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 .
Status of Claims
This action is reply to the Application Number 18/494,097 filed on 01/30/2026.
Claims 1, 2, 4, 5, 7 – 18, 20 and 21 are currently pending and have been examined. Claims 1, 4, 7, 18 and 20 have been amended. Claims 3, 6 and 19 have been cancelled.
This action is made FINAL.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 11 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al. (Lane Information Extraction for High Definition Maps Using Crowdsourced Data).
Regarding claim 11, Zhou teaches a method for resolving discrepancies in map data, the method comprising: (Zhou: Page 7780, Abstract: “The proposed method is quantitatively evaluated against a real-world HD map produced by a mobile mapping vehicle. The experimental results show that more than 80% of the extracted lane markings meet the accuracy requirements of HD maps. In conclusion, our method can be used as a low-cost and efficient approach for updating the lane information in HD maps for autonomous vehicles.”)
comparing each of a plurality of crowdsourced map datasets with a first map dataset and a second map dataset to determine one or more common lane lines using one or more central computers, (Zhou: Page 7784, A. Data Management, paragraph 1: “The lane information in the HD map has a fixed update period. During this cycle, the lane information collected through multiple crowdsourced vehicles keep on accumulating. Accordingly, a data management method must be designed to make the best advantage of the large amount of crowdsourced data. First, position data g was used to match with the existing map to determine the road to which the lane information belongs to through an HMM-based map-matching method [34] and fuse the data from different vehicles.”,
Supplemental Note: the lane lines are compared and then placed in a fused map)
wherein the plurality of crowdsourced map datasets, (Zhou: Page 7782, D. Extraction From Road Images, paragraph 3: “Table I summarizes all of the abovementioned methods with their own merits and demerits. This study extracts lane information from road images collected by multiple crowdsourced vehicles.”; Page 7784, A. Data Management, paragraph 1: “The lane information in the HD map has a fixed update period. During this cycle, the lane information collected through multiple crowdsourced vehicles keep on accumulating. Accordingly, a data management method must be designed to make the best advantage of the large amount of crowdsourced data. First, position data g was used to match with the existing map to determine the road to which the lane information belongs to through an HMM-based map-matching method [34] and fuse the data from different vehicles.”,
Supplemental Note: the HD map is updated based on the lane information of the crowdsourced vehicles, therefore interpreted as crowdsourced map datasets)
the first map dataset, and the second map dataset represent a predefined geographical area, (Zhou: Page 7782, D. Extraction From Road Images, paragraph 2: “Compared to traditional methods, the deep learning based method can learn more road features. With that said, the deep learning-based road semantic mapping is worthy of exploration [30]. However, visual sensors are usually sensitive to light conditions, and the accuracy of the lane information collected from road images is unstable. To improve this accuracy, a large number of crowdsourced data on the same road should be collected to extract the lane information.”)
and wherein each of the plurality of crowdsourced map datasets, the first map dataset, and the second map dataset includes a plurality of points representing one or more lane lines; and
determining a fused map dataset using the one or more central computers based at least in part on the first map dataset, the second map dataset, the plurality of crowdsourced map datasets, and the one or more common lane lines (Zhou: Page 7784, A. Data Management, paragraph 1: “The lane information in the HD map has a fixed update period. During this cycle, the lane information collected through multiple crowdsourced vehicles keep on accumulating. Accordingly, a data management method must be designed to make the best advantage of the large amount of crowdsourced data. First, position data g was used to match with the existing map to determine the road to which the lane information belongs to through an HMM-based map-matching method [34] and fuse the data from different vehicles.”; Page 7785, C. Data Fitting, paragraphs 1 – 2: “Each cluster was divided into several parts with the same width along the vehicle trajectory direction to automatically construct the topological lane information. The improved DBSCAN algorithm was used again in each part to distinguish different lane marking types. The point from which the distances to all the other points in the cluster were the smallest was chosen as the main point. The lane information on an HD map must be fit to an appropriate curve to effectively and accurately express and store the lane’s geometrical information. Considering that the position and speed of the vehicle are continuously changing when it is driven on the road, a B-spline was utilized herein to fit the lane information. The B-spline represents the geometrical information for the lane via a set of control points and knots that can reduce the number of control points as long as the fitting accuracy is maintained. A gradual fitting algorithm was employed to determine the control points and the B-spline parameters in accordance with the main points. This algorithm consisted of three steps: (1) calculation of the initial fitting vertices; (2) fitting of the initial B-spline; and (3) gradual optimization.”,
Supplemental Note: the lane lines are compared and then placed in a fused map. The lane information of an HD map includes points which are associated with the lane lines found in the crowd sourced images).
Allowable Subject Matter
Claims 1, 2, 4, 5, 7 – 10, 18 and 20 – 21 are allowed.
Claims 12-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Independent claim 1, independent claim 18 and claim 12 state the limitation of generating “a plurality of aligned map datasets… wherein the plurality of aligned map datasets includes a first subset and a second subset, wherein the first subset is generated by aligning each of the plurality of crowdsourced map datasets with the first map dataset by executing a map-matching registration algorithm, and wherein the second subset is generated by aligning each of the plurality of crowdsourced map datasets with the second map dataset by executing the map-matching registration algorithm;”, determining “the one or more common lane lines using the one or more central computers based at least in part on the plurality of aligned map datasets”. The closest prior found is:
Lee et al. (US 2019234745 A1) – teaches matching visual road information to a first map data (interpreted as the claimed “crowdsourced map data”) but does not teach an aligned map data set in which there are two subsets or using a map-matching registration algorithm. Lee also teaches a second matching score, however that is matching between static objects such as curbs, buildings, etc. and not lane lines which is what the first matching score is based upon.
Claims 2, 4, 5, 7 – 10, 13 – 17, 20 and 21 are also allowed per their dependency on the allowed claimed.
Response to Arguments
Applicant’s arguments, see section Specification of the REMARKS, filed 01/30/2026, with respect to the specification objection have been fully considered and are persuasive. The specification objection has been withdrawn.
Applicant’s arguments, see section Rejection Under 35 U.S.C. 102 of the REMARKS, filed 01/30/2026, with respect to the 35 U.S.C. prior art rejection of claims 1 – 3, 11 – 12 and 18 – 19 have been fully considered but are not fully persuasive.
Applicant states regarding claim 11 that Zhou does not teach the claim limitation of “comparing each of a plurality of crowdsourced map datasets with a first map dataset and a second map dataset to determine one or more common lane lines… determining a fused map dataset using the one or more central computers based at least in part on the first map dataset, the second map dataset, the plurality of crowdsourced map datasets, and the one or more common lane lines”. Applicant states that Zhou does not each three distinct datasets as the Zhou is not using the images captured from the vehicle sensors which are cited to be interpreted as the claimed first and second map data set. Examiner respectfully disagrees. Zhou clearly states multiple times that the study extracts lane information from road images and the lane marking detection is a vehicle-based image sequence (Zhou: Page 7782, D. Extraction From Road Images, paragraph 3; Page 7782, III. Lane Detection, paragraph 1). The data from these images are projected in which the projections are compared with the HD map (Zhou: Page 7788, B. Results of Lane Detection, paragraph 6), therefore all three of the distinct datasets are taught by Zhou.
The remainder of the claims have allowable subject matter and therefore overcome the previously used prior art rejections.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIVAM SHARMA whose telephone number is (703)756-1726. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/SHIVAM SHARMA/ Examiner, Art Unit 3665
/Erin D Bishop/ Supervisory Patent Examiner, Art Unit 3665
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