NON-FINAL REJECTION, FIRST DETAILED ACTION
Status of Prosecution
The present application 18/192,372, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on March 29, 2023 and claims priority to European Office application EP22177 192.6 filed on June 3, 2022.
Claims 1-18 are pending and are all rejected in this rejection. Claims 1, 6 and 12 are independent claims.
Status of Claims
Claims 1-2 and 4-18 are rejected under 35 USC § 103 as being unpatentable over Naseef, United States Patent Application Publication 2012/0248488, published on Aug. 12, 2021 in view of Kleinbeck et al. (“Kleinbeck”), United States Patent Application Publication 2017/0374573, published on Dec. 28, 2017.
Claim 3 is rejected under 35 USC § 103 as being unpatentable over Naseef in view of Kleinbeck in further view of Cheng et al., (“Cheng”), United States Patent Application Publication 2020/0050182, published on Feb. 13, 2020.
Objection
Claim 8 is objected to as to what appears to be a typographical error. It references “fingerprint data” which is not recited in its stated parent claim 6, but instead is recited in claim 7. For examination purposes, Examiner will construe it as a typographical error that should depend from claim 7 instead. Correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
A.
Claims 1-2 and 4-18 are rejected under 35 USC § 103 as being unpatentable over Naseef, United States Patent Application Publication 2012/0248488, published on Aug. 12, 2021 in view of Kleinbeck et al. (“Kleinbeck”), United States Patent Application Publication 2017/0374573, published on Dec. 28, 2017.
As to Claim 1, Naseef teaches: A computer-implemented method of training an artificial intelligence circuit, the method comprising the steps of
providing a training data set, the training data set encompassing training data of a frequency spectrum (Naseef: par. Par. 0064, training data set D is premised on a predefined scenario; pars. 0066-67, the scenario may describe the RF signals that would be measured at a specific location) and
feeding the training data set into the artificial intelligence circuit to be trained (Naseef: Fig. 1, par. 0051, the trained machine learning circuit [18] has the training data set D fed into it to be trained), which processes the training data set in order to learn a standard frequency spectrum and/or a standard radio frequency power (Naseef: pars. 0084-85, the signals which include information of the signal spectrum and power are stored as the training data set D).
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Naseef may not explicitly teach: providing a training data set, the training data set encompassing training data of a frequency spectrum comprising a guard band and/or training data concerning a radio frequency power in the guard band;
feeding the training data set into the artificial intelligence circuit to be trained, which processes the training data set in order to learn a standard frequency spectrum and/or a standard radio frequency power in the guard band, thereby enabling the artificial intelligence circuit, when trained, to determine a deviation from the standard frequency spectrum and/or to determine a deviation from the standard radio frequency power in the guard band.
Kleinbeck teaches in general concepts related to automatic signal detection with a learning and conflict detection engine (Kleinbeck: Abstract). Specifically, Kleinbeck teaches that teaches that machine learning is used to identify open spaces such as guard bands, white spaces and combinations thereof (Kleinbeck: par. 0160). Anomalies (i.e. deviations) are identified using machine learning techniques (Kleinbeck: par. 0250, “ If there is a narrow band interference signal where there typically is a wide band signal, the system will identify it as an anomaly because it does not match the pattern of what is usually in that space.”).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Naseef disclosures and teachings by consolidating the steps for the detection of the deviations into the guard band as taught and suggested by Kleinbeck. Such a person would have been motivated to do so with a reasonable expectation of success to allow for an autonomous detection of the anomalous to reduce cognitive burden on the user (Kleinbeck: par. 0251).
As to Claim 2, Naseef and Klienbeck teaches the limitations of claim 1.
Naseef and Kleinbeck as combined further teaches: wherein the training data is indicative of the standard frequency spectrum and/or the standard radio frequency power in the guard band (Naseef: pars. 0084-85, the signals which include information of the signal spectrum and power are stored as the training data set D; Kleinbeck: par. 0160, open space such as guard bands are considered).
As to Claim 4, Naseef and Klienbeck teaches the limitations of claim 1.
Klienbeck further teaches: wherein the artificial intelligence circuit processes the training data set, thereby extracting at least one fingerprint data encompassed in the training data set, wherein the at least one fingerprint data is used for determining the deviation from the standard frequency spectrum and/or determining the deviation from the standard radio frequency power in the guard band (Kleinbeck: pars. 0265, as radio signals shift, fingerprint data may be used to identify and understand the source of radio signals; Examiner asserts that this information would be used with the other teachings of Klienbeck to determine the deviations).
As to Claim 5, Naseef and Klienbeck teaches the limitations of claim 1.
Naseef further teaches: wherein the training data is generated in a computer-aided manner and/or wherein the training data is collected by a radio receiver (Naseef: par. 0085, the training data set D is create electronically and stored).
As to Claim 6, Naseef teaches: A monitoring method for identifying an anomaly in a radio frequency spectrum, the monitoring method comprises the steps of:
receiving at least one radio frequency signal by at least one receiver that processes the at least one radio frequency signal, thereby providing radio frequency data (Naseef: pars. 0084-85, the signals which include information of the signal spectrum and power are stored as the training data set D),
feeding the radio frequency data to at least one evaluation unit comprising a trained artificial intelligence circuit (Naseef: Fig. 1, par. 0051, the trained machine learning circuit [18] has the training data set D fed into it to be trained).
Naseef may not explicitly teach: analyzing the radio frequency data by the trained artificial intelligence circuit in order to identify a deviation from the standard frequency spectrum and/or a deviation from the standard radio frequency power in the guard band.
Kleinbeck teaches in general concepts related to automatic signal detection with a learning and conflict detection engine (Kleinbeck: Abstract). Specifically, Kleinbeck teaches that teaches that machine learning is used to identify open spaces such as guard bands, white spaces and combinations thereof (Kleinbeck: par. 0160). Anomalies (i.e. deviations) are identified using machine learning techniques (Kleinbeck: par. 0250, “If there is a narrow band interference signal where there typically is a wide band signal, the system will identify it as an anomaly because it does not match the pattern of what is usually in that space.”).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Naseef disclosures and teachings by consolidating the steps for the detection of the deviations into the guard band as taught and suggested by Kleinbeck. Such a person would have been motivated to do so with a reasonable expectation of success to allow for an autonomous detection of the anomalous to reduce cognitive burden on the user (Kleinbeck: par. 0251).
As to Claim 7, Naseef and Klienbeck teaches the limitations of claim 6.
Kleinbeck further teaches: wherein the trained artificial intelligence circuit processes the radio frequency data, thereby extracting at least one fingerprint data being encompassed in the radio frequency data, wherein the at least one fingerprint data extracted is used for determining the deviation from the standard frequency spectrum and/or determining the deviation from the standard radio frequency power in the guard band (Kleinbeck: pars. 0265, as radio signals shift, fingerprint data may be used to identify and understand the source of radio signals; Examiner asserts that this information would be used with the other teachings of Klienbeck to determine the deviations).
As to Claim 8, Naseef and Klienbeck teaches the limitations of claim 7.
Kleinbeck further teaches: wherein the trained artificial intelligence circuit compares the at least one fingerprint data extracted with corresponding fingerprint data trained (Examiner asserts that there is a comparison with the fingerprint data in the anomaly training data as taught and disclosed by Kleinbeck).
As to Claim 9, Naseef and Klienbeck teaches the limitations of claim 6.
Kleinbeck further teaches: wherein a notification is outputted in case the trained artificial intelligence circuit identifies a respective deviation from the standard frequency spectrum and/or the standard radio frequency power in the guard band (Kleibeck: par. 0122, indication of signal identification or not identified may be displayed (block [326])).
As to Claim 10, Naseef and Klienbeck teaches the limitations of claim 6.
Kleinbeck further teaches: wherein a direction finding takes place, thereby identifying the direction of a source of the at least one radio frequency signal received (Kleinbeck: pars. 0265, as radio signals shift, fingerprint data may be used to identify and understand the source of radio signals).
As to Claim 11, Naseef and Klienbeck teaches the limitations of claim 6.
Kleinbeck further teaches: wherein at least one environmental parameter is additionally captured by a sensor unit, wherein the environmental parameter is also taken into account by the evaluation unit when analyzing the radio frequency data (Kleinbeck: par. 0013, discussing relevant prior art Kadambe, notes a sensor that senses radio frequency signal and noise data (i.e. environmental parameter)).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Naseef-Klienbeck disclosures and teachings by utilizing the environmental sensor as taught and suggested by Kleinbeck’s reference to Kadambe. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the consideration of noise data in radio spectrum predictions.
As to Claim 12, it is rejected for similar reasons as claim 6.
As to Claim 13, Naseef and Klienbeck teaches the limitations of claim 12.
Kleinbeck further teaches: wherein the at least one receiver is configured to process the at least one radio frequency signal received, thereby obtaining in-phase and quadrature (I/Q) data (Kleinbeck: par. 0102, a spectral analysis receiver is able to obtain the I/Q data).
As to Claim 14, Naseef and Klienbeck teaches the limitations of claim 13.
Naseef and Kleinbeck as combined further teaches: wherein the in-phase and quadrature (I/Q) data is further processed in order to obtain the radio frequency data that is forwarded to the evaluation unit.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Naseef-Klienbeck disclosures and teachings by utilizing the processed I/Q data for evaluation and training.. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the consideration of the I/Q data in its training process.
As to Claim 15, Naseef and Klienbeck teaches the limitations of claim 14.
Kleinbeck further teaches: wherein the in-phase and quadrature (I/Q) data is further processed by a Fourier transform (Kleinbeck: par. 0107, the data is transformed using a FFT or other DSP).
As to Claim 16, Naseef and Klienbeck teaches the limitations of claim 12.
Naseef further teaches: wherein a storage medium is provided that is configured to store the radio frequency data (Naseef: par. 0085, the training data set D is create electronically and stored).
As to Claim 17, Naseef and Klienbeck teaches the limitations of claim 12.
Kleinbeck further teaches: wherein the system comprises at least one amplifier configured to amplify the at least one radio frequency signal received and/or at least one signal classification unit configured to identify a signal baseline and/or an interfering signal (Kleinbeck: par. 0101, a low noise amplified received radio RF energy from an antenna and filters and amplifies the RF energy, which is then later used in analysis).
As to Claim 18, Naseef and Klienbeck teaches the limitations of claim 12.
Kleinbeck further teaches: wherein a sensor is provided that is configured to additionally capture at least one environmental parameter, wherein the sensor is connected with the at least one evaluation unit such that the at least one evaluation unit is configured to take the environmental parameter also into account when analyzing the radio frequency data (Kleinbeck: par. 0013, discussing relevant prior art Kadambe, notes a sensor that senses radio frequency signal and noise data (i.e. environmental parameter)).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Naseef-Klienbeck disclosures and teachings by utilizing the environmental sensor as taught and suggested by Kleinbeck’s reference to Kadambe. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the consideration of noise data in radio spectrum predictions.
B.
Claim 3 is rejected under 35 USC § 103 as being unpatentable over Naseef, United States Patent Application Publication 2012/0248488, published on Aug. 12, 2021 in view of Kleinbeck et al. (“Kleinbeck”), United States Patent Application Publication 2017/0374573, published on Dec. 28, 2017 and in further view of Cheng et al., (“Cheng”), United States Patent Application Publication 2020/0050182, published on Feb. 13, 2020.
As to Claim 3, Naseef and Klienbeck teaches the limitations of claim 1.
Naseef and Kleinbeck may not explicitly teach: wherein the training data set comprises anomaly data indicative of a spill over or leakage into the guard band and/or unwanted radio frequency signals in the guard band, based on which the artificial intelligence circuit is trained to identify a deviation from the standard frequency spectrum and/or the standard radio frequency power in the guard band.
Cheng in general teaches detection of anomaly precursor events (Cheng: Abstract). Specifically, Cheng teaches that training datasets for a neural network may include system anomalies (Cheng: par. 0047).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Naseef-Kleinbeck disclosures and teachings by including anomalies in the training data set as taught and suggested by Kleinbeck. Such a person would have been motivated to do so with a reasonable expectation of success to allow for better training and better fitting on the model with anomalous data.
Conclusion
Additional relevant prior art made of the record:
Shima, US PG Pub 2018/032495 (Nov. 8, 2018) (describing spectral sensing and allocation using deep machine learning);
Khanna et al., US PG Pub 2021/0116982 (Apr. 22, 2021) (describing methos to optimize a guard band in a hardware resource);
Non-patent literature, J. Mitola III, et al. “Cognitive Radio: Making Software radios more personal,” (IEEE, 1999).
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/JAMES T TSAI/ Primary Examiner, Art Unit 2147