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 .
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.
Claim 1-9, 11-18, 20-21, 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without “significantly more”. Claim(s) 1-9, 11-18, 20-21, 24 is/are directed to Abstract Idea such as an idea standing alone such as an instantiated concept, pan or scheme, as well as a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper for example collecting, analyzing and reporting information to the management system.
The apparatus and the method claim 1, 21 and 24 recites limitation, “a node of a wireless communication network obtaining event data related to one or more wireless devices connected to the wireless communication network; based on analyzing the event data, the node detecting that at least one of the one or more wireless devices corresponds to a rogue unmanned aerial vehicle, UAV; and the node reporting the detected at least one wireless device to an aircraft traffic management system”. Since the claim is directed to a process and a machine, which is one of the statutory categories of the invention (Step 1: YES).
The claim is then analyzed to determine whether it is directed to any judicial exception. The claim recites a node of a wireless communication network obtaining event data; based on analyzing the event data, the node detecting that at least one of the one or more wireless devices corresponds to a rogue unmanned aerial vehicle, UAV; and the node reporting the detected at least one wireless device to an aircraft traffic management system. Current claims do recite a mental process because it contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include. For example a claim to "collecting information i.e., a node of a wireless communication network obtaining event data, analyzing it i.e., based on analyzing the event data, and displaying certain results i.e., the node reporting the detected at least one wireless device to an aircraft traffic management system of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) recited in the claim is no more than an abstract idea i.e., mental process of estimating, etc. (Step 2A: Prong One Abstract Idea=Yes).
The claim is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. In the current claims, there is no additional elements that would integrate the abstract idea into a practical application (Step 2A: Prong Two Abstract Idea=Yes).
Next the claim as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. The claim requires the additional limitation of a computer with the central processing unit, memory, a printer, an input and output terminal and a program. These generic computer components are claimed to perform the basic functions of storing, retrieving and processing data through the program that enables. In the current scenario, there are no additional elements that would amount to significantly more than the abstract idea. Therefore, the claim does not amount to significantly more than the abstract idea itself (Step 2B: No). Accordingly, the claim is not patent eligible.
Further, dependent claims do not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims.
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.
(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) 1-9, 14-18, 20, 21, 24 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. Pub. No. US 20220051570 A1
Regarding Claim 1, Zhang teaches a method of controlling wireless communication, the method (Fig. 2 and 3 and Para 99 and 100, a method for detecting an unauthorized uncrewed aerial vehicle) comprising:
a node of a wireless communication network (Fig. 2 and 3, AMF) obtaining event data related to one or more wireless devices (Para 130, after a request of the network device is received, the user equipment may report the attribute information of the user equipment. After receiving the attribute information reported by the user equipment, the network device may send, to the AMF, the attribute information reported by the user equipment i.e., obtaining event data related to one or more wireless devices) connected to the wireless communication network (S202 and Para 126, the network device configures the user equipment in the coverage area of the network device i.e., UE connected to the wireless communication network);
based on analyzing the event data (Para 87 and 136 and Step S205, The AMF identifies the identity status of the user equipment based on the attribute information of the user equipment), the node detecting that at least one of the one or more wireless devices corresponds to a rogue unmanned aerial vehicle, UAV (Para 89 and 137, The identity status includes an authorized uncrewed aerial vehicle or an unauthorized uncrewed aerial vehicle); and
the node reporting the detected at least one wireless device to an aircraft traffic management system (Fig. 3 Step S206 and Para 93 and 145, The AMF sends an unauthorized uncrewed aerial vehicle reporting message to the uncrewed aerial vehicle traffic management (UTM) network element device).
Regarding Claim 2, Zhang teaches wherein the event data comprise mobility data indicative of mobility of the one or more wireless devices (Para 81).
Regarding Claim 3, Zhang teaches wherein the mobility data comprise data indicating position of the wireless device and corresponding time information (Para 81).
Regarding Claim 4, Zhang teaches wherein the event data comprise communication data indicative of data communication between the one or more wireless devices and the wireless communication network (Para 81 and 127).
Regarding Claim 5, Zhang teaches wherein the communication data comprise data indicative of user plane activity of the one or more wireless devices (Para 81).
Regarding Claim 6, Zhang teaches wherein the communication data comprise data describing one or more data flows established the one or more wireless devices (Para 81).
Regarding Claim 7, Zhang teaches wherein the communication data comprise data indicative of communication destinations of the one or more wireless devices (Para 131).
Regarding Claim 8, Zhang teaches wherein the event data are filtered based on a geographical area of interest (Para 81).
Regarding Claim 9, Zhang teaches wherein the event data are filtered based on a list of identifiers of the one or more wireless devices (Para 81).
Regarding Claim 14, Zhang teaches wherein said reporting comprises indicating an identifier of the detected at least one wireless device (Para 132).
Regarding Claim 15, Zhang teaches wherein said reporting comprises indicating a position of the detected at least one wireless device (Para 93).
Regarding Claim 16, Zhang teaches wherein said reporting comprises indicating an estimated future trajectory of the detected at least one wireless device (Para 147).
Regarding Claim 17, Zhang teaches wherein said reporting comprises indicating a metric representing a level of confidence that the detected at least one wireless device corresponds to a rogue UAV (Para 147).
Regarding Claim 18, Zhang teaches further comprising: triggering at least one action for the detected at least one wireless device, wherein the at least one action comprises one or more of: redirecting traffic of the detected at least one wireless device, blocking traffic of the detected at least one wireless device, disabling a subscription associated with the detected at least one wireless device, reporting traffic of the detected at least one wireless device, and enforcing authentication of the detected at least one wireless device (Para 148).
Regarding Claim 20, Zhang teaches wherein the node performs said analyzing and reporting in response to a subscription from the aircraft traffic management system (Para 88).
Regarding Claim 21, it has been rejected for the same reasons as claim 1 and further Zhang teaches a node for a wireless communication network (Fig. 6 and 7 and Para 213, AMF), the node comprising: a processing unit (Fig. 6 Unit 602, processing unit), and a memory containing program code executable by the processing unit (Para 76, the processor is configured to read the program instructions in the memory, and perform, according to the program instructions in the memory).
Regarding Claim 24, it has been rejected for the same reasons as claim 1 and further Zhang teaches a non-transitory computer readable storage medium storing program code to be executed by at least one processor of a node of a wireless communication network, wherein execution of the program code (Para 77).
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.
Claim(s) 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. Pub. No. US 20220051570 A1 in view of Correnti et al. Pub. No. US 20190199756 A1.
Regarding Claim 11, Zhang does not specifically teach wherein said analyzing of the event data is based on a machine learning model, and the machine learning model is trained based on training event data related to one or more wireless devices classified as rogue UAV.
However, in the same field of endeavor, Correnti teaches the drone detection unit 116 may delay instructing the network adjustment unit 118 to initiate performance of one or more operations to secure the network 140 until the drone detection unit 116 determines whether the unauthorized drone 105 is a hacker drone. The drone detection unit 116 may determine whether the drone 105 is hacker drone by analyzing the radio frequency (RF) signals output by a radio transmitter 105a of the drone 105. Analyzing the RF signals output by the drone's 105 radio transmitter 105a may include determining whether the drone 105 is communicating with, or is attempting to communicate with, the network 140. Alternatively, or in addition, in some implementations, the drone detection unit 116 may employ one or more artificial intelligence models (e.g., neural networks) to analyze features of network communication data transfers in order to determine if the communications associated with such data transfers are associated with a hack attempt by one or more malicious parties such as a hacking drone 105 i.e., analyzing of the event data is based on a machine learning model, and the machine learning model is trained based on training event data related to one or more wireless devices classified as rogue UAV (Para 43).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Zhang with the method of Correnti so as to secure the network by adjust the network parameter based on selected network adjustment policies (See Correnti Abstract).
Regarding Claim 12, Zhang does not specifically teach wherein said analyzing of the event data is based on a machine learning model, and the machine learning model is trained based on training event data related to one or more wireless devices classified as regular UAV.
However, in the same field of endeavor, Correnti teaches that the monitoring unit can determine 240 that the unauthorized drone is communicating, or attempting to communicate, with a network associated with the property. For example, the monitoring unit can determine whether the unauthorized drone is communicating, or attempting to communicate, with the network by analyzing the radio frequency (RF) signals output by a radio transmitter of the drone. Alternatively, or in addition, in some implementations, the monitoring unit may employ one or more artificial intelligence models (e.g., neural networks) to analyze features of network communication data transfers in order to determine if the communications associated with such data transfers are associated with a hack attempt by one or more malicious parties such as a hacking drone. The features of the network communication data analyzed by the monitoring unit may be obtained by, and transmitted to, the monitoring unit by one or more drone detecting sensors or one or more other components of the monitoring system. In response to determining that an unauthorized drone is communicating, or attempting to communicate, with a network associated with the property, the monitoring unit may select 250 one or more network adjustment policies that can be initiated to secure the network.
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Zhang with the method of Correnti so as to secure the network by adjust the network parameter based on selected network adjustment policies (See Correnti Abstract).
Regarding Claim 13, Zhang does not specifically teach wherein said analyzing of the event data is based on a machine learning model, and the machine learning model is based on reinforcement learning.
However, in the same field of endeavor, Correnti teaches In response to determining that an unauthorized drone is communicating, or attempting to communicate, with a network associated with the property, the monitoring unit may select 250 one or more network adjustment policies that can be initiated to secure the network (Para 66).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Zhang with the method of Correnti so as to secure the network by adjust the network parameter based on selected network adjustment policies (See Correnti Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Panchal et al. Pub. No. US 20210160721 A1 - Method for detecting, classifying and managing unauthorized airborne device, involves performing action to identify target device as unauthorized airborne device to manage target device based on confirming whether target device is airborne by using device
Duchin et al. Patent No. US 9690937 B1 - Recommending a set of malicious activity detection rules in an automated, data-driven manner
Rockwell Pub. No. US 20030027550 A1 - Airborne security manager
Defending airports from UAS: A survey on cyber-attacks and counter-drone sensing technologies – 2020
Cybersecurity of the internet of drones: Vulnerabilities analysis and IMECA based assessment - 2018
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NIZAR N. SIVJI
Primary Examiner
Art Unit 2647
/NIZAR N SIVJI/ Primary Examiner, Art Unit 2647
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