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 .
Election/Restrictions
Applicant’s election without traverse of Species 1 corresponding to claims 1-10 and 17-20 in the reply filed on 2/9/2026 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly
claiming the subject matter which the applicant regards as his invention.
Claims 1 – 10 and 17 – 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The terms/limitations: “flexible”, “efficient”, “mitigating wireless network load and reducing network latency”, “significantly reducing”, “accurate”, “effective” in claims 1 and 17; “optimal” in claim 3 are relative terms which renders the claims indefinite. The above recited terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of examination, the relative terms will be interpreted, with broadest and reasonable interpretation, as simply the function following the terms. For example, the limitation “flexible and efficient communication” will be interpreted as simply “configured for communication.” Similar interpretations will be applied to the other relative terms recited above. Subsequent dependent claims recite similar relevant terms and are analyzed and rejected for the same reasons as claims 1 and 17.
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.
Claim(s) 1 – 8 and 17 - 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jha et al. (Pub. No.: US 2022/0343241 A1).
Regarding claims 1 and 17, Jha discloses an intelligent cooperative perception (iCOOPER) system and method (Cooperative perception ¶ 15) for autonomous air vehicles (AAVs) (drones, UAVs, ¶ 304), comprising at least two AAVs (Vehicles 1352a and b, FIG. 13), each of the at least two AAVs being provided with at least one object detection sensor (Each equipped with sensors 1310, FIG. 13), and the at least two AAVs configured to perform:
an information-centric networking (ICN) scheme configured for flexible and efficient communications among the at least two AAVs and infrastructure sensors (Sensors 1310 and communication interface 1353 may be LTE/NR proximity service, WiFI based links, personal area network based on protocols including ZigBee, IPv6, etc. ¶ 317);
a deep reinforcement learning scheme including selecting sensory data to be transmitted, selecting data compression format, mitigating wireless network load, and reducing network latency (self-learning systems ¶ 14 Deep learning accelerators ¶ 335 machine learning ¶¶ 359, 430);
an efficient real-time compression scheme of 3D point cloud streams (3D LiDAR Point cloud ¶ 151) based on recurrent neural network (RNN) algorithms (AI accelerators, neural compute stick, neuromorphic hardware ¶ 359) and including significantly reducing data amount exchanged among the at least two AAVs, network load and delay while maintaining accurate cooperative perception; and
an effective point cloud fusion scheme including compensating network latency and accurately fusing sensed data from the at least two AAVs and the infrastructure sensors (3D Point cloud and data sensor fusion ¶¶ 151, 299, 302).
Regarding claim 2, Jha discloses the iCOOPER system, wherein the deep reinforcement learning scheme comprises a deep reinforcement learning-based adaptive transmission scheme latency (self-learning systems ¶ 14, adaptation entity ¶ 294, Deep learning accelerators ¶ 335 machine learning ¶¶ 359, 430).
Regarding claims 3 and 18, Jha discloses the iCOOPER system and method, wherein the deep reinforcement learning-based adaptive transmission scheme includes dynamically determining an optimal transmission policy of real-time sensed data of a detected object based on the importance of the sensed data, location and trajectory of the detected object, and wireless network state (optimized for collective perception especially for the scenarios like presence of high density of same types/classes of objects ¶ 208).
Regarding claim 4, Jha discloses the iCOOPER system of, wherein the ICN scheme includes naming, publishing, requesting, retrieving and/or subscribing sensory data (Collective Perception Layer 104, FIG. 1).
Regarding claim 5, Jha discloses the iCOOPER system, wherein the ICN scheme includes naming and accessing sensory data based on sensed regions with multiple resolutions (151, FIG. 1 and Master Layers 201-208, FIG. 2).
Regarding claim 6, Jha discloses the iCOOPER system, wherein the 3D point cloud streams include sensory data of objects sensed by the at least one sensor (3D LiDAR Point cloud ¶ 151).
Regarding claims 7 and 19, Jha discloses the iCOOPER system and method, wherein the at least one sensor includes one selected from a group consisting of LIDAR, stereo camera, and radar (¶ 36).
Regarding claims 8 and 20, Jha discloses the iCOOPER system and method, wherein the effective point cloud fusion scheme including velocity vector estimation at a sender and network latency compensation at a receiver, the sender being a first AAV of the at least two AAVs and the receiver being a second AAV of the at least two AAVs (¶¶ 304-306).
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 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jha et al. (Pub. No.: US 2022/0343241 A1) as applied to claim 1 above, and further in view of Sharma et al. (Pub. No.: US 2019/022003 A1).
Regarding claim 9, Jha is silent to the iCOOPER system, wherein the ICN scheme comprises a data preprocessing and naming scheme including organizing sensory data based on its region in an Octree structure with multiple resolutions. However, Sharma teaches 3D mapping of an environment around an autonomous vehicle (See Abstract). Vehicle sensors may gather data relating to the environment around the vehicles (¶ 16) and more specifically, the 3D map environment may be built based on a vehicle broadcast of a subset of an octree (¶ 59 and FIG. 6).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify Jha to comprise a data preprocessing and naming scheme including organizing sensory data based on its region in an Octree structure with multiple resolutions as taught by Sharma to enhance situational awareness and as a result, enhance vehicle safety (¶ 2).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Jha et al. (Pub. No.: US 2022/0343241 A1) as applied to claim 1 above, and further in view of Zhang et al. (Pub. No.: US 2021/0232922 A1).
Regarding claim 10, Zhang teaches the iCOOPER system, wherein the deep reinforcement learning scheme comprises an Actor Critic neural network (Actor ensemble and the Critic neural network, See Abstract, ¶ 156; and applied in aircraft, UAVs ¶ 144).
It would have been obvious to modify Jha to wherein the deep reinforcement learning scheme comprises an Actor Critic neural network as taught by Zhang to optimize data processing (¶ 157).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYLER J LEE whose telephone number is (571)272-9727. The examiner can normally be reached M-F 7:30-5:00.
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/TYLER J LEE/Primary Examiner, Art Unit 3663