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 .
Application Status
This office action is in response to the application filed on 07/30/2025. The claims and drawings are objected to as detailed below. Claims 1-20 are pending and rejected as detailed below. This action is non-final.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Applicant has claimed priority to foreign application KR10-2025-0026233, filed on 02/27/2025.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the "output a signal indicating the identified tunnel ceiling, and control, based on the signal, autonomous driving of the vehicle," of claims 1, 11, and 19 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered. Applicant’s Fig. 3, which describes the method, does not include these steps. According to Fig. 3, it would appear that the system merely filters and flags noise. There is no outputting of the signal of the ceiling, and certainly no controlling of the vehicle. Applicant further uses Fig. 7 to describe how the autonomous driving control occurs. Fig. 7 is merely a generic figure describing a computer that lacks any elements that could control the vehicle. Therefore, the drawings do not show how the vehicle is controlled via the signal.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Fig. 4, point 404 is missing as described in [00127]
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Fig. 5, item 520
Fig. 7, items 1310 and 1320
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 9 and 10 are objected to because of the following informalities:
A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim.
A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n).
Appropriate correction is required. In general applicant can correct this by cancelling claims 6, 9, and 10 and readding them as a new series of claims at the end of the claims. In general this will be corrected if the application proceeds to allowance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
The offending limitations are in the independent claims 1, 11, and 19. Claims 2-10, 12-18, and 20 would be rejected due to their dependence on rejected claims. As the independent claims all use similar language only one explanation will be given. Regarding claim 1, the spec does not appear to enable the limitations of, “output a signal indicating the identified tunnel ceiling, and control, based on the signal, autonomous driving of the vehicle.” The claim as written leaves the examiner questioning what exactly is occurring here. The figures do not show how these two steps feed into the overall flow of the method, as noted in the drawing objections. Further while Fig. 7 is supposed to show a computer that enables the autonomous driving of the vehicle, it is merely what appears to be a generic computing device, that lacks sensors, vehicle controllers, or any other output/control devices. Fig. 1, at least has a lidar but again no controlling elements. Applicant broadly speaks of what an autonomous vehicle is in [0046]-[0052], but these amount to merely describing what an autonomous vehicle is in general. The applicant does not describe how exactly the tunnel ceiling signal is used to control a vehicle. Going through the Wands Factors, the examiner has determined, that the claim language is broad in scope, the invention is related to vehicle sensors, the prior art does use Lidar to generate point clouds for vehicle control, one of ordinary skill would likely know what is occurring, the art can be predictable, the inventor did not provide enough direction, this may exist, and someone would need to experiment quite a bit. The examiner is left with a series of questions, what is the signal output to, how does the computer control the vehicle, is the vehicle autonomy level lowered, does the vehicle merely follow the ceiling, what elements of the vehicle are autonomous, how does the ceiling signal relate to the various autonomy levels, and the like. In light of this the examiner finds not all parts of claim 1 are enabled. Claims 11 and 19 are rejected for similar reasons. Dependent claims are rejected due to their dependence on rejected claims.
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.
Claim(s) 1-4, 7, 11-14, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (KR-20210135753-A) in view of Soni (US PG Pub 2021/0061064).
Regarding claim 1, Lee teaches an apparatus of a vehicle, ([0027]-[0030] teaches a vehicle with a sensor apparatus) the apparatus comprising:
a processor; ([0033] and [0177]-[0179] teaches a processor in the apparatus) and
a memory storing at least one instruction that, when executed by the processor communicating with the memory, ([0178]-[0179] teaches a memory equipped to store programs executed by the processor) is configured to cause the apparatus to:
generate, based on sensor data from a sensor of the vehicle, point cloud data representing a surrounding environment of the vehicle, ([0028] and [0035]-[0036] teach the system using a sensor to generate a point cloud of the surrounding environment)
detect, from the point cloud data, a plurality of candidate points ([0035]-[0036], [0040]-[0044], and [0129]-[0130] teach the system determining a series of points that could be a tunnel ceiling, or structure on top of the tunnel, by excluding data of some kind.)
determine a slope for each of the plurality of candidate points, ([0104]-[0106] teaches the vehicle determining a reference linear pattern for each of the plurality of points in the cloud. This reference pattern is based on angle and distance of the points)
exclude, from the plurality of candidate points, each candidate point having the determined slope being greater than or equal to a second threshold value, ([0107]-[0108] teaches the system comparing the reference pattern to the detected results and determining an abnormal shape. [0115] teaches the system excluding abnormal point data)
identify, based on the remaining candidate points and based on ([0106] and [0129]-[0140] teach the system based on the observed data, determining the pattern of the structure above the vehicle in the tunnel, i.e. the ceiling)
output a signal indicating the identified tunnel ceiling, ([0133]-[0140] and [0168]-[0169] teach the system determining the vehicle position as a function of an output of the indicating ceiling structure) and
control, based on the signal, autonomous driving of the vehicle. ([0182]-[0183] teach the system using an output signal to perform autonomous driving)
Lee does not teach by excluding a point from among points input from an upper channel of the sensor, based on a height of the point being less than or equal to a first threshold value and a learning model stored in the memory.
However, Sonni teaches “by excluding a point from among points input from an upper channel of the sensor, based on a height of the point being less than or equal to a first threshold value” ([0048] and [0058] teach the system using a height threshold to remove a series of points from the collected point cloud data. This height threshold is based in part on the determination that the remaining points may belong to a tunnel ceiling) and “a learning model stored in the memory.” ([0043] and [0051] teach the system using a machine learned model to determine that a surface is the ceiling of a tunnel)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee with Soni; and have a reasonable expectation of success. Both relate to vehicle control systems that use point clouds to detect structures around the vehicle, specifically tunnels. By excluding points lower than a certain height the system can ensure that it is not processing data that would not be the tunnel ceiling. While Lee teaches near this, it does not use height for this. AS Soni teaches in [0058] using various threshold heights for filtering data allows for unique situations to be processed based on what is happening around the vehicle. By eliminating extraneous data points the process is optimized. Additionally, the use of a machine learned process will allow the output of processed images quicker and more optimized than traditional processes as described in [0051].
Claims 11 and 19 are substantially similar in language and scope and would be rejected for the same rationale as recited above.
Regarding claim 2, Lee teaches the apparatus of claim 1, wherein at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to: identify an adjacent point located in a traveling direction of the vehicle, ([0135]-[0137] teaches comparing the information of adjacent points in the vehicle’s travel) and wherein the slope is determined based on a height difference between a candidate point and the adjacent point and based on a horizontal distance between the candidate point and the adjacent point, ([0131]-[0140] teach the system comparing the height of adjacent points over a time period, i.e. distance, to determine the linear shape of the detected points) and
identify, ([0106] and [0129]-[0140] teach the system based on the observed data, determining the pattern of the structure above the vehicle in the tunnel, i.e. the ceiling)
Lee does not teach a learning model stored in the memory.
However, Sonni teaches “a learning model stored in the memory.” ([0043] and [0051] teach the system using a machine learned model to determine that a surface is the ceiling of a tunnel)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee with Soni; and have a reasonable expectation of success. Both relate to vehicle control systems that use point clouds to detect structures around the vehicle, specifically tunnels. By excluding points lower than a certain height the system can ensure that it is not processing data that would not be the tunnel ceiling. While Lee teaches near this, it does not use height for this. AS Soni teaches in [0058] using various threshold heights for filtering data allows for unique situations to be processed based on what is happening around the vehicle. By eliminating extraneous data points the process is optimized. Additionally, the use of a machine learned process will allow the output of processed images quicker and more optimized than traditional processes as described in [0051].
Claim 12 is substantially similar in language and scope and would be rejected for the same rationale as recited above.
Regarding claim 3, Lee teaches the apparatus of claim 1, wherein the second threshold value is a variable in units of frame over time, and wherein the second threshold value is determined based on the following equation: slopeth = weightth * slopet-1 + (1 – weightth) * slopet, and wherein the slopeth is a second threshold value applied at time 't', the weightth is a weight measured by using empirical data, the slopet is a slope of a candidate point measured at time 't', and the slopet-1 is a slope of the candidate point measured at time 't-1'. (Fig. 5(b), [0093]-[0096], [0101]-[0109], and [0141] teach the system determining a value of the point cloud for each frame. This is used to generate a linear pattern, i.e. slope, this linear pattern is used to determine the shape and location of point cloud data over a given time period/distance in order to determine the detected pattern of the data, based on frame information, time, vertical distance, horizontal distance, intensity, and range. Applicant’s specific formula is a design choice to determine the slope threshold, and the formula of Lee would be analogous.)
Claim 13 is substantially similar in language and scope and would be rejected for the same rationale as recited above.
Regarding claim 4, Lee teaches the apparatus of claim 3, wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to recognize, as a tunnel ceiling point, a candidate point whose measured slope is less than the second threshold value corresponding to a ceiling determination value, even after the slopeth reaches the ceiling determination value. (Fig. 6 and [0145]-[0148] teach the system determining the ceiling slope even when the system determines an abnormal pattern as the same as the reference linear pattern)
Claim 14 is substantially similar in language and scope and would be rejected for the same rationale as recited above.
Regarding claim 7, Lee teaches the apparatus of claim 1, wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to, before detecting the plurality of candidate points, determine, based on a slope of a first point satisfying a predetermined ground slope threshold value, the first point as a ground point, wherein the first point is from among points input from a lower channel of the sensor, and wherein the ground point is a point estimated not to correspond to the tunnel ceiling. ([0064] and [0099]-[0106] teaches the system determining the "floor" of the tunnel which is analogous to the ground of the tunnel. The system can exclude the determined ground points as described in [0081])
Claim 17 is substantially similar in language and scope and would be rejected for the same rationale as recited above.
Regarding claim 20, Lee teaches the method of claim 19, further comprising, prior to generation of the plurality of candidate points: identifying, based on a ground slope threshold value, one or more ground points from the data; ([0064] and [0099]-[0106] teaches the system determining the "floor" of the tunnel which is analogous to the ground of the tunnel) and
excluding the one or more ground points from the data. ([0081] teaches the system excluding ground points from the collected points)
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee and Soni in view of Akiyama (US PG Pub 2024/0385298).
Regarding claim 5 the combination of Lee and Sonni teach the apparatus of claim 1.
The combination of Lee and Soni does not teach wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to generate a noise flag for each candidate point excluded from the plurality of candidate points.
However, Akiyama teaches “wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to generate a noise flag for each candidate point excluded from the plurality of candidate points.” (Fig. 4, S300; and [0056] and [0068] teach a noise removal process by determining a noise element in LiDAR data and then flagging it and removing it)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Soni with Akiyama; and have a reasonable expectation of success. All relate to the control of sensor devices for capturing data. As Akiyama teaches in [0032]-[0034] the use of LiDAR for capturing data is useful however, it can be subject to light noise. This noise is the product of a number of issues, but can cause the system to improperly process data and cause the system to find structures that don’t actually exist. By identifying and removing the noise it allows for a smoother processing and prevents erroneous data from being captured.
Claim 15 is substantially similar in language and scope and would be rejected for the same rationale as recited above.
Claim(s) 6, 9-10, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee and Soni in view of Chinthalapudi (US PG Pub 2026/0126525).
Regarding claim 6 the combination of Lee and Sonni teach the apparatus of claim 1.
The combination of Lee and Soni does not teach wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to detect the plurality of candidate points based on applying a width threshold value and a length threshold value to the points input from the upper channel of the sensor.
However, Chinthalapudi teaches “wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to detect the plurality of candidate points based on applying a width threshold value and a length threshold value to the points input from the upper channel of the sensor.” (Fig. 3 and [0038]-[0041] teach the system determining a spread of points in a given axis. This spread is applied over the width and length of a point cloud cluster to determine what the points correspond to)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Soni with Chinthalapudi; and have a reasonable expectation of success. All relate to vehicle sensor devices and capturing point cloud data. As Chinthalapudi teaches in [0003] the systems used can often detect ghost images. These artifacts don’t exist in real life but are left over from the data capturing. [0004] adds that by clustering the data in specific ways it can be classified based on the variances of the zones to detect different objects. This allows for the detection of various elements of the tunnel or tunnels.
Claim 16 is substantially similar in language and scope and would be rejected for the same rationale as recited above.
Regarding claim 9 the combination of Lee and Sonni teach the apparatus of claim 6.
The combination of Lee and Soni does not teach wherein the width threshold value, the length threshold value, and the first threshold value are set differently for each tunnel of a plurality of tunnels having tunnel ceilings.
However, Chinthalapudi teaches “wherein the width threshold value, the length threshold value, and the first threshold value are set differently for each tunnel of a plurality of tunnels having tunnel ceilings.” (Fig. 3 and [0039]-[0042] teaches the system having different threshold spreads depending on what is being detected, tunnel, wall, natural tunnel, etc. [0023]-[0024] teaches that different tunnels may require different sensor perceptions to detect them. [0030] further teaches that different values are necessary depending on the entrance way of the tunnel)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Soni with Chinthalapudi; and have a reasonable expectation of success. All relate to vehicle sensor devices and capturing point cloud data. As Chinthalapudi teaches in [0003] the systems used can often detect ghost images. These artifacts don’t exist in real life but are left over from the data capturing. [0004] adds that by clustering the data in specific ways it can be classified based on the variances of the zones to detect different objects. This allows for the detection of various elements of the tunnel or tunnels.
Regarding claim 10 the combination of Lee and Sonni teach the apparatus of claim 9.
The combination of Lee and Soni does not teach wherein the width threshold value, the length threshold value, and the first threshold value are set differently for each of a plurality of predefined sections within a tunnel having the tunnel ceiling.
However, Chinthalapudi teaches “wherein the width threshold value, the length threshold value, and the first threshold value are set differently for each of a plurality of predefined sections within a tunnel having the tunnel ceiling.” (Fig. 3 and [0039]-[0040] teaches the system determining the spread of points with different values depending on the section of tunnel the vehicle is in)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Soni with Chinthalapudi; and have a reasonable expectation of success. All relate to vehicle sensor devices and capturing point cloud data. As Chinthalapudi teaches in [0003] the systems used can often detect ghost images. These artifacts don’t exist in real life but are left over from the data capturing. [0004] adds that by clustering the data in specific ways it can be classified based on the variances of the zones to detect different objects. This allows for the detection of various elements of the tunnel or tunnels.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee and Soni in view of Keilaf (US PG Pub 2024/0255645).
Regarding claim 8, the combination of Lee and Sonni teach the apparatus of claim 1.
The combination of Lee and Soni does not teach wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to generate history information about misrecognition, wherein the misrecognition corresponds to a case where there is a point, which is incorrectly identified as a tunnel ceiling point of the tunnel ceiling
However, Keilaf teaches “wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the apparatus to generate history information about misrecognition, wherein the misrecognition corresponds to a case where there is a point, which is incorrectly identified as a tunnel ceiling point of the tunnel ceiling.” ([0088] and [0112] teach the use of historical lidar data in order to ensure accurate sensor collection in the current environment)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Soni with Keilaf; and have a reasonable expectation of success. All relate to the use of sensor data to generate point clouds. As Kelad teaches in [0088] the use of historical data provides advantages when comparing new data to it. The historical data can be used to determine whether or not there are errors. The historic data can also be used for error checking and debugging processes. This allows for cleaner and more complete data processing.
Claim 18 is substantially similar in language and scope and would be rejected for the same rationale as recited above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Heo (US PG Pub 2017/0059713) teaches a vehicle that detects a lane using measurement data of a lidar sensor and a lane detection method are provided. The vehicle includes a distance sensor and a processor that is configured to determine data that indicates a road lane among data obtained by the distance sensor. Additionally, the processor accumulates the determined lane data using vehicle speed information and determines the road lane using the accumulated lane data.
Gupta (US Pat 12,019,448) teaches methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining map data and detection data for an area in an environment; determining a change in the area based on the map data and the detection data; and generating control data based on the change.
Aluru (US PG Pub 2025/0231048) teaches a tunnel detection system includes an imager configured to capture image data and including a microcontroller, radar configured to capture radar data, and an ambient light sensor configured to capture light data. An electronic control unit (ECU) is communicatively coupled with each of the imager, the radar, and the ambient light sensor. The ECU includes data processing hardware that includes a tunnel detection application and a navigation application. The tunnel detection application is configured to compare an exposure value of the image data and a light value of the light data with the radar data to define tunnel data. The tunnel detection application is further configured to identify a tunnel based on the tunnel data.
Wygant (US PG Pub 2020/0025578) teaches a process for constructing highly accurate three-dimensional mappings of objects along a rail tunnel in which GPS signal information is not available includes providing a vehicle for traversing the tunnel on the rails, locating on the vehicle a LiDAR unit, a mobile GPS unit, an inertial navigation system, and a speed sensor to determine the speed of said vehicle.
Baek (US PG Pub 2023/0311896) teaches a vehicle for preventing accidents when entering a tunnel includes: a front camera provided in the vehicle and configured to acquire front image data, a light wave detection and ranging (LiDAR) sensor provided in the vehicle and configured to acquire point cloud data, a front radar provided in the vehicle and configured to acquire front radar data, and a driver assist system including at least one of a cruise control system, an emergency braking control system, or a body control module. The vehicle further includes a controller that recognizes a tunnel positioned in front of the vehicle based on the front image data and the point cloud data, detects a preceding vehicle based on the front radar data, and controls an operation of the driver assist system based on the recognition of the tunnel in front of the vehicle and a relative speed between the preceding vehicle and the vehicle.
He (US PG Pub 2020/0182969) teaches methods, apparatuses, and systems are provided for detecting overhead obstructions along a path segment. One exemplary method includes receiving three-dimensional data collected by a depth sensing device traveling along a path segment, wherein the three-dimensional data comprises point cloud data positioned above a ground plane of the path segment. The method further includes identifying data points of the point cloud data positioned within a corridor positioned above the ground plane. The method further includes projecting the identified data points onto a plane. The method further includes detecting the overhead obstruction based on a concentration of point cloud data positioned within a plurality of cells of the plane. The method further includes storing the detected overhead obstruction above the path segment within a map database.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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