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 action is in response to amendments and remarks filed on [DATE]. No claims have been amended, cancelled, or added. Claim(s) are pending examination. This action is made final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 101 rejection is improper. Applicant asserts that the claims are not an abstract idea and are a practical application.
Applicant asserts that the 35 USC 103 rejection is improper. Applicant asserts that the prior art fails to teach all claim limitations as written.
Applicant's arguments filed 09/02/2025 have been fully considered but they are not persuasive.
Regarding applicant’s argument A, the examiner respectfully disagrees. The applicant alleges that the claims are significantly more than an abstract idea. In particular the applicant states, “this results in more accurate and reliable map data used directly in navigation, semi-autonomous vehicle control, and road network modeling,” (Page 7). However, the claims do not explicitly or implicitly use the technology in this manner. The claims amount to steps of receiving data, determining a plurality of information on this data, constructing a representation of this, and storing the data. Receiving and storing of data are generally understood to be insignificant extra-solution activity. MPEP 2106.05(g) clearly states that data gathering is insignificant extra-solution activity. Meanwhile the storing of the data would be a well-known limitation as there is not a specific way the data is stored; it amounts to pressing save in a database. Regarding the determination steps, there is no claimed step that takes it outside of the human mind. Applicant alleges that noise, data volume, number of vehicle, and other issues would be present and that filtering is necessary, Pages 6-8. However, the claims as written do not reflect this reality. As written a person could receiver a representation of a line in data, this could be two points, they then compare the points to a map database, determine if the points are oriented in an angular manner, determine if the distance from a base point is more or less than a value, and then weight this information. There is claim limitation that require multiple vehicles, the allegation of noisy and large amounts of sensor data is just that, an allegation, and a human working in a generic computing environment would be able to receive and process the data as recited. For the final step of constructing a representation, this could be interpreted to just mean creating an image or picture of the road, a person using a generic pen and paper would be able to construct a representation of the linear feature by drawing a picture of the road. When taken together there is nothing in the claims that would move it out of an abstract idea.
If the applicant amended the claims to include some form of vehicle control based on the linear feature, it may take it out of the 101 rejection, but this would depend on the way the claim limitation is written.
Regarding applicant’s argument B, the examiner respectfully disagrees. The applicant alleges that the prior art does not teach all claim limitations. The arguments fail to persuade the examiner in this regard. The applicant provides mere allegation and assertion that the prior art does not teach the claims as written. Applicant fails to cite anything in either the application or the prior art to prove their point and merely states what they believe to be true. Applicant states that the prior art doesn’t teach something but offers no evidence of this statement. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. For this reason the examiner will maintain the rejections as written.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-9, 11-12, 14-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Analysis of the claim(s) regarding subject matter eligibility utilizing the 2019 Revised Patent Subject Matter Eligibility Guidance is described below.
STEP 1: STATUTORY CATEGORIES
Claim(s) 1, 3-9, 11-12, 14-17, and 19-20 do fall into at least one of the four statutory subject matter categories.
STEP 2A: JUDICIAL EXCEPTIONS
PRONG 1: RECITATION OF A JUDICIAL EXCEPTION
The claim(s) recite(s):
- Claims 1, 12, and 17 recite(s) an abstract idea belonging to the grouping of mental processes. The claims use similar language so only the language of claim 1 will be used for the explanation. Claim 1 recites, “receiving two or more linear feature detections that respectively represent a linear feature of a road as a line segment delimited by two feature points,” “map matching the two or more linear feature detections to a road link segment of a geographic database,” “clustering the two or more linear feature detections based on a lateral distance from a centerline of the map matched road link segment,” “determining an orientation difference for each of the two or more linear feature detections,” “determining a feature orientation of the linear feature based on an aggregation of the orientation difference for each of the two or more linear feature detections, wherein the aggregation is weighted based on a length of each of the two or more linear feature detections, wherein a longest length of each of the two or more linear feature detections is weighted the highest;” “constructing a representation of the linear feature based at least in part on the feature orientation;” and “storing the representation of the linear feature in a geographic database.” The process of claim 1 amount to receiving data, matching it to existing data, comparing the data to determine differences, and then creating a representation for said data. A person using a generic computing device could achieve the same results. A person could receive a collection of data, and compare it to existing data, then represent what has changed and create a file or other representation for said data. This claim amounts to data gathering and processing which is well understood and routine in the art so there is not a significantly more exception. The amount of data collected, compared, and represented is not an infinite amount of data and as explained in the spec can be as little as one small section of a linear feature of a road. For these reasons the examiner believes this to be an abstract idea and claims 1, 12, and 17 are rejected due to it.
- Claim 3 recites “wherein the orientation difference is a signed acute angle difference.” This would be insignificant extrasolution activity.
- Claim 4 recites, “wherein the aggregation is a median or an average.” This would be insignificant extrasolution activity.
- Claims 5, 14, and 19 recite(s) an abstract idea belonging to the grouping of mental processes. The claims use similar language so only the language of claim 5 will be used for the explanation. Claim 5 recites, “determining a feature location of the linear feature based on respective locations associated with the two or more linear feature detections.” The determination of a location relative to known information is something a person with a generic computing device could reasonably do. If a person knows the location of points in their environment they can very often determine where they are in space relative to those points.
- Claims 6, 15, and 20 recite, “wherein the representation of the linear feature is constructed further based on the feature location.” This would be insignificant extrasolution activity.
- Claims 7 and 16 recite, “wherein the representation of the linear feature is constructed as a line through the feature location at the feature orientation.” This would be insignificant extrasolution activity.
- Claim 8 recites, “wherein the feature location is determined based on an average of respective distances of the two feature points of the two or more linear feature detections.” This would be insignificant extrasolution activity.
- Claim 9 recites, “wherein the average is weighted based on respective lengths of the two or more linear feature detections.” This would be insignificant extrasolution activity.
- Claim 11 recites, “wherein the linear feature is a road boundary, a road lane marking, a road median, a road curb, a line-based road object, a vehicle path, or a combination thereof.” This would be insignificant extrasolution activity.
PRONG 2: INTEGRATION INTO A PRACTICAL APPLICATION
The additional element(s) recited in the claim(s) beyond the judicial exception are the use of a computer to implement the claims as well as the use of weighted averages and signed angle measurements of the linear feature data. The additional element(s) do not integrate the judicial exception into a practical application because the additional element(s) do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and add insignificant extra-solution activity to the judicial exception. The computer elements are merely used as a tool to perform the abstract idea, and the use of the judicial exception is generally linked to the particular technological environment of autonomous vehicle driving and map making without using the judicial exception in some other meaningful way (MPEP 2106.04(d)).
STEP 2B: INVENTIVE CONCEPT/SIGNIFICANTLY MORE
The additional elements recited in the claim(s) are not sufficient to amount to significantly more than the judicial exception because they do not add more than insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), and the computer functions of receiving and transmitting data have been recognized by the courts as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(d)). Further, the additional elements of a “memory” and a “processor” recited in the claim(s) are well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality (MPEP2106.05 (d)).
Based on the above analysis, claim(S) 1, 3-9, 11-12, 14-17, and 19-20 is/are not eligible subject matter and is/are rejected under 35 U.S.C 101.
As Noted before incorporation of some kind of control step to control the vehicle based on the linear feature, may overcome the 101.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-, 3-9, 11-12, 14-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US PG Pub 2021/0033406) in view of Wang, (US PG Pub 2014/0095062) Aviv, (US Pat 9,208,403) and Piao (US PG Pub 2020/0393265).
Regarding claim 1, Watanabe teaches a computer-implemented method comprising: receiving two or more linear feature detections that respectively represent a linear feature of a road as a line segment ([0028] teaches a measurement acquisition device that can determine linear features around a vehicle traveling along a road) delimited by two feature points; ([0041] teaches determining of a lane marking limited by distinct points)
map matching the two or more linear feature detections to a road link segment of a geographic database; ([0035]-[0036] teach determining the reliability of information for a given area, this is done by comparing data from collected information to other information to match it)
clustering the two or more linear feature detections ([0083] teaches grouping the lane points and plotting them based on the assigned groups)
([0036] teaches a weighted sum of planimetric features of the extracted linear element)
constructing a representation of the linear feature based at least in part on the feature orientation ([0069] teaches representing the planimetric features of the map data, which would be the linear features) and
storing the representation of the linear feature in a geographic database. (Fig. 1, item 16 and [0031] teach storing the representation in a geographic database)
Watanabe does not teach, clustering based on a lateral distance from a centerline of the map matched road link segment, determining an orientation difference for each of the two or more linear feature detections, wherein the orientation difference is an angle difference between each of the two or more linear feature detections and a link orientation of the map matched road link segment; determining a feature orientation of the linear feature based on an aggregation of the orientation difference for each of the two or more linear feature detections, wherein a longest length of each of the two or more linear feature detections is weighted the highest.
However, Wang teaches “determining an orientation difference for each of the two or more linear feature detections,” ([0016] teaches determining an orientation of data points) “wherein the orientation difference is an angle difference between each of the two or more linear feature detections and a link orientation of the map matched road link segment;” ([0016] teaches comparing the orientation of the data point to the direction of travel for a map segment, and creating an angle between them) and “determining a feature orientation of the linear feature based on an aggregation of the orientation difference for each of the two or more linear feature detections;” ([0017] teaches comparing the differences in angular determination between segments and keeping them if they are within reason, if the orientations are close enough aligned the road feature is kept in the map data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe and Wang; and have a reasonable expectation of success. Both teach ways to determine map information from data collected by sensors. The data can then be used to determine the shape, size, heading, and other properties of linear road features. Watanabe teaches determining the planimetric features of these road elements including how parallel they might be in relation to other elements. Determining an orientation difference between data is obvious to try for one of ordinary skill. If you are mapping what should be “straight” features such as road lines or curbs if the data gathered has large angular cuts it can be assumed that the data is faulty. As only certain lines tend to bend and they do it at 90 degree angles such as stop lines. Incorporating and measuring these angles would provide an obvious way to determine that the lane line data you have collected is faulty to an extent. As Wang teaches in [0001] the tracing of road lines allows for updating of linear features. In [0020] Wang teaches that the removal of unhelpful line segments ensure that roads defined on maps are accurate and useful to an end user. Using averages and weights as well would be obvious. Averages are well known to engineers/scientists and would allow the computer to ensure that the decisions are not made based upon outliers. For these reasons the examiner finds the claim matter obvious.
Neither Watanabe nor Wang teach wherein a longest length of each of the two or more linear feature detections is weighted the highest, and clustering based on a lateral distance from a centerline of the map matched road link segment.
However, Aviv teaches “wherein a longest length of each of the two or more linear feature detections is weighted the highest” (Col. 10, lines 27-37; teaches weighing detected linear features in an image, the weights may reflect the length of the linear features where the longer the linear feature in the image the higher the weight; this would be analogous to the claim as by following the logic of Aviv the longest linear feature would have the highest weight)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe and Wang with Aviv; and have a reasonable expectation of success. As Aviv teaches it can be challenging for a computer system to track linear features in images. The use of various mathematical operations such as a Hough Transform may be used to ease said line tracking. The use of weighted segments as taught in Col. 10, lines 35-37, may contribute to better accuracy of a generated representative line segment. This would allow one of ordinary skill in the art to use a similar weighting scheme as claimed here, where the longer the line the higher the weight. By following the logic of Aviv one would obviously make the longest length have the highest weight.
None of the prior art teaches clustering based on a lateral distance from a centerline of the map matched road link segment.
However, Piao teaches clustering, “based on a lateral distance from a centerline of the map matched road link segment.” (Fig. 13D and [0142] teaches the system of clustering detected points of a road linear feature. This clustering is based on the detected points distance from each other. And the distance from a parallel linear structure. This parallel linear structure would be analogous to a centerline of a road.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe, Wang, and Aviv with Piao; and have a reasonable expectation of success. All relate to vehicle controls and the detection of road structures. It would be obvious to try to base the clustering on the lateral distance from a centerline. As Piao shows, clustering is already done based on a lateral distance from parallel linear features. These features can be lane lines and centerlines. As Piao teaches in [0142] the distances between detected points can be used to ensure that the points are close enough to each other to be linear feature. By basing the clustering off this distance the system can ensure that a detected lane line point is at the same distance from a known reference and the accuracy can be improved.
Regarding claim 3, Watanabe teaches the method of claim 1.
Watanabe does not teach the orientation difference is a signed acute angle difference.
However, Wang teaches “the orientation difference is a signed acute angle difference.” ([0018] teaches determining the angle between two line points and if too large removing it occurs)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe and Wang; and have a reasonable expectation of success. Both teach ways to determine map information from data collected by sensors. The data can then be used to determine the shape, size, heading, and other properties of linear road features. Watanabe teaches determining the planimetric features of these road elements including how parallel they might be in relation to other elements. Determining an orientation difference between data is obvious to try for one of ordinary skill. If you are mapping what should be “straight” features such as road lines or curbs if the data gathered has large angular cuts it can be assumed that the data is faulty. As only certain lines tend to bend and they do it at 90 degree angles such as stop lines. The use of signed angles is obvious as they can be used to tell you if the feature detection goes to the left or right of the anticipated heading. Them being acute angles would also be obvious as you are measuring a “straight” feature so an angle more than 90 degrees would not tell you any information but because we know the rules of angles if an angle is more than 90 there must be a corresponding angle that is less than 90 as they are singed so the totals for left/right sides are only 180 degrees. Therefore a signed acute angle would be an obvious way to measure the orientation of a linear feature on a road. For these reasons the examiner finds the claim matter obvious.
Regarding claim 4, Watanabe teaches the method of claim 1, wherein the aggregation is a median or an average. ([0036] teaches a weighted sum of planimetric features of the extracted linear element)
Regarding claim 5, Watanabe teaches the method of claim 1, further comprising: determining a feature location of the linear feature based on respective locations associated with the two or more linear feature detections. ([0041] teaches basing the location of a linear feature based on point clod data)
Regarding claim 6, Watanabe teaches the method of claim 5, wherein the representation of the linear feature is constructed further based on the feature location. ([0041] teaches constructing the line feature in a map based on point cloud location data)
Regarding claim 7, Watanabe teaches the method of claim 5, wherein the representation of the linear feature is constructed as a line through the feature location at the feature orientation. ([0041] teaches plotting the lane as a line in a map based upon point cloud and planimetric data)
Regarding claim 8, Watanabe teaches the method of claim 5, wherein the feature location is determined based on an average of respective distances of the two feature points of the two or more linear feature detections. ([0081]-[0083] teaches plotting lanes based on detected location and the reliability of such information, the reliability is an average assumption of the planimetric features of the detected object)
Regarding claim 9, Watanabe teaches the method of claim 8, wherein the average is weighted based on respective lengths of the two or more linear feature detections. ([0081]-[0083] teaches plotting lanes based on detected location and the reliability of such information, the reliability is an average assumption of the planimetric features of the detected object)
Regarding claim 11, Watanabe teaches the method of claim 1, wherein the linear feature is a road boundary, a road lane marking, a road median, a road curb, a line-based road object, a vehicle path, or a combination thereof. ([0039] teaches a determination of linear features that may be lane lines, curbs, guardrails, shoulder areas, etc.)
Regarding claim 12, Watanabe teaches an apparatus comprising: at least one processor; ([0072] teaches a processor) and at least one memory including computer program code for one or more programs, ([0072] teaches a memory for storing a program) the at least one memory and the computer program code configured to, within the at least one processor, cause the apparatus to perform at least the following, ([0075] teaches a processor executing a program stored in memory)
receive two or more linear feature detections that respectively represent a linear feature of a road as a line segment ([0028] teaches a measurement acquisition device that can determine linear features around a vehicle traveling along a road) delimited by two feature points; ([0041] teaches determining of a lane marking limited by distinct points)
map match the two or more linear feature detections to a road link segment of a geographic database; ([0035]-[0036] teach determining the reliability of information for a given area, this is done by comparing data from collected information to other information to match it)
cluster the two or more linear feature detections ([0083] teaches grouping the lane points and plotting them based on the assigned groups)
([0036] teaches a weighted sum of planimetric features of the extracted linear element)
construct a representation of the linear feature based at least in part on the feature orientation ([0069] teaches representing the planimetric features of the map data, which would be the linear features) and
store the representation of the linear feature in a geographic database. (Fig. 1, item 16 and [0031] teach storing the representation in a geographic database)
Watanabe does not teach, clustering based on a lateral distance from a centerline of the map matched road link segment, determine an orientation difference for each of the two or more linear feature detections, wherein the orientation difference is an angle difference between each of the two or more linear feature detections and a link orientation of the map matched road link segment; determine a feature orientation of the linear feature based on an aggregation of the orientation difference for each of the two or more linear feature detections, wherein a longest length of each of the two or more linear feature detections is weighted the highest.
However, Wang teaches “determine an orientation difference for each of the two or more linear feature detections,” ([0016] teaches determining an orientation of data points) “wherein the orientation difference is an angle difference between each of the two or more linear feature detections and a link orientation of the map matched road link segment;” ([0016] teaches comparing the orientation of the data point to the direction of travel for a map segment, and creating an angle between them) and “determine a feature orientation of the linear feature based on an aggregation of the orientation difference for each of the two or more linear feature detections;” ([0017] teaches comparing the differences in angular determination between segments and keeping them if they are within reason, if the orientations are close enough aligned the road feature is kept in the map data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe and Wang; and have a reasonable expectation of success. Both teach ways to determine map information from data collected by sensors. The data can then be used to determine the shape, size, heading, and other properties of linear road features. Watanabe teaches determining the planimetric features of these road elements including how parallel they might be in relation to other elements. Determining an orientation difference between data is obvious to try for one of ordinary skill. If you are mapping what should be “straight” features such as road lines or curbs if the data gathered has large angular cuts it can be assumed that the data is faulty. As only certain lines tend to bend and they do it at 90 degree angles such as stop lines. Incorporating and measuring these angles would provide an obvious way to determine that the lane line data you have collected is faulty to an extent. As Wang teaches in [0001] the tracing of road lines allows for updating of linear features. In [0020] Wang teaches that the removal of unhelpful line segments ensure that roads defined on maps are accurate and useful to an end user. Using averages and weights as well would be obvious. Averages are well known to engineers/scientists and would allow the computer to ensure that the decisions are not made based upon outliers. For these reasons the examiner finds the claim matter obvious.
Neither Watanabe nor Wang teach wherein a longest length of each of the two or more linear feature detections is weighted the highest and clustering based on a lateral distance from a centerline of the map matched road link segment.
However, Aviv teaches “wherein a longest length of each of the two or more linear feature detections is weighted the highest” (Col. 10, lines 27-37; teaches weighing detected linear features in an image, the weights may reflect the length of the linear features where the longer the linear feature in the image the higher the weight; this would be analogous to the claim as by following the logic of Aviv the longest linear feature would have the highest weight)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe and Wang with Aviv; and have a reasonable expectation of success. As Aviv teaches it can be challenging for a computer system to track linear features in images. The use of various mathematical operations such as a Hough Transform may be used to ease said line tracking. The use of weighted segments as taught in Col. 10, lines 35-37, may contribute to better accuracy of a generated representative line segment. This would allow one of ordinary skill in the art to use a similar weighting scheme as claimed here, where the longer the line the higher the weight. By following the logic of Aviv one would obviously make the longest length have the highest weight.
None of the prior art teaches clustering based on a lateral distance from a centerline of the map matched road link segment.
However, Piao teaches clustering, “based on a lateral distance from a centerline of the map matched road link segment.” (Fig. 13D and [0142] teaches the system of clustering detected points of a road linear feature. This clustering is based on the detected points distance from each other. And the distance from a parallel linear structure. This parallel linear structure would be analogous to a centerline of a road.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe, Wang, and Aviv with Piao; and have a reasonable expectation of success. All relate to vehicle controls and the detection of road structures. It would be obvious to try to base the clustering on the lateral distance from a centerline. As Piao shows, clustering is already done based on a lateral distance from parallel linear features. These features can be lane lines and centerlines. As Piao teaches in [0142] the distances between detected points can be used to ensure that the points are close enough to each other to be linear feature. By basing the clustering off this distance the system can ensure that a detected lane line point is at the same distance from a known reference and the accuracy can be improved.
Regarding claim 14, Watanabe teaches the apparatus of claim 12, wherein the apparatus is further caused to: determine a feature location of the linear feature based on respective locations associated with the two or more linear feature detections. ([0041] teaches basing the location of a linear feature based on point clod data)
Regarding claim 15, Watanabe teaches the apparatus of claim 14, wherein the representation of the linear feature is constructed further based on the feature location. ([0041] teaches constructing the line feature in a map based on point cloud location data)
Regarding claim 16, Watanabe teaches the apparatus of claim 14, wherein the representation of the linear feature is constructed as a line through the feature location at the feature orientation. ([0041] teaches plotting the lane as a line in a map based upon point cloud and planimetric data)
Regarding claim 17, Watanabe teaches a non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform: ([0075] teaches a processor executing a program stored in memory) receiving two or more linear feature detections that respectively represent a linear feature of a road as a line segment ([0028] teaches a measurement acquisition device that can determine linear features around a vehicle traveling along a road) delimited by two feature points; ([0041] teaches determining of a lane marking limited by distinct points)
map matching the two or more linear feature detections to a road link segment of a geographic database; ([0035]-[0036] teach determining the reliability of information for a given area, this is done by comparing data from collected information to other information to match it)
clustering the two or more linear feature detections ([0083] teaches grouping the lane points and plotting them based on the assigned groups)
([0036] teaches a weighted sum of planimetric features of the extracted linear element)
constructing a representation of the linear feature based at least in part on the feature orientation; ([0069] teaches representing the planimetric features of the map data, which would be the linear features) and
storing the representation of the linear feature in a geographic database. (Fig. 1, item 16 and [0031] teach storing the representation in a geographic database)
Watanabe does not teach, clustering based on a lateral distance from a centerline of the map matched road link segment, determining an orientation difference for each of the two or more linear feature detections, wherein the orientation difference is an angle difference between each of the two or more linear feature detections and a link orientation of the map matched road link segment; determining a feature orientation of the linear feature based on an aggregation of the orientation difference for each of the two or more linear feature detections, wherein a longest length of each of the two or more linear feature detections is weighted the highest.
However, Wang teaches “determining an orientation difference for each of the two or more linear feature detections,” ([0016] teaches determining an orientation of data points) “wherein the orientation difference is an angle difference between each of the two or more linear feature detections and a link orientation of the map matched road link segment;” ([0016] teaches comparing the orientation of the data point to the direction of travel for a map segment, and creating an angle between them) and “determining a feature orientation of the linear feature based on an aggregation of the orientation difference for each of the two or more linear feature detections;” ([0017] teaches comparing the differences in angular determination between segments and keeping them if they are within reason, if the orientations are close enough aligned the road feature is kept in the map data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe and Wang; and have a reasonable expectation of success. Both teach ways to determine map information from data collected by sensors. The data can then be used to determine the shape, size, heading, and other properties of linear road features. Watanabe teaches determining the planimetric features of these road elements including how parallel they might be in relation to other elements. Determining an orientation difference between data is obvious to try for one of ordinary skill. If you are mapping what should be “straight” features such as road lines or curbs if the data gathered has large angular cuts it can be assumed that the data is faulty. As only certain lines tend to bend and they do it at 90 degree angles such as stop lines. Incorporating and measuring these angles would provide an obvious way to determine that the lane line data you have collected is faulty to an extent. As Wang teaches in [0001] the tracing of road lines allows for updating of linear features. In [0020] Wang teaches that the removal of unhelpful line segments ensure that roads defined on maps are accurate and useful to an end user. Using averages and weights as well would be obvious. Averages are well known to engineers/scientists and would allow the computer to ensure that the decisions are not made based upon outliers. For these reasons the examiner finds the claim matter obvious.
Neither Watanabe nor Wang teach wherein a longest length of each of the two or more linear feature detections is weighted the highest, and clustering based on a lateral distance from a centerline of the map matched road link segment.
However, Aviv teaches “wherein a longest length of each of the two or more linear feature detections is weighted the highest” (Col. 10, lines 27-37; teaches weighing detected linear features in an image, the weights may reflect the length of the linear features where the longer the linear feature in the image the higher the weight; this would be analogous to the claim as by following the logic of Aviv the longest linear feature would have the highest weight)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe and Wang with Aviv; and have a reasonable expectation of success. As Aviv teaches it can be challenging for a computer system to track linear features in images. The use of various mathematical operations such as a Hough Transform may be used to ease said line tracking. The use of weighted segments as taught in Col. 10, lines 35-37, may contribute to better accuracy of a generated representative line segment. This would allow one of ordinary skill in the art to use a similar weighting scheme as claimed here, where the longer the line the higher the weight. By following the logic of Aviv one would obviously make the longest length have the highest weight.
None of the prior art teaches clustering based on a lateral distance from a centerline of the map matched road link segment.
However, Piao teaches clustering, “based on a lateral distance from a centerline of the map matched road link segment.” (Fig. 13D and [0142] teaches the system of clustering detected points of a road linear feature. This clustering is based on the detected points distance from each other. And the distance from a parallel linear structure. This parallel linear structure would be analogous to a centerline of a road.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Watanabe, Wang, and Aviv with Piao; and have a reasonable expectation of success. All relate to vehicle controls and the detection of road structures. It would be obvious to try to base the clustering on the lateral distance from a centerline. As Piao shows, clustering is already done based on a lateral distance from parallel linear features. These features can be lane lines and centerlines. As Piao teaches in [0142] the distances between detected points can be used to ensure that the points are close enough to each other to be linear feature. By basing the clustering off this distance the system can ensure that a detected lane line point is at the same distance from a known reference and the accuracy can be improved.
Regarding claim 19, Watanabe teaches the non-transitory computer-readable storage medium of claim 17, wherein the apparatus is caused to further perform: determining a feature location of the linear feature based on respective locations associated with the two or more linear feature detections. ([0041] teaches basing the location of a linear feature based on point clod data)
Regarding claim 20, Watanabe teaches the non-transitory computer-readable storage medium of claim 19, wherein the representation of the linear feature is constructed further based on the feature location. ([0041] teaches constructing the line feature in a map based on point cloud location data)
Conclusion
THIS ACTION IS MADE FINAL. 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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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665