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 .
Summary
This communication is a First Office Action Non-Final Rejection on the merits.
Claims 1 – 7 and 21 – 33 are currently pending and 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 6, 21, 24, 27 – 28, 31 and 33are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lockwood et al. (Hereinafter Lockwood) (US 10268191 B1).
As per claim 1, Lockwood teaches the limitations of:
a method comprising:
generating at least one image representing a map of an environment using data
values indicating one or more arrival times of an object detected in the environment to
positions in the environment, the one or more arrival times corresponding to potential
trajectories for the object based at least on one or more values of one or more motion
parameters corresponding to the object (See at least column 7 line 20 – 60 and column 8 line 24 – 37 and column 9 line 23 – 48; the example vehicle system 202 includes a plurality of sensors 204, for example, configured to sense movement of the vehicle 102 through the environment 100, sense environmental data (e.g., ambient temperature, pressure, and humidity), and/or sense conditions of an interior of the vehicle 102 (e.g., passenger count, interior temperature, noise level). In some examples, the sensors 204 may include sensors configured to identify a location on a map. The sensors 204 may include, for example, one or more light detection and ranging sensors (LIDAR), one or more cameras (e.g. RGB-cameras, intensity (grey scale) cameras, infrared cameras, depth cameras, stereo cameras, and the like), one or more radio detection and ranging sensors (RADAR), sound navigation and ranging (SONAR) sensors, one or more microphones for sensing sounds in the environment 100, such as sirens from law enforcement and emergency vehicles or for selectively noise-cancelling an interior of the vehicle 102, and other sensors related to the operation of the vehicle 102. … The external sources may include global satellites for facilitating operation of a global positioning system (GPS) and/or a wireless network for communicating and receiving information related to the vehicle's location, such as map data. The location systems 210 may also include sensors configured to assist with navigation of the vehicle 102, such as wheel encoders for sensing the rotation of the wheels 128, inertial navigation sensors, such as gyroscopes and/or accelerometers and/or magnetometers, and/or cameras for obtaining image data for visual odometry or visio-inertial navigation. … the collision predictor system 220 may be configured to use the data representing the object type, the predicted object behavior, the data representing the track of the object, and/or the data representing the trajectory of the vehicle 102, to predict a collision between the vehicle 102 and the object. In some examples, the collision predictor system 220 may be used to predict a collision between the vehicle 102 and an object in the environment 100 based on the object type, whether the object is moving, the trajectory of the vehicle 102, the predicted path of the object obtained from the planner 214. For example, a collision may be predicted based in part on the object type due to the object moving, the trajectory of the object being in potential conflict with the trajectory of the vehicle 102, and the object having an object classification that indicates the object is a likely collision threat. In some examples, the kinematics calculator 222 may be configured to determine data representing one or more scalar and/or vector quantities associated with motion of objects in the environment 100, including, but not limited to, velocity, speed, acceleration, momentum, local pose, and/or force. Data from the kinematics calculator 222 may be used to compute other data, including, but not limited to, data representing an estimated time to impact between an object and the vehicle 102, and data representing a distance between the object and the vehicle 102.);
analyzing, using the at least one image, one or more locations associated with a
machine in the environment (See at least column 8 line 17 – 38 and column 9 line 23 – 38; the planner 214 may be configured to generate data representative of a trajectory of the vehicle 102, for example, using data representing a location of the vehicle 102 in the environment 100 and other data, such as local pose data, that may be included in the location data 212. In some examples, the planner 214 may also be configured to determine projected trajectories predicted to be executed by the vehicle 102. The planner 214 may, in some examples, be configured to calculate data associated with a predicted motion of an object in the environment 100, and may determine a predicted object path associated with the predicted motion of the object. In some examples, the object path may include the predicted object path. In some examples, the object path may include a predicted object trajectory. In some examples, the planner 214 may be configured to predict more than a single predicted object trajectory. For example, the planner 214 may be configured to predict multiple object trajectories based on, for example, probabilistic determinations or multi-modal distributions of predicted positions, trajectories, and/or velocities associated with an object. … the collision predictor system 220 may be configured to use the data representing the object type, the predicted object behavior, the data representing the track of the object, and/or the data representing the trajectory of the vehicle 102, to predict a collision between the vehicle 102 and the object. In some examples, the collision predictor system 220 may be used to predict a collision between the vehicle 102 and an object in the environment 100 based on the object type, whether the object is moving, the trajectory of the vehicle 102, the predicted path of the object obtained from the planner 214. For example, a collision may be predicted based in part on the object type due to the object moving, the trajectory of the object being in potential conflict with the trajectory of the vehicle 102, and the object having an object classification that indicates the object is a likely collision threat); and
causing performance of one or more control operations corresponding to the
machine based at least on the analysis of the at least one image (See at least column 10 line 5 – 24; the safety system actuator 224 may be configured to activate one or more safety systems of the autonomous vehicle 102 when a collision is predicted by the collision predictor 220 and/or the occurrence of other safety related events, such as, for example, an emergency maneuver by the vehicle 102, such as hard braking or a sharp acceleration. The safety system actuator 224 may be configured to activate an interior safety system (e.g., including seat belt pre-tensioners and/or air bags), an exterior safety system (e.g., including warning sounds and/or warning lights), the drive system 232 configured to execute an emergency maneuver to avoid a collision and/or a maneuver to come to a safe stop, and/or any combination thereof. For example, the drive system 232 may receive data for causing a steering system of the vehicle 102 to change the travel direction of the vehicle 102, and a propulsion system of the vehicle 102 to change the speed of the vehicle 102 to alter the trajectory of vehicle 102 from an initial trajectory to a trajectory for avoiding a collision.).
As per claim 4, Lockwood teaches the limitations of:
wherein the data values indicate that the machine arriving at one or more corresponding positions during at least one arrival time of the one or more arrival times would not result in a collision with the object (See at least column 8 line 24 – 37, column 9 line 23 – 48 and column 9 line 54 – column 10 line 41. The Examiner construes that since the Lockwood actuate safety system when there is possibility for a collision, if the safety system is not activated, then the data is interpreted by the system as the arrival time(s) would be safe and not be considered to cause a collision with the object).
As per claim 6, Lockwood teaches the limitations of:
wherein data values of the at least one image are representative of respective safe time intervals for particular target positions in the environment (See at least column 8 line 24 – 37, column 9 line 23 – 48 and column 9 line 54 – column 10 line 41. The Examiner construes that since the Lockwood actuate safety system when there is possibility for a collision, if the safety system is not activated, then the data is interpreted by the system as the arrival time(s) would be safe and not be considered to cause a collision with the object)..
As per claim 27, Lockwood teaches the limitations of:
wherein 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 light transport simulation;
a system for performing deep learning operations; or
a system implemented at least partially using cloud computing resources (See at least column 4 line 23 – 39).
Regarding claims 21, 24, 28, 31 and 33:
Claims 21, 24, 28, 31 and 33 are rejected using the same rationale, mutatis mutandis, applied to claims 1, 4, 6 and 27 above, respectively.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 22 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Lockwood in view of Shalev-Shwartz et al. (Hereinafter Shalev) (US 2018/0032082 A1).
As per claim 2, Lockwood does teach wherein the analyzing of the one or more locations includes applying the at least one image to one or more machine learning models (MLMs) (See at least column 19 line 57 – 63), but does not explicitly teaches the limitations of:
applying the at least one image to one or more machine learning models (MLMs) to produce output data indicating one or more of:
at least one trajectory generated using the one or more MLMs;
whether one or more trajectories are unlikely to result in a collision between the
machine and the object; or
a level of safety for the one or more trajectories.
Shalev teaches the limitations of:
applying the at least one image to one or more machine learning models (MLMs) to produce output data indicating one or more of:
at least one trajectory generated using the one or more MLMs;
whether one or more trajectories are unlikely to result in a collision between the
machine and the object; or
a level of safety for the one or more trajectories (See at least paragraph 174 and 221).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include applying the at least one image to one or more machine learning models (MLMs) to produce output data indicating one or more of: at least one trajectory generated using the one or more MLMs; whether one or more trajectories are unlikely to result in a collision between the machine and the object; or a level of safety for the one or more trajectories as taught by Shalev in the system of Lockwood, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 22 and 29:
Claims 22 and 29 are rejected using the same rationale, mutatis mutandis, applied to claim 2 above, respectively.
Claims 3, 5, 7, 23, 25 - 26, 30 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Lockwood in view of Ebrahimi Afrouzi et al. (Hereinafter Ebrahimi) (US 2021/0089040 A1).
As per claim 3, Lockwood teaches limitation of:
wherein the at least one image indicating the one or more arrival times in association with corresponding ones of the positions (See at least column 7 line 20 – 60 and column 8 line 24 – 37 and column 9 line 23 – 48), but does not teach the limitation of
the at least one image includes pixel values.
Ebrahimi teaches the limitation of:
the at least one image includes pixel values (See at least paragraph 422).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the at least one image includes pixel values as taught by Ebrahimi in the system of Lockwood, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, the combination of Lockwood and Ebrahimi teaches limitation of:
wherein the analyzing includes:
comparing locations associated with the machine along at least one proposed trajectory for the machine to corresponding pixel values of the at least one image (Ebrahimi, see at least paragraph 422 – 423); and
based at least on the comparing, determining, for at least one location of the locations, that a corresponding arrival time of the one or more arrival times would be unsafe for the machine, wherein the one or more control operations are performed using a different trajectory than the at least one proposed trajectory based at least on the determining the corresponding arrival time would be unsafe (Lockwood, See at least column 7 line 20 – 60, column 8 line 24 – 37, column 9 line 23 – 48 and column 10 line 5 – 24).
As per claim 7, the combination of Lockwood and Ebrahimi teaches limitation of:
wherein the map forms a time-valued gradient around the object, the time-valued gradient capturing relationships between the positions and the one or more arrival times for the machine to arrive at the positions (Ebrahimi, see at least paragraph 747 – 750).
As per claim 26, the combination of Lockwood and Ebrahimi teaches limitation of:
one or more central processing units (CPUs) (Lockwood, see at least column 6 line 16 – 30);
one or more graphics processing units (GPUs) (Lockwood, see at least column 6 line 16 – 30);
one or more hardware accelerators (Ebrahimi, see at least paragraph 253); and
one or more external sensors having one or more fields of view or one or more sensory fields, and the system causes the machine to perform the operations (Lockwood, see at least column 7 line 12 – 44).
Regarding claims 23, 25, 30 and 32:
Claims 23, 25, 30 and 32 are rejected using the same rationale, mutatis mutandis, applied to claims 3, 5, 7 and 26 above, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gray (US 10007269 B1) discloses collision-avoidance system for autonomous-capable vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IG T AN whose telephone number is (571)270-5110. The examiner can normally be reached M - F: 10:00AM- 4:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached at (571) 270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IG T AN
Primary Examiner
Art Unit 3662
/IG T AN/Primary Examiner, Art Unit 3662
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