FINAL REJECTION, SECOND 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.
The Office mailed a first detailed action, non-final rejection on March 9, 2026.
Applicant filed amendments with accompanying remarks and arguments on June 9, 2026, the subject of the instant action.
Claims 1, 3, 5-6, 9-18 are pending and are all rejected in this rejection. Claims 2, 4, and 7-8 are canceled by amendment. Claims 1, 6 and 12 are independent claims.
Status of Claims
Claims 1, 5-6 and 9-18 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.
Response to Remarks and Arguments
Examiner thanks Applicant for the amendments and submitted remarks and arguments.
First regarding the objection to claim 8: it is rejected as it is now moot due to the cancellation of the claim.
Next, turning to the prior art rejections, Examiner has considered Applicant’s amendments and arguments. Initially, as to the argument that Naseef fails to disclose the feature of Claim 1 according to which the artificial intelligence “processes the training data set in order to learn the standard frequency spectrum or the standard radio frequency power in the guard band.” (Remarks: pp. 7-8). Naseef is characterized instead to “classify what type of signal is present … not to learn what a “standard” or “normal” spectrum/power level looks like in order to detect deviations from it. (Remarks: p. 8). Examiner respectfully disagrees.
Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the time of the invention. The words of the claim must be given their plain meaning unless the plain meaning is inconsistent with the specification. In re Zletz, 893 F.2d 319, 321, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) MPEP 2111.01.
Here, Examiner gave a plain meaning interpretation to what “standard” is recognized by the machine learning module may mean, which would included the standard type of signal, which Applicant agrees to as what Naseef teaches (Remarks: p. 8). If Applicant wishes to narrow or clarify that “standard” is meant differently, Examiner suggests amending the language of the claims accordingly to respective industrial standards “relate[d] to requirements concerning Adjacent Channel Leakage Ratio (ACLR) and Adjacent Channel Power Ratio (ACPR).” As recited in the Specification (Specification as filed, Background section).
Next, Applicant contends that the prior art fails to disclose the features of originally filed claim 4 which relate to the fingerprint data encompassed in the training data set and how it is related to the frequency spectrum and/or the radio frequency power in the guard band (Remarks: p. 8). First, Applicant argues that a person of ordinary skill would not be motivated to modify Naseef to incorporate Kleinbeck’s anomaly detection concepts, because Naseer’s purpose, as characterized by Applicant is to “generate synthetic training data for a signal classifier – it does not monitor any live environment for anomalies,” which would change the nature and purpose of Naseer’s architecture.(Remarks: p. 9).
Examiner respectfully disagrees. The reason or motivation to modify the reference may often suggest what the inventor has done, but for a different purpose or to solve a different problem. It is not necessary that the prior art suggest the combination to achieve the same advantage or result discovered by applicant. See, e.g., In re Kahn, 441 F.3d 977, 987, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006); MPEP 2144(IV).
As noted in Naseef, the creation of the training data sets are intended to incorporate “real life relevance,” and scenarios (Naseef: pars. 0014, 0016). Therefore, even though some embodiments in Naseef do disclose cable, non-over-the-air environments, they do not detract from the architecture and intent of creating real-life scenarios including anomalous signals in Kleinbeck. Examiner is not persuaded on this point.
Next, Applicant argues that the fingerprint extraction, among other things, is hardware based and does not take place during the real-time monitoring nor the guard band frequency spectrum. (Remarks: pp. 10-11). As necessitated by the amended language, Examiner has adjusted and clarified the application of Kleinbeck here. Regarding the remaining arguments though, in general Klienbeck teaches that, “the device in the present invention is trained to distinguish random and coherent energy patterns over time, it can clearly pick out the pattern of a signal (Kleinbeck. par. 0254). Kleinbeck also teaches that signal identification (i.e. patterns or fingerprints) may be captured and stored and uploaded and matched against databases (Kleinbeck: pars. 0258, “The device is a watchful eye in an RF environment, and a partner to an operator who is trying to manage, analyze, understand and operate in the RF environment.).
Examiner is therefore not persuaded.
Regarding the arguments for claim 6, et seq., Examiner incorporates many of the similar arguments above in response.
The claims stand rejected.
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, 5-6 and 9-18 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) wherein the training data is indicative of a standard frequency spectrum or a 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); 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 the standard frequency spectrum or the standard radio frequency power in the guard band, thereby selecting and extracting at least one fingerprint data encompassed in the training data set, wherein the at least one fingerprint data relates to the frequency spectrum or the 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., based on the at least one fingerprint data extracted.
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.”). Kleinbeck also teaches that signal identification (i.e. patterns or fingerprints) may be captured and stored and uploaded and matched against databases (Kleinbeck: pars. 0258, “The device is a watchful eye in an RF environment, and a partner to an operator who is trying to manage, analyze, understand and operate in the RF environment.). Further, in general Klienbeck teaches that, “the device in the present invention is trained to distinguish random and coherent energy patterns over time, it can clearly pick out the pattern of a signal (Kelibenck. par. 0254). Guard bands are considered in the analysis and classification process (Kleinbeck: par. 0160, open space such as guard bands are considered).
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 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 or a deviation from the standard radio frequency power in the guard band, 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 relates to the frequency spectrum or the radio frequency power in the guard band, and wherein the trained artificial intelligence circuit compares the at least one fingerprint data extracted with corresponding fingerprint data trained in order to determine the deviation from the standard frequency spectrum or the 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.”). Kleinbeck also teaches that signal identification (i.e. patterns or fingerprints) may be captured and stored and uploaded and matched against databases (Kleinbeck: pars. 0258, “The device is a watchful eye in an RF environment, and a partner to an operator who is trying to manage, analyze, understand and operate in the RF environment.). Further, in general Klienbeck teaches that, “the device in the present invention is trained to distinguish random and coherent energy patterns over time, it can clearly pick out the pattern of a signal (Kelibenck. par. 0254). Guard bands are considered in the analysis and classification process (Kleinbeck: par. 0160, open space such as guard bands are considered).
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 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
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.
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
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