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 .
1. This action is responsive to application filed Sep. 26, 2024.
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.
2. Claims 1-6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mohseni et al., U.S. Patent Application Publication No. 2021/0142160 (referred to hereafter as Mohseni).
As to claims 1 and 5-6, Mohseni teaches a method, device and non-transitory machine-readable storage medium for creating a digital map, comprising the following steps:
receiving first training data sets, wherein each of the first training data sets represents a first image of a corresponding surrounding area (see para. 27, step 302);
generating second training data sets using a generative neural network by determining a corresponding second, perturbed image of the same surrounding area in each case for each first image (see para. 28, step 304);
training a further neural network, which is configured to merge the first image of a first training data set and the corresponding second perturbed image of a second training data set according to specified criteria (see para. 59, step 306);
receiving surrounding area data sets, which in each case represent a surrounding area image of a corresponding vehicle surrounding area, wherein the surrounding area data sets include a position description of the corresponding vehicle surrounding area (see para. 127 and 181);
creating the digital map by merging the surrounding area images, depending on the position description, using a scan matching method, wherein the scan matching method includes at least the further neural network; and providing the digital map (see para. 126-127 and 211).
As to claim 2, Mohseni teaches the method according to claim 1, wherein the generative neural network was previously trained using training image pairs, wherein each training image pair represents two different images of a spatial scene (see para. 94 and 157).
As to claim 3, Mohseni teaches the method according to claim 1, wherein the first training data sets represent the first image and/or the second training data sets represent the second image and/or the surrounding area data sets represent the surrounding area image, in each case in the form of point clouds (see para. 186, 190 and 214).
As to claim 4, Mohseni teaches the method according to claim 1, wherein the digital map is provided for operating an automated vehicle (see para. 52).
3. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUSSEIN A EL CHANTI whose telephone number is (571)272-3999. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Mehdizadeh can be reached at 571-272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUSSEIN ELCHANTI/Primary Examiner, Art Unit 3669