Office Action 1: Non-Final Rejection (2023-10-05)
Rejection types: 101 - Patent-Eligible Subject Matter, 103 - Obviousness

CLAIMS:
IN THE CLAIMS: Claims 6 and 7 have been amended herein. All of the pending claims are presented below. This listing of claims will replace all prior versions and listings of claims in the application. Please enter these claims as amended.

Listing of the Claims:

1\. (Original) A device fingerprinting system, comprising:
one or more servers configured to store, in a database, fingerprint data and known identification codes associated with a plurality of identified transmitting devices;
a receiver configured to receive, from an unidentified transmitting device, wireless communications; and
a machine learning computing device configured to:
determine a device fingerprint responsive to the wireless communications using few-shot learning techniques; and
compare the determined device fingerprint to the stored fingerprint data in the database.

2\. (Original) The system of claim 1, wherein the machine learning computing device is further configured to:
provide a portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN);
capture features in the portion of each of the wireless communications; and
average the captured features.

3\. (Original) The system of claim 2, wherein the portion of each of the wireless communications is substantially identical to respective portions of others of the wireless communications.

4\. (Original) The system of claim 3, wherein the portion of each of the wireless communications comprises a preamble of each of the wireless communications.

5\. (Original) The system of claim 1, wherein each of the wireless communications comprises a demodulated identification code.

6\. (Currently amended) The system of claim 5, wherein the machine learning computing device is further configured to ompare demodulated identification codes of the wireless communications with the known identification codes associated with the plurality of identified transmitting devices responsive to a determination that the determined device fingerprint matches the stored fingerprint data in the database.

7\. (Currently amended) The system of claim 5, wherein the wireless communications are automatic dependent surveillance-broadcast (ADS-B) messages from an aircraft and demodulated identification codes are international civil aviation organization (ICAO) aircraft addresses.

8\. (Original) The system of claim 7, wherein the ADS-B messages indicate one or more of aircraft types, encoded latitudes, encoded longitudes, airborne velocities, surface positions, airborne positions, altitudes, aircraft identifications, and parity bits for error detection.

9\. (Original) The system of claim 1, wherein the wireless communications further comprise geolocation data indicating a current location of the unidentified transmitting device.

10\. (Original) The system of claim 1, wherein the machine learning computing device is further configured to add the determined device fingerprint to the database responsive to a determination that the determined device fingerprint does not match the stored fingerprint data in the database.

11\. (Original) The system of claim 1, wherein the one or more servers are remotely located from the receiver and the machine learning computing device.

12\. (Original) The system of claim 1, wherein using few-shot learning techniques comprises using prototypical networks.

13\. (Original) A device fingerprinting method comprising:
storing fingerprint data indicating device fingerprint features detected for a plurality of identified transmitting devices in a database;
receiving, at a communication device, wireless communications from an unidentified transmitting device;
determining a device fingerprint responsive to a portion of each of the wireless communications using few-shot learning techniques; and
comparing the determined device fingerprint to the stored fingerprint data in the database.

14\. (Original) The method of claim 13, wherein determining a device fingerprint comprises:
providing the portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN);
capturing features in the portion of each of the wireless communications; and
averaging the captured features of each of the wireless communications.

15\. (Original) The method of claim 13, further comprising determining whether the determined device fingerprint matches the stored device fingerprint features for one of the plurality of identified transmitting devices.

16\. (Original) The method of claim 15, wherein receiving the wireless communications indicates a demodulated identification code associated with the unidentified transmitting device.

17\. (Original) The method of claim 16, further comprising:
responsive to determining that the device fingerprint does not match the stored device fingerprint features, adding the device fingerprint and the demodulated identification code associated with the unidentified transmitting device to the database.

18\. (Original) The method of claim 16, further comprising:
responsive to determining that the device fingerprint matches the stored device fingerprint features, determining whether the demodulated identification code matches a known identification code associated with the matched device fingerprint features.

19\. (Original) A computer-readable medium having computer-readable instructions stored thereon, the computer-readable instructions configured to instruct one or more processors to:
train a neural network for fingerprinting an unidentified transmitter device;
capture features of signals received from the unidentified transmitter device using the trained neural network;
determine a prototype for the signals by averaging the captured features of each of the signals;
and compare the prototype to previously stored fingerprint data contained in a database.

20\. (Original) The computer-readable medium of claim 19, wherein the computer- readable instructions are further configured to instruct the one or more processors to:
responsive to determining that the prototype matches the previously stored fingerprint data in the database, determine whether a demodulated identification code associated with the signals matches a known identification code associated with the previously stored fingerprint data.

21\. (Original) The computer-readable medium of claim 19, wherein the computer- readable instructions are further configured to instruct the one or more processors to:
responsive to a determination that the prototype does not match the previously stored fingerprint data in the database, add the prototype and a demodulated identification code associated with the signals to the database.

22\. (Original) The computer-readable medium of claim 19, wherein the neural network is a convolutional neural network (CNN).
4 5 6 7 8

OFFICE ACTION:
DETAILED ACTION
1. 	This is a reply to the application No. 17/666,353 filed on 02/07/2022. Claims 1-22 are submitted for examination and claims 1, 13 and 19 are independent.
Notice of Pre-AIA  or AIA  Status
2.	The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3.	This application doesn’t claim priority. Thus, the effective filling date for the subject matter defined in the pending claims of this application is February 7th, 2022.
Preliminary Amendment
4.	The preliminary amendment filed on 01/17/2023 amending both the specification and claims 6-7 has been acknowledged.
Information Disclosure Statement
5.	The information disclosure statements (IDS) submitted on 02/08/2022 and 06/20/2023 have been considered. The submission is in-compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Drawings
6.	The drawings filed on February 7th, 2022 are accepted.
Specification
7.	The specification filed on February 7th, 2022 is also accepted.
Claim Rejections - 35 USC § 101
8.	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.
9	Claims 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because of claims 19-22 being directed to a computer- readable medium. There is not found within the Applicant's Specification support for said “computer-readable medium” being limited to a statutory embodiment. The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media/medium (or non-transitory media/medium) and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable medium, particularly when the specification is silent (or absent of a controlling definition in the specification). See MPEP §2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). Amending this limitation to a non-transitory computer-readable medium’ overcomes this ground of rejection.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
10.	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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
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 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.
11.	Claims 1-9, 11-16, 19-20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Zha Haoran et al (herein after referred as Haoran) NPL document, titled, “Real-World ADS-B signal recognition based on Radio Frequency Fingerprinting) (IEEE, 2020)(Pages 1-6) in view of Fan Sheng-Hung et al (herein after referred as Sheng-Hung] (NPL Document titled, “A Few-Shot learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-identification”) (IEEE, Pages 133650-133662) (September 2021) (Both prior arts are provided/submitted with the IDS)
The following is referring to independent claims 1, 13 and 19:
As per independent claim 1, Haoran discloses a device fingerprinting system [See figure 1 and 5], comprising: one or more servers configured to store, in a database, fingerprint data and known identification codes associated with a plurality of identified transmitting devices [See at least figure 5 that shows a laptop computer which functions as the server to receive signals originating for a radio or an aircraft];
a receiver [ADS-B signal detection] configured to receive, from an unidentified transmitting device [radio or ADS-B system], wireless communications; and a machine learning computing device [This device may be integrated in the laptop of figure 5 or be a separate entity as shown in the schematic drawing of figure 1, for instance in the box labeled “data processing”] configured to:
determine a device fingerprint responsive to the wireless communications [ See at least page 1, right column, line 9-11]
compare the determined device fingerprint to the stored fingerprint data in the database [See page 2, left column line 21-23].
Hoaran substantially discloses all the limitation recited in the claim but doesn’t explicitly disclose the following underlined or bolded claim limitation: “determine a device fingerprint responsive to the wireless communications using few-shot learning techniques”
However, Sheng-Hung discloses: “determine a device fingerprint responsive to the wireless communications using few-shot learning techniques” [See page 133,651, right column, line 26-34]
Hoaran and Sheng-Hung are an analogous/in the same field of endeavor as they both are directed to determine a device fingerprint.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement in the system of Hoaran a mechanism of adding a security feature such as “determine a device fingerprint responsive to the wireless communications using few-shot learning techniques” as per teaching Sheng-Hung because this would enhance the security of the system by selecting a better and suitable machine learning technique for determining a device fingerprint [See Sheng-Hung at least See page 133,651, right column, line 26-34]
As per independent claim 13, Independent claim 13 is rejected for the same reason as that of the above independent claim 1 as they both recite similar claims/having the similar scope.
As per independent claim 19, Independent claim 19 is rejected for the same reason as that of the above independent claim 1. Independent claim 19 further recites a limitation: training a neural network for fingerprinting an unidentified transmitter device; capture features of signals received from the unidentified transmitter device using the trained neural network; determine a prototype for the signals by averaging the captured features of each of the signals. This extra limitation is taught by Hoaran [See page 1, right column, line 22-26 and the training under III Experiments and Discussion, which includes those technical features which are part of the discussion of Hoaran and when this is combined with few-shot learning techniques discussed by Sheng-Hung at least See page 133,651, right column, line 26-34, the combination teaches all the limitation recited in independent claim 19.
The following is referring to dependent claims 2-9, 11-12, 14-16, 20 and 22
As per dependent claim 2, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method wherein, the machine learning computing device is further configured to: provide a portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN); capture features in the portion of each of the wireless communications; and average the captured features [See at least page 1, right column, line 22-26 and the feature of captueing features and average them, See page 3, right column, line 7-11]
As per dependent claim 14, dependent claim 14 is rejected for the same reason as that of the above dependent claim 2 as they both recite similar claims/having the similar scope.
As per dependent claim 3, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method,
wherein the portion of each of the wireless communications is substantially identical to respective portions of others of the wireless communications [See at least page 3, right column, line 14-21].
As per dependent claim 4, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method,
wherein the portion of each of the wireless communications comprises a preamble of each of the wireless communications [See at least page 2, right column, line 10-12].
As per dependent claim 5, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method, wherein each of the wireless communications comprises a demodulated identification code [See abstract where the reader is informed that ADS-B signals are not encrypted, hence they comprise demodulated information of all kinds].
As per dependent claim 16, dependent claim 16 is rejected for the same reason as that of the above dependent claim 5 as they both recite similar claims/having the similar scope.
As per dependent claim 6, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the machine learning computing device is further configured to compare the compare demodulated identification codes of the wireless communications with the known identification codes associated with the plurality of identified transmitting devices responsive to a determination that the determined device fingerprint matches the stored fingerprint data in the database [See at least page 5, left column, line 11 to right column, line 1].
As per dependent claim 15, dependent claim 15 is rejected for the same reason as that of the above dependent claim 6 as they both recite similar claims/having the similar scope.
As per dependent claim 7, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the wireless communications are automatic dependent surveillance-broadcast (ADS-B) messages from an aircraft and the and demodulated identification codes are international civil aviation organization (ICAO) aircraft addresses [See at least page 3, left column, line 19-25].
As per dependent claim 8, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the ADS-B messages indicate one or more of aircraft types, encoded latitudes, encoded longitudes, airborne velocities, surface positions, airborne positions, altitudes, aircraft identifications, and parity bits for error detection [See at least page 3, left column, line 28-29].
As per dependent claim 9, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the wireless communications further comprise geolocation data indicating a current location of the unidentified transmitting device [See at least page 1, left column, line 31-34]
As per dependent claim 11, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the one or more servers are remotely located from the receiver and the machine learning computing device [See at least page 1, left column, line 42-44, in systems applied for air-traffic control it is typical to have various devices remotely located to each other].
As per dependent claim 12, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Sheng-Hung discloses the method/system, wherein using few-shot learning techniques comprises using prototypical networks [See at least page 133,652, right column, line 22-23].
As per dependent claim 20, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein responsive to determining that the prototype matches the previously stored fingerprint data in the database, determine whether a demodulated identification code associated with the signals matches a known identification code associated with the previously stored fingerprint data [See at least page 5, left column, line 11 to right column, line 1].
As per dependent claim 22, the combination of Hoaran and Sheng-Hung discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the neural network is a convolutional neural network (CNN) [See at least page 1, right column, line 22-26].
12.	Claims 10, 17 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zha Haoran et al (herein after referred as Haoran) NPL document, titled, “Real-World ADS-B signal recognition based on Radio Frequency Fingerprinting) (IEEE, 2020)(Pages 1-6) in view of Fan Sheng-Hung et al (herein after referred as Sheng-Hung] (NPL Document titled, “A Few-Shot learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-identification”) (IEEE, Pages 133650-133662) (September 2021) and further in view of  Shen Guanxiong et al (herein after referred as Guanxiong) (NPL document, titled, “ Radio Frequency Fingerprint identification for LoRa Using Deep Learing”) (IEEE,  June 2021, Pages 2604-2616) (All the  prior arts are provided/submitted with the IDS)
As per dependent claim 10, the combination of Hoaran and Sheng-Hung doesn’t explicitly disclose the limitation: wherein: “the machine learning computing device is further configured to add the determined device fingerprint to the database responsive to a determination that the determined device fingerprint does not match the stored fingerprint data in the database”
However, Guanxiong discloses the machine learning computing device is further configured to add the determined device fingerprint to the database responsive to a determination that the determined device fingerprint does not match the stored fingerprint data in the database [See at least page 2606, left column, line 7-16]
Hoaran, Sheng-Hung and Guanxiong are an analogous/in the same field of endeavor as they all are directed to determine a device fingerprint.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement in the system of Hoaran and Sheng-Hung a mechanism of adding a security feature such as “the machine learning computing device is further configured to add the determined device fingerprint to the database responsive to a determination that the determined device fingerprint does not match the stored fingerprint data in the database” as per teaching Guanxiong because this would enhance the security of the system by adding device fingerprint to the database that are not already stored.[See at least page 2606, left column, line 7-16]
As per dependent claim 17, dependent claim 17 is rejected for the same reason as that of the above dependent claim 10 as they both recite similar claims/having the similar scope.
As per dependent claim 21, dependent claim 21 is rejected for the same reason as that of the above dependent claim 10 as they both recite similar claims/having the similar scope.
13.	Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Zha Haoran et al (herein after referred as Haoran) NPL document, titled, “Real-World ADS-B signal recognition based on Radio Frequency Fingerprinting) (IEEE, 2020)(Pages 1-6) in view of Fan Sheng-Hung et al (herein after referred as Sheng-Hung] (NPL Document titled, “A Few-Shot learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-identification”) (IEEE, Pages 133650-133662) (September 2021) and further in view of Anu Jagannath et al (herein after referred as Jagannath) (NPL document submitted with IDS, titled “A comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges”) (Cornell University Library January 2022)
As per dependent claim 18, the combination of Hoaran and Sheng-Hung doesn’t explicitly disclose the limitation: “responsive to determining that the device fingerprint matches the stored device fingerprint features, determining whether the demodulated identification code matches a known identification code associated with the matched device fingerprint features”.
However, Jagannath discloses: “responsive to determining that the device fingerprint matches the stored device fingerprint features, determining whether the demodulated identification code matches a known identification code associated with the matched device fingerprint features” [See at least page 6, right column, line 22-26]
Hoaran, Sheng-Hung and Jagannath are an analogous/in the same field of endeavor as they all are directed in determining a device fingerprint.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement in the system of Hoaran and Sheng-Hung a mechanism of adding a security feature such as “responsive to determining that the device fingerprint matches the stored device fingerprint features, determining whether the demodulated identification code matches a known identification code associated with the matched device fingerprint features” as per teaching Jagannath because this would enhance the security of the system by adding extra verification by matching to a known identification code [See at least page 6, right column, line 22-26]
Conclusion
14.	The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. 	US Patent No.  10951618 B2 to Porras discloses is a method and apparatus for wireless network security. for securing a wireless computing network includes receiving a communication from an unidentified transmitter, identifying the transmitter in accordance with a fingerprint generated from one or more radio frequency signal characteristics extracted from the communication, and taking action in response to an identity of the transmitter.
B.	US Publication No.  20200382961 A1 to Shattil discloses a system that detects and identifies unmanned vehicles (UVs) from radio communications between UVs and their controllers. One or more radio frequency (RF) signal detectors can detect RF signals, including downlink signals transmitted by a UV or uplink signals transmitted by a UV controller. A feature extractor can extract signal features from detected RF signals, and a classifier performs machine learning to identify at least one of the UV and the UV controller based on the signal features. Machine learning can employ an artificial neural network, which may perform deep learning.
C.	US Publication No.  20190253905 A1 to Kleinbeck discloses systems, methods and apparatus for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.
D. 	See the other cited prior arts.


NEXT OFFICE ACTION: DETAILED ACTION
1. 	This office action is in response to an amendment filed on 01/04/2024. Claims 2-4 are canceled and claims 1 and 5-20 are pending and claims 1, 13 and 19 are independent. Each independent claim 1, 13 and 19 is amended.
Notice of Pre-AIA  or AIA  Status
2.	The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
3.	On March 28, 2024, applicant’s attorney David T Ostler, Reg. No. 80,014 and examiner conducted examiner-initiated telephone interview. The summary of the interview is attached.
4.	The amendment made to 19-20 overcomes the 35 U.S.C. 101 rejection set forth in the previous office action. Thus, this particular rejection is withdrawn.
Response to Arguments
5.	Applicant’s remarks/arguments filed on January 4th, 2024 with respect to the amended independent claims 13, 19 and 1 have also been fully 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.
Independent claim 19 is amended. The amendment changes the scope of the claim and necessitated new grounds of rejection.
Independent claim 13 is amended. The amendment changes the scope of the claim and necessitated new grounds of rejection.
Independent claim 1 is amended. The amended independent claim 1 incorporates the canceled claims 1, 2 and 4 without incorporating dependent claim 3 even though claim 4 depended on the canceled claim 3. Thus, incorporating dependent claim 1, 2 and 4 without incorporating dependent claim 3 while claim 4 depended on claim 3, changes the scope of independent claim 1 and necessitated new grounds of rejection.
Regarding independent claim 19, Applicant argued that the following amended/underlined claim limitation recited in independent claim 19, “capture features of a preamble portion of signals received from the unidentified transmitter device using the trained neural network” and the similar amendment made to independent claim 13 and 1 is not disclosed by the prior art for the record.
Examiner would like to point out that, the newly applied prior art in particular, NPL document titled, “Robust Wireless Fingerprinting via Complex-Valued Neural Networks, Aug. 26, 2019” discloses the above amended claim limitation:
“capture features of a preamble portion of signals received from the unidentified transmitter device using the trained neural network” [See figure “Complex Conv. Layer, kernel size 40 followed by kernel size 5; and stride 20 followed by stride 1, 100 filters followed by another 100 filters see how ADS-B preamble signal received from transmitter device using convolutional neural network (CNN). Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint. See also page column 2, “….using the preamble, which is common to all packets from all transmitters, suffices to obtain reasonable accuracies, despite the relatively short length of the preamble compared to the length of the entire message” page 2, right column, “We then focus on learning fingerprints from the preamble”. See VI Conclusions, “In this paper, we have demonstrated the efficacy of complexvalued CNNs for wireless fingerprinting. This technique does not rely on signal domain knowledge and, as illustrated by our experiments with WiFi and ADS-B data, can be used across diverse wireless protocols. We show the vulnerability of the approach to “cheating” using transmitter ID when using the entire message to extract the fingerprint. When using
the preamble alone, reasonably high accuracies are obtained, and performance is significantly enhanced by noise augmentation” See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for
fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types. We report accuracies for the following networks: • ADS-B: 100 C 40×20 – 100 C 5×1 – | · |2 – Avg – 100 D. • WiFi: 100 C 20×10 – 100 C 10×1 – | · |2 – Avg – 100 D. The notation should be read as follows:
filters> C×, andD, where C represents a convolutional layer and D a fully connected layer, with complex-valued layers prior to the | · |2
layer and real-valued layers afterward. ‘Avg’ denotes a temporal averaging layer. We train networks for 200 epochs with a batch size of 100, using the Adam optimizer with default hyperparameters and ℓ2 regularization constant of 10-3]
Regarding independent claim 1, applicant’s representative argued that the following amended claim limitation is not disclosed by the prior art of the record.
“provide a preamble portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN); capture features in the preamble portion of each of the wireless communications via the CNN; and average the captured feature”
Examiner would like to point out that, the newly applied prior art in particular, NPL document titled, “Robust Wireless Fingerprinting via Complex-Valued Neural Networks, Aug. 26, 2019” discloses the above amended claim limitation:
“provide a preamble portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN) [See figure 1, See how ADS-B Preamble portion is provided as input to a trained convolutional n neural network (CNN)]
; capture features in the preamble portion of each of the wireless communications via the CNN [See figure “Complex Conv. Layer, kernel size 40 followed by kernel size 5; and stride 20 followed by stride 1, 100 filters followed by another 100 filters see how ADS-B preamble signal received from transmitter device using convolutional neural network (CNN). Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint. See also page column 2, “….using the preamble, which is common to all packets from all transmitters, suffices to obtain reasonable accuracies, despite the relatively short length of the preamble compared to the length of the entire message” page 2, right column, “We then focus on learning fingerprints from the preamble”. See VI Conclusions, “In this paper, we have demonstrated the efficacy of complex valued CNNs for wireless fingerprinting. This technique does not rely on signal domain knowledge and, as illustrated by our experiments with WiFi and ADS-B data, can be used across diverse wireless protocols. We show the vulnerability of the approach to “cheating” using transmitter ID when using the entire message to extract the fingerprint. When using the preamble alone, reasonably high accuracies are obtained, and performance is significantly enhanced by noise augmentation” See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types.]
; and average the captured feature”[ See figure 1, “Temporal average,” , Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types. We report accuracies for the following networks: • ADS-B: 100 C 40×20 – 100 C 5×1 – | · |2 – Avg – 100 D. • WiFi: 100 C 20×10 – 100 C 10×1 – | · |2 – Avg – 100 D. The notation should be read as follows:C×, andD, where C represents a convolutional layer and D a fully connected layer, with complex-valued layers prior to the | · |2] layer and real-valued layers afterward. ‘Avg’ denotes a temporal averaging layer. We train networks for 200 epochs with a batch size of 100, using the Adam optimizer with default hyperparameters and ℓ2 regularization constant of 10-3]
Regarding independent claim 13, applicant’s representative argued that the following amended claim limitation is not disclosed by the prior art of the record.
“providing a preamble portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN) [; determining, via the trained CNN, a device fingerprint responsive to the preamble….”
Examiner would like to point out that, the newly applied prior art in particular, NPL document titled, “Robust Wireless Fingerprinting via Complex-Valued Neural Networks, Aug. 26, 2019” discloses the above amended claim limitation:
“providing a preamble portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN) [[See figure 1, See how ADS-B Preamble portion is provided as input to a trained convolutional n neural network (CNN)]; determining, via the trained CNN, a device fingerprint responsive to the preamble….”[[See figure “Complex Conv. Layer, kernel size 40 followed by kernel size 5; and stride 20 followed by stride 1, 100 filters followed by another 100 filters see how ADS-B preamble signal received from transmitter device using convolutional neural network (CNN). Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint. See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types. Abstract, in this paper, we explore the use of neural networks with complex-valued weights to learn fingerprints using supervised learning. Page 1, right column, Contribution, We propose a protocol-agnostic fingerprinting technique using complex-valued CNNs and demonstrate its robustness to various real-world imperfections We then focus on learning fingerprints from the preamble]
6.	Thus, regarding independent claims 1, 13 and 19, in response to the 35 U.S.C. 103 rejection set forth in the previous office action, applicant amended at least independent 1, 13 and 19 presumably to overcome the 103 rejection set forth in the previous office action.
7.	Since the newly amended claims changed the scope and necessitated new grounds of rejection, Applicant’s arguments are moot. The analysis of the claims under consideration, as amended, follows in the corresponding section below.
Claim Rejections - 35 USC § 103
8.	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 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.
9.	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.
10.	The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Examiner’s note: text in bold corresponds to the claimed limitations; text in italics underlined or not underlined correspond to the cited prior art reference (i.e., verbatim, and/or examiner’s clarification. Meaning, text after a limitation in brackets [ ] corresponds to examiner’s mapping (including further explanation and/or comments) and/or prior art reference citations. Furthermore text in brackets [ ] points out explanation how the claim limitation is taught or explicitly taught by the reference being cited for that particular limitation or part of the limitation]
11.	Claims 1, 5-9, 11-16, 19-20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Zha Haoran et al (herein after referred as Haoran) NPL document, titled, “Real-World ADS-B signal recognition based on Radio Frequency Fingerprinting) (IEEE, 2020)(Pages 1-6) in view of Fan Sheng-Hung et al (herein after referred as Sheng-Hung] (NPL Document titled, “A Few-Shot learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-identification”) (IEEE, Pages 133650-133662) (September 2021) (Both prior arts are provided/submitted with the IDS) and further in view of Sorrya Gopalakrishnan (herein after referred as Gopalakrishnan) (NPL document titled, “Robust Wireless Fingerprinting via Complex-Valued Neural Networks, Aug. 26, 2019”)
The following is referring to independent claims 1, 13 and 19:
As per independent claim 1, Haoran discloses a device fingerprinting system [See figure 1 and 5], comprising: one or more servers configured to store, in a database, fingerprint data and known identification codes associated with a plurality of identified transmitting devices [See at least figure 5 that shows a laptop computer which functions as the server to receive signals originating for a radio or an aircraft];
a receiver [ADS-B signal detection] configured to receive, from an unidentified transmitting device [radio or ADS-B system], wireless communications; and
a machine learning computing device [This device may be integrated in the laptop of figure 5 or be a separate entity as shown in the schematic drawing of figure 1, for instance in the box labeled “data processing”] configured to:
“
determine a device fingerprint responsive to the wireless communications [ See at least page 1, right column, line 9-11]
compare the determined device fingerprint to the stored fingerprint data in the database [See page 2, left column line 21-23].
Hoaran substantially discloses all the limitation recited in the claim but doesn’t explicitly disclose the following underlined or bolded claim limitation: “determine a device fingerprint responsive to the wireless communications using few-shot learning techniques”
However, Sheng-Hung discloses: “determine a device fingerprint responsive to the wireless communications using few-shot learning techniques” [See page 133,651, right column, line 26-34]
Hoaran and Sheng-Hung are an analogous/in the same field of endeavor as they both are directed to determine a device fingerprint.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement in the system of Hoaran a mechanism of adding a security feature such as “determine a device fingerprint responsive to the wireless communications using few-shot learning techniques” as per teaching Sheng-Hung because this would enhance the security of the system by selecting a better and suitable machine learning technique for determining a device fingerprint [See Sheng-Hung at least See page 133,651, right column, line 26-34]
Hoaran and Sheng-Hung  substantially discloses all the limitation recited in the claim but doesn’t explicitly disclose the following underlined or bolded amended claim limitation: “
“provide a preamble portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN); capture features in the preamble portion of each of the wireless communications via the CNN; and “average the captured feature”
However, Gopalakrishnan discloses the above claim limitation. In particular Gopalakrishnan discloses:
“provide a preamble portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN) [See figure 1, See how ADS-B Preamble portion is provided as input to a trained convolutional n neural network (CNN)]
; capture features in the preamble portion of each of the wireless communications via the CNN [See figure “Complex Conv. Layer, kernel size 40 followed by kernel size 5; and stride 20 followed by stride 1, 100 filters followed by another 100 filters see how ADS-B preamble signal received from transmitter device using convolutional neural network (CNN). Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint. See also page column 2, “….using the preamble, which is common to all packets from all transmitters, suffices to obtain reasonable accuracies, despite the relatively short length of the preamble compared to the length of the entire message” page 2, right column, “We then focus on learning fingerprints from the preamble”. See VI Conclusions, “In this paper, we have demonstrated the efficacy of complex valued CNNs for wireless fingerprinting. This technique does not rely on signal domain knowledge and, as illustrated by our experiments with WiFi and ADS-B data, can be used across diverse wireless protocols. We show the vulnerability of the approach to “cheating” using transmitter ID when using the entire message to extract the fingerprint. When using the preamble alone, reasonably high accuracies are obtained, and performance is significantly enhanced by noise augmentation” See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types.]
; and average the captured feature”[ See figure 1, “Temporal average,” , Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types. We report accuracies for the following networks: • ADS-B: 100 C 40×20 – 100 C 5×1 – | · |2 – Avg – 100 D. • WiFi: 100 C 20×10 – 100 C 10×1 – | · |2 – Avg – 100 D. The notation should be read as follows:C×, andD, where C represents a convolutional layer and D a fully connected layer, with complex-valued layers prior to the | · |2] layer and real-valued layers afterward. ‘Avg’ denotes a temporal averaging layer. We train networks for 200 epochs with a batch size of 100, using the Adam optimizer with default hyperparameters and ℓ2 regularization constant of 10-3]
Hoaran and Sheng-Hung and Gopalakrishnan are an analogous/in the same field of endeavor as they all are directed to determine a device fingerprint.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement in the system of Hoaran and Sheng-Hung a mechanism of adding a security feature such as “provide a preamble portion of each of the wireless communications as an input to a previously trained convolutional neural network (CNN); capture features in the preamble portion of each of the wireless communications via the CNN; and “average the captured feature” as per teaching  Gopalakrishnan because this would enhance the security of the system by generating a wireless fingerprint that is robust against standard spoofing techniques. [See  Gopalakrishnan at least the abstract and page 1, column 2, We demonstrate that a completely protocol-agnostic CNN is vulnerable to such involuntary cheating, and then show that using the preamble, which is common to all packets from all transmitters, suffices to obtain reasonable accuracies, despite the relatively short length of the preamble compared to the length of the entire message]
As per independent claim 13, Independent claim 13 is rejected for the same reason as that of the above independent claim 1 as they both recite similar claims/having the similar scope.
As per independent claim 19, Independent claim 19 is rejected for the same reason as that of the above independent claim 1 as they both recite similar claims/having the similar scope.
The following is referring to dependent claims 5-9, 11-12, 14-16, 20 and 22
As per dependent claim 5, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method, wherein each of the wireless communications comprises a demodulated identification code [See abstract where the reader is informed that ADS-B signals are not encrypted, hence they comprise demodulated information of all kinds].
As per dependent claim 16, dependent claim 16 is rejected for the same reason as that of the above dependent claim 5 as they both recite similar claims/having the similar scope.
As per dependent claim 6, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the machine learning computing device is further configured to compare the compare demodulated identification codes of the wireless communications with the known identification codes associated with the plurality of identified transmitting devices responsive to a determination that the determined device fingerprint matches the stored fingerprint data in the database [See at least page 5, left column, line 11 to right column, line 1].
As per dependent claim 15, dependent claim 15 is rejected for the same reason as that of the above dependent claim 6 as they both recite similar claims/having the similar scope.
As per dependent claim 7, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the wireless communications are automatic dependent surveillance-broadcast (ADS-B) messages from an aircraft and the and demodulated identification codes are international civil aviation organization (ICAO) aircraft addresses [See at least page 3, left column, line 19-25].
As per dependent claim 8, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the ADS-B messages indicate one or more of aircraft types, encoded latitudes, encoded longitudes, airborne velocities, surface positions, airborne positions, altitudes, aircraft identifications, and parity bits for error detection [See at least page 3, left column, line 28-29].
As per dependent claim 9, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the wireless communications further comprise geolocation data indicating a current location of the unidentified transmitting device [See at least page 1, left column, line 31-34]
As per dependent claim 11, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the one or more servers are remotely located from the receiver and the machine learning computing device [See at least page 1, left column, line 42-44, in systems applied for air-traffic control it is typical to have various devices remotely located to each other].
As per dependent claim 12, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Sheng-Hung discloses the method/system, wherein using few-shot learning techniques comprises using prototypical networks [See at least page 133,652, right column, line 22-23].
As per dependent claim 14, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Gopalakrishnan
Capturing features in the preamble portion of each of the wireless communications [[See figure “Complex Conv. Layer, kernel size 40 followed by kernel size 5; and stride 20 followed by stride 1, 100 filters followed by another 100 filters see how ADS-B preamble signal received from transmitter device using convolutional neural network (CNN). Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint. See also page column 2, “….using the preamble, which is common to all packets from all transmitters, suffices to obtain reasonable accuracies, despite the relatively short length of the preamble compared to the length of the entire message” page 2, right column, “We then focus on learning fingerprints from the preamble”. See VI Conclusions, “In this paper, we have demonstrated the efficacy of complex valued CNNs for wireless fingerprinting. This technique does not rely on signal domain knowledge and, as illustrated by our experiments with WiFi and ADS-B data, can be used across diverse wireless protocols. We show the vulnerability of the approach to “cheating” using transmitter ID when using the entire message to extract the fingerprint. When using the preamble alone, reasonably high accuracies are obtained, and performance is significantly enhanced by noise augmentation” See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types.];
and averaging the captured features of each of the wireless communications [See figure 1, “Temporal average,” , Fig. 1 depicts a sample complex convolutional architecture for ADS-B signals. We use a series of complex 1D convolutions followed by an | · |2 layer to convert complex representations to real ones, and then a series of real-valued layers after a temporal averaging layer to obtain the fingerprint See page 3, first column, “Performance”, We provide results for an external database for two different wireless protocols: WiFi 802.11a (5.8 GHz) and 802.11g (2.4 GHz) commercial off-the-shelf emitters with a signal bandwidth of 20 MHz, and ADS-B (1.09 GHz) narrowband air traffic control signals. We start by using only the preamble for fingerprinting, with signals normalized to unit power. When sampled at 20 MHz, the length of the preamble is 320 I/Q samples for both protocol types. We report accuracies for the following networks: • ADS-B: 100 C 40×20 – 100 C 5×1 – | · |2 – Avg – 100 D. • WiFi: 100 C 20×10 – 100 C 10×1 – | · |2 – Avg – 100 D. The notation should be read as follows:C×, andD, where C represents a convolutional layer and D a fully connected layer, with complex-valued layers prior to the | · |2] layer and real-valued layers afterward. ‘Avg’ denotes a temporal averaging layer. We train networks for 200 epochs with a batch size of 100, using the Adam optimizer with default hyperparameters and ℓ2 regularization constant of 10-3]
As per dependent claim 20, the combination of Hoaran, Sheng-Hung and Gopalakrishnan   discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein responsive to determining that the prototype matches the previously stored fingerprint data in the database, determine whether a demodulated identification code associated with the signals matches a known identification code associated with the previously stored fingerprint data [See at least page 5, left column, line 11 to right column, line 1].
As per dependent claim 22, the combination of Hoaran, Sheng-Hung and Gopalakrishnan  discloses the method/system as applied to claims above. Furthermore, Hoaran discloses the method/system, wherein the neural network is a convolutional neural network (CNN) [See at least page 1, right column, line 22-26].
12.	Claims 10, 17 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zha Haoran et al (herein after referred as Haoran) NPL document, titled, “Real-World ADS-B signal recognition based on Radio Frequency Fingerprinting) (IEEE, 2020)(Pages 1-6) in view of Fan Sheng-Hung et al (herein after referred as Sheng-Hung] (NPL Document titled, “A Few-Shot learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-identification”) (IEEE, Pages 133650-133662) (September 2021) and further in view of Sorrya Gopalakrishnan (herein after referred as Gopalakrishnan) (NPL document titled, “Robust Wireless Fingerprinting via Complex-Valued Neural Networks, Aug. 26, 2019”) and further in view of  Shen Guanxiong et al (herein after referred as Guanxiong) (NPL document, titled, “ Radio Frequency Fingerprint identification for LoRa Using Deep Learing”) (IEEE,  June 2021, Pages 2604-2616) (All the  prior arts are provided/submitted with the IDS)
As per dependent claim 10, the combination of Hoaran, Sheng-Hung and Gopalakrishnan doesn’t explicitly disclose the limitation: wherein: “the machine learning computing device is further configured to add the determined device fingerprint to the database responsive to a determination that the determined device fingerprint does not match the stored fingerprint data in the database”
However, Guanxiong discloses the machine learning computing device is further configured to add the determined device fingerprint to the database responsive to a determination that the determined device fingerprint does not match the stored fingerprint data in the database [See at least page 2606, left column, line 7-16]
Hoaran, Sheng-Hung, Gopalakrishnan and Guanxiong are an analogous/in the same field of endeavor as they all are directed to determine a device fingerprint.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement in the system of Hoaran, Sheng-Hung and  Gopalakrishnan a mechanism of adding a security feature such as “the machine learning computing device is further configured to add the determined device fingerprint to the database responsive to a determination that the determined device fingerprint does not match the stored fingerprint data in the database” as per teaching Guanxiong because this would enhance the security of the system by adding device fingerprint to the database that are not already stored.[See at least page 2606, left column, line 7-16]
As per dependent claim 17, dependent claim 17 is rejected for the same reason as that of the above dependent claim 10 as they both recite similar claims/having the similar scope.
As per dependent claim 21, dependent claim 21 is rejected for the same reason as that of the above dependent claim 10 as they both recite similar claims/having the similar scope.
13.	Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Zha Haoran et al (herein after referred as Haoran) NPL document, titled, “Real-World ADS-B signal recognition based on Radio Frequency Fingerprinting) (IEEE, 2020)(Pages 1-6) in view of Fan Sheng-Hung et al (herein after referred as Sheng-Hung] (NPL Document titled, “A Few-Shot learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-identification”) (IEEE, Pages 133650-133662) (September 2021) and further in view of Sorrya Gopalakrishnan (herein after referred as Gopalakrishnan) (NPL document titled, “Robust Wireless Fingerprinting via Complex-Valued Neural Networks, Aug. 26, 2019”) and further in view of Anu Jagannath et al (herein after referred as Jagannath) (NPL document submitted with IDS, titled “A comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges”) (Cornell University Library January 2022)
As per dependent claim 18, the combination of Hoaran, Sheng-Hung and Gopalakrishnan doesn’t explicitly disclose the limitation: “responsive to determining that the device fingerprint matches the stored device fingerprint features, determining whether the demodulated identification code matches a known identification code associated with the matched device fingerprint features”.
However, Jagannath discloses: “responsive to determining that the device fingerprint matches the stored device fingerprint features, determining whether the demodulated identification code matches a known identification code associated with the matched device fingerprint features” [See at least page 6, right column, line 22-26]
Hoaran, Sheng-Hung, Gopalakrishnan and Jagannath are an analogous/in the same field of endeavor as they all are directed in determining a device fingerprint.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement in the system of Hoaran, Sheng-Hung and Gopalakrishnan a mechanism of adding a security feature such as “responsive to determining that the device fingerprint matches the stored device fingerprint features, determining whether the demodulated identification code matches a known identification code associated with the matched device fingerprint features” as per teaching Jagannath because this would enhance the security of the system by adding extra verification by matching to a known identification code [See at least page 6, right column, line 22-26]
Conclusion
14.	The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A.	NPL document titled, Machine Learning for the Detection and Identification of Internet of Things Devices: A Survey” to Liu discloses how to generate a fingerprint of wireless device form the preamble.
B.	NPL document titled, to Shen “Radio Frequency Fingerprint Identification for LoRa
Using Spectrogram and CNN” discloses how to generate a fingerprint of wireless device form the preamble.
C.	See the other cited prior arts.
15.	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 extension fee 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 date of this final action.

CITED REFERENCE SUMMARIES: Ref US20070025265A1: Claims:
A method for securing a wireless computing network, the method comprising: receiving a communication from an unidentified transmitter; identifying the transmitter in accordance with a fingerprint generated from one or more radio frequency signal characteristics extracted from the communication; and making one or more access control decisions in response to an identity of the transmitter.
receiving a communication from an unidentified transmitter;
identifying the transmitter in accordance with a fingerprint generated from one or more radio frequency signal characteristics extracted from the communication; and
making one or more access control decisions in response to an identity of the transmitter.
The method of claim 1 , wherein the one or more radio frequency signal characteristics are extracted from a physical layer frame of the communication.
The method of claim 1 , wherein the identifying comprises: comparing the generated fingerprint to one or more stored fingerprints representing known transmitters.
comparing the generated fingerprint to one or more stored fingerprints representing known transmitters.
The method of claim 3 , wherein the comparing comprises: generating for each comparison a similarity score indicative of a degree of similarity between the generated fingerprint and a respective one of the one or more stored fingerprints.
generating for each comparison a similarity score indicative of a degree of similarity between the generated fingerprint and a respective one of the one or more stored fingerprints.
The method of claim 3 , wherein the comparing comprises: converting the generated fingerprint to a generated fingerprint model, the generated fingerprint model comprising one or more categorical measurements for radio frequency signal characteristics corresponding to the generated fingerprint; and comparing the generated fingerprint model to a stored fingerprint model of a stored fingerprint, the stored fingerprint model comprising one or more categorical measurements for radio frequency signal characteristics corresponding to the stored fingerprint.
converting the generated fingerprint to a generated fingerprint model, the generated fingerprint model comprising one or more categorical measurements for radio frequency signal characteristics corresponding to the generated fingerprint; and
comparing the generated fingerprint model to a stored fingerprint model of a stored fingerprint, the stored fingerprint model comprising one or more categorical measurements for radio frequency signal characteristics corresponding to the stored fingerprint.
The method of claim 5 , wherein the stored fingerprint model further comprises: a tolerance associated with each of the one or more categorical measurements, each tolerance specifying a degree of similarity that an associated categorical measurement must meet with regard to a corresponding categorical measurement of the generated fingerprint model in order to be considered a match.
a tolerance associated with each of the one or more categorical measurements, each tolerance specifying a degree of similarity that an associated categorical measurement must meet with regard to a corresponding categorical measurement of the generated fingerprint model in order to be considered a match.
The method of claim 1 , wherein the making one or more access control decisions comprises: registering the transmitter such that the transmitter is allowed to communicate with an infrastructure of the wireless computing network.
registering the transmitter such that the transmitter is allowed to communicate with an infrastructure of the wireless computing network.
The method of claim 1 , wherein the making one or more access control decisions comprises: authenticating the transmitter if the generated fingerprint substantially matches a stored fingerprint corresponding to an authorized user of the wireless computing network.
authenticating the transmitter if the generated fingerprint substantially matches a stored fingerprint corresponding to an authorized user of the wireless computing network.
The method of claim 1 , wherein the making one or more access control decisions comprises: authenticating the transmitter if the generated fingerprint indicates that an estimated location of the transmitter is within a permissible range.
authenticating the transmitter if the generated fingerprint indicates that an estimated location of the transmitter is within a permissible range.
The method of claim 1 , wherein the making one or more access control decisions comprises: generating an alert if the generated fingerprint substantially matches a stored fingerprint corresponding to a known source of malicious activity, the alert indicative of a possible intrusion in the wireless computing network; and forwarding the alert to an intrusion prevention system.
generating an alert if the generated fingerprint substantially matches a stored fingerprint corresponding to a known source of malicious activity, the alert indicative of a possible intrusion in the wireless computing network; and
forwarding the alert to an intrusion prevention system.
The method of claim 1 , wherein the making one or more access control decisions comprises: rating an alert if the generated fingerprint does not substantially match a stored fingerprint corresponding to an authorized user of the wireless computing network, the alert indicative of a possible intrusion in the wireless computing network; and forwarding the alert to an intrusion prevention system.
rating an alert if the generated fingerprint does not substantially match a stored fingerprint corresponding to an authorized user of the wireless computing network, the alert indicative of a possible intrusion in the wireless computing network; and
The method of claim 11 , wherein the alert includes information about the generated fingerprint.
The method of claim 1 , wherein the making one or more access control decisions comprises: determining that the transmitter is not an authorized user of the wireless computing device, in accordance with the generated fingerprint; and disrupting communication sessions associated with the transmitter.
determining that the transmitter is not an authorized user of the wireless computing device, in accordance with the generated fingerprint; and
disrupting communication sessions associated with the transmitter.
The method of claim 13 , wherein the disrupting is accomplished in accordance with a frame injection technique.
The method of claim 1 , wherein the making one or more access control decisions comprises: sharing the fingerprint with at least one other computing network.
sharing the fingerprint with at least one other computing network.
The method of claim 15 , further comprising: tracking a location of the transmitter in accordance with the shared fingerprint.
tracking a location of the transmitter in accordance with the shared fingerprint.
The method of claim 1 , further comprising: providing one or more data link layer frames associated with the transmitter to a network firewall; and indicating to the firewall whether the transmitter has been identified as an authorized user of the wireless computing network or an unauthorized user of the wireless computing network, in accordance with the generated fingerprint.
providing one or more data link layer frames associated with the transmitter to a network firewall; and
indicating to the firewall whether the transmitter has been identified as an authorized user of the wireless computing network or an unauthorized user of the wireless computing network, in accordance with the generated fingerprint.
The method of claim 1 , wherein the making one or more access control decisions comprises: marking the transmitter as blacklisted if the generated fingerprint does not substantially match a stored fingerprint corresponding to an authorized user of the wireless computing network.
marking the transmitter as blacklisted if the generated fingerprint does not substantially match a stored fingerprint corresponding to an authorized user of the wireless computing network.
The method of claim 1 , wherein the fingerprint is generated periodically.
A computer readable medium containing an executable program for securing a wireless computing network, where the program performs the steps of: receiving a communication from an unidentified transmitter; identifying the transmitter in accordance with a fingerprint generated from one or more radio frequency signal characteristics extracted from the communication; and making one or more access control decisions in response to an identity of the transmitter.
The computer readable medium of claim 20 , wherein the one or more radio frequency signal characteristics are extracted from a physical layer frame of the communication.
The computer readable medium of claim 20 , wherein the identifying comprises: comparing the generated fingerprint to one or more stored fingerprints representing known transmitters.
The computer readable medium of claim 22 , wherein the comparing comprises: generating for each comparison a similarity score indicative of a degree of similarity between the generated fingerprint and a respective one of the one or more stored fingerprints.
The computer readable medium of claim 22 , wherein the comparing comprises: converting the generated fingerprint to a generated fingerprint model, the generated fingerprint model comprising one or more categorical measurements for radio frequency signal characteristics corresponding to the generated fingerprint; and comparing the generated fingerprint model to a stored fingerprint model of a stored fingerprint, the stored fingerprint model comprising one or more categorical measurements for radio frequency signal characteristics corresponding to the stored fingerprint.
The computer readable medium of claim 24 , wherein the stored fingerprint model further comprises: a tolerance associated with each of the one or more categorical measurements, each tolerance specifying a degree of similarity that an associated categorical measurement must meet with regard to a corresponding categorical measurement of the generated fingerprint model in order to be considered a match.
The computer readable medium of claim 20 , wherein the making one or more access control decisions comprises: registering the transmitter such that the transmitter is allowed to communicate with an infrastructure of the wireless computing network.
The computer readable medium of claim 20 , wherein the making one or more access control decisions comprises: authenticating the transmitter if the generated fingerprint substantially matches a stored fingerprint corresponding to an authorized user of the wireless computing network.
The computer readable medium of claim 20 , wherein the making one or more access control decisions comprises: authenticating the transmitter if the generated fingerprint indicates that an estimated location of the transmitter is within a permissible range.
The computer readable medium of claim 20 , wherein the taking action comprises: generating an alert if the generated fingerprint substantially matches a stored fingerprint corresponding to a known source of malicious activity, the alert indicative of a possible intrusion in the wireless computing network; and forwarding the alert to an intrusion prevention system.
The computer readable medium of claim 20 , wherein the making one or more access control decisions comprises: generating an alert if the generated fingerprint does not substantially match a stored fingerprint corresponding to an authorized user of the wireless computing network, the alert indicative of a possible intrusion in the wireless computing network; and forwarding the alert to an intrusion prevention system.
generating an alert if the generated fingerprint does not substantially match a stored fingerprint corresponding to an authorized user of the wireless computing network, the alert indicative of a possible intrusion in the wireless computing network; and
The computer readable medium of claim 30 , wherein the alert includes information about the generated fingerprint.
The computer readable medium of claim 20 , wherein the making one or more access control decisions comprises: determining that the transmitter is not an authorized user of the wireless computing device, in accordance with the generated fingerprint; and disrupting communication sessions associated with the transmitter.
The computer readable medium of claim 32 , wherein the disrupting is accomplished in accordance with a frame injection technique.
The computer readable medium of claim 20 , wherein the making one or more access control decisions comprises: sharing the fingerprint with at least one other computing network.
The computer readable medium of claim 34 , further comprising: tracking a location of the transmitter in accordance with the shared fingerprint.
The computer readable medium of claim 20 , further comprising: providing one or more data link layer frames associated with the transmitter to a network firewall; and indicating to the firewall whether the transmitter has been identified as an authorized user of the wireless computing network or an unauthorized user of the wireless computing network, in accordance with the generated fingerprint.
The computer readable medium of claim 20 , wherein the making one or more access control decisions comprises: marking the transmitter as blacklisted if the generated fingerprint does not substantially match a stored fingerprint corresponding to an authorized user of the wireless computing network.
The computer readable medium of claim 20 , wherein the fingerprint is generated periodically.
A system for securing a wireless computing network, comprising: means for receiving a communication from an unidentified transmitter; means for identifying the transmitter in accordance with a fingerprint generated from one or more radio frequency signal characteristics extracted from the communication; and means for making one or more access control decisions in response to an identity of the transmitter.
means for receiving a communication from an unidentified transmitter;
means for identifying the transmitter in accordance with a fingerprint generated from one or more radio frequency signal characteristics extracted from the communication; and
means for making one or more access control decisions in response to an identity of the transmitter.
The system of claim 39 , wherein the means for receiving comprises one or more antennae.
The system of claim 39 , wherein the means for identifying comprises: a standard processing module configured to extract a data link layer frame from a raw radio frequency signal associated with the communication; and a fingerprint processing module configured to produce the fingerprint.
a standard processing module configured to extract a data link layer frame from a raw radio frequency signal associated with the communication; and
a fingerprint processing module configured to produce the fingerprint.
The system of claim 41 , wherein the fingerprint processing module is further configured to extract a medium access control address contained in the communication.
The system of claim 41 , wherein the fingerprint processing module is further configured to demodulate and decode additional radio frequency signals not associated with the transmitter.
The method of claim 41 , wherein the fingerprint processing module comprises an expert reasoning engine for detecting location-fixed transmitter position violations.
The system of claim 39 , further comprising: a fingerprint store comprising fingerprint data for one or more transmitters known to the wireless computing network.
a fingerprint store comprising fingerprint data for one or more transmitters known to the wireless computing network.
The system of claim 45 , wherein the fingerprint store is an extension of an address resolution protocol cache.
The system of claim 45 , wherein at least some of the contents of the fingerprint store are exported to other wireless computing network security systems.
The system of claim 45 , wherein at least some of the contents of the fingerprint store are imported from other wireless computing network security systems.
The system of claim 39 , wherein the means for making one or more access control decisions comprises: a firewall module configured to accept or deny data link layer frames of the communication based on the fingerprint.
a firewall module configured to accept or deny data link layer frames of the communication based on the fingerprint.
The system of claim 49 , wherein the firewall module is further configured to: pass data link layer frames associated with authorized transmitters to a network stack module; and drop data link layer frames that are not associated with authorized transmitters.
pass data link layer frames associated with authorized transmitters to a network stack module; and
drop data link layer frames that are not associated with authorized transmitters.
The system of claim 49 , wherein the firewall module is further configured to send an alert to an intrusion prevention system if the data link layer frames are not associated with authorized transmitters.
The system of claim 49 . wherein the firewall module is further configured to disrupt communications of unauthorized transmitters.
The system of claim 39 , further comprising: an intrusion prevention system configured to perform inline packet inspection and anomaly detection.
an intrusion prevention system configured to perform inline packet inspection and anomaly detection.
Ref US20200382961A1: Claims:
A system for unmanned vehicle (UV) detection in a radio frequency (RF) environment, comprising: one or more radio frequency (RF) signal detectors configured to detect RF signals, including RF signals transmitted by at least one of a UV and a UV controller; a feature extractor configured to extract signal features from detected RF signals; and a classifier that employs machine learning to identify at least one of the UV and the UV controller based on the signal features.
one or more radio frequency (RF) signal detectors configured to detect RF signals, including RF signals transmitted by at least one of a UV and a UV controller;
a feature extractor configured to extract signal features from detected RF signals; and
a classifier that employs machine learning to identify at least one of the UV and the UV controller based on the signal features.
The system of claim 1 , further comprising a filter coupled between the one or more RF signal detectors and the feature extractor, the filter configured to remove one or more unwanted frequencies from the detected RF signals, temporally filter the detected RF signals to retain only temporally relevant signals, or spatially filter the detected RF signals.
The system of claim 2 , further comprising a computer-readable memory for storing filtered RF signal data for later analysis.
The system of claim 1 , further comprising a decoder coupled between the one or more RF signal detectors and the feature extractor, the decoder configured to decode at least one of the detected RF signals.
The system of claim 4 , wherein the decoder is configured to perform blind-adaptive decoding.
The system of claim 1 , wherein the classifier is configured to compare one or more of the signal features to known signal features in order to distinguish a signal of interest from other signals.
The system of claim 1 , wherein the classifier is configured to receive a device identifier from the detected RF signals, and comparing the device identifier to an authentication database to determine if the at least one of the UV and the UV controller is permitted.
The system of claim 1 , wherein the one or more RF signal detectors are responsive to a sensor output indicating an incursion of a perimet
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