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 .
DETAILED ACTION
Applicant’s arguments, see Response After Final action, filed on 01/21/2026, with respect to
Final Rejection have been fully considered and are persuasive. The Non-Final rejection of 02/23/2026 has been withdrawn.
No claims have been amended. Claims 1-20 are pending in this case. Claims 1, 8 and 15 are independent claims.
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.
Claim(s) 1-2, 8-9 and 15 are rejected under 35 U.S.C 103 as being unpatentable over “Learning Risk-aware Costmaps for Traversability in Challenging Environments”, https://arxiv.org/abs/2107.11722, Fan et al, 2022, hereinafter referred to as Fan, in view of “TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation”, https://arxiv.org/abs/2303.15771, Meng et al, 2023, hereinafter referred to as Meng and further in view of Manivasagam et al. (Pub No.: 20200301799 A1), hereinafter referred to as Manivasagam.
With respect to claim 1, Fan disclose:
A machine-learning based (ML-based) method for determining traversability with uncertainty object estimation for one or more robot devices to navigate through one or more terrains, the ML-based method comprising (On page 1 (Abstract), Fan teaches a neural-network architecture that takes point-cloud-derived information and learns traversability risk. The system determines traversability costs/risk associated with terrain and produces a traversability cost map.)
Obtaining, by one or more hardware processors, optimum-fidelity scan data in a form of point cloud from one or more scanner devices, wherein the optimum-fidelity scan data generated by the one or more scanner devices comprise at least one of: one or more three-dimensional point clouds, information associated with optimum-resolution surfaces, depth and distance measurements, color and intensity attributes, spatial coordinates, associated with one or more environments (On page 4 (D. Obtaining Ground Truth Labels & A. Dataset), Fan teach a robotic platform equipped with Velodyne VLP-16 LiDAR sensor for obtaining LiDAR point-cloud measurement of the surrounding terrain. Furthermore, Fan teach aggregates the LiDAR point clouds over time using robot odometry and processes the resulting three-dimensional point-cloud based on spatial and elevation informaiton, including z-heights, to distinguish ground, obstacle, and ceiling points. Thus, Fan teaches obtaining three-dimensional point-cloud scan data from a scanner device associated with the robot's surrounding environment.)
Generating, by the one or more hardware processors, an elevation map including elevation cells depicting changes in elevation associated with features of the one or more terrains and free space of the one or more environments by applying at least one of: an elevation mapping model and free-space detection model on the point cloud (On page 4 (B. Computing Traversability Cost), Fan teaches aggregating LiDAR point clouds, performing ground segmentation to isolate ground, obstacle, and ceiling points, and thereafter performing elevation mapping that probabilistically constructs a 2.5-D elevation map using the ground points. Furthermore, the Fan derives geometric terrain information from the elevation map and point cloud, including slope, step size, roughness, negative obstacles, and collision obstacles. Accordingly, Fan teaches to apply elevation mapping to pony-cloud data to generate an elevation representation characterizing the terrain and regions surrounding obstacles.)
Predicting, by the one or more hardware processors, one or more traversability features from the (On page 8 (D. Comparisons against baselines), Fan teaches using a learned neural-network model to predict traversability characteristics, including traversability costs, for an environment. Thereby enabling evaluation of navigability/traversability using simulated environmental sensor data. )
Determining, by the one or more hardware processors, the traversability with the uncertainty object estimation, which adapts the one or more robot devices to navigate on the one or more terrains, based on the one or more traversability features predicted from the ML model, wherein determining the traversability with the uncertainty object estimation includes calculating, by the one or more hardware processors, the traversability as a probability the predicted one or more traversability features are below respective critical threshold values (On page 4 (first paragraph), Fan teaches a robot collects environmental and position data while moving and determines a traverability cose for the sampled environment. The traversability determination may be uncertain or stochastic, and the learning approach is designed to capture this uncertainty. A neural network learns risk measures such as VaR and CVaR associated with the traversability costs. The approach is then applied to learn the distribution of 2-D traversability costs from 3D point clouds. On page 4 (Computing Traversability Cost), Fan teaches about computing traversability costs from LiDAR points clouds collected by the robot. The point clouds are processed to identify ground and obstacles points and construct an elevation map, from which features such as slope, step size, roughness, and obstacles are determined. These features are combined to produce a traversability cose and associated variance, with the cost ranging from 0 (safe) to 1 (lethal). The resulting cost distribution is then used to predict CVaR, representing risk associated with traversing the environment. )
With respect to claim 1, Fan do not explicitly disclose:
Generating, by the one or more hardware processors, a dense point cloud based upon the point cloud from one or more scanner devices and including one or more ground-truth map features from the elevation map of the one or more environments
Generating, by the one or more hardware processors, a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments
Synthetic point cloud
However, it is known by Meng to disclose:
Generating, by the one or more hardware processors, a dense point cloud based upon the point cloud from one or more scanner devices and including one or more ground-truth map features from the elevation map of the one or more environments (On page 6 (D. Multi-head Terrain Inpainting Module & B. Dataset), Meng teaches recording LiDAR scans, determining gravity-aligned poses for the respective scans using SLAM, and aggregating the scan to obtain a globally aligned dense point cloud. The dense point cloud is subsequently divided into spatial cells and analyzed according to the elevation of the points within each cell to determine ground elevation information. Furthermore, Meng teaches to use the LiDAR-derived representation to produce high-quality ground-truth data, including ground-truth semantic and elevation maps, for the environment.)
Fan and Meng are analogous because both references are directed to machine-learning-based terrain traversability assessment for autonomous robotic navigation. Accordingly, 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 Fan with determining where the robot can or cannot safely move as taught by Fan, with estimating traversability for autonomous off-road driving as taught by Meng. The motivation for doing so would have been to the improve robot traversability assessment in uncertain, difficult environment-specifically, how a robot determines which terrain is safe or risk to traverse from point-cloud data (See (Page 1 (Abstract) of Fan.)
With respect to claim 1, Fan and Meng do not explicitly disclose:
Generating, by the one or more hardware processors, a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments
Synthetic point cloud
However, it is known by Manivasagam to disclose:
Generating, by the one or more hardware processors, a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments (In paragraph [0040], Manivasagam discloses generating synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In paragraph [0048], Manivasagam discloses obtaining a three-dimensional map of an environment and performing ray casting on the three-dimensional map according to the trajectory of a virtual object to generate an initial three-dimensional point cloud simulating LiDAR data. The ray-casting engine receives the trajectory, including a desired six-degree-of freedom sensor pose and velocity, and casts ray from a simulated virtual LiDAR system into the environment. The resulting map-relative frame to provide the returned LiDAR points, thereby generating a synthetic point cloud corresponding to the virtual LiDAR viewpoint.)
Synthetic point cloud (In paragraph [0040], Manivasagam discloses generating synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In paragraph [0048], Manivasagam discloses obtaining a three-dimensional map of an environment and performing ray casting on the three-dimensional map according to a trajectory of a virtual object to generate an initial three-dimensional point cloud simulating LiDAR data. )
Fan in view of Meng and Manivasagam are analogous because both references are directed to machine-learning-based terrain traversability assessment for autonomous robotic navigation. Accordingly, 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 Manivasagam with a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud as taught by Manivasagam. The motivation for doing so would have been to the improve the safety of passengers of an autonomous vehicle, improve the safety of the surroundings of the autonomous vehicle, improve the experience of the rider and/or operator of the autonomous vehicle, as well as provide other improvements as described herein (See [0013] of Manivasagam.)
With respect to claim 8, Fan disclose:
A machine-learning based (ML-based) system for determining traversability with uncertainty object estimation for one or more robot devices to navigate through one or more terrains, the ML-based system comprising: one or more hardware processors (On page 1 (Abstract), Fan teaches a neural-network architecture that takes point-cloud-derived information and learns traversability risk. The system determines traversability costs/risk associated with terrain and produces a traversability cost map. Although Fan does not necessarily characterize each disclosed operation as being performed by a “hardware processor”, Fan expressly describes computer-implemented operation including processing point-cloud data, training and executing a neutral network, and computing traversability information. Such operations necessarily require processing hardware for their execution.)
A memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises: a data obtaining subsystem configured to obtain optimum-fidelity scan data in a form of point cloud from one or more scanner devices, wherein the optimum-fidelity scan data generated by the one or more scanner devices comprise at least one of: one or more three-dimensional point clouds, information associated with optimum-resolution surfaces, depth and distance measurements, color and intensity attributes, spatial coordinates, associated with one or more environments (On page 4 (D. Obtaining Ground Truth Labels & A. Dataset), Fan teach a robotic platform equipped with Velodyne VLP-16 LiDAR sensor for obtaining LiDAR point-cloud measurement of the surrounding terrain. Furthermore, Fan teach aggregates the LiDAR point clouds over time using robot odometry and processes the resulting three-dimensional point-cloud based on spatial and elevation informaiton, including z-heights, to distinguish ground, obstacle, and ceiling points. Thus, Fan teaches obtaining three-dimensional point-cloud scan data from a scanner device associated with the robot's surrounding environment.)
Elevation map generating subsystem configured to generate an elevation map including elevation cells depicting changes in elevation associated features of the one or more terrains and free space of the one or more environments by applying at least one of: an elevation mapping model and free-space detection model on the point cloud (On page 4 (B. Computing Traversability Cost), Fan teaches aggregating LiDAR point clouds, performing ground segmentation to isolate ground, obstacle, and ceiling points, and thereafter performing elevation mapping that probabilistically constructs a 2.5-D elevation map using the ground points. Furthermore, the Fan derives geometric terrain information from the elevation map and point cloud, including slope, step size, roughness, negative obstacles, and collision obstacles. Accordingly, Fan teaches to apply elevation mapping to pony-cloud data to generate an elevation representation characterizing the terrain and regions surrounding obstacles.)
A traversability predicting subsystem configured to:predict one or more traversability features from the (On page 8 (D. Comparisons against baselines), Fan teaches using a learned neural-network model to predict traversability characteristics, including traversability costs, for an environment. Thereby enabling evaluation of navigability/traversability using simulated environmental sensor data. )
Determine the traversability with the uncertainty object estimation, which adapts the one or more robot devices to navigate on the one or more terrains, based on the one or more traversability features predicted from the ML model, wherein to determine the traversability with the uncertainty object estimation includes calculating the traversability as a probability the predicted one or more traversability features are below respective critical threshold values (On page 4 (first paragraph), Fan teaches a robot collects environmental and position data while moving and determines a traverability cost for the sampled environment. The traversability determination may be uncertain or stochastic, and the learning approach is designed to capture this uncertainty. A neural network learns risk measures such as VaR and CVaR associated with the traversability costs. The approach is then applied to learn the distribution of 2-D traversability costs from 3D point clouds. On page 4 (Computing Traversability Cost), Fan teaches about computing traversability costs from LiDAR points clouds collected by the robot. The point clouds are processed to identify ground and obstacles points and construct an elevation map, from which features such as slope, step size, roughness, and obstacles are determined. These features are combined to produce a traversability cose and associated variance, with the cost ranging from 0 (safe) to 1 (lethal). The resulting cost distribution is then used to predict CVaR, representing risk associated with traversing the environment. )
With respect to claim 8, Fan do not explicitly disclose:
Generate a dense point cloud based upon the point cloud from one or more scanner devices and including one or more ground-truth map features from the elevation map of the one or more environments
Generate a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments
Synthetic point cloud
However, it is known by Meng to disclose:
Generate a dense point cloud based upon the point cloud from one or more scanner devices and including one or more ground-truth map features from the elevation map of the one or more environments (On page 6 (D. Multi-head Terrain Inpainting Module & B. Dataset), Meng teaches recording LiDAR scans, determining gravity-aligned poses for the respective scans using SLAM, and aggregating the scan to obtain a globally aligned dense point cloud. The dense point cloud is subsequently divided into spatial cells and analyzed according to the elevation of the points within each cell to determine ground elevation information. Furthermore, Meng teaches to use the LiDAR-derived representation to produce high-quality ground-truth data, including ground-truth semantic and elevation maps, for the environment.)
Fan and Meng are analogous because both references are directed to machine-learning-based terrain traversability assessment for autonomous robotic navigation. Accordingly, 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 Fan with determining where the robot can or cannot safely move as taught by Fan, with estimating traversability for autonomous off-road driving as taught by Meng. The motivation for doing so would have been to the improve robot traversability assessment in uncertain, difficult environment-specifically, how a robot determines which terrain is safe or risk to traverse from point-cloud data (See (Page 1 (Abstract) of Fan.)
With respect to claim 8, Fan and Meng do not explicitly disclose:
Generate a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments
Synthetic point cloud
However, it is known by Manivasagam to disclose:
Generate a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments (In paragraph [0040], Manivasagam discloses generating synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In paragraph [0048], Manivasagam discloses obtaining a three-dimensional map of an environment and performing ray casting on the three-dimensional map according to the trajectory of a virtual object to generate an initial three-dimensional point cloud simulating LiDAR data. The ray-casting engine receives the trajectory, including a desired six-degree-of freedom sensor pose and velocity, and casts ray from a simulated virtual LiDAR system into the environment. The resulting map-relative frame to provide the returned LiDAR points, thereby generating a synthetic point cloud corresponding to the virtual LiDAR viewpoint.)
Synthetic point cloud (In paragraph [0040], Manivasagam discloses generating synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In paragraph [0048], Manivasagam discloses obtaining a three-dimensional map of an environment and performing ray casting on the three-dimensional map according to a trajectory of a virtual object to generate an initial three-dimensional point cloud simulating LiDAR data. )
Fan in view of Meng and Manivasagam are analogous because both references are directed to machine-learning-based terrain traversability assessment for autonomous robotic navigation. Accordingly, 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 Manivasagam with a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud as taught by Manivasagam. The motivation for doing so would have been to the improve the safety of passengers of an autonomous vehicle, improve the safety of the surroundings of the autonomous vehicle, improve the experience of the rider and/or operator of the autonomous vehicle, as well as provide other improvements as described herein (See [0013] of Manivasagam.)
With respect to claim 15, Fan disclose:
A non-transitory computer-readable storage medium having instructions stored therein that when executed by one or more hardware processors, cause the one or more hardware processors to execute operations of: obtaining optimum-fidelity scan data in a form of point cloud from one or more scanner devices, wherein the optimum-fidelity scan data generated by the one or more scanner devices comprise at least one of: one or more three-dimensional point clouds, information associated with optimum-resolution surfaces, depth and distance measurements, color and intensity attributes, spatial coordinates, associated with one or more environments including one or more one or more terrains (On page 1 (Abstract), Fan teaches a neural-network architecture that takes point-cloud-derived information and learns traversability risk. The system determines traversability costs/risk associated with terrain and produces a traversability cost map. Although Fan does not necessarily characterize each disclosed operation as being performed by a “hardware processor”, Fan expressly describes computer-implemented operation including processing point-cloud data, training and executing a neutral network, and computing traversability information. Such operations necessarily require processing hardware for their execution. On page 4 (D. Obtaining Ground Truth Labels & A. Dataset), Fan teach a robotic platform equipped with Velodyne VLP-16 LiDAR sensor for obtaining LiDAR point-cloud measurement of the surrounding terrain. Furthermore, Fan teach aggregates the LiDAR point clouds over time using robot odometry and processes the resulting three-dimensional point-cloud based on spatial and elevation information, including z-heights, to distinguish ground, obstacle, and ceiling points. Thus, Fan teaches obtaining three-dimensional point-cloud scan data from a scanner device associated with the robot's surrounding environment.)
Generating an elevation map including elevation cells depicting changes in elevation associated features of the one or more terrains and free space of the one or more environments by applying at least one of: an elevation mapping model and free-space detection model on the point cloud (On page 4 (B. Computing Traversability Cost), Fan teaches aggregating LiDAR point clouds, performing ground segmentation to isolate ground, obstacle, and ceiling points, and thereafter performing elevation mapping that probabilistically constructs a 2.5-D elevation map using the ground points. Furthermore, the Fan derives geometric terrain information from the elevation map and point cloud, including slope, step size, roughness, negative obstacles, and collision obstacles. Accordingly, Fan teaches to apply elevation mapping to pony-cloud data to generate an elevation representation characterizing the terrain and regions surrounding obstacles.)
Predicting one or more traversability features from the (On page 8 (D. Comparisons against baselines), Fan teaches using a learned neural-network model to predict traversability characteristics, including traversability costs, for an environment. Thereby enabling evaluation of navigability/traversability using simulated environmental sensor data. )
Determining the traversability with uncertainty object estimation, which adapts one or more robot devices to navigate on the one or more terrains, based on the one or more traversability features predicted from the ML model, wherein determining the traversability with the uncertainty object estimation includes calculating, by the one or more hardware processors, the traversability as a probability the predicted one or more traversability features are below respective critical threshold values (On page 4 (first paragraph), Fan teaches a robot collects environmental and position data while moving and determines a traverability cost for the sampled environment. The traversability determination may be uncertain or stochastic, and the learning approach is designed to capture this uncertainty. A neural network learns risk measures such as VaR and CVaR associated with the traversability costs. The approach is then applied to learn the distribution of 2-D traversability costs from 3D point clouds. On page 4 (Computing Traversability Cost), Fan teaches computing traversability costs from LiDAR points clouds collected by the robot. The point clouds are processed to identify ground and obstacles points and construct an elevation map, from which features such as slope, step size, roughness, and obstacles are determined. These features are combined to produce a traversability cose and associated variance, with the cost ranging from 0 (safe) to 1 (lethal). The resulting cost distribution is then used to predict CVaR, representing risk associated with traversing the environment. )
With respect to claim 15, Fan do not explicitly disclose:
Generating a dense point cloud based upon the point cloud from one or more scanner devices and including one or more ground-truth map features from the elevation map of the one or more environments
Generating a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments
Synthetic point cloud
However, it is known by Meng to disclose:
Generating a dense point cloud based upon the point cloud from one or more scanner devices and including one or more ground-truth map features from the elevation map of the one or more environments (On page 6 (D. Multi-head Terrain Inpainting Module & B. Dataset), Meng teaches recording LiDAR scans, determining gravity-aligned poses for the respective scans using SLAM, and aggregating the scan to obtain a globally aligned dense point cloud. The dense point cloud is subsequently divided into spatial cells and analyzed according to the elevation of the points within each cell to determine ground elevation information. Furthermore, Meng teaches to use the LiDAR-derived representation to produce high-quality ground-truth data, including ground-truth semantic and elevation maps, for the environment.)
Fan and Meng are analogous because both references are directed to machine-learning-based terrain traversability assessment for autonomous robotic navigation. Accordingly, 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 Fan with determining where the robot can or cannot safely move as taught by Fan, with estimating traversability for autonomous off-road driving as taught by Meng. The motivation for doing so would have been to the improve robot traversability assessment in uncertain, difficult environment-specifically, how a robot determines which terrain is safe or risk to traverse from point-cloud data (See (Page 1 (Abstract) of Fan.)
With respect to claim 15, Fan and Meng do not explicitly disclose:
Generating a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments
Synthetic point cloud
However, it is known by Manivasagam to disclose:
Generating a synthetic point cloud based on projecting the dense point cloud of the one or more environments into at least one frame defined by a respective at least one pose representing a virtual viewpoint of the one or more environments (In paragraph [0040], Manivasagam discloses generating synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In paragraph [0048], Manivasagam discloses obtaining a three-dimensional map of an environment and performing ray casting on the three-dimensional map according to the trajectory of a virtual object to generate an initial three-dimensional point cloud simulating LiDAR data. The ray-casting engine receives the trajectory, including a desired six-degree-of freedom sensor pose and velocity, and casts ray from a simulated virtual LiDAR system into the environment. The resulting map-relative frame to provide the returned LiDAR points, thereby generating a synthetic point cloud corresponding to the virtual LiDAR viewpoint.)
Synthetic point cloud (In paragraph [0040], Manivasagam discloses generating synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In paragraph [0048], Manivasagam discloses obtaining a three-dimensional map of an environment and performing ray casting on the three-dimensional map according to a trajectory of a virtual object to generate an initial three-dimensional point cloud simulating LiDAR data. )
Fan in view of Meng and Manivasagam are analogous because both references are directed to machine-learning-based terrain traversability assessment for autonomous robotic navigation. Accordingly, 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 Manivasagam with a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud as taught by Manivasagam. The motivation for doing so would have been to the improve the safety of passengers of an autonomous vehicle, improve the safety of the surroundings of the autonomous vehicle, improve the experience of the rider and/or operator of the autonomous vehicle, as well as provide other improvements as described herein (See [0013] of Manivasagam.)
Regarding claim 2, Fan in view of Meng and Manivasagam disclose elements of claim 1. In addition Fan disclose:
The ML-based method of claim 1, further comprising extracting, by the one or more hardware processors, the one or more traversability features comprising at least one of: step, slope, and roughness, of the one or more terrains, from one or more neighborhoods of one or more elevation cells, based on an analysis of the elevation map of the one or more environments (On page 1 (paragraph 5), Fan teach geometric terrain information from the elevation map and point cloud, including slope, step size, roughness, negative obstacles, and collision obstacles.)
Regarding claim 9, Fan in view of Meng and Manivasagam disclose elements of claim 8. In addition Fan disclose:
The ML-based system of claim 8, wherein the traversability predicting subsystem is configured to extract the one or more traversability features comprising at least one of: step, slope, and roughness, of the one or more terrains, from one or more neighborhoods of one or more elevation cells, based on an analysis of the elevation map of the one or more environment (On page 1 (paragraph 5), Fan teach geometric terrain information from the elevation map and point cloud, including slope, step size, roughness, negative obstacles, and collision obstacles.)
Claims 3, 10 and 6 are rejected under 35 U.S.C 103 as being unpatentable over Fan in view of Meng, Manivasagam and further in view of STENNETH et al. (US Pub No.: 20200050973 A1), hereinafter referred to as STENNETH.
Regarding claim 3, Fan in view of Meng and Manivasagam disclose elements of claim 1. Fan in view of Meng and Manivasagam does not explicitly disclose:
The ML-based method of claim 1, further comprising training, by the one or more hardware processors, the ML model, by: obtaining, by the one or more hardware processors, one or more training datasets from the generated dense point cloud with the one or more ground-truth map features
training, by the one or more hardware processors, the ML model with the one or more training datasets obtained from the generated dense point cloud with the one or more ground-truth map features
predicting, by the one or more hardware processors, the one or more traversability features using the trained ML model
However, STENNETH disclose the limitations:
The ML-based method of claim 1, further comprising training, by the one or more hardware processors, the ML model, by: obtaining, by the one or more hardware processors, one or more training datasets from the generated dense point cloud with the one or more ground-truth map features (In Fig. 5 and paragraph [0101], STENNETH discloses extracting a plurality of features for the pre-processed road observations, wherein the plurality of features include sensor-based features and map-based features with ground truth data. )
training, by the one or more hardware processors, the ML model with the one or more training datasets obtained from the generated dense point cloud with the one or more ground-truth map features (In Fig. 5 and paragraph [0102], STENNETH discloses training an ML model based on a plurality of sensors based on features and the plurality of map-based features with ground-truth data.)
predicting, by the one or more hardware processors, the one or more traversability features using the trained ML model (In Fig. 5 and paragraph [0105], STENNETH discloses predicting the location of the road sign based on the trained machine learning model. )
Fan in view of Meng, Manivasagam and STENNETH are analogous pieces of art because both references concern the method of navigation of robots. Accordingly, 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 STENNETH, with training a machine learning model based on the association of the ground truth data with the set of sensor based features and the set of map based features as taught by STENNETH. The motivation for doing so would have been to improve the cost and performance aspects of the navigation (See [0031] of STENNETH.)
Regarding claim 10, Fan in view of Meng and Manivasagam disclose elements of claim 8. Fan in view of Meng and Manivasagam does not explicitly disclose:
The ML-based system of claim 8, further comprising a training subsystem configured to train the ML model, by: obtaining one or more training datasets from the generated dense point cloud with the one or more ground-truth map features
training the ML model with the one or more training datasets obtained from the generated dense point cloud with the one or more ground-truth map features
predicting the one or more traversability features using the trained ML model
However, STENNETH disclose the limitations:
The ML-based system of claim 8, further comprising a training subsystem configured to train the ML model, by: obtaining one or more training datasets from the generated dense point cloud with the one or more ground-truth map features (In Fig. 5 and paragraph [0101], STENNETH discloses extracting a plurality of features for the pre-processed road observations, wherein the plurality of features include sensor-based features and map based features with ground truth data.)
training the ML model with the one or more training datasets obtained from the generated dense point cloud with the one or more ground-truth map features (In Fig. 5 and paragraph [0102], STENNETH discloses training an ML model based on a plurality of sensors based on features and the plurality of map-based features with ground-truth data.)
predicting the one or more traversability features using the trained ML model (In Fig. 5 and paragraph [0105], STENNETH discloses predicting the location of the road sign based on the trained machine learning model. )
Fan in view of Meng, Manivasagam and STENNETH are analogous pieces of art because both references concern the method of navigation of robots. Accordingly, 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 STENNETH, with training a machine learning model based on the association of the ground truth data with the set of sensor based features and the set of map based features as taught by STENNETH. The motivation for doing so would have been to improve the cost and performance aspects of the navigation (See [0031] of STENNETH.)
Regarding claim 16, Fan in view of Meng and Manivasagam disclose elements of claim 15. Fan in view of Meng and Manivasagam does not explicitly disclose:
The non-transitory computer-readable storage medium of claim 15, further comprising training the ML model, by: obtaining one or more training datasets from the generated dense point cloud with the one or more ground-truth map features
training the ML model with the one or more training datasets obtained from the generated dense point cloud with the one or more ground-truth map features
predicting the one or more traversability features using the trained ML model
However, STENNETH disclose the limitations:
The non-transitory computer-readable storage medium of claim 15, further comprising training the ML model, by: obtaining one or more training datasets from the generated dense point cloud with the one or more ground-truth map features (In Fig. 5 and paragraph [0101], STENNETH discloses extracting a plurality of features for the preprocessed road observations, wherein the plurality of features include sensor-based features and map-based features with ground truth data.)
training the ML model with the one or more training datasets obtained from the generated dense point cloud with the one or more ground-truth map features (In Fig. 5 and paragraph [0102], STENNETH discloses training an ML model based on a plurality of sensors based on features and the plurality of map-based features with ground-truth data.)
predicting the one or more traversability features using the trained ML model (In Fig. 5 and paragraph [0105], STENNETH discloses predicting the location of the road sign based on the trained machine learning model. )
Fan in view of Meng, Manivasagam and STENNETH are analogous pieces of art because both references concern the method of navigation of robots. Accordingly, 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 STENNETH, with training a machine learning model based on the association of the ground truth data with the set of sensor based features and the set of map based features as taught by STENNETH. The motivation for doing so would have been to improve the cost and performance aspects of the navigation (See [0031] of STENNETH.)
Claims 4, 11 and 17 are rejected under 35 U.S.C 103 as being unpatentable over Fan in view of Meng, Manivasagam, STENNETH and further in view of Botonjic et al. (US Patent No.: 11,308,639 B2), hereinafter referred to as Botonjic.
Regarding claim 4, Fan in view of Meng, Manivasagam and STENNETH disclose elements of claim 3. Fan in view of Meng, Manivasagam and STENNETH does not explicitly disclose:
The ML-based method of claim 1, wherein generating the synthetic point cloud based on the dense point cloud of the one or more environments, comprises: collecting, by the one or more hardware processors, one or more poses indicating one or more virtual viewpoints, in one or more free spaces in the one or more environments
projecting, by the one or more hardware processors, the generated dense point cloud into one or more frames defined by the collected one or more poses
cropping, by the one or more hardware processors, the one or more ground-truth map features upon a transformation process on the one or more ground-truth map features, based on the collected one or more poses
applying, by the one or more hardware processors, noising data to the synthetic point cloud associated with the one or more training datasets to make the synthetic point cloud having outputs similar to outputs of one or more low-resolution sensors associated with the one or more robot devices
However, Botonjic disclose the limitation:
The ML-based method of claim 1, wherein generating the synthetic point cloud based on the dense point cloud of the one or more environments, comprises: collecting, by the one or more hardware processors, one or more poses indicating one or more virtual viewpoints, in one or more free spaces in the one or more environments (In Col. 12, lines 50–60, Botonjic discloses receiving a point cloud from a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor; processing the point cloud to produce a voxelized frame including a plurality of voxels; processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons)
projecting, by the one or more hardware processors, the generated dense point cloud into one or more frames defined by the collected one or more poses (In Col. 12, lines 50–60, Botonjic discloses processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons; and outputting the location of the determined one or more persons and the pose for each of the determined one or more persons)
cropping, by the one or more hardware processors, the one or more ground-truth map features upon a transformation process on the one or more ground-truth map features, based on the collected one or more poses (In Col. 17-18, lines 50-10, Botonjic disclose the annotation tool can automatically find important areas in the point cloud frame and crop it to show only those areas. The tool may send the point cloud frame to a deep neural network that can identify people and their poses.)
applying, by the one or more hardware processors, noising data to the synthetic point cloud associated with the one or more training datasets to make the synthetic point cloud having outputs similar to outputs of one or more low-resolution sensors associated with the one or more robot devices (In Col. 11, lines 36–46, Botonjic discloses that it processes the voxel frame to find one or more people near the LiDAR sensor and figure out their poses. Then, Microprocessor 22 can give the location of these people and their poses.)
Fan in view of Meng, Manivasagam, STENNETH and Botonjic are analogous pieces of art because both references concern the method of navigation of robots. Accordingly, 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 Botonjic, with training a neural network to more accurately identify and label poses in point cloud data in real-time as taught by Botonjic. The motivation for doing so would have been to provide a more accurate determination of a person and pose estimation, while 2D convolutional layers generally provide for a quicker determination of a person and pose estimation (See (Col. 2, lines 6-11) of Botonjic).
Regarding claim 11, Fan in view of Meng, Manivasagam and STENNETH disclose elements of claim 8. Fan in view of Meng, Manivasagam and STENNETH does not explicitly disclose:
The ML-based system of claim 8, wherein in generating the synthetic point cloud based on the dense point cloud of the one or more environments, the point cloud generating subsystem is configured to: collect one or more poses indicating one or more virtual viewpoints, in one or more free spaces in the one or more environment
project the generated dense point cloud into one or more frames defined by the collected one or more poses
crop the one or more ground-truth map features upon a transformation process on the one or more ground-truth map features, based on the collected one or more poses
apply noising data to the synthetic point cloud associated with the one or more training datasets to make the synthetic point cloud having outputs similar to outputs of one or more low-resolution sensors associated with the one or more robot devices
However, Bontonjic disclose the limitation:
The ML-based system of claim 8, wherein in generating the synthetic point cloud based on the dense point cloud of the one or more environments, the point cloud generating subsystem is configured to: collect one or more poses indicating one or more virtual viewpoints, in one or more free spaces in the one or more environment (In Col. 12, lines 50–60, Botonjic discloses receiving a point cloud from a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor; processing the point cloud to produce a voxelized frame including a plurality of voxels; processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons)
project the generated dense point cloud into one or more frames defined by the collected one or more poses (In Col. 12, lines 50–60, Botonjic discloses processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons; and outputting the location of the determined one or more persons and the pose for each of the determined one or more persons).
crop the one or more ground-truth map features upon a transformation process on the one or more ground-truth map features, based on the collected one or more poses (In Col. 17- 18, lines 50-10, Botonjic disclose the annotation tool can automatically find important areas in the point cloud frame and crop it to show only those areas. The tool may send the point cloud frame to a deep neural network that can identify people and their poses.)
apply noising data to the synthetic point cloud associated with the one or more training datasets to make the synthetic point cloud having outputs similar to outputs of one or more low-resolution sensors associated with the one or more robot devices (In Col. 11, lines 36–46, Botonjic discloses that it processes the voxel frame to find one or more people near the LiDAR sensor and figure out their poses. Then, Microprocessor 22 can give the location of these people and their poses.)
Fan in view of Meng, Manivasagam, STENNETH and Botonjic are analogous pieces of art because both references concern the method of navigation of robots. Accordingly, 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 Botonjic, with training a neural network to more accurately identify and label poses in point cloud data in real-time as taught by Botonjic. The motivation for doing so would have been to provide a more accurate determination of a person and pose estimation, while 2D convolutional layers generally provide for a quicker determination of a person and pose estimation (See (Col. 2, lines 6-11) of Botonjic).
Regarding claim 17, Fan in view of Meng, Manivasagam and STENNETH disclose elements of claim 15. Fan in view of Meng, Manivasagam and STENNETH does not explicitly disclose:
The non-transitory computer-readable storage medium of claim 15, wherein generating the synthetic point cloud based on the dense point cloud of the one or more environments, comprises: collecting one or more poses indicating one or more virtual viewpoints, in one or more free spaces in the one or more environments
projecting the generated dense point cloud into one or more frames defined by the collected one or more poses
cropping the one or more ground-truth map features upon a transformation process on the one or more ground-truth map features, based on the collected one or more poses
applying noising data to the synthetic point cloud associated with the one or more training datasets to make the synthetic point cloud having outputs similar to outputs of one or more low-resolution sensors associated with the one or more robot devices
However, Botonjic disclose the limitation:
The ML-based system of claim 8, wherein in generating the synthetic point cloud based on the dense point cloud of the one or more environments, the point cloud generating subsystem is configured to: collect one or more poses indicating one or more virtual viewpoints, in one or more free spaces in the one or more environment (In Col. 12, lines 50–60, Botonjic discloses receiving a point cloud from a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor; processing the point cloud to produce a voxelized frame including a plurality of voxels; processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons)
project the generated dense point cloud into one or more frames defined by the collected one or more poses (In Col. 12, lines 50–60, Botonjic discloses processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons; and outputting the location of the determined one or more persons and the pose for each of the determined one or more persons)
crop the one or more ground-truth map features upon a transformation process on the one or more ground-truth map features, based on the collected one or more poses (In Col. 17- 18, lines 50-10, Botonjic disclose the annotation tool can automatically find important areas in the point cloud frame and crop it to show only those areas. The tool may send the point cloud frame to a deep neural network that can identify people and their poses.)
apply noising data to the synthetic point cloud associated with the one or more training datasets to make the synthetic point cloud having outputs similar to outputs of one or more low-resolution sensors associated with the one or more robot devices (In Col. 11, lines 36–46, Botonjic discloses that it processes the voxel frame to find one or more people near the LiDAR sensor and figure out their poses. Then, Microprocessor 22 can give the location of these people and their poses.)
Fan in view of Meng, Manivasagam, STENNETH and Botonjic are analogous pieces of art because both references concern the method of navigation of robots. Accordingly, 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 Botonjic, with training a neural network to more accurately identify and label poses in point cloud data in real-time as taught by Botonjic. The motivation for doing so would have been to provide a more accurate determination of a person and pose estimation, while 2D convolutional layers generally provide for a quicker determination of a person and pose estimation (See (Col. 2, lines 6-11) of Botonjic).
Claims 5, 12 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Fan in view of Meng, Manivasagam and further in view of Goforth et al. (US Pub No.: 20230289999 A1), hereinafter referred to as Goforth.
Regarding claim 5, Fan in view of Meng and Manivasagam disclose elements of claim 1. Fan in view of Meng and Manivasagam does not explicitly disclose:
The ML-based method of claim 1, wherein predicting the one or more traversability features from the synthetic point cloud associated with the one or more environments using the machine learning (ML) model, comprises: defining, by the one or more hardware processors, one or more metric regions around an ego-pose comprising at least one of: a resolution, width and height, in the synthetic point cloud
passing, by the one or more hardware processors, one or more points in the one or more metric regions through a point pillars network comprising at least one of: a point net and a cell-wise max-pooling
and generating, by the one or more hardware processors, a cell-wise and factorized gaussian distribution for the one or more traversability features based on the point pillars network with the one or more points in the one or more metric regions, using the ML model
However, Goforth disclose the limitation:
The ML-based method of claim 1, wherein predicting the one or more traversability features from the synthetic point cloud associated with the one or more environments using the machine learning (ML) model, comprises: defining, by the one or more hardware processors, one or more metric regions around an ego-pose comprising at least one of: a resolution, width and height, in the synthetic point cloud (In paragraph [0050], Goforth discloses both partial and complete views of detailed 3D models of different vehicles. For partial views, points from the surfaces of several models are picked using the simulated sensor at a certain height.)
passing, by the one or more hardware processors, one or more points in the one or more metric regions through a point pillars network comprising at least one of: a point net and a cell-wise max-pooling (In paragraph [0042], Goforth discloses the geometric information in the input point cloud, and uses max pooling on F to get a global feature g, which captures the most important information.)
generating, by the one or more hardware processors, a cell-wise and factorized gaussian distribution for the one or more traversability features based on the point pillars network with the one or more points in the one or more metric regions, using the ML model (In paragraph [0042], Goforth discloses that the deep neural network model creates a simple distribution for the traversability features using the point pillars network and points in specific areas.)
Fan in view of Meng, Manivasagam and Goforth are analogous pieces of art because both references concern the method of navigation of robots. . Accordingly, 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 Goforth, with generating an estimated pose and shape of the object based on the code as taught by Goforth. The motivation for doing so would have been to improve both the accuracy and efficiency of shape estimation and/or pose estimation based on sensor data (See [0068] of Goforth.)
Regarding claim 12 Fan in view of Meng and Manivasagam disclose elements of claim 8. Fan in view of Meng and Manivasagam does not explicitly disclose:
The ML-based system of claim 8, wherein in predicting the one or more traversability features from the synthetic point cloud associated with the one or more environments using the machine learning (ML) model, the traversability predicting subsystem is configured to: define one or more metric regions around an ego-pose comprising at least one of: a resolution, width and height, in the synthetic point cloud
pass one or more points in the one or more metric regions through a point pillars network comprising at least one of: a point net and a cell-wise max-pooling
generate a cell-wise and factorized gaussian distribution for the one or more traversability features based on the point pillars network with the one or more points in the one or more metric regions, using the ML model
However, Goforth disclose the limitation:
The ML-based system of claim 8, wherein in predicting the one or more traversability features from the synthetic point cloud associated with the one or more environments using the machine learning (ML) model, the traversability predicting subsystem is configured to: define one or more metric regions around an ego-pose comprising at least one of: a resolution, width and height, in the synthetic point cloud (In paragraph [0050], Goforth discloses both partial and complete views of detailed 3D models of different vehicles. For partial views, points from the surfaces of several models are picked using the simulated sensor at a certain height.)
pass one or more points in the one or more metric regions through a point pillars network comprising at least one of: a point net and a cell-wise max-pooling (In paragraph [0042], Goforth discloses the geometric information in the input point cloud, and uses max pooling on F to get a global feature g, which captures the most important information.)
generate a cell-wise and factorized gaussian distribution for the one or more traversability features based on the point pillars network with the one or more points in the one or more metric regions, using the ML model (In paragraph [0042], Goforth discloses that the deep neural network model creates a simple distribution for the traversability features using the point pillars network and points in specific areas.)
Fan in view of Meng, Manivasagam and Goforth are analogous pieces of art because both references concern the method of navigation of robots. . Accordingly, 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 Goforth, with generating an estimated pose and shape of the object based on the code as taught by Goforth. The motivation for doing so would have been to improve both the accuracy and efficiency of shape estimation and/or pose estimation based on sensor data (See [0068] of Goforth.)
Regarding claim 18 Fan in view of Meng and Manivasagam disclose elements of claim 15. Fan in view of Meng and Manivasagam does not explicitly disclose:
The non-transitory computer-readable storage medium of claim 15, wherein predicting the one or more traversability features from the synthetic point cloud associated with the one or more environments using the machine learning (ML) model, comprises: defining one or more metric regions around an ego-pose comprising at least one of: a resolution, width and height, in the synthetic point cloud
passing one or more points in the one or more metric regions through a point pillars network comprising at least one of: a point net and a cell-wise max-pooling
generating a cell-wise and factorized gaussian distribution for the one or more traversability features based on the point pillars network with the one or more points in the one or more metric regions, using the ML model
However, Goforth disclose the limitation:
The non-transitory computer-readable storage medium of claim 15, wherein predicting the one or more traversability features from the synthetic point cloud associated with the one or more environments using the machine learning (ML) model, comprises: defining one or more metric regions around an ego-pose comprising at least one of: a resolution, width and height, in the synthetic point cloud (In paragraph [0050], Goforth discloses both partial and complete views of detailed 3D models of different vehicles. For partial views, points from the surfaces of several models are picked using the simulated sensor at a certain height.)
passing one or more points in the one or more metric regions through a point pillars network comprising at least one of: a point net and a cell-wise max-pooling (In paragraph [0042], Goforth discloses the geometric information in the input point cloud, and uses max pooling on F to get a global feature g, which captures the most important information.)
generating a cell-wise and factorized gaussian distribution for the one or more traversability features based on the point pillars network with the one or more points in the one or more metric regions, using the ML model (In paragraph [0042], Goforth discloses that the deep neural network model creates a simple distribution for the traversability features using the point pillars network and points in specific areas.)
Fan in view of Meng, Manivasagam and Goforth are analogous pieces of art because both references concern the method of navigation of robots. . Accordingly, 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 Goforth, with generating an estimated pose and shape of the object based on the code as taught by Goforth. The motivation for doing so would have been to improve both the accuracy and efficiency of shape estimation and/or pose estimation based on sensor data (See [0068] of Goforth.)
Claims 7, 14 and 20 are rejected under 35 U.S.C 103 as being unpatentable over Fan in view of Meng, Manivasagam and further in view of AMBRUS et al. (US Pub No.: 20230177850 A1), hereinafter referred to as AMBRUS
Regarding claim 7, Fan in view of Meng and Manivasagam disclose elements of claim 1. Fan in view of Meng and Manivasagam does not explicitly disclose:
The ML-based method of claim 1, further comprising re-training, by the one or more hardware processors, the ML model for the one or more robot devices based on at least one of: static traversability estimation at an execution time, changing of cost function, and one or more user requirements
However, AMBRUS disclose the limitation (In paragraph [0077], AMBRUS discloses that during the training phase of the monocular depth prediction network, a loss function helps improve the depth predictions based on a true depth map. This loss function is adjusted to include depth uncertainty. These changes allow the network to better estimate how uncertain each depth prediction is. In this process, the predicted depth map and the depth uncertainty are given as outputs from the network to a 3D object detection system.)
Fan in view of Meng, Manivasagam and AMBRUS are analogous pieces of art because both references concern the method of navigating autonomous agents (e.g., vehicles, robots, etc.) through one or more uncertainty regions, with detecting objects and accurately locating them in three-dimensional space as taught by AMBRUS. The motivation for doing so would have been to improve a 3D object detection network (See [0081] of AMBRUS).
Regarding claim 14 Fan in view of Meng and Manivasagam disclose elements of claim 8. Fan in view of Meng and Manivasagam does not explicitly disclose:
The ML-based system of claim 8, further comprising a re-training subsystem configured to re-train the ML model for the one or more robot devices based on at least one of: static traversability estimation at an execution time, changing of cost function, and one or more user requirements
However, AMBRUS disclose the limitation (In paragraph [0077], AMBRUS discloses that during the training phase of the monocular depth prediction network, a loss function helps improve the depth predictions based on a true depth map. This loss function is adjusted to include depth uncertainty. These changes allow the network to better estimate how uncertain each depth prediction is. In this process, the predicted depth map and the depth uncertainty are given as outputs from the network to a 3D object detection system)
Fan in view of Meng, Manivasagam and AMBRUS are analogous pieces of art because both references concern the method of navigating autonomous agents (e.g., vehicles, robots, etc.) through one or more uncertainty regions, with detecting objects and accurately locating them in three-dimensional space as taught by AMBRUS. The motivation for doing so would have been to improve a 3D object detection network (See [0081] of AMBRUS).
Regarding claim 20 Fan in view of Meng and Manivasagam disclose elements of claim 15. Fan in view of Meng and Manivasagam does not explicitly disclose:
The non-transitory computer-readable storage medium of claim 15,further comprising retraining the ML model for the one or more robot devices based on at least one of: static traversability estimation at an execution time, changing of cost function, and one or more user requirements
However, AMBRUS disclose the limitation (In paragraph [0077], AMBRUS discloses that during the training phase of the monocular depth prediction network, a loss function helps improve the depth predictions based on a true depth map. This loss function is adjusted to include depth uncertainty. These changes allow the network to better estimate how uncertain each depth prediction is. In this process, the predicted depth map and the depth uncertainty are given as outputs from the network to a 3D object detection system)
Fan in view of Meng, Manivasagam and AMBRUS are analogous pieces of art because both references concern the method of navigating autonomous agents (e.g., vehicles, robots, etc.) through one or more uncertainty regions, with detecting objects and accurately locating them in three-dimensional space as taught by AMBRUS. The motivation for doing so would have been to improve a 3D object detection network (See [0081] of AMBRUS).
Claims 13 and 19 are rejected under 35 U.S.C 103 as being unpatentable over Fan in view of Meng, Manivasagam and further in view of TOKMAKOV et al. (US Pub No.: 20240269844 A1), hereinafter referred to as TOKMAKOV.
Regarding claim 13, Fan in view of Meng and Manivasagam disclose elements of claim 8. Fan in view of Meng and Manivasagam does not explicitly disclose:
The ML-based system of claim 8, wherein the traversability predicting subsystem is further configured to: analyze the traversability as probability that the predicted one or more traversability features are below to critical threshold values
analyze the traversability with the uncertainty object estimation when the predicted one or more traversability features are exceeded to the critical threshold values
However, TOKMAKOV disclose the limitation:
The ML-based system of claim 8, wherein the traversability predicting subsystem is further configured to: analyze the traversability as probability that the predicted one or more traversability features are below to critical threshold values (In paragraph [0076], TOKMAKOV disclose the model can predict trajectory from a previous application of motion prediction model using a mathematical notation, wherein the estimated point must be less than the threshold "y".)
analyze the traversability with the uncertainty object estimation when the predicted one or more traversability features are exceeded to the critical threshold values (In paragraph [0089], TOKMAKOV discloses that the models' predicted trajectory estimated is greater than or equal to the threshold.)
Fan in view of Meng, Manivasagam and TOKMAKOV are analogous pieces of art because both references concern the method of navigating autonomous agents (e.g., vehicles, robots, etc.) through one or more uncertainty regions, with identifying one or more uncertainty regions in an environment based on an estimated trajectory of an object in the environment as taught by TOKMAKOV. The motivation for doing so would have been to improve of an accuracy of objection motion predictions and may also improve a precision and efficiency of agent-object interactions as taught by TOKMAKOV (See [0025] of TOKMAKOV.)
Regarding claim 19, Fan in view of Meng and Manivasagam disclose elements of claim 15. Fan in view of Meng and Manivasagam does not explicitly disclose:
The non-transitory computer-readable storage medium of claim 15, further comprising at least one of: analyzing the traversability as probability that the predicted one or more traversability features are below to critical threshold values
analyzing the traversability with the uncertainty object estimation when the predicted one or more traversability features are exceeded to the critical threshold values
However, TOKMAKOV discloses the limitation:
The non-transitory computer-readable storage medium of claim 15, further comprising at least one of: analyzing the traversability as probability that the predicted one or more traversability features are below to critical threshold values (In paragraph [0076], TOKMAKOV disclose the model can predict trajectory from a previous application of motion prediction model using a mathematical notation, wherein the estimated point must be less than the threshold "y".)
analyzing the traversability with the uncertainty object estimation when the predicted one or more traversability features are exceeded to the critical threshold values (In paragraph [0089], TOKMAKOV discloses that the models' predicted trajectory estimated is greater than or equal to the threshold.)
Fan in view of Meng, Manivasagam and TOKMAKOV are analogous pieces of art because both references concern the method of navigating autonomous agents (e.g., vehicles, robots, etc.) through one or more uncertainty regions, with identifying one or more uncertainty regions in an environment based on an estimated trajectory of an object in the environment as taught by TOKMAKOV. The motivation for doing so would have been to improve of an accuracy of objection motion predictions and may also improve a precision and efficiency of agent-object interactions as taught by TOKMAKOV (See [0025] of TOKMAKOV.)
Response to Arguments
Applicant's arguments filed on 05/26/2026 have been fully considered, and in part are persuasive in combination of applicants amendments and arguments.
Pertaining to Rejection under 101
Applicant’s argument in regard to 101 is persuasive and rejection is withdrawn.
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection,
Conclusion
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
