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 .
The following is a Final Office action in response to communications received on 05/03/2026.
Response to Amendment
Claims 8 and 15 have been amended.
Claims 8-20 have been examined.
Applicant’s arguments with respect to claims 8 and 15 regarding the new limitations: “the one or more trained ML-based attack detection models are deployed at one or more fog nodes of a decentralized environment based on an edge-fog-cloud computing paradigm to detect global positioning system (GPS) spoofing attacks on UAVs of the UAM network by detecting slow shifting patterns in GPS signals received from the UAVs” in claim 8 and “wherein the lightweight blockchain is deployed in a decentralized environment based on an edge-fog-cloud computing paradigm comprising an edge computing layer containing UAVs as edge devices, a fog computing layer containing fog nodes, and a cloud computing layer, wherein raw mission data captured at the UAVs in the edge computing layer is transmitted to the fog computing layer for data aggregation and higher-level analytic services” in claim 15 have been considered but are moot in view of the new ground of rejection presented in the current office action.
Claim Rejections - 35 USC § 103
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 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over prior art of record LightMAN: A Lightweight Microchained Fabric for Assurance and Resilience Oriented Urban Air Mobility Networks by Xu et al (hereinafter Xu), US 11032022 to Sen et al (hereinafter Sen), and Efficient Detection of GPS Spoofing Attacks on Unmanned Aerial Vehicles Using Deep Learning by Agyapong et al (hereinafter Agyapong).
As per claim 8, Xu teaches:
A method for forming a lightweight blockchain fabric, comprising:
providing an urban air mobility (UAM) network, wherein the UAM network provides an on-demand automated transportation service (Xu: page 6: lines 254-256: A UAM network encompasses air traffic operations for manned and unmanned aircraft systems in a metropolitan area. The left part of Figure 1 shows a UAV application that provides on-demand, automated transportation services);
providing a lightweight blockchain module, wherein the lightweight blockchain module includes a first sub-system and a second sub-system, the first sub-system includes a lightweight consensus protocol that relies on a randomly selected consensus committee to achieve a low latency when committing transactions on a distributed ledger, and the second sub-system includes a hybrid on-chain and off-chain storage that improves efficiency and privacy- preservation (Xu: page 8: 3.2. Microchain Fabric for UAM Data Sharing 330 As right part of Figure 1 shows, microchain fabric consists of two sub-systems: i) a lightweight consensus protocol that relies on a randomly selected consensus committee to achieve low latency of committing transactions on the distributed ledger; ii) a hybrid on-chain and off-chain storage strategy that improves efficiency and privacy-preservation);
providing a machine learning (ML)-based anomaly detection module, wherein the ML- based anomaly detection module includes one or more trained ML-based attack detection models, (Xu: page 3: lines 99-100: (2) A machine learning based anomaly detection (MLAD) method to monitor the UAM networks in real-time. Pages 7 and 8: lines 288-289 and 318-327: Our objective is to develop machine learning (ML) based anomaly detection (MLAD) and Reinforcement Learning (RL) artificial agents can achieve a good level of performance and generality on diagnostic and prognostic. (iii) Deep Learning Based Detection: LightMAN applies DL techniques (e.g., L-CNN, RNN/LSTM, etc.) to characterize the dynamic state of the monitored system. With the trained model in place, the operator can conduct the detection and classification of potential attacks. In the online monitoring process, LightMAN monitoring tools collect real-time flight data and the processed traffic data are sent to the learned classifier for anomaly detection); and
forming the lightweight blockchain fabric that includes the UAM network, the lightweight blockchain module, and the ML-based anomaly detection module (Xu: page 6: lines 252-253: Figure 1 demonstrates the LightMAN architecture that consists of two sub-frameworks: i) UAM network; and ii) Microchain fabric. Page 3: lines 99-100: (2) A machine learning based anomaly detection (MLAD) method to monitor the UAM 99 networks in real-time. Page 13, lines 483-484 and 494-496: we illustrate a comprehensive system architecture, along with details on ML-based UAM monitoring and lightweight microchain implementation. This paper presents LightMAN which combines DL powered UAM security and a lightweight microchained fabric to support assurance and resilience oriented UAM networks.).
Xu teaches using machine learning to perform anomaly detection using flight data of UAVs but does not teach: the one or more trained ML-based attack detection models are deployed at one or more fog nodes of a decentralized environment based on an edge-fog-cloud computing paradigm to detect global positioning system (GPS) spoofing attacks on UAVs of the UAM network by detecting slow shifting patterns in GPS signals received from the UAVs. However, Sen teaches:
the one or more trained ML-based attack detection models are deployed at one or more fog nodes of a decentralized environment based on an edge-fog-cloud computing paradigm to detect (Sen: column 2, lines 32-52: Analytics engines may be hosted by a server in a cloud, at an edge processor (e.g., an edge server, a client device, remote sensors, other edge devices), or both. The disposition of the data, such as analysis results, may be used by edge devices to provision an action, such as …, identifying jamming and other radio interference sources, identifying spoofed radio signals (spoofing attack), etc. The analytics engine may pre-trained through supervised machine learning using a set of predefined training data, such as pre-classified signals, data patterns, and the like. Column 8, line 65-column 9, line 6: Clusters of edge devices 101-110 may be equipped to communicate with the cloud 120. This may allow the edge devices 101-110 to form a cluster of devices, allowing them to function as a single device, which may be termed a fog device (edge-fog-cloud computing paradigm). column 3, line 59-column 4, line 2: The method comprises receiving training data based upon sensor measurements of at least one unmanned autonomous vehicle for processing in a cognitive learning and inference system. The system performs a plurality of machine learning operations on the training data to generate a cognitive profile of the at least one UAV. A cognitive insight is generated based upon the cognitive profile, and a countermeasure may be enacted against the UAV based upon the cognitive insight. Column 15, lines 49-67: Machine learning 302 comprises generating a cognitive profile. In certain aspects, the cognitive profile may include data associated with a device's interaction with the CUAS, such as geolocation data from remote sensors (e.g., radar, lidar, acoustical sensors, optical sensors, cameras, GPS data), etc.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ the teachings of Sen in the invention of Xu to include the above limitations. The motivation to do so would be to detect, identify, track, and enact countermeasure against remote-controlled vehicles, such as unmanned autonomous vehicles (UAVs) (column 1, lines 18-21).
Xu in view of Sen teaches detecting spoofing attacks on UAVs but fails to teach: detect global positioning system (GPS) spoofing attacks on UAVs of the UAM network by detecting slow shifting patterns in GPS signals received from the UAVs. However, Agyapong teaches:
detect global positioning system (GPS) spoofing attacks on UAVs of the UAM network by detecting slow shifting patterns in GPS signals received from the UAVs (Agyapong: page 4: left column: paragraphs 1-2: After building the model, we then deploy the model for test on the simulated UAV in the Gazebo simulator. This is done by developing the model as a plugin in the Gazebo simulator. To further validate the inference time of the models, we deploy them on Intel® NCS2 hardware, an inference device used to run deep learning models on the edge. Page 5, right column: last paragraph and page 6, left column, first paragraph: The generated telemetry dataset consists of both benign and spoofed trajectory data and its associated sensory readings of six UAVs models that include: Typhoon H480, Standard Plane, Standard VTOL, Tailsitter VTOL, the default Quadrotor, and Intel_R Aero RTF. The spoofed data was collected while the spoofing plugin in Gazebo was active. The amount of deviation, ϵ, that was introduced ranges from as low as 5 meters up to 150 meters. Page 6, right column, last paragraph: The models have competitive results in detecting GPS spoofing attacks that cause minor deviation (slow shifting patterns), obtaining a 93.97% accuracy for the generalized detector in the case of the binary classifier and 90.94% in the case of the autoencoder one-class classifier).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ the teachings of Agyapong in the invention of Xu in view of Sen to include the above limitations. The motivation to do so would be to telemetry data that is readily available during flights missions to detect GPS spoofing attacks (Agyapong: page 7: Conclusion: right column).
As per claim 9, Xu in view of Sen and Agyapong teaches:
The method according to claim 8, wherein the lightweight blockchain module acts as a security and trust networking infrastructure to provide decentralized security and privacy- preserving guarantees for UAM data (Xu: page 6: lines 269-270: The microchain fabric acts as a security and trust networking infrastructure to provide decentralized security and privacy preserving guarantees for UAM data).
As per claim 10, Xu in view of Sen and Agyapong teaches:
The method according to claim 8, wherein unmanned aerial vehicle (UAV) data and flight logs are stored securely and distributively without relying on any centralized server (Xu: page 7: lines 276-278: microchain integrates a lightweight consensus protocol with a hybrid on-chain and off-chain storage to ensure UAV data and flight logs are stored securely and distributively without relying on any centralized server).
As per claim 11, Xu in view of Sen and Agyapong teaches:
The method according to claim 8, wherein the hybrid on-chain and off-chain storage includes distributed data storage (DDS) built on a swarm network (Xu: page 3: lines 91-92: our LightMAN allows encrypted data to be stored on a distributed data storage (DDS). Page 8: lines 353-357: The organization of on-chain and off-chain storage is illustrated by the upper right part of Figure 1. The Distributed Data Storage (DDS), which is built on a Swarm network, is used as off-chain storage).
As per claim 12, Xu in view of Sen and Agyapong teaches:
The method according to claim 11, wherein unmanned aerial vehicle (UAV) data and flight logs are saved on the DDS and accessible by a swarm hash (Xu: page 8, lines 357-360: The UAV data and flight logs that require heterogeneous format and various sizes are saved on the DDS and they can be easily addressed by their swarm hash. As an optimal manner, each transaction only contains a swarm hash as a reference pointing to its raw data on the DDS).
As per claim 13, Xu in view of Sen and Agyapong teaches:
The method according to claim 12, wherein a transaction only includes the swarm hash as a reference pointing to corresponding raw data on the DDS (Xu: page 8, lines 357-360: As an optimal manner, each transaction only contains a swarm hash as a reference pointing to its raw data on the DDS).
As per claim 14, Xu in view of Sen and Agyapong teaches:
The method according to claim 8, wherein the one or more trained ML-based attack detection models include a long short-term memory (LSTM) model and/or an XceptionTime model (Xu: page 7: lines 318-321: (iii) Deep Learning Based Detection: LightMAN applies DL techniques (e.g., L-CNN, RNN/LSTM, etc.) to characterize the dynamic state of the monitored system. With the trained model in place, the operator can conduct the detection and classification of potential attacks).
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu and Design Guidelines for Blockchain-Assisted 5G-UAV Networks by Aloqaily et al (hereinafter Aloqaily).
As per claim 15, Xu teaches:
A method for forming a lightweight blockchain, comprising:
providing a first sub-system, wherein the first sub-system includes a lightweight consensus protocol that relies on a randomly selected consensus committee to achieve a low latency when committing transactions on a distributed ledger (Xu: page 8: 3.2. Microchain Fabric for UAM Data Sharing 330 As right part of Figure 1 shows, microchain fabric consists of two sub-systems: i) a lightweight consensus protocol that relies on a randomly selected consensus committee to achieve low latency of committing transactions on the distributed ledger);
providing a second sub-system, wherein the second sub-system includes a hybrid on-chain and off-chain storage that improves efficiency and privacy-preservation (Xu: page 8: 3.2. Microchain Fabric for UAM Data Sharing 330 As right part of Figure 1 shows, microchain fabric consists of two sub-systems: ii) a hybrid on-chain and off-chain storage strategy that improves efficiency and privacy-preservation), the hybrid on-chain and off-chain storage includes distributed data storage (DDS) built on a swarm network (Xu: page 3: lines 91-92: our LightMAN allows encrypted data to be stored on a distributed data storage (DDS). Page 8: lines 353-357: The organization of on-chain and off-chain storage is illustrated by the upper right part of Figure 1. The Distributed Data Storage (DDS), which is built on a Swarm network, is used as off-chain storage), the DDS is arranged to save unmanned aerial vehicle (UAV) data and flight logs, and the UAV data and flight logs are accessible by a swarm hash (Xu: page 8, lines 357-360: The UAV data and flight logs that require heterogeneous format and various sizes are saved on the DDS and they can be easily addressed by their swarm hash. As an optimal manner, each transaction only contains a swarm hash as a reference pointing to its raw data on the DDS); and
forming the lightweight blockchain that includes the first and second sub-systems (Xu: page 2, lines 82-83: , this paper proposes LightMAN, a lightweight microchained fabric for data assurance and operation resilience oriented UAM networks. Page 8: lines 331: As right part of Figure 1 shows, microchain fabric consists of two sub-systems).
Xu does not teach: wherein the lightweight blockchain is deployed in a decentralized environment based on an edge-fog-cloud computing paradigm comprising an edge computing layer containing UAVs as edge devices, a fog computing layer containing fog nodes, and a cloud computing layer, wherein raw mission data captured at the UAVs in the edge computing layer is transmitted to the fog computing layer for data aggregation and higher-level analytic services. However, Aloqaily teaches:
wherein the lightweight blockchain is deployed in a decentralized environment based on an edge-fog-cloud computing paradigm comprising an edge computing layer containing UAVs as edge devices, a fog computing layer containing fog nodes, and a cloud computing layer, wherein raw mission data captured at the UAVs in the edge computing layer is transmitted to the fog computing layer for data aggregation and higher-level analytic services (Aloqaily: page 65, left column, last 3 lines-right column, lines 1-12 and last paragraph: Coupled with the most advanced paradigms of communication (i.e., 5G), data storage (using edge, fog, cloud, blockchain technologies), and processing (e.g., ML and AI), drones as a service (DaaS) is able to provide a wide range of smart services (Fig. 1). Indeed, drones are able to perform any task on demand using the different communication infrastructures deployed around the city. The various types of collected data, the users’ requests, or the transactions among the service providers can be processed and stored by edge or cloud computing, allowing the extraction of valuable information. UAVs are considered cost-effective flying IoT devices used to collect truthful information for various applications. Surveillance, monitoring, and environment measurements are popular examples of IoT application-based drones. Page 65, figure 1 and page 66, figure 2 show drones as edge devices, a fog computing layer containing fog nodes, and a cloud computing layer. Figure 2 also shows the blockchain deployed in the edge-fog-cloud computing paradigm and data from drones being transmitted to the fog nodes. Page 68: left column: last 2 paragraphs: Blockchain can be used to integrate drones with fog and cloud by ensuring data integrity and secure communication. This in return makes the system more reliable and trustworthy).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ the teachings of Aloqaily in the invention of Xu to include the above limitations. The motivation to do so would be to ensure data integrity and secure communication (Aloqaily: Page 68: left column: last 2 paragraphs).
As per claim 16, Xu in view of Aloqaily teaches:
The method according to claim 15, wherein a transaction only includes the swarm hash as a reference pointing to corresponding raw data on the DDS (Xu: page 8, lines 357-360: As an optimal manner, each transaction only contains a swarm hash as a reference pointing to its raw data on the DDS).
As per claim 17, Xu in view of Aloqaily teaches:
The method according to claim 15, wherein a consensus mechanism runs on a predetermined small number of validators (Xu: page 10: lines 423-428:Fig. 4, committee size K represented by the number of validators. As microchain executes an efficient consensus protocol within a small consensus committee, it brings lower total latency).
As per claim 18, Xu in view of Aloqaily teaches:
The method according to claim 15, wherein a structure of the lightweight blockchain includes a plurality of confirmed blocks and a plurality of finalized blocks and each of the plurality of confirmed blocks and the plurality of finalized blocks uses a prehash to point to a corresponding parent block and extend a chain (page 8: lines 346-356: The block proposal leverages an efficient Proof-of-Credit (PoC) algorithm, which allows the consensus committee to continuously publish blocks (confirmed blocks) containing transactions and extend main chain length. The block proposal process keeps running multiple rounds until the end of an epoch. Then a voting based chain finality protocol allows committee members to make agreement on a checkpointing block. As a result, temporary fork chains are pruned and these committed blocks are finalized on the unique main-chain. As the basic unit of on-chain data recorded on the distributed ledger, a block contains header information (e.g., previous block hash and block height) and orderly transactions).
As per claim 19, Xu in view of Aloqaily teaches:
The method according to claim 15, wherein a chain height follows an increasing sequence of a plurality of finalized blocks (Xu: page 8: lines 354-356: As the basic unit of on-chain data recorded on the distributed ledger, a block contains header information (e.g., previous block hash and block height) and orderly transactions. It was well known to one of ordinary skill in the art before the effective filing date of the claimed invention that block height shows the number of blocks that have been added to a chain since the genesis block and the block height increases with each added block).
As per claim 20, Xu in view of Aloqaily teaches:
The method according to claim 15, wherein a workflow of the lightweight blockchain includes an initialization step, a committee selection step, a block proposal step, a chain finality step, and a committee change step (Xu: page 8: lines 335-339: The core functionalities and work flows are briefly described as follows: The lifetime of a committee is defined as a Dynasty, and all nodes within the network use a random committee election mechanism to construct a new committee at the beginning of a new dynasty (committee selection step). Until the current dynasty’s lifetime is ending, committee members utilize an epoch randomness generation protocol to cooperatively propose a global random seed for next committee election. Given a synchronous network environment, operations of consensus processes are coordinated in sequential rounds called Epoch. The block proposal leverages an efficient Proof-of-Credit (PoC) algorithm, which allows the consensus committee to continuously publish blocks containing transactions and extend main chain length. The block proposal process keeps running multiple rounds until the end of an epoch (block proposal step). Then a voting based chain finality protocol allows committee members to make agreement on a checkpointing block. As a result, temporary fork chains are pruned and these committed blocks are finalized on the unique main-chain (chain finality step). Fig. 1 shows committee selection step, a block proposal step, chain finality step and a committee change step. An initialization step where all the systems are initialized before the workflow process is started is well known to one of ordinary skill in the art before the effective filing date of the claimed invention).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
RODAD: Resilience Oriented Decentralized Anomaly Detection for Urban Air Mobility Networks by Wei et al: In this paper, we develop a Resilience Oriented Decentralized Anomaly Detection (RODAD) framework to maximize UAM capability to secure data access among aircraft and ATS service providers based on microservices technologies in an edge-fog-cloud computing paradigm. Machine learning based anomaly detection (MLAD) is developed to detect anomaly behaviors (e.g., aircraft route anomaly) against both single feature and multi-feature spoofing attacks across avionics mission data. Two GPS spoofing attack scenarios (e.g., restricted and generalized) with four attacking types (e.g., continuous, interim, biased, random) are crafted for the performance evaluation. A hardware-in-the-loop (HITL) implementation is also developed to demonstrate the effectiveness of RODAD for supporting real-time resilient analysis. Our experiments validate the performance of RODAD in detection accuracy and efficiency against spoofing attacks for UAM.
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 MADHURI R HERZOG whose telephone number is (571)270-3359. The examiner can normally be reached 8:30AM-4:30PM.
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MADHURI R. HERZOG
Primary Examiner
Art Unit 2438
/MADHURI R HERZOG/Primary Examiner, Art Unit 2438
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