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 .
Response to Amendment
This Office Action is in response to Applicant’s amendment filed 12/18/25 which has
been entered and made of record. Claims 1, 3, 7, 14, and 16-17 have been amended. Claim 21 has been newly added. Claim 8 has been cancelled. Claims 1-7 and 9-21 are pending in the application. Applicant’s amendments to the Claims and Drawings have overcome each and every objection previously set forth in the Non-Final Office Action mailed October 8th 2025.
Response to Arguments
Applicant’s arguments, see remarks, page 2, filed 12/18/25, with respect to reference 80 in fig. 6 have been fully considered and are persuasive. The objection of fig. 6 has been withdrawn.
Applicant’s arguments with respect to claim(s) 1-7 and 9-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument (due to applicant’s arguments directed to newly amend limitation(s) which is addressed by new prior art presented in this Office Action).
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.
Claim(s) 1, 7-9, 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seiler (U.S. Patent Application Publication No. 2024/0094027), hereinafter referenced as Seiler, in view of Cornell (U.S. Patent No. 8,730,258), hereinafter referenced as Cornell, Karagiorgou et al. (On Vehicle Tracking Data-Based Road Network Generation), hereinafter referenced as Karagiorgou, Staempfle et al. (U.S. Patent No. 8,630,762), hereinafter referenced as Staempfle, Lin et al. (U.S. Patent Application Publication No. 2025/0207969), hereinafter referenced as Lin, and ZHANG et al. (U.S. Patent Application Publication No. 2020/0279478), hereinafter referenced as Zhang.
Regarding claim 1, Seiler teaches a system for telemetry data collected from a plurality of vehicles into map content, the system comprising: (abstract teaches "system…map generation assembly (MGA) including a processing assembly and an electronic memory assembly, the map generation assembly being in data communication with the data communications network and arranged to receive the position reports, the MGA being configured to; generate batches of points from the position reports, the points corresponding to vehicle positions for respective vehicles at respective times"); this shows using multiple vehicle's telemetry data such as positions for map generation assembly; one or more central computers in wireless communication with the plurality of vehicles that are each situated within a predefined geofenced area, (paragraph 8 teaches "provided a method for issuing updates of a map in respect of a geographical area", paragraph 116 " System 101 includes the data network 31 for placing the vehicle communications system 36 of each haul truck 2-1, . . . , 2-I in data communication with MGA 33", and fig. 4 reference 33 shows the computer/MGA 33 communicating wirelessly using data network 31); geographical area mentioned for the haul roads would be the predefined geofenced area; the one or more central computers executing instructions to: receive road network data representing a road network for the predefined geofenced area, (paragraph 147 teaches "in a first step, the MGA 33 groups GPS points from the position reports 25 grouped into clusters of points that share similar locations and orientations. A second step then updates the connections between the clusters to form a road network."); this shows the road network formed from received points and cluster data of road network for the geographical/geofenced area; wherein the road network is a network graph that models roadways based on a plurality of road segments (paragraphs 91-92 teach "FIG. 6 is a graphical representation of GPS data points received in the course of travel of a vehicle and the pairing of the GPS data points to form input records containing source and target position pairs.
FIG. 7 is a graphical representation of a map generated by the MGA prior to a post-processing cleaning."); these figures show network graph modeling roadways that consist of road segments; execute one or more line drawing programs that draw a line including a plurality of discrete pixels, (figs. 11a-11h show lines drawn including discrete points/pixels, paragraph 97 teaches " FIGS. 11A to 11H progressively illustrate map creation in response to new position reports for the vehicles being received over the data network." and paragraph 199 teaches " basic idea behind area marking is, that areas that allow for driving freely exhibit a multitude of paths that split and join and form a multiply connected graph whereas roads leading into areas tend to form simple lines. The processing is performed individually for each area"); this shows multiple lines drawn (using computer/program) which have discrete/GPS points/pixels (as shown); wherein the plurality of discrete pixels, which are expressed in image space, are representative of the interpolated telemetry data points, which are expressed in Euclidean space (paragraph 151 teaches "Distances between clusters and GPS points are measured using a weighted Euclidean metric with a weight allocated to the angular difference. For example, 1 degree may be set to equal 1 m, although other weights will also be workable." and paragraph 147 teaches "position reports 25 grouped into clusters of points that share similar locations and orientations."); Euclidean metric shows Euclidean space, GPS points act as discrete points/pixels and would be displayed in an image space (for the user to see), the clusters of points would include the interpolated points from Staempfle as described below since cluster includes points with similar locations and orientations and thus, the discrete/GPS points/pixels would also be representative of the cluster of points/interpolated telemetry data points in Euclidean space since they can also be grouped together with similar locations and orientations to form cluster.
However, Seiler fails to explicitly teach rasterizing …data…into map; wherein the one or more central computers receive the telemetry data from each of the plurality of vehicles; execute one or more map matching algorithms to perform topology matching that aligns a plurality of telemetry data points with a topology of the road network, wherein the plurality of telemetry data points each represent a trajectory of one of the plurality of vehicles; calculate a spline that connects the plurality of telemetry data points to one another, wherein the spline represents a trajectory of a single vehicle; determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; and determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles.
However, Cornell explicitly teaches rasterizing …data…into map (Cornell, col. 1, lines 31-34 teach "One method of rendering a map image is to store map images within the map database as sets of raster or pixelated images made up of numerous pixel data points"). Cornell is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of rasterizing map data and anti-aliasing lines. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Seiler's invention with the raster and anti-alias techniques of Cornell to efficiently render individual lines with sufficient anti-aliasing to produce a pleasing rendering of a line at any orientation (Cornell, col. 14, lines 25-27). This ensures better experience for viewer/user due to smoother image.
However, the combination of Seiler and Cornell fails to explicitly teach wherein the one or more central computers receive the telemetry data from each of the plurality of vehicles (although, Seiler paragraph 139 teaches "Each position report 25 contains GPS data in respect of one of the vehicles 2-1, . . . , 2-I, which have been respectively generated by the position trackers 32 of each vehicle."); execute one or more map matching algorithms to perform topology matching that aligns a plurality of telemetry data points with a topology of the road network, wherein the plurality of telemetry data points each represent a trajectory of one of the plurality of vehicles; calculate a spline that connects the plurality of telemetry data points to one another, wherein the spline represents a trajectory of a single vehicle; determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; and determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles.
However, Karagiorgou explicitly teaches wherein the one or more central computers receive the telemetry data from each of the plurality of vehicles (Karagiorgou, page 90, right column, last paragraph explicitly teaches "The vehicle tracking data is commonly referred to as Floating
car data (FCD), i.e., data generated by one vehicle as a sample to assess to overall traffic conditions (“cork swimming in the river”). Typically these data comprise basic vehicle telemetry such as speed, direction and most importantly the position of the vehicle in the form of vehicle tracking data."); this shows tracking data of each vehicle along with position would include other telemetry data as well for each of the aforementioned vehicles; execute one or more map matching algorithms to perform topology matching that aligns a plurality of telemetry data points with a topology of the road network, (Karagiorgou, page 98, section 6, paragraph 1 teaches "our algorithm produces road networks that closely resemble the actual road network provided sufficient tracking data is available" and paragraph 2 teaches "Assessing the quality of the generated road network is important and our aim is to improve our method towards directly considering topological network properties."); networks (from tracked data) closely resembling actual road network and considering topological network properties shows the telemetry data is consistent (or aligned with/mapped) to the topology of the road network; wherein the plurality of telemetry data points each represent a trajectory of one of the plurality of vehicles (Karagiorgou, page 90, section 3, first paragraph teaches "the tracking data is essentially reduced to the actual road network geometry. In addition, road categories are derived based on the amount of data that is available for particular road network portions. Our task will be to align the vehicle trajectories, so as to derive the actual road network underlying it."); tracking data includes the telemetry data as aforementioned and the task is to align vehicle trajectories using it which shows tracking/telemetry data representing trajectory of a vehicle. Karagiorgou is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of road network generation from vehicle tracking. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler and Cornell with the vehicle tracking and road network generation techniques of Karagiorgou to ensure having large amounts of vehicles collecting such data for a given spatial area such as a city (e.g., taxis, public transport, utility vehicles, private vehicles), it will create an accurate picture of the traffic conditions in time and space (Karagiorgou, page 90, right column, last paragraph). Larger sets of data would also provide other similar benefits such as more accuracy.
However, the combination of Seiler, Cornell and Karagiorgou fails to teach calculate a spline that connects the plurality of telemetry data points to one another, wherein the spline represents a trajectory of a single vehicle; determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; and determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles.
However, Staempfle teaches calculate a spline that connects the plurality of telemetry data points to one another, (Staempfle, claim 13 teaches "wherein the lateral speed of the motor vehicle is taken into account, in the determining…trajectory..spline" ); trajectory accounts for the tracking/telemetry data and speed/telemetry data; wherein the spline represents a trajectory of a single vehicle (Staempfle, claim 13 teaches "a motor vehicle...trajectory is described by at least one of a constant-curvature spline "); determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points (Staempfle, claim 13 teaches "trajectory is described by at least one of a constant-curvature spline and a polyline having equidistant interpolation points."); when both spline and equidistant interpolation points exist then the interpolated points would be interpolated telemetry data points because the interpolation is for trajectory and since trajectory accounts for the tracking/telemetry data (thus points thereof which are transformed to interpolated points) and speed/telemetry data as described above, and this is along the spline since both spline and interpolated equidistant points describe the same trajectory. Staempfle is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of trajectory calculation of vehicle using specific mathematical algorithms. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell and Karagiorgou with the mathematical calculation techniques of Staempfle to enable a very effective, simple, and fast trajectory calculation for vehicles (Staempfle, col. 2, lines 6-7).
However, the combination of Seiler, Cornell, Karagiorgou, and Staempfle fails to teach determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; and determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles.
However, Lin teaches and determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles (Lin, paragraph 24 teaches "calculating possibility scores of vehicle positioning data points matching lanes;", paragraph 25 teaches "S43. fitting by adopting a Gaussian distribution model" and paragraph 29 teaches "obtaining the spatial-temporal distribution of the vehicles on the bridge deck based on a bridge group lane-level road network simulation mode"); Gaussian distribution model can be multi-dimensional inclusive of two when modeling speed and location of vehicle from above, distribution indicating probability density is shown by distribution of vehicles on the bridge (and possibility scores thereof), since this is spatio-temporal it would be over a period of time(such as regular time intervals from Karagiorgou on page 94, section 3.5, first paragraph), and one of ordinary skill in the art would understand that a bridge can be a road segment in a geographical(of Seiler)/geofenced area which would be based on lines of figs. 11a-11h from Seiler as described above. Lin is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of probability distribution of vehicles on a road segment. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, and Staempfle with the probability distribution and density techniques of Lin to reduce monitoring costs and time consumption and so the optimal matching result between the vehicle positioning point and the road section lane is obtained, which provides accurate vehicle trajectories and the lane where the bridge entrance is located for the bridge deck vehicle spatial-temporal distribution identification method (Lin, paragraph 92). This provides more accurate data and increased overall accuracy to the probability distribution density of the vehicles.
However, the combination of Seiler, Cornell, Karagiorgou, Staempfle and Lin fails to explicitly teach determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points.
However, Zhang explicitly teaches determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points (Zhang, paragraph 65 teaches “the computer 155 may specify the time period for data collection to be a default time period such as 30 minutes”, paragraph 70 teaches “receipt of a periodic signal such as a clock signal from a clock internal or external to the computer 155, such as a time-of-day signal, indicating that a time, e.g., five minutes, has passed since a previous trigger event, receiving a command from the remote computer 170 to begin the process 500, and receiving data” and the rightmost column in the table in paragraph 57 shows this data collection for trajectory/telemetry points having the lane position relative to centerline varying); 30 minutes of data collection and/or 5 minutes of trigger time for data collection shows temporally equidistant and the varying position (from data collected shown in table) shows being positioned spatially unequal to one another. Zhang is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of having temporal equidistance but spatial inequivalence of telemetry/trajectory data points. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to the combination of Seiler, Cornell, Karagiorgou, Staempfle and Lin with the temporal and spatial techniques of Zhang to identify locations where the values of same or like operating parameters for the plurality of vehicles vary from a baseline value (Zhang, paragraph 29). This would better show errors and discrepancies which can then be addressed.
Regarding claim 7, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang teaches wherein the one or more central computers execute instructions to: filter the telemetry data based on one or more telemetry data parameters (Seiler, paragraph 182 teaches "Chord Angle Filtering" and paragraph 183 teaches "To avoid connecting to clusters that do not give rise to a reasonable path, clusters are filtered depending on whether their heading seems reasonable for an intermediate way point. The filter heuristic works as follows: the angle of the straight-line connection between the current cluster in the Dijkstra search and the goal is calculated. If the heading of the next cluster has a similar angle (e.g. seat the tolerance to 30 degrees), it is accepted as a neighbour of the Dijkstra search, otherwise it is rejected" and Karagiorgou, page 91, left column, second paragraph teaches "some heuristics that allow us to filter out outliers in the data"); this shows filtering of telemetry data (and connection paths from it) based on degrees (angles of cluster) of the telemetry data; wherein the one or more telemetry data parameters include one or more of the following: GPS boundary error, vehicle trajectories including temporal gaps that exceed a predefined period of time, a number of trajectories, and sample speed (Karagiorgou, page 91, left column, second paragraph teaches "recorded vehicle trajectories are affected by a measurement error due to the limited GPS accuracy", first paragraph teaches "resulting data comprises vehicle trajectories, which can be modeled as a list of space-time points," page 98, left column, second paragraph teaches "tracking data such as sampling rate and vehicle speed highly affect the produced result." and first paragraph teaches "Intersection nodes are effectively used to bundle vehicle trajectories and links between intersections are derived by merging them"); measurement error due to limited GPS accuracy shows GPS boundary error, space-time points for trajectories shows trajectories including temporal gap exceeding predefined time of 0, sampling rate along vehicle speed shows sample speed, and bundling trajectories shows having a number of trajectories. The same motivations used in claim 1 apply here in claim 7.
Regarding claim 9, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang teaches wherein the one or more central computers execute instructions to: execute a parametric spline algorithm to calculate the spline (Staempfle, col. 7, lines 12-15 teach "The description of trajectory 13 using a spline arrangement that the integral can be solved analytically, and the optimization criterion can be indicated directly as a function of the parameters of the splines."); equation solved using parameters of spline shows a parametric spline algorithm to calculate spline. The same motivations used in claim 1 apply here in claim 9.
Regarding claim 11, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang teaches wherein a distance measured between the equidistant intervals is determined based on the specific application of the map content (Seiler, paragraph 188 teaches "When clusters are to be connected the connecting line is subdivided by equidistant points"); this shows the equidistance distances measured/subdivided being based on the specific application of the map content which is for Mapping of Haul Roads as described in Seiler's title.
Regarding claim 12, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang teaches wherein the line is an anti-aliased line (Cornell, col. 5, lines 1-4 teach "a set of vector data comprising data defining one or more straight line image objects and a second routine executes on the processor to determine an extended width of the straight line to be used in anti-aliasing the straight line when the straight line is rendered"); this shows lines in map data such as that of Seiler, being anti-aliased. The same motivations used in claim 1 apply here in claim 12.
Regarding claim 13, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang teaches wherein the telemetry data indicates one or more of the following: a position of a specific vehicle, GPS boundary error, vehicle speed, heading, elevation, and a hashed vehicle identifier corresponding to each timestamp (Karagiorgou, page 90, right column, last paragraph teaches "vehicle telemetry such as speed, direction and most importantly the position of the vehicle in the form of vehicle tracking data.", page 91, left column, second paragraph teaches "recorded vehicle trajectories are affected by a measurement error due to the limited GPS accuracy", page 98, left column, second paragraph teaches "considering topological network properties", and Seiler, paragraph 131 teaches "stores vehicle positions (including location, timestamp and vehicle ID)"); this shows telemetry data including position and speed of vehicle as well as heading/direction, measurement error due to limited GPS accuracy shows GPS boundary error, considering topological properties shows elevation also being considered, and one of ordinary skill in the art would understand the vehicle ID at the timestamp can be hashed to provide uniqueness and data integrity. The same motivations used in claim 1 apply here in claim 13.
Regarding claim 14, the method claim 14 recites similar limitations as system claim 1, and thus is rejected under similar rationale.
Claim(s) 2-3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang as applied to claim 1 and 14 above, and further in view of Funayama et al. (U.S. Patent No. 6,332,038), hereinafter referenced as Funayama.
Regarding claim 2, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang fails to teach wherein the one or more central computers execute instructions to: transform the two-dimensional probability distribution into a rendered probability density image.
However, Funayama teaches wherein the one or more central computers execute instructions to: transform the two-dimensional probability distribution into a rendered probability density image (Funayama, col. 15, lines 37-41 teach "image extraction processing device 12 calculates a probability density function from this distribution information, and, by applying the probability density function to the foregoing partial image, extracts a domain, smaller than the foregoing partial image"); resulting image is the rendered probability density image and it is based on distribution information (such as the above explained gaussian/2d probability distribution from Lin). Funayama is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of probability density image being rendered using image processing. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang with the probability density image techniques of Funayama to ensure improving the precision of extraction of a partial image (Funayama, col. 21, lines 44-45). This would mean more accuracy in the probability density image that is rendered.
Regarding claim 3, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang and Funayama teaches wherein the rendered probability density image is expressed as one of the following: grayscale or a red, green, blue (RGB) image (Funayama, col. 14, lines 18-22 teach "if the distribution of pixel values in a domain judged to be a single color by human eyes is investigated, there are cases in which this distribution can be approximated by a normal distribution (normal probability density function) like that shown in FIG. 17." and col. 20, lines 21-26 teach "the image processing device explained in either of the first and second embodiments above can be given a structure...calculated from the RGB values of the pixel values making up an inputted digital image (base image)"); one of ordinary skill in the art would understand single color can be gray and this also shows using RGB for calculations which would result in RGB. The same motivations used in claim 2 apply here in claim 3.
Regarding claim 15, the method claim 15 recites similar limitations as system claim 2, and thus is rejected under similar rationale.
Claim(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang as applied to claim 1 above, and further in view of Okada et al. (U.S. Patent Application Publication No. 2016/0098605), hereinafter referenced as Okada.
Regarding claim 4, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang fails to teach wherein the two-dimensional probability distribution includes an aggregated line and a probability boundary.
However, Okada teaches wherein the two-dimensional probability distribution includes an aggregated line and a probability boundary (Okada, paragraph 98 teaches "When a solid lane boundary line has been detected in step S2, the lane boundary line probability information acquiring unit 9 updates the probability Pt1 in step S631 by using the equation (13). On the other hand, if no solid lane boundary line was detected in step S2, the lane boundary line probability information acquiring unit 9 updates the probability Pt1 in step S631 by using the equation (14)."); this shows probability (two-dimensional probability distribution from above) including equation 13 representing the solid/aggregated line and equation 14 representing the lane boundary line probability not having a solid line. Okada is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of probability boundary and aggregated/solid lines in vehicle roadways. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang with the probability boundary and aggregated/solid lines techniques of Okada so it is possible to increase the accuracy of the lane boundary line probability information (Okada, paragraph 79). This means more accurate data received from vehicle leading to a better road network map.
Regarding claim 5, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang, and Okada teaches wherein the aggregated line is a solid line and represents an aggregation of the plurality of lines that each correspond to one of the plurality of vehicles (Okada, paragraph 98 teaches "When a solid lane boundary line has been detected in step S2" and fig. 2, step S1 teaches "acquire images captured by in-vehicle camera" before detecting lines); this solid line is the aggregated line, this is from images (plural/multiple) captured meaning it is a plurality of lines and corresponds to one of the vehicles since using an in-vehicle camera of a vehicle. The same motivations used in claim 4 apply here in claim 5.
Regarding claim 6, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang, and Okada teaches wherein the probability boundary surrounds the aggregated line and is representative of a global positioning system (GPS) boundary error of each of the plurality of vehicles located within the predefined geofenced area (Karagiorgou, page 95, section 4.1, second paragraph and fig. 12 teach "the example of Figure 12, the dashed black lines represent the buffer region, the grey lines the partial road network and the yellow lines an instance of the trajectory data" and page 91, left column, second paragraph teaches "recorded vehicle trajectories are affected by a measurement error due to the limited GPS accuracy”); when viewed in combination with Okada, solid is aggregated and dashed lines would be the probability boundary from Okada since they represent a buffer region (which can be probability boundary), as shown in fig. 12 the dashed lines surround aggregated/solid lines, measurement error shows GPS boundary error and this would be for each of the vehicle trajectories located within the geofenced area from Seiler. The same motivations used in claims 1 and 4 apply here in claim 6.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang as applied to claim 1 above, and further in view of Bulan et al. (U.S. Patent Application Publication No. 2020/0394838), hereinafter referenced as Bulan.
Regarding claim 21, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang fails to teach wherein the map content is created for an autonomous driving system and the distance between the equidistant intervals is one meter.
However, Bulan teaches wherein the map content is created for an autonomous driving system (Bulan, paragraph 23 teaches “present techniques provide for generating map features based on aerial data and telemetry data without the need for specialized driving mapping vehicles typically used to generate map features. That is, the present techniques generate medium definition maps having the same features as high definition maps and in the same format that autonomous vehicles can utilize”); this shows maps generated for autonomous driving systems; and the distance between the equidistant intervals is one meter (Bulan, paragraph 48 teaches “sample points are taken at regular intervals (e.g. 0.5 meters, 1 meter, etc.) along the curve. These are depicted in the image 718a as the “dots” corresponding to the lane markings. As illustrated, the points are evenly spaced (i.e., at regular intervals).”); one meter as regular interval shows distance between equidistant intervals is one meter. Bulan is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of maps for autonomous vehicles and having a set distance between intervals. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang with the autonomous vehicles and set distance interval techniques of Bulan to ensure a cost-effective solution for creating high definition map features for autonomous vehicles and scalable and reduce lead-time for creating maps (Bulan, paragraph 23). This ensures a more efficient invention overall.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang as applied to claim 9 above, and further in view of Maekawa et al. (Curvature continuous path generation for autonomous vehicle using B-spline curves), hereinafter referenced as Maekawa.
Regarding claim 10. the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang fails to teach wherein the parametric spline algorithm is a parametric cubic B-spline algorithm.
However, Maekawa teaches wherein the parametric spline algorithm is a parametric cubic B-spline algorithm (Maekawa, page 352, left column, last paragraph teaches "path generation" for vehicle using "cubic b-spline curve", page 353, equation 12 teaches parameter values for each data point and equation 14 teaches vector of cubic B-spline curve computation of first steps); this shows a parametric spline algorithm for vehicle trajectories and paths can be cubic b-spline algorithm. Maekawa is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of using parametric cubic b-spline algorithm for vehicle trajectory/path. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang with the cubic b-spline algorithm techniques of Maekawa so that when a path cannot be generated, the system reports error messages (Maekawa, page 358, left column, second paragraph). This provides the advantage of user knowing when there's an error (leading to better and faster troubleshooting) instead of extraneous solutions or steps following and enlarging the problem which would also make this algorithm more efficient overall.
Claim(s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seiler in view of Cornell, Karagiorgou, Staempfle, Lin, Zhang and Funayama.
Regarding claim 16, Seiler teaches a system for telemetry data collected from a plurality of vehicles into map content, the system comprising: (abstract teaches "system…map generation assembly (MGA) including a processing assembly and an electronic memory assembly, the map generation assembly being in data communication with the data communications network and arranged to receive the position reports, the MGA being configured to; generate batches of points from the position reports, the points corresponding to vehicle positions for respective vehicles at respective times"); this shows using multiple vehicle's telemetry data such as positions for map generation assembly; one or more central computers in wireless communication with the plurality of vehicles that are each situated within a predefined geofenced area, (paragraph 8 teaches "provided a method for issuing updates of a map in respect of a geographical area", paragraph 116 " System 101 includes the data network 31 for placing the vehicle communications system 36 of each haul truck 2-1, . . . , 2-I in data communication with MGA 33", and fig. 4 reference 33 shows the computer/MGA 33 communicating wirelessly using data network 31); geographical area mentioned for the haul roads would be the predefined geofenced area; the one or more central computers executing instructions to: receive road network data representing a road network for the predefined geofenced area, (paragraph 147 teaches "in a first step, the MGA 33 groups GPS points from the position reports 25 grouped into clusters of points that share similar locations and orientations. A second step then updates the connections between the clusters to form a road network."); this shows the road network formed from received points and cluster data of road network for the geographical/geofenced area; wherein the road network is a network graph that models roadways based on a plurality of road segments (paragraphs 91-92 teach "FIG. 6 is a graphical representation of GPS data points received in the course of travel of a vehicle and the pairing of the GPS data points to form input records containing source and target position pairs.
FIG. 7 is a graphical representation of a map generated by the MGA prior to a post-processing cleaning."); these figures show network graph modeling roadways that consist of road segments; execute one or more line drawing programs that draw a line including a plurality of discrete pixels, (figs. 11a-11h show lines drawn including discrete points/pixels, paragraph 97 teaches " FIGS. 11A to 11H progressively illustrate map creation in response to new position reports for the vehicles being received over the data network." and paragraph 199 teaches " basic idea behind area marking is, that areas that allow for driving freely exhibit a multitude of paths that split and join and form a multiply connected graph whereas roads leading into areas tend to form simple lines. The processing is performed individually for each area"); this shows multiple lines drawn (using computer/program) which have discrete/GPS points/pixels (as shown); wherein the plurality of discrete pixels, which are expressed in image space, are representative of the interpolated telemetry data points, which are expressed in Euclidean space (paragraph 151 teaches "Distances between clusters and GPS points are measured using a weighted Euclidean metric with a weight allocated to the angular difference. For example, 1 degree may be set to equal 1 m, although other weights will also be workable." and paragraph 147 teaches "position reports 25 grouped into clusters of points that share similar locations and orientations."); Euclidean metric shows Euclidean space, GPS points act as discrete points/pixels and would be displayed in an image space (for the user to see), the clusters of points would include the interpolated points from Staempfle as described below since cluster includes points with similar locations and orientations and thus, the discrete/GPS points/pixels would also be representative of the cluster of points/interpolated telemetry data points in Euclidean space since they can also be grouped together with similar locations and orientations to form cluster.
However, Seiler fails to explicitly teach rasterizing …data…into map; wherein the one or more central computers receive the telemetry data from each of the plurality of vehicles; execute one or more map matching algorithms to perform topology matching that aligns a plurality of telemetry data points with a topology of the road network, wherein the plurality of telemetry data points each represent a trajectory of one of the plurality of vehicles; calculate a spline that connects the plurality of telemetry data points to one another, wherein the spline represents a trajectory of a single vehicle; determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of anti-aliased lines that each correspond to one of the plurality of vehicles; and transform the two-dimensional probability distribution into a rendered probability density image.
However, Cornell explicitly teaches rasterizing …data…into map (Cornell, col. 1, lines 31-34 teach "One method of rendering a map image is to store map images within the map database as sets of raster or pixelated images made up of numerous pixel data points"); anti-aliased lines (Cornell, col. 5, lines 1-4 teach "a set of vector data comprising data defining one or more straight line image objects and a second routine executes on the processor to determine an extended width of the straight line to be used in anti-aliasing the straight line when the straight line is rendered"); this shows lines in map data such as that of Seiler, being anti-aliased. Cornell is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of rasterizing map data and anti-aliasing lines. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Seiler's invention with the raster and anti-alias techniques of Cornell to efficiently render individual lines with sufficient anti-aliasing to produce a pleasing rendering of a line at any orientation (Cornell, col. 14, lines 25-27). This ensures better experience for viewer/user due to smoother image.
However, the combination of Seiler and Cornell fails to explicitly teach wherein the one or more central computers receive the telemetry data from each of the plurality of vehicles (although, Seiler paragraph 139 teaches "Each position report 25 contains GPS data in respect of one of the vehicles 2-1, . . . , 2-I, which have been respectively generated by the position trackers 32 of each vehicle."); execute one or more map matching algorithms to perform topology matching that aligns a plurality of telemetry data points with a topology of the road network, wherein the plurality of telemetry data points each represent a trajectory of one of the plurality of vehicles; calculate a spline that connects the plurality of telemetry data points to one another, wherein the spline represents a trajectory of a single vehicle; determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles; and transform the two-dimensional probability distribution into a rendered probability density image.
However, Karagiorgou explicitly teaches wherein the one or more central computers receive the telemetry data from each of the plurality of vehicles (Karagiorgou, page 90, right column, last paragraph explicitly teaches "The vehicle tracking data is commonly referred to as Floating
car data (FCD), i.e., data generated by one vehicle as a sample to assess to overall traffic conditions (“cork swimming in the river”). Typically these data comprise basic vehicle telemetry such as speed, direction and most importantly the position of the vehicle in the form of vehicle tracking data."); this shows tracking data of each vehicle along with position would include other telemetry data as well for each of the aforementioned vehicles; execute one or more map matching algorithms to perform topology matching that aligns a plurality of telemetry data points with a topology of the road network, (Karagiorgou, page 98, section 6, paragraph 1 teaches "our algorithm produces road networks that closely resemble the actual road network provided sufficient tracking data is available" and paragraph 2 teaches "Assessing the quality of the generated road network is important and our aim is to improve our method towards directly considering topological network properties."); networks (from tracked data) closely resembling actual road network and considering topological network properties shows the telemetry data is consistent (or aligned with/mapped) to the topology of the road network; wherein the plurality of telemetry data points each represent a trajectory of one of the plurality of vehicles (Karagiorgou, page 90, section 3, first paragraph teaches "the tracking data is essentially reduced to the actual road network geometry. In addition, road categories are derived based on the amount of data that is available for particular road network portions. Our task will be to align the vehicle trajectories, so as to derive the actual road network underlying it."); tracking data includes the telemetry data as aforementioned and the task is to align vehicle trajectories using it which shows tracking/telemetry data representing trajectory of a vehicle. Karagiorgou is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of road network generation from vehicle tracking. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler and Cornell with the vehicle tracking and road network generation techniques of Karagiorgou to ensure having large amounts of vehicles collecting such data for a given spatial area such as a city (e.g., taxis, public transport, utility vehicles, private vehicles), it will create an accurate picture of the traffic conditions in time and space (Karagiorgou, page 90, right column, last paragraph). Larger sets of data would also provide other similar benefits such as more accuracy.
However, the combination of Seiler, Cornell and Karagiorgou fails to teach calculate a spline that connects the plurality of telemetry data points to one another, wherein the spline represents a trajectory of a single vehicle; determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles; and transform the two-dimensional probability distribution into a rendered probability density image.
However, Staempfle teaches calculate a spline that connects the plurality of telemetry data points to one another, (Staempfle, claim 13 teaches "wherein the lateral speed of the motor vehicle is taken into account, in the determining…trajectory..spline" ); trajectory accounts for the tracking/telemetry data and speed/telemetry data; wherein the spline represents a trajectory of a single vehicle (Staempfle, claim 13 teaches "a motor vehicle...trajectory is described by at least one of a constant-curvature spline "); determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points (Staempfle, claim 13 teaches "trajectory is described by at least one of a constant-curvature spline and a polyline having equidistant interpolation points."); when both spline and equidistant interpolation points exist then the interpolated points would be interpolated telemetry data points because the interpolation is for trajectory and since trajectory accounts for the tracking/telemetry data (thus points thereof which are transformed to interpolated points) and speed/telemetry data as described above, and this is along the spline since both spline and interpolated equidistant points describe the same trajectory. Staempfle is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of trajectory calculation of vehicle using specific mathematical algorithms. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell and Karagiorgou with the mathematical calculation techniques of Staempfle to enable a very effective, simple, and fast trajectory calculation for vehicles (Staempfle, col. 2, lines 6-7).
However, the combination of Seiler, Cornell, Karagiorgou, and Staempfle fails to teach determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles; and transform the two-dimensional probability distribution into a rendered probability density image.
However, Lin teaches determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area based on a plurality of lines that each correspond to one of the plurality of vehicles (Lin, paragraph 24 teaches "calculating possibility scores of vehicle positioning data points matching lanes;", paragraph 25 teaches "S43. fitting by adopting a Gaussian distribution model" and paragraph 29 teaches "obtaining the spatial-temporal distribution of the vehicles on the bridge deck based on a bridge group lane-level road network simulation mode"); Gaussian distribution model can be multi-dimensional inclusive of two when modeling speed and location of vehicle from above, distribution indicating probability density is shown by distribution of vehicles on the bridge (and possibility scores thereof), since this is spatio-temporal it would be over a period of time(such as regular time intervals from Karagiorgou on page 94, section 3.5, first paragraph), and one of ordinary skill in the art would understand that a bridge can be a road segment in a geographical(of Seiler)/geofenced area which would be based on lines of figs. 11a-11h from Seiler as described above. Lin is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of probability distribution of vehicles on a road segment. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, and Staempfle with the probability distribution and density techniques of Lin to reduce monitoring costs and time consumption and so the optimal matching result between the vehicle positioning point and the road section lane is obtained, which provides accurate vehicle trajectories and the lane where the bridge entrance is located for the bridge deck vehicle spatial-temporal distribution identification method (Lin, paragraph 92). This provides more accurate data and increased overall accuracy to the probability distribution density of the vehicles.
However, the combination of Seiler, Cornell, Karagiorgou, Staempfle and Lin fails to explicitly teach determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points; and transform the two-dimensional probability distribution into a rendered probability density image.
However, Zhang explicitly teaches determine a plurality of interpolated telemetry data points positioned at equidistant intervals from one another along the spline by transforming the plurality of the telemetry data points, which are temporally equidistant but are positioned spatially unequal to one another, into the plurality of interpolated telemetry data points (Zhang, paragraph 65 teaches “the computer 155 may specify the time period for data collection to be a default time period such as 30 minutes”, paragraph 70 teaches “receipt of a periodic signal such as a clock signal from a clock internal or external to the computer 155, such as a time-of-day signal, indicating that a time, e.g., five minutes, has passed since a previous trigger event, receiving a command from the remote computer 170 to begin the process 500, and receiving data” and the rightmost column in the table in paragraph 57 shows this data collection for trajectory/telemetry points having the lane position relative to centerline varying); 30 minutes of data collection and/or 5 minutes of trigger time for data collection shows temporally equidistant and the varying position (from data collected shown in table) shows being positioned spatially unequal to one another. Zhang is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of having temporal equidistance but spatial inequivalence of telemetry/trajectory data points. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to the combination of Seiler, Cornell, Karagiorgou, Staempfle and Lin with the temporal and spatial techniques of Zhang to identify locations where the values of same or like operating parameters for the plurality of vehicles vary from a baseline value (Zhang, paragraph 29). This would better show errors and discrepancies which can then be addressed.
However, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang fails to teach and transform the two-dimensional probability distribution into a rendered probability density image.
However, Funayama teaches and transform the two-dimensional probability distribution into a rendered probability density image (Funayama, col. 15, lines 37-41 teach "image extraction processing device 12 calculates a probability density function from this distribution information, and, by applying the probability density function to the foregoing partial image, extracts a domain, smaller than the foregoing partial image"); resulting image is the rendered probability density image and it is based on distribution information (such as the above explained gaussian/2d probability distribution from Lin). Funayama is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of probability density image being rendered using image processing. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin and Zhang with the probability density image techniques of Funayama to ensure improving the precision of extraction of a partial image (Funayama, col. 21, lines 44-45). This would mean more accuracy in the probability density image that is rendered.
Regarding claim 17, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang and Funayama teaches wherein the rendered probability density image is expressed as one of the following: grayscale or a red, green, blue (RGB) image (Funayama, col. 14, lines 18-22 teach "if the distribution of pixel values in a domain judged to be a single color by human eyes is investigated, there are cases in which this distribution can be approximated by a normal distribution (normal probability density function) like that shown in FIG. 17." and col. 20, lines 21-26 teach "the image processing device explained in either of the first and second embodiments above can be given a structure...calculated from the RGB values of the pixel values making up an inputted digital image (base image)"); one of ordinary skill in the art would understand single color can be gray and this also shows using RGB for calculations which would result in RGB. The same motivations used in claim 16 apply here in claim 17.
Claim(s) 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang and Funayama as applied to claim 16 above, and further in view of Okada.
Regarding claim 18, Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang and Funayama fails to teach
wherein the two-dimensional probability distribution includes an aggregated line and a probability boundary.
However, Okada teaches wherein the two-dimensional probability distribution includes an aggregated line and a probability boundary (Okada, paragraph 98 teaches "When a solid lane boundary line has been detected in step S2, the lane boundary line probability information acquiring unit 9 updates the probability Pt1 in step S631 by using the equation (13). On the other hand, if no solid lane boundary line was detected in step S2, the lane boundary line probability information acquiring unit 9 updates the probability Pt1 in step S631 by using the equation (14)."); this shows probability (two-dimensional probability distribution from above) including equation 13 representing the solid/aggregated line and equation 14 representing the lane boundary line probability not having a solid line. Okada is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of probability boundary and aggregated/solid lines in vehicle roadways. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang and Funayama with the probability boundary and aggregated/solid lines techniques of Okada so it is possible to increase the accuracy of the lane boundary line probability information (Okada, paragraph 79). This means more accurate data received from vehicle leading to a better road network map.
Regarding claim 19, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang, Funayama and Okada teaches wherein the aggregated line is a solid line and represents an aggregation of the plurality of lines that each correspond to one of the plurality of vehicles (Okada, paragraph 98 teaches "When a solid lane boundary line has been detected in step S2" and fig. 2, step S1 teaches "acquire images captured by in-vehicle camera" before detecting lines); this solid line is the aggregated line, this is from images (plural/multiple) captured meaning it is a plurality of lines and corresponds to one of the vehicles since using an in-vehicle camera of a vehicle. The same motivations used in claim 18 apply here in claim 19.
Regarding claim 20, the combination of Seiler, Cornell, Karagiorgou, Staempfle, Lin, Zhang, Funayama and Okada teaches wherein the probability boundary surrounds the aggregated line and is representative of a GPS boundary error of each of the plurality of vehicles located within the predefined geofenced area (Karagiorgou, page 95, section 4.1, second paragraph and fig. 12 teach "the example of Figure 12, the dashed black lines represent the buffer region, the grey lines the partial road network and the yellow lines an instance of the trajectory data" and page 91, left column, second paragraph teaches "recorded vehicle trajectories are affected by a measurement error due to the limited GPS accuracy”); when viewed in combination with Okada, solid is aggregated and dashed lines would be the probability boundary from Okada since they represent a buffer region (which can be probability boundary), as shown in fig. 12 the dashed lines surround aggregated/solid lines, measurement error shows GPS boundary error and this would be for each of the vehicle trajectories located within the geofenced area from Seiler. The same motivations used in claims 16 and 18 apply here in claim 6.
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.
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/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
/N.U.A./ Examiner, Art Unit 2611
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