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 .
Status of the Claims
Claims 1-20, as amended, are currently pending and have been considered below.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5-9, 11, 12 and 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Romero, Alberto, “Tesla AI Day 2021 Review- Part 2: Training Data. How Does a Car Learn?” Towards Data Science, Sept 22, 2021, hereinafter, “Romero”.
As per claim 1, Romero discloses a method comprising:
inputting, by a processor using one or more cameras of an ego object, image data of a space around the ego object into an artificial intelligence model (Romero, pages 4-5, The challenge of unusual scenarios, vector space predictions ... vector space datasets ... use their car fleet to do auto labeling. The idea is to have the cars drive around - either on Autopilot or not - capturing videos that are then sent to a supercomputer that labels them for further training; Romero, page 4, 1. Manual labeling - From 2D images to 4D vectors, vector space is built from the eight cameras and multiple frames ... a pipeline for auto labeling; Romero, page 5, 2. Auto labeling - Humans out-of-the-loop, for the most part the cars could train without human intervention. To generate auto labeled data they ideated a three-step pipeline: Capturing clips: They collect a clip that consists of different pieces of data - videos, IMU, GPS, odometry, etc. The cars drive around, capturing the data as they would normally but this time with the intention of using it for further training ... Feature extraction: This data is passed through offline NNs that extract different types of intermediate features - depth, segmentation masks, point matching, etc. ... Labeling: The intermediate features are passed through other algorithms that generate the labels for the data; Romero, pages 5-6, 2. Auto labeling - Human out-of-the-loop, Let's imagine that our Tesla car passes by a pedestrian. As it moves forward, the front, lateral, and back cameras capture the person and generate single-cam videos from different angles. These videos can then be passed through a neural network that generates a vector space representation of the environment and the pedestrian);
predicting, by the processor executing the artificial intelligence model, a surface attribute of one or more surfaces of the space around the ego object (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view); and
generating, by the processor, a dataset based on the one or more surfaces and their corresponding surface attribute (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road. A car could then sweep out many miles, generating data on the go that could be used to label entire areas ... After the labels are generated for the surface, … a human could go into the loop and check them to correct noise).
As per claim 5, Romero discloses the method of claim 1, wherein the dataset is a queryable dataset configured to transmit the surface attribute to an autonomous driving protocol of the ego object (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road; Romero, pages 4-5, The challenge of unusual scenarios, vector space predictions ... vector space datasets ... use their car fleet to do auto labeling. The idea is to have the cars drive around - either on Autopilot or not - capturing videos).
As per claim 6, Romero discloses the method of claim 1, wherein the artificial intelligence model is trained using a sensor attribute of the one or more surfaces (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road).
As per claim 7, Romero discloses the method of claim 1, wherein the ego object is an autonomous vehicle executing a driving protocol based on the dataset (Romero, pages 4-5, The challenge of unusual scenarios, vector space predictions ... vector space datasets ... use their car fleet to do auto labeling. The idea is to have the cars drive around - either on Autopilot or not - capturing videos that are then sent to a supercomputer that labels them for further training; Romero, page 4, 1. Manual labeling - From 2D images to 4D vectors, vector space is built from the eight cameras and multiple frames ... a pipeline for auto labeling).
As per claim 8, Romero discloses the method of claim 1, wherein the surface attribute corresponds to an elevation (Romero, page 4, 1. Manual labeling - From 2D images to 4D vectors, vector space is built from the eight cameras and multiple frames ... a pipeline for auto labeling; Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road).
As per claim 9, Romero discloses the method of claim 1, wherein the surface attribute indicates whether the one or more surfaces are navigable surfaces (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road).
As per claim 11, Romero discloses the method of claim 1, wherein the surface attribute indicates whether the one or more surfaces correspond to one or more curbs (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks).
As per claim 12, Romero discloses an ego object comprising:
one or more cameras (Romero, page 4, 1. Manual labeling - From 2D images to 4D vectors, vector space is built from the eight cameras and multiple frames);
a processor; and a non-transitory computer-readable medium containing an artificial intelligence model configured to be executed by the processor, wherein the processor is configured to:
input, using the one or more cameras of an ego object, image data of a space around the ego object into the artificial intelligence model (Romero, pages 4-5, The challenge of unusual scenarios, vector space predictions ... vector space datasets ... use their car fleet to do auto labeling. The idea is to have the cars drive around - either on Autopilot or not - capturing videos that are then sent to a supercomputer that labels them for further training; Romero, page 4, 1. Manual labeling - From 2D images to 4D vectors, vector space is built from the eight cameras and multiple frames ... a pipeline for auto labeling; Romero, page 5, 2. Auto labeling - Humans out-of-the-loop, for the most part the cars could train without human intervention. To generate auto labeled data they ideated a three-step pipeline: Capturing clips: They collect a clip that consists of different pieces of data - videos, IMU, GPS, odometry, etc. The cars drive around, capturing the data as they would normally but this time with the intention of using it for further training ... Feature extraction: This data is passed through offline NNs that extract different types of intermediate features - depth, segmentation masks, point matching, etc. ... Labeling: The intermediate features are passed through other algorithms that generate the labels for the data; Romero, pages 5-6, 2. Auto labeling - Human out-of-the-loop, Let's imagine that our Tesla car passes by a pedestrian. As it moves forward, the front, lateral, and back cameras capture the person and generate single-cam videos from different angles. These videos can then be passed through a neural network that generates a vector space representation of the environment and the pedestrian);
predict, executing the artificial intelligence model, a surface attribute of one or more surfaces of the space around the ego object (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view); and
generate a dataset based on the one or more surfaces and their corresponding surface attribute (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road. A car could then sweep out many miles, generating data on the go that could be used to label entire areas ... After the labels are generated for the surface, … a human could go into the loop and check them to correct noise).
As per claim 16, Romero discloses the ego object of claim 12, wherein the dataset is a queryable dataset configured to transmit the surface attribute to an autonomous driving protocol of the ego object (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road; Romero, pages 4-5, The challenge of unusual scenarios, vector space predictions ... vector space datasets ... use their car fleet to do auto labeling. The idea is to have the cars drive around - either on Autopilot or not - capturing videos).
As per claim 17, Romero discloses the ego object of claim 12, wherein the artificial intelligence model is trained using a sensor attribute of the one or more surfaces (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road).
As per claim 18, Romero discloses the ego object of claim 12, wherein the ego object is an autonomous vehicle executing a driving protocol based on the dataset (Romero, pages 4-5, The challenge of unusual scenarios, vector space predictions ... vector space datasets ... use their car fleet to do auto labeling. The idea is to have the cars drive around - either on Autopilot or not - capturing videos that are then sent to a supercomputer that labels them for further training; Romero, page 4, 1. Manual labeling - From 2D images to 4D vectors, vector space is built from the eight cameras and multiple frames ... a pipeline for auto labeling).
As per claim 19, Romero discloses the ego object of claim 12, wherein the surface attribute corresponds to an elevation (Romero, page 4, 1. Manual labeling - From 2D images to 4D vectors, vector space is built from the eight cameras and multiple frames ... a pipeline for auto labeling; Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road).
As per claim 20, Romero discloses the ego object of claim 12, wherein the surface attribute indicates whether the one or more surfaces are navigable surfaces (Romero, page 6, Restructuring the road, One of the tasks the auto labeling pipeline can do is reconstructing the road surface ... The idea is to have a neural network implicitly represent the road surface. Then, they input x, y image coordinates to the network which extracts both the height (z) and various semantics that define what's at that exact location - lane lines, curbs, crosswalks ... These 3D points can be reprojected into the eight cameras, and because the network has the coordinates and the semantic class, it knows where to locate the objects in each camera view. After doing this process for thousands of queries, each single-cam frame would be entirely populated with points referring to the different semantic classes (line, curb, crosswalk...). Each frame populated with points could then be jointly optimized with the image space segmentation of the same scene to generate precise labels of what's in the road).
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.
Claim(s) 2-4 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Romero, Alberto, “Tesla AI Day 2021 Review- Part 2: Training Data. How Does a Car Learn?” Towards Data Science, Sept 22, 2021, hereinafter, “Romero” as applied to claims 1 and 12 above, and further in view of Tesla AI Day – The Presentation (I) - Elon Musk Interviews, Wordpress, 2021/08/31, hereinafter, “Wordpress”.
As per claim 2, Romero discloses the method of claim 1, but does not explicitly disclose the following limitations as further recited however Wordpress discloses further comprising:
generating, by the processor, an output representing the space around the ego object and illustrating the one or more surfaces and their corresponding surface attributes (Wordpress, page 2, in the vision component, what we’re trying to do is, we’re trying to design a neural network that processes the raw information, which, in our case, is the eight cameras that are positioned around the vehicle, and they send us images. And we need to process that in real time into what we call the vector space. And this is a three dimensional representation of everything you need for driving. So, this is the three dimensional positions of lanes, edges, curbs, traffic signs, traffic lights, cars, their positions, orientations, depths, velocities, and so on ... Here I’m showing the video of the raw inputs that come into the stack, and then neural net processes that into the vector space. And you are seeing parts of that vector space rendered in the instrument cluster on the car).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Wordpress and Romero because they are in the same field of endeavor. One skilled in the art would have been motivated to include the instrument cluster rendering as taught by Wordpress in the system of Romero in order to refine the locations of objects with respect to a drivable or navigable surface (Wordpress, page 14).
As per claim 3, Romero and Wordpress disclose the method of claim 2, wherein the one or more surfaces are visually distinct in accordance with their respective surface attributes (Wordpress, page 2, in the vision component, what we’re trying to do is, we’re trying to design a neural network that processes the raw information, which, in our case, is the eight cameras that are positioned around the vehicle, and they send us images. And we need to process that in real time into what we call the vector space. And this is a three dimensional representation of everything you need for driving. So, this is the three dimensional positions of lanes, edges, curbs, traffic signs, traffic lights, cars, their positions, orientations, depths, velocities, and so on ... Here I’m showing the video of the raw inputs that come into the stack, and then neural net processes that into the vector space. And you are seeing parts of that vector space rendered in the instrument cluster on the car; Wordpress, page 14, on the left, we are seeing what we had before, and on the right, we’re now seeing significantly improved predictions coming directly out of the neural net. This is a multi-camera network predicting directly in vector space. And you can see that it’s basically night and day –you can actually drive on this).
As per claim 4, Romero and Wordpress disclose the method of claim 2, further comprising:
displaying, by the processor, the output on a screen associated with the ego object (Wordpress, page 2, in the vision component, what we’re trying to do is, we’re trying to design a neural network that processes the raw information, which, in our case, is the eight cameras that are positioned around the vehicle, and they send us images. And we need to process that in real time into what we call the vector space. And this is a three dimensional representation of everything you need for driving. So, this is the three dimensional positions of lanes, edges, curbs, traffic signs, traffic lights, cars, their positions, orientations, depths, velocities, and so on ... Here I’m showing the video of the raw inputs that come into the stack, and then neural net processes that into the vector space. And you are seeing parts of that vector space rendered in the instrument cluster on the car).
As per claim 13, Romero discloses the ego object of claim 12, but does not explicitly disclose the following limitations as further recited however Wordpress discloses wherein the processor is further configured to generate an output representing the space around the ego object and illustrating the one or more surfaces and their corresponding surface attributes (Wordpress, page 2, in the vision component, what we’re trying to do is, we’re trying to design a neural network that processes the raw information, which, in our case, is the eight cameras that are positioned around the vehicle, and they send us images. And we need to process that in real time into what we call the vector space. And this is a three dimensional representation of everything you need for driving. So, this is the three dimensional positions of lanes, edges, curbs, traffic signs, traffic lights, cars, their positions, orientations, depths, velocities, and so on ... Here I’m showing the video of the raw inputs that come into the stack, and then neural net processes that into the vector space. And you are seeing parts of that vector space rendered in the instrument cluster on the car).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Wordpress and Romero because they are in the same field of endeavor. One skilled in the art would have been motivated to include the instrument cluster rendering as taught by Wordpress in the system of Romero in order to refine the locations of objects with respect to a drivable or navigable surface (Wordpress, page 14).
As per claim 14, Romero and Wordpress disclose the ego object of claim 13, wherein the one or more surfaces are visually distinct in accordance with their respective surface attributes (Wordpress, page 2, in the vision component, what we’re trying to do is, we’re trying to design a neural network that processes the raw information, which, in our case, is the eight cameras that are positioned around the vehicle, and they send us images. And we need to process that in real time into what we call the vector space. And this is a three dimensional representation of everything you need for driving. So, this is the three dimensional positions of lanes, edges, curbs, traffic signs, traffic lights, cars, their positions, orientations, depths, velocities, and so on ... Here I’m showing the video of the raw inputs that come into the stack, and then neural net processes that into the vector space. And you are seeing parts of that vector space rendered in the instrument cluster on the car; Wordpress, page 14, on the left, we are seeing what we had before, and on the right, we’re now seeing significantly improved predictions coming directly out of the neural net. This is a multi-camera network predicting directly in vector space. And you can see that it’s basically night and day –you can actually drive on this).
As per claim 15, Romero and Wordpress disclose the ego object of claim 13, wherein the processor is further configured to display the output on a screen associated with the ego object (Wordpress, page 2, in the vision component, what we’re trying to do is, we’re trying to design a neural network that processes the raw information, which, in our case, is the eight cameras that are positioned around the vehicle, and they send us images. And we need to process that in real time into what we call the vector space. And this is a three dimensional representation of everything you need for driving. So, this is the three dimensional positions of lanes, edges, curbs, traffic signs, traffic lights, cars, their positions, orientations, depths, velocities, and so on ... Here I’m showing the video of the raw inputs that come into the stack, and then neural net processes that into the vector space. And you are seeing parts of that vector space rendered in the instrument cluster on the car).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Romero, Alberto, “Tesla AI Day 2021 Review- Part 2: Training Data. How Does a Car Learn?” Towards Data Science, Sept 22, 2021, hereinafter, “Romero” as applied to claim 1 above, and further in view of Srinivasan, U.S. Publication No. 2020/0410259, hereinafter, “Srinivasan”.
As per claim 10, Romero discloses the method of claim 1, but does not explicitly disclose the following limitation as further recited however Srinivasan discloses wherein the surface attribute indicates a material associated with the one or more surfaces (Srinivasan, ¶0011, Examples of environmental attributes may include features such as sidewalks, crosswalks, bike lanes, lane markers on a drivable surface, signs, fences, grass or other ground cover material, for instance; Srinivasan, ¶0034, While the example described in relation to FIGS. 3, 4, and 5 includes a pedestrian in a crosswalk, other examples of leveraging relative locations are also considered. In some examples, the relative location of the object 204 to the environmental attribute 402 may further take into account additional environmental attributes such as a ground plane, drivable surface, and/or an edge or intersection of the object 204 with the environmental attribute 402. For instance, of the edge of the crosswalk 206 intersects the object 204, a determination may be made that the object 204 is in the crosswalk 206).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Srinivasan and Romero because they are in the same field of endeavor. One skilled in the art would have been motivated to include materials as taught by Srinivasan in the surface reconstruction as taught by Romero as an alternate means to refine locations of objects and depth estimates (Srinivasan, Abstract).
Conclusion
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/TRACY MANGIALASCHI/Primary Examiner, Art Unit 2668
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