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 .
This Office Action is in response to the Election filed following a Restriction Requirement. Applicant elects Group I and the corresponding claims 1-9, 11-17, and 19 without traverse for examination.
Claim Interpretation
During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111; See also, MPEP 2173.02. Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also, In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow”). The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
The Examiner respectfully requests of the Applicant in preparing responses, to consider fully the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN.
Claim Rejections - 35 USC § 102
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) 1-9, 11-17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2019/0025843 A1 to Wilkinson et al. (hereinafter Wilkinson).
With regards to claim 1, Wilkinson discloses:
1. One or more processors comprising one or more circuits to:
generate, based at least on image data of an environment, input training data comprising a first representation of a three-dimensional (3D) surface of a component of the environment (see, detailed description, including, The data acquisition system(s) can acquire sensor data (e.g., lidar data, radar data, image data, etc.) associated with one or more objects (e.g., pedestrians, vehicles, etc.) that are proximate to the autonomous vehicle and/or sensor data associated with the vehicle path (e.g., path shape, boundaries, markings, etc.). The sensor data can include information that describes the location (e.g., in three-dimensional space relative to the autonomous vehicle) of points that correspond to objects within the surrounding environment of the autonomous vehicle (e.g., at one or more times). The data acquisition system(s) can provide such sensor data to the vehicle computing system, para. 0025); and
generate, based at least on LiDAR data associated with a capture session corresponding to the image data, ground truth training data corresponding to the input training data and comprising a second representation of the 3D surface of the component (see, detailed description, including, ground truth label training data could be generated based on analysis of other driver behavior in the surrounding environment of the autonomous vehicle. For instance, sensors, such as lidar, radar, image capture devices, and/or the like, can capture data regarding the behavior of other vehicles around the autonomous vehicle in certain situations. The data can then be analyzed to extract context awareness scenarios which can be added to a training data set, para. 0051).
With regards to claim 2, Wilkinson discloses:
2. The one or more processors of claim 1, wherein the one or more circuits are further to generate the first representation of the 3D surface based at least on an estimated 3D representation of the 3D surface generated using the image data, and a projected representation of the estimated 3D representation (see, detailed description, including, LIDAR system, the sensor data can include the location (e.g., in three-dimensional space relative to the LIDAR system) of a number of points that correspond to objects that have reflected a ranging laser. For example, LIDAR system can measure distances by measuring the Time of Flight (TOF) that it takes a short laser pulse to travel from the sensor to an object and back, calculating the distance from the known speed of light, para. 0067).
With regards to claim 3, Wilkinson discloses:
3. The one or more processors of claim 1, wherein the one or more circuits are further to generate the first representation of the 3D surface based at least on a projected representation of a set of points on the 3D surface selected from an estimated 3D representation of the 3D surface using extracted classification data representing the component of the environment (see, detailed description, including, or one or more cameras, various processing techniques (e.g., range imaging techniques such as, for example, structure from motion, structured light, stereo triangulation, and/or other techniques) can be performed to identify the location (e.g., in three-dimensional space relative to the one or more cameras) of a number of points that correspond to objects that are depicted in imagery captured by the one or more cameras. Other sensor systems can identify the location of points that correspond to objects as well, para. 0069).
With regards to claim 4, Wilkinson discloses:
4. The one or more processors of claim 1, wherein the one or more circuits are further to generate the second representation of the 3D surface based at least on determining one or more missing values of the LiDAR data using Delaunay triangulation (see, detailed description, including, the positioning system 120 can determine actual or relative position by using a satellite navigation positioning system (e.g. a GPS system, a Galileo positioning system, the Global Navigation satellite system (GLONASS), the BeiDou Satellite Navigation and Positioning system), an inertial navigation system, a dead reckoning system, based on IP address, by using triangulation and/or proximity to cellular towers or WiFi hotspots, and/or other suitable techniques for determining position. The position of the autonomous vehicle 102 can be used by various systems of the vehicle computing system 106, para. 0064).
With regards to claim 5, Wilkinson discloses:
5. The one or more processors of claim 1, wherein the one or more circuits are further to generate one or more updates to one or more neural networks based at least on the first and second representations of the 3D surface, and cancel out at least one of the one or more updates that do not correspond to the component based at least on ground truth classification data (see, detailed description, including, neural networks (e.g., deep neural networks), or other multi-layer non-linear models. Neural networks can include recurrent neural networks (e.g., long, short-term memory recurrent neural networks), feed-forward neural networks, convolutional neural networks, and/or other forms of neural networks, para. 0040) and
the machine-learned model(s) can be trained using ground truth labels (e.g., providing speed labels based on particular contexts/situations) from one or more sources such that the machine-learned model can “learn” suitable speed for an autonomous vehicle to be driven given certain scenarios. In some implementations, the training data for the machine-learned model(s) can include continuous labels over sequences, for example absolute speed and distance, versus discrete decisions, para. 0043).
With regards to claim 6, Wilkinson discloses:
6. The one or more processors of claim 1, wherein the one or more circuits are further to update one or more neural networks using a loss function that compares one or more predicted values to one or more ground truth values represented by the second representation of the 3D surface (see, detailed description, including, neural networks (e.g., deep neural networks), or other multi-layer non-linear models. Neural networks can include recurrent neural networks (e.g., long, short-term memory recurrent neural networks), feed-forward neural networks, convolutional neural networks, and/or other forms of neural networks, para. 0040).
With regards to claim 7, Wilkinson discloses:
7. The one or more processors of claim 1, wherein the one or more circuits are further to include the input training data and the ground truth training data in a training dataset (see, detailed description, including, More particularly, the machine-learned model(s) can be trained using ground truth labels (e.g., providing speed labels based on particular contexts/situations) from one or more sources such that the machine-learned model can “learn” suitable speed for an autonomous vehicle to be driven given certain scenarios. In some implementations, the training data for the machine-learned model(s) can include continuous labels over sequences, for example absolute speed and distance, versus discrete decisions, para. 0043).
With regards to claim 8, Wilkinson discloses:
8. The one or more processors of claim 1, wherein the one or more circuits are further to include the first and second representations of the 3D surface in a training dataset comprising one or more corresponding representations of one or more simulated 3D surfaces generated based at least on one or more parametric mathematical models (see, detailed description, including, or LIDAR system, the sensor data can include the location (e.g., in three-dimensional space relative to the LIDAR system) of a number of points that correspond to objects that have reflected a ranging laser. For example, LIDAR system can measure distances by measuring the Time of Flight (TOF) that it takes a short laser pulse to travel from the sensor to an object and back, calculating the distance from the known speed of light, para. 0067).
With regards to claim 9, Wilkinson discloses:
9. The one or more processors of claim 1, wherein the one or more circuits are further to include the first and second representations of the 3D surface in a training dataset comprising one or more corresponding representations of one or more simulated 3D surfaces generated using a simulation system that generates a simulated environment using a physics engine (see, detailed description, including, the vehicle computing system can determine a speed limit and/or a target offset from the nominal path for the autonomous vehicle. For instance, in some implementations, a model, such as a machine-learned model, can determine a speed limit for the autonomous vehicle based at least in part on the features (e.g., the autonomous vehicle features and the context features). Additionally, or alternatively, the machine-learned model can determine a target offset from the nominal path for the autonomous vehicle based at least in part on the features (e.g., the autonomous vehicle features and the context features) para. 0052).
With regard to claim 11, claim 11 (a system claim) recites substantially similar limitations to claim 1 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 12, claim 12 (a system claim) recites substantially similar limitations to claim 2 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 13, claim 13 (a system claim) recites substantially similar limitations to claim 3 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 14, claim 14 (a system claim) recites substantially similar limitations to claim 5 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 15, claim 15 (a system claim) recites substantially similar limitations to claim 6 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 16, claim 16 (a system claim) recites substantially similar limitations to claim 7 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 17, claim 17 (a system claim) recites substantially similar limitations to claim 8 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 19, claim 19 (a method claim) recites substantially similar limitations to claim 1 (a processor claim) and is therefore rejected using the same art and rationale set forth above.
A sampling of the prior art made of record and not relied upon and considered
pertinent to Applicants’ disclosure includes: U.S. Patent No. 11,170,299 B2 A1 to Kwon et al. that discusses: In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D. TITCOMB whose telephone number is (571)270-5190. The examiner can normally be reached 9:30 AM - 6:30 PM (M-F).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen C. Hong can be reached at 571-272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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WILLIAM D. TITCOMB
Primary Examiner
Art Unit 2178
/WILLIAM D TITCOMB/Primary Examiner, Art Unit 2178 8-11-2026
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