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 .
Priority
Applicant’s claim for domestic benefit under 35 U.S.C. 119(e) is acknowledged.
Information Disclosure Statement
The information disclosure statement submitted has been considered by the Examiner and made of record in the application file.
Election/Restrictions
Applicant’s election without traverse of group I in the reply filed on 8/5/2026 is acknowledged. Therefore, claims 1-7 and 15-27 are pending and under examination.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent Nos. 11308338, 11170299, 11769052, 11790230, and 12093824 in view of Alaniz et al. USPN 2016/0210775. Although the claims at issue are not identical, they are not patentably distinct from each other because it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter to arrive at the more broadly claimed instant invention. See comparison table below.
Claim 1:
Instant application
Pat # 11308338
Comments
1. A method comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
1. A method comprising: applying, to a neural network, first data representative of an image of a field of view of an image sensor and second data representative of a distortion map generated to correspond to a field-of-view of the image sensor, the neural network trained based at least in part on ground truth information generated using at least one of a LIDAR sensor or a RADAR sensor; computing, using the neural network and based at least in part on the first data and the second data, third data representative of one or more depth values corresponding to the image;
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
determining one or more pixels of the image that correspond to an object depicted in the image; and associating, with the object, a depth value of the one or more depth values that corresponds to the one or more pixels.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 2-7 are obvious over claims 2-20 of Patent # 11308338.
Claim 15:
Instant application
Pat # 11308338
Comments
15. One or more processors comprising processing circuitry to perform operations comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more objects,
20. A system comprising: an image sensor to generate first data representative of an image of an environment; a computing device including one or more processing devices and one or more memory devices communicatively coupled to the one or more processing devices and storing programmed instructions thereon that, when executed using the one or more processing devices, cause the instantiation of: a depth determiner to compute depth values using a neural network and based at least in part on the first data and second data representative of a distortion map generated to correspond to a field-of-view of the image sensor, the neural network trained using at least one of LIDAR data or RADAR data as ground truth; an object assigner to assign one or more of the depth values to one or more objects depicted in the image based at least in part on third data representative of one or more bounding shapes corresponding to the one or more objects; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment generated, at least in part, using one or more ray-tracing techniques.
a control component to perform one or more operations associated with control of the vehicle based at least in part on the one or more depth values and the third data.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 16-20 are obvious over claims 2-20 of Patent # 11308338.
Claim 21:
Instant application
Pat # 11308338
Comments
21. (New) A system comprising: one or more processors to: apply simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
20. A system comprising: an image sensor to generate first data representative of an image of an environment; a computing device including one or more processing devices and one or more memory devices communicatively coupled to the one or more processing devices and storing programmed instructions thereon that, when executed using the one or more processing devices, cause the instantiation of: a depth determiner to compute depth values using a neural network and based at least in part on the first data and second data representative of a distortion map generated to correspond to a field-of-view of the image sensor, the neural network trained using at least one of LIDAR data or RADAR data as ground truth; an object assigner to assign one or more of the depth values to one or more objects depicted in the image based at least in part on third data representative of one or more bounding shapes corresponding to the one or more objects; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
a control component to perform one or more operations associated with control of the vehicle based at least in part on the one or more depth values and the third data.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 22-27 are obvious over claims 2-20 of Patent # 11308338.
Claim 1:
Instant application
Pat # 11170299
Comments
1. A method comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
1. A method comprising: applying image data representative of a field of view of an image sensor to a deployed neural network; computing, using the deployed neural network and based at least in part on the image data, a depth map comprising first depth data corresponding to one or more objects in the field of view and second depth data corresponding to a free-space boundary associated with the field of view; associating the first depth data with the one or more objects and associating the second depth data with the free-space boundary; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
performing one or more operations by an ego-vehicle based at least in part on the first depth data and the second depth data.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 2-7 are obvious over claims 2-19 of Patent # 11170299.
Claim 15:
Instant application
Pat # 11170299
Comments
15. One or more processors comprising processing circuitry to perform operations comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more objects,
1. A method comprising: applying image data representative of a field of view of an image sensor to a deployed neural network; computing, using the deployed neural network and based at least in part on the image data, a depth map comprising first depth data corresponding to one or more objects in the field of view and second depth data corresponding to a free-space boundary associated with the field of view; associating the first depth data with the one or more objects and associating the second depth data with the free-space boundary; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment generated, at least in part, using one or more ray-tracing techniques.
performing one or more operations by an ego-vehicle based at least in part on the first depth data and the second depth data.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 16-20 are obvious over claims 2-19 of Patent # 11170299.
Claim 21:
Instant application
Pat # 11170299
Comments
21. (New) A system comprising: one or more processors to: apply simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
1. A method comprising: applying image data representative of a field of view of an image sensor to a deployed neural network; computing, using the deployed neural network and based at least in part on the image data, a depth map comprising first depth data corresponding to one or more objects in the field of view and second depth data corresponding to a free-space boundary associated with the field of view; associating the first depth data with the one or more objects and associating the second depth data with the free-space boundary; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
performing one or more operations by an ego-vehicle based at least in part on the first depth data and the second depth data.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 21-27 are obvious over claims 2-19 of Patent # 11170299.
Claim 1:
Instant application
Pat # 11769052
Comments
1. A method comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
1. A processor comprising: processing circuitry to: compute, using a deep neural network (DNN) and based at least in part on sensor data generated using one or more sensors of an ego-machine, first data representative of first depth values corresponding to one or more objects and second data representative of second depth values corresponding to one or more free-space boundaries; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
perform one or more operations for controlling the ego-machine based at least in part on the first depth values and the second depth values.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 2-7 are obvious over claims 2-20 of Patent # 11769052.
Claim 15:
Instant application
Pat # 11769052
Comments
15. One or more processors comprising processing circuitry to perform operations comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more objects,
1. A processor comprising: processing circuitry to: compute, using a deep neural network (DNN) and based at least in part on sensor data generated using one or more sensors of an ego-machine, first data representative of first depth values corresponding to one or more objects and second data representative of second depth values corresponding to one or more free-space boundaries; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment generated, at least in part, using one or more ray-tracing techniques.
perform one or more operations for controlling the ego-machine based at least in part on the first depth values and the second depth values.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 15-20 are obvious over claims 2-20 of Patent # 11769052.
Claim 21:
Instant application
Pat # 11769052
Comments
21. (New) A system comprising: one or more processors to: apply simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
1. A processor comprising: processing circuitry to: compute, using a deep neural network (DNN) and based at least in part on sensor data generated using one or more sensors of an ego-machine, first data representative of first depth values corresponding to one or more objects and second data representative of second depth values corresponding to one or more free-space boundaries; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
perform one or more operations for controlling the ego-machine based at least in part on the first depth values and the second depth values.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 22-27 are obvious over claims 2-20 of Patent # 11769052.
Claim 1:
Instant application
Pat # 11790230
Comments
1. A method comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
1. A processor comprising: processing circuitry to: compute, using one or more machine learning models and based at least in part on image data representative of an image generated using one or more image sensors of an ego-machine, data indicative of one or more depth values corresponding to one or more pixels of an image;
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
determine at least one pixel of the one or more pixels that corresponds to a location of one or more bounding shapes associated with an object depicted in the image; determine an associated depth value for the object based at least in part on a depth value of the one or more depth values that corresponds to the at least one pixel; and perform one or more operations for controlling the ego-machine through an environment based at least in part on the associated depth value for the object.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 2-7 are obvious over claims 2-20 of Patent # 11790230.
Claim 15:
Instant application
Pat # 11790230
Comments
15. One or more processors comprising processing circuitry to perform operations comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more objects,
9. A system comprising: one or more processors comprising processing circuitry to: compute at least a first location of a first bounding shape and a second location of a second bounding shape; determine, using a clustering algorithm and based at least in part on the first location and the second location, that the first bounding shape and the second bounding shape correspond to a same object; generate, based at least in part on the determination, a final bounding shape based at least in part on the first bounding shape and the second bounding shape; compute, using one or more machine learning models and based at least in part on an image generated using one or more image sensors of an ego-machine, one or more depth values corresponding to one or more pixels of the image;
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment generated, at least in part, using one or more ray-tracing techniques.
associate at least one depth value of the one or more depth values with the final bounding shape based at least in part on the at least one depth value corresponding to a pixel of the one or more pixels that corresponds to the final bounding shape; and perform one or more operations for controlling the ego-machine based at least in part on the final bounding shape and the at least one depth value.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 16-20 are obvious over claims 2-20 of Patent # 11790230.
Claim 21:
Instant application
Pat # 11790230
Comments
21. (New) A system comprising: one or more processors to: apply simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
15. A processor comprising: processing circuitry to control, at least in part, an ego-machine based at least in part on one or more depth values associated with one or more object bounding shapes,
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
the one or more depth values computed using one or more machine learning models and based at least in part on image data generated using an image sensor of the ego-machine and a distortion map corresponding to a difference between at least one parameter of the image sensor and a reference image sensor used to generate training data for training the one or more machine learning models.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 22-27 are obvious over claims 2-20 of Patent # 11790230.
Claim 1:
Instant application
Pat # 12093824
Comments
1. A method comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
1. A method comprising: generating, using one or more machine learning models and based at least on sensor data obtained using one or more sensors of a machine, depth data that classifies one or more points as corresponding to one or more free- space locations within the environment that the machine is capable of navigating safely; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
performing, based at least on the depth data, one or more operations associated with the machine.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 2-7 are obvious over claims 2-10 and 12-21 of Pat # 12093824.
Claim 15:
Instant application
Pat # 12093824
Comments
15. One or more processors comprising processing circuitry to perform operations comprising: applying simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more objects,
10. (Currently Amended) A system comprising: one or more processing units to: determine, using one or more machine learning models and based at least on sensor data obtained using one or more sensors of a machine, location information indicating one or more free-space boundaries that separate one or more drivable locations within an environment from one or more non-drivable locations within the environment; and
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment generated, at least in part, using one or more ray-tracing techniques.
perform, based at least on the location information, one or more operations associated with the machine.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 16-20 are obvious over claims 2-21 of Pat # 12093824.
Claim 21:
Instant application
Pat # 12093824
Comments
21. (New) A system comprising: one or more processors to: apply simulated data to one or more machine learning models to compute data indicating depth information corresponding to one or more free-space boundaries,
19. A processor comprising: one or more processing units to cause performance of one or more control operations associated with a machine based on at least one or more free-space boundaries that separate one or more drivable locations within an environment from one or more non-drivable locations within the environment,
Instantly claimed subject matter is broader.
the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques.
wherein the one or more free-space boundaries are determined using an output of one or more machine learning models generated based at least on the one or more machine learning models processing sensor data obtained using one or more sensors of the machine.
Instantly claimed subject matter is broader. See Alaniz [0026-0027] for ray tracing.
Claims 22-27 are obvious over claims 2-10 and 12-21 of Pat # 12093824.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7 and 15-27 are rejected under 35 U.S.C. 103 as being unpatentable over Alaniz et al. USPN 2016/0210775 in view Barfield JR. et al. USPN 201/0136332.
Consider claim 1, Alaniz discloses a method comprising: applying simulated data to one or more machine learning models to compute data indicating virtual scenarios presented in the virtual environment 150, the user can navigate the virtual vehicle through the virtual environment 150 to test sign and obstacle detection processes, observe autonomous driving process performance, or experiment with switching between autonomous and manual driving modes. The virtual environment 150 may, in real time, present the output… one or more virtual sensors may be positioned within the virtual environment. Each virtual sensor may output virtual sensor data such as camera image data or ray-traced sensor data, depending on the sensor type, to shared memory where it can be accessed and processed in accordance with signal processing code. The data may be processed in a way that reflects the limitations of rear world sensors prior to be output to, e.g., an object detection module. The object detection module may process the simulated sensor data and output information including relative position, size, and object type about any detected objects…”), the simulated data being generated using a simulated environment that is rendered, at least in part, using one or more ray-tracing techniques (see [0027]: “…computing device may provide access to the full raw data output from the sensors via ray tracing. For example, the virtual lidar sensor may output the full point cloud that a real world lidar sensor may output…”).
While Alaniz discloses LIDAR and point cloud, however, does not disclose depth information and free-space boundaries.
In the related field of endeavor, Barfield discloses depth information and free-space boundaries (see [0048]: “…two-dimensional point cloud data set is mapped to the corresponding image data. Although transformed into two dimensional space, because LIDAR data includes information regarding how far away an object surface is from the LIDAR system (i.e., depth), the transformed LIDAR data set may also include depth and direction information for the surfaces of objects represented by pixels of the image via mapping of the two-dimensional point cloud data…”).
Therefore, it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter to combine the virtual vehicle simulator of Alaniz and the depth information of Barfield in order to arrive at the instant recitation and provide optimized autonomous virtual vehicle control simulation.
Consider claim 2 as applied to respective claim, Alaniz as modified discloses computing one or more locations of the one or more free-space boundaries; and associating the data with the one or more free-space boundaries based at least in part on the one or more locations (see [0026]: “…object detection module may process the simulated sensor data and output information including relative position, size, and object type about any detected objects. Detected objects may be displayed using markings and labels overlaid on a simulation window that shows each sensor's point of view…” locations of object and other markings are indicative of “free-space” boundaries, where a vehicle may not drive).
Consider claim 3 as applied to respective claim, Alaniz as modified discloses the computing the one or more locations is executed using at least one of a deep neural network (DNN) or a computer vision algorithm (see Barfield [0009]: “…controller may be configured for providing, or causing the applying of, or applying itself, the image evaluation range data set to a machine leaning model, such as a deep learning algorithm, a convolutional neural network, a neural network, support vector machines (“SVM”), regression, or other similar techniques…”).
Consider claim 4 as applied to respective claim, Alaniz as modified discloses the simulated data corresponds to sensor data obtained using one or more virtual sensors of a simulated machine within the simulated environment (see Alaniz [0026]: “…one or more virtual sensors may be positioned within the virtual environment. Each virtual sensor may output virtual sensor data such as camera image data or ray-traced sensor data, depending on the sensor type, to shared memory where it can be accessed and processed…”).
Consider claim 5 as applied to respective claim, Alaniz as modified discloses the one or more machine learning models further compute data indicating one or more bounding shape locations based at least on the one or more machine learning models processing the simulated data (see [0010]: “…machine learning process may take in these images as input along with the position and bounding box of the road signs in them, generate features using image processing techniques and train classifiers to recognize each sign type…”).
Consider claim 6 as applied to respective claim, Alaniz as modified discloses the applying of the simulated data to the one or more machine learning models is part of testing or validating the one or more machine learning models (see [0014]: “…output of the computing device 110 may include virtual sensor data that may be used for testing purposes, training purposes, or both, and may represent the sensor data collected by virtual sensors as a result of virtually navigating a virtual vehicle through the virtual environment. The virtual sensor data may ultimately be used to generate calibration data that can be uploaded to the vehicle system 105 so that one or more subsystems of the autonomous vehicle 100 (a real-world vehicle) may be calibrated according to the virtual sensor data collected during the testing or training that occurs when navigating the virtual vehicle through the virtual environment…”).
Consider claim 7 as applied to respective claim, Alaniz as modified discloses using at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using a machine; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (see [0025]: “…computing device 110 integrates a virtual driving environment, created using three-dimensional modeling and animation tools, with sensor models to produce the virtual sensor data in large quantities in a relatively short amount of time. Relevant parameters such as lighting and road sign orientation, in the case of sign detection, may be randomized in the recorded data to ensure a diverse dataset with minimal bias…”).
Examiner Note: See detailed rejection analysis of independent claim 1 for the remaining independent claim rejections.
Consider claim 15, Alaniz discloses one or more processors comprising processing circuitry to perform operations comprising: applying simulated data to one or more machine learning models to compute data indicating ray tracing. For example, the virtual lidar sensor may output the full point cloud that a real world lidar sensor may output…”).
While Alaniz discloses LIDAR and point cloud, however, does not disclose depth information and free-space boundaries.
In the related field of endeavor, Barfield discloses depth information and free-space boundaries (see [0048]: “…two-dimensional point cloud data set is mapped to the corresponding image data. Although transformed into two dimensional space, because LIDAR data includes information regarding how far away an object surface is from the LIDAR system (i.e., depth), the transformed LIDAR data set may also include depth and direction information for the surfaces of objects represented by pixels of the image via mapping of the two-dimensional point cloud data…”).
Therefore, it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter to combine the virtual vehicle simulator of Alaniz and the depth information of Barfield in order to arrive at the instant recitation and provide optimized autonomous virtual vehicle control simulation.
Consider claim 16 as applied to respective claim, Alaniz as modified discloses processing circuitry to: compute one or more locations of one or more bounding shapes corresponding to the one more objects; and associate the data with the one or more bounding shapes based at least on the one or more locations (see [0026]: “…object detection module may process the simulated sensor data and output information including relative position, size, and object type about any detected objects. Detected objects may be displayed using markings and labels overlaid on a simulation window that shows each sensor's point of view…” locations of object and other markings are indicative of “free-space” boundaries, where a vehicle may not drive).
Consider claim 17 as applied to respective claim, Alaniz as modified discloses the one or more machine learning models include at least one of a deep neural network (DNN) or a computer vision algorithm (see Barfield [0009]: “…controller may be configured for providing, or causing the applying of, or applying itself, the image evaluation range data set to a machine leaning model, such as a deep learning algorithm, a convolutional neural network, a neural network, support vector machines (“SVM”), regression, or other similar techniques…”).
Consider claim 18 as applied to respective claim, Alaniz as modified discloses the simulated data corresponds to sensor data generated using one or more virtual sensors of a simulated machine located within the simulated environment (see Alaniz [0026]: “…one or more virtual sensors may be positioned within the virtual environment. Each virtual sensor may output virtual sensor data such as camera image data or ray-traced sensor data, depending on the sensor type, to shared memory where it can be accessed and processed…”).
Consider claim 19 as applied to respective claim, Alaniz as modified discloses one or more ray-tracing techniques are executed using one or more ray-tracing hardware accelerators (see [0027]: “…some graphics engines are limited, and the computing device may allow sensor models and signal processing code to interface with high-end graphics engines capable of producing photorealistic camera data with realistic reflections, shadows, textures, and physical irregularities desirable for thorough testing. Additionally, in terms of other sensor types, the computing device may provide access to the full raw data output from the sensors via ray tracing…” specialized hardware are obvious within the scope of Alaniz).
Consider claim 20 as applied to respective claim, Alaniz as modified discloses the simulated data is used to update one or more parameters of the one or more machine learning models during a training process (see [0034]: “…the output data, or an aggregation of output data, may be loaded into the vehicle system 105 as, e.g., calibration data operating in a real-world autonomous vehicle 100. When the calibration data is loaded into the vehicle system 105, the autonomous driving sensors 130 may apply the appropriate settings to properly identify objects under the circumstances…”).
Consider claim 21, Alaniz discloses a system comprising: one or more processors to: apply simulated data to one or more machine learning models to compute data indicating environment that is rendered, at least in part, using one or more ray-tracing techniques (see [0027]: “…computing device may provide access to the full raw data output from the sensors via ray tracing. For example, the virtual lidar sensor may output the full point cloud that a real world lidar sensor may output…”).
While Alaniz discloses LIDAR and point cloud, however, does not disclose depth information and free-space boundaries.
In the related field of endeavor, Barfield discloses depth information and free-space boundaries (see [0048]: “…two-dimensional point cloud data set is mapped to the corresponding image data. Although transformed into two dimensional space, because LIDAR data includes information regarding how far away an object surface is from the LIDAR system (i.e., depth), the transformed LIDAR data set may also include depth and direction information for the surfaces of objects represented by pixels of the image via mapping of the two-dimensional point cloud data…”).
Therefore, it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter to combine the virtual vehicle simulator of Alaniz and the depth information of Barfield in order to arrive at the instant recitation and provide optimized autonomous virtual vehicle control simulation.
Consider claim 22 as applied to respective claim, Alaniz as modified discloses the one or more processors are further to compute one or more locations of the one or more free-space boundaries; and associate the data with the one or more free-space boundaries based at least in part on the one or more locations (see [0026]: “…object detection module may process the simulated sensor data and output information including relative position, size, and object type about any detected objects. Detected objects may be displayed using markings and labels overlaid on a simulation window that shows each sensor's point of view…” locations of object and other markings are indicative of “free-space” boundaries, where a vehicle may not drive).
Consider claim 23 as applied to respective claim, Alaniz as modified discloses the computing the one or more locations is executed using at least one of a deep neural network (DNN) or a computer vision algorithm (see Barfield [0009]: “…controller may be configured for providing, or causing the applying of, or applying itself, the image evaluation range data set to a machine leaning model, such as a deep learning algorithm, a convolutional neural network, a neural network, support vector machines (“SVM”), regression, or other similar techniques…”).
Consider claim 24 as applied to respective claim, Alaniz as modified discloses the simulated data corresponds to sensor data obtained using one or more virtual sensors of a simulated machine within the simulated environment (see Alaniz [0026]: “…one or more virtual sensors may be positioned within the virtual environment. Each virtual sensor may output virtual sensor data such as camera image data or ray-traced sensor data, depending on the sensor type, to shared memory where it can be accessed and processed…”).
Consider claim 25 as applied to respective claim, Alaniz as modified discloses the one or more machine learning models further compute data indicating one or more bounding shape locations based at least on the one or more machine learning models processing the simulated data (see [0010]: “…machine learning process may take in these images as input along with the position and bounding box of the road signs in them, generate features using image processing techniques and train classifiers to recognize each sign type…”).
Consider claim 26 as applied to respective claim, Alaniz as modified discloses the simulated data is applied to the one or more machine learning models as part of testing or validating the one or more machine learning models (see [0014]: “…output of the computing device 110 may include virtual sensor data that may be used for testing purposes, training purposes, or both, and may represent the sensor data collected by virtual sensors as a result of virtually navigating a virtual vehicle through the virtual environment. The virtual sensor data may ultimately be used to generate calibration data that can be uploaded to the vehicle system 105 so that one or more subsystems of the autonomous vehicle 100 (a real-world vehicle) may be calibrated according to the virtual sensor data collected during the testing or training that occurs when navigating the virtual vehicle through the virtual environment…”).
Consider claim 27 as applied to respective claim, Alaniz as modified discloses the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using a machine; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (see [0025]: “…computing device 110 integrates a virtual driving environment, created using three-dimensional modeling and animation tools, with sensor models to produce the virtual sensor data in large quantities in a relatively short amount of time. Relevant parameters such as lighting and road sign orientation, in the case of sign detection, may be randomized in the recorded data to ensure a diverse dataset with minimal bias…”).
Conclusion
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Fayyaz Alam
August 22, 2026
/FAYYAZ ALAM/
Primary Examiner, Art Unit 2646
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