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 § 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 (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 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dosovitskiy et al. (CARLA: An Open Urban Driving Simulator, 2017, CoRL, Pages 1-16), hereinafter “Dosovitskiy”, in view of Goodin et al. (Unmanned Ground Vehicle Simulation with the Virtual Autonomous Navigation Environment, 2017, International Conference on Military Technologies (ICMT), Pages 160-165), hereinafter “Goodin”.
Claim 1 is met by the combination of Dosovitskiy and Goodin, wherein
Dosovitskiy teaches:
A method (See the Abstract.) comprising:
determining, within a simulation environment, one or more object to lane assignments based at least on one or more neural networks (See the first two paragraphs under section 3.3 on pages 5-6: “Our third method is deep reinforcement learning, which trains a deep network based on a reward signal provided by the environment, with no human driving traces...The reward is a weighted sum of five terms: positively weighted speed and distance traveled towards the goal, and negatively weighted collision damage, overlap with the sidewalk, and overlap with the opposite lane.” Also see page 16 regarding types of infractions: “Opposite lane: More than 30% of the car’s footprint is over wrong-way lanes.”) processing simulated data generated using the simulation environment…(See page 3, first paragraph: “CARLA simulates a dynamic world and provides a simple interface between the world and an agent that interacts with the world. To support this functionality, CARLA is designed as a server-client system, where the server runs the simulation and renders the scene.”); and
performing, within the simulation environment, one or more operations associated with a virtual machine based at least on the one or more object to lane assignments (See page 4, first full paragraph: “Measurements concerning traffic rules include the percentage of the vehicle’s footprint that impinges on wrong-way lanes or sidewalks, as well as states of the traffic lights and the speed limit at the current location of the vehicle. Finally, CARLA provides access to exact locations and bounding boxes of all dynamic objects in the environment. These signals play an important role in training and evaluating driving policies.” Also see page 6, paragraph before section 5: “Infractions, such as driving on the sidewalk or collisions, do not lead to termination of an episode, but are logged and reported.”).
Dosovitskiy does not disclose the following; however, Goodin discloses:
the simulation environment rendered using one or more ray-tracing algorithms (See page 161, right column: “The VANE camera model also uses high fidelity ray-tracing to generate photo-realistic synthetic imagery.”)
Dosovitskiy and Goodin together disclose the limitations of claim 1. Goodin is directed to a similar field of art (unmanned/autonomous vehicle navigation simulation). Therefore, Dosovitskiy and Goodin are combinable. Modifying the system and method of Dosovitskiy by simple substitution of the rendered scenes for the ray-traced scenes of Goodin would yield the expected and predictable result of generating photo-realistic synthetic imagery. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Dosovitskiy and Goodin in this way.
Claim 2 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 1, wherein the determining the one or more object to lane assignments comprises:
And Dosovitskiy further discloses:
determining, based at least on the one or more neural networks processing the simulated data, one or more bounding shapes associated with one or more objects; and determining the one or more object to lane assignments based at least on the one or more bounding shapes (See page 4: “Finally, CARLA provides access to exact locations and bounding boxes of all dynamic objects in the environment.”).
Claim 3 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 2, further comprising:
And Dosovitskiy further discloses:
determining, based at least on cropping one or more portions of the one or more bounding shapes (See the paragraph bridging pages 12 & 15, cropping of the image followed by input to the network: “Before feeding a raw 800 × 600 image to the network, we cropped 171 pixels at the top and 45 at the bottom”.), one or more object fences associated with the one or more objects, wherein the determining the one or more object to lane assignments is based at least on the one or more object fences (See page 4: “Measurements concerning traffic rules include the percentage of the vehicle’s footprint that impinges on wrong-way lanes or sidewalks”.).
Claim 4 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 3, wherein the cropping the one or more portions of the one or more bounding shapes comprises
And Dosovitskiy further discloses:
cropping at least one of: one or more first portions of the one or more bounding shapes that are associated with drivable freespace; one or more second portions of the one or more bounding shapes that are outside of a drivable surface (See the paragraph bridging pages 12 & 15: “Before feeding a raw 800 × 600 image to the network, we cropped 171 pixels at the top and 45 at the bottom”.); or one or more upper portions of the one or more bounding shapes.
Claim 5 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 3, wherein the cropping the one or more portions of the one or more bounding shapes comprises
And Dosovitskiy further discloses:
The method of claim 1, wherein the determining the one or more object to lane assignments comprises: determining, based at least on the one or more neural networks processing the simulated data, one or more object fences associated with one or more objects; and determining the one or more object to lane assignments based at least on the one or more object fences (See page 4: “Measurements concerning traffic rules include the percentage of the vehicle’s footprint that impinges on wrong-way lanes or sidewalks”.).
Claim 6 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 1, wherein the determining the one or more object to lane assignments comprises:
And Dosovitskiy further discloses:
determining, based at least on the one or more neural networks processing the simulated data, one or more drivable freespace locations; determining, based at least on the one or more drivable freespace locations, one or more object fences associated with one or more objects (See the paragraph bridging pages 3-4: “Our semantic segmentation pseudo-sensor provides 12 semantic classes: road, lane-marking, traffic sign, sidewalk, fence, pole, wall, building, vegetation, vehicle, pedestrian, and other.”); and determining the one or more object to lane assignments based at least on the one or more object fences (See page 4: “Measurements concerning traffic rules include the percentage of the vehicle’s footprint that impinges on wrong-way lanes or sidewalks”.).
Claim 7 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 1, wherein the determining the one or more object to lane assignments comprises:
And Dosovitskiy further discloses:
The method of claim 1, wherein the determining the one or more object to lane assignments comprises: determining, based at least on the one or more neural networks processing the simulated data, one or more locations associated with one or more lanes; and determining the one or more object to lane assignments based at least on the one or more locations associated with the one or more lanes (See page 5, second full paragraph: “The probability distributions provided by the network are used to estimate the ego-lane based on the road area and the lane markings.”).
Claim 8 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 1, wherein the determining the one or more object to lane assignments comprises:
And Dosovitskiy further discloses:
determining, based at least on the one or more neural networks processing the simulated data, one or more object fences associated with one or more objects (See page 4: “Finally, CARLA provides access to exact locations and bounding boxes of all dynamic objects in the environment.”); determining, based at least on the simulated data, one or more locations associated with one or more lanes (See page 5, second full paragraph: “The probability distributions provided by the network are used to estimate the ego-lane based on the road area and the lane markings.”); and determining the one or more object to lane assignments based at least on the one or more object fences and the one or more locations associated with the one or more lanes (See the first two paragraphs under section 3.3 on pages 5-6: “Our third method is deep reinforcement learning, which trains a deep network based on a reward signal provided by the environment, with no human driving traces...The reward is a weighted sum of five terms: positively weighted speed and distance traveled towards the goal, and negatively weighted collision damage, overlap with the sidewalk, and overlap with the opposite lane.” Also see page 16 regarding types of infractions: “Opposite lane: More than 30% of the car’s footprint is over wrong-way lanes.”).
Claim 9 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The method of claim 8, wherein the determining the one or more object to lane assignments comprises:
And Dosovitskiy further discloses:
determining one or more amounts of overlap between the one or more object fences and the one or more locations associated with the one or more lanes; and determining the one or more object to lane assignments based at least on the one or more amounts of overlap (See the first two paragraphs under section 3.3 on pages 5-6: “Our third method is deep reinforcement learning, which trains a deep network based on a reward signal provided by the environment, with no human driving traces...The reward is a weighted sum of five terms: positively weighted speed and distance traveled towards the goal, and negatively weighted collision damage, overlap with the sidewalk, and overlap with the opposite lane.” Also see page 16 regarding types of infractions: “Opposite lane: More than 30% of the car’s footprint is over wrong-way lanes.”).
The system of claim 10 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 1.
Claim 11 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 2.
Claim 12 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 3.
Claim 13 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 4.
Claim 14 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 5.
Claim 15 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claims 1 and 2.
Claim 16 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 9.
Claim 17 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The system of claim 10, wherein the system is comprised in at least one of:
And Dosovitskiy further discloses:
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 (See page 4, first full paragraph: “Measurements concerning traffic rules include the percentage of the vehicle’s footprint that impinges on wrong-way lanes or sidewalks…These signals play an important role in training and evaluating driving policies.”); a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; 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.
The system of claim 18 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 1.
Claim 19 is met by the combination of Dosovitskiy and Goodin, wherein
The combination of Dosovitskiy and Goodin discloses:
The one or more processors of claim 18, wherein
And Dosovitskiy further discloses:
the object to lane assignments are determined using one or more object fences that indicate one or more first portions of the one or more objects that are associated with one or more driving surfaces without indicating one or more second portions of the one or more objects that are outside of the one or more driving surfaces (See the type of infraction detected on page 16: “Opposite lane: More than 30% of the car’s footprint is over wrong-way lanes.”).
Claim 20 is met by the combination of Dosovitskiy and Goodin for the reasons given in the treatment of claim 17.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN S LEE whose telephone number is (571)272-1981. The examiner can normally be reached 11:30 AM - 7:30 PM.
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/Jonathan S Lee/Primary Examiner, Art Unit 2677
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