.
See also Cox – see par 55 - The AV 100 can further be configured to communicate the presence of the road surface feature to a server computing device that controls various operations of a fleet of AVs, responsive to confirming that the road surface feature is present. With reference now to FIG. 4, an exemplary AV fleet system 400 is shown, wherein the fleet system 400 includes the AV 100, a plurality of additional AVs 402-406, and a server computing device 408 that is in communication with each of the AVs 100 and 402-406 by way of a network 410. The server computing device 408 includes a data store 412 that has map data 414 loaded thereon. The map data 414 can be master map data that is used by the server computing device 408 in connection with coordinating or controlling operation of the various AVs 100, 402-406 in the fleet system 400), measurement of one or more road surface states of the inspection target road or transmission of measured data obtained by the measurement (Shimada – see par 38 - The road surface condition measurement vehicle 130 travels on the inspection target road section. The road surface condition measurement apparatus 131 performs measurement (referred to as “road surface condition measurement”) such as a step measurement of the road with a laser scan unit, road surface imaging with a camera image capturing part, etc., in order to derive MCI (Maintenance Control Index) values. See par 39 - The road surface condition measurement apparatus 131 performs the road surface condition measurement in the identified kilometer post section. In other words, according to the measurement system 100 of the road surface state, the section in which the road surface condition measurement is to be performed with the road surface condition measurement apparatus 131 is limited within the inspection target road section;
see also Cox – see par 6 - the lidar sensor system can output data indicative of positions of the road surface and any road surface features on or in a portion of the road surface in the travel path of the AV (e.g., potholes, uneven pavement, speed bumps, utility access covers, and the like). see par 54 - In a non-limiting example, the road surface analysis component 124 can output a first probability that a speed bump is present at a location on the roadway and a second probability that a pothole is present at the location on the roadway);
measuring the one or more road surface states of the inspection target road (Shimada – see par 52 - The laser scan unit 401 emits laser light to the road surface, and measures a distance to a laser spot position to perform the step measurement, etc., of the road. The camera image capturing part 402 captures the image of the road surface to generate images of the road surface. See par 105 - Further, if the determination part 1502 determines that the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323, the determination part 1502 outputs an acquisition instruction to the laser measurement value acquisition part 1503 and the captured image acquisition part 1504
see also Cox see par 34 - The road surface analysis component 124 is configured to determine whether a road surface feature is present on or in a roadway on which the AV 100 is traveling based upon lidar data output by the lidar sensor system 102. par 57 - AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present… or is not still present; The AV 100 can therefore keep road surface feature data in the map data 130 up-to-date by allowing for the revisiting of road surfaces indicated in the map data 130 as including road surface features, when enough time has passed that the road surface features may no longer exist. When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data; See par 59 - the perception component 122 can indicate a high probability that a first road surface feature is a pot hole and a high probability that a second road surface feature is motor vehicle accident debris (e.g., based upon output of the neural network component 308). In the example, the perception component 122 updates the map data 130 with indications that the first road surface feature and the second road surface feature are present. The indication that the first road surface feature is present can include an indication that the first road surface feature is likely to be a pot hole;
see also Johnson – see par 181-185 – quality scoring metrics applied to sensor data; sensed characteristics can be on [0185] – potholes or road degradation); and
transmitting measured data obtained by the measurement of the one or more road surface states (Shimada – par 51 – road surface condition measurement apparatus 131 includes storage 404 and a communication part 405; par 55 – communication part 405 performs communication with an external apparatus
see also Cox –see par 55 - In an exemplary embodiment, the perception system 122 of the AV 100 confirms the presence of a road surface feature at a particular location based upon acceleration data output by the IMU 104. Subsequently, the AV 100 can transmit map update data to the server computing device 408, wherein the map update data causes the server computing device 408 to update the map data 414 stored at the data store 412 with an indication that the road surface feature is present at the particular location);
wherein the processor further performs instructions based on the need and … to control at least one of whether or not to perform measurement of the one or more road surface states of the inspection target road by a measurement vehicle that is capable of measuring the one or more road surface states, (Shimada – see par 38-39 [as in claim 1]; See par 108 - The storage control part 1505 stores road surface condition measurement information 1410 in which the obtained latitude and longitude, the laser measurement values, and the captured images are associated with date and time of the acquisition thereof in the road surface condition measurement information DB 420. See par 111 - Further, an inspection report document, etc., is made using the road surface condition measurement information of the kilometer post section(s) whose MCI (Maintenance Control index) value is less than or equal to 2, among the derived MCI values
see also Cox – See par 57 – as in claim 1- The AV 100 can sometimes be routed over a previously-identified road surface feature (e.g., previously identified by the AV 100 or another AV that shares map data with the AV 100 such as the AVs 402-406 of FIG. 4). When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present… or is not still present; The AV 100 can therefore keep road surface feature data in the map data 130 up-to-date by allowing for the revisiting of road surfaces indicated in the map data 130 as including road surface features, when enough time has passed that the road surface features may no longer exist. When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data),… the required quality level being needed for the measured data and being set for the road segments (Cox – see par 34 - The road surface analysis component 124 is configured to determine whether a road surface feature is present on or in a roadway on which the AV 100 is traveling based upon lidar data output by the lidar sensor system 102. See par 59 - the perception component 122 can indicate a high probability that a first road surface feature is a pot hole and a high probability that a second road surface feature is motor vehicle accident debris (e.g., based upon output of the neural network component 308). In the example, the perception component 122 updates the map data 130 with indications that the first road surface feature and the second road surface feature are present. The indication that the first road surface feature is present can include an indication that the first road surface feature is likely to be a pot hole).
Shimada discloses vehicle with a road surface condition measurement apparatus 131 (fig. 4) with a laser scan 401 and camera imaging to capture the image of the road (See par 51) and analyzing road surface condition (See par 128). Cox discloses having a perception system (122) with cameras/imaging/Lidar (See par 27) and gives details on lidar in a driving environment for obtaining points 220 representative of surface of roadway (see par 36-37) where training a neural network is used for determining higher probability that road surface has a feature that is present (e.g. pothole – see par 48).
Johnson discloses:
based on the need “and whether or not a required quality level is satisfied” to control at least one of whether or not to perform measurement of the one or more road surface states of the inspection target road by a measurement vehicle that is capable of measuring the one or more road surface states (Johnson discloses based on broadest reasonable interpretation in light of the specification the entire limitation [same as claim 1 – see par 197; see par 198 - maintenance planning application 710 issues a probe management request to TMC 704 (1100) in response to detecting a problem with roadway 106. The probe management request may, for instance, ask TMC 704 to task vehicles 714 in the vicinity of the sign to capture a higher-resolution image of the pavement, or may ask TMC 704 to ask vehicles 714 in the vicinity of the section of roadway 106 under review for increased resolution in one or more of the image capture or sensor readings).
Shimada, Cox, and Johnson disclose:
and whether or not to transmit measured data obtained by the measurement.. (Shimada- par 51 – road surface condition measurement apparatus 131 includes storage 404 and a communication part 405; par 55 – communication part 405 performs communication with an external apparatus;).
see also Cox – see par 6 - the lidar sensor system can output data indicative of positions of the road surface and any road surface features on or in a portion of the road surface in the travel path of the AV (e.g., potholes, uneven pavement, speed bumps, utility access covers, and the like). see par 54 - In a non-limiting example, the road surface analysis component 124 can output a first probability that a speed bump is present at a location on the roadway and a second probability that a pothole is present at the location on the roadway. see par 55 - In an exemplary embodiment, the perception system 122 of the AV 100 confirms the presence of a road surface feature at a particular location based upon acceleration data output by the IMU 104. Subsequently, the AV 100 can transmit map update data to the server computing device 408, wherein the map update data causes the server computing device 408 to update the map data 414 stored at the data store 412 with an indication that the road surface feature is present at the particular location)
Johnson also discloses:
“and whether or not to transmit measured data obtained by the measurement,” as well as “the required quality level being needed for the measured data and being set for the road segments” (Johnson [as above for the “quality level”] - see par 136 - In some examples, service component 538 may determine that the quality metric is more than one standard deviation below the mean for similar infrastructure articles. In some examples, service component 538 may determine an anomaly in a sensor of a vehicle or an environment of the vehicle. In some examples, service component 538 may send an indication of the quality metric to at least one other vehicle for use to modify an operation of the at least one other vehicle in response to detection of the infrastructure article. see par 137 - For example, the infrastructure data may be the result of pre-processing by the respective vehicle of raw sensor data, wherein the classification comprises less data than the raw data on which the classification is generated. In some examples, infrastructure component 536 may select different sets of infrastructure data from a set of infrastructure data generated by a larger number of vehicles than the set of vehicles. That is, infrastructure component 536 may discard or ignore certain sets of infrastructure data from infrastructure data 532 based on one or more criteria (e.g., anomalous criteria, temporal criteria, locational criteria, or any other suitable criteria) (see par 181-182, 185 – infrastructure examples sensed include potholes or road degradation); see par 152 - Furthermore, in some such approaches, at least some of the information includes a confidence score that can be used with information from other vehicles 714 to better assess the condition of the roadway; see par 197 - information may include data elements such as the presence and location of potholes or cracked pavement and may also include a confidence level associated with the quality of the roadway, or the quality of the underlying information.)
It would have been obvious to combine Shimada and Cox and Johnson for the same reasons as claim 1.
Concerning independent claim 12, Shimada and Cox and Johnson disclose:
A server, that is capable of communicating a measurement vehicle measuring one or more road surface states of an inspection target road and transmitting measured data obtained by the measurement of the one or more road surface states (Shimada – See FIG. 1 – patrol vehicle 110, portable terminal 111, road surface condition measurement vehicle 130, server 120; see par 36 – server apparatus 120 obtains information for road sections for which road surface state is to be inspected; the road section whose road surface state is to be inspected is referred to as “inspection target road section”), the server comprising:
at least one processor to perform instructions (Shimada – FIG. 1 has patrol vehicle with portable terminal 111; par 40 - FIG. 2 is a diagram illustrating a hardware configuration of the portable terminal. The portable terminal 111 includes a CPU (Central Processing Unit) 200; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see FIG. 1 – measurement vehicle 130 has road surface condition measurement apparatus 131; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404), and
at least one memory storing the instructions to implement (Shimada - see par 43 - The CPU 200 executes programs stored in the storage 203; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404):
determining, regarding road segments, each of which is a unit obtained by dividing the inspection target road, a need for inspection for the each of the road segments (Shimada see par 36-37 – as in claim 1, 10 above); and
transmitting an instruction to the measurement vehicle in response to a query about a need for inspection for each of the road segments from the measurement vehicle (Shimada see par 38-39 ; Cox par 34, 57, 59 – as in claim 1, 10 above), the instruction being used for controlling measurement of one or more road surface states of each of the road segments to which the query is directed or transmission of measured data obtained by the measurement (Shimada – see par 51-52, 55, 108, 111; FIG. 7, par 105-106, 108; Cox par 55 – as in claim 1, 4, 10 above),
wherein the processor further performs instructions based on the need and … to control at least one of whether or not to perform measurement of the one or more road surface states of the inspection target road by a measurement vehicle that is capable of measuring the one or more road surface states, (Shimada – see par 38-39 [as in claim 1, 10]; See par 108, 111
see also Cox – See par 57),… the required quality level being needed for the measured data and being set for the road segments (Cox – [as in claim 1, 10] - see par 34, par 59).
Johnson discloses:
based on the need “and whether or not a required quality level is satisfied” to control at least one of whether or not to perform measurement of the one or more road surface states of the inspection target road by a measurement vehicle that is capable of measuring the one or more road surface states… (Johnson discloses based on broadest reasonable interpretation in light of the specification the entire limitation [same as claim 1, 10 – see par 136-137, 197; see par 198);
and whether or not to transmit measured data obtained by the measurement.. (Shimada- par 51, par 55).
see also Cox – see par 6, 54, 55)
Johnson also discloses:
“and whether or not to transmit measured data obtained by the measurement,” as well as “the required quality level being needed for the measured data and being set for the road segments” (Johnson [as above for the “quality level”] - see par 136 , 137, 181-182, 185, 152, 197)
It would have been obvious to combine Shimada and Cox and Johnson for the same reasons as claim 1.
Concerning claim 2, Shimada discloses having a date and time of acquisition of measurement information (See par 60).
Cox discloses:
The road inspection system according to claim 1; wherein the at least one processor performs instructions to determine based on one or more past inspection records of each of the road segments (Cox – See par 57 - The AV 100 can sometimes be routed over a previously-identified road surface feature (e.g., previously identified by the AV 100 or another AV that shares map data with the AV 100 such as the AVs 402-406 of FIG. 4). When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present… or is not still present; The AV 100 can therefore keep road surface feature data in the map data 130 up-to-date by allowing for the revisiting of road surfaces indicated in the map data 130 as including road surface features, when enough time has passed that the road surface features may no longer exist).
It would have been obvious to combine Shimada and Cox for the same reasons as claim 1.
Concerning claim 3, Shimada discloses:
The road inspection system according to claim 1; wherein the each of the road segments is a unit obtained by dividing the inspection target road based on one or more inspection conditions of the inspection target road (Shimada –See par 39 - In other words, according to the measurement system 100 of the road surface state, the section in which the road surface condition measurement is to be performed with the road surface condition measurement apparatus 131 is limited within the inspection target road section; see par 66 - the acceleration in the up-and-down direction whose magnitude is greater than or equal to a predetermined threshold is recognized among the acceleration in the up-and-down direction stored in the measurement information 510, and the latitude and the longitude associated with the recognized acceleration in the up-and-down direction are extracted. see par 93 - In the examples “13a” and “13b” illustrated in FIG. 13, the measurement target section information generation part 702 determines that the road surface degradation positions identified by the combination of the latitudes and the longitudes included in the road surface degradation position information 710 are included in the road section A).
Concerning claim 4, Shimada and Cox disclose:
The road inspection system according to claim 1;
wherein the measurement vehicle includes:
at least one processor ( Shimada – see par 34 - As illustrated in FIG. 1, a measurement system 100 of a road surface state includes a portable terminal 111 and a server apparatus 120) to perform instructions to, and at least one memory storing the instructions to implement (Shimada – FIG. 1 has patrol vehicle with portable terminal 111; par 40 - FIG. 2 is a diagram illustrating a hardware configuration of the portable terminal. The portable terminal 111 includes a CPU (Central Processing Unit) 200; see par 43 - The CPU 200 executes programs stored in the storage 203. see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404):
measuring one or more road surface states (Shimada – see par 103 - The road surface condition measurement apparatus 131 according to the embodiment includes a latitude and longitude acquisition part 1501, a determination part 1502, a laser measurement value acquisition part 1503, a captured image acquisition part 1504, and a storage control part 1505. see par 105 - Further, if the determination part 1502 determines that the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323, the determination part 1502 outputs an acquisition instruction to the laser measurement value acquisition part 1503 and the captured image acquisition part 1504);
querying a predetermined server including the controlling about the need for inspection each of the road segments on which the measurement vehicle is to run or is running (Shimada – see FIG. 7 – server apparatus 120 has “measurement target section information output part 703” and “measurement target section information 323”; see par 105 - determination part 1502 determines whether the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323 supplied from the server apparatus 120.
See also Cox – see par 55 - The AV 100 can further be configured to communicate the presence of the road surface feature to a server computing device that controls various operations of a fleet of AVs, responsive to confirming that the road surface feature is present. With reference now to FIG. 4, an exemplary AV fleet system 400 is shown, wherein the fleet system 400 includes the AV 100, a plurality of additional AVs 402-406, and a server computing device 408 that is in communication with each of the AVs 100 and 402-406 by way of a network 410. The server computing device 408 includes a data store 412 that has map data 414 loaded thereon. The map data 414 can be master map data that is used by the server computing device 408 in connection with coordinating or controlling operation of the various AVs 100, 402-406 in the fleet system 400.); and
transmitting measured data obtained by the measurement (Shimada – par 51 – road surface condition measurement apparatus 131 includes storage 404 and a communication part 405; par 55 – communication part 405 performs communication with an external apparatus
see also Cox – see par 6 - the lidar sensor system can output data indicative of positions of the road surface and any road surface features on or in a portion of the road surface in the travel path of the AV (e.g., potholes, uneven pavement, speed bumps, utility access covers, and the like). see par 54 - In a non-limiting example, the road surface analysis component 124 can output a first probability that a speed bump is present at a location on the roadway and a second probability that a pothole is present at the location on the roadway. see par 55 - In an exemplary embodiment, the perception system 122 of the AV 100 confirms the presence of a road surface feature at a particular location based upon acceleration data output by the IMU 104. Subsequently, the AV 100 can transmit map update data to the server computing device 408, wherein the map update data causes the server computing device 408 to update the map data 414 stored at the data store 412 with an indication that the road surface feature is present at the particular location); and
wherein in response to a reply to the query, the measurement is controlled on each of the road segments on which the measurement vehicle is to run or is running or transmission of the measured data obtained by the measurement is controlled (Shimada – see par 105, FIG. 15 - Further, if the determination part 1502 determines that the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323, the determination part 1502 outputs an acquisition instruction to the laser measurement value acquisition part 1503 and the captured image acquisition part 1504. See par 106 - The laser measurement value acquisition part 1503 obtains the laser measurement values detected by the laser scan unit 401 during a period in which the acquisition instruction is output from the determination part 1502. See par 108 - The storage control part 1505 stores road surface condition measurement information 1410 in which the obtained latitude and longitude, the laser measurement values, and the captured images are associated with date and time of the acquisition thereof in the road surface condition measurement information DB 420.
see also Cox - See par 57 - The AV 100 can sometimes be routed over a previously-identified road surface feature (e.g., previously identified by the AV 100 or another AV that shares map data with the AV 100 such as the AVs 402-406 of FIG. 4). When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present; When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data).
It would have been obvious to combine Shimada and Cox for the same reasons as claim 1.
Concerning claim 5 and 19, Shimada and Cox disclose:
The road inspection system according to claim 4;
wherein the at least one processor performs instructions to identify each of the road segments on which the measurement vehicle is to run or is running based on location information about the measurement vehicle and road segment information indicating a geographical location each of the road segments (Shimada – see par 38 - The road surface condition measurement vehicle 130 travels on the inspection target road section. The road surface condition measurement apparatus 131 performs measurement (referred to as “road surface condition measurement”) such as a step measurement of the road with a laser scan unit, road surface imaging with a camera image capturing part, etc., in order to derive MCI (Maintenance Control Index) values. See par 39 - The road surface condition measurement apparatus 131 performs the road surface condition measurement in the identified kilometer post section. In other words, according to the measurement system 100 of the road surface state, the section in which the road surface condition measurement is to be performed with the road surface condition measurement apparatus 131 is limited within the inspection target road section; see par 51 – road surface condition measurement apparatus 131 includes… GPS unit 403;
see also Cox – see par 27 – AV includes… GPS sensor; see par 55 - an exemplary AV fleet system 400 is shown, wherein the fleet system 400 includes the AV 100, a plurality of additional AVs 402-406, and a server computing device 408 that is in communication with each of the AVs 100 and 402-406 by way of a network 410. See par 50 - The road surface analysis component 124 can determine that a conflict exists between the lidar height map and the pre-defined height map when the heights of a location on the road surface indicated by each of the height maps differ by greater than a threshold amount. see par 57 - The AV 100 can sometimes be routed over a previously-identified road surface feature (e.g., previously identified by the AV 100 or another AV that shares map data with the AV 100 such as the AVs 402-406 of FIG. 4) (communicating over a network 410)) and
query the predetermined server about the need for inspection for each of the road segments (Shimada – see FIG. 7 – server apparatus 120 has “measurement target section information output part 703” and “measurement target section information 323”; see par 105 - determination part 1502 determines whether the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323 supplied from the server apparatus 120.
See also Cox – see par 53 - the control system 126 can control the AV 100 to decelerate prior to reaching the road surface feature while not causing the AV 100 to avoid the road surface feature. Subsequently, as the AV 100 passes over the road surface feature, the IMU 104 outputs acceleration data that is indicative of an acceleration of the AV 100. when the AV 100 travels over a speed bump or a pot hole, the AV 100 is accelerated vertically as the vehicle travels over the feature. the IMU 104 provides a confirmation of the presence of a road surface feature when the AV 100 travels over the road surface feature. see par 55 - The AV 100 can further be configured to communicate the presence of the road surface feature to a server computing device that controls various operations of a fleet of AVs, responsive to confirming that the road surface feature is present. With reference now to FIG. 4, an exemplary AV fleet system 400 is shown, wherein the fleet system 400 includes the AV 100, a plurality of additional AVs 402-406, and a server computing device 408 that is in communication with each of the AVs 100 and 402-406 by way of a network 410. The server computing device 408 includes a data store 412 that has map data 414 loaded thereon. The map data 414 can be master map data that is used by the server computing device 408 in connection with coordinating or controlling operation of the various AVs 100, 402-406 in the fleet system 400; see par 57 - The AV 100 can therefore keep road surface feature data in the map data 130 up-to-date by allowing for the revisiting of road surfaces indicated in the map data 130 as including road surface features, when enough time has passed that the road surface features may no longer exist. When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data).
It would have been obvious to combine Shimada and Cox for the same reasons as claim 1.
Concerning claim 6 and 20, Shimada and Cox disclose:
The road inspection system according to claim 1;
wherein the at least one processor performs instructions to hold inspection need/nonneed information indicating the need for inspection for each of the road segments, and control, based on the inspection need/nonneed information and location information about the measurement vehicle, the measurement of the one or more road surface states performed by the measurement vehicle or transmission of the measured data obtained by the measurement (Shimada - See par 39 - The road surface condition measurement apparatus 131 performs the road surface condition measurement in the identified kilometer post section. In other words, according to the measurement system 100 of the road surface state, the section in which the road surface condition measurement is to be performed with the road surface condition measurement apparatus 131 is limited within the inspection target road section; See par 105 - Further, if the determination part 1502 determines that the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323, the determination part 1502 outputs an acquisition instruction to the laser measurement value acquisition part 1503 and the captured image acquisition part 1504
see also Cox – see par 30 - the memory 120 comprises a perception system 122 that is configured to identify objects (in proximity to the AV 100) captured in sensor signals output by the sensor systems 102-108. The perception system 122 includes a road surface analysis component 124; see par 54 - In a non-limiting example, the road surface analysis component 124 can output a first probability that a speed bump is present at a location on the roadway and a second probability that a pothole is present at the location on the roadway. See par 57 - When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present; When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data).
It would have been obvious to combine Shimada and Cox for the same reasons as claim 1.
Concerning claim 7, Shimada and Cox disclose:
The road inspection system according to claim 6, wherein the road inspection system comprises a predetermined server, the predetermined server including
at least one processor to perform instructions (Shimada – FIG. 1 has patrol vehicle with portable terminal 111; par 40 - FIG. 2 is a diagram illustrating a hardware configuration of the portable terminal. The portable terminal 111 includes a CPU (Central Processing Unit) 200; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see FIG. 1 – measurement vehicle 130 has road surface condition measurement apparatus 131; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404), and
at least one memory storing the instructions to implement (Shimada - see par 43 - The CPU 200 executes programs stored in the storage 203; see par 46, FIG. 3 – CPU 301 of server 120 executes programs stored in storage; see par 51-54, FIG. 4 – road surface condition measurement apparatus 131 has CPU 400 executing programs in storage 404):
transmitting the inspection need/nonneed information to the measurement vehicle, based on at least one of a … or the location information about the measurement vehicle (Shimada - see par 105 - determination part 1502 determines whether the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323 supplied from the server apparatus 120.
Shimada discloses alternative on “location”.
Though not required at this time, for purposes of compact prosecution, Cox discloses the other alternatives:
transmitting the inspection need/nonneed information to the measurement vehicle, based on at least one of a certain period, a certain time point, a timing at which one or more inspection records is updated, a timing at which the predetermined server receives a request from the measurement vehicle, or the location information about the measurement vehicle (Cox – See par 57 - The AV 100 can sometimes be routed over a previously-identified road surface feature (e.g., previously identified by the AV 100 or another AV that shares map data with the AV 100 such as the AVs 402-406 of FIG. 4). When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present… or is not still present; The AV 100 can therefore keep road surface feature data in the map data 130 up-to-date by allowing for the revisiting of road surfaces indicated in the map data 130 as including road surface features, when enough time has passed that the road surface features may no longer exist. When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data ).
It would have been obvious to combine Shimada and Cox for the same reasons as claim 1.
Concerning claim 8 and 21, Shimada and Cox disclose:
The road inspection system according to claim 1; wherein at least one processor performs instructions to control the measurement of the one or more road surface states or transmission of the measured data obtained by the measurement, based on whether or not the measurement vehicle satisfies a required quality level that is set for each of the road segments (Shimada – par 51 – road surface condition measurement apparatus 131 includes storage 404 and a communication part 405; par 55 – communication part 405 performs communication with an external apparatus
see also Cox – see par 6 - the lidar sensor system can output data indicative of positions of the road surface and any road surface features on or in a portion of the road surface in the travel path of the AV (e.g., potholes, uneven pavement, speed bumps, utility access covers, and the like). see par 54 - In a non-limiting example, the road surface analysis component 124 can output a first probability that a speed bump is present at a location on the roadway and a second probability that a pothole is present at the location on the roadway. see par 55 - In an exemplary embodiment, the perception system 122 of the AV 100 confirms the presence of a road surface feature at a particular location based upon acceleration data output by the IMU 104. Subsequently, the AV 100 can transmit map update data to the server computing device 408, wherein the map update data causes the server computing device 408 to update the map data 414 stored at the data store 412 with an indication that the road surface feature is present at the particular location).
Shimada discloses vehicle with a road surface condition measurement apparatus 131 (fig. 4) with a laser scan 401 and camera imaging to capture the image of the road (See par 51) and analyzing road surface condition (See par 128). Cox discloses having a perception system (122) with cameras/imaging/Lidar (See par 27) and gives details on lidar in a driving environment for obtaining points 220 representative of surface of roadway (see par 36-37) where training a neural network is used for determining higher probability that road surface has a feature that is present (e.g. pothole – see par 48).
Johnson discloses:
based on at least one of one or more measurement capabilities of the measurement vehicle (claim in alternative – Applicant’s [0050], FIG> 8 gives examples of “resolution (pixels) of camera”, frame rate, bit rate, “object recognition function”, a speed of the measurement vehicle (Applicant’s [0052] gives example for this alternative - the image quality deteriorates as the speed increases, and therefore, the speed (range) of the measurement vehicle may be designated as the required quality level.), or an environmental state of the measurement vehicle (Applicant’s [0078] as published gives example of this alternative - environmental state of the measurement vehicle (ambient brightness, weather, etc.)
Johnson discloses based on broadest reasonable interpretation in light of the specification – see par 197 - maintenance planning application 710 has received information on the quality of roadway 106 from vehicles 714 as the vehicles 714 moved along the roadway 106. The information may include data elements such as the presence and location of potholes or cracked pavement and may also include a confidence level associated with the quality of the roadway, or the quality of the underlying information. see par 198 - maintenance planning application 710 issues a probe management request to TMC 704 (1100) in response to detecting a problem with roadway 106. The probe management request may, for instance, ask TMC 704 to task vehicles 714 in the vicinity of the sign to capture a higher-resolution image of the pavement, or may ask TMC 704 to ask vehicles 714 in the vicinity of the section of roadway 106 under review for increased resolution in one or more of the image capture or sensor readings).
Shimada, Cox, and Johnson are analogous art as they are directed to analyzing road conditions using imaging such as lasers/Lidar (see Shimada Abstract; See Cox Abstract;). Shimada discloses vehicle with a road surface condition measurement apparatus 131 (fig. 4) with a laser scan 401 and camera imaging to capture the image of the road (See par 51) and analyzing road surface condition (See par 128). Cox discloses having a perception system (122) with cameras/imaging/Lidar (See par 27) and gives details on lidar in a driving environment for obtaining points 220 representative of surface of roadway (see par 36-37) where training a neural network is used for determining higher probability that road surface has a feature that is present (e.g. pothole – see par 48). Johnson improves upon Shimada and Cox by disclosing requesting a higher-resolution image for a particular section of a roadway related to the pavement when detecting a problem with a roadway 106. One of ordinary skill in the art would be motivated to further include having differing resolution images requested to efficiently improve upon the road surfaces inspected in Shimada and the training of a neural network to determine probability of a road surface feature in Cox.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the measuring of a road state using a road surface condition measurement from a vehicle in Shimada to further revisit a road surface feature if enough time has passed to check if still exists as disclosed in Cox, and to further have different resolution images for assessing roadway conditions as disclosed in Johnson, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 9 and 22, Shimada and Cox and Johnson disclose:
The road inspection system according to claim 8; wherein the required quality level is set based on at least one of an importance level of each of the road segments or one or more inspections pass/fail criteria used by the road surface inspection (Shimada – see par 114, FIG. 17 - , the server apparatus 120 receives the measurement information 510 from the portable terminal 111, identifies the road surface degradation position(s) via the comparison with the predetermined threshold, and then generates the measurement target section information;
See also Johnson -see par 170 - safety is a high (or highest) priority for the agencies that manage and operate the roadways, safety for the drivers, and the maintenance crews. Another high priority is efficiently spending taxpayer dollars to maximize the safety of the roadway. Techniques of this disclosure may enable optimization or improvement of one or more priorities by using the infrastructure quality scores to prioritize the roadways with the highest opportunities in both infrastructure improvement and safety improvement based on actual roadway data. see par 197 – FIG. 11 - maintenance planning application 710 has received information on the quality of roadway 106 from vehicles 714 as the vehicles 714 moved along the roadway 106. The information may include data elements such as the presence and location of potholes or cracked pavement and may also include a confidence level associated with the quality of the roadway, or the quality of the underlying information. See par 228 - Each vehicle entering the work zone may be tasked by maintenance planning application 710 to collect a series of data elements such as lane keep line classification & confidence and object detection, classification and confidence. The system can then process this disparate data to monitor for degradation or changes in performance, trending, or various other conditions of interest within the work zone.)
It would have been obvious to combine Shimada and Cox and Johnson for the same reasons as claim 8. In addition, Johnson discloses that a particular road segment (e.g. a work zone) can have extra monitoring attention for degradation related to potholes or cracked pavement.
Concerning claim 13, Shimada and Cox disclose:
The server according to claim 12, wherein the at least one processor performs instructions to control measurement of a road surface state(s) of the road or transmission of measured data obtained by the measurement, based on the inspection need/nonneed information and location information, and transmit measured data obtained by the measurement of the road surface state(s), and
wherein the server provides the measurement vehicle with the inspection need/nonneed information (Shimada - See par 39 - The road surface condition measurement apparatus 131 performs the road surface condition measurement in the identified kilometer post section. In other words, according to the measurement system 100 of the road surface state, the section in which the road surface condition measurement is to be performed with the road surface condition measurement apparatus 131 is limited within the inspection target road section; See par 105 - Further, if the determination part 1502 determines that the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323, the determination part 1502 outputs an acquisition instruction to the laser measurement value acquisition part 1503 and the captured image acquisition part 1504
see also Cox – see par 30 - the memory 120 comprises a perception system 122 that is configured to identify objects (in proximity to the AV 100) captured in sensor signals output by the sensor systems 102-108. The perception system 122 includes a road surface analysis component 124; see par 54 - In a non-limiting example, the road surface analysis component 124 can output a first probability that a speed bump is present at a location on the roadway and a second probability that a pothole is present at the location on the roadway. See par 57 - When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present; When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data).
It would have been obvious to combine Shimada and Cox for the same reasons as claim 1.
Concerning claim 18, Shimada and Cox disclose:
The measurement vehicle according to claim 10;
wherein the at least one processor performs instructions to query a predetermined server including the controlling about the need for inspection for the road segment on which the measurement vehicle is to run or is running (Shimada – see FIG. 7 – server apparatus 120 has “measurement target section information output part 703” and “measurement target section information 323”; see par 105 - determination part 1502 determines whether the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323 supplied from the server apparatus 120.
See also Cox – see par 55 - The AV 100 can further be configured to communicate the presence of the road surface feature to a server computing device that controls various operations of a fleet of AVs, responsive to confirming that the road surface feature is present. With reference now to FIG. 4, an exemplary AV fleet system 400 is shown, wherein the fleet system 400 includes the AV 100, a plurality of additional AVs 402-406, and a server computing device 408 that is in communication with each of the AVs 100 and 402-406 by way of a network 410. The server computing device 408 includes a data store 412 that has map data 414 loaded thereon. The map data 414 can be master map data that is used by the server computing device 408 in connection with coordinating or controlling operation of the various AVs 100, 402-406 in the fleet system 400); and
wherein in response to a reply to the query, the measurement on the road segment on which the measurement vehicle is to run or is running is controlled or the transmission of the measured data obtained by the measurement is controlled (Shimada – see par 105, FIG. 15 - Further, if the determination part 1502 determines that the position identified by the obtained latitude and longitude is within the kilometer post section identified by the measurement target section information 323, the determination part 1502 outputs an acquisition instruction to the laser measurement value acquisition part 1503 and the captured image acquisition part 1504. See par 106 - The laser measurement value acquisition part 1503 obtains the laser measurement values detected by the laser scan unit 401 during a period in which the acquisition instruction is output from the determination part 1502. See par 108 - The storage control part 1505 stores road surface condition measurement information 1410 in which the obtained latitude and longitude, the laser measurement values, and the captured images are associated with date and time of the acquisition thereof in the road surface condition measurement information DB 420.
see also Cox - See par 57 - The AV 100 can sometimes be routed over a previously-identified road surface feature (e.g., previously identified by the AV 100 or another AV that shares map data with the AV 100 such as the AVs 402-406 of FIG. 4). When the AV 100 is routed over a previously-identified road surface feature, the perception system 122 of the AV 100 confirms, based upon output of the IMU 104, whether the previously-identified road surface feature is still present; When a fleet of AVs configured in a similar fashion to the AV 100 is operated in an operational region, the fleet of AVs can maintain current road surface feature data).
It would have been obvious to combine Shimada and Cox for the same reasons as claim 1.
Response to Arguments
Applicant's arguments filed 2/18/26 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
Applicant’s arguments are moot over the new rejections necessitated by the amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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