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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/31/2026 has been entered.
Status of Claims
Claims 1-20 are pending. Claims 1, 3, 8, 14 and 16 are amended.
Response to Arguments
Applicant’s amendment of independent Claims 1 and 14, which has altered the scope of the claims of the instant application, has necessitated the new ground(s) of rejection presented in this office action with respect to claims of the instant application. Accordingly, in response to Applicant’s arguments that are merely directed to the amended portion of the claims, new analyses have been presented below, which make Applicant’s arguments moot.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 7, 8, 10, 11, 13, 14, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2021/0302992 A1), in view of Lee et al. (US 2021/0358296 A1) and in further view of Hao et al. (US 2021/0209368 A1).
Regarding claim 1, Chen teaches, A system for (Chen, ¶0019: “a control system for controlling a motion of a vehicle”) generating perception data, (Chen, ¶0003: “the perception of environment… through two-dimensional (2D) object detection based on camera data”) the system comprising: a processor (Chen, ¶0039: “the control system 100 includes an image processor 106”) operating in an offline processing environment; (Chen, ¶0037: “control system 100 for controlling a motion of a vehicle 116”) and a memory storing machine-readable instructions that, (Chen, ¶0042: “control system 100 includes a memory 108 that stores instructions executable by a controller 104”) when executed by the processor, cause the processor to: (Chen, ¶0042: “controller 104 may be configured to execute the stored instructions in order to control operations”) extract first features (Chen, ¶0094: “multi-head neural network 110 executes feature extraction operation”) from an entire time sequence of perceptual sensor data (Chen, ¶0051: “a sequence of video frames including the one or more objects in the environment captured by the camera”) to generate a first set of bird's-eye-view (BEV) feature images; (Chen, ¶0007: “environmental state is detected based on a bird's eye view (BEV) map”) extract second features from the first set of BEV feature images (Chen, ¶0055: “extract the spatio-temporal features from the BEV maps”) using a BEV feature extractor that performs feature-level temporal aggregation including (Chen, ¶0055: “multi-head neural network 110 is configured to extract the spatio-temporal features from the BEV maps”). However, Chen does not explicitly teach, stored in the offline processing environment in a non-casual manner and both forward recurrence and backward recurrence to generate a second set of BEV feature images, wherein each BEV feature image in the second set of BEV feature images corresponds to a distinct time step in the entire time sequence of perceptual sensor data and incorporates information from the past, present, and future from all time steps in the entire time sequence of perceptual sensor data; and consume the second set of BEV feature images using one or more neural-network heads to perform one of: generating automatically labeled perception data to train one or more of an online perception model, an online prediction model, -or an online planning model used to control an autonomous robot; -or validating performance of an online autonomous stack used to control an autonomous robot.
In an analogous field of endeavor, Lee teaches, both forward recurrence and backward recurrence (Lee, ¶0112: “flows in both the forward and the backward directions from the two bird's-eye view image embeddings can be calculated”) to generate a second set of BEV feature images, wherein each BEV feature image in the second set of BEV feature images corresponds to (Lee, ¶0109: “aggregator 552 receives all of the pillar features from the various points of all BeV images”) a distinct time step in the time sequence of perceptual sensor data (Lee, ¶0105: “two birds-eye view images 531, 532… one representing the first point cloud (e.g., the point cloud at time t−1) and one representing the second point cloud (e.g., the point cloud at time t”) and and consume the second set of BEV feature images using (Lee, ¶0107: “two birds-eye view images 531, 532 are aggregated to train classifiers for the features”) one or more neural-network heads (Lee, ¶0108: “classifiers may include, for example, Classification And Regression Tree (CART), K-nearest neighbor, neural network and mixture models”) to perform one of: generating automatically labeled perception data to train one or more of an online perception model, (Lee, ¶0102: “training data for the model may be autonomously labelled”) an online prediction model, or an online planning model (Lee, ¶0008: “Online versions typically model this state…. with a recurrent network trained by self-supervised labeling to predict future states”) used to control an autonomous robot; (Lee, ¶0040: “The systems and methods disclosed herein may be implemented for use in scene flow estimation for robotics, autonomous vehicles and other automated technologies”) or validating performance of an online autonomous stack used to control an autonomous robot. (Lee, ¶0005: “develop a new evaluation by looking at the 3D reconstruction quality of dynamic models”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen using the teachings of Lee to introduce generating automatic labels from forward and backward recurrences to train a classification model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of detecting the surrounding of an autonomous robot for automatic operation. Therefore, it would have been obvious to combine the analogous arts Chen and Lee to obtain the above-described limitations in claim 1. However, the combination of Chen and Lee does not explicitly teach, (sensor data) stored in the offline processing environment in a non-casual manner and (feature image) incorporates information from the past, present, and future from all time steps in the entire time sequence of perceptual sensor data.
In another analogous field of endeavor, Hao teaches, stored in the offline processing environment in a non-casual manner (Hao, ¶0021: “improve detection accuracy for video, such as offline video, to better support high-level visual processing task”) and (feature image) incorporates information from the past, present, and future from all time steps (Hao, ¶0023: “paired patches between the current and previous frames and paired patches between the current and subsequent frames, respectively, are detected. Thereby, forward and backward similar patches are detected”) in the entire time sequence of perceptual sensor data. (Hao, ¶0022: “detection is performed on each of a current frame, a previous frame, and a subsequent frame of video (e.g., on all frames of a video sequence) to determine initial feature maps”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen in view of Lee using the teachings of Hao to introduce feature maps including data from past, present and future timesteps. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of using the temporal data of each timestep to train an autonomous robot for autonomous operation. Therefore, it would have been obvious to combine the analogous arts Chen, Lee and Hao to obtain the invention in claim 1.
Regarding claim 3, Chen in view Lee and in further view of Hao teaches, The system of claim 1, wherein the entire time sequence of perceptual sensor data includes one or more of camera images, Light Detection and Ranging (LIDAR) data, radar data, sonar data, map data, -or audio data. (Chen, ¶0050: “a plurality of sensors on the vehicle 116 such as a light detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR) sensor, a camera, and the like”).
Regarding claim 4, Chen in view Lee and in further view of Hao teaches, The system of claim 1, wherein the one or more neural-network heads include (Chen, ¶0040: “The multi-head neural network 110 includes”) one or more of a three-dimensional (3D) detection head, (Chen, ¶0062: “the classification head may execute… 3D object detection based on LiDAR data, or fusion-based detection”) a 3D semantic-occupancy head, an occupancy-flow head, (Chen, ¶0066: “motion state estimation head outputs occupancy information indicating a state of each of the one or more objects”) a map-elements head, (Chen, ¶0058: “cell classification head executes BEV map segmentation”) an instance-segmentation head, a panoptic-segmentation head, (Chen, ¶0059: “the semantic segmentation corresponds to classification of every pixel of the BEV maps into a corresponding class”) a drivable- surface-estimation head, (Chen, ¶0004: “OGM pipeline can be utilized to specify a future drivable space and thereby provide support for motion planning”) -or an elevation-estimation head. (Chen, ¶0054: “converts the 3D voxel lattice into a 2D pseudo-image with a height dimension”).
Regarding claim 5, Chen in view Lee and in further view of Hao teaches, The system of claim 1, wherein the autonomous robot is an autonomous vehicle. (Chen, ¶0038: “The vehicle 116 may be an autonomous vehicle or a semi-autonomous vehicle”).
Regarding claim 7, Chen in view Lee and in further view of Hao teaches, The system of claim 1, wherein the online autonomous stack includes perception, prediction, and planning models. (Chen, ¶0002: “autonomous systems such as autonomous vehicles… facilitates motion planning… (1) perception, which identifies the foreground objects from the background; and (2) motion prediction”).
Regarding claim 8, it recites a computer-readable medium including instructions corresponding to the elements of the system recited in claim 1. Therefore, the recited instructions of the computer-readable medium of claim 8 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 1. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim. In addition, Chen teaches, A non-transitory computer-readable medium for generating perception data and storing instructions that, when executed by a processor, cause the processor to: (Chen, ¶0109: “the program code or code segments to perform the necessary tasks may be stored in a machine readable medium. A processor(s) may perform the necessary tasks”).
Regarding claim 10, it recites a computer-readable medium including instructions corresponding to the elements of the system recited in claim 4. Therefore, the recited instructions of the computer-readable medium of claim 10 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 4. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim.
Regarding claim 11, it recites a computer-readable medium including instructions corresponding to the elements of the system recited in claim 5. Therefore, the recited instructions of the computer-readable medium of claim 11 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 5. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim.
Regarding claim 13, it recites a computer-readable medium including instructions corresponding to the elements of the system recited in claim 7. Therefore, the recited instructions of the computer-readable medium of claim 13 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 7. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim.
Regarding claim 14, it recites a method with steps corresponding to the elements of the system recited in claim 1. Therefore, the recited steps of the method claim 14 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 1. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim. In addition, Chen teaches, A method (Chen, ¶00111: “Embodiments of the present disclosure may be embodied as a method”).
Regarding claim 16, it recites a method with steps corresponding to the elements of the system recited in claim 3. Therefore, the recited steps of the method claim 16 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 3. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim.
Regarding claim 17, it recites a method with steps corresponding to the elements of the system recited in claim 4. Therefore, the recited steps of the method claim 17 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 4. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim.
Regarding claim 18, it recites a method with steps corresponding to the elements of the system recited in claim 5. Therefore, the recited steps of the method claim 18 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 5. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 5, apply to this claim.
Regarding claim 20, it recites a method with steps corresponding to the elements of the system recited in claim 7. Therefore, the recited steps of the method claim 20 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 7. Additionally, the rationale and motivation to combine Chen, Lee and Hao presented in rejection of claim 1, apply to this claim.
Claims 2, 6, 9, 12, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2021/0302992 A1), in view of Lee et al. (US 2021/0358296 A1), in further view of Hao et al. (US 2021/0209368 A1) and still in further view of Beaver et al. (US 2023/0274541 A1).
Regarding claim 2, Chen in view Lee and in further view of Hao teaches, The system of claim 1. However, the combination of Chen, Lee and Hao does not explicitly teach, wherein the BEV feature extractor includes one of a plurality of Gated Recurrent Units (GRUs), a plurality of Long Short-Term Memory (LSTM) networks, -or a plurality of transformer networks to perform the feature-level temporal aggregation including both forward recurrence and backward recurrence.
In an analogous field of endeavor, Beaver teaches, wherein the BEV feature extractor includes one of a plurality of Gated Recurrent Units (GRUs), a plurality of Long Short-Term Memory (LSTM) networks, and a plurality of transformer networks to perform the feature-level temporal aggregation including both forward recurrence and backward recurrence. (Beaver, ¶0019: “a sequence-to-sequence model such a recurrent neural network (RNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and/or a Bidirectional Encoder Representations from Transformers (BERT) transformer network, may be used to process individual pixels or groups of pixels as a sequence”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen in view of Lee and in further view of Hao using the teachings of Beaver to introduce a plurality of extractor models. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of detecting the surroundings of an autonomous robot using extracted features. Therefore, it would have been obvious to combine the analogous arts Chen, Lee, Hao and Beaver to obtain the invention in claim 2.
Regarding claim 6, Chen in view Lee and in further view of Hao teaches, The system of claim 1. However, the combination of Chen, Lee and Hao does not explicitly teach, wherein the autonomous robot is one of a search and rescue robot, a delivery robot, an aerial drone, -or an indoor robot.
In an analogous field of endeavor, Beaver teaches, wherein the autonomous robot is one of a search and rescue robot, a delivery robot, an aerial drone, and an indoor robot. (Beaver, ¶0030: “An individual robot 108.sub.1-M may take various forms, such as an unmanned aerial vehicle 108-1”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen in view of Lee and in further view of Hao using the teachings of Beaver to introduce an unmanned aerial vehicle. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of autonomously operating an aerial drone using the perception data. Therefore, it would have been obvious to combine the analogous arts Chen, Lee, Hao and Beaver to obtain the invention in claim 6.
Regarding claim 9, it recites a computer-readable medium including instructions corresponding to the elements of the system recited in claim 2. Therefore, the recited instructions of the computer-readable medium of claim 9 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 2. Additionally, the rationale and motivation to combine Chen, Lee, Hao and Beaver presented in rejection of claim 2, apply to this claim.
Regarding claim 12, it recites a computer-readable medium including instructions corresponding to the elements of the system recited in claim 6. Therefore, the recited instructions of the computer-readable medium of claim 12 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 6. Additionally, the rationale and motivation to combine Chen, Lee, Hao and Beaver presented in rejection of claim 6, apply to this claim.
Regarding claim 15, it recites a method with steps corresponding to the elements of the system recited in claim 2. Therefore, the recited steps of the method claim 15 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 2. Additionally, the rationale and motivation to combine Chen, Lee, Hao and Beaver presented in rejection of claim 2, apply to this claim.
Regarding claim 19, it recites a method with steps corresponding to the elements of the system recited in claim 6. Therefore, the recited steps of the method claim 19 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 6. Additionally, the rationale and motivation to combine Chen, Lee, Hao and Beaver presented in rejection of claim 6`, apply to this claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHRAZUL ISLAM whose telephone number is (571)270-0489. The examiner can normally be reached Monday-Friday: 8am-5pm.
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/MEHRAZUL ISLAM/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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