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 .
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.
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4,7,12 and 24-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bao (J. Bao, J. Nie, C. Liu, B. Jiang, F. Zhu and J. He, "Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs," in IEEE Access, vol. 7, pp. 97117-97129, 2019) in view of Yu (Jianyuan Yu, "Multiple Angles of Arrival Estimation using Neural Networks." arXiv:2002.00541, 2020) and Zhou (CN 101808334 A).
Regarding claim 1, Bao discloses:
“A spectrum sensing method, comprising: determining feature data of a target sub-band; and” (page 97119: “A cognitive base station (CBS) first detects PU signals in the detection channel at all divided frequency bands.”)
“inputting the feature data into a trained spectrum sensing model to obtain a spectrum sensing result, wherein the spectrum sensing result comprises a first result, the first result being used for indicating whether the target sub-band is occupied…” (page 97123: “Namely, the output is ‘‘+1’’ or ‘‘−1’’ corresponding to the spectrum occupied by PUs or not.” Also see Fig. 4.)
Bao does not disclose “in response to the first result indicating that the target sub-band is occupied, the spectrum sensing result further comprises a second result, the second result being used for indicating an angle of occupation of the target sub-band.”
However, Yu discloses the missing feature “the second result being used for indicating an angle of occupation of the target sub-band.” (page 13: “Since our neural network work not only for one angle estimation but also work for several angles estimation.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, having the teachings of Bao and Yu, to modify the technique as disclosed by Bao, to also determine angle as disclosed by Yu. The motivation for doing so is that it allows for improved resource allocation. Therefore, it would have been obvious to combine Bao with Yu to obtain the invention as specified in the instant claim.
Yu also does not disclose “in response to the first result indicating that the target sub-band is occupied, the spectrum sensing result further comprises a second result.”
However, Zhou discloses the missing feature “in response to the first result indicating that the target sub-band is occupied, the spectrum sensing result further comprises a second result.” (Abstract: “The method comprises the following steps that: a linear array antenna system receives the signal; the spatial spectrum of the signal is obtained through multiple signal classification algorithm; the statistical average of the spatial spectrum Lambda serves as judgment reference; the threshold Gamma which meets false alarm probability Pf is obtained through a test; Lambda and threshold Gamma are compared to judge if the authorized user exists; and if the authorized user exists, the angle of arrival of the authorized user is obtained through minimum optimization searching.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, having the teachings of Bao, Yu, and Zhao to modify the technique as disclosed by Yu, to be conditional as disclosed by Zhou. The motivation for doing so is that it improves resource efficiency. Therefore, it would have been obvious to combine Bao with Yu and Zhao to obtain the invention as specified in the instant claim.
Regarding claim 2, Bao in view of Yu and Zhao discloses all the features of the parent claim.
Bao further discloses “wherein the spectrum sensing model comprises a signal energy … configured for performing feature extraction of the feature data to obtain a signal energy feature… outputting the first result according to the signal energy feature and the signal angle feature, and outputting the second result in response to the first result indicating that the target sub-band is occupied.” (See Fig. 4, wherein “in response to” is taught by Zhou as discussed in relation to the parent claim.)
Bao does not disclose “angle sensing model.”
However, Yu discloses the missing feature “angle sensing model” (See section 2.2, starting on page 10)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, having the teachings of Bao and Yu, to modify the technique as disclosed by Bao, to utilize an angle sensing model as disclosed by Yu. The motivation for doing so is that it allows for improved resource allocation. Therefore, it would have been obvious to combine Bao with Yu to obtain the invention as specified in the instant claim.
Regarding claim 3, Bao in view of Yu and Zhao discloses all the features of the parent claim.
Bao further discloses “wherein the spectrum sensing model comprises a signal energy sensing model…” (page 97123: “Namely, the output is ‘‘+1’’ or ‘‘−1’’ corresponding to the spectrum occupied by PUs or not.” Also see Fig. 4.)
Bao does not disclose “and a signal angle sensing model, wherein: the signal energy sensing model is configured for outputting the first result according to the feature data; and the signal angle sensing model is configured for outputting the second result according to the feature data in response to the first result indicating that the target sub-band is occupied.”
However, Yu discloses the missing feature “and a signal angle sensing model” (page 13: “Since our neural network work not only for one angle estimation but also work for several angles estimation.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, having the teachings of Bao and Yu, to modify the technique as disclosed by Bao, to also determine angle as disclosed by Yu. The motivation for doing so is that it allows for improved resource allocation. Therefore, it would have been obvious to combine Bao with Yu to obtain the invention as specified in the instant claim.
Yu also does not disclose “the signal energy sensing model is configured for outputting the first result according to the feature data; and the signal angle sensing model is configured for outputting the second result according to the feature data in response to the first result indicating that the target sub-band is occupied.”
However, Zhou discloses the missing feature “the signal energy sensing model is configured for outputting the first result according to the feature data; and the signal angle sensing model is configured for outputting the second result according to the feature data in response to the first result indicating that the target sub-band is occupied.” (Abstract: “The method comprises the following steps that: a linear array antenna system receives the signal; the spatial spectrum of the signal is obtained through multiple signal classification algorithm; the statistical average of the spatial spectrum Lambda serves as judgment reference; the threshold Gamma which meets false alarm probability Pf is obtained through a test; Lambda and threshold Gamma are compared to judge if the authorized user exists; and if the authorized user exists, the angle of arrival of the authorized user is obtained through minimum optimization searching.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, having the teachings of Bao, Yu, and Zhao to modify the technique as disclosed by Yu, to be conditional as disclosed by Zhou. The motivation for doing so is that it improves resource efficiency. Therefore, it would have been obvious to combine Bao with Yu and Zhao to obtain the invention as specified in the instant claim.
Regarding claim 4, Bao in view of Yu and Zhao discloses all the features of the parent claim.
Bao further discloses “wherein the feature data comprises at least one of a horizontal-antenna covariance matrix, a vertical-antenna covariance matrix, or an all antenna covariance matrix.” (page 97123: “One is the computation of the covariance matrix, where M(M + 1)N/2 multiplications and M(M + 1)(N − 1)/2 additions are required. Here, M,N are the number of antennas in an SU and the total number of sensing samples in one observation obtained in a sensing slot as described in Subsection II(A).”)
Regarding claim 7, Bao in view of Yu and Zhao discloses all the features of the parent claim.
Bao further discloses “determining feature data of a target sub-band comprises: acquiring first frequency-domain data of the target sub-band according to a predefined sensing granularity; and obtaining an antenna covariance matrix of the target sub-band through calculation according to the first frequency-domain data.” (page 97119: “A cognitive base station (CBS) first detects PU signals in the detection channel at all divided frequency bands.”)
Regarding claim 12, Bao in view of Yu and Zhao discloses all the features of the parent claim.
Bao further discloses “wherein before acquiring first frequency-domain data of the target sub-band according to a predefined sensing granularity, the method further comprises: dividing a full bandwidth into a plurality of sub-bands, wherein a number of frequency-domain sensing units in each of the sub-bands is less than or equal to a number of frequency-domain sensing units indicated by a frequency-domain sensing granularity.” (page 97119: “A cognitive base station (CBS) first detects PU signals in the detection channel at all divided frequency bands.”)
Claims 24 and 25 are substantially similar to claim 1 and are rejected for similar reasons, wherein the examiner takes official notice that the differences would be well known to one of ordinary skill in the art.
Allowable Subject Matter
Claims 5-6, 8-9, 13-17, and 19-21 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. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 5, of the closest prior arts Bao (J. Bao, J. Nie, C. Liu, B. Jiang, F. Zhu and J. He, "Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs," in IEEE Access, vol. 7, pp. 97117-97129, 2019) in view of Yu (Jianyuan Yu, "Multiple Angles of Arrival Estimation using Neural Networks." arXiv:2002.00541, 2020) and Zhou (CN 101808334 A) discloses all the features of the parent claims as disclosed above. However, Bao in view of Yu and Zhao does not disclose “wherein in response to the feature data comprising the horizontal-antenna covariance matrix and the vertical-antenna covariance matrix, the signal energy sensing model comprises: a first signal energy sensing submodel, configured for performing feature extraction on the horizontal-antenna covariance matrix and outputting a horizontal antenna energy feature; a second signal energy sensing submodel, configured for performing feature extraction on the vertical-antenna covariance matrix and outputting a vertical antenna energy feature; and a third signal energy sensing submodel, configured for outputting the first result according to the horizontal antenna energy feature and the vertical antenna energy feature.” The cited references fail to anticipate or render the above limitations in combination with all the recited limitations of claims 5 obvious, over any of the prior art of record, alone or in combination.
Regarding claim 6, of the closest prior arts Bao in view of Yu and Zhou discloses all the features of the parent claims as disclosed above. However, Bao in view of Yu and Zhao does not disclose “wherein in response to the feature data comprising the horizontal-antenna covariance matrix and the vertical-antenna covariance matrix, the signal angle sensing model comprises: a first signal angle sensing submodel, configured for performing feature extraction on the horizontal-antenna covariance matrix and outputting a horizontal antenna angle feature; a second signal angle sensing submodel, configured for performing feature extraction on the vertical-antenna covariance matrix and outputting a vertical antenna angle feature; and a third signal angle sensing submodel, configured for outputting the second result according to the horizontal antenna angle feature and the vertical antenna angle feature.” The cited references fail to anticipate or render the above limitations in combination with all the recited limitations of claims 6 obvious, over any of the prior art of record, alone or in combination.
Regarding claim 8, of the closest prior arts Bao in view of Yu and Zhou discloses all the features of the parent claims as disclosed above. However, Bao in view of Yu and Zhao does not disclose “wherein the predefined sensing granularity comprises a time-domain sensing granularity and a frequency-domain sensing granularity, the time-domain sensing granularity is used for indicating a number of time-domain sensing units, and the frequency-domain sensing granularity is used for indicating a number of frequency-domain sensing units; and acquiring first frequency-domain data of the target sub-band according to a predefined sensing granularity comprises: acquiring a time-domain received signal from a target antenna; performing a discrete Fourier transform on the time-domain received signal to obtain full-bandwidth frequency-domain data of the target antenna; and acquiring the first frequency-domain data of the target sub-band in the target antenna from the full-bandwidth frequency-domain data, wherein the first frequency-domain data comprises a plurality of pieces of second frequency-domain data, each corresponding to one time-domain sensing unit and one frequency-domain sensing unit.” The cited references fail to anticipate or render the above limitations in combination with all the recited limitations of claims 8 obvious, over any of the prior art of record, alone or in combination. Claim 9 depends on claim 8 and contains allowable subject matter based on its dependence.
Regarding claim 13, of the closest prior arts Bao in view of Yu and Zhou discloses all the features of the parent claims as disclosed above. Bao further discloses “wherein a process of training the spectrum sensing model comprises: acquiring a training sample set of a sub-band, wherein the training sample set comprises a plurality of first training samples and a plurality of second training samples, the first training samples each comprise sample feature data corresponding to the sub-band in an unoccupied state and a first label, the second training samples each comprise sample feature data corresponding to the sub-band in an occupied state, a second sample label … the first sample label is used for indicating that the sub-band is not occupied, the second sample label is used for indicating that the sub-band is occupied … and training the spectrum sensing model based on the training sample set until a preset training ending condition is satisfied, to obtain the trained spectrum sensing model.” However, Bao in view of Yu and Zhao does not disclose “and the third sample label is used for indicating an angle of occupation of the sub-band.” The cited references fail to anticipate or render the above limitations in combination with all the recited limitations of claims 13 obvious, over any of the prior art of record, alone or in combination. Claims 14-17 and 19-21 depends on claim 13 and contain allowable subject matter based on their dependence.
Conclusion
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/SAAD KHAWAR/ Primary Examiner, Art Unit 2412
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