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 .
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because:
The claimed invention is directed to an abstract idea without significantly more.
2. Step 1: Statutory Category Analysis
Claims 1, 8, & 15 are directed to a method, a system, and a system, respectively, which are recognized statutory categories under § 101.
3. Step 2A, Prong One: Judicial Exception (Abstract Idea) Analysis
4. Claims 1, 8, & 15 recite the following limitations:
defining a first polygon for a first lidar data set and defining a second polygon for a second lidar data set;
forming a first buffer around the first polygon and a second buffer around the second polygon;
defining an overlap region between the first buffer and the second buffer;
for each lidar point of the first lidar data set or the second lidar data set within the overlap region, determining a lidar point pair by finding a nearest neighbor of the other of the first lidar data set or the second lidar data set;
determining a positional offset for each lidar point pair; and
determining a polygon offset based at least in part on the positional offsets for the lidar point pairs.
These limitations, when considered at a high level, encompass processes that can be performed mentally or with pen and paper. For example, a person could observe lidar data sets and draw boundaries around them. The buffers are merely an extension of the first boundaries. A person could evaluate these areas of overlap to determine which lidar points correlate between data sets. The distance between the selected points of similarity can then be used to define an offset for each point, which can be extrapolated to the offset between the boundaries themselves. Such observation, evaluation, and organization are forms of mental processes, which are identified as abstract ideas under the judicial exceptions to patent eligibility.
5. Step 2A, Prong Two: Integration into a Practical Application
6. Claims 8 & 15 recite the following additional limitations:
one or more processors; (Claims 8 & 15)
a memory storing instructions; (Claims 8 & 15)
a lidar scanner operable to scan an environment to generate lidar points that define lidar data sets; (Claim 15)
However, these elements are recited at a high level of generality and are merely invoked as tools to carry out the abstract idea. The claims do not specify how the scanners, processors, or memory are uniquely configured or operate in a manner that integrates the abstract idea into a practical application. Instead, they generally link the abstract idea to a particular technological environment, which is insufficient under MPEP 2106.05(h). Merely implementing an abstract idea on generic hardware does not render the claims patent-eligible.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
7. Step 2B: Inventive Concept Analysis
The additional elements— scanners, processors, and memory —are described generically and do not amount to significantly more than the abstract idea itself. The claims do not provide any inventive concept that transforms the nature of the abstract idea into a patent-eligible application. Thus, at least claims 1, 8, & 15 are not directed to patent-eligible subject matter.
8. Dependent claims 2–7, 9–14, & 16-20 are also rejected under § 101. These claims depend from independent claims 1, 8, & 15 and merely further specify aspects of the abstract idea or recite additional generic steps or features. They do not add any meaningful limitations that would remedy the deficiency of the independent claims or amount to significantly more than the abstract idea itself.
Claims 2-5, & 7: Directed to an abstract idea.
Claim 6: Extra solution activity (display the abstract idea).
Claims 9-12, & 14: Apply the abstract idea.
Claim 13: Extra solution activity (display the abstract idea).
Claims 16-18, & 20: Apply the abstract idea.
Claim 19: Extra solution activity (display the abstract idea).
Claim Rejections – 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form
the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –(a)(2) the claimed invention was described in a
patent issued under section 151, or in an application for patent published or deemed
published under section 122(b), in which the patent or application, as the case may be, names
another inventor and was effectively filed before the effective filing date of the claimed
invention.
9. Claims 1-5, 8-12, & 15-18 are rejected under 35 U.S.C. 102(a)(2) as being
unpatentable over Wheeler et al (US 20190120946 A1), hereinafter Wheeler.
10. Regarding Claims 1, 8, & 15
Wheeler teaches a method for resolving positional offsets between adjacent lidar data sets, ([0075]: To record data describing the environment surrounding the autonomous vehicle, multiple sensors are mounted to the vehicle to capture the surrounding environment. In one embodiment, the sensors are mounted such that they rotate 360° over a vertical axis. Examples of potential sensors include, but are not limited to, rolling shutter cameras, sonar sensors, or light detection and ranging (LIDAR) sensors. In embodiments in which the plurality of sensors are LIDAR sensors, each LIDAR sensor records an image of the environment as a point cloud). Wheeler further teaches, ([0091]: Before determining the relative transform based on a comparison of scan data, the scan data segmenting module 940 organizes secondary scan data into segments based on timestamps used to segment the primary scan data. Each of the resulting segments describes a complete rotation of both the primary sensor and the secondary sensor but accounts for the orientation offset between the primary sensor and the secondary sensor). Wheeler goes on to teach, ([0101]: There are three ways to estimate the LiDAR's relative motion (T), i.e., the unwinding transform, during the course of each scan: (1) Using GPS-IMU, (2) Run pairwise point cloud registration using raw, consecutive LiDAR point clouds, (3) Performing a global alignment of various point clouds and computing the relative transform from the adjacent LiDAR poses). See figures 6B, 8A-B, & 13. Wheeler teaches defining a first polygon for a first lidar data set and defining a second polygon for a second lidar data set, ([0063]: The online HD map system 110 divides a physical area into geographical regions and stores a separate representation of each geographical region. Each geographical region represents a continuous physical area bounded by a geometric shape, for example, a square, a rectangle, a quadrilateral or a general polygon. In an embodiment, the online HD map system 110 divides a physical area into geographical regions of the same size independent of the amount of data required to store the representation of each geographical region. In another embodiment, the online HD map system 110 divides a physical area into geographical regions of different sizes based on the amount of data required to represent the geographical region). See figures 8A-B. Wheeler teaches forming a first buffer around the first polygon and a second buffer around the second polygon, ([0067]: According to an embodiment, as illustrated in FIG. 6, each geographical region has a buffer of a predetermined width around it. The buffer comprises redundant map data around all 4 sides of a geographical region (in the case that the geographical region is bounded by a rectangle). FIG. 6A shows a boundary 620 for a buffer of 50 meters around the geographical region 610a and a boundary 630 for buffer of 100 meters around the geographical region 610a. The vehicle computing system 120 switches the current geographical region of a vehicle from one geographical region to the neighboring geographical region when the vehicle crosses a threshold distance within this buffer). Wheeler teaches defining an overlap region between the first buffer and the second buffer, ([0067]: For example, as shown in FIG. 6B, a vehicle starts at location 650a in the geographical region 610a. The vehicle traverses along a route to reach a location 650b where it cross the boundary of the geographical region 610 but stays within the boundary 620 of the buffer. Accordingly, the vehicle computing system 120 continues to use the geographical region 610a as the current geographical region of the vehicle. Once the vehicle crosses the boundary 620 of the buffer at location 650c, the vehicle computing system 120 switches the current geographical region of the vehicle to geographical region 610b from 610a. The use of a buffer prevents rapid switching of the current geographical region of a vehicle as a result of the vehicle travelling along a route that closely tracks a boundary of a geographical region). Wheeler teaches for each lidar point of the first lidar data set or the second lidar data set within the overlap region, determining a lidar point pair by finding a nearest neighbor of the other of the first lidar data set or the second lidar data set and determining a positional offset for each lidar point pair, ([0101]: (2) Run pairwise point cloud registration using raw, consecutive LiDAR point clouds). Wheeler further teaches, ([0082]: The alignment module 950 compares secondary scan data with primary scan data recorded over the same range of time and determines a relative transform between the primary scan data and secondary scan data. In embodiments in which the primary and secondary sensors are LIDAR sensors, the scan data resembles point clouds and the alignment module determines the relative transform between the scans of the primary and secondary sensors is determined using an iterative closest point technique, for example point-to-point ICP, point-to-plane ICP, or an alternate ICP technique. Because the primary scan data and the secondary scan data are both scans of the same surrounding environment, the alignment module 950 may detect common features between two the point clouds. Using the relative transform and the identified common features in each set of scan data, the alignment module 950 aligns primary scan data with secondary scan data to aggregate the two sets of scan data into a representation of the surrounding environment, for example, a point cloud representation. In such embodiments, the aligned point cloud is stored in the point cloud store 960. The alignment of primary scan data and secondary scan data is further described with reference to FIGS. 14-15). Wheeler teaches determining a polygon offset based at least in part on the positional offsets for the lidar point pairs, ([0084]: FIG. 10 illustrates a flow chart of the process for calibrating a set of sensors mounted on a vehicle, according to an embodiment. The illustrated process is executed by the various components of the system architecture described with reference to FIG. 9. As described above, the calibration module 260 identifies 1010 a primary sensor and identifies the remaining sensors as secondary sensors. The primary sensor records scan data and stores the scan data in the primary sensor data store 910 while the secondary sensor records scan data and stored the scan data in the secondary sensor data store 920. The primary sensor rotation tracker 930 assigns scan-start times and/or a scan-end times to the stored primary scan data in order to segment the data into individual rotations of the primary sensor. The scan data segmenting module 940 segments secondary scan data recorded by each secondary sensor based on the scan-start and scan-end times assigned to the primary scan data resulting in segments of secondary scan data representative of a complete rotation of the secondary sensor. The alignment module 950 aligns 1030 the primary scan data with the secondary scan data based on a comparison of scan data recorded by the primary sensor and the secondary sensor using. In some embodiments in which the sensors record point-cloud representations of the environment, the alignment module 950 uses ICP (iterative closest point) techniques. Based on the alignment of the primary and secondary scan data, the alignment module 950 computes 1040 the relative transforms between the sets of scan data). See figure 10, especially steps 1030 & 1040 in the flowchart.
11. Regarding Claims 8 & 15:
Wheeler teaches a lidar scanner operable to scan an environment to generate lidar points that define lidar data sets, ([0075]: To record data describing the environment surrounding the autonomous vehicle, multiple sensors are mounted to the vehicle to capture the surrounding environment. In one embodiment, the sensors are mounted such that they rotate 360° over a vertical axis. Examples of potential sensors include, but are not limited to, rolling shutter cameras, sonar sensors, or light detection and ranging (LIDAR) sensors. In embodiments in which the plurality of sensors are LIDAR sensors, each LIDAR sensor records an image of the environment as a point cloud). Wheeler teaches, one or more processors, and a memory storing instructions, ([0107]: FIG. 19 is a block diagram illustrating components of an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller), according to an embodiment. Specifically, FIG. 19 shows a diagrammatic representation of a machine in the example form of a computer system 1900 within which instructions 1924 (e.g., software) for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment). Wheeler further teaches, ([0109]: The example computer system 1900 includes a processor 1902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any combination of these), a main memory 1904, and a static memory 1906, which are configured to communicate with each other via a bus 1908).
12. Regarding Claims 2, 9 & 16:
Wheeler teaches the positional offset is a vertical offset, ([0005]: Using the primary scan data and the secondary scan data, the system calibrates the plurality of sensors by determining a relative transform for transforming points between the primary scan data and the secondary scan data. Calibrating each sensor mounted on the autonomous vehicle to synchronize the entire sensor system ensures that the computing system is able to align the scan data collected by each sensor, for example based on common landmarks within each scan). Wheeler further teaches, ([0059]: FIG. 5 illustrates the components of an HD map, according to an embodiment. The HD map comprises maps of several geographical regions. The HD map 510 of a geographical region comprises a landmark map (LMap) 520 and an occupancy map (OMap) 530. The landmark map comprises information describing lanes including spatial location of lanes and semantic information about each lane. The spatial location of a lane comprises the geometric location in latitude, longitude and elevation at high prevision, for example, at or below 10 cm precision. The semantic information of a lane comprises restrictions such as direction, speed, type of lane (for example, a lane for going straight, a left turn lane, a right turn lane, an exit lane, and the like), restriction on crossing to the left, connectivity to other lanes and so on. The landmark map may further comprise information describing stop lines, yield lines, spatial location of crosswalks, safely navigable space, spatial location of speed bumps, curb, and road signs comprising spatial location and type of all signage that is relevant to driving restrictions. Examples of road signs described in an HD map include stop signs, traffic lights, speed limits, one-way, do-not-enter, yield (vehicle, pedestrian, animal), and so on).
13. Regarding Claims 3 & 10:
Wheeler teaches the positional offset is a lateral offset.
See Claims 2, 9 & 16.
14. Regarding Claims 4, 11 & 18:
Wheeler teaches the first lidar data set and the second lidar data set represent a road, ([0040]: The perception module 210 receives sensor data 230 from the sensors 105 of the vehicle 150. This includes data collected by cameras of the car, LIDAR, IMU, GPS navigation system, and so on. The perception module 210 uses the sensor data to determine what objects are around the vehicle, the details of the road on which the vehicle is travelling, and so on).
15. Regarding Claims 5, 12 & 17:
Wheeler teaches applying a correction factor to one or both of the first lidar data set and the second lidar data set based at least in part on the polygon offset, ([0091]: Before determining the relative transform based on a comparison of scan data, the scan data segmenting module 940 organizes secondary scan data into segments based on timestamps used to segment the primary scan data. Each of the resulting segments describes a complete rotation of both the primary sensor and the secondary sensor but accounts for the orientation offset between the primary sensor and the secondary sensor. By segmenting secondary scan data based on the same boundary conditions (e.g., the scan-start time and scan-end time) as the primary scan data, the system ensures that the environment being compared between the two segments is the same environment. Continuing from the exemplary configuration of FIG. 12, FIG. 13 illustrates a representation of the calibration of a primary sensor and a secondary sensor as a function of time, according to an embodiment. Compared to FIG. 12, FIG. 13 describes alternative representations of the range of motion 1250 for the primary sensor and the range of motion 1260 for the secondary sensor. The scan-start time for rotations of both the primary sensor 1210 and the secondary sensor 1220 are labeled using T0. As described with reference to FIG. 12, at T0, the primary sensor is oriented at 0° and the secondary sensor is oriented at 45°. As the primary sensor 1210 passes the reference angle, the primary sensor rotation tracker 930 records the scan-end time of the current rotation/scan-start time of the following rotation (T1). Accordingly, the scan data segmenting module 940 generates a first segment between T0 and T1 which represents the 360° rotations of the primary sensor from the initial 0° orientation and the secondary sensor from the initial 45° orientation. Having accounted for the initial 45° offset in the first segment, the scan data segmenting module 940 continues to receive scan-start times from the primary sensor rotation tracker 930 and identifies secondary scan data associated with a matching timestamp to generate the second, third, and fourth segments as illustrated).
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not
identically disclosed as set forth in section 102, if the differences between the claimed invention and the
prior art are such that the claimed invention as a whole would have been obvious before the effective filing
date of the claimed invention to a person having ordinary skill in the art to which the claimed invention
pertains. Patentability shall not be negated by the manner in which the invention was made.
16. Claims 6, 13 & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler et al (US 20190120946 A1), hereinafter Wheeler, as applied to Claims 1, 8, & 15, in view of He et al (CN 106600690 A), hereinafter He.
17. Regarding Claims 6, 13 & 19:
Wheeler does not teach displaying a visual representation of the polygon offset between the first polygon and the second polygon
However, He teaches a point cloud registration method using (Terrestrial Laser Scanning, TLS), ([Abstract]: The invention claims a method based on point cloud data of complex three-dimensional modelling method, comprising the following steps: step 1, carrying out laser scanning on the building to obtain an integral building surface laser point cloud data, step 2, constructing a two-dimensional line graph, step 3, reconstructing the three-dimensional solid model. The modelling method can overcome the limitation of traditional modelling method to realize fast, accurately and efficiently establishing three-dimensional model of the building). He further teaches, ([0085]: Building the three-dimensional model into 3DMAX in the software becomes editable polygon, selecting a face to be mapping, opening the material editor corresponding to the processed photo dragged, then attribute information column modifying texture photograph of ratio and offset position, to make it conform to the real building information. is mapping texture mapping and rendering three-dimensional model display as shown in FIG. 10). He further teaches, ([Fig. 9]: Shows the visual representation of the polygon offset with a menu including the offset).
It would have been obvious for one of ordinary skill in the art at the time of filing to modify Wheeler with He to include displaying a visual representation of the polygon offset between the first polygon and the second polygon, since it is the same field of endeavor and results would have been predictable. One of ordinary skill in the art at the time of filing would have been motivated to modify Wheeler with He since, such a visual representation of the polygon offset can help the user to isolate local drift, highlight anomalies caused by dynamic objects, moving vehicles, or sparse data regions that disrupt matching algorithms, and provides an intuitive, human-readable check to confirm whether alignment meets the strict safety thresholds required for autonomous navigation. In addition, the display of such data will show how well iterative algorithms (like ICP) minimize the residual distance between overlapping frames, enabling immediate tuning of registration parameters before errors accumulate into the final online high-definition (HD) map.
18. Regarding Claims 13 & 19:
Wheeler teaches an electronic display, ([0109]: The example computer system 1900 includes a processor 1902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any combination of these), a main memory 1904, and a static memory 1906, which are configured to communicate with each other via a bus 1908. The computer system 1900 may further include graphics display unit 1910 (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT))).
19. Claims 7, 14 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler et al (US 20190120946 A1), hereinafter Wheeler, as applied to Claims 1, 8, & 15, in view of Van der Merwe et al (US 20210278851 A1), hereinafter Van der Merwe.
20. Regarding Claims 7, 14 & 20:
Wheeler does not teach the first polygon is defined by forming a first concave hull around lidar points of the first lidar data set and the second polygon is defined by forming a second concave hull around lidar points of the second lidar data set.
However, Van der Merwe teaches a system and method for controlling an autonomous vehicle using maps and onboard sensors, including Lidar, ([Abstract]: A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates). Van der Merwe further teaches, ([0128]: Continuing to refer to FIG. 1G, in some configurations, polygons 10759 can be processed by removing outliers by conventional means such as, for example, but not limited to, statistical analysis techniques such as those available in the Point Cloud Library, http://pointclouds.org/documentation/tutorials/statistical_outlier.php. Filtering can include downsizing segments 10137 (FIG. 1D) by conventional means including, but not limited to, a voxelized grid approach such as is available in the Point Cloud Library, http://pointclouds.org/documentation/tutorials/voxel_grid.php. Concave polygons 10263 can be created, for example, but not limited to, by the process set out in the process set out in A New Concave Hull Algorithm and Concaveness Measure for n-dimensional Datasets, Park et al., Journal of Information Science and Engineering 28, pp. 587-600, 2012).
It would have been obvious for one of ordinary skill in the art at the time of filing to modify Wheeler with Van der Merwe to include the first polygon is defined by forming a first concave hull around lidar points of the first lidar data set and the second polygon is defined by forming a second concave hull around lidar points of the second lidar data set, since it is the same field of endeavor and results would have been predictable. One of ordinary skill in the art at the time of filing would have been motivated to modify Wheeler with Van der Merwe since, using concave hull in point cloud registration provides a tighter boundary fit, unlike convex hulls that artificially bridge gaps and swallow empty space (such as curves, street furniture, or road recesses), concave hulls tightly hug the actual non-convex contours of road segments and structures. In addition, the use of concave hulls enables better feature matching due to more precise polygon boundaries, as well as reduction of false overlaps, leading to a more reliable transformation matrix during registration, when compared with convex hulls.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
US 10684372 B2: Discloses systems and methods for autonomous vehicle localization.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES W NAPIER whose telephone number is (571)272-7451. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Helal Algahaim can be reached at (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.W.N./Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645
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