DETAILED ACTION
Status of Claims
Claims 1, 5-7, 9-10, 12-14, and 16-20 are currently pending and have been examined in this application. This action is NON-FINAL.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/31/2026 has been entered.
Response to Arguments
Applicant's arguments filed 08/31/2026 have been fully considered but they are not persuasive.
Applicant argues:
Regarding the 35 USC 101 rejection, “In particular, currently amended claim 1 explicitly recites that the vehicle control signal is generated to control travel of the vehicle based on the travel path determined based on the selected road boundary. Therefore, the vehicle control signal is not mere data output, but rather a technical process used for vehicle travel control. Thus, even assuming arguendo that claim 1 includes some elements of an abstract idea, currently amended claim 1 integrates the abstract idea into a practical application, namely vehicle travel control. Accordingly, claim 1 possesses patent eligibility under 35 U.S.C. § 101.” (Remarks, pg. 10)
Regarding the 35 USC 103 rejection, “Kudo relates to driving assistance based on road conditions, such as surrounding vehicles, oncoming vehicles, and diverging diamond interchange (DDI). Even assuming arguendo that Kudo discloses assisting the driving of a subject vehicle by using traveling directions or positional relationships of other vehicles, Kudo fails to disclose a configuration of determining a plurality of road boundaries in an in-vehicle road image, assuming an invisible road boundary corresponding to a blocked area, and selecting an approachable road boundary by using the invisible road boundary. That is, Kudo fails to disclose at least the aforementioned features [1] to [5] of currently amended claim 1.
Furthermore, the purpose of Kudo is to provide driving assistance according to oncoming vehicles or surrounding traffic conditions, and Kudo would not be naturally combined by those of ordinary skill in the art with the high-definition map generation processing of Jin or the lane line identification processing of He.” (Remarks, pg. 11)
Regarding the 35 USC 103 rejection, “He relates to lane line recognition. He discloses a technique for identifying lane lines by using two-dimensional filtering, connected components, linear fitting, or the like when the recognition accuracy of lane lines decreases due to shadows, occlusions, or the like.
However, He fails to disclose assuming an invisible road boundary corresponding to a blocked area and select a road boundary approachable by a vehicle by using the invisible road boundary. The process in He relates to improving a lane line identification model or enhancing lane line detection accuracy. He does not select an approachable road boundary by using an estimated invisible road boundary, determine a driving path of a vehicle based on the selection result, and generate a vehicle control signal for controlling driving of the vehicle. That is, He fails to disclose at least the aforementioned features [1] to [5] of currently amended claim 1.” (Remarks, pg. 12)
Examiner respectfully disagrees.
Regarding point (a), the independent claims recite generating a control signal at a high level of generality and does not integrate the abstract idea into practical application. The claims do not recite controlling the vehicle based on the generated signal, but merely recites the generation of a control signal, so the step of generating a control signal does not constitute a control step. Therefore, “generating a vehicle control signal based on the selected road boundary,” is mere data outputting which is considered insignificant extra solution activity and does not integrate the mental process into practical application. Applicant is advised to amend the limitation to “generating a vehicle control signal to control the vehicle based on the selected road boundary, wherein”.
Regarding point (b), Kudo teaches determining the location of a vehicle in an ego lane based on map data (Kudo, para. [0040] “The own-vehicle position estimating section 12a may perform map matching of the acquired coordinates of the current position of the own vehicle on the map information to estimate the own-vehicle position (i.e., the current position) on the road map. The own-vehicle position estimating section 12a may also identify the traveling lane on which the own vehicle is traveling and retrieve information on the road shapes of the traveling lanes and the merging lanes, the interchanges provided on the set traveling route, and the like from the map information, and store these data items in a sequential manner.”). Kudo further teaches detecting a traveling lane based on image data that includes driving direction of other vehicles (Kudo, para. [0078] “The DDI may be provided with the region in which the relative positional relationship is reversed in the lateral direction between the own-vehicle lane on which the own vehicle is traveling and the oncoming-vehicle lane on which other vehicles are traveling in the direction opposite to the traveling direction of the own vehicle. The region may be located in part of a general road including the entrance and the exit of the highway.”). Since Kudo teaches determining the vehicle location on a road map and identifying the traveling lane of a vehicle, Kudo teaches determining an ego lane where the vehicle is located based on at least one of: traffic signs identified in the road image, driving direction of other vehicle identified in the road image, or position information of the vehicle based on pre-obtained map data. It would be obvious to combine the boundary selection method taught by Jin that identifies a road boundary that the vehicle is able to enter (Jin, Col. 7 lines 17-46) with the ego lane identification system taught by Kudo because they are both in the same field of controlling a vehicle based on map information and determining information about the surroundings of a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Jin with Kudo to include determining an own-vehicle lane based on the direction of another vehicle in order to ensure a more efficient traffic flow and high safety while mitigating traffic congestion (Kudo, para. [0011]).
Regarding point (c), He discloses identifying lane boundaries that are not visible (He, para. [0035] “An image of a driving road is captured, and the image includes image data of a lane line. Conventional lane line identification methods have the problems of low adaptability and low identification accuracy. Specifically, once the image acquiring environment changes, for example, the lane line in the image is blocked by a large number of other objects or a large number of shadow regions appear in the image, the identification result of the lane line in the image may be a false alarm or a misjudgement.”). He further teaches determining where the boundary should be marked (He, para. [0064] “After the filtering and binarization, several connected domains are formed inside the lane line identification model. Because illumination may be uneven in the image or the lane line may be blocked by other objects, the actual boundary of the connected domain acquired may not be a straight line. Therefore, this embodiment the boundary of the connected domain is linearly fitted by using an improved ransac algorithm.”). In other words, He teaches determining if a lane line is blocked and assume the shape and location of the lines when they are blocked. Since He teaches identifying the lane boundaries that are invisible, He teaches selecting a road boundary based on one or more invisible road boundaries. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine selecting a boundary that the vehicle is allowed to enter based on image and map data as taught by the combination of Jin and Kudo with the invisible boundary identification method taught by He in order to improve the map information with improved adaptability and accuracy of lane line identification (He, para. [0036]).
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, 5-7, 9-10, 12-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis: Step 1
Claim 1 is directed to a method, which is one of the statutory categories of invention.
101 Analysis: Step 2A, Prong I (MPEP § 2106.04)
The examiner has identified method claim 1 as the claim that represents the claimed invention for analysis. claim 1 recites:
A method for road boundary detection, performed by an electronic device, the method comprising:
identifying a road image acquired by an image acquisition device arranged on a vehicle, and determining a plurality of road boundaries in the road image; and
detecting a blocked area based on the road image, the blocked area being at least a part of the plurality of road boundaries which is not directly visible;
assuming one or more invisible road boundaries corresponding to the blocked area, based on the road image;
determining an ego lane where the vehicle is located, based on at least one of:
traffic signs identified in the road image;
a driving direction of other vehicle identified in the road image; or position information of the vehicle based on pre-obtained map data;
selecting a road boundary into which the vehicle is able to enter from the plurality of road boundaries, based on the ego lane, the plurality of road boundaries, and the one or more invisible road boundaries; and
generating a vehicle control signal based on the selected road boundary.
The examiner submits that foregoing the bolded claim limitations constitute a “mental process” as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation. “selecting a road boundary into which the vehicle is able to enter from the plurality of road boundaries, based on the ego lane, the plurality of road boundaries, and the one or more invisible road boundaries;” is equivalent to a person determining the edges of a road from an image and determining which edge a vehicle could cross, i.e. a mental process of judgement based on observation. “detecting a blocked area based on the road image, the blocked area being at least a part of the plurality of road boundaries which is not directly visible; assuming one or more invisible road boundaries corresponding to the blocked area, based on the road image;” is equivalent to a mental process of determining an area that is not visible to the camera and observing where the road boundaries should be. “determining a plurality of road boundaries in the road image; and determining an ego lane where the vehicle is located, based on at least one of: traffic signs identified in the road image; a driving direction of other vehicle identified in the road image; or position information of the vehicle based on pre-obtained map data;” is equivalent to the mental process of determining the ego lane based on observations regarding the traffic signs, driving direction, or position of the vehicle and selecting a vehicle boundary for the vehicle to enter based on the observed ego lane and the observed invisible road boundaries.
Accordingly, claim 1 recites an abstract idea.
101 Analysis: Step 2A, Prong II (MPEP § 2106.04)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitation” while the bolded portions continue to represent the “abstract idea”):
A method for road boundary detection, performed by an electronic device, the method comprising:
identifying a road image acquired by an image acquisition device arranged on a vehicle, and determining a plurality of road boundaries in the road image; and
detecting a blocked area based on the road image, the blocked area being at least a part of the plurality of road boundaries which is not directly visible;
assuming one or more invisible road boundaries corresponding to the blocked area, based on the road image;
determining an ego lane where the vehicle is located, based on at least one of:
traffic signs identified in the road image;
a driving direction of other vehicle identified in the road image; or position information of the vehicle based on pre-obtained map data;
selecting a road boundary into which the vehicle is able to enter from the plurality of road boundaries, based on the ego lane, the plurality of road boundaries, and the one or more invisible road boundaries; and
generating a vehicle control signal based on the selected road boundary.
Regarding the limitations, “…performed by an electronic device…” and “…an image acquisition device arranged on a vehicle,” the examiner submits that this is an attempt to generally link additional elements to a technologic environment. The electronic device and image acquisition device are recited at a high level of generality and merely automates the identifying, determining, and selecting steps, therefore acting as a generic computer component.
Regarding the limitation “identifying a road image acquired by…” the examiner submits that this is an example of mere data gathering. In particular, the road image is recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Regarding the limitation “generating a vehicle control signal based on the selected road boundary,” the examiner submits that this is an example of mere data outputting. In particular, the control signal generated is recited at a high level of generality and amounts to mere data outputting, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05).
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis: Step 2B (MPEP § 2106.05)
Step 2B of the Revised Guidance analyzes the claims to determine if the claims recite additional limitations that amount to significantly more than the judicial exception.
When considered individually or in combination, the additional limitations of claim 1 do not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application. The additional limitations of claim 1 are examples of adding insignificant extra-solution activity (pre-solution, post-solution) to the judicial exception as it is mere data gathering conducted by a generic computer component.
Dependent claims 5-7, 9-10, 12-13, and 16-19 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, similar to the claims shown above.
Claim 5 recites the additional limitations “wherein determining the ego lane where the vehicle is located based on the traffic signs, comprises: in a case that the traffic signs indicate that the ego lane where the vehicle is located is not a one-way lane, and the traffic signs comprise designated road markings, determining the ego lane where the vehicle is located based on the designated road markings.” The examiner submits that this recites a mental process equivalent to a person observing traffic signs and road markings and mentally determining what lane the vehicle is in based on that observation.
Claim 9 recites the additional limitations “wherein selecting the road boundary into which the vehicle is able to enter from the plurality of road boundaries based on the ego lane where the vehicle is located, comprises: determining the road boundary into which the vehicle is able to enter from the plurality of road boundaries based on the traffic signs and the ego lane where the vehicle is located.” The examiner submits that this recites a mental process equivalent to a person observing lane and road boundaries from an image of the road and mentally connecting the ends of the boundaries.
Independent claims 14 and 20 recite similar limitations to claim 1 and are rejected for the same reasons.
Therefore, claims 1, 5-7, 9-10, 12-14, and 16-20 recite abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception, thus are directed toward non-statutory subject matter and are rejected under 35 U.S.C. 101.
Applicant is advised to amend the limitation “generating a vehicle control signal based on the selected road boundary” of the independent claims to “generating a vehicle control signal to control the vehicle based on the selected road boundary”
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.
Claims 1, 6-7, 9-10, 14, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 12055410 B2; hereinafter Jin) in view of Kudo (US 20220144274 A1; hereinafter Kudo) and in further view of He et al. (US 20180225527 A1; hereinafter He).
Regarding claim 1,
Jin teaches:
A method for road boundary detection, performed by an electronic device, the method comprising:
identifying a road image acquired by an image acquisition device arranged on a vehicle, and determining a plurality of road boundaries in the road image; and
(Jin – [Col. 6 line 59 – Col. 7 line 16] “In certain embodiments, the boundary lines may be identified or labelled automatically by processing the original road map with certain image recognition algorithm… For example, the original road map may include original lane separation lines, and thus the lane boundaries can follow these original lane separation lines using an image recognition algorithm. In some cases where the original lane separation lines are not provided in the original road map, the lane boundaries may be identified based on the boundary lines and traffic rules.”)
selecting a road boundary into which the vehicle is able to enter from the plurality of road boundaries, based on the ego lane, the plurality of road boundaries,
(Jin – [Col. 7 lines 17-46] “In certain embodiments, the reference path is linked with navigation information related to traffic properties. The navigation information to be linked may include lane width, lane's hard/soft boundaries (i.e., whether a vehicle is allowed to pass through the lane boundary) and speed limit, etc. In certain embodiments, the navigation information is linked with the waypoints contained in the reference path such that an autonomous vehicle can use the waypoints in conjunction with the sensory information to plan the path and control the motion. In certain embodiment, waypoints are set in every about 0.5 or more meters in the reference path. In certain embodiments, the navigation information linked with reference path or waypoints may include traffic property such as “pass through prohibited” which prohibits a vehicle from changing lanes, or a traffic property “pass through permitted” which permits a vehicle to change lane.” [Col. 8 lines 6-24] “Similar to the reference path in lanes, the reference path in a crossroad junction may include waypoints. As a result, in certain embodiments, a complete reference path that can guide a vehicle driving from one lane to another through a crossroad junction includes a first section in an inward lane, a second section in an outward lane, and a third section connecting the first section and the second section. Each section comprises a sequence of waypoints that link to related navigation information and are conveyed to an autonomous vehicle to guide navigation. As illustrated in FIG. 4, reference path 302 and 311 can guide a vehicle driving from inward lane 211 to outward lane 234, and from inward lane 212 to outward land 233, respectively.”)
generating a vehicle control signal based on the selected road boundary.
(Jin – [Col. 7 lines 16-26] “In certain embodiments, the reference path is linked with navigation information related to traffic properties. The navigation information to be linked may include lane width, lane's hard/soft boundaries (i.e., whether a vehicle is allowed to pass through the lane boundary) and speed limit, etc. In certain embodiments, the navigation information is linked with the waypoints contained in the reference path such that an autonomous vehicle can use the waypoints in conjunction with the sensory information to plan the path and control the motion.”)
Jin does not explicitly teach the following limitations, however Kudo teaches:
determining an ego lane where the vehicle is located, based on at least one of:
traffic signs identified in the road image;
a driving direction of other vehicle identified in the road image; or
(Kudo – [0078] “The DDI may be provided with the region in which the relative positional relationship is reversed in the lateral direction between the own-vehicle lane on which the own vehicle is traveling and the oncoming-vehicle lane on which other vehicles are traveling in the direction opposite to the traveling direction of the own vehicle. The region may be located in part of a general road including the entrance and the exit of the highway.”)
position information of the vehicle based on pre-obtained map data;
(Kudo – [0040] “For example, the own-vehicle position estimating section 12a may acquire the coordinates (latitude and longitude) of the current position of the own vehicle on the basis of positioning signals received from a global navigation satellite system (GNSS) receiver 14. The own-vehicle position estimating section 12a may perform map matching of the acquired coordinates of the current position of the own vehicle on the map information to estimate the own-vehicle position (i.e., the current position) on the road map. The own-vehicle position estimating section 12a may also identify the traveling lane on which the own vehicle is traveling and retrieve information on the road shapes of the traveling lanes and the merging lanes, the interchanges provided on the set traveling route, and the like from the map information, and store these data items in a sequential manner.”)
Kudo is considered to be analogous to the claimed invention because it is in the same field of determining information surrounding a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Jin with Kudo to include determining an own-vehicle lane based on the direction of another vehicle in order to ensure a more efficient traffic flow and high safety while mitigating traffic congestion (Kudo, para. [0011]).
The combination of Jin and Kudo does not explicitly teach the following, however, He teaches:
detecting a blocked area based on the road image, the blocked area being at least a part of the plurality of road boundaries which is not directly visible;
(He – [0035] “An image of a driving road is captured, and the image includes image data of a lane line. Conventional lane line identification methods have the problems of low adaptability and low identification accuracy. Specifically, once the image acquiring environment changes, for example, the lane line in the image is blocked by a large number of other objects or a large number of shadow regions appear in the image, the identification result of the lane line in the image may be a false alarm or a misjudgement.”)
assuming one or more invisible road boundaries corresponding to the blocked area, based on the road image;
(He – [0064] “After the filtering and binarization, several connected domains are formed inside the lane line identification model. Because illumination may be uneven in the image or the lane line may be blocked by other objects, the actual boundary of the connected domain acquired may not be a straight line. Therefore, this embodiment the boundary of the connected domain is linearly fitted by using an improved ransac algorithm.”)
selecting a road boundary
(He – [0064] “After the filtering and binarization, several connected domains are formed inside the lane line identification model. Because illumination may be uneven in the image or the lane line may be blocked by other objects, the actual boundary of the connected domain acquired may not be a straight line. Therefore, this embodiment the boundary of the connected domain is linearly fitted by using an improved ransac algorithm.”)
He is considered to be analogous to the claimed invention because it is in the same field of determining lane boundaries for a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Jin and Kudo with He to include detecting and assuming invisible lane lines in order to improve the adaptability and accuracy of lane line identification (He, para. [0036]).
Regarding claim 6,
The combination of Jin, Kudo, and He teaches the limitations of claim 1.
Kudo further teaches:
wherein determining the ego lane where the vehicle is located based on the driving direction of the other vehicle, comprises:
in a case that the driving direction of the other vehicle is opposite to a driving direction of the vehicle, determining the ego lane where the vehicle is located based on a lane where the other vehicle is located.
(Kudo – [0078] “The DDI may be provided with the region in which the relative positional relationship is reversed in the lateral direction between the own-vehicle lane on which the own vehicle is traveling and the oncoming-vehicle lane on which other vehicles are traveling in the direction opposite to the traveling direction of the own vehicle. The region may be located in part of a general road including the entrance and the exit of the highway.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Jin with Kudo to include determining an own-vehicle lane based on the direction of another vehicle in order to ensure a more efficient traffic flow and high safety while mitigating traffic congestion (Kudo, para. [0011]).
Regarding claim 7,
The combination of Jin, Kudo, and He teaches the limitations of claim 1.
Jin further teaches:
wherein selecting the road boundary into which the vehicle is able to enter from the plurality of road boundaries based on the ego lane where the vehicle is located, comprises:
determining the road boundary into which the vehicle is able to enter from the plurality of road boundaries based on the traffic signs and the ego lane where the vehicle is located.
(Jin – [Col. 9 lines 19-36] “A polygonal hub 400 is then generated to encompass the crossroad junction 250. As used herein, the term “encompass” when referring to the relationship between a polygonal hub and a crossroad junction means that the polygonal hub generally delineates the crossroad junction, i.e., the shape of the polygonal hub generally matches the shape of the crossroad junction. For example, a hub for a crossroad junction that connects to four roads can be quadrilateral. A hub for a crossroad junction that connects to five roads can be a pentagon. A hub for a three-way junction, i.e. a T-shape crossroad, can be quadrilateral. The polygonal hub should contain inside the crossroad junction and also certain neighboring regions in the lanes that connect to the crossroad junction. In particular, the polygonal hub contains regions and objects that are related to the navigation information necessary for guiding a vehicle driving through the crossroad junction, such as crosswalk regions, stop lines, stop signs, traffic lights, etc.”)
Regarding claim 9,
The combination of Jin, Kudo, and He teaches the limitations of claim 1.
Jin further teaches:
wherein determining the plurality of road boundaries in the road image, comprises:
detecting a plurality of lanes in the road image, and determining the plurality of road boundaries by connecting ends of the plurality of lanes.
(Jin – [Col. 6 lines 12-28] “Moreover, an HD vector map generated is a vector map which includes vector-based collections of geographic information system (GIS) data about a place at various levels of detail. For example, the HD vector map may contain various hubs or junctions connected by lanes in form of a tree structure consisting of nodes connected by edges. In some embodiments, the tree-structure vector map facilitates routing planning and navigation by using various graph searching algorithms.”)
Regarding claim 10,
The combination of Jin, Kudo, and He teaches the limitations of claim 1.
Jin further teaches:
wherein determining the plurality of road boundaries in the road image, comprises:
detecting a freespace in the road image, and determining the plurality of road boundaries in the road image based on contour lines of the freespace.
(Jin – [Col. 3 line 59 – Col. 4 line 3] “The information in the HD vector map is used by many other modules of the autonomous driving system. In the first place, a localization module depends on the HD vector map to determine the exact location of the autonomous vehicle. The BD vector map also helps a perception module to sense the environment around the autonomous vehicle when the surrounding area is out of the range of the sensors or blocked by an obstacle. The HD vector map also helps a planning module to find suitable driving space and to identify multiple driving routes. The HD vector map allows the planning module to accurately plan a path and choose the best maneuver.”)
Regarding claim 14,
Claim 14 recites an apparatus comprising substantially the same limitation as claim 1 above, therefore it is rejected for the same reasons. Additionally, Jin further teaches:
a processor, configured to:
(Jin – [Col. 6 lines 59-66] “As used herein, the term “automatically” when used in the context of identification, labeling or generation of objects or linkages in an HD vector map means the identification, labeling or generation is performed by a computer processor without substantial human input.”)
Regarding claim 17,
The combination of Jin, Kudo, and He teaches the limitations of claim 14.
Kudo further teaches:
wherein the processor is further configured to:
identify the driving direction of the other vehicle in the road image; and
(Kudo – [0126] “The process for detecting an oncoming vehicle executed by the oncoming vehicle detector 23 is schematically described below. For example, the oncoming vehicle detector 23 may detect mobile objects in the oncoming-vehicle emerging expected region. Among the objects detected in the oncoming-vehicle emerging expected region, an object having a velocity component directed toward the own vehicle M1 along the length direction of the own vehicle M1 (e.g., a velocity component Y1 indicated by an arrow in FIG. 4) and a velocity component moving from right to left in parallel with the width direction of the own vehicle M1 (e.g., a velocity component W1 indicated by an arrow in FIG. 4) may be detected as an oncoming vehicle against which the warning control and the contact avoidance brake control are executed.”)
determine the ego lane where the vehicle is located based on the driving direction of the other vehicle.
(Kudo – [0078] “The DDI may be provided with the region in which the relative positional relationship is reversed in the lateral direction between the own-vehicle lane on which the own vehicle is traveling and the oncoming-vehicle lane on which other vehicles are traveling in the direction opposite to the traveling direction of the own vehicle. The region may be located in part of a general road including the entrance and the exit of the highway.”)
Kudo is considered to be analogous to the claimed invention because it is in the same field of determining information surrounding a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Jin with Kudo to include determining an own-vehicle lane based on the direction of another vehicle in order to ensure a more efficient traffic flow and high safety while mitigating traffic congestion (Kudo, para. [0011]).
Regarding claim 19,
Claim 19 recites an apparatus comprising substantially the same limitation as claim 6 above, therefore it is rejected for the same reasons.
Regarding claim 20,
Claim 20 recites a non-transitory computer readable storage medium comprising substantially the same limitation as claim 1 above, therefore it is rejected for the same reasons. Additionally, Jin further teaches:
A non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements a method for road boundary detection, wherein the method comprises:
(Jin – [Col. 1 lines 59-67] “In another aspect of the application, there is provided a computer readable storage medium for storing a program of instructions executable by a computer to perform a process.”)
Claims 5, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 12055410 B2; hereinafter Jin) in view of Kudo (US 20220144274 A1; hereinafter Kudo) in further view of He et al. (US 20180225527 A1; hereinafter He) and in further view of Kim (US 20200089224 A1; hereinafter Kim).
Regarding claim 5,
The combination of Jin, Kudo, and He teaches the limitations of claim 1.
The combination of Jin, Kudo, and He does not explicitly teach the following limitations, however, Kim teaches:
wherein determining the ego lane where the vehicle is located based on the traffic signs, comprises:
in a case that the traffic signs indicate that the ego lane where the vehicle is located is not a one-way lane, and the traffic signs comprise designated road markings, determining the ego lane where the vehicle is located based on the designated road markings.
(Kim – [0065] “The processor 140 may determine whether a high-pass sign is present in front of a vehicle or whether the high-pass road marking is present in the lane, when a high-pass guidance lane is not present in the current driving lane of the vehicle and a high-pass gate is not present in front of the vehicle; the processor 140 may determine that the driving lane is a high-pass lane candidate, when the high-pass sign is present in front of the vehicle or the high-pass road marking is present in the lane.”)
It would have been obvious to one skilled in the art before the effective filing date to modify the combination of Jin, Kudo, and He with Kim to include determining an ego lane based on the road signs and markings detected from image data in order to reduce the risk of traffic accidents and assist safety driving (Kim, para. [0004]).
Regarding claim 16,
The combination of Jin, Kudo, and He teaches the limitations of claim 14.
The combination of Jin, Kudo, and He does not explicitly teach the following limitations, however, Kim teaches:
wherein the processor is further configured to:
determine the ego lane where the vehicle is located based on the traffic signs.
(Kim – [0065] “The processor 140 may determine whether a high-pass sign is present in front of a vehicle or whether the high-pass road marking is present in the lane, when a high-pass guidance lane is not present in the current driving lane of the vehicle and a high-pass gate is not present in front of the vehicle; the processor 140 may determine that the driving lane is a high-pass lane candidate, when the high-pass sign is present in front of the vehicle or the high-pass road marking is present in the lane.”)
Jin and Kim are both considered to be analogous to the claimed invention because they are both in the same field of a perception system for an autonomous vehicle. It would have been obvious to one skilled in the art before the effective filing date to modify the combination of Jin, Kudo, and He with Kim to include determining an ego lane based on the road signs and markings detected from image data in order to reduce the risk of traffic accidents and assist safety driving (Kim, para. [0004]).
Regarding claim 18,
Claim 18 recites an apparatus comprising substantially the same limitation as claim 5 above, therefore it is rejected for the same reasons.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 12055410 B2; hereinafter Jin) in view of Kudo (US 20220144274 A1; hereinafter Kudo) in further view of He et al. (US 20180225527 A1; hereinafter He) and in further view of Jia et al. (US 20220274601 A1; hereinafter Jia).
Regarding claim 12,
The combination of Jin, Kudo, and He teaches the limitations of claim 1.
The combination of Jin, Kudo, and He does not explicitly teach the following limitation, however, Jia teaches:
further comprising:
setting a first region of interest based on the road boundary into which the vehicle is able to enter, and obtaining an image corresponding to the first region of interest at a first resolution; wherein the road image is obtained at a second resolution, and the second resolution is smaller than the first resolution.
(Jia – [0089] “FIG. 4 illustrates an example static grid 400 generated by a grid-based road model with multiple layers and for which the road-perception system 108 can determine lane-boundary cells 404. The road-perception system 108 can use the static grid 400 to represent attributes of a roadway (e.g., the roadway 120) on which the vehicle 102 is traveling. The static grid 400 can include or be inclusive of the grid 302 described with respect to FIG. 3. The static grid 400 includes multiple cells 402. In FIG. 4, the cells 402 are represented as having uniform resolution. In other implementations, the cells 402 can have a higher resolution in areas of interest (e.g., lane boundaries, lane markers, lane centers).”)
Jia is considered to be analogous to the claimed invention because it is in the same field of collecting images for a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Jin, Kudo, and He with Jia to have images in areas of interest have a higher resolution in order to accurate model the roadway and elements thereof (Jia, para. [0016]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 12055410 B2; hereinafter Jin) in view of Kudo (US 20220144274 A1; hereinafter Kudo) in further view of He et al. (US 20180225527 A1; hereinafter He) and in further view of Shiga et al. (US 20170267178 A1; hereinafter Shiga).
Regarding claim 13,
The combination of Jin, Kudo, and He teaches the limitations of claim 1.
The combination of Jin, Kudo, and He does not explicitly teach the following limitation, however, Shiga teaches:
further comprising:
setting a second region of interest based on the road boundary into which the vehicle is able to enter, and obtaining an image corresponding to the second region of interest at a first frame rate; wherein the road image is obtained at a second frame rate, and the second frame rate is smaller than the first frame rate.
(Shiga – [0263] “Since, according to Variant Embodiment #1 as explained above, settings are established according to actuation of the turn signal switch 11 in order to make the image capture conditions for the region of attention 82 and the image capture conditions for the imaging region 81 to be different, accordingly, if for example the vehicle is turning right or left at an intersection, then the opposite vehicle lane is included in the region of attention 82 so that it is possible reliably to detect oncoming vehicles, and/or the road edge is included in the region of attention 82 so that it is possible to prevent involvement in an accident, and as a result it is possible to set the region of attention 82 in an appropriate manner. Furthermore, the frame rate for the region of attention 82 is set to be higher as compared to that for the imaging region 81 and so on, so that it is possible to set the image capture conditions for the imaging region 81 and for the region of attention 82 in an appropriate manner.”)
Shiga is considered to be analogous to the claimed invention because it is in the same field of capturing images for a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Jin, Kudo, and He with Shiga to capture some regions with a higher frame rate in order to reliably detect oncoming vehicles and the road edge and prevent involvement in an accident (Shiga, para. [0263]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Filimonov et al. (US 20240362926 A1) discloses a turn line does not indicate a road border section of a main road where one or more of the following apply: the main road is crossed by a crosswalk, the main road is curved without comprising any of a junction, intersection or crossroads, an edge of a road border section is invisible on the training image, and/or an edge of a road that is not an edge of a carriageway of the road.
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/M.G.H./Examiner, Art Unit 3668 /JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668
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