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 .
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 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D1.1
With regard to claim 1, D1 teach receiving, as input to a transformer model, sensor data corresponding to a two- dimensional (2D) image of a current scene of a vehicle (see abstract, § IIIB ¶ 1, fig. 2: input image to transformer model); encoding, using a birds-eye view encoder, the sensor data to generate a corresponding sequence of feature embeddings, the sequence of feature embeddings corresponding to a three- dimensional (3D) representation of the current scene of the vehicle (see fig. 2: BEV encoder to generate embeddings); decoding the sequence of feature embeddings using two or more feature-specific decoders executing in parallel (see fig. 2: HD map decoder and lane network decoder in parallel, see also abstract); processing, using a prediction network, the decoded sequence of feature embeddings to convert the decoded sequence feature of embeddings into semantic features (see fig. 2: decoded feature embeddings; see p. 2172 col 2 ¶ 2: semantic categories); and processing, using a topology network, the semantic features and the decoded sequence of feature embeddings to generate an adjacency matrix representing a 3D view of the current scene of the vehicle (see fig. 2, p. 2172 col 2 ¶ 2: adjacency matrix).
With regard to claim 2, D1 teach method of Claim 1, wherein the two or more feature-specific decoders executing in parallel each include a plurality of transformer layers (see abstract, fig. 2: HD map decoder and lane network decoder in parallel).
With regard to claim 3, D1 teach method of Claim 2, wherein each transformer layer includes a cross-attention head (see fig. 2: cross attention head).
With regard to claim 4, D1 teach method of Claim 2, wherein decoding the sequence of feature embeddings using the two or more feature-specific decoders executing in parallel comprises executing cross- attention of the sequence of feature embeddings between corresponding transformer layers of the two or more feature-specific decoders (see fig. 2, abstract: two decoders in parallel with corresponding cross attention heads).
With regard to claim 5, D1 teach method of Claim 1, wherein the sensor data includes a set of image frames (see fig. 2: input plurality of frames).
With regard to claim 6, D1 teach method of Claim 5, wherein operations further comprise, for each image frame of the set of image frames, extracting feature embeddings of the current scene (see fig. 2: e4xtracting feature embeddings).
With regard to claim 7, D1 teach method of Claim 6, wherein encoding, using the birds-eye view encoder, the sensor data to generate the corresponding sequence of feature embeddings comprises projecting the sensor data into the corresponding sequence of feature embeddings (see fig. 2: birds eye vide encoder).
With regard to claim 8, D1 teach method of Claim 1, wherein the prediction network comprises a multilayer perceptron network (see fig. 2, § III ¶ 2: MLP).
With regard to claim 9, D1 teach method of Claim 1, wherein the 2D image of the current scene of the vehicle comprises at least two elements (see fig. 2, § III ¶ 2: image comprises plurality of elements or features).
With regard to claim 10, D1 teach method of Claim 9, wherein the adjacency matrix predicts a strength of the relationship between the at least two elements (see fig. 2, p. 2171 col 2: adjacency matrix).
With regard to claims 11-19, see analysis of corresponding claims above.
With regard to claim 20, see discussion of claim 1. D1 further teach wherein the 2D image includes at least two elements (see fig. 2, § III ¶ 2: image comprises plurality of elements or features).
Pertinent Art
Kum et al.2 is related to a method and system for graph-based bird's-eye-view (BEV) driving environment perception for autonomous driving.
Conclusion
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 Zhu, Tianyi, et al. "Lanemapnet: Lane network recognization and hd map construction using curve region aware temporal bird’s-eye-view perception." 2024 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2024.
2 US Publication No. 2025/0139986.
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