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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/12/2026 has been entered. Claims 1 and 11 have been amended. Claims 2 and 12 are cancelled. Claims 1, 3-11 and 13-21 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments and remarks filed on 03/12/2026 have been fully considered.
Applicant’s arguments provided for the U.S.C. §102 and §103 rejections of claims 1, 3-11 and 13-21 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 Objections
Claims 1 and 11 are objected to because of the following informalities: the claims use the acronym “ADS-B” without defining it. The acronym should be defined upon its first use in the claims: “Automatic Dependent Surveillance-Broadcast (ADS-B)”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Examiner interprets claim limitations of claim 1 under 35 U.S.C. 112(f). Supporting structure from the specification for these limitations is detailed below:
Regarding claim 1, structure for “an ADS-B processing module configured to extract data from the ADS-B waveform…” can be found in Fig. 1B, flow diagram surrounding ADS-B processing module 102 and related description in paragraph [0042]; “With reference to Fig. 1B, after receiving an ADS-B track waveform 100 from the antenna 126, an ADS-B processing module 102 within the ADS-B receiver 128 extracts analysis data 106 from the track 100 and forwards the analysis data 106 to a verifying unit 130…” Support is also found in paragraph [0069]: “With reference to Fig. 6, the method of the present disclosure includes selecting a plurality of spoofing detection tests and associated weighting factors 600 that will be applied to a detected ADS-B waveform by the one or more processors in the ADS-B receiver 128.”
Regarding claim 1, structure for “a verifying unit configured to: receive from the ADS-B processing module the extracted data from the ADS-B waveform; apply a plurality of detection tests to the extracted data, each of said detection tests resulting in a score indicating a degree of likelihood that the apparent ADS-B track is a valid ADS-B track; apply a weighting factor to each of the scores; and combine the scores to obtain a confidence level predicting whether the ADS-B waveform is a valid ADS-B waveform containing accurate ADS-B track information, or a spoofed waveform containing inaccurate or specious ADS-B track information …” can be found in Fig. 1B, flow diagram for verifying unit 130, and associated description in paragraph [0042]; “A plurality of software/firmware detection tests 104 associated with one or more processors are applied by the verifying unit 130 to the analysis data 106.”
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, 3-11 and 13-21 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.
Claims 1 and 11 recite the limitation "the ADS-B standard" in lines 15 and 15-16, respectively. There is insufficient antecedent basis for this limitation in the claim, as an ADS-B standard has not previously been recited. A possible correction would be to introduce an ADS-B standard in the claim preamble.
Dependent claims are likewise rejected.
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.
Claims 1, 3, 6-8, 11, 13 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Manesh et al. (US-20220094710-A1; hereinafter Manesh) in view of Dormiani (US-20200241144-A1; hereinafter Dormiani).
Regarding claim 1, Manesh discloses:
An apparatus for discriminating between spoofed and valid ADS-B tracks (see at least Fig. 1, ADS-B cyber-attack detection and mitigation system 100), the apparatus comprising:
an input configured to receive an ADS-B waveform (see at least Fig. 1, block 110 input ADS-B signals) detected by one or more antennae (see at least [0010]; “These solutions do not require implementing additional array antennas or an encrypted or any other new communication protocol.”), an apparent ADS-B track being encoded in the ADS-B waveform (see at least [0003]; “The ADS-B systems use Global Navigation Satellite Systems (GLASS) such as the Global Positioning System (GPS) to gather and broadcast flight information, such as aircraft identity, heading, three-dimensional position, velocity, and other flight information. The information is broadcast in the form of unencrypted plaintext messages, allowing other aircraft and ground stations to receive and process the information for air traffic awareness and control purposes.”);
an ADS-B processing module configured to extract data from the ADS-B waveform (see at least Fig. 1, block 120 signal characteristics calculation / subsystem A), said data including digital data reported by the apparent ADS-B track (see at least [0013]; “Subsystem A 120 may implement signal characteristics calculations. In operation, Subsystem A receives input ADS-B signals 110 and extract and calculate various parameters from the received ADS-B signals. In an embodiment, these parameters may be classified into two broad categories as (1) information taken from the content of the message (e.g., transmitter latitude, longitude, heading, velocity), and (2) information extracted from the ADS-B physical signal itself (e.g., received signal strength (RSS), frequency of the received signal).”); and
a verifying unit (see at least Fig. 1, subsystems B, C and D) configured to:
receive from the ADS-B processing module the extracted data from the ADS-B waveform;
apply a plurality of detection tests to the extracted data (see at least [0029]; “Subsystem B 130, the attack detection subsystem, may implement attack detection using various attack detection blocks based on parameters received from Subsystem A 120. Subsystem B 130 may include three machine learning algorithms, including jamming attack detection 132, injection attack detection 134, and modification attack detection 136. In an embodiment, a random forest algorithm is used for jamming attack detection 132, and neural networks are used for injection attack detection 134, and modification attack detection 136.”), each of said detection tests resulting in a score indicating a degree of likelihood that the apparent ADS-B track is a valid ADS-B track (see at least [0030]; “Jamming attack detection 132 may receive the group of jamming attack parameters 122, which includes mean eigenvalue (EGV), signal-to-noise ratio (SNR), received signal strength (RSS), had packet ratio (BPR), and energy, test statistic (E). These parameters are provided to a random forest algorithm to detect abnormalities in the signals. This jamming detection algorithm includes an ensemble of decision tree classifiers in which each classifier is created by means of a vector sampled randomly and independently from the input vector, and each tree provides a unit vote for the most popular class to classify an input vector. The classification of data is performed by taking the majority voted class from all the tree predictors in the forest… Jamming attack detection block 132 provides this classification decision Ŷ as the jamming attack detection output to Subsystem C 140.” See also [0031]; “Injection attack detection 134 may receive the group of injection attack parameters 124, including ownship latitude (LatR), ownship longitude (LonR), transmitter latitude (LatT), transmitter longitude (LonT), velocity difference (Δv), doppler shift (Δf), and received signal strength (RSS). Injection attack detection 134 uses its received parameters to provide Subsystem C 140 with a binary decision about presence or absence of the attack.”);
apply a weighting factor to each of the scores (see at least [0034]; “In the Bayesian models used in Subsystem C 140, prior knowledge of the attacks is used to compute the posterior probability distribution of the attacks when a new ADS-B message is received.” This computation is performed in equation 28, where the prior probability distribution PriorAtt is used to scale the computed attack probability and thus can be described as a weighting factor.); and
combine the scores to obtain a confidence level predicting whether the ADS-B waveform is a valid ADS-B waveform containing accurate ADS-B track information, or a spoofed waveform containing inaccurate or specious ADS-B track information (see at least [0033]; “Subsystem C 140 may implement Bayesian inference processing for each type of attack. Subsystem C 140 may use Bayesian models to calculate the probability that each type of attack will happen, to calculate the probability of detection, or provide other calculations. Subsystem C 140 uses Bayesian methodologies to generate an estimate of the probability of detection, probability of false alarm, and accuracy of the attack based on past and present data.” See also [0038]; “Subsystem D 150 may implement decision-making and provide an ADS-B system output 160, which may include the ADS-B cyber-attack type and probability. Subsystem D 150 may receive the output from Subsystem C 140 from all three detection algorithms, including attack type, attack rate, PostAtt, and the attack presence probability, ATProb. Subsystem D 150 synthesizes (e.g., analyzes and combines) the information, and provide an attack type and probability of attack as ADS-B system output 160.”).
However, Manesh does not explicitly teach:
wherein the plurality of detection tests includes a multi-band detection analysis applied to the digital data reported by the ADS-B track, wherein the ADS-B standard provides 2 channels at different frequencies, and wherein embodiments of the apparatus include a dual band receiver that can monitor both ADS-B channels simultaneously, so that apparently rebroadcast ADS-B tracks can be confirmed as authentic.
Manesh discloses methods for detection of ADS-B cyber-attacks, and Dormiani is directed to methods for air traffic tracking by autonomous aerial vehicle using ADS-B. Manesh discloses:
a multi-band detection analysis applied to the digital data reported by the ADS-B track see at least [0081]; “The method 500 could still further entail determining that a first incoming tracking message received on the first transmission technology and a second incoming tracking message received on the second transmission technology both contain concurrent real-time tracking data for the same flight vehicle other than the AV.”), wherein the ADS-B standard provides 2 channels at different frequencies (see at least [0004]; “Under ADS-B, two different transmission link technologies have been implemented that are compliant with the standard but not directly compatible with each other. Both technologies employ radio frequency (RF) transmission and reception, differing mainly according to RF operating frequency and transponder technology, as well as certain aspects of message structure. The two technologies are Universal Access Transmitter (UAT), which operates at 978 MHz, and 1090 MHz Extended Squitter (1090ES), which operates at 1,090 MHz.”), and wherein embodiments of the apparatus include a dual band receiver that can monitor both ADS-B channels simultaneously, so that apparently rebroadcast ADS-B tracks can be confirmed as authentic (see at least [0081]; “The method 500 could still further entail determining that a first incoming tracking message received on the first transmission technology and a second incoming tracking message received on the second transmission technology both contain concurrent real-time tracking data for the same flight vehicle other than the AV. In the example of ADS-B described above, this could occur, for instance when a 1090ES message is directly received from another vehicle, while a duplicate is received on UAT via a ground relay station. Or the reverse could occur: a UAT message could be directly received from another vehicle, while a duplicate is received on 1090ES is received via a ground relay station.” Determining that messages received from another vehicle and from a ground relay station are duplicates means that the ground relay station messages can be confirmed as authentic).
Both Manesh and Dormiani teach methods related to ADS-B. Dormiani shows that it is known in the art to receive both the 978 MHz and 1090 MHz ADS-B transmission technologies in the same aircraft receiver. Arteaga (US-20240363014-A1) further confirms that such a reception scheme is well-known and used in the art, see [0029]: “Antenna(s) 28 and unit(s) 37 can be configured to receive ADS-B messages on the 978 MHz frequency and/or the 1090 MHz frequency as is known in the art.” It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the receiver of Dormiani in the system of Manesh (see Manesh Abs: “These solutions operate based on current ADS-B receiver technology, and can be combined with existing ADS-B receivers…”). Use of such a receiver would thus include the multi-band detection analysis of Dormiani in the detection tests of Manesh.
Regarding claim 3, Manesh in view of Dormiani discloses the apparatus of claim 1. Manesh further discloses:
wherein the data extracted from the ADS-B waveform further includes a detected power level at which the ADS-B waveform was detected (see at least Table 1 and [0015]; “Received signal strength (RSS): This parameter has a correlation with the great circle distance, which is calculated from the coordinates of the ownship and the transmitter. RSS is given by the Friis transmission equation as follows…”), and wherein the plurality of detection tests includes a power level validation applied to the detected power (see at least [0032]; “Modification attack detection 136 may receive the group of message modification attack parameters 126, including received signal strength (RSS), bad packet ratio (BPR), rate of climb (cr), rate of turn (tr), speed rate (vr), and displacement rate (dr). Similar to the neural network algorithm for injection attack detection 134, the neural network of the modification attack detector 136 provides an output determination as to whether the signal under consideration is modified or not.”).
Regarding claim 6, Manesh in view of Dormiani discloses the apparatus of claim 1. Manesh further discloses:
wherein the verifying unit further includes a weighting factor library containing a plurality of sets of weighting factors, and wherein applying a weighting factor to each of the scores includes selecting one of the sets of weighting factors from the weighting factor library, and applying the weighting factors of the selected set to the scores (see at least [0034]; “In the Bayesian models used in Subsystem C 140, prior knowledge of the attacks is used to compute the posterior probability distribution of the attacks when a new ADS-B message is received… PriorAtt is the prior probability distribution, and PostAtt is the posterior probability distribution. The maximum value of PostAtt is considered as the attack rate and taken as PriorOcc for the next iteration.” Examiner maps all values of PostAtt to the plurality of sets of weighting factors, selecting maximum value of PostAtt to selecting a set of weighting factors, and use of the selected value in the subsequent Bayesian analysis to applying the weighting factor to the selected set of scores.).
Regarding claim 7, Manesh in view of Dormiani discloses the apparatus of claim 1. Manesh further discloses:
wherein the verifying unit further includes a detection test library containing a plurality of detection tests (see at least [0029]; “Subsystem B 130, the attack detection subsystem, may implement attack detection using various attack detection blocks based on parameters received from Subsystem A 120.”), and wherein applying the plurality of detection tests to the extracted data includes selecting a plurality of detection tests from the detection test library, and applying the selected detection tests to the extracted data (see at least [0029]; “Subsystem B 130 may include three machine learning algorithms, including jamming attack detection 132, injection attack detection 134, and modification attack detection 136. In an embodiment, a random forest algorithm is used for jamming attack detection 132, and neural networks are used for injection attack detection 134, and modification attack detection 136. Each of the detection blocks 132-136 classify the incoming signals in real-time based on the provided characteristics of the signal.”).
Regarding claim 8, Manesh in view of Dormiani discloses the apparatus of claim 1. Manesh further discloses:
wherein the verifying unit is further configured to generate display data according to apparent ADS-B tracks encoded in a plurality of detected ADS-B waveforms and their associated confidence levels, and to forward the display data to a "situational awareness" display (SA) (see at least [0039]; “The ADS-B system output 160 may include identification of one or more warnings, alerts, and attacks. The ADS-B system output 160 may be associated with specific visible, audible, tactile, or other indicators. For example, ADS-B system output 160 may be used to notify an air controller by using red, yellow, and green visual indicators to indicate one or more attack types, attack probabilities, attack severities, and other attack information.”).
Regarding claim 11, Manesh discloses:
A method of discriminating between spoofed and valid ADS-B tracks (see at least Abs; “The present subject matter provides various technical solutions to technical problems facing ADS-B cyber-attacks. One technical solution for detecting and mitigating ADS-B cyber-attacks includes receiving extracting information from received ADS-B signals, detecting a cyber-attack based on a selected subset of ADS-B information, determining a detection probability, and outputting a ADS-B cyber-attack type and probability.”), the method comprising:
receiving an ADS-B waveform, an apparent ADS-B track being encoded in the ADS-B waveform (see at least [0013]; “In operation, Subsystem A receives input ADS-B signals 110 and extract and calculate various parameters from the received ADS-B signals. In an embodiment, these parameters may be classified into two broad categories as (1) information taken from the content of the message (e.g., transmitter latitude, longitude, heading, velocity), and (2) information extracted from the ADS-B physical signal itself (e.g., received signal strength (RSS), frequency of the received signal).”);
extracting data from the ADS-B waveform, said data including digital data reported by the apparent ADS-B track (see again [0013]);
applying a plurality of detection tests to the data extracted from the ADS-B waveform (see at least [0029]; “Subsystem B 130, the attack detection subsystem, may implement attack detection using various attack detection blocks based on parameters received from Subsystem A 120. Subsystem B 130 may include three machine learning algorithms, including jamming attack detection 132, injection attack detection 134, and modification attack detection 136. In an embodiment, a random forest algorithm is used for jamming attack detection 132, and neural networks are used for injection attack detection 134, and modification attack detection 136.”), each of said detection test applications resulting in a corresponding score indicating a degree of likelihood that the apparent ADS-B track is a valid ADS-B track (see at least [0030]; “Jamming attack detection 132 may receive the group of jamming attack parameters 122, which includes mean eigenvalue (EGV), signal-to-noise ratio (SNR), received signal strength (RSS), had packet ratio (BPR), and energy, test statistic (E). These parameters are provided to a random forest algorithm to detect abnormalities in the signals. This jamming detection algorithm includes an ensemble of decision tree classifiers in which each classifier is created by means of a vector sampled randomly and independently from the input vector, and each tree provides a unit vote for the most popular class to classify an input vector. The classification of data is performed by taking the majority voted class from all the tree predictors in the forest… Jamming attack detection block 132 provides this classification decision Ŷ as the jamming attack detection output to Subsystem C 140.” See also [0031]; “Injection attack detection 134 may receive the group of injection attack parameters 124, including ownship latitude (LatR), ownship longitude (LonR), transmitter latitude (LatT), transmitter longitude (LonT), velocity difference (Δv), doppler shift (Δf), and received signal strength (RSS). Injection attack detection 134 uses its received parameters to provide Subsystem C 140 with a binary decision about presence or absence of the attack.”);
applying a weighting factor to each of the scores (see at least [0034]; “In the Bayesian models used in Subsystem C 140, prior knowledge of the attacks is used to compute the posterior probability distribution of the attacks when a new ADS-B message is received.” This computation is performed in equation 28, where the prior probability distribution PriorAtt is used to scale the computed attack probability and thus can be described as a weighting factor.); and
combining the weighted scores to obtain a confidence level predicting whether the ADS-B waveform is a valid ADS-B waveform containing accurate ADS-B track information, or a spoofed waveform containing inaccurate or specious ADS-B track information (see at least [0033]; “Subsystem C 140 may implement Bayesian inference processing for each type of attack. Subsystem C 140 may use Bayesian models to calculate the probability that each type of attack will happen, to calculate the probability of detection, or provide other calculations. Subsystem C 140 uses Bayesian methodologies to generate an estimate of the probability of detection, probability of false alarm, and accuracy of the attack based on past and present data.” See also [0038]; “Subsystem D 150 may implement decision-making and provide an ADS-B system output 160, which may include the ADS-B cyber-attack type and probability. Subsystem D 150 may receive the output from Subsystem C 140 from all three detection algorithms, including attack type, attack rate, PostAtt, and the attack presence probability, ATProb. Subsystem D 150 synthesizes (e.g., analyzes and combines) the information, and provide an attack type and probability of attack as ADS-B system output 160.”).
However, Manesh does not explicitly teach:
wherein the plurality of detection tests includes a multi-band detection analysis applied to the digital data reported by the ADS-B track, wherein the ADS-B standard provides 2 channels at different frequencies, and wherein embodiments of the apparatus include a dual band receiver that can monitor both ADS-B channels simultaneously, so that apparently rebroadcast ADS-B tracks can be confirmed as authentic.
Manesh discloses methods for detection of ADS-B cyber-attacks, and Dormiani is directed to methods for air traffic tracking by autonomous aerial vehicle using ADS-B. Manesh discloses:
a multi-band detection analysis applied to the digital data reported by the ADS-B track see at least [0081]; “The method 500 could still further entail determining that a first incoming tracking message received on the first transmission technology and a second incoming tracking message received on the second transmission technology both contain concurrent real-time tracking data for the same flight vehicle other than the AV.”), wherein the ADS-B standard provides 2 channels at different frequencies (see at least [0004]; “Under ADS-B, two different transmission link technologies have been implemented that are compliant with the standard but not directly compatible with each other. Both technologies employ radio frequency (RF) transmission and reception, differing mainly according to RF operating frequency and transponder technology, as well as certain aspects of message structure. The two technologies are Universal Access Transmitter (UAT), which operates at 978 MHz, and 1090 MHz Extended Squitter (1090ES), which operates at 1,090 MHz.”), and wherein embodiments of the apparatus include a dual band receiver that can monitor both ADS-B channels simultaneously, so that apparently rebroadcast ADS-B tracks can be confirmed as authentic (see at least [0081]; “The method 500 could still further entail determining that a first incoming tracking message received on the first transmission technology and a second incoming tracking message received on the second transmission technology both contain concurrent real-time tracking data for the same flight vehicle other than the AV. In the example of ADS-B described above, this could occur, for instance when a 1090ES message is directly received from another vehicle, while a duplicate is received on UAT via a ground relay station. Or the reverse could occur: a UAT message could be directly received from another vehicle, while a duplicate is received on 1090ES is received via a ground relay station.” Determining that messages received from another vehicle and from a ground relay station are duplicates means that the ground relay station messages can be confirmed as authentic).
Both Manesh and Dormiani teach methods related to ADS-B. Dormiani shows that it is known in the art to receive both the 978 MHz and 1090 MHz ADS-B transmission technologies in the same aircraft receiver. Arteaga (US-20240363014-A1) further confirms that such a reception scheme is well-known and used in the art, see [0029]: “Antenna(s) 28 and unit(s) 37 can be configured to receive ADS-B messages on the 978 MHz frequency and/or the 1090 MHz frequency as is known in the art.” It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the receiver of Dormiani in the system of Manesh (see Manesh Abs: “These solutions operate based on current ADS-B receiver technology, and can be combined with existing ADS-B receivers…”). Use of such a receiver would thus include the multi-band detection analysis of Dormiani in the detection tests of Manesh.
Regarding claim 13, Manesh in view of Dormiani discloses the method of claim 11. Manesh further discloses:
wherein extracting data from the ADS-B waveform includes determining a detection power at which the ADS-B waveform was detected (see at least Table 1 and [0015]; “Received signal strength (RSS): This parameter has a correlation with the great circle distance, which is calculated from the coordinates of the ownship and the transmitter. RSS is given by the Friis transmission equation as follows…”),
and wherein applying the plurality of detection tests includes applying a power level analysis to the detection power (see at least [0032]; “Modification attack detection 136 may receive the group of message modification attack parameters 126, including received signal strength (RSS), bad packet ratio (BPR), rate of climb (cr), rate of turn (tr), speed rate (vr), and displacement rate (dr). Similar to the neural network algorithm for injection attack detection 134, the neural network of the modification attack detector 136 provides an output determination as to whether the signal under consideration is modified or not.”).
Regarding claim 17, Manesh in view of Dormiani discloses the method of claim 11. Manesh further discloses:
wherein applying a weighting factor to each of the validity scores includes selecting a set of weighting factors from a weighting factor library, and applying the weighting factors of the selected set to the scores (see at least [0034]; “In the Bayesian models used in Subsystem C 140, prior knowledge of the attacks is used to compute the posterior probability distribution of the attacks when a new ADS-B message is received… PriorAtt is the prior probability distribution, and PostAtt is the posterior probability distribution. The maximum value of PostAtt is considered as the attack rate and taken as PriorOcc for the next iteration.” Examiner maps all values of PostAtt to the plurality of sets of weighting factors, selecting maximum value of PostAtt to selecting a set of weighting factors, and use of the selected value in the subsequent Bayesian analysis to applying the weighting factor to the selected set of scores.).
Regarding claim 18, Manesh in view of Dormiani discloses the method of claim 11. Manesh further discloses:
wherein applying the plurality of detection tests to the extracted data includes selecting a plurality of detection tests from a detection test library (see at least [0029]; “Subsystem B 130, the attack detection subsystem, may implement attack detection using various attack detection blocks based on parameters received from Subsystem A 120.”), and applying the selected detection tests to the data extracted from the ADS-B waveform (see at least [0029]; “Subsystem B 130 may include three machine learning algorithms, including jamming attack detection 132, injection attack detection 134, and modification attack detection 136. In an embodiment, a random forest algorithm is used for jamming attack detection 132, and neural networks are used for injection attack detection 134, and modification attack detection 136. Each of the detection blocks 132-136 classify the incoming signals in real-time based on the provided characteristics of the signal.”).
Regarding claim 19, Manesh in view of Dormiani discloses the method of claim 11. Manesh further discloses:
further comprising generating display data according to apparent ADS-B tracks encoded in a plurality of detected ADS-B waveforms and their associated confidence levels, and forwarding the display data to a "situational awareness" display (SA) (see at least [0039]; “The ADS-B system output 160 may include identification of one or more warnings, alerts, and attacks. The ADS-B system output 160 may be associated with specific visible, audible, tactile, or other indicators. For example, ADS-B system output 160 may be used to notify an air controller by using red, yellow, and green visual indicators to indicate one or more attack types, attack probabilities, attack severities, and other attack information.”).
Claims 4-5 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Manesh in view of Dormiani, further in view of Stayton et al. (US-20120041620-A1; hereinafter Stayton).
Regarding claim 4, Manesh in view of Dormiani discloses the apparatus of claim 1. However, Manesh does not explicitly disclose:
wherein:
the one or more antennae includes at least two antennae;
the data extracted from the ADS-B waveform further includes detection power levels at which the ADS-B waveform was detected by each of the at least two antennae; and
the plurality of detection tests includes an antenna diversity analysis applied to the detection power levels.
Manesh is directed to detecting cyber-attacks in ADS-B systems, and Stayton is directed to detecting spoofing in systems such as ADS-B systems. Stayton teaches:
wherein:
the one or more antennae includes at least two antennae;
the data extracted from the ADS-B waveform further includes detection power levels at which the ADS-B waveform was detected by each of the at least two antennae; and
the plurality of detection tests includes an antenna diversity analysis applied to the detection power levels (see at least [0054] – [0060]; “FIG. 7 illustrates a method according to certain embodiments of the present invention. The method includes, at 710, receiving, on a device, a signal providing a report for an aircraft. The device can be located in own aircraft. The report can be or include a position report. The signal can include or encode an ADS-B message…
The method of FIG. 7 also includes, at 720, determining, with the device, a first parameter for the aircraft from information in the report. The method can further include, at 730, determining, with the device, a second parameter for the aircraft from at least one signal characteristic of the signal.
The method, as illustrated in FIG. 7, additionally includes, at 740, determining, with the device, a validity status of the report based on comparing the first parameter and the second parameter. Determining the validity status based on the comparing can include, at 742, determining that the report is valid when a difference between the first parameter and the second parameter is less than a predetermined threshold. The validity here can refer to whether or not the report is a spoof report for a non-existent aircraft…
The signal characteristic can be or include at least one of the following: radio frequency power level of the signal; differential time measurement of receipt of the signal; or Doppler frequency change of the signal.
RF power level can be used in various ways. For example, RF power level can be used in terms of an expected power level with respect to range. Additionally, or alternatively, however, RF power level can be used with respect to a comparison of the signal strength received by a top antenna and a bottom antenna. If an aircraft is above own aircraft, the top antenna is expected to have a higher signal strength then the bottom antenna, for example, particularly when the aircraft is reported to be close (for example, within one mile) to own aircraft.”).
Both Manesh and Stayton teach a plurality of methods to detect spoofing or attacks on ADS-B messages. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the metrics used in Manesh to include comparing the received power in top and bottom antennas as taught by Stayton. One of ordinary skill would be motivated to include this antenna power comparison in order to detect spoofing by determining the signal’s direction of origin and comparing to the believed position of the transmitting aircraft, as recognized by Stayton (see Stayton at least [0054] – [0060]).
Regarding claim 5, Manesh in view of Dormiani discloses the apparatus of claim 1. Manesh further discloses [note that what Manesh does not teach is strike-through]:
wherein the data extracted from the ADS-B waveform further includes a Doppler offset (see at least Table 1 and [0017]; “Doppler shift, Δf: This parameter is the difference between the frequency of the received signal, fR, and the frequency of the transmitted signal, f0, which is 1090 MHz in 1090ES mode. This parameter is calculated as Δf=fR−f0.”), and wherein the plurality of detection tests includes using the Doppler offset with velocities, headings, and/or changes thereof that are reported by the apparent ADS-B track (see at least [0031]; “Injection attack detection 134 may receive the group of injection attack parameters 124, including ownship latitude (LatR), ownship longitude (LonR), transmitter latitude (LatT), transmitter longitude (LonT), velocity difference (Δv), doppler shift (Δf), and received signal strength (RSS). Injection attack detection 134 uses its received parameters to provide Subsystem C 140 with a binary decision about presence or absence of the attack.”).
However, Manesh does not explicitly teach comparing the Doppler offset with velocities, headings, and/or changes thereof that are reported by the apparent ADS-B track.
Stayton teaches:
wherein the data extracted from the ADS-B waveform (see at least [0054]; “The signal can include or encode an ADS-B message.”) further includes a Doppler offset (see at least [0059]; “The signal characteristic can be or include at least one of the following: radio frequency power level of the signal; differential time measurement of receipt of the signal; or Doppler frequency change of the signal.”) and wherein the plurality of detection tests includes comparing the Doppler offset with velocities, headings, and/or changes thereof that are reported by the apparent ADS-B track (see at least [0056] – [0057]; “The method, as illustrated in FIG. 7, additionally includes, at 740, determining, with the device, a validity status of the report based on comparing the first parameter and the second parameter. Determining the validity status based on the comparing can include, at 742, determining that the report is valid when a difference between the first parameter and the second parameter is less than a predetermined threshold. The validity here can refer to whether or not the report is a spoof report for a non-existent aircraft. The first parameter and/or the second parameter can include a bearing of the aircraft with respect to own aircraft.”).
Both Manesh and Stayton use Doppler and heading information to detect spoofing or cyber-attacks in ADS-B signals. Stayton explicitly performs a comparison between the various sources of information, while Manesh uses the information as inputs to a neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, based on the teachings of Stayton, that the invention of Manesh either does or easily could perform a comparison between the gathered information to determine the presence of a cyber-attack.
Regarding claim 14, Manesh in view of Dormiani teaches the method of claim 11. However, Manesh does not explicitly disclose:
wherein extracting the data from the ADS-B waveform includes determining detection power levels at which the ADS-B waveform was detected by at least two antennae, and wherein applying the plurality of detection tests includes applying an antenna diversity analysis to the detection power levels.
Manesh is directed to detecting cyber-attacks in ADS-B systems, and Stayton is directed to detecting spoofing in systems such as ADS-B systems. Stayton teaches:
wherein extracting the data from the ADS-B waveform includes determining detection power levels at which the ADS-B waveform was detected by at least two antennae, and wherein applying the plurality of detection tests includes applying an antenna diversity analysis to the detection power levels (see at least [0054] – [0060], quoted above regarding claim 4).
Both Manesh and Stayton teach a plurality of methods to detect spoofing or attacks on ADS-B messages. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the metrics used in Manesh to include comparing the received power in top and bottom antennas as taught by Stayton. One of ordinary skill would be motivated to include this antenna power comparison in order to detect spoofing by determining the signal’s direction of origin and comparing to the believed position of the transmitting aircraft, as recognized by Stayton (see Stayton at least [0054] – [0060]).
Regarding claim 15, Manesh in view of Dormiani teaches the method of claim 11. Manesh further discloses [note that what Manesh does not teach is strike-through]:
wherein extracting the data from the ADS-B waveform includes determining a Doppler offset (see at least Table 1 and [0017]; “Doppler shift, Δf: This parameter is the difference between the frequency of the received signal, fR, and the frequency of the transmitted signal, f0, which is 1090 MHz in 1090ES mode. This parameter is calculated as Δf=fR−f0.”), and wherein applying the plurality of detection tests includes using the Doppler offset with velocities, headings, and/or changes thereof that are reported by the apparent ADS-B track (see at least [0031]; “Injection attack detection 134 may receive the group of injection attack parameters 124, including ownship latitude (LatR), ownship longitude (LonR), transmitter latitude (LatT), transmitter longitude (LonT), velocity difference (Δv), doppler shift (Δf), and received signal strength (RSS). Injection attack detection 134 uses its received parameters to provide Subsystem C 140 with a binary decision about presence or absence of the attack.”).
However, Manesh does not explicitly teach comparing the Doppler offset with velocities, headings, and/or changes thereof that are reported by the apparent ADS-B track.
Stayton teaches:
wherein extracting the data from the ADS-B waveform (see at least [0054]; “The signal can include or encode an ADS-B message.”) includes determining a Doppler offset (see at least [0059]; “The signal characteristic can be or include at least one of the following: radio frequency power level of the signal; differential time measurement of receipt of the signal; or Doppler frequency change of the signal.”), and wherein applying the plurality of detection tests includes comparing the Doppler offset with velocities, headings, and/or changes thereof that are reported by the apparent ADS-B track (see at least [0056] – [0057]; “The method, as illustrated in FIG. 7, additionally includes, at 740, determining, with the device, a validity status of the report based on comparing the first parameter and the second parameter. Determining the validity status based on the comparing can include, at 742, determining that the report is valid when a difference between the first parameter and the second parameter is less than a predetermined threshold. The validity here can refer to whether or not the report is a spoof report for a non-existent aircraft. The first parameter and/or the second parameter can include a bearing of the aircraft with respect to own aircraft.”).
Both Manesh and Stayton use Doppler and heading information to detect spoofing or cyber-attacks in ADS-B signals. Stayton explicitly performs a comparison between the various sources of information, while Manesh uses the information as inputs to a neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, based on the teachings of Stayton, that the invention of Manesh either does or easily could perform a comparison between the gathered information to determine the presence of a cyber-attack.
Regarding claim 16, Manesh in view of Dormiani and Stayton teaches the method of claim 15. Manesh further discloses:
wherein determining the Doppler offset includes estimating the Doppler offset from baseband waveform samples over a duration of a pulse train (see at least [0017]; “Doppler shift, Δf: This parameter is the difference between the frequency of the received signal, fR, and the frequency of the transmitted signal, f0, which is 1090 MHz in 1090ES mode. This parameter is calculated as Δf=fR−f0.”).
Claims 9-10 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Manesh in view of Dormiani, further in view of Ballestros et al. (US-20170236425-A1; hereinafter Ballestros).
Regarding claim 9, Manesh in view of Dormiani discloses the apparatus of claim 8. However, Manesh does not explicitly teach:
wherein generating the display data includes associating annotations with the apparent ADS-B tracks, said annotations being indicative of the confidence levels associated with the apparent ADS-B tracks.
Manesh discloses detecting cyber-attacks targeting ADS-B messages, and Ballestros is directed to determining whether ADS-B messages are truthful or not. Ballestros teaches:
wherein generating the display data includes associating annotations with the apparent ADS-B tracks, said annotations being indicative of the confidence levels associated with the apparent ADS-B tracks (see at least [0038]; “The system 1 may be configured so that only those verified ADS-B messages are sent to the ADS-B system 7, or all the ADS-B messages are sent to the ADS-B system 7 but each of them labeled as TRUTHFUL or UNTRUTHFUL for the flight crew's information. The information is shown to the flight crew by means of a visual representation in a screen 9.”).
Manesh visually displays data indicating confidence levels in ADS-B messages but does not explicitly disclose associating the annotations with the specific ADS-B tracks. Ballestros similarly visually displays data indicating confidence levels in ADS-B messages, with annotations associated with each message. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the display used in Manesh to include association with the tracks from which the confidence levels are derived, as taught by Ballestros. One of ordinary skill would be motivated to include this associated information in order to provide relevant information to the flight crew, as recognized by Ballestros (see Ballestros at least [0038]).
Regarding claim 10, Manesh in view of Dormiani discloses the apparatus of claim 8. However, Manesh does not explicitly teach:
wherein generating the display data includes excluding from the display data apparent ADS-B tracks having confidence levels associated therewith that are below a specified minimum confidence level.
Ballestros teaches:
wherein generating the display data includes excluding from the display data apparent ADS-B tracks having confidence levels associated therewith that are below a specified minimum confidence level (see at least [0038]; “The system 1 may be configured so that only those verified ADS-B messages are sent to the ADS-B system 7, or all the ADS-B messages are sent to the ADS-B system 7 but each of them labeled as TRUTHFUL or UNTRUTHFUL for the flight crew's information. The information is shown to the flight crew by means of a visual representation in a screen 9.”).
Both Manesh and Ballestros verify the authenticity of ADS-B messages. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system used in Manesh to include a visual representation of the verified messages, as taught by Ballestros. One of ordinary skill would be motivated to include such a display in order to provide relevant information to the flight crew, as recognized by Ballestros (see Ballestros at least [0038]).
Regarding claim 20, Manesh in view of Dormiani discloses the method of claim 19. However, Manesh does not explicitly teach:
wherein generating the display data includes associating annotations with the apparent ADS-B tracks, said annotations being indicative of the confidence levels associated with the ADS-B tracks.
Manesh discloses detecting cyber-attacks targeting ADS-B messages, and Ballestros is directed to determining whether ADS-B messages are truthful or not. Ballestros teaches:
wherein generating the display data includes associating annotations with the apparent ADS-B tracks, said annotations being indicative of the confidence levels associated with the ADS-B tracks (see at least [0038]; “The system 1 may be configured so that only those verified ADS-B messages are sent to the ADS-B system 7, or all the ADS-B messages are sent to the ADS-B system 7 but each of them labeled as TRUTHFUL or UNTRUTHFUL for the flight crew's information. The information is shown to the flight crew by means of a visual representation in a screen 9.”).
Regarding claim 21, Manesh in view of Dormiani discloses the method of claim 19. However, Manesh does not explicitly teach:
wherein generating the display data includes excluding from the display data apparent ADS-B tracks having confidence levels associated therewith that are below a specified minimum confidence level.
Ballestros teaches:
wherein generating the display data includes excluding from the display data apparent ADS-B tracks having confidence levels associated therewith that are below a specified minimum confidence level (see at least [0038]; “The system 1 may be configured so that only those verified ADS-B messages are sent to the ADS-B system 7, or all the ADS-B messages are sent to the ADS-B system 7 but each of them labeled as TRUTHFUL or UNTRUTHFUL for the flight crew's information. The information is shown to the flight crew by means of a visual representation in a screen 9.”).
Both Manesh and Ballestros verify the authenticity of ADS-B messages. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system used in Manesh to include a visual representation of the verified messages, as taught by Ballestros. One of ordinary skill would be motivated to include such a display in order to provide relevant information to the flight crew, as recognized by Ballestros (see Ballestros at least [0038]).
Conclusion
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/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/OLUMIDE AJIBADE AKONAI/Primary Examiner, Art Unit 3648
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