DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed on June 16, 2026 has been entered. Claims 1, 5, 11–14, 20 and 21 are currently pending. Claims 1, 11–14, and 20 have been amended, and claim 21 has been added.
Response to Arguments
Applicant's arguments filed 06/16/2026 have been fully considered. Arguments are persuasive with respect to the previous claim objections and the previous rejection of claims 1, 5, 11–14, and 20 under 35 U.S.C. § 103 over Gao et al. in view of Kheyrollahi et al., as explained below.
Applicant's arguments filed 06/16/2026 are persuasive with respect to overcoming the prior claim objections. Applicant's arguments, see Remarks filed 06/16/2026, state that claims 11–14, 18, and 20 have been amended to correct the typographical errors “keypointsof keypoints” and “keypoints of keypoints” previously identified. Upon reconsideration, the Examiner agrees that the amendments resolve the informalities previously identified. Therefore, the prior objections to claims 11–14, 18, and 20 have been withdrawn. It is noted that new objections are set forth below based on additional informalities identified in the amended claims.
Applicant's arguments regarding the rejection of claims 1, 5, 11–14, and 20 under 35 U.S.C. § 103 over Gao et al. in view of Kheyrollahi et al. have been fully considered. Upon reconsideration, the Examiner agrees that the previously cited portions of Gao and Kheyrollahi do not teach or suggest the claimed categorization of travel lane elements as including a “false detections” category. Although Kheyrollahi teaches false-positive filtering and false-detection handling in the context of road-marking recognition, the cited portions of Kheyrollahi do not expressly disclose organizing keypoints into subgroups categorized as road boundaries, travel lane markers, and false detections. Therefore, the previous rejection of claims 1, 5, 11–14, and 20 under 35 U.S.C. § 103 as unpatentable over Gao et al. in view of Kheyrollahi et al. has been withdrawn. However, a new ground of rejection under 35 U.S.C. § 103 is made in this Office Action.
Applicant's arguments regarding claim 11 have been fully considered. Applicant's arguments are persuasive with respect to the previous rejection of claim 11 under 35 U.S.C. § 103 over Gao et al. in view of Kheyrollahi et al., to the extent that the previous rejection did not account for the amended scope of claim 1 from which claim 11 depends. Upon reconsideration, the Examiner agrees that the previous rejection of claim 11 based on Gao in view of Kheyrollahi did not address the additional limitations incorporated into claim 11 through amended claim 1, including the categorization of travel lane elements comprising road boundaries, travel lane markers, and false detections. Therefore, the previous rejection of claim 11 under 35 U.S.C. § 103 over Gao in view of Kheyrollahi has been withdrawn. However, the updated rejection incorporates the amended limitations of claim 1 through the teachings of Xu. Therefore, Applicant's arguments do not overcome the new rejection of claim 11, and the rejection of claims 12–13 depending therefrom, is maintained.
Applicant's additional arguments have been fully considered. The newly added limitations directed to false detections and ground-truth comparison have been addressed through newly cited prior art. The necessity to apply Xu to address the amended limitations, and the withdrawal and replacement of the prior rejection under 35 U.S.C. § 103, were directly necessitated by Applicant's substantive amendments. Because the new grounds of rejection under 35 U.S.C. § 103 and 35 U.S.C. § 112(b), and the withdrawal and replacement of the prior grounds of rejection, were directly necessitated by Applicant's amendments, this action is properly made final in accordance with MPEP § 706.07(a).
Based on these facts, this action is made FINAL.
Claim Objections
At the beginning of the claim listing, before claim 1, the filing includes the following unnumbered language: “further comprising sending the object embedding information item to a false positive removal module, and performing a false positive removal operation by the false positive removal module”. That language is not associated with any claim number or claim-status identifier, and it uses terminology that does not otherwise appear in the presently examined claims. It appears to be stray or inadvertently retained language from another claim set or draft. Applicant should delete it or identify the claim to which it is intended to belong.
Claims 1, 11, 14, and 20 are objected to because of the following informalities:
Claims 1, 11, 14, and 20 recite “one or more subgroup” and/or “one or more organized subgroup.” Because the claims refer to one or more such groups, “subgroup” should be amended to “subgroups” at each applicable occurrence.
Claim 14 additionally lacks terminal punctuation following “travel lane marker”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Amended claim 1 recites that the categories of travel lane elements comprise road boundaries, travel lane markers, and false detections. Claim 13, which depends from claim 1 through claims 11 and 12, additionally recites classifying a subgroup of keypoints as indicative of an incidental marking.
It is unclear whether an “incidental marking” is intended to be encompassed by the “false detections” category recited in claim 1, or whether “incidental marking” is intended to represent a separate category of travel lane elements. The claims therefore fail to define the relationship between these categories, such that the scope of claim 13 cannot be reasonably ascertained.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are:
Claim 21 recites “comparing the keypoints to ground truth.” However, claim 1 introduces multiple keypoint-related limitations, including “a plurality of keypoints,” “one or more subgroups of keypoints,” and “one or more organized subgroup of keypoints.” Claim 21 does not provide antecedent basis identifying which of these keypoint sets is intended by “the keypoints.” It is unclear whether the claimed comparison is performed using the originally generated keypoints, the organized subgroup(s) of keypoints, or another representation derived from the keypoints, such as the embeddings generated therefrom. Because the claim does not identify the particular keypoints being compared to ground truth, and because the relationship between the comparison and the embedding-based classification recited in claim 1 is unclear, the scope of claim 21 is not reasonably ascertainable.
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.
Claims 1, 5, 11–13, 14 and 20-21 are rejected under 35 U.S.C. §103 as being unpatentable over Gao (Gao et al, US 2021/0150350 A1, 2021), in view of Xu (Xu et al, US 2021/0266418 A1, 2019), further in view of Kheyrollahi (Kheyrollahi et al., "Automatic real-time road marking recognition using a feature driven approach", 2010).
Regarding claim 1, Gao teaches a method for travel lane element classification, comprising:
obtaining, via a processing circuit, information indicative of a travel lane including one or more travel lane elements located within an environment of a vehicle;
( Gao, [Fig. 3, Step 302] & [0030], teaching a system receiving / obtaining data including map features of a map of the environment surrounding the vehicle; wherein map features, in [0031], are characterized by scene data, the environment includes road lane boundaries.)
generating a plurality of keypoints from the information;
( Gao: in [Fig. 3, Step 304 - 306] & [0056–0057], teaches that a lane boundary contains multiple control points / key points that build a spline and that geographic entities can be approximated as polylines defined by one or more control points; in [0058] explained these polylines are sets of vectors; also in [0072] teaches how to generate these vectors that connect a plurality of keypoints along the map feature, including uniformly sampling keypoints along the splines. )
organizing the plurality of keypoints into one or more subgroups of keypoints, wherein each of the one or more subgroup of keypoints is indicative of one or more categories of travel lane elements,
( Gao: in [Fig. 3, Step 304 - 306], [0031], & [0071-0072], Gao organizes keypoints into "a respective polyline of each of the features of the map that represents the feature as a sequence of one or more vector". The system can then sequentially connect the neighboring key points along the map feature into vectors, thereby organizing the keypoints into polyline subgroups, polyline subgroup(s) indicative per road lane boundaries, crosswalk, stoplight, road signs categories respectively. )
wherein the one or more categories of travel lane elements comprise road boundaries, travel lane markers
( Gao, [0031], [0056], [0071]–[0074]: Gao teaches that scene data includes map features of the environment surrounding the vehicle, where the map features include lane boundaries and other road features. Gao further teaches that geographic entities, such as lane boundaries, are represented as polylines defined by control points/keypoints, and that the system generates polylines for map features by connecting a plurality of keypoints along the feature. Gao additionally teaches that each vector representing a map feature may include an identifier of a road feature type, such as a lane boundary, thereby organizing keypoints into subgroups corresponding to categories of road/lane-related elements. )
generating one or more embeddings of the one or more subgroup of keypoints; and
( Gao: in [Fig. 3, Step 308] & [0078-0079], teaches processing the respective polylines (including polylines of map features such as lane boundaries) using an encoder neural network to generate polyline features [embeddings] )
predicting, based on the one or more embeddings, the one or more organized subgroup of keypoints as indicative of a travel lane marker;
( Gao, as said in [0061], generating a trajectory prediction for a given one of the agents in the environment by processing the polyline features of the polyline that represents the trajectory of the agent using a trajectory decoder neural network; then in [0074], teaches each vector can include an identifier of road feature type including lane boundary, and thus the polyline subgroup is associated with a travel-lane-element category (lane boundary / lane marker); then in [0080] the system generates a predicted trajectory for the agent from the polyline features which classifies polylines relative to map feature polylines (lane boundaries = travel lane markers) through "self-attention mechanism model interactions between polyline embeddings; see also in [0005] explains that object classification is one of trajectory prediction task. )
wherein the predicting triggers a determination of a driving related operation to be executed by the vehicle.
( Gao, in [0040–0042], [0061], teaches an on-board planning system that makes autonomous / semi-autonomous driving decisions by generating a planned vehicle path, and uses trajectory prediction model outputs to generate planning decisions and cause the vehicle to follow the planned path (e.g., by autonomously controlling steering) )
Gao however fails to explicitly disclose categorizing travel lane elements as including false detections where Xu discloses:
wherein the one or more categories of travel lane elements comprise road boundaries, travel lane markers, and false detections;
( Xu, [0038–0039], [0043–0047], [0168–0169]: Xu teaches an autonomous vehicle lane detection system that identifies and classifies driving-surface features, including road boundaries and lane markings. Xu teaches generating multi-class segmentation masks having different classes corresponding to different lane markings and boundaries, including road boundaries and solid/ dashed lane markings. Xu further teaches evaluating detected lane features by comparing detection polyline points with ground-truth polyline points and determining false detections based on the comparison. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Gao’s vehicle travel-lane-element classification system with Xu’s lane detection and false-detection determination techniques. Gao already provides the framework of obtaining vehicle-environment road feature information, organizing keypoints into road-feature polylines, generating learned feature representations, and using the representations for vehicle planning. Xu provides the predictable improvement of classifying lane-related features using road-boundary and lane-marking categories and identifying false detections by comparing detected lane features with ground-truth information. Such a modification would have been the combination of known prior-art elements according to their established functions to improve detection reliability and avoid incorrect vehicle operations caused by false lane-element classifications, with a reasonable expectation of success.
Gao [as modified by Xu] provides a vehicle-based lane-element detection and categorization framework; however, Gao [as modified by Xu] does not expressly disclose where Kheyrollahi teaches:
classifying, based on the one or more embeddings, the one or more organized subgroup of keypoints as indicative of a travel lane marker;
( Kheyrollahi, in [Page 4, Sec. 4 (Road-marking extraction)], teaches extracting a road-marking candidate as an organized set of points / pixels (i.e., an object / connected marking region), generating a feature-vector descriptor (i.e., an embedding / vector representation) for the extracted marking; then providing the feature-vector descriptor to a trained classifier to classify the marking into a road-marking category [Page 6-7, Sec. 5 (Road marking recognition)]; Because travel lane markers are road markings, Kheyrollahi’s classification of a road-marking candidate into a marking category corresponds to classifying the organized subgroup of points / keypoints as indicative of a travel lane marker when the classifier output indicates a lane-marking category. )
wherein the classifying triggers a determination of a driving related operation to be executed by the vehicle.
( Kheyrollahi, in [Page 1, (Abstract)], teaches that automatic road marking recognition lends support to both autonomous driving and augmented driver assistance such as situationally aware navigation systems”; in [Page 1, Sec. 1 (Introduction), Col. 2, paragraph 4]: further teaches that the results of the road-marking classification are post-processed “for either driver display or potential use by an autonomous driving decision engine”, i.e., the classification output is used by a vehicle decision engine to determine an appropriate driving-related operation. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify Gao [as modified by Xu] with Kheyrollahi’s road-marking classification techniques. Gao [as modified by Xu] already provides the framework of obtaining vehicle-environment road feature information, organizing keypoints into road-feature representations, generating feature representations, categorizing lane-related elements, identifying false detections, and using the classification results for vehicle operation. Kheyrollahi provides the predictable improvement of generating feature-vector representations of extracted road-marking candidates and applying a trained classifier to classify such representations into road-marking categories. Such a modification would have been the combination of known prior-art elements according to their established functions to improve the accuracy and reliability of travel lane element classification and enable more accurate identification of travel lane markers for vehicle decision-making, with a reasonable expectation of success.
Regarding claim 5, Gao [as modified by Xu and Kheyrollahi] teaches the method of claim 1, wherein the obtaining step comprises obtaining, by an imaging sensor, a field of view image of a viewable area including the vehicle environment.
( Gao, in [0026] & [0028], discloses camera systems and sensor subsystems captures the surrounding vehicle environment, corresponding to obtain a field of view image of a viewable area including the vehicle environment. )
Regarding claim 11, Gao [as modified by Xu and Kheyrollahi] teaches the method of claim 1, wherein the organizing step comprises training a classifier to classify each of the one or more subgroup of keypoints based on the aspect ratios.
( Gao, [0071–0075], teaches organizing keypoints into polyline representations corresponding to map features, including lane-related features, by generating vectors connecting a plurality of keypoints along each map feature. Gao further teaches that the polyline representations include learned feature representations generated from the corresponding map features. Xu, [0038–0039], [0043–0047], teaches detecting and classifying lane-related driving-surface features, including road boundaries and lane markings, and assigning categories to the detected lane-related features. Kheyrollahi, [Page 6–7, Sec. 5 (Road marking recognition)], teaches extracting a feature-vector descriptor for each road-marking candidate, wherein the feature-vector descriptor includes an aspect ratio of the road-marking candidate, and providing the feature-vector descriptor to a trained neural network classifier for recognition of the road-marking candidate. Accordingly, Gao [as modified by Xu and Kheyrollahi] teaches classifying organized lane-related feature subgroups using aspect-ratio information as part of the classifier input. )
Regarding claim 12, Gao [as modified by Xu and Kheyrollahi] teaches the method of claim 11, further comprising classifying, based on the organizing step, at least a second subgroup of keypoints as indicative of a road boundary.
( [Fig. 3, Step 304 - 306], [0031], & [0071-0072], & [0083]: Gao teaches forming the organized subgroup (polyline) for each map feature from keypoints/vectors and include an identifier of road feature type including lane boundary: polyline subgroup(s) as indicative of road boundary respectively. Kheyrollahi teaches performing classification of an organized set of points/pixels representing a road marking [Page 6-7, Sec. 5 (Road marking recognition)].
Accordingly, when Gao’s organized polyline subgroup (road-boundary lane-boundary marking) is provided as the road-marking candidate to Kheyrollahi’s trained classifier (e.g., using a feature-vector descriptor derived from the subgroup’s geometry), the classifier outputs the road boundary / lane boundary category for that second subgroup, thereby classifying the second subgroup as indicative of a road boundary. It would have been obvious to use Kheyrollahi’s trained marking-classification stage to explicitly classify Gao’s organized lane-boundary polyline subgroup as “road boundary” because Gao already represents lane boundaries as distinct map-feature polylines and Kheyrollahi teaches that marking candidates are routinely encoded into a feature vector and classified by a trained classifier, yielding predictable results and improving downstream interpretation for vehicle planning/control. )
Regarding claim 13, Gao [as modified by Xu and Kheyrollahi] teaches the method of claim 12, further comprising classifying, based on the organizing step, at least a third subgroup of keypoints as indicative of an incidental marking.
( Gao: in [0031], [0056], [0069], Gao teaches forming additional organized polyline subgroups (from keypoints/vectors) for non-lane road features besides lane boundaries (including crosswalks, stoplight, stop sign, vehicle, pedestrian, cyclist and other surrounding agents) via the “road feature type” identifier associated with each polyline / map feature: polyline subgroup(s) as indicative of an incidental marking respectively. Kheyrollahi, [Page 6-7, Sec. 5 (Road marking recognition)], also teaches classification of organized set of extracted road-marking objects (non-lane / other marking candidates) (i.e., incidental markings) as distinct from lane markers.
Accordingly, when Gao’s organized polyline subgroup (road-boundary lane-boundary marking) is provided as the road-marking candidate to Kheyrollahi’s trained classifier (e.g., using a feature-vector descriptor derived from the subgroup’s geometry), the classifier outputs a non-lane / other marking category for that third subgroup, thereby classifying the third subgroup as indicative of an incidental marking. It would have been obvious to use Kheyrollahi’s trained marking-classification stage to explicitly classify Gao’s organized non-lane polyline subgroup(s) as “incidental marking” because Gao already represents multiple non-lane markings / features as distinct map-feature polylines and Kheyrollahi teaches that marking candidates are routinely encoded into a feature vector and classified by a trained classifier, yielding predictable results and improving downstream interpretation for vehicle planning/control. )
Regarding claims 14 and 20, the rationale provided for claim 1 is incorporated herein. In addition, the method of claim 1 corresponds to the non-transitory computer-readable medium of claim 14, as well as the system of claim 20, and performs the steps disclosed herein. Therefore, the claims are all ineligible.
Regarding claim 21, Gao [as modified by Xu and Kheyrollahi] teaches the method of claim 1, wherein the classifying is accomplished by comparing the keypoints to ground truth.
( Xu, [0168–0169]: Xu teaches evaluating detected lane features by comparing detection polyline points with ground-truth polyline points. Xu teaches that lane detection performance may be measured based on precision, recall, and average closest point distance between detection polyline points and ground-truth polyline points. Xu further teaches determining a false detection when a ground-truth point is identified but a corresponding prediction/detection point is not identified. Accordingly, the classification of lane-related keypoints/ features is accomplished based on comparison between detected keypoints/ points and ground-truth information. )
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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