DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/21/2024 and 09/08/2025 are in compliance with the provisions of 35 CFR 1.97. Accordingly, the IDS have been considered by the examiner.
Claim Objections
Claim 1 objected to because of the following informalities: The claim recites “receive signal that reflect or echo” which contains subject-verb disagreement. It should be either “receive signals that reflect or echo” or “receive a signal that reflects or echoes“.
Claim 2 objected to because of the following informalities: The claim recites the parenthetical “(e.g., narrow beam vs. broad beam)” is informal language inappropriate for claim drafting. It may render the claim indefinite since it is unclear whether the limitation following “e.g.” is required or merely exemplary.
Claims 2-5 objected to because of the following informalities: The claims recite "The system of claim 1 wherein" which should be "The system of claim 1, wherein".
Claim 3 objected to because of the following informalities: The claim recites “simultaneously acquire signal” which should be “simultaneously acquire signals” (plural).
Claim 6 objected to because of the following informalities: The claim recites "The (extra spacing between words needs to be removed).
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rejected under 35 U.S.C. § 112(b) as being indefinite for improperly mixing statutory claim categories. Claim 1 is directed to “A system for suppressing radio frequency interference…” which is an apparatus claim. However, the body of the claim recites method steps using:
“…comprising: “simultaneously obtaining RFI data…”, “simultaneously training a model…”, “applying the trained model…”, and “removing RFI signals…”. The claim recites no structural elements (e.g., processor, memory, transmitter, receiver, antenna hardware) that would define the system. The specification at paragraph [0009] confirms the invention focuses on “computational algorithms (as described in steps (1)-(4))” and “design/deployment of reference radar antennas”—yet no such structure appears in the claim.
The claim should recite structural elements (e.g., “A system comprising: at least one primary antenna; a plurality of reference antennas; a processor configured to…”); or reformat as a method claim (e.g., “A method for suppressing RFI, comprising the steps of: obtaining…; training…; applying…; removing…”) or similar.
Specification Support: The specification at paragraph [0008] states: “This method of the present invention entails both computational algorithms (as described in steps (1)-(4) above) and design/deployment of reference radar antennas.” This confirms the claims are directed to methods/algorithms, not system structure. No hardware structure is described that corresponds to the claimed “system.”
Claim 1 recites “removing RFI signals received by primary antenna” which lacks proper antecedent basis. The claim earlier recites “at least one primary antenna” (potentially plural), but later refers to “primary antenna” (singular) and “the primary antenna” (definite singular). It is unclear whether the limitation applies to one antenna or all antennas when multiple primary antennas are present.
Claim 1 recites “simultaneously obtaining RFI data” and “simultaneously training a model.” The term “simultaneously” renders the claim indefinite because:
It is unclear what operations occur simultaneously with what other operations.
Looking to the specification (paragraph [0021]), “simultaneously” refers to primary and reference antennas acquiring signals at the same time—but Claim 1 could be read to require obtaining and training to occur simultaneously with each other.
Simultaneous implies that multiple actions happen at the same time, however, it appears that only one action is being performed.
Claim 4 recites “trained using the RFI characterization signals.” There is no prior recitation of “RFI characterization signals” in Claims 1 or 4. Claim 3 introduces this term, but Claim 4 depends from Claim 1, not Claim 3. Therefore, “the RFI characterization signals” lacks antecedent basis.
Allowable Subject Matter
Claims 1-6 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
The following is a statement of reasons for the indication of allowable subject matter:
Axel et al. (US 5,217,016 A), teaches interference suppression using auxiliary antennas with correlation-based estimation and real-time subtraction, but does not teach: Training a model to learn mappings, non-linear mapping functions, predictive inference separate from measurement, and machine learning or neural network-based approaches. The disclosed system is deterministic and analytical, not model-based.
Morrell (US 6,268,728 B1), teaches adaptive cancellation using reference channels with continuous linear adaptation, but does not teach: Training a model that learns and generalizes, non-linear inference, prediction using a previously trained model and separation of training and inference phases.
Hanley (US 2006/0232272 A1), teaches receiver-based interference mitigation using algorithmic estimation and subtraction, but does not teach: Machine-learned models, trained predictive models, non-linear mapping learning, and CNN-based or neural network-based approaches
Webler (US 2007/0055142 A1), addresses interference suppression using signal processing blocks and filtering, but does not teach: Model training, non-linear mapping inference, predictive modeling paradigm, and machine learning approaches.
Nguyen et al. (US 2010/0141508 A1), discusses interference cancellation using reference signals and adaptive calibration-based techniques, does not teach: Training a model on RFI-only data, non-linear mapping learning, and subsequent predictive application of trained model.
Rothberg et al. (US 2016/0069968 A1), discloses signal conditioning and interference suppression using algorithmic and deterministic techniques, but does not teach: Model training, predictive inference using trained models, and non-linear mapping learning.
Harris et al. (US 2019/0004137 A1), discusses machine learning in wireless systems but applies ML to classification, detection, and scheduling tasks, but does not teach: Training models to predict interference waveforms for subtraction, reference-antenna-based RFI prediction architecture, non-linear antenna-to-antenna interference modeling, and the specific training-then-prediction paradigm claimed.
Zaiss et al. (US 2020/0072931 A1), discloses AI-assisted wireless optimization with system-level learning, but does not teach: Predicting interference signals at a primary antenna based on reference antenna data, subtraction of predicted waveforms, and the claimed RFI suppression architecture.
Gang et al. (CN103760541A), discloses radar interference suppression using adaptive linear cancellation, but does not teach: Trained models, non-linear inference, machine learning approaches, and prediction of interference in the presence of signals of interest
Harvey et al. (US 2017/0108569 A1), discloses MRI RF noise suppression using “sniffer coils” (RF noise detection coils) placed outside the imaging volume to detect environmental RF noise. The system operates as follows:
Calibration Phase (RF excitation disabled): Acquires “calibration magnetic resonance data” from imaging coils, “reference radio frequency data” from sniffer coils, where both acquired simultaneously during “dummy” periods without RF excitation and then calculates a “noise calibration” by comparing signals.
Imaging Phase (RF excitation enabled): Acquires “imaging magnetic resonance data” from imaging coils, “noise radio frequency data” from sniffer coils, where both are acquired simultaneously during actual MRI scanning.
Noise Removal: Uses the noise calibration to relate sniffer coil measurements to imaging coil noise, calculates predicted noise, and subtracts predicted noise from imaging data.
Deterministic calibration: Harvey calculates scale factors by direct comparison of simultaneously acquired signals (see equation in FIG. 6, [0091]-[0102]). Linear scaling: The relationship is a simple multiplicative scale factor calculated analytically. No machine learning: Harvey does not train a model; instead, it performs correlation-based calibration. Synchronous measurement: Harvey relies on measuring noise simultaneously in both imaging and sniffer coils, then applying a calculated ratio
Harvey does not teach or suggest: Training a model (machine learning, neural network, or otherwise) to learn relationships between coils. Non-linear signal mappings—Harvey’s approach is explicitly based on linear scaling factors (ratios). Learning from data—Harvey calculates deterministic relationships, not learned patterns. Predictive inference—Harvey estimates noise based on synchronized measurements and calculated ratios, not model-based prediction.
While Harvey is the closest prior art in terms of system architecture (primary coils, reference coils, calibration phase, subtraction), Harvey’s deterministic, linear, calculation-based approach is fundamentally different from the claimed invention’s model training, non-linear mapping learning, and predictive inference approach. Harvey’s system performs well-understood electromagnetic coupling analysis and applies calculated ratios. It does not teach or suggest training a model (e.g., CNN) to learn non-linear antenna-to-antenna mappings from RFI-only data for subsequent prediction during signal acquisition.
The core invention - training a model to learn non-linear mappings then applying that trained model for prediction – is not taught individually or in combination by the closest prior art of record.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REMASH R GUYAH whose telephone number is (571)270-0115. The examiner can normally be reached M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire can be reached at (571) 270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REMASH R GUYAH/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648