DETAILED ACTION
This action is responsive to the application filed on 5/6/2024.
Claims 1-21 are pending in this application. Claims 1 and 12 are independent claims.
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 .
Information Disclosure Statement
The information disclosure statements (IDS)s submitted on 5/6/2024 and 6/9/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 1 and 12 are objected to because of the following informalities:
Claims 1 and 12, the last limitation of each replace “… receives as input the response, an indication of the motion tracked between the first and second aerial images, and the second aerial image when ...” with “… receives, as input: the response, an indication of the motion tracked between acquiring the first and second aerial images, and the second aerial image, when ...”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 12-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because independent claim 12 recites “At least one machine-readable medium…” in the preamble which may be categorized based on the specifications ([0046]-[0047]) as a signal/carrier wave, which is a signal per se, and which is not a “process,” a “machine,” a “manufacture,” or a “composition of matter” as defined in 35 U.S.C. § 101. Neither of claims 13-21 incorporates any additional limitation(s) to resolve this issue. Examiner respectfully suggests adding the word “non-transitory” before “machine-readable medium” in the claims in order to overcome the rejection.
Allowable Subject Matter
Claims 1-11 are allowed.
Claims 12-21 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: While various pieces of cited art teach features relevant to the usage of neural networks for sematic segmentation of aerial images as well as UAV object detection using a distributed Edge-Cloud collaborative framework, none of the art teaches using, within a UAV delivery service, as input, for UAV onboard object identification at the delivery destination: 1) a response from a cloud-based neural network identifying objects, 2) an indication of motion tracked between a first image used for the cloud-based object identification and a second image, and as 3) the second image, in conjunction with the other limitations recited in the independent claim. See the description of cited art below. Furthermore, the sequence of processing indicated in the claim limitations would not have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, based on the combinations of teachings of the cited art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Examiner notes from the cited art:
US 20200151500 A1, KOSSYK et al., which teaches using time series of aerial images for predicting changes in certain features [see abstract and front figure].
US 20220114740 A1, SHIMIZU et al., which teaches using camera motion information for 3D reconstruction.
US 20240124137 A1, Shoeb, which teaches image analysis for obstacle avoidance that can be done onboard the UAV or offloaded to the cloud [see e.g.[0023]-[0025]].
“Yuan, Yazhou, et al. "Edge-cloud collaborative UAV object detection: Edge-embedded lightweight algorithm design and task offloading using fuzzy neural network." IEEE Transactions on Cloud Computing 12.1 (2024): 306-318” which teaches UAV object detection using a distributed Edge-Cloud collaborative framework [see title, abstract, and fig. 1 and its corresponding description] but does not teach using, as input, for UAV onboard object identification: 1) a response from a cloud-based neural network identifying objects together with 2) an indication of motion tracked between a first image used for the cloud-based object identification and a second image as well as 3) the second image. It merely teaches using outputs from each of the edge-based detection and the cloud-based detection for decision making [see fig. 4 and corresponding description].
“Wang, Chenyang, Benjamin Carlson, and Qi Han. "Object recognition offloading in augmented reality assisted UAV-UGV systems." Proceedings of the Ninth Workshop on Micro Aerial Vehicle Networks, Systems, and Applications. 2023” which teaches offloading part of the computation in object recognition [see abstract] but, again, does not teach using, as input, for UAV onboard object identification: 1) a response from a cloud-based neural network identifying objects together with 2) an indication of motion tracked between a first image used for the cloud-based object identification and a second image as well as 3) the second image.
“Slyusar, Vadym, et al. "Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs." Eastern-European Journal of Enterprise Technologies 6.2 (2021): 114” which teaches the usage of neural networks for sematic segmentation of aerial images [see abstract] but, again, does not teach using, as input, for UAV onboard object identification: 1) a response from a cloud-based neural network identifying objects together with 2) an indication of motion tracked between a first image used for the cloud-based object identification and a second image as well as 3) the second image.
US 20200103552 A1, Phelan et al., which teaches onboard and cloud options for neural networks to detect features of interest within UAV acquired aerial imagery [see [0159]-[0163]] but, again, does not teach using, as input, for UAV onboard object identification: 1) a response from a cloud-based neural network identifying objects together with 2) an indication of motion tracked between a first image used for the cloud-based object identification and a second image as well as 3) the second image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA S AYAD whose telephone number is (571)272-2743. The examiner can normally be reached Monday-Friday, 7:30 am - 4:30 pm. Alt, Friday, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Queler can be reached at (571) 272-4140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIA S AYAD/Primary Examiner, Art Unit 2172
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