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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/13/2026 has been entered.
Status of Claims
This action is in reply to the RCE filed on 05/11/2026.
Claims 1-4, 6-13, and 15-24 are currently pending and have been examined.
Claims 1, 7, 10, 16, and 19 are amended.
Claims 22-24 are cancelled.
Claim 25 is added.
Claims 1-4, 6-13, 15-21, and 25 are currently rejected.
This action is made NON-FINAL.
Response to Arguments
Applicant’s arguments filed 04/13/2026 have been fully considered but they are not persuasive.
Regarding the 101 rejections applications argument are not persuasive. As previously stated in the advisory action, the inclusion of machine learning is a very high-level of generality such that the mental process is being “applied to” the machine learning that is being performed on generic computer hardware. The claims also do not include any limitations that preclude the ability of the mental process steps to be performed by a person though the use of a physical aid such as paper and pencil, by requiring the mental process to be performed in real time or process an amount of data that would be unfeasible for a person. However as the claims currently stand, are performable by a person. A person is capable of generating a map of a roadway and then subdividing that map into sections such that each section has the same number of lanes. The machine learning applied on a computer is just speeding up the process and providing the generated map in order to perform construction or repair is insignificant post-solution activity. Therefore the 101 rejections are being maintained.
Applicant’s arguments with regards to the art rejections have been considered and appear to be directed solely to the instant amendments to the claims. Accordingly, the claims are addressed in the body of the rejections below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-13, 15-21, and 25 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-4, 6-13, 15-21, and 25 are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES)
The examiner has identified independent system/method/product Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent Claims 10 and 19. Claim 1 recites the limitations of:
A computing system for carriageway chopping in a mapping structure comprising:
one or more processors; and
memory coupled to the one or more processors to store instructions, which when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
determining, by a self-supervised machine learning (ML) model, according to parameters of a carriageway:
a face of a lane connector of a first lane of the carriageway;
a heading of the lane connector at the face, wherein the heading of the lane connector at the face is indicative of the direction of traffic at the end of the lane connector;
a candidate slice to the lane connector at the face; and
a second candidate slice to a second lane connector of a second lane at a second face;
aligning the candidate slice of the lane connector and the second candidate slice of the second lane connector;
aligning the candidate slice of the lane connector and the second candidate slice of the second lane connector;
transforming the carriageway mapping structure into a plurality of slices by chopping the carriageway into the aligned candidate slices and creating a carriageway link at the aligned candidate slices;
and generating, by the self-supervised ML model, a mapped layout of the carriageway according to the transformed carriageway mapping structure and the carriageway link;
generating, based on the transformed carriageway mapping structure and the mapped layout, multiple carriageway links corresponding to respective portions of the carriageway; and
utilizing the mapped layout and the multiple carriageway links to perform construction or repair of the carriageway.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Determining features of a road and performing analysis of those features recites concepts performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea.)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: processors and memory in Claim 1 is just applying generic computer components to the recited abstract limitations. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than instructions to apply the exception using a generic computer component. Therefore, claims 1, 10, and 19 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application.)
The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware and machine learning amounts to no more than mere instructions to apply the exception using a generic computer components that simply aid in speeding up the process capable of being performed in the mind. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Altering the construction of a map is a mental process that is performed on generic computer hardware that does not integrate itself into a particular use. Using the map to perform either repair or construction of a road is insignificant post solution activity. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 10, and 19 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more.)
Dependent claims further define the abstract idea that is present in their respective independent claims 1, 10, and 19 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1-4, 6-13, 15-21, and 25 are not patent-eligible.
Amending the claims to control the vehicle based on the map chopping performed in the claims or limiting the claims to a mental process that is incapable of being performed in the human mind would overcome the 101 rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 7-11, 16-19, and 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et. al. (US 2023/0410536), herein Jiang in view of Chen et. al. (US 2019/0086928), herein Chen and Nepomuceno et. al. (US 10,571,283), herein Nepomuceno.
Regarding claim 1:
Jiang teaches:
A computing system (fig. 16, system 1600) for carriageway chopping (Partition the map into a grid of slots and generate, for each of the slots, a list of lanes intersecting with the slot based on the lane content. [0050]) in a carriageway mapping structure (The dotting plot DP illustrates the shape of the road (e.g., a highway) with polylines. FIG. 3 is a diagram illustrating the shape of the road 200 with polylines in accordance with an embodiment of the present disclosure [0028]) comprising:
one or more processors (fig. 16, processor 12604); and
memory coupled to the one or more processors to store instructions (fig. 16, storage device 1602), which when executed by the one or more processors, cause the one or more processors to perform operations (The storage device is arranged to store a programming code PROG. When the programming code PROG is executed by the processor 1604, the method 1400 or the method 1500 is executed. [0066]), the operations comprising:
a face of a lane connector (Second, a polyline segment is selected from the polyline segments (e.g., the polyline segments PS1 to PS9 as shown in FIG. 4) as an incident polyline segment… in FIG. 6A, the polyline segment PS2 is selected as the incident polyline segment [0032]) of a first lane of the carriageway (fig. 6a, PS2 in the first lane);
a heading of the lane connector at the face (the left-hand side of FIG. 4 is determined as the front of each lane by the system 100. The right-hand side of FIG. 4 is determined as the back of each lane by the system 100 [0029]; As mentioned in the description of FIG. 4, the left-hand side of FIG. 7B is considered as the front of each lane while the right-hand side of FIG. 7B is considered as the back of each lane. With such configurations, the backward boundary M of the lane segment S3 shown in FIG. 7B is overlapped with the forward boundary N of the lane segment S6 [0040]);
a candidate slice (fig. 6b, PS2) to the lane connector at the face (The polyline segment PS2 includes two end points A and B. At the end point B, several candidate polyline segments are identified. For example, the polyline segments PS3 and PS4 are connected to the end point B. Therefore, the polyline segments PS3 and PS4 are identified as the candidate polyline segments for the incident polyline segment. [0032]); and
a second candidate slice (fig. 6b, PS4) to a second lane connector of a second lane at a second face (The polyline segment PS2 includes two end points A and B. At the end point B, several candidate polyline segments are identified. For example, the polyline segments PS3 and PS4 are connected to the end point B. Therefore, the polyline segments PS3 and PS4 are identified as the candidate polyline segments for the incident polyline segment. [0032]);
aligning (examiner notes that in going from fig. 6c to 7b the individual slices PS2 and PS4 are aligned and combined into one slice that cuts across all the lanes.) the candidate slice of the lane connector and the second candidate slice of the second lane connector (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]);
transforming (see transformation from fig. 3 to fig. 7B) the carriageway mapping structure (FIG. 3 is a diagram illustrating the shape of the road 200 with polylines [0028]) into a plurality of slices (see at least fig. 7B) by chopping the carriageway into the aligned candidate slices (In FIG. 7B, all the lane segments (e.g., the lane segments S1 to S11) are constructed by the lane segment circuit 508. As shown in FIG. 7B, when all the lane segments are constructed, there is no arrow remaining. [0039]) and creating a carriageway link at the aligned candidate slices (fig. 7B, S1-S4, S5-S7, S8-S10, and S11);
generating, [by the self-supervised ML model], a mapped layout of the carriageway (fig. 7B) according to the transformed carriageway mapping structure (The visualization circuit 110 is arranged to receive the raw image data DATA, and generate a dotting plot DP according to the raw image data DATA. The dotting plot DP illustrates the shape of the road (e.g., a highway) with dots [0027]) and the carriageway link (fig. 7B, S1-S4, S5-S7, S8-S10, and S11);
generating (The lane content generation circuit 150 is arranged to generate a lane content LC for the respective lane based on the lane geometry data LGD, wherein the lane content LC includes graphical representation of lanes, the waypoints of the lanes, or some auxiliary factors for the respective lane which will be described in the following paragraphs [0030]; Partition the map into a grid of slots and generate, for each of the slots, a list of lanes intersecting with the slot based on the lane content [0050]), based on the transformed carriageway mapping structure and the mapped layout (fig. 7B, road 200), multiple carriageway links corresponding to respective portions of the carriageway (fig. 7B, S1-S4, S5-S7, S8-S10, and S11); and
Jiang does not explicitly teach, however Chen teaches:
determining, by a self-supervised machine learning (ML) model, according to parameters of a carriageway (a feature detector, lane line detector, trained neural network, and/or the like may be used to identify and extract geo-coded lane boundaries from the image data [0056]):
a heading of the lane connector at the face (see at least fig. 7 showing heading arrows at face of each section of lanes indicating direction of traffic.), wherein the heading of the lane connector at the face is indicative of the direction of traffic at the end of the lane connector (a direction of traffic flow along the lane (e.g., an expected heading of a vehicle traveling in the lane at a particular location along the lane, and/or the like) [0052]);
generating, by the self-supervised ML model, a mapped layout of the carriageway (the map data of the lane-centric road network model may be generated, updated, managed, and/or the like by a model apparatus 10 [0030]) according to the parameters of the carriageway (an image capturing system 50 and/or vehicle apparatus 20 may capture image data that includes portions of a road network and provide (e.g., transmit) the image data. [0055]) and the carriageway link (see fig. 7 showing links.).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang to include the teachings as taught by Chen with a reasonable expectation of success. Both arts are in the same field of endeavor of mapping roadways. Chen also teaches that “Autonomous driving requires high accuracy road network models that accurately describe lane topology. Continuing the example of the left turn lane from above, if an autonomous vehicle is in the left turn lane, the vehicle will need to know the location of the stop line for the left turn lane rather than a virtual location of a virtual amalgamate stop line configured to represent the stop line for entire road segment. Thus, a road network model to be used for navigation of an autonomously driven vehicle should be able to accurately describe road segments having non-trivial topologies. [Chen, 0003]”.
Jiang in view of Chen does not explicitly teach, however Nepomuceno teaches:
utilizing the mapped layout [and the multiple carriageway links] to perform construction or repair of the carriageway (The risk map may be a heat map, and may be transmitted, via wireless communication or data transmission over one or more radio frequency links or wireless communication channels to government entity servers (to facilitate road repair or construction of high risk roads and intersections [col 13, lines 7-12]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang and Chen to include the teachings as taught by Nepomuceno with a reasonable expectation of success. All arts are in the same field of endeavor of mapping roadways. Jiang teaches creating a map of a roadway that is separate into carriageway links but does not explicitly teach what the created map can be used for. Nepomuceno teaches using a map of a roadway “to government entity servers (to facilitate road repair or construction of high risk roads and intersections) [Nepomuceno, col 14, lines 26-28]” to overcome the issue of “environmental factors contributing to the riskiness of an area may not always be readily apparent, observable, or quantifiable. For example, even if a civil engineer identifies a number of one-lane bridges as potentially dangerous, she may have no way of quantifying how risky these one-lane bridges are relative to one another. Moreover, the engineer may overlook a two-lane bridge that is seemingly safe, but which is in actuality riskier than many of the identified one-lane bridges. Because the environmental factors contributing to risk may not always be apparent, observable, or quantifiable, these environmental risk factors may go unnoticed. Thus, engineers and government officials may never identify certain high-risk areas, much less identify solutions to mitigate the risk and improve the safety of the areas for vehicle drivers and passengers. [Nepomuceno, col 1, line 55 – col 2, line 2]”. It would be obvious to one having ordinary skill in the art to provide the map as created by Jiang to aid in road construction or repair as taught by Nepomuceno because in addition to the safety benefits outlined by Nepomuceno, it would be applying known methods to achieve a predictable result.
Regarding claim 2:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 1, upon which this claim is dependent.
Jiang further teaches:
wherein the lane connector comprises at least one of a merging lane (fig. 7b, S5), a branching lane (fig. 7b, S7), an off ramp lane (fig. 7b, S11), an on ramp lane (fig. 7b, S1), a straight lane (fig. 7b, S2-S4, S6, and S8-S10), a parking lane (examiner is interpreting this limitation in the alternative.) and a turning lane (examiner is interpreting this limitation in the alternative.).
Regarding claim 7:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 1, upon which this claim is dependent.
Jiang further teaches:
wherein each of the multiple carriageway links (fig. 7B, S1, S2, S3, and S4) are between two chops of the carriageway (fig. 7B, lines at the ends of S1-S4) and a first carriageway link (fig. 7B, S1-S4) between a first set of the two chops (fig. 7B, lines at the ends of S1-S4) includes a consistent number of lanes of the carriageway throughout (fig. 7B, S1-S4 only has 4 lanes in this section).
Regarding claim 8:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 1, upon which this claim is dependent.
Jiang further teaches:
wherein the candidate slice is perpendicular to the heading of the lane connector (the first boundary segment and the second boundary segment extends perpendicular to a direction of the plurality of lanes [claim 18]).
Regarding claim 9:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 1, upon which this claim is dependent.
Jiang further teaches:
wherein the second lane connector of the second lane is adjacent to the lane connector of the first lane of the carriageway (For example the left boundary L of the lane segment S3 is overlapped with the right boundary R of the lane segment S2. Therefore, the lane segments S3 and S2 are determined to be located in different lanes. In some embodiments, after the lane segments are constructed into lanes, the forward and backward boundaries of each lane segment may be removed from the map such that each lane is illustrated as a contiguous lane space defined by a left boundary and a right boundary. [0041]).
Regarding claim 10:
Jiang teaches:
A method for carriageway chopping in a mapping structure (The method includes receiving a grid data of the map comprising lane segments, the grid data comprising an array of grids each associated with a list including none or at least one of the lane segments intersecting the respective grid; receiving coordinates of a location; identifying a first grid including the location based on the grid data; identifying a target grid that has an associated list including at least one of the lane segments as a first lane segment(s); and outputting the first lane segment. [0004]), the method comprising:
determining a face of a lane connector (Second, a polyline segment is selected from the polyline segments (e.g., the polyline segments PS1 to PS9 as shown in FIG. 4) as an incident polyline segment… in FIG. 6A, the polyline segment PS2 is selected as the incident polyline segment [0032]) of a first lane of a carriageway (fig. 6a, PS2 in the first lane);
determining a heading of the lane connector at the face (the left-hand side of FIG. 4 is determined as the front of each lane by the system 100. The right-hand side of FIG. 4 is determined as the back of each lane by the system 100 [0029]; As mentioned in the description of FIG. 4, the left-hand side of FIG. 7B is considered as the front of each lane while the right-hand side of FIG. 7B is considered as the back of each lane. With such configurations, the backward boundary M of the lane segment S3 shown in FIG. 7B is overlapped with the forward boundary N of the lane segment S6 [0040]);
determining a candidate slice (fig. 6b, PS2) to the lane connector at the face (The polyline segment PS2 includes two end points A and B. At the end point B, several candidate polyline segments are identified. For example, the polyline segments PS3 and PS4 are connected to the end point B. Therefore, the polyline segments PS3 and PS4 are identified as the candidate polyline segments for the incident polyline segment. [0032]);
determining a second candidate slice (fig. 6b, PS4) to a second lane connector of a second lane at a second face (The polyline segment PS2 includes two end points A and B. At the end point B, several candidate polyline segments are identified. For example, the polyline segments PS3 and PS4 are connected to the end point B. Therefore, the polyline segments PS3 and PS4 are identified as the candidate polyline segments for the incident polyline segment. [0032]);
aligning (examiner notes that in going from fig. 6c to 7b the individual slices PS2 and PS4 are aligned and combined into one slice that cuts across all the lanes.) the candidate slice of the lane connector and the second candidate slice of the second lane connector (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]);
transforming (see transformation from fig. 3 to fig. 7B) the carriageway mapping structure (FIG. 3 is a diagram illustrating the shape of the road 200 with polylines [0028]) into a plurality of slices (see at least fig. 7B) by chopping the carriageway into the aligned candidate slices (In FIG. 7B, all the lane segments (e.g., the lane segments S1 to S11) are constructed by the lane segment circuit 508. As shown in FIG. 7B, when all the lane segments are constructed, there is no arrow remaining. [0039]) and creating a carriageway link at the aligned candidate slices (fig. 7B, S1-S4, S5-S7, S8-S10, and S11);
generating, [by the self-supervised ML model], a mapped layout of the carriageway (fig. 7B) according to the transformed carriageway mapping structure (The visualization circuit 110 is arranged to receive the raw image data DATA, and generate a dotting plot DP according to the raw image data DATA. The dotting plot DP illustrates the shape of the road (e.g., a highway) with dots [0027]) and the carriageway link (fig. 7B, S1-S4, S5-S7, S8-S10, and S11);
generating (The lane content generation circuit 150 is arranged to generate a lane content LC for the respective lane based on the lane geometry data LGD, wherein the lane content LC includes graphical representation of lanes, the waypoints of the lanes, or some auxiliary factors for the respective lane which will be described in the following paragraphs [0030]; Partition the map into a grid of slots and generate, for each of the slots, a list of lanes intersecting with the slot based on the lane content [0050]), based on the transformed carriageway mapping structure and the mapped layout (fig. 7B, road 200), multiple carriageway links corresponding to respective portions of the carriageway (fig. 7B, S1-S4, S5-S7, S8-S10, and S11); and
Jiang does not explicitly teach, however Chen teaches:
determining, by a self-supervised machine learning (ML) model, according to parameters of a carriageway (a feature detector, lane line detector, trained neural network, and/or the like may be used to identify and extract geo-coded lane boundaries from the image data [0056]):
a heading of the lane connector at the face (see at least fig. 7 showing heading arrows at face of each section of lanes indicating direction of traffic.), wherein the heading of the lane connector at the face is indicative of the direction of traffic at the end of the lane connector (a direction of traffic flow along the lane (e.g., an expected heading of a vehicle traveling in the lane at a particular location along the lane, and/or the like) [0052]);
generating, by the self-supervised ML model, a mapped layout of the carriageway (the map data of the lane-centric road network model may be generated, updated, managed, and/or the like by a model apparatus 10 [0030]) according to the parameters of the carriageway (an image capturing system 50 and/or vehicle apparatus 20 may capture image data that includes portions of a road network and provide (e.g., transmit) the image data. [0055]) and the carriageway link (see fig. 7 showing links.).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang to include the teachings as taught by Chen with a reasonable expectation of success. Both arts are in the same field of endeavor of mapping roadways. Chen also teaches that “Autonomous driving requires high accuracy road network models that accurately describe lane topology. Continuing the example of the left turn lane from above, if an autonomous vehicle is in the left turn lane, the vehicle will need to know the location of the stop line for the left turn lane rather than a virtual location of a virtual amalgamate stop line configured to represent the stop line for entire road segment. Thus, a road network model to be used for navigation of an autonomously driven vehicle should be able to accurately describe road segments having non-trivial topologies. [Chen, 0003]”.
Jiang in view of Chen does not explicitly teach, however Nepomuceno teaches:
utilizing the mapped layout [and the multiple carriageway links] to perform construction or repair of the carriageway (The risk map may be a heat map, and may be transmitted, via wireless communication or data transmission over one or more radio frequency links or wireless communication channels to government entity servers (to facilitate road repair or construction of high risk roads and intersections [col 13, lines 7-12]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang and Chen to include the teachings as taught by Nepomuceno with a reasonable expectation of success. All arts are in the same field of endeavor of mapping roadways. Jiang teaches creating a map of a roadway that is separate into carriageway links but does not explicitly teach what the created map can be used for. Nepomuceno teaches using a map of a roadway “to government entity servers (to facilitate road repair or construction of high risk roads and intersections) [Nepomuceno, col 14, lines 26-28]” to overcome the issue of “environmental factors contributing to the riskiness of an area may not always be readily apparent, observable, or quantifiable. For example, even if a civil engineer identifies a number of one-lane bridges as potentially dangerous, she may have no way of quantifying how risky these one-lane bridges are relative to one another. Moreover, the engineer may overlook a two-lane bridge that is seemingly safe, but which is in actuality riskier than many of the identified one-lane bridges. Because the environmental factors contributing to risk may not always be apparent, observable, or quantifiable, these environmental risk factors may go unnoticed. Thus, engineers and government officials may never identify certain high-risk areas, much less identify solutions to mitigate the risk and improve the safety of the areas for vehicle drivers and passengers. [Nepomuceno, col 1, line 55 – col 2, line 2]”. It would be obvious to one having ordinary skill in the art to provide the map as created by Jiang to aid in road construction or repair as taught by Nepomuceno because in addition to the safety benefits outlined by Nepomuceno, it would be applying known methods to achieve a predictable result.
Regarding claim 11:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 10, upon which this claim is dependent.
Jiang further teaches:
wherein the lane connector comprises at least one of a merging lane (fig. 7b, S5), a branching lane (fig. 7b, S7), an off ramp lane (fig. 7b, S11), an on ramp lane (fig. 7b, S1), a straight lane (fig. 7b, S2-S4, S6, and S8-S10), a parking lane (examiner is interpreting this limitation in the alternative.) and a turning lane (examiner is interpreting this limitation in the alternative.).
Regarding claim 16:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 10, upon which this claim is dependent.
Jiang further teaches:
wherein each of the multiple carriageway links (fig. 7B, S1, S2, S3, and S4) are between two chops of the carriageway (fig. 7B, lines at the ends of S1-S4) and a first carriageway link (fig. 7B, S1-S4) between a first set of the two chops (fig. 7B, lines at the ends of S1-S4) includes a consistent number of lanes of the carriageway throughout (fig. 7B, S1-S4 only has 4 lanes in this section).
Regarding claim 17:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 10, upon which this claim is dependent.
Jiang further teaches:
wherein the candidate slice is perpendicular to the heading of the lane connector (the first boundary segment and the second boundary segment extends perpendicular to a direction of the plurality of lanes [claim 18]).
Regarding claim 18:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 10, upon which this claim is dependent.
Jiang further teaches:
wherein the second lane connector of the second lane is adjacent to the lane connector of the first lane of the carriageway (For example the left boundary L of the lane segment S3 is overlapped with the right boundary R of the lane segment S2. Therefore, the lane segments S3 and S2 are determined to be located in different lanes. In some embodiments, after the lane segments are constructed into lanes, the forward and backward boundaries of each lane segment may be removed from the map such that each lane is illustrated as a contiguous lane space defined by a left boundary and a right boundary. [0041]).
Regarding claim 19:
Jiang teaches:
A non-transitory machine-readable medium (fig. 16, storage device 1602) having instructions stored therein, which when executed by a processor(The storage device is arranged to store a programming code PROG. When the programming code PROG is executed by the processor 1604, the method 1400 or the method 1500 is executed. [0066]), cause the processor to perform operations (fig. 16, processor 12604), the operations comprising:
determining a face of a lane connector (Second, a polyline segment is selected from the polyline segments (e.g., the polyline segments PS1 to PS9 as shown in FIG. 4) as an incident polyline segment… in FIG. 6A, the polyline segment PS2 is selected as the incident polyline segment [0032]) of a first lane of a carriageway (fig. 6a, PS2 in the first lane);
determining a heading of the lane connector at the face (the left-hand side of FIG. 4 is determined as the front of each lane by the system 100. The right-hand side of FIG. 4 is determined as the back of each lane by the system 100 [0029]; As mentioned in the description of FIG. 4, the left-hand side of FIG. 7B is considered as the front of each lane while the right-hand side of FIG. 7B is considered as the back of each lane. With such configurations, the backward boundary M of the lane segment S3 shown in FIG. 7B is overlapped with the forward boundary N of the lane segment S6 [0040]);
determining a candidate slice (fig. 6b, PS2) to the lane connector at the face (The polyline segment PS2 includes two end points A and B. At the end point B, several candidate polyline segments are identified. For example, the polyline segments PS3 and PS4 are connected to the end point B. Therefore, the polyline segments PS3 and PS4 are identified as the candidate polyline segments for the incident polyline segment. [0032]);
determining a second candidate slice (fig. 6b, PS4) to a second lane connector of a second lane at a second face (The polyline segment PS2 includes two end points A and B. At the end point B, several candidate polyline segments are identified. For example, the polyline segments PS3 and PS4 are connected to the end point B. Therefore, the polyline segments PS3 and PS4 are identified as the candidate polyline segments for the incident polyline segment. [0032]);
aligning (examiner notes that in going from fig. 6c to 7b the individual slices PS2 and PS4 are aligned and combined into one slice that cuts across all the lanes.) the candidate slice of the lane connector and the second candidate slice of the second lane connector (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]);
transforming (see transformation from fig. 3 to fig. 7B) the carriageway mapping structure (FIG. 3 is a diagram illustrating the shape of the road 200 with polylines [0028]) into a plurality of slices (see at least fig. 7B) by chopping the carriageway into the aligned candidate slices (In FIG. 7B, all the lane segments (e.g., the lane segments S1 to S11) are constructed by the lane segment circuit 508. As shown in FIG. 7B, when all the lane segments are constructed, there is no arrow remaining. [0039]) and creating a carriageway link at the aligned candidate slices (fig. 7B, S1-S4, S5-S7, S8-S10, and S11);
generating, [by the self-supervised ML model], a mapped layout of the carriageway (fig. 7B) according to the transformed carriageway mapping structure (The visualization circuit 110 is arranged to receive the raw image data DATA, and generate a dotting plot DP according to the raw image data DATA. The dotting plot DP illustrates the shape of the road (e.g., a highway) with dots [0027]) and the carriageway link (fig. 7B, S1-S4, S5-S7, S8-S10, and S11);
generating (The lane content generation circuit 150 is arranged to generate a lane content LC for the respective lane based on the lane geometry data LGD, wherein the lane content LC includes graphical representation of lanes, the waypoints of the lanes, or some auxiliary factors for the respective lane which will be described in the following paragraphs [0030]; Partition the map into a grid of slots and generate, for each of the slots, a list of lanes intersecting with the slot based on the lane content [0050]), based on the transformed carriageway mapping structure and the mapped layout (fig. 7B, road 200), multiple carriageway links corresponding to respective portions of the carriageway (fig. 7B, S1-S4, S5-S7, S8-S10, and S11); and
Jiang does not explicitly teach, however Chen teaches:
determining, by a self-supervised machine learning (ML) model, according to parameters of a carriageway (a feature detector, lane line detector, trained neural network, and/or the like may be used to identify and extract geo-coded lane boundaries from the image data [0056]):
a heading of the lane connector at the face (see at least fig. 7 showing heading arrows at face of each section of lanes indicating direction of traffic.), wherein the heading of the lane connector at the face is indicative of the direction of traffic at the end of the lane connector (a direction of traffic flow along the lane (e.g., an expected heading of a vehicle traveling in the lane at a particular location along the lane, and/or the like) [0052]);
generating, by the self-supervised ML model, a mapped layout of the carriageway (the map data of the lane-centric road network model may be generated, updated, managed, and/or the like by a model apparatus 10 [0030]) according to the parameters of the carriageway (an image capturing system 50 and/or vehicle apparatus 20 may capture image data that includes portions of a road network and provide (e.g., transmit) the image data. [0055]) and the carriageway link (see fig. 7 showing links.).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang to include the teachings as taught by Chen with a reasonable expectation of success. Both arts are in the same field of endeavor of mapping roadways. Chen also teaches that “Autonomous driving requires high accuracy road network models that accurately describe lane topology. Continuing the example of the left turn lane from above, if an autonomous vehicle is in the left turn lane, the vehicle will need to know the location of the stop line for the left turn lane rather than a virtual location of a virtual amalgamate stop line configured to represent the stop line for entire road segment. Thus, a road network model to be used for navigation of an autonomously driven vehicle should be able to accurately describe road segments having non-trivial topologies. [Chen, 0003]”.
Jiang in view of Chen does not explicitly teach, however Nepomuceno teaches:
utilizing the mapped layout [and the multiple carriageway links] to perform construction or repair of the carriageway (The risk map may be a heat map, and may be transmitted, via wireless communication or data transmission over one or more radio frequency links or wireless communication channels to government entity servers (to facilitate road repair or construction of high risk roads and intersections [col 13, lines 7-12]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang and Chen to include the teachings as taught by Nepomuceno with a reasonable expectation of success. All arts are in the same field of endeavor of mapping roadways. Jiang teaches creating a map of a roadway that is separate into carriageway links but does not explicitly teach what the created map can be used for. Nepomuceno teaches using a map of a roadway “to government entity servers (to facilitate road repair or construction of high risk roads and intersections) [Nepomuceno, col 14, lines 26-28]” to overcome the issue of “environmental factors contributing to the riskiness of an area may not always be readily apparent, observable, or quantifiable. For example, even if a civil engineer identifies a number of one-lane bridges as potentially dangerous, she may have no way of quantifying how risky these one-lane bridges are relative to one another. Moreover, the engineer may overlook a two-lane bridge that is seemingly safe, but which is in actuality riskier than many of the identified one-lane bridges. Because the environmental factors contributing to risk may not always be apparent, observable, or quantifiable, these environmental risk factors may go unnoticed. Thus, engineers and government officials may never identify certain high-risk areas, much less identify solutions to mitigate the risk and improve the safety of the areas for vehicle drivers and passengers. [Nepomuceno, col 1, line 55 – col 2, line 2]”. It would be obvious to one having ordinary skill in the art to provide the map as created by Jiang to aid in road construction or repair as taught by Nepomuceno because in addition to the safety benefits outlined by Nepomuceno, it would be applying known methods to achieve a predictable result.
Regarding claim 25:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 19, upon which this claim is dependent.
Jiang further teaches:
wherein each of the multiple carriageway links (fig. 7B, S1, S2, S3, and S4) are between two chops of the carriageway (fig. 7B, lines at the ends of S1-S4) and a first carriageway link (fig. 7B, S1-S4) between a first set of the two chops (fig. 7B, lines at the ends of S1-S4) includes a consistent number of lanes of the carriageway throughout (fig. 7B, S1-S4 only has 4 lanes in this section).
Claim(s) 3-4, 12-13, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et. al. (US 2023/0410536), herein Jiang in view of Chen et. al. (US 2019/0086928), herein Chen and Nepomuceno et. al. (US 10,571,283), herein Nepomuceno in further view of Eagelberg et. al. (US 2018/0120859), herein Eagelberg.
Regarding claim 3:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 1, upon which this claim is dependent.
Jiang further teaches:
wherein the determining the face of the lane connector (Second, a polyline segment is selected from the polyline segments (e.g., the polyline segments PS1 to PS9 as shown in FIG. 4) as an incident polyline segment… in FIG. 6A, the polyline segment PS2 is selected as the incident polyline segment [0032]) comprises:
Jiang in view of Chen and Nepomuceno does not explicitly teach, however Eagelberg teaches:
determining a type of the lane connector (a lane mark characteristic may indicate a feature of a lane mark, which the system may use to determine a type of the lane mark (e.g., whether the lane mark is a merge lane mark or a split lane mark). [0163]); and
determining the face of the lane connector according to the type (if the detected lane mark characteristic(s) include a lateral distance between lane marks 1004A and 1004C and/or between lane marks 1004B and 1004C, a decreasing lateral distance between the lane marks over a distance forward of the host vehicle may indicate that the lane mark type for lane mark 1004C is a merge lane [0185]; if a detected lane mark characteristic includes a split of lane mark 1204B into lane mark 1204B and lane mark 1204C, processing unit 110 may determine that the lane mark type is a split lane. [0216]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang in view of Chen to include the teachings as taught by Eagelberg with a reasonable expectation of success. Eagelberg teaches “the autonomous vehicle may need to take these lane changes and any potential maneuvers neighboring vehicles may make in view of the lane changes into consideration while navigating. Moreover, when these lane changes occur, the autonomous vehicle may need to make an adjustment to its navigational path or speed to travel safely and accurately. [Eagelberg, 0004]”. Additionally, both Jiang and Eagelberg are in the same field of endeavor of processing navigational maps.
Regarding claim 4:
Jiang in view of Chen, Nepomuceno, and Eagelberg teaches all the limitations of claim 3, upon which this claim is dependent.
Jiang further teaches:
wherein the type of the lane connector comprises at least one of branch left (fig. 7b, S7), branch right (examiner is interpreting this limitation in the alternative.), left (examiner is interpreting this limitation in the alternative.), right (examiner is interpreting this limitation in the alternative.), merge left (fig. 7b, S5), merge right, loop (examiner is interpreting this limitation in the alternative.), loop entry (examiner is interpreting this limitation in the alternative.), loop exit (examiner is interpreting this limitation in the alternative.), straight (fig. 7b, S2-S4, S6, and S8-S10) and u-turn (examiner is interpreting this limitation in the alternative.).
Regarding claim 12:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 10, upon which this claim is dependent.
Jiang further teaches:
wherein the determining the face of the lane connector (Second, a polyline segment is selected from the polyline segments (e.g., the polyline segments PS1 to PS9 as shown in FIG. 4) as an incident polyline segment… in FIG. 6A, the polyline segment PS2 is selected as the incident polyline segment [0032]) comprises:
Jiang in view of Chen and Nepomuceno does not explicitly teach, however Eagelberg teaches:
determining a type of the lane connector (a lane mark characteristic may indicate a feature of a lane mark, which the system may use to determine a type of the lane mark (e.g., whether the lane mark is a merge lane mark or a split lane mark). [0163]); and
determining the face of the lane connector according to the type (if the detected lane mark characteristic(s) include a lateral distance between lane marks 1004A and 1004C and/or between lane marks 1004B and 1004C, a decreasing lateral distance between the lane marks over a distance forward of the host vehicle may indicate that the lane mark type for lane mark 1004C is a merge lane [0185]; if a detected lane mark characteristic includes a split of lane mark 1204B into lane mark 1204B and lane mark 1204C, processing unit 110 may determine that the lane mark type is a split lane. [0216]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang in view of Chen and Nepomuceno to include the teachings as taught by Eagelberg with a reasonable expectation of success. Eagelberg teaches “the autonomous vehicle may need to take these lane changes and any potential maneuvers neighboring vehicles may make in view of the lane changes into consideration while navigating. Moreover, when these lane changes occur, the autonomous vehicle may need to make an adjustment to its navigational path or speed to travel safely and accurately. [Eagelberg, 0004]”. Additionally, both Jiang and Eagelberg are in the same field of endeavor of processing navigational maps.
Regarding claim 13:
Jiang in view of Chen, Nepomuceno, and Eagelberg teaches all the limitations of claim 12, upon which this claim is dependent.
Jiang further teaches:
wherein the type of the lane connector comprises at least one of branch left (fig. 7b, S7), branch right (examiner is interpreting this limitation in the alternative.), left (examiner is interpreting this limitation in the alternative.), right (examiner is interpreting this limitation in the alternative.), merge left (fig. 7b, S5), merge right, loop (examiner is interpreting this limitation in the alternative.), loop entry (examiner is interpreting this limitation in the alternative.), loop exit (examiner is interpreting this limitation in the alternative.), straight (fig. 7b, S2-S4, S6, and S8-S10) and u-turn (examiner is interpreting this limitation in the alternative.).
Regarding claim 21:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 19, upon which this claim is dependent.
Jiang further teaches:
wherein the determining the face of the lane connector (Second, a polyline segment is selected from the polyline segments (e.g., the polyline segments PS1 to PS9 as shown in FIG. 4) as an incident polyline segment… in FIG. 6A, the polyline segment PS2 is selected as the incident polyline segment [0032]) comprises:
Jiang in view of Chen and Nepomuceno does not explicitly teach, however Eagelberg teaches:
determining a type of the lane connector (a lane mark characteristic may indicate a feature of a lane mark, which the system may use to determine a type of the lane mark (e.g., whether the lane mark is a merge lane mark or a split lane mark). [0163]); and
determining the face of the lane connector according to the type (if the detected lane mark characteristic(s) include a lateral distance between lane marks 1004A and 1004C and/or between lane marks 1004B and 1004C, a decreasing lateral distance between the lane marks over a distance forward of the host vehicle may indicate that the lane mark type for lane mark 1004C is a merge lane [0185]; if a detected lane mark characteristic includes a split of lane mark 1204B into lane mark 1204B and lane mark 1204C, processing unit 110 may determine that the lane mark type is a split lane. [0216]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang in view of Chen and Nepomuceno to include the teachings as taught by Eagelberg with a reasonable expectation of success. Eagelberg teaches “the autonomous vehicle may need to take these lane changes and any potential maneuvers neighboring vehicles may make in view of the lane changes into consideration while navigating. Moreover, when these lane changes occur, the autonomous vehicle may need to make an adjustment to its navigational path or speed to travel safely and accurately. [Eagelberg, 0004]”. Additionally, both Jiang and Eagelberg are in the same field of endeavor of processing navigational maps.
Claim(s) 6, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et. al. (US 2023/0410536), herein Jiang in view of Chen et. al. (US 2019/0086928), herein Chen and Nepomuceno et. al. (US 10,571,283), herein Nepomuceno in further view of Maheshwari et. al. (US 2021/0209941), herein Maheshwari.
Regarding claim 6:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 1, upon which this claim is dependent.
Jiang further teaches:
wherein the aligning the candidate slice of the lane connector and the second candidate slice of the second lane connector (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]) comprises:
aligning the candidate slices of the lane connectors () by moving the face of the lane connector or the second face of the second lane connector to position the heading of each lane connector in the same direction and the candidate slice of each lane connector on the same latitudinal line across the carriageway (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]).
Jiang in view of Chen and Nepomuceno does not explicitly teach, however Maheshwari teaches:
determining a longitudinal distance between the second candidate slice to the second lane connector and the candidate slice to the lane connector (he lane detection module 104 may compare the measured distances (643, 644) to a threshold distance, and the threshold distance may be predetermined or dynamically determined as part of a machine learning model [0100]);
determining the longitudinal distance is within a distance threshold (If the lane detection module 104 determines that the distance 643 associated with the first pixel 641 is less than the threshold distance, the lane detection module 104 may determine that the first pixel 641 should be linked in a subsection 645a with the beginning pixel 637 and any other pixels in between that satisfy the distance threshold [0100]); and
aligning the candidate slices of the lane connectors (Once the lane marking pixels are identified, the lane marking detection module 104 may estimate a left boundary and a right boundary relative to the lane marking pixels (block 906). The left and the right boundaries may be substantially aligned with a direction of a lane indicated by the lane marking pixels. [0151]) by moving the face of the lane connector or the second face of the second lane connector to position the heading of each lane connector in the same direction and the candidate slice of each lane connector on the same latitudinal line across the carriageway (the lane detection module 104 may analyze the control points 679a-679e, 679e-679f, 679e-679j, and 681a-681f to fit the splines 680a-680c and 682, respectively. Generally, the lane detection module 104 may use any suitable splining method to generate the spline, and in embodiments, the lane detection module 104 may use a Catmull-Rom spline method to generate the spline through the control points. [0109]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang in view of Chen and Nepomuceno to include the teachings as taught by Maheshwari with a reasonable expectation of success. Maheshwari teaches “detecting boundaries of lanes on a road comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further comprises partitioning, by the one or more processors, the set of pixels into a plurality of groups, wherein each of the plurality of groups is associated with one or more control points. The method further comprises generating, by the one or more processors, a spline that traverses the control points of the plurality of groups, such that the spline describes a boundary of a lane. [Maheshwari, 0006]”.
Regarding claim 15:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 10, upon which this claim is dependent.
Jiang further teaches:
wherein the aligning the candidate slice of the lane connector and the second candidate slice of the second lane connector (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]) comprises:
aligning the candidate slices of the lane connectors () by moving the face of the lane connector or the second face of the second lane connector to position the heading of each lane connector in the same direction and the candidate slice of each lane connector on the same latitudinal line across the carriageway (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]).
Jiang in view of Chen and Nepomuceno does not explicitly teach, however Maheshwari teaches:
determining a longitudinal distance between the second candidate slice to the second lane connector and the candidate slice to the lane connector (he lane detection module 104 may compare the measured distances (643, 644) to a threshold distance, and the threshold distance may be predetermined or dynamically determined as part of a machine learning model [0100]);
determining the longitudinal distance is within a distance threshold (If the lane detection module 104 determines that the distance 643 associated with the first pixel 641 is less than the threshold distance, the lane detection module 104 may determine that the first pixel 641 should be linked in a subsection 645a with the beginning pixel 637 and any other pixels in between that satisfy the distance threshold [0100]); and
aligning the candidate slices of the lane connectors (Once the lane marking pixels are identified, the lane marking detection module 104 may estimate a left boundary and a right boundary relative to the lane marking pixels (block 906). The left and the right boundaries may be substantially aligned with a direction of a lane indicated by the lane marking pixels. [0151]) by moving the face of the lane connector or the second face of the second lane connector to position the heading of each lane connector in the same direction and the candidate slice of each lane connector on the same latitudinal line across the carriageway (the lane detection module 104 may analyze the control points 679a-679e, 679e-679f, 679e-679j, and 681a-681f to fit the splines 680a-680c and 682, respectively. Generally, the lane detection module 104 may use any suitable splining method to generate the spline, and in embodiments, the lane detection module 104 may use a Catmull-Rom spline method to generate the spline through the control points. [0109]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang in view of Chen and Nepomuceno to include the teachings as taught by Maheshwari with a reasonable expectation of success. Maheshwari teaches “detecting boundaries of lanes on a road comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further comprises partitioning, by the one or more processors, the set of pixels into a plurality of groups, wherein each of the plurality of groups is associated with one or more control points. The method further comprises generating, by the one or more processors, a spline that traverses the control points of the plurality of groups, such that the spline describes a boundary of a lane. [Maheshwari, 0006]”.
Regarding claim 20:
Jiang in view of Chen and Nepomuceno teaches all the limitations of claim 19, upon which this claim is dependent.
Jiang further teaches:
wherein the aligning the candidate slice of the lane connector and the second candidate slice of the second lane connector (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]) comprises:
aligning the candidate slices of the lane connectors () by moving the face of the lane connector or the second face of the second lane connector to position the heading of each lane connector in the same direction and the candidate slice of each lane connector on the same latitudinal line across the carriageway (When the outline polyline segments are selected as boundaries of the general outline GO by the outline tracking circuit 502, the invalid polyline segment is removed by the validation circuit 504, and at least two joined polyline segments are optionally merged into a single polyline segment by the data merging circuit 506. As a result, the general outline GO is constructed as shown in FIG. 6C. [0037]).
Jiang in view of Chen and Nepomuceno does not explicitly teach, however Maheshwari teaches:
determining a longitudinal distance between the second candidate slice to the second lane connector and the candidate slice to the lane connector (he lane detection module 104 may compare the measured distances (643, 644) to a threshold distance, and the threshold distance may be predetermined or dynamically determined as part of a machine learning model [0100]);
determining the longitudinal distance is within a distance threshold (If the lane detection module 104 determines that the distance 643 associated with the first pixel 641 is less than the threshold distance, the lane detection module 104 may determine that the first pixel 641 should be linked in a subsection 645a with the beginning pixel 637 and any other pixels in between that satisfy the distance threshold [0100]); and
aligning the candidate slices of the lane connectors (Once the lane marking pixels are identified, the lane marking detection module 104 may estimate a left boundary and a right boundary relative to the lane marking pixels (block 906). The left and the right boundaries may be substantially aligned with a direction of a lane indicated by the lane marking pixels. [0151]) by moving the face of the lane connector or the second face of the second lane connector to position the heading of each lane connector in the same direction and the candidate slice of each lane connector on the same latitudinal line across the carriageway (the lane detection module 104 may analyze the control points 679a-679e, 679e-679f, 679e-679j, and 681a-681f to fit the splines 680a-680c and 682, respectively. Generally, the lane detection module 104 may use any suitable splining method to generate the spline, and in embodiments, the lane detection module 104 may use a Catmull-Rom spline method to generate the spline through the control points. [0109]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Jiang in view of Chen and Nepomuceno to include the teachings as taught by Maheshwari with a reasonable expectation of success. Maheshwari teaches “detecting boundaries of lanes on a road comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further comprises partitioning, by the one or more processors, the set of pixels into a plurality of groups, wherein each of the plurality of groups is associated with one or more control points. The method further comprises generating, by the one or more processors, a spline that traverses the control points of the plurality of groups, such that the spline describes a boundary of a lane. [Maheshwari, 0006]”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott R Jagolinzer whose telephone number is (571)272-4180. The examiner can normally be reached M-Th 8AM - 4PM Eastern.
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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