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Patent Application 18061986 - THIRD-PARTY ENABLED INTERFERENCE CLASSIFICATION - Rejection

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Patent Application 18061986 - THIRD-PARTY ENABLED INTERFERENCE CLASSIFICATION

Title: THIRD-PARTY ENABLED INTERFERENCE CLASSIFICATION PLATFORM

Application Information

  • Invention Title: THIRD-PARTY ENABLED INTERFERENCE CLASSIFICATION PLATFORM
  • Application Number: 18061986
  • Submission Date: 2025-04-10T00:00:00.000Z
  • Effective Filing Date: 2022-12-05T00:00:00.000Z
  • Filing Date: 2022-12-05T00:00:00.000Z
  • National Class: 370
  • National Sub-Class: 329000
  • Examiner Employee Number: 86659
  • Art Unit: 2645
  • Tech Center: 2600

Rejection Summary

  • 102 Rejections: 0
  • 103 Rejections: 2

Cited Patents

The following patents were cited in the rejection:

Office Action Text


    DETAILED ACTION
This Office Action is in response to the Applicant's communication filed on 12/20/2022. In virtue of this communication, claims 1 - 20 are currently pending in the instant application.
Claim Rejections - 35 USC § 103
2.	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 of this title, 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.

3.	Claims 1 – 4, 6, 10 – 14, and 16 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (hereinafter “Singh”) (Pub # US 2021/0234622 A1) in view of Beadles et al. (hereinafter “Beadles”) (Pub # US 2018/0323815 A1).
Regarding claims 1, 11, and 16, Singh discloses a method/a system (see 105 in FIG. 1, FIG. 11), comprising: a processor (see 1199 in FIG. 11); and a memory having instructions (see Interference Analysis System 105 in FIG. 7, 1105 in FIG. 11, [0108], [0109]) stored thereon which, when executed on the processor, performs operations (see FIG. 5, [0101] – [0104], [0108],  [0109]) comprising: 
receiving data describing radio frequency characteristics (i.e., time-frequency characteristic) of a wireless network transmission environment (i.e., mobile network 110 in FIG. 1) (see FIG. 1, [0019] for interference analysis system functions to generate the analysis, which can include one or more of generated interference information, mobile network control information, mobile network configuration information, and a graphical user interface, and see FIG. 5, FIG. 7, FIG. 8, [0023], [0029], [0035] – [0040] for an analysis datastore (e.g., 120 shown in FIG. 1, FIG. 8) receives the telemetry data includes time-frequency characteristic data for at least one base station receive antenna); 
presenting the data for display on a graphical user interface (GUI) associated with a network device (see1111 in FIG. 11) in the wireless network (see [0101], [0102], [0106] for network adapter device provides one or more wired or wireless interfaces (i.e., GUI) for exchanging data and commands); 
receiving input from the GUI identifying the data (see [0095], [0101] for the user interface includes user interface elements constructed to receive user input from the operator device, and display output of interference analysis based on the received user input, wherein user input includes selection of at least one of: a start and end time during which to perform interference analysis, a QoE impact threshold used to filter and/or prioritize classified instances of detected interference, and a mobile network base station for which the interference analysis is to be performed); and 
training a machine learning (ML) model (see machine learning system 190 in FIG. 1, [0031]) to classify an interferer in the wireless network transmission environment based on the identified one or more portions of the data (see [0033] - [0035] for the detection engine 130 can include a trained machine learning model which implement one or more detection algorithms that function to detect interference by processing at least a subset of accessed telemetry data of the mobile network).
Singh discloses user input includes selection of at least one of: a start and end time during which to perform interference analysis, a QoE impact threshold used to filter and/or prioritize classified instances of detected interference, and a mobile network base station for which the interference analysis is to be performed, and the model trained to detect interference from a single set of time-frequency characteristic data (see [0033] – [0035], [0095]). Thus, Singh obviously teaches receiving input identifying one or more portions of the data. If this is in question, using Beadles as below.
In an analogous art, Beadles discloses receiving input identifying one or more portions of the data (see Beadles, FIG. 2, [0038] – [0041] for the server selects pattern characterization data that characterizes the detected signal patterns of the selected pattern type).
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date the invention was made, to modify the invention of Singh, and have receiving input identifying one or more portions of the data such that the server and its method facilitate tracking interference levels for particular types of interference over an entire network, facilitate the prioritization of individual interference sources that impact large geographical regions, thereby provides easily view the impact of such changes to interference levels across the network by comparing the pattern characterization data, as discussed by Beadles (see Beadles, [0041]). 
Regarding claims 2, 12, and 17, Singh in view of Beadles disclose wherein the data describing the wireless network transmission environment is captured at a wireless access point (AP), and wherein the GUI is associated with at least one of: (i) the AP or (ii) a controller in the network environment (see Singh, FIG. 1, FIG. 7, FIG. 8, [0019], [0020] – [0023] for the system functions to detect radio interference affecting a mobile network within a geographical area of the mobile network maintained by an operator, and an infrastructure element (e.g., 111c shown in FIG. 8) of the mobile network (e.g., an MME) provides the telemetry data to the interference analysis system).
Regarding claims 3, 13, and 18, Singh in view of Beadles disclose transmitting the trained ML model to the AP, wherein the AP is configured to classify the interferer using the trained ML model (see Singh, [0022] for the system performs one or more of classifying detected interference determining one or more root causes for detected interference, determining a prioritization of detected interference with respect to service degradation of a subscriber, and determining interference location probabilities within an area of the mobile network, see [0034], [0035], [0038], [0040] for the detection engine/ classification engine includes at least a trained machine learning model to detect / classify interference from a single set of time-frequency characteristic data).
Regarding claims 4, 14, and 19, Singh in view of Beadles disclose wherein receiving input from the GUI identifying one or more portions of the data comprises receiving input identifying one or more portions of the data reflecting the interferer (see Singh, FIG. 4, [0064], [0095]).
Regarding claim 6, Singh in view of Beadles disclose identifying one or more known signals in the received data based on using one or more previously generated classifiers with the received data (see Beadles, [0055] for pattern comparison functional compares the saved mathematical analysis data to known signal patterns variables); and displaying one or more unknown signals in the data on the GUI, based on the identified one or more known signals (see Singh, FIG. 7, [0022], [0043], [0044] for interference generated by an interference source external to the mobile network, thus there are unknown signals).
Regarding claim 10, Singh in view of Beadles disclose configuring one or more parameters for the wireless network based on classifying the interferer using the trained ML model (see Singh, FIG. 5, [0091], [0097] for uses a trained machine learning model to identify interference, and generate control parameters which are generated to reduce impact of interference).
4.	Claims 5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (hereinafter “Singh”) (Pub # US 2021/0234622 A1) in view of Beadles et al. (hereinafter “Beadles”) (Pub # US 2018/0323815 A1) as applied to claim 1 above, and further in view of EI-Akkad et al. (hereinafter “EI-Akkad”) (Pub # US 2024/0097352 A1).
Regarding claim 5, Singh in view of Beadles disclose wherein the data describing radio frequency characteristics of the wireless network transmission environment comprises radio frequency and time characteristics of the wireless network transmission environment, and wherein receiving the input from the GUI identifying one or more portions of the data comprises receiving identification of one or more portions of the radio frequency and time characteristics (see Singh, [0029], [0035], [0037], [0038], [0040]).
Singh in view of Beadles teach that mobile network can include wireless spectrum network (see Singh, [0023]).
 Singh in view of Beadles do not disclose specifically that the data describing radio frequency characteristics comprises spectrogram data describing radio frequency and time characteristics.
In an analogous art, EI-Akkad discloses that the data describing radio frequency characteristics comprises spectrogram data describing radio frequency and time characteristics (see EI-Akkad, [0313] – [0316], where EI-Akkad is discussing the spectrogram can describe and/or display the raw RF signal data as a function of frequency, time).
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date the invention was made, to modify the invention of Singh/ Beadles, and have the data describing radio frequency characteristics comprises spectrogram data describing radio frequency and time characteristics such that provides only signal in a specific frequency or narrow range of frequencies can be transmitted, thereby increasing power efficiency, as discussed by EI-Akkad (see EI-Akkad, [0013]). 
Regarding claim 9, Singh in view of Beadles and EI-Akkad disclose wherein the wireless network transmission environment comprises a WiFi network supporting a 6GHz band, and wherein the interferer in the wireless network transmission environment operates using at least a portion of the 6GHz band (see Singh, FIG. 7,  [0015], [0020], [0023], and see EI-Akkad, [0089] for the wireless network may include one or more wireless networks, such as Bluetooth, WiFi, infrared, cellular, sub-6 GHz, or any other type of wireless network).
Allowable Subject Matter
Claims 7 and 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 20 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.

Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONG-THUY THI TRAN whose telephone number is (571)270-3199. The examiner can normally be reached Monday-Friday: 9AM - 6PM (IFP).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANTHONY ADDY can be reached on (571)272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.





/MONG-THUY T TRAN/           Primary Examiner, Art Unit 2645                                                                                                                                                                                             


    
        
            
        
            
    


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