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
The Amendment filed on 16 March 2026 has been acknowledged.
Claims 1, 11 and 19 are amended.
Claim 10 is cancelled.
Claim 21 is newly presented.
Currently, claims 1 – 9 and 11 – 21 are pending and considered as set forth.
Response to Amendment
Applicant’s amendments to the claims are sufficient to overcome the 35 U.S.C. 101 rejections set forth in the previous office action.
Response to Arguments
Applicant’s arguments with respect to claims 1, 11 and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1 – 9, and 11 – 21 are rejected under 35 U.S.C. 103 as being unpatentable over Mirkovic et al. (Hereinafter Mirkovic)(US 2024/0190452 A1) in view of Gogna et al. (Hereinafter Gogna)(US 2021/0181750 A1).
As per claim 1, Mirkovic teaches the limitations of:
A method comprising:
generating, using one or more neural networks and based at least on first sensor data generated using one or more first sensors of one or more first sensor modalities, first data indicating one or more first locations associated with one or more first objects in an environment (See at least paragraph 33 and 39; The sensor system 111 may include one or more sensors that are coupled to and/or are included within the AV 102, as illustrated in FIG. 11. For example, such sensors may include, without limitation, a light detection and ranging (LiDAR) system, a radio detection and ranging (radar) system, a laser detection and ranging (LADAR) system, a sound navigation and ranging (sonar) system, one or more cameras (for example, visible spectrum cameras, infrared cameras, etc.), temperature sensors, position sensors (for example, a global positioning system (GPS), etc.), location sensors, fuel level sensors, motion sensors (for example, an inertial measurement unit (IMU), etc.), humidity sensors, occupancy sensors, or the like. The sensor data can include information that describes the location of objects within the surrounding environment of the AV 102, information about the environment itself, information about the motion of the AV 102, information about a route of the vehicle, or the like. … he perception system 202 includes sensors that capture information about moving actors and other objects that exist in the vehicle's environment or surroundings. Example sensors include cameras, LiDAR systems, and radar systems. The data captured by such sensors (such as a digital image, lidar point cloud data, or radar data) is known as perception data. Methods of identifying objects and assigning categorical labels to objects are well known in the art, and any suitable classification process may be used, such as those that make bounding box classifications for detected objects in a scene and use convolutional neural networks or other computer vision models.);
generating, using one or more algorithmic processing techniques and based at least on second sensor data generated using one or more second sensors of one or more second sensor modalities different from the one or more first sensor modalities, second data indicating one or more first attributes associated with one or more second objects in the environment (See at least paragraph 39; The perception system 202 includes sensors that capture information about moving actors and other objects that exist in the vehicle's environment or surroundings. Example sensors include cameras, LiDAR systems, and radar systems. The data captured by such sensors (such as a digital image, lidar point cloud data, or radar data) is known as perception data. The perception system may include one or more processors, along with a computer-readable memory with programming instructions and/or trained artificial intelligence models that, during a run of the vehicle, will process the captured data to identify objects and assign categorical labels and unique identifiers to each object detected in a scene. Categorical labels may include categories such as vehicle, bicyclist, pedestrian, building, and the like. Methods of identifying objects and assigning categorical labels to objects are well known in the art, and any suitable classification process may be used, such as those that make bounding box classifications for detected objects in a scene and use convolutional neural networks or other computer vision models.); and
performing one or more control operations that cause a machine to navigate within the environment based at least on the third data (See at least paragraph 41 and 64 – 65; In an AV, the vehicle's perception system 202, as well as the vehicle's forecasting system 203, will deliver data and information to the vehicle's motion planning system 204 and motion control system 205 so that the receiving systems may assess such data and initiate any number of reactive motions to such data. The motion planning system 204 and control system 205 include and/or share one or more processors and computer-readable programming instructions that are configured to process data received from the other systems, determine a trajectory for the vehicle, and output commands to vehicle hardware to move the vehicle according to the determined trajectory. Example actions that such commands may cause the vehicle hardware to take include causing the vehicle's brake control system to actuate, causing the vehicle's acceleration control subsystem to increase speed of the vehicle, or causing the vehicle's steering control subsystem to turn the vehicle.).
Mirkovic does not explicitly teach the limitation of:
generating, based at least on at least a first portion of the first data and at least a second portion of the second data, third data comprising at least one of: an updated version of the first data indicating one or more second locations associated with the one or more first objects; or an updated version of the second data indicating one or more second attributes associated with the one or more second objects.
Gogna teaches the limitation of:
generating, based at least on fusing at least a portion of the first data and at least a portion of the second data, third data comprising at least one of: an updated version of the first data modified based at least on the portion of the second data, the updated version of the first data indicating one or more second locations associated with the one or more first objects; or an updated version of the second data modified based on at least on the portion of the first data, the updated version of the second data indicating one or more second attributes associated with the one or more second objects (See at least paragraph 37; the first portion 204 may comprise one or more images based at least in part on sensor data associated with a sensor of the vehicle 202. In such examples, the one or one or more images may represent scenes captured by a perception system of a vehicle computer system in the vehicle 202. As depicted in FIG. 2, multiple images may convey different scenes (front, back, right side, and left side) around the vehicle 202 including detected objects such as vehicles, pedestrians, bicyclists, and buildings just to name a few. … sensor data from the vehicle 202 may continuously determine a location and/or orientation of the vehicle 202 within the environment (using a localization component of the vehicle, for example) and may also continuously detect objects. As shown in FIG. 2, the vehicle 202 may travel along the road segment 214 (e.g., a lane of a roadway) from a first location to a second location without encountering an obstacle that impedes progress. The road segment 214 may be associated with map feature data describing attributes of the road segment (e.g., a start point, an endpoint, road condition(s), a road segment identification, a lane number, and so on). Some or all of the attributes of the road segment 214 may be transmitted to the vehicle if the road segment 214 (or a portion thereof) becomes a blocked region. The road segment 214 may, in some examples, correspond to a corridor associated with a safety margin. For instance, the computer device may determine a drivable surface, determine a corridor, detect objects, and/or fuse the objects into the corridor. In such an example, a safety margin for the vehicle 202 is created during fusion of the detected objects into the corridor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include generating, based at least on at least a first portion of the first data and at least a second portion of the second data, third data comprising at least one of: an updated version of the first data indicating one or more second locations associated with the one or more first objects; or an updated version of the second data indicating one or more second attributes associated with the one or more second objects as taught by Gogna in the system of Mirkovic, 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 2, Mirkovic teaches the limitations of:
wherein: the first data is instantaneous data indicating the one or more first locations associated with the one or more first objects in the environment surrounding the machine at an instance of time, and the second data is temporal data and the one or more first attributes are tracked over a period of time that at least partially precedes the instance of time (See at least paragraph 97).
As per claim 3, Mirkovic teaches the limitations of:
wherein: the first data further indicates one or more occluded portions of the environment, and the updated version of the first data further indicates whether the one or more occluded portions of the environment are occupied by at least one of the one or more first objects or at least one of the one or more second objects (See at least abstract and paragraph 57).
As per claim 4, Mirkovic teaches the limitations of:
wherein: an attribute of the one or more first attributes includes at least one of:
a first bounding shape associated with an object of the one or more second objects, a first location associated with the object, a first pose associated with the object, a first trajectory associated with the object, or a first classification associated with the object, and an attribute of the one or more second attributes includes at least one of: a second bounding shape associated with the object, a second location associated with the object, a second pose associated with the object, a second trajectory associated with the object, or a second classification associated with the object (See at least paragraph 39 and 71).
As per claim 5, the combination of Mirkovic and Gogna teaches the limitations of:
wherein the first data is a dense occupancy representation of the environment from a top-down perspective, the dense occupancy representation including one or more points representing one or more samples obtained using the one or more first sensors at an instance of time, wherein one or more first points of the one or more points correspond to the one or more first locations associated with the one or more first objects and one or more second points of the one or more points correspond to one or more unoccupied locations in the environment at the instance of time (Mirkovic, see at least paragraph 61, and Gogna, see at least paragraph 53).
As per claim 6, the combination of Mirkovic and Gogna teaches the limitations of:
wherein one or more values of the one or more points correspond to at least one of a height or a confidence associated with the one or more samples (Mirkovic, See at least paragraph 56 – 57).
As per claim 7, Mirkovic teaches the limitations of:
determining that a first object of the one or more first objects corresponds to a second object of the one or more second objects, wherein the generating the third data is further based at least on the first object corresponding to the second object (See at least paragraph 41 and 64 – 65).
As per claim 8, Mirkovic teaches the limitations of:
generating fourth data indicating one or more prior locations associated with the one or more first objects in the environment, the fourth data including one or more points representing one or more prior samples obtained using the one or more first sensors over a period of time and refined based at least on the one or more first attributes, wherein the generating the third data is further based at least on the fourth data (See at least paragraph 41 and 64 – 65).
As per claim 9, Mirkovic teaches the limitations of:
wherein a first point of the one or more points included in the fourth data is indicative of a velocity associated with an object of the one or more first objects (See at least paragraph 44 and 46).
As per claim 10, Mirkovic teaches the limitations of:
causing the machine to perform one or more operations based at least on at least one of the updated version of the first data or the updated version of the second data (See at least paragraph 41 and 64 – 65).
As per claim 17, Mirkovic teaches the limitations of:
wherein the one or more first sensors include one or more of: an image sensor; a radar sensor; an ultrasonic sensor; or a LiDAR sensor (See at least paragraph 33).
As per claim 18, Mirkovic 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 digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system implementing one or more large language models (LLMs); 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 at least abstract).
As per claim 21, the combination of Mirkovic and Gogna teaches the limitations of:
wherein generating the fused perception information comprises fusing at least a portion of the occupancy representation and at least a portion of the attribute information to modify the occupancy representation based at least on the attribute information or to modify the attribute information based at least on the occupancy representation (See at least
Regarding claims 11 – 16 and 19 - 20:
Claims 11 – 16 and 19 - 20 are rejected using the same rationale, mutatis mutandis, applied to claims 1 – 9 and 17 - 18 above, respectively.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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