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 .
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Status of the Claims
This Final Action is in response to Applicant’s amendment of 13 December 2025. Claims 1, 3, 5-6 and 9 are pending and have been considered as follows. Claims 2, 4 and 7-8 are cancelled.
Response to Argument
Applicant's amendments and arguments with respect to the rejection of claim 1-9 under 35 USC 101 as set forth in the office action of 25 September 2025 have been considered and are persuasive. Therefore, the rejection of claim 1-9 under 35 USC 101 as set forth in the office action of 25 September 2025 has been withdrawn.
Applicant’s amendments and/or arguments with respect to the rejection of claims 2-4 under 35 USC 103 have been considered and are NOT persuasive. Specifically, Applicant argues:
Tsukamoto fails to disclose calculating curvature of a travel trajectory. Although the Office may argue that Tohriyama discloses calculating curvature of a travel trajectory and using a curvature threshold to determine a lane terminal point (LnE) within a node having no stop line or crosswalk, Tohriyama fails to disclose switching a lane-shape estimation method based on the curvature value itself. In particular, Tohriyama fails to disclose estimating lane shape based on both travel trajectory and entrance/exit geometry when curvature is equal to or greater than a threshold, while estimating lane shape based only on entrance/exit geometry when curvature is less than the threshold.
Accordingly, neither Tsukamoto nor Tohriyama, alone or in combination, disclose or render obvious the claimed curvature-dependent lane-shape estimation logic, and none of the other cited references disclose or suggest switching a lane-shape estimation method based on a comparison between curvature and a threshold value.
The Examiner’s Response:
Examiner has carefully considered Applicant's arguments and respectfully disagrees. Regarding Claims 2 and 4, Tohriyama, under broadest reasonable interpretation, teaches estimating the lane shape at an intersection with an approximate curve based on average trajectories in a driving log and the lane shape at an entrance and exit when the curvature is higher than a predetermined value as seen in [0115] “Furthermore, as depicted in FIG. 7D, in the case where there is no stop line or crosswalk in the node where the terminal end LnE of the lane exists, if the lane ends after making a right or left turn in the node, a curvature ρ(γγ/V) may be calculated from the vehicle speed V and the yaw rate γ in the driving log in the vicinity of the terminal end LnE of the lane (which is determined based on the road map information in the road map information database) along a driving route, and a spot where the curvature p changes from a value equal to or larger than a predetermined value to a value equal to or smaller than the predetermined value may be specified as the position of the terminal end LnE of the lane.” Furthermore, Tohriyama, under broadest reasonable interpretation, does teach that in situations where a curvature is not above a predetermined value (and thus curvature is not relevant for estimating a lane shape), an estimation method that uses an approximate curve (in this case a straight line) and the entrance and exit at an intersection to estimate a lane shape “It should be noted herein that the predetermined value for the curvature ρ may be, for example, 0.01 (1/m) (because the lane can be regarded as a substantially straight line when the curvature ρ is smaller than 0.01 (1/m)). On the other hand, as depicted in FIG. 7E, in the case where there is no stop line or crosswalk in the node where the terminal end LnE of the lane exists and the lane ends after extending straight in the node, the position of the terminal end LnE may be determined as a position Os_.sub.TL+Os_e) obtained by adding the distance Os_e from a landmark located immediately short of the terminal end LnE of the lane to the terminal end LnE of the lane, which is determined based on the road map information in the road map information database, to a driving course distance Os_.sub.TL from the leading end LnS of the lane to the landmark located immediately short of the terminal end LnE of the lane (e.g., the stop line TL on the entrance side of the node or the like) (which is determined based on the road map information in the road map information database).”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Tsukamoto (US 20230296400 A1) in view of Gao (US 20230314170 A1) in view of Lukarski (US 11465620 B1) in further view of Tohriyama (US 20200225044 A1).
Regarding Claim 1, Tsukamoto teaches A map generation apparatus, comprising (see at least [¶018]):
a processor and a memory coupled to the processor (A processor coupled with a memory device. see at least [¶020, 026-027 & 029-031]),
recognizing surrounding situation of the vehicle (Recognizing the surrounding situation of the vehicle using a camera. see at least [¶023]);
calculating a curvature of the travel trajectory in a specific scene where traveling map information has not been acquired, based on the travel trajectory information stored in the memory (Calculating the curvature of a travel/vehicle trajectory in a specific scene/intersection where traveling map information has not been acquired for the lanes. The travel/vehicle trajectory used is the one stored in the memory. see at least [¶047-049, 052-056 & FIG 5-6]);
estimating a lane shape in the specific scene based on the curvature (Estimating the lane shape in the specific scene/intersection based on the curvature of the travel/vehicle trajectory. see at least [¶047-049, 052-056 & FIG 5-6]);
and adding the lane shape to the traveling map information (Adding the lane shape to the traveling map information at the intersections that lack lane information. see at least [¶047-049 & 050-056]).
wherein the specific scene includes an intersection without a guidance marking for guiding a travel route (The specific scene/intersection includes an intersection with no specific guidance marking for guiding the travel route of a vehicle. see at least [¶047-049, 052-056 & FIG 5-6]);
Tsukamoto does not explicitly teach wherein the memory is configured to store travel trajectory information indicating a travel trajectory of a vehicle in a manual driving mode that requires driving operation of a driver.
However, Gao does teach wherein the memory is configured to store travel trajectory information indicating a travel trajectory of a vehicle in a manual driving mode that requires driving operation of a driver (The travel/vehicle trajectory information indicates a trajectory of a vehicle that is being driven manually by a driver and can be stored in memory to be used for mapping of areas. see at least [¶05, 019 & 032]).
Gao would be in a similar field as it also deals in the area of map generation from trajectories. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Tsukamoto to use the technique of having the memory be configured to store travel trajectory information indicating a travel trajectory of a vehicle in a manual driving mode that requires driving operation of a driver as taught by Gao. Doing so would lead to improved accuracy in the generation of a lane map (see at least [¶011]).
Tsukamoto and Gao do not explicitly teach the traveling map information being used in an automated driving mode that does not require the driving operation of the driver; and in the automated driving mode, generating a target trajectory based on the traveling map information and controlling a traveling actuator of the vehicle such that the vehicle travels along the target trajectory.
However, Lukarski does teach the traveling map information being used in an automated driving mode that does not require the driving operation of the driver (The traveling map information with the generated lanes is used in an automated driving mode of a vehicle with no driver input. see at least [Column 5-6, Lines 25-14 and Column 7, Lines 4-26]).
and in the automated driving mode, generating a target trajectory based on the traveling map information and controlling a traveling actuator of the vehicle such that the vehicle travels along the target trajectory (In the automated driving mode, generating a target trajectory/path based on the traveling map information with the generated lane segments and controlling the travelling actuator of the vehicle so it travels along the target trajectory/path. see at least [Column 5-6, Lines 25-14 and Column 7, Lines 4-26]).
Lukarski would be in a similar field as it also deals in the area of lane generation. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Tsukamoto and Gao to use the technique of having the traveling map information being used in an automated driving mode that does not require the driving operation of the driver; and in the automated driving mode, generating a target trajectory based on the traveling map information and controlling a traveling actuator of the vehicle such that the vehicle travels along the target trajectory as taught by Lukarski. Doing so would lead to improved automated vehicle control at intersections with generated lanes (see at least [Column 5, Lines 25-42]).
Tsukamoto, Gao and Lukarski do not explicitly teach estimating the lane shape in the intersection from an approximate curve calculated based on the average value of the travel trajectory and the lane shape at an entrance and an exit of the intersection when the curvature is equal to or more than a predetermined value, while estimating the lane shape in the intersection from an approximate curve calculated based on the lane shape at the entrance and the exit of the intersection when the curvature is less than the predetermined value and adding the lane shape in the intersection to the traveling map information.
However, Tohriyama does teach wherein the processor is configured to perform calculating the curvature of an average value of the travel trajectory in the intersection based on the travel trajectory information (Calculating the curvature of an average value of the travel trajectory at an intersection based on the driving log with travel trajectory information. see at least [¶0115 & 0123])
estimating the lane shape in the intersection from an approximate curve calculated based on the average value of the travel trajectory and the lane shape at an entrance and an exit of the intersection when the curvature is equal to or more than a predetermined value (Estimating the lane shape at an intersection with an approximate curve based on average trajectories in a driving log and the lane shape at an entrance and exit when the curvature is higher than a predetermined value. see at least [¶0115-0117, 0122-0126, 0131-0132 & FIG 7D]),
while estimating the lane shape in the intersection from an approximate curve calculated based on the lane shape at the entrance and the exit of the intersection when the curvature is less than the predetermined value (Estimating the lane shape at an intersection with an approximate curve based on average trajectories in a driving log and the lane shape at an entrance and exit when the curvature is less than a predetermined value. For instance, when the curvature is low, the lane shape is estimated to be a straight line and the lane shape is obtained based on the entrance and exit of lanes found at an intersection. see at least [¶0113-0117 & FIG 7E])
and adding the lane shape in the intersection to the traveling map information (Adding the lane shape information to the traveling map information. see at least [¶0131-0132]).
Tohriyama would be in a similar field as it also deals in the area of lane map generation. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Tsukamoto, Gao and Lukarski to use the technique of having the processor is configured to perform calculating the curvature of an average value of the travel trajectory in the intersection based on the travel trajectory information; estimating the lane shape in the intersection from an approximate curve calculated based on the average value of the travel trajectory and the lane shape at an entrance and an exit of the intersection when the curvature is equal to or more than a predetermined value, while estimating the lane shape in the intersection from an approximate curve calculated based on the lane shape at the entrance and the exit of the intersection when the curvature is less than the predetermined value and adding the lane shape in the intersection to the traveling map information as taught by Tohriyama. Doing so would lead to improved lane accuracy used for driving support (see at least [¶0131-0132]).
Regarding Claim 3, Tsukamoto, Gao, Lukarski and Tohriyama teaches all of the limitations of claim 1 as shown above, Tsukamoto discloses the necessary formulas to estimate a lane change at an intersection using a vehicle trajectory. However, it is silent as to the specifics of applying mathematical formula for finding the lane shape using linear approximation on the average value of the vehicle trajectory in relation to the right and left lane lines to find a curve for the lane.
Nevertheless, applying any mathematical formulae, including that of the claimed invention, would have been an obvious design choice for one of ordinary skill in the art because it facilitates known mathematical means to compute the lane shape at an intersection using a vehicle trajectory, as shown by Tsukamoto. Since the invention failed to provide novel or unexpected results from the usage of said claimed formula, use of any mathematical means, including that of the claimed invention, would be an obvious matter of design choice within the skill of the art.
Furthermore, Tsukamoto does teach and adding the lane shape in the intersection to the traveling map information (Adding the lane shape to the traveling map information at the intersections that lack lane information. see at least [¶047-049 & 050-056]).
Regarding Claim 9, Tsukamoto, Gao, Lukarski and Tohriyama teach all of the limitations of claim 1 as shown above, furthermore, Tsukamoto teaches and a server device configured to acquire and store the travel trajectory information from a plurality of the vehicle (A server is configured to acquire and store travel/vehicle trajectories from a plurality of vehicles. see at least [¶020, 027-029 & 031]),
wherein the processor is configured to perform calculating the curvature of the travel trajectory in the specific scene based on the travel trajectory information of the vehicle other than a two-wheeled vehicle from among the travel trajectory information stored in the server device (The processor is able to calculate the curvature of the travel/vehicle trajectory in a specific scene/intersection based on additional (4 wheeled) vehicles. see at least [¶020, 027-029, 031, 047-049, 052-056 & FIG 1]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Tsukamoto ‘400 (US 20230296400 A1) in view of Gao (US 20230314170 A1) in view of Lukarski (US 11465620 B1) in view of Tohriyama (US 20200225044 A1) in further view of Tsukamoto ‘700 (US 20220219700 A1).
Regarding Claim 5, Tsukamoto ‘400, Gao, Lukarski and Tohriyama teach all of the limitations of claim 1 as shown above, Tsukamoto ‘400, Gao, Lukarski and Tohriyama do not explicitly teach wherein the specific scene further includes an intersection with the guidance marking, wherein the processor is configured to perform estimating the lane shape in the intersection with the guidance marking from an approximate curve calculated based on the lane shape at an entrance and an exit of the intersection with the guidance marking and the guidance marking; and adding the lane shape in the intersection with the guidance marking to the traveling map information.
However, Tsukamoto ‘700 does teach wherein the specific scene further includes an intersection with the guidance marking, wherein the processor is configured to perform estimating the lane shape in the intersection with the guidance marking from an approximate curve calculated based on the lane shape at an entrance and an exit of the intersection with the guidance marking and the guidance marking (The intersection can have guidance marking on the road that are detected, and the processor is able to estimate the lane shape of at the intersection with the guidance markings by using an approximate curve that connects the lane shape at the entrance and exit with guidance marking. Guidance marking is taken to mean any marking on the road, be it lines, arrows or signs. see at least [¶061-062, 068-075 and FIG 5]);
and adding the lane shape in the intersection with the guidance marking to the traveling map information (Adding the lane shape to the intersection with guidance markers to the map. see at least [¶061-062, 068-075 and FIG 6]).
Tsukamoto ‘700 would be in a similar field as it also deals in the area of lane generation. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Tsukamoto ‘400, Gao, Lukarski and Tohriyama to use the technique of having the specific scene further includes an intersection with the guidance marking, wherein the processor is configured to perform estimating the lane shape in the intersection with the guidance marking from an approximate curve calculated based on the lane shape at an entrance and an exit of the intersection with the guidance marking and the guidance marking; and adding the lane shape in the intersection with the guidance marking to the traveling map information as taught by Tsukamoto ‘700. Doing so would lead to improved vehicle travel with generated lanes from markings (see at least [¶060]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Tsukamoto (US 20230296400 A1) in view of Gao (US 20230314170 A1) in view of Lukarski (US 11465620 B1) in view of Tohriyama (US 20200225044 A1) in further view of Ma (US 20160102986 A1).
Regarding Claim 6, Tsukamoto, Gao, Lukarski and Tohriyama teach all of the limitations of claim 1 as shown above, Tsukamoto, Gao, Lukarski and Tohriyama do not explicitly teach wherein the processor is configured to perform detecting a statistical outlier from a plurality of pieces of the travel trajectory information; and calculating the curvature based on the travel trajectory information excluding the outlier.
However, Ma does teach wherein the processor is configured to perform detecting a statistical outlier from a plurality of pieces of the travel trajectory information; and calculating the curvature based on the travel trajectory information excluding the outlier (Detecting a statistical outlier from the pieces of the travel/vehicle trajectory information and calculating the curvature with trajectory information that excludes the outlier. see at least [¶080-084]).
Ma would be in a similar field as it also deals in the area of lane mapping intersections. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Tsukamoto, Gao, Lukarski and Tohriyama to use the technique of having the processor is configured to perform detecting a statistical outlier from a plurality of pieces of the travel trajectory information; and calculating the curvature based on the travel trajectory information excluding the outlier as taught by Ma. Doing so would lead to improved accuracy in map generation (see at least [¶080]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MOISES GASCA ALVA/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667