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 . The objections and rejections from the Office Action of 6/17/2026 are hereby withdrawn. New grounds for rejection are presented below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 20 and 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Massarella et al. (US 20180024220 A1)[hereinafter “Massarella”] and DePoy et al. (US 20230144796 A1)[hereinafter “DePoy”].
Regarding Claim 20, Massarella discloses a direction finding system, comprising: a direction finding sensor array [See Figs. 1a, 3, 8a, 8b, and 9a and corresponding text.Paragraph [0041] – “FIG. 1a schematically illustrates an Adcock antenna array for use in Watson-Watt direction finding”];
one or more processors; and one or more computer-readable recording media that store executable instructions that are executable by the one or more processors [See Fig. 3 and Paragraph [0083] – “In this example, the incident angle determining module 14 is implemented by a suitably programmed microprocessor. However, the incident angle determining module 14 may alternatively be provided by a FPGA or by a suitably programmed computer.” The program instructions are inherently stored in memory.] to configure the direction finding system to:
access a baseline neural network [Paragraph [0177] – “Alternatively, the incident signal angles θ.sub.k at each given frequency f.sub.k may be directly calculated using a multi-layer perceptron, or other suitable neural network, provided by the incident angle determining module 14. In this case, the inputs would be the measured values of signal power P.sub.n(f.sub.k), in which 1≦n≦N, and the network would be trained, during calibration of the system using known RF signal sources 5, using back-propagation of weight values or another suitable training method.”] initially configured to imitate behavior of a two-argument arctangent function [Paragraph [0042] – “FIG. 1b schematically illustrates ambiguities which can arise when Watson-Watt methods are applied to signal power values”See Equation (5) which is based off of the two arguments presented by Equations (3) and (4).].
Massarella discloses the use of Watson-Watt direction finding [Paragraph [0041] – “FIG. 1a schematically illustrates an Adcock antenna array for use in Watson-Watt direction finding”] without utilizing a lookup table for error correction [Use of the neural network of Paragraph [0177] “alternatively” to the use of the look-up table mentioned elsewhere (for example, Paragraph [0095])] and the use of machine learning [Paragraph [0176] – “The hereinbefore described methods of estimating incident signal angles may be modified to incorporate machine learning methods. For example, machine learning methods may be employed to optimise, during calibration and/or during operation, corrections to compensate for bias errors which may arise in any of the hereinbefore described methods.”], but fails to disclose that the system is configured to:
apply transfer learning to the baseline neural network to generate a calibrated neural network, wherein the transfer learning calibrates the baseline neural network.
However, DePoy discloses to apply transfer learning to a baseline neural network to generate a calibrated neural network, wherein the transfer learning calibrates the baseline neural network [See the neural network of Paragraphs [0152]-[0153].Paragraph [0153] – “In some implementations, training begins by using a generic model trained to perform DoA regression in the simulation domain. The model is updated for a specific (physical) array or deployment scenario by capturing target signals from one or more test positions using the target sensor/array. Such an implementation is shown in FIG. 1 in the case where the environment 102 is simulated. These recordings are then used to generate training examples to update the model trained in simulation (e.g., transfer learning). This manual training process can be repeated until the quality of the model is suitable (e.g., meets minimum criteria for accuracy/precision, satisfies a threshold, among others). Once a minimally viable model is produced, a semi-supervised training method may be deployed in order to iteratively enhance the quality of the model without the need for additional data collection. A semi-supervised training method can include generating ground truth data based on traditional DoA methods as discussed in reference to FIG. 1.”].
Massarella, as modified, would disclose that the system is configured to:
access measurement data acquired via the direction finding sensor array [See Figs. 1a, 3, 8a, 8b, and 9a and corresponding text.Paragraph [0041] – “FIG. 1a schematically illustrates an Adcock antenna array for use in Watson-Watt direction finding”], the baseline neural network comprising a first neural network and a second neural network [Paragraph [0177], each layer of the “multi-layer perceptron” considered a separate neural network as they each inherently comprise neurons], the first neural network and the second neural network comprising separate instances of a single initially trained neural network [Paragraph [0177], the layers of the initially trained multi-layer perceptron prior to calibration];
generate preprocessed data by applying one or more preprocessing operations to the measurement data [See Equation (2) and Paragraph [0069] – “The antennae are arranged in pairs: north-south and east-west. The signals from these antennas are processed by combining the voltage signals from the north-south pair in a passive circuit known as a 180 degree hybrid.”];
utilize the preprocessed data as input to the calibrated [Per the calibration of DePoy] neural network [Paragraph [0177] – “Alternatively, the incident signal angles θ.sub.k at each given frequency f.sub.k may be directly calculated using a multi-layer perceptron, or other suitable neural network, provided by the incident angle determining module 14. In this case, the inputs would be the measured values of signal power P.sub.n(f.sub.k), in which 1≦n≦N, and the network would be trained, during calibration of the system using known RF signal sources 5, using back-propagation of weight values or another suitable training method.”See Equations (6.1) and (6.2), used further on in the data processing.]; and
output angle of arrival data [Fig. 3, element 17. See Paragraph [0083].], the angle of arrival data comprising output of the calibrated neural network [Per updating of the neural network via DePoy].
Regarding Claim 24, the combination would disclose that applying transfer learning [per DePoy] to the baseline neural network comprises retraining the first neural network and the second neural network [Layers of the multi-layer perceptron of Massarella] utilizing transfer learning data comprising measurement data and ground truth angle of arrival data [Paragraph [0153] – “In some implementations, training begins by using a generic model trained to perform DoA regression in the simulation domain. The model is updated for a specific (physical) array or deployment scenario by capturing target signals from one or more test positions using the target sensor/array. Such an implementation is shown in FIG. 1 in the case where the environment 102 is simulated. These recordings are then used to generate training examples to update the model trained in simulation (e.g., transfer learning). This manual training process can be repeated until the quality of the model is suitable (e.g., meets minimum criteria for accuracy/precision, satisfies a threshold, among others). Once a minimally viable model is produced, a semi-supervised training method may be deployed in order to iteratively enhance the quality of the model without the need for additional data collection. A semi-supervised training method can include generating ground truth data based on traditional DoA methods as discussed in reference to FIG. 1.”].
Regarding Claim 25, DePoy fails to disclose that the transfer learning data further comprises anchor point data associated with one or more beam peaks or one or more beam crossovers. However, Massarella discloses that the arrangement would have measurement conditions corresponding to the recited anchor point data associated with one or more beam peaks or one or more beam crossovers [See Fig. 9a]. It would have been obvious to make use of such features when performing transfer learning in order to improve the accuracy of the resulting model.
Regarding Claim 26, Massarella discloses that the measurement data comprises radiation pattern data [See Figs. 1a and 9a], and wherein applying the one or more preprocessing operations to the measurement data [See Equation (2) and Paragraph [0069] – “The antennae are arranged in pairs: north-south and east-west. The signals from these antennas are processed by combining the voltage signals from the north-south pair in a passive circuit known as a 180 degree hybrid.”] comprises mapping the radiation pattern data to sine pattern data [See Equation (6.1)] and cosine pattern data [See Equation (6.2)].
Allowable Subject Matter
Claims 21-23 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.
Response to Arguments
Applicant argues:
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115
792
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Examiner’s Response:
The corresponding objection is hereby withdrawn.
Applicant argues:
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558
788
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Examiner’s Response:
The corresponding rejections are hereby withdrawn.
Applicant argues:
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148
787
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270
788
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Examiner’s Response:
The corresponding rejections are hereby withdrawn.
Applicant argues:
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335
786
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Examiner’s Response:
The corresponding rejection is hereby withdrawn.
Applicant argues:
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270
788
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202
782
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Examiner’s Response:
The Examiner respectfully disagrees. The term “neural network” is broad in scope and the Examiner is interpreting that limitation as a machine learning algorithm that comprises neurons. For example, see Page 2 of i2tutorials, Understanding single layer Perceptron and difference between Single Layer vs Multilayer Perceptron, 9.6.2019 (available at: https://i2tutorials.com/what-is-single-layer-perceptron-and-difference-between-single-layer-vs-multilayer-perceptron/) – “Single layer perceptron is a simple Neural Network which contains only one layer.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20190346532 A1 – METHOD FOR DIRECTION FINDING OF AT LEAST ONE STATIONARY AND/OR MOVING TRANSMITTER AS WELL AS SYSTEM FOR DIRECTION FINDING
US 20160018509 A1 – ELECTRICALLY SMALL, RANGE AND ANGLE-OF-ARRIVAL RF SENSOR AND ESTIMATION SYSTEM
Friedrichs et al., A Compact Machine Learning Architecture for Wideband Amplitude-Only Direction Finding, IEEE, July 2022
Oestreich et al., Miniaturized Watson-Watt Direction Finder: An Advancement in Vehicle Safety, IEEE, 2012
Scorrano et al., Estimation of the Direction-of-Arrival of Incoming EM Wavefronts through a Neural Network Approach, PIERS, 2017
Scorrano et al., Compact Direction Finding Array for Tactical Aircraft Radios Through Artificial Neural Networks Estimator, IEEE, 2018
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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, Arleen Vazquez can be reached at (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R QUIGLEY/ Primary Examiner, Art Unit 2857
