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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/18/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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(s) 1-3, 5-9, 11-13, 15-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cho et. al. (United States Patent Application Publication US 2026/0109373 A1).
Regarding claim 1, Cho et. al. discloses a method comprising: executing an extraction model to generate a plurality of lane features from a lane image (Cho et. al. [0022]: To facilitate autonomous navigation, the ego can use a lane detection algorithm to recognize lane segments on a road of the environment as the ego traverses. For example, the ego can acquire sensor data (e.g., Light Detection and Ranging (LiDAR) and optical images) and apply an image segmentation model to detect lines along a road surface to recognize lane segments on the road.); executing a coarse model to generate a plurality of coarse boundary embeddings and a coarse lane graph from the plurality of lane features and a plurality of prior boundary embeddings using a transformer decoder, wherein the plurality of prior boundary embeddings is generated from a prior lane graph (Cho et. al. [0023]-[0024]: To address these and other technical constraints, a computing system on the ego can be configured with a set of AI algorithms and ML models to generate a graph defining a set of lane segments (sometimes referred to herein as pathways) and connectivity. To that end, the computing system on the ego can acquire sensor data (e.g., optical camera images and LiDAR) as well as map data (e.g., navigation map) of the surroundings of the ego. Using a first set of encoders, the computing system can generate a set of tensors (or embeddings) as a reduced dimensional representation of the sensor data.); and executing a refinement model to update the prior lane graph with a refined lane graph to form an updated lane graph, wherein the refined lane graph is generated from a plurality of refined boundary embeddings that is output from a transformer encoder, wherein the transformer encoder generates the plurality of refined boundary embeddings from the plurality of coarse boundary embeddings combined with a plurality of point embeddings corresponding to the plurality of coarse boundary embeddings (Cho et. al. [0024], [0088]- [0089], [0098]: based on the set of embeddings, the computing system can produce, output, or otherwise generate at least one token for at least one of the pathways through the environment. With the generation, the computing system can add, insert, or otherwise include the token in the graph. The computing system can update the graph to include the token to be used autonomously navigate the ego through environment via the pathways.).
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Regarding claim 11, which discloses a system comprising: at least one processor; and an application that, when executing on the at least one processor, performs a plurality of operations comprising of the method of claim 1, which the rejection analysis is incorporated herein.
Regarding claim 20, which discloses a non-transitory computer readable medium comprising instructions executable by at least one processor to perform operations comprising of the method of claim 1, which the rejection analysis is incorporated herein.
Regarding claim 2 and 12, Cho et. al. discloses the method of claim 1 and the system of claim 11, further comprising: loading the refined lane graph to an autonomous system, wherein the autonomous system executes a set of maneuvers to remain in a lane identified by the refined lane graph, and wherein the refined lane graph comprises a node corresponding to a point of a boundary of a lane identified from the lane image and an edge corresponding to a relationship between the node and a different node (Cho et. al. [0122]).
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Regarding claim 3 and 13, Cho et. al. discloses the method of claim 1 and the system of claim 11, wherein executing the extraction model further comprises: down sampling the lane image to generate a down sampled image; and executing a set of feature extraction models to generate the plurality of lane features from the down sampled image, wherein the set of feature extraction models includes a residual network model and a feature pyramid network model, wherein the plurality of lane features comprises a feature map with a set of channels, and wherein the set of channels comprises a lane marking channel to identify a plurality of lane markings within the down sampled image (Cho et. al. [0098]- [0099]: The grid can specify or define the set of grid points (or coordinates) at a resolution coarser or lower than an original resolution of the grid point as defined by the map. This effectively down samples the image. [0069]- [0071]: The ML models in the visual component can include, for example, a set of self-regulated networks (RegNets, hereinafter generally referred to as residual networks), a set of feature pyramid networks (FPNs, hereinafter generally referred to as feature pyramid networks), at least one transformer, and at least one video module, among others.).
Regarding claim 5 and 15, Cho et. al. discloses the method of claim 1 and the system of claim 11, wherein executing the coarse model further comprises: executing the transformer decoder to generate the plurality of coarse boundary embeddings from the plurality of prior boundary embeddings using cross attention between the plurality of prior boundary embeddings and the plurality of lane features, wherein the plurality of prior boundary embeddings is used as a plurality of query parameters by the transformer decoder, and wherein the plurality of lane features is used as a plurality of key parameters and a plurality of value parameters by the transformer decoder (Cho et. al. [0063]: the input can include the collected data, such as sensor data (e.g., video or image from one or more cameras) and map data (e.g., navigation map) from egos. The output can include environment features (e.g., attributes gathered from sensor data), map features (e.g., attributes in navigation map such as topological features and road layouts), classifications (e.g., a type of topology), and an output token (e.g., a combination of environment features, map features, and classifications) to be included in a graph defining lane segments, among others. [0078]-[0079]: The point can correspond to a starting point from which the ego is to navigate through the environment as defined by the map. The grid can specify or define the set of grid points (or coordinates) at a resolution coarser or lower than an original resolution of the grid point as defined by the map.).
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Regarding claim 6 and 16, Cho et. al. discloses the method of claim 1 and the system of claim 11, wherein executing the coarse model further comprises: executing a plurality of coarse embeddings models to generate the coarse lane graph, existence data, and classification data from the plurality of coarse boundary embeddings, wherein the plurality of coarse boundary embeddings are a subset of decoder output embeddings that do not correspond to the plurality of prior boundary embeddings (Cho et. al. [0063]: the input can include the collected data, such as sensor data (e.g., video or image from one or more cameras) and map data (e.g., navigation map) from egos. The output can include environment features (e.g., attributes gathered from sensor data), map features (e.g., attributes in navigation map such as topological features and road layouts), classifications (e.g., a type of topology), and an output token (e.g., a combination of environment features, map features, and classifications) to be included in a graph defining lane segments, among others. [0078]- [0079]: The point can correspond to a starting point from which the ego is to navigate through the environment as defined by the map. The grid can specify or define the set of grid points (or coordinates) at a resolution coarser or lower than an original resolution of the grid point as defined by the map. [0081]- [0086]: machine learning model architecture).
Regarding claim 7 and 17, Cho et. al. discloses the method of claim 1 and the system of claim 11, wherein executing the refinement model further comprises: up sampling the lane image to generate an up sampled image; densifying a plurality of lane boundaries from the coarse lane graph to form a plurality of boundary points corresponding to the plurality of lane boundaries for the lane image; sampling from the up sampled image and from the plurality of lane features using the plurality of boundary points to generate a plurality of point samples, wherein the plurality of point samples comprises a point sample corresponding to a boundary point of the plurality of boundary points, to a lane boundary of the plurality of lane boundaries, and to a coarse boundary embedding of the plurality of coarse boundary embeddings; and executing a point embedding model to generate the plurality of point embeddings from the plurality of point samples, wherein the plurality of point embeddings comprises a point embedding corresponding to the point sample and corresponding to the coarse boundary embedding (Cho et. al. [0080]: The point can correspond to the starting point from which the ego is to navigate through the environment as defined by the map. The second grid can specify or define the set of grid points (or coordinates) at the resolution finer or higher than the resolution of the first grid. This effectively up samples the image. [0081]-[0087], Fig. 3A-3H neural network architecture. [0078]: The grid can specify or define the set of grid points (or coordinates) at a resolution coarser or lower than an original resolution of the grid point as defined by the map.)
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Regarding claim 8 and 18, Cho et. al. discloses the method of claim 1 and the system of claim 11, wherein executing the refinement model further comprises: executing the transformer encoder to generate the plurality of refined boundary embeddings from the plurality of coarse boundary embeddings combined with the plurality of point embeddings using self-attention (Cho et. al. Fig. 3A-3H, [0081]- [0085]).
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Regarding claim 9 and 19, Cho et. al. discloses the method of claim 1 and the system of claim 11, wherein executing the refinement model further comprises: executing a plurality of refined embeddings models to generate offset data and connectivity data, wherein the offset data and the connectivity data are combined with a plurality of boundary points to form the refined lane graph; and executing a graph combination model to generate the updated lane graph from the prior lane graph and the refined lane graph (Cho et. al. [0024], [0088]- [0089], [0098]: based on the set of embeddings, the computing system can produce, output, or otherwise generate at least one token for at least one of the pathways through the environment. With the generation, the computing system can add, insert, or otherwise include the token in the graph. The computing system can update the graph to include the token to be used autonomously navigate the ego through environment via the pathways.).
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.
Claim(s) 4, 10, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho et. al. (United States Patent Application Publication US 2026/0109373 A1) in view of Lilja et. al. (United States Patent Application Publication US 2025/0078519 A1).
Regarding claim 4 and 14, Cho et. al. discloses the method of claim 1 and the system of claim 11, wherein executing the coarse model further comprises: executing a prior graph model to generate the plurality of prior boundary embeddings from the prior lane graph (Cho et. al. [0098]: updated lane graph). However, Cho et. al. fails to disclose wherein the prior graph model comprises a multilayer perceptron.
Lilja et. al. teaches wherein the prior graph model comprises a multilayer perceptron (Lilja et. al. [0053]- [0054]: the extracted set of object embeddings are decoded by using one or more Multi-Layer Perceptron). The multilayer perceptron is important to the claimed invention because it is a type of artificial neural network that consists of multiple layers of neurons. It enables the learning of complex patterns in data and are widely used for tasks such as classification, regression, and pattern recognition. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Cho et. al. and Lilja et. al. such the multi-layer perceptron is included as part of the architecture of the machine learning models of Cho et. al.
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Regarding claim 10, Cho et. al. discloses the method of claim 1, further comprising: training a combination of the coarse model and the refinement model using a lane graph metric (Cho et. al. [0024], [0029]: the vehicle having the ego computing device may transmit its camera feed to the trained AI model(s) and may generate a graph defining lane segments in the environment. [0088]- [0089], [0098]: based on the set of embeddings, the computing system can produce, output, or otherwise generate at least one token for at least one of the pathways through the environment. With the generation, the computing system can add, insert, or otherwise include the token in the graph. The computing system can update the graph to include the token to be used autonomously navigate the ego through environment via the pathways.). However, Cho et. al. fails to teach training the coarse model using a loss.
Lilja et. al. teaches training the coarse model using a loss (Lilja et. al. [0068]: the program code may be configured to, with the one or more processors, cause the system to form a loss function based on the output position and class of each road object and a ground-truth dataset.). The loss function calculates the amount of error within the machine learning model and refines the training, leading to increased overall accuracy. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Cho et. al. and Lilja et. al. so that a loss is computed for the machine learning models.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Homayounfar et. al. (United States Patent Application Publication US 2020/0302662 A1) is relevant to the claimed invention because it discloses a computing system for generating a graph representing lane boundaries associated with the portion of the travel way by identifying a respective node location for the respective lane boundary based in part on identified feature data associated with lane boundary information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 September 1, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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