DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10-17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10-17 recites the limitation "The system of claim 9" in line 1. There is insufficient antecedent basis for this limitation in the claim. For purposes of examination, examiner will read claim 9 as “A system comprising…”, examiner recommends amending claim 9 in a similar manner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al (US 20230298263 A1) in view of Towal et al (US 20190384304 A1)
Regarding claim 1, Yang discloses A method ([0017] target object reconstruction in a virtual world. ) comprising:
obtaining sensor data of an environment, the sensor data representing one or more sensor rays being associated with first starting points and ending points within the environment ([0021] autonomous system includes various types of sensors, such as LiDAR sensors amongst other types, which are used to obtain measurements of the real-world environment and cameras that capture images from the real world environment.);
generating, using the at least the portion of the sensor rays that is associated with the driving surface, one or more virtual rays ([0017] particular virtual sensor location (e.g., virtual camera, virtual lidar sensor), ray tracing is performed to simulate the effect of sensor input (e.g., light, lidar) on the target object) that are associated with one or more second starting points within the environment and the one or more ending points ([0017] Each ray has a first endpoint at the virtual sensor and a second endpoint in the virtual world according to the view of the virtual sensor); and
training, based at least on the at least the portion of the sensor rays and the one or more virtual rays, one or more neural networks to perform neural rendering ([0025] The evaluator (110) may be implemented as a data-driven neural network that learns to distinguish between good and bad driving behavior. [0034] pending on the asset representation (e.g., of stationary and nonstationary objects), embodiments may use graphics-based rendering for assets with textured meshes, neural rendering, or a combination of multiple rendering schemes.).
Towal discloses determining at least a portion of the sensor rays that is associated with a driving surface located within the environment, the at least the portion of the sensor rays being associated with one or more ending points from the ending points ([0035] image data captured from this perspective may be useful for perception when navigating—e.g., within a lane, through a lane change, through a turn, through an intersection, etc. - because a forward-facing camera may include a field of view (e.g., the field of view of the forward-facing stereo camera 1168 and/or the wide-view camera 1170 of FIG. 11B) that includes both a current lane of travel of the vehicle 1100, adjacent lane(s) of travel of the vehicle 1100, and/or boundaries of the driving surface.)
Yang and Towal are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify target object reconstruction of Yang to include determining at least a portion of the sensor rays that is associated with a driving surface located within the environment, the at least the portion of the sensor rays being associated with one or more ending points from the ending points as described by Towal
The motivation for doing so would have been for path detection for autonomous machines using deep neural networks (Towal [0006]).
Therefore, it would have been obvious to combine Yang and Towal to obtain the invention as specified in claim 1.
Regarding claim 2, Yang discloses determining one or more classifications associated with the sensor rays ([0035] asset model provides the information used by the simulator to represent and simulate the asset in the simulated environment),
wherein the determining the at least the portion of the sensor rays that is associated with the driving surface is based at least on segmenting the sensor rays using at least one of the one or more classifications ([0035] an asset model may include geometry and bounding volume, the asset's interaction with light at various wavelengths of interest (e.g., visible for camera, infrared for LiDAR, microwave for RADAR), animation information describing deformation (e.g. rigging) or lighting changes (e.g., turn signals), material information such as friction for different surfaces, and metadata such as the asset's semantic class and key points of interest.).
Regarding claim 3, Yang discloses determining one or more parameters associated with generating the one or more virtual rays, the one or more parameters including at least one of a vertical parameter or a horizontal parameter associated with deviating from one or more poses associated with the at least the portion of the sensor rays ([0058] he signed distance function of the NeRSDF model maps a location in three dimensional space to the location's signed distance from the object surface.),
wherein the generating the one or more virtual rays is based at least on the one or more parameters ([0056] he geometry model (402) is a Neural Reflectance signed distance function (NeRSDF) model parameterized by an MLP surface model that models the surface of the target object.).
Regarding claim 4, Yang discloses the vertical parameter includes at least one of a vertical distance or a vertical angle with respect to deviating from the one or more poses ([0059] to prevent the gradient from α(x) to the location signed distance model from vanishing during training, β is set as a learnable parameter that is adjusted during training.); and
the horizontal parameter includes at least one of a horizontal distance or a horizontal angle with respect to deviating from the one or more poses ([0087] a lidar loss accumulated across a subset of lidar rays is calculated using lidar points determined for the lidar rays and sensor data for the same viewing direction and angle as the lidar ray).
Regarding claim 5, Yang is silent to determining, based at least on a first starting point of the first starting points that is associated with the sensor ray, a second starting point of the one or more second starting points associated with the virtual ray;
determining that the sensor ray is associated with an ending point of the one or more ending points; and
generating the virtual ray to project from the second starting point to the ending point.
Towal discloses determining, based at least on a first starting point of the first starting points that is associated with the sensor ray, a second starting point of the one or more second starting points associated with the virtual ray ([0130] anchor points 506 and/or anchor lines 508 may have static pixel positions that may only change to a same relative location after augmentation (e.g., up-scaling, down-scaling, cropping, rotating, shifting, etc.) of an image(s));
determining that the sensor ray is associated with an ending point of the one or more ending points ([0118] the ground truth data 504 may include anchor point(s) 506, anchor line(s) 508, path label(s) 510, and/or path type(s) 512 (e.g., encoded to correspond to one or more of the anchor points 506 and/or anchor lines 508); and
generating the virtual ray to project from the second starting point to the ending point ([0118] ages captured from within a virtual environment used for testing and/or generating training images (e.g., a virtual camera of a virtual vehicle within a virtual or simulated environment).)
Yang and Towal are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify target object reconstruction of Yang to include determining, based at least on a first starting point of the first starting points that is associated with the sensor ray, a second starting point of the one or more second starting points associated with the virtual ray; determining that the sensor ray is associated with an ending point of the one or more ending points; and generating the virtual ray to project from the second starting point to the ending point as described by Towal
The motivation for doing so would have been for path detection for autonomous machines using deep neural networks (Towal [0006]).
Therefore, it would have been obvious to combine Yang and Towal to obtain the invention as specified in claim 5.
Regarding claim 6, Yang discloses sampling a batch of sensor rays, a first percentage of the batch of sensor rays including the at least the portion of the sensor rays and a second percentage of the batch of sensor rays including the one or more virtual rays, the second percentage being less than the first percentage ([0087] The observed lidar point is compared to the simulated color value calculated using the operations of FIG. 6. Specifically, the difference between the depths in the observed lidar point value and the simulated depth in the simulated lidar point value is calculated as the depth difference),
wherein the training the one or more neural networks is based at least on the batch of sensor rays ([0091] training may be performed using ray batches or batches of rays).
Regarding claim 7, Yang discloses wherein the method further comprising:
generating, for at least a second portion of the sensor rays that is associated with the driving surface located within the environment, one or more second virtual rays that are associated with one or more third starting points within the environment and one or more second ending points from the ending points ([0044] The simulated sensor data that is output by the sensor simulation model (114) may be in or converted to the exact message format that the virtual driver takes as input as if the virtual driver were in the real world, and the virtual driver can then run as a black box virtual driver with the simulated latencies incorporated for components that run sequentially); and
further training, during a second iteration and based at least on the at least the second portion of the sensor rays and the one or more second virtual rays, the one or more neural networks ([0049] during training, the evaluator provides feedback to the virtual driver. Thus, the parameters of the virtual driver are updated to improve performance of the virtual driver in a variety of scenarios).
Regarding claim 8, Yang discloses determining one or more poses associated with one or more image sensors within the environment ([0043] the scenario specification (140) may describe the initial state of the scene, such as the current state of autonomous system (e.g., the full 6D pose, velocity and acceleration),); and
generating, using the one or more neural networks and based at least on the one or more poses, one or more virtual scenes associated with the environment, the one or more virtual scenes depicting at least the driving surface ([0046] generate camera output and lidar sensor output, respectively, for a virtual camera and a virtual lidar sensor,).
Regarding claim 9, Yang discloses a method ([0017] target object reconstruction in a virtual world. ) comprising:
one or more processors ([0093] computing system (900) may include one or more computer processors) to:
d
generate one or more virtual outputs [0017] particular virtual sensor location (e.g., virtual camera, virtual lidar sensor), ray tracing is performed to simulate the effect of sensor input (e.g., light, lidar) on the target object) that are associated with one or more second starting points within the environment and the one or more ending points ([0017] Each ray has a first endpoint at the virtual sensor and a second endpoint in the virtual world according to the view of the virtual sensor);
and update, based at least on the one or more real outputs and the one or more virtual outputs, one or more neural networks that are associated with neural rendering ([0025] The evaluator (110) may be implemented as a data-driven neural network that learns to distinguish between good and bad driving behavior. [0034] pending on the asset representation (e.g., of stationary and nonstationary objects), embodiments may use graphics-based rendering for assets with textured meshes, neural rendering, or a combination of multiple rendering schemes.).
Towal discloses determine at least a portion of sensor data that is associated with a surface located within an environment, the at least the portion of the sensor data representing one or more real outputs being associated with one or more first starting points and one or more ending points within the environment ([0035] image data captured from this perspective may be useful for perception when navigating—e.g., within a lane, through a lane change, through a turn, through an intersection, etc. - because a forward-facing camera may include a field of view (e.g., the field of view of the forward-facing stereo camera 1168 and/or the wide-view camera 1170 of FIG. 11B) that includes both a current lane of travel of the vehicle 1100, adjacent lane(s) of travel of the vehicle 1100, and/or boundaries of the driving surface.)
Yang and Towal are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify target object reconstruction of Yang to include determine at least a portion of sensor data that is associated with a surface located within an environment, the at least the portion of the sensor data representing one or more real outputs being associated with one or more first starting points and one or more ending points within the environment as described by Towal
The motivation for doing so would have been for path detection for autonomous machines using deep neural networks (Towal [0006]).
Therefore, it would have been obvious to combine Yang and Towal to obtain the invention as specified in claim 9.
Regarding claim 10, Yang discloses determine classifications associated with the sensor data , the classifications including at least a first classification associated with the surface and one or more second classifications associated with one or more second surfaces located within the environment ([0035] an asset model may include geometry and bounding volume, the asset's interaction with light at various wavelengths of interest (e.g., visible for camera, infrared for LiDAR, microwave for RADAR), animation information describing deformation (e.g. rigging) or lighting changes (e.g., turn signals), material information such as friction for different surfaces, and metadata such as the asset's semantic class and key points of interest.).,
wherein the at least the portion of the sensor data is determined based at least on the classifications ([0035] asset model provides the information used by the simulator to represent and simulate the asset in the simulated environment)
Regarding claim 11, Yang discloses determine one or more parameters associated with generating the one or more virtual outputs, the one or more parameters including at least one or more distances or one or more angles,
wherein the one or more virtual outputs are generated based at least on the one or more parameters.
Regarding claim 12, Yang discloses the one or more distances include at least a maximum distance for deviating from the one or more first starting points to generate the one or more second starting points ([0056] he geometry model (402) is a Neural Reflectance signed distance function (NeRSDF) model parameterized by an MLP surface model that models the surface of the target object.).; and
the one or more angles include at least a maximum angle for deviating from one or more first angles associated with the one or more real outputs to generate one or more second angles associated with the one or more virtual outputs ([0058] he signed distance function of the NeRSDF model maps a location in three dimensional space to the location's signed distance from the object surface.),
Regarding claim 13, Yang is silent to determining, based at least on a first starting point of the one or more first starting points that is associated with the real output, a second starting point of the one or more second starting points associated with the virtual output;
determining that the real output is associated with an ending point of the one or more ending points; and
generating the virtual output to project from the second starting point to the ending point.
Towal discloses determining, based at least on a first starting point of the one or more first starting points that is associated with the real output, a second starting point of the one or more second starting points associated with the virtual output ([0130] anchor points 506 and/or anchor lines 508 may have static pixel positions that may only change to a same relative location after augmentation (e.g., up-scaling, down-scaling, cropping, rotating, shifting, etc.) of an image(s));
determining that the real output is associated with an ending point of the one or more ending points ([0118] the ground truth data 504 may include anchor point(s) 506, anchor line(s) 508, path label(s) 510, and/or path type(s) 512 (e.g., encoded to correspond to one or more of the anchor points 506 and/or anchor lines 508); and
generating the virtual output to project from the second starting point to the ending point ([0118] ages captured from within a virtual environment used for testing and/or generating training images (e.g., a virtual camera of a virtual vehicle within a virtual or simulated environment).).
Yang and Towal are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify target object reconstruction of Yang to include determining, based at least on a first starting point of the one or more first starting points that is associated with the real output, a second starting point of the one or more second starting points associated with the virtual output; determining that the real output is associated with an ending point of the one or more ending points; and generating the virtual output to project from the second starting point to the ending point as described by Towal
The motivation for doing so would have been for path detection for autonomous machines using deep neural networks (Towal [0006]).
Therefore, it would have been obvious to combine Yang and Towal to obtain the invention as specified in claim 13
Regarding claim 14, Yang discloses wherein the one or more processors are further to:
sampling a batch of outputs, a first percentage of the batch of outputs including the one or more real outputs and a second percentage of the batch of output including the one or more virtual outputs, the second percentage being different than the first percentage ([0087] The observed lidar point is compared to the simulated color value calculated using the operations of FIG. 6. Specifically, the difference between the depths in the observed lidar point value and the simulated depth in the simulated lidar point value is calculated as the depth difference),
wherein the one or more neural networks are updated based at least on the batch of outputs ([0091] training may be performed using ray batches or batches of rays).
Regarding claim 15, Yang discloses determine at least a second portion of the sensor data that is also associated with the surface, the at least the second portion of the sensor data representing one or more second real outputs being associated with one or more third starting points and one or more second ending points within the environment ([0044] The simulated sensor data that is output by the sensor simulation model (114) may be in or converted to the exact message format that the virtual driver takes as input as if the virtual driver were in the real world, and the virtual driver can then run as a black box virtual driver with the simulated latencies incorporated for components that run sequentially);
generate one or more second virtual outputs associated with one or more fourth starting points within the environment and the one or more second ending points ([0089] for location xi with surface normal ni, the symmetrized point and surface normal are xi′ and ni′. And s(xi) is the predicted signed distance at location xi, a(xi) is the predicted diffuse albedo at location xi, as(xi) is the predicted specular albedo at location xi, γ(xi) is the predicted material shininess at location xi.); and
further update, based at least on the one or more second real outputs and the one or more second virtual outputs, the one or more neural networks ([0049] during training, the evaluator provides feedback to the virtual driver. Thus, the parameters of the virtual driver are updated to improve performance of the virtual driver in a variety of scenarios).
Regarding claim 16, Yang discloses determine one or more poses associated with one or more image sensors within the environment ([0043] the scenario specification (140) may describe the initial state of the scene, such as the current state of autonomous system (e.g., the full 6D pose, velocity and acceleration),); and
generate, using the one or more neural networks and based at least on the one or more poses, one or more virtual scenes associated with the environment, the one or more virtual scenes depicting at least the surface ([0046] generate camera output and lidar sensor output, respectively, for a virtual camera and a virtual lidar sensor,)..
Regarding claim 17, Yang discloses wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine ([0020] An autonomous system is a self-driving mode of transportation);
a perception system for an autonomous or semi-autonomous machine ([0020] the autonomous system includes a virtual driver that is the decision making portion of the autonomous system.);
a system for performing one or more simulation operations ([0022] a simulator (100) is configured to train and test a virtual driver);
a system for performing one or more digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system that provides one or more cloud gaming applications;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system implemented using a robot ([0020] of autonomous systems include self-driving vehicles (e.g., self-driving trucks and cars), drones, airplanes, robots, etc);
a system for performing one or more generative AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing operations using one or more multi-modal language models;
a system for performing one or more conversational AI operations;
a system for generating synthetic data ([0020] generating a simulated environment for training and testing of autonomous systems.);
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
systems using or deploying one or more inference microservices;
systems that incorporate one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center ([0093] Any combination of mobile, desktop, server); or
a system implemented at least partially using cloud computing resources ([0094] a wide area network (WAN) such as the Internet, mobile network, or any other type of network) and/or to another device, such as another computing device.).
Regarding claim 18, Yang discloses one or more processors ([0093] computing system (900) may include one or more computer processors) comprising:
processing circuitry to:
update one or more neural networks that are associated with reconstructing an environment based at least on a batch of outputs associated with the environment ([0025] The evaluator (110) may be implemented as a data-driven neural network that learns to distinguish between good and bad driving behavior. [0034] pending on the asset representation (e.g., of stationary and nonstationary objects), embodiments may use graphics-based rendering for assets with textured meshes, neural rendering, or a combination of multiple rendering schemes.),
wherein the batch of outputs includes at least one or more sensor outputs that are associated with one or more first starting points and one or more ending points within the environment and one or more virtual outputs that are associated with one or more second starting points and the one or more ending points within the environment.
Towal discloses wherein the batch of outputs includes at least one or more sensor outputs that are associated with one or more first starting points and one or more ending points within the environment and one or more virtual outputs that are associated with one or more second starting points and the one or more ending points within the environment ([0035] image data captured from this perspective may be useful for perception when navigating—e.g., within a lane, through a lane change, through a turn, through an intersection, etc. - because a forward-facing camera may include a field of view (e.g., the field of view of the forward-facing stereo camera 1168 and/or the wide-view camera 1170 of FIG. 11B) that includes both a current lane of travel of the vehicle 1100, adjacent lane(s) of travel of the vehicle 1100, and/or boundaries of the driving surface.)
Yang and Towal are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify target object reconstruction of Yang to include wherein the batch of outputs includes at least one or more sensor outputs that are associated with one or more first starting points and one or more ending points within the environment and one or more virtual outputs that are associated with one or more second starting points and the one or more ending points within the environment as described by Towal
The motivation for doing so would have been for path detection for autonomous machines using deep neural networks (Towal [0006]).
Therefore, it would have been obvious to combine Yang and Towal to obtain the invention as specified in claim 18.
Regarding claim 19, Yang is silent to wherein the processing circuitry is to generate the one or more virtual outputs using the one or more sensor outputs, at least a virtual output of the one or more virtual outputs generated, at least, by:
determining, based at least on a first starting point of the one or more first starting points that is associated with a sensor output of the one or more sensor outputs, a second starting point of the one or more second starting points associated with the virtual output;
determining that the real output is associated with an ending point of the one or more ending points; and
generating the virtual output to project from the second starting point to the ending point.
Towal discloses determining, based at least on a first starting point of the one or more first starting points that is associated with a sensor output of the one or more sensor outputs, a second starting point of the one or more second starting points associated with the virtual output ([0130] anchor points 506 and/or anchor lines 508 may have static pixel positions that may only change to a same relative location after augmentation (e.g., up-scaling, down-scaling, cropping, rotating, shifting, etc.) of an image(s));
determining that the real output is associated with an ending point of the one or more ending points ([0118] the ground truth data 504 may include anchor point(s) 506, anchor line(s) 508, path label(s) 510, and/or path type(s) 512 (e.g., encoded to correspond to one or more of the anchor points 506 and/or anchor lines 508); and
generating the virtual output to project from the second starting point to the ending point ([0118] ages captured from within a virtual environment used for testing and/or generating training images (e.g., a virtual camera of a virtual vehicle within a virtual or simulated environment).).
Yang and Towal are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify target object reconstruction of Yang to include wherein the processing circuitry is to generate the one or more virtual outputs using the one or more sensor outputs, at least a virtual output of the one or more virtual outputs generated, at least, by: determining, based at least on a first starting point of the one or more first starting points that is associated with a sensor output of the one or more sensor outputs, a second starting point of the one or more second starting points associated with the virtual output; determining that the real output is associated with an ending point of the one or more ending points; and generating the virtual output to project from the second starting point to the ending point as described by Towal
The motivation for doing so would have been for path detection for autonomous machines using deep neural networks (Towal [0006]).
Therefore, it would have been obvious to combine Yang and Towal to obtain the invention as specified in claim 19
Regarding claim 20, Yang discloses wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine ([0020] An autonomous system is a self-driving mode of transportation);
a perception system for an autonomous or semi-autonomous machine ([0020] the autonomous system includes a virtual driver that is the decision making portion of the autonomous system.);
a system for performing one or more simulation operations ([0022] a simulator (100) is configured to train and test a virtual driver);
a system for performing one or more digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system that provides one or more cloud gaming applications;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system implemented using a robot ([0020] of autonomous systems include self-driving vehicles (e.g., self-driving trucks and cars), drones, airplanes, robots, etc);
a system for performing one or more generative AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing operations using one or more multi-modal language models;
a system for performing one or more conversational AI operations;
a system for generating synthetic data ([0020] generating a simulated environment for training and testing of autonomous systems.);
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
systems using or deploying one or more inference microservices;
systems that incorporate one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center ([0093] Any combination of mobile, desktop, server); or
a system implemented at least partially using cloud computing resources ([0094] a wide area network (WAN) such as the Internet, mobile network, or any other type of network) and/or to another device, such as another computing device.).
Conclusion
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/SHIVANG I PATEL/Primary Examiner, Art Unit 2615
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