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 § 103
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.
Claims 1 and 3-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20250381983; hereinafter Zhang) in view of Yan et al. (US 20250078531; hereinafter Yan).
Regarding claim 1, Zhang teaches a system comprising: one or more processors (see at least, [0209] A system, comprising: a data store storing computer-executable instructions; and at least one processor) to: determine, using one or more encoders and based at least on image data obtained using one or more image sensors (see at least, [0099] The images 501 for a particular scene (also referred herein as a set of images 501) may include image data from one or more sensors in a sensor suite) of a machine (see at least, [0111] The BEV Generator 505 may be implemented using an image to BEV encoder), one or more first Bird's Eye View (BEV) features associated with a current time instance (see at least, [0178] The lidar-based set of object queries can be generated based in part on BEV feature map(s) generated during the lidar BEV stage 504b; [0179] At block 708, the perception system 402 enriches the object queries based on feature maps of the current time step); determine, using one or more neural networks and based at least on the one or more first BEV features, the one or more second BEV features, and the heatmap, a BEV representation indicating one or more locations of one or more objects located within an environment at the current time instance (see at least, Fig 4C, [ 0078] a convolutional neural network (CNN) 420; [0116] determines (or helps determine) a probable location of the respective object queries within the vehicle scene…may use heat maps based on the BEV feature map 602 that indicate expected or probable movements or trajectories of objects within the vehicle scene); and cause the machine to perform one or more planning, control, or navigation operations based at least on the BEV representation (see at least, [0186] FIG. 8 routine 800 implemented by at least one processor to navigate a vehicle based on at least one bounding box generated based on object tracking data and enriched object queries).
Zhang does not explicitly teach generate a heatmap indicating an amount of overlap associated with the one or more first BEV features and one or more second BEV features, the one or more second BEV features determined using the one or more encoders and associated with one or more previous time instances. However, Yan teaches this limitation.
Yan teaches generate a heatmap indicating an amount of overlap associated with the one or more first BEV features and one or more second BEV features (see at least, [0061] a BEV feature extraction system that can extract a set of BEV features from sensor data (e.g., camera data and radar data), using a set of feature networks, generate a fused BEV feature by fusing each BEV feature of the set of BEV features, and process the fused BEV feature using a BEV feature network to generate the at least one heatmap), the one or more second BEV features determined using the one or more encoders (see at least, Fig 5E, Encoder 540) and associated with one or more previous time instances (see at least, [0073] BEV feature generation component 440 can generate set of BEV features 442 from the extracted BEV features (e.g., the camera BEV feature and the radar BEV feature)…generating
the fused BEV feature includes performing temporal aggregation based on a set of prior frames).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to include generate a heatmap indicating an amount of overlap associated with the one or more first BEV features and one or more second BEV features, the one or more second BEV features determined using the one or more encoders and associated with one or more previous time instances as taught by Yan so that driving algorithm can process the information about the environment and to provide correct instructions to the AV controls and the drivetrain (Yan, [0002]).
Regarding claim 3, the combination of Zhang and Yan teaches the system of claim 1. Yan further teaches wherein the generation of the heatmap comprises: determining one or more first regions of the environment that are represented by the one or more first BEV features (see at least, [0065] Camera
BEV feature extraction component 420 can receive camera data 412 and extract a camera BEV
feature from camera data 412) ; determining one or more second regions of the environment that are represented by the one or more second BEV features (see at least, [0072] Radar BEV feature extraction component 430 can receive radar data 414 and extract a radar BEV feature from radar data); and generating the heatmap to indicate the amount of overlap between the one or more first regions and at least a portion of the one or more second regions (see at least, [0061] extract a set of BEV features from sensor data (e.g., camera data and radar data), using a set of feature networks, generate a fused BEV feature by fusing each BEV feature of the set of BEV features, and process the fused BEV feature using a BEV feature network to generate the at least one heatmap).
Regarding claim 4, the combination of Zhang and Yan teaches the system of claim 1. Yan further teaches wherein the one or more processors are further to: generate a fused input based at least on concatenating the one or more first BEV features, the one or more second BEV features, and the heatmap (see at least, [0080] generating the transformer input includes concatenating set of BEV features 442 and at least one heatmap 452 to obtain a concatenated 3D feature, downsampling the concatenated 3D feature to obtain a 2D feature, and flattening the 2D feature to obtain a one-dimensional (1D) feature as the transformer input),wherein the representation is determined using the one or more neural networks and based at least on the fused input (see at least, [0082] An encoder of the transformer can receive the transformer input (e.g., the 1D feature), and process the transformer input through multiple layers of self-attention and feed-forward neural networks to generate an encoder output. The encoder output is an encoded representation of the transformer input).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to include generate a fused input based at least on concatenating the one or more first BEV features, the one or more second BEV features, and the heatmap, wherein the representation is determined using the one or more neural networks and based at least on the fused input as taught by Yan so that driving algorithm can process the information about the environment and to provide correct instructions to the AV controls and the drivetrain (Yan, [0002]).
Regarding claim 5, the combination of Zhang and Yan teaches the system of claim 1. Zhang further teaches wherein the determination of the BEV representation comprises: determining, using the one or more neural networks and based at least on the one or more first BEV features, the one or more second BEV features, and the heatmap, one or more unified BEV features associated with the current time instance (see at least, Fig 4C, [ 0078] a convolutional neural network (CNN) 420; [0116] determines (or helps determine) a probable location of the respective object queries within the vehicle scene…may use heat maps based on the BEV feature map 602 that indicate expected or probable movements or trajectories of objects within the vehicle scene); and determining, using one or more decoders and based at least on the one or more unified BEV features, the BEV representation indicating the one or more locations of the one or more objects located within the environment (see at least, [0031] The queries can be used to generate bounding boxes for objects with the feature map or set of feature maps…The combination of image data of various modalities and object tracking data can result in the decoder converging on a result that more accurately detects object and generates corresponding bounding boxes within a vehicle scene).
Regarding claim 6, the combination of Zhang and Yan teaches the system of claim 5. Zhang further teaches wherein the one or more processors are further to: store data representative of the one or more second BEV features in one or more memories (see at least, [0145] the object queries enriched by the lidar object query cross-attention stage 509a may be stored for further use during the operation phase); and replace at least one of the one or more second BEV features with the one or more unified BEV features in the one or more memories (see at least, [0162] A lidar-based set of object queries and a camera-based set of object queries can be generated. After generation, the lidar-based set of object queries and a camera-based set of object queries can be fused together to generate a set of object queries for the time step).
Regarding claim 7, the combination of Zhang and Yan teaches the system of claim 1. Zhang further teaches wherein the one or more processors are further to: generate one or more updated BEV features by at least updating the one or more second BEV features based at least on a motion associated with the machine (see at least, Fig 6B, [0190] The target feature update can be configured to refine features of object queries of the current time step for data association…The tracking process can include a plurality of stages based on input modalities used for the data association process…a lidar object query data association stage can use a set of lidar-based enriched object queries generated based on the lidar object query cross-attention stage) wherein the BEV representation is determined using the one or more neural networks and based at least on the one or more first BEV features, the one or more updated BEV features, and the heatmap (see at least, Fig 4C, [ 0078] a convolutional neural network (CNN) 420; [0116] determines (or helps determine) a probable location of the respective object queries within the vehicle scene…may use heat maps based on the BEV feature map 602 that indicate expected or probable movements or trajectories of objects within the vehicle scene).
Regarding claim 8, the combination of Zhang and Yan teaches the system of claim 1. Zhang further teaches wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (see at least, [0044] vehicle 200 includes autonomous system 202, powertrain control system 204, steering control system 206, and brake system 208);
a perception system for an autonomous or semi-autonomous machine (see at least, [0045] The data generated by the one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located);
a system for performing one or more simulation operations;
a system for performing one or more digital twin operations;
a system for performing one or more light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more wireless cellular transmissions using a wireless cellular network (see at least, [0039] Network 112 includes one or more wired and/or wireless networks…includes a cellular network);
a system that provides one or more cloud gaming applications;
a system for performing one or more deep learning operations (see at least, [0075] control system 408 implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder);
a system implemented using an edge device;
a system implemented using a robot;
a system for performing one or more generative AI operations;
a system for performing one or more conversational AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing operations using one or more multi-modal language models (MMLMs); a system for performing operations using one or more vision-language-action (VLA) models;
a system for performing one or more conversational AI operations;
a system for performing one or more synthetic data generation operations;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
systems using or deploying one or more inference microservices;
systems that incorporate deploy one or more machine learning models in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center (see at least, [0034] vehicles 102 are configured to be in communication with V2I device 110, remote AV system 114, fleet
management system 116, and/or V2I system 118 via network 1); or
a system implemented at least partially using cloud computing resources (see at least, [0039] Network 112 includes…a cloud computing network).
Regarding claim 9, Zhang teaches a method comprising: storing, in one or more memories, one or more unified feature maps associated with one or more first time instances (see at least, [0145] the object queries enriched by the lidar object query cross-attention stage 509a may be stored for further use during the operation phase); generating, based at least on sensor data obtained using one or more sensors of a machine, a feature map associated with a second time instance subsequent the one or more first time instances (see at least, [0112] feature maps 601…generate object queries 603 based on images 501 for the current time step…generate object queries 603 based on lidar images 501a and/or camera images 501b generated during the initialization time step, t=0…generate object queries 603
based on lidar images 501a and/or camera images 501b generated during the operation time step, t=1); and causing the machine to perform one or more planning, navigation, or control operations based at least on the representation (see at least, [0186] FIG. 8 routine 800 implemented by at least one processor to navigate a vehicle based on at least one bounding box generated based on object tracking data and enriched object queries).
Yan does not explicitly teach generating, using one or more neural networks and based at least on the one or more unified feature maps and the feature map, a representation indicating information associated with one or more objects located within an environment at the second time instance. However, Yan teaches this limitation.
Yan teaches generating, using one or more neural networks and based at least on the one or more unified feature maps and the feature map (see at least, [0082] An encoder of the transformer can receive the transformer input (e.g., the 1D feature), and process the transformer input through multiple layers of self-attention and feed-forward neural networks to generate an encoder output), a representation indicating information associated with one or more objects located within an environment at the second time instance (see at least, [0064] An image obtained by any of sensors can include a corresponding intensity map…can be any set of coordinates, including three-dimensional (spherical, cylindrical, Cartesian, etc.) coordinates (e.g., in the instances of lidar and/or radar images)…Coordinates of various objects…can be determined from directional data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to include generating, using one or more neural networks and based at least on the one or more unified feature maps and the feature map, a representation indicating information associated with one or more objects located within an environment at the second time instance as taught by Yan so that driving algorithm can process the information about the environment and to provide correct instructions to the AV controls and the drivetrain (Yan, [0002]).
Regarding claim 10, the combination of Zhang and Yan teaches the method of claim 9. Zhang further teaches wherein the one or more unified feature maps associated with the one or more first time instances include a plurality of unified feature maps associated with a plurality of time instances (see at least, [0178] The lidar-based set of object queries can be generated based in part on BEV feature
map(s) generated during the lidar BEV stage 504b. The camera-based set of object queries can be generated based in part on BEV feature map(s) generated during the camera BEV stage 504a. After generation, the lidar-based set of object queries and the camera-based set of object queries can be fused together to generate one set of object queries for the time step).
Regarding claim 11, the combination of Zhang and Yan teaches the method of claim 9. Yan further teaches wherein the generating the representation comprises: generating a fused feature map by at least concatenating the one or more unified feature maps with the feature map (see at least, [0080] generating the transformer input includes concatenating set of BEV features 442 and at least one heatmap 452 to obtain a concatenated 3D feature, downsampling the concatenated 3D feature to obtain a 2D feature, and flattening the 2D feature to obtain a one-dimensional (1D) feature as the transformer input); and generating, using the one or more neural networks and based at least on the fused feature map, the representation indicating the information associated with the one or more objects (see at least, [0082] An encoder of the transformer can receive the transformer input (e.g., the 1D feature), and process the transformer input through multiple layers of self-attention and feed-forward neural networks to generate an encoder output. The encoder output is an encoded representation of the transformer input).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to include generating a fused feature map by at least concatenating the one or more unified feature maps with the feature map; and generating, using the one or more neural networks and based at least on the fused feature map, the representation indicating the information associated with the one or more objects as taught by Yan so that driving algorithm can process the information about the environment and to provide correct instructions to the AV controls and the drivetrain (Yan, [0002]).
Regarding claim 12, the combination of Zhang and Yan teaches the method of claim 9. Zhang further teaches comprising: generating one or more updated unified feature maps by at least updating the one or more unified feature maps based at least on a motion associated with the machine (see at least, Fig 6B, [0190] The target feature update can be configured to refine features of object queries of the current time step for data association), wherein the generating the representation is based at least on the one or more updated unified feature maps and the feature map (see at least, [0190] The tracking process can include a plurality of stages based on input modalities used for the data association process…a lidar object query data association stage can use a set of lidar-based enriched object queries generated based on the lidar object query cross-attention stage)
Regarding claim 13, the combination of Zhang and Yan teaches the method of claim 9. Zhang further teaches wherein the generating the representation comprises: generating, using the one or more neural networks and based at least on the one or more unified feature maps and feature map, a second unified feature map associated with the second time instance (see at least, [0112] feature maps 601…generate object queries 603 based on images 501 for the current time step…generate object queries 603 based on lidar images 501a and/or camera images 501b generated during the initialization time step, t=0…
generate object queries 603 based on lidar images 501a and/or camera images 501b generated during the operation time step, t=1); and generating, based at least on the second unified feature map, the representation indicating the information associated with the one or more objects (see at least, Fig 4C, [0078] a convolutional neural network (CNN) 420; [0116] determines (or helps determine) a probable location of the respective object queries within the vehicle scene…may use heat maps based on the BEV feature map 602 that indicate expected or probable movements or trajectories of objects within the vehicle scene).
Regarding claim 14, the combination of Zhang and Yan teaches the method of claim 13. Zhang further teaches comprising replacing at least one of the one or more unified feature maps stored in the one or more memories with the second unified feature map (see at least, [0162] A lidar-based set of object queries and a camera-based set of object queries can be generated. After generation, the lidar-based set of object queries and a camera-based set of object queries can be fused together to generate a set of object queries for the time step).
Regarding claim 15, the combination of Zhang and Yan teaches the method of claim 9. Zhang further teaches comprising: generating an image indicating an overlap associated with the one or more unified feature maps and the feature map (see at least, [0111] the BEV Generator 505 may relate or group grid cells from the feature maps that map to the same grid cell of the BEV feature map; [0106] one or more of the regions may overlap with multiple feature maps corresponding to different images), wherein the generating the representation is further based at least on the image (see at least, [0104] The grid cells may include semantic data (or features) extracted from (pixels in) the image(s) from which the feature map was generated).
Regarding claim 16, the combination of Zhang and Yan teaches the method of claim 15. Zhang further teaches wherein the image is associated with one or more cells corresponding to one or more regions of the environment (see at least, [0106] the multi-view stage 502 may group grid cells by dividing a feature map into multiple regions (also referred to herein as windows) and/or assign different grid cells of a feature map to the different regions or windows), an individual cell of the one or more cells indicating an amount of temporal information associated with a region of the one or more regions (see at least, [0109] the multi-view stage 502 may update a particular value of a particular grid cell using corresponding weighted values of some or all of the other grid cells in the group).
Regarding claim 17, the combination of Zhang and Yan teaches the method of claim 15. Zhang further teaches wherein the generating the image comprises: determining one or more first regions of the environment that are represented by the one or more unified feature maps (see at least, [0102] the
image feature extractor 503b receives six images corresponding to six cameras placed at different locations around the vehicle and oriented in different ways…may generate six feature maps); determining one or more second regions of the environment that are represented by the feature map ([0102] The image feature extractor 503 may generate one or more feature maps using the images
501. The image feature extractor 503 may receive different types of images based…a point cloud from Lidar sensor(s)); and generating the image to indicate the overlap between at least a portion of the one or more first regions and the one or more second regions (see at least, [0106] one or more of the regions may overlap with multiple feature maps corresponding to different images).
Regarding claim 18, Zhang teaches one or more processors comprising processing circuitry (see at least, [0209] A system, comprising: a data store storing computer-executable instructions; and at least one processor) to: cause performance of one or more operations of a machine based at least on a representation indicating information associated with one or more objects located within an environment of the machine (see at least, [0186] FIG. 8 routine 800 implemented by at least one processor to navigate a vehicle based on at least one bounding box generated based on object tracking data and enriched object queries), wherein the representation is generated using one or more neural networks and based at least on one or more first features associated with first sensor data corresponding to a time instance (see at least, Fig 4C, [ 0078] a convolutional neural network (CNN) 420; [0116] determines (or helps determine) a probable location of the respective object queries within the vehicle scene…may use heat maps based on the BEV feature map 602 that indicate expected or probable movements or trajectories of objects within the vehicle scene).
Zhang does not explicitly teach one or more second features associated with second sensor data corresponding to one or more previous time instances, and an indication of an amount of overlap between the one or more first features and the one or more second features. However, Yan teaches this limitation.
Yan teaches one or more second features associated with second sensor data corresponding to one or more previous time instances (see at least, [0073] BEV feature generation component 440 can generate set of BEV features 442 from the extracted BEV features (e.g., the camera BEV feature and the radar BEV feature)…generating the fused BEV feature includes performing temporal aggregation based on a set of prior frames), and an indication of an amount of overlap between the one or more first features and the one or more second features (see at least, [0061] a BEV feature extraction system that can extract a set of BEV features from sensor data (e.g., camera data and radar data), using a set of feature networks, generate a fused BEV feature by fusing each BEV feature of the set of BEV features, and process the fused BEV feature using a BEV feature network to generate the at least one heatmap).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to include one or more second features associated with second sensor data corresponding to one or more previous time instances, and an indication of an amount of overlap between the one or more first features and the one or more second features as taught by Yan so that driving algorithm can process the information about the environment and to provide correct instructions to the AV controls and the drivetrain (Yan, [0002]).
Regarding claim 19, the combination of Zhang and Yan teaches the one or more processors of claim 18. Zhang further teaches wherein the performance of the one or more operations comprises at least one of: causing the machine to navigate within an environment; or causing an update to one or more maps associated with the environment (see at least, [0186] FIG. 8 routine 800 implemented by at least one processor to navigate a vehicle based on at least one bounding box generated based on object tracking data and enriched object queries).
Regarding claim 20, the combination of Zhang and Yan teaches the one or more processors of claim 18. Zhang further teaches wherein the one or more processors are comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (see at least, [0044] vehicle 200 includes autonomous system 202, powertrain control system 204, steering control system 206, and brake system 208);
a perception system for an autonomous or semi-autonomous machine (see at least, [0045] The data generated by the one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located);
a system for performing one or more simulation operations;
a system for performing one or more digital twin operations;
a system for performing one or more light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more wireless cellular transmissions using a wireless cellular network (see at least, [0039] Network 112 includes one or more wired and/or wireless networks…includes a cellular network);
a system that provides one or more cloud gaming applications;
a system for performing one or more deep learning operations (see at least, [0075] control system 408 implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder);
a system implemented using an edge device;
a system implemented using a robot;
a system for performing one or more generative AI operations;
a system for performing one or more conversational AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing operations using one or more multi-modal language models (MMLMs); a system for performing operations using one or more vision-language-action (VLA) models;
a system for performing one or more conversational AI operations;
a system for performing one or more synthetic data generation operations;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
systems using or deploying one or more inference microservices;
systems that incorporate deploy one or more machine learning models in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center (see at least, [0034] vehicles 102 are configured to be in communication with V2I device 110, remote AV system 114, fleet
management system 116, and/or V2I system 118 via network 1); or
a system implemented at least partially using cloud computing resources (see at least, [0039] Network 112 includes…a cloud computing network).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20250381983; hereinafter Zhang) in view of Yan et al. (US 20250078531; hereinafter Yan) in further view of Hong et al. (US 11195418 B1; hereinafter Hong).
Regarding claim 2, the combination of Zhang and Yan teaches the system of claim 1. The combination does not explicitly teach wherein the heatmap represents one or more cells corresponding to one or more regions of the environment, an individual cell of the one or more cells indicating an amount of temporal information associated with a region of the one or more regions, and the amount of temporal information being associated with the amount of overlap between the one or more first BEV features and the one or more second BEV features. However, Hong teaches these limitations.
Hong teaches wherein the heatmap represents one or more cells corresponding to one or more regions of the environment (see at least, Col 3 lines 51-55, A heat map can represent a discretized region of the environment proximate to the autonomous vehicle…can represent a 64×64 grid (or J×K sized grid) representing a 100 meter by 100 meter region around the autonomous vehicle), an individual cell of the one or more cells indicating an amount of temporal information associated with a region of the one or more regions (see at least, Col 3 lines 59-62, Each cell can comprise a prediction probability representing a probability that the agent will be at the corresponding location in the environment at the time corresponding to the heat map), and the amount of temporal information being associated with the amount of overlap between the one or more first BEV features and the one or more second BEV features (see at least, Col 4 lines 37-50, the heat maps may comprise prediction probabilities that may represent a plurality of predicted trajectories for the agent in the environment…the operation can include masking, covering, or otherwise removing prediction probabilities of the heat maps that correspond to the first predicted trajectory).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Zhang and Yan to include the heatmap represents one or more cells corresponding to one or more regions of the environment, an individual cell of the one or more cells indicating an amount of temporal information associated with a region of the one or more regions, and the amount of temporal information being associated with the amount of overlap between the one or more first BEV features and the one or more second BEV features as taught by Han in order to generate predicted trajectories based at least in part on a top-down representation of an environment including information associated with an agent in the environment (Hong, Fig 6).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Das et al. (US 12313727 B2) discloses generate a fused input based at least on concatenating the one or more first BEV features, the one or more second BEV features, and the heatmap (e.g., Col 14 lines 19-22, heatmap can be computed using a feature map corresponding to a result of combining lidar feature map 224 and radar feature map 226 based on element-wise operations
…e.g., concatenation, summation, or maxpooling).
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to TOYA PETTIEGREW whose telephone number is (313)446-6636. The examiner can normally be reached 8:30pm - 5:00pm M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at 571-270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662
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