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 .
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 2-4, 7-9, and 12-14 are 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.
Regarding claims 2, 7, and 12, “adding the target weight vector-based loss function to a value of the target beam pattern-based loss function multiplied by a weight of a loss function relative to the target weight vector with respect to the target beam pattern” renders the claims indefinite because it is unclear if the weight is multiplied to the target beam pattern-based loss function and then that product is added to the weight vector-based loss function or if the two loss functions are added and their sum is multiplied by the weight.
Regarding claims 3-4, 8-9, and 13-14, “into artificial neural network” renders the claims indefinite because it is unclear if antecedence is claimed to the artificial neural network of the independent claim or if a new neural network is established.
Claims 4, 9, and 14 are indefinite by virtue of dependency on an indefinite claim.
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.
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, 3, 6, 8, 11, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Woodsum (US20160261330A1) in view of Sallam (Sallam, Tarek, et al. "A neural-network-based beamformer for phased array weather radar." IEEE Transactions on Geoscience and Remote Sensing 54.9 (2016): 5095-5104.)
Regarding claim 1, Woodsum teaches an artificial neural network model training method performed by an electronic device (¶53), the method comprising:
preparing data including digital intermediate frequency data based on a signal received from an antenna array of a multiple input multiple output (MIMO) system as input data (¶25 – Digital intermediate frequency data from a MIMO system is gathered, i.e. prepared), and at least one of a target beam pattern and a target weight vector as output data (¶26, the matrix, i.e. target weight vector, is output data) and
wideband frequency uniform multi-beam generation using the data (Figure 2, 30 to 32).
Woodsum does not teach the particulars of the neural network model training method, in particular where the digital intermediate frequency data is training data, where the target weight vector is output data utilized in training, training an artificial neural network model for wideband frequency uniform multi-beam generation using the training data, wherein a weight vector of the artificial neural network model is recursively trained using at least one of a target beam pattern-based loss function and a target weight vector-based loss function necessary to generate desired multiple beams.
Sallam teaches training a neural network to generate an appropriate weight matrix for beamforming, including preparing training data (§II, subsection E2) and output data (§II, subsection E2, where the Wiener matrix is utilized as the target weight vector), training the artificial neural network model for multi-beam generation (§II, subsection E2), wherein a weight vector of the artificial neural network model is recursively trained using at least one of a target beam pattern-based loss function and a target weight vector-based loss function necessary to generate desired multiple beams (§II, Subsection E2, supervised learning is used to tune the weights of the neural network, “The weights from the hidden to the output layer are determined by considering a supervised learning procedure.”, the loss function can be considered “a target weight vector-based loss function” because the network is training to generate a target weight vector, i.e. the Wiener matrix).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Woodsum to utilize the neural network and training procedure of Sallam in beamforming in order to provide a computationally efficient method of beamforming.
Regarding claim 3, Woodsum as modified teaches all of the limitations of claim 1, wherein
the training includes converting the digital intermediate frequency data to a complex correlation matrix and inputting the converted complex correlation matrix into artificial neural network model (see Sallam, §II, E2, Rx).
Regarding claims 6 and 11, Woodsum as modified accord to claim 1 covers the method of claim 6 and the non-transitory computer readable medium of claim 11 when implemented on a computer (see Figure 2, 30, the CPU).
Regarding claims 8 and 13, Woodsum as modified according to claim 3 covers claims 8 and 13.
Claim(s) 5, 10, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Woodsum (US20160261330A1) in view of Sallam (Sallam, Tarek, et al. "A neural-network-based beamformer for phased array weather radar." IEEE Transactions on Geoscience and Remote Sensing 54.9 (2016): 5095-5104.), further in view of Kobayashi (US20170061329A1).
Regarding claims 5, 10, and 15, Woodsum as modified teaches all of the limitations of claim 3.
Woodsum does not teach where the training includes tuning hyperparameters for obtaining the weight vector based on search results through neural network structure search.
Kobayashi teaches tuning hyperparameters for obtaining the weight vector based on search results through neural network structure search (¶209-211).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Woodsum as modified to include tuning hyperparameters for obtaining the weight vector based on search results through neural network structure search in order to provide an optimized neural network.
Allowable Subject Matter
Claim 2, 4, 7, 9, 12, and 14 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding claims 2, 7, and 12, the prior art does not establish a prima facie case of obviousness for utilizing both the weight vector based loss function and the target beam pattern based loss function for obtaining the weight vector. Doing so would require impermissible hindsight.
Regarding claims 4, 9, and 12, the prior art does not establish a prima facie case of obviousness for determining a beamforming azimuth with unsupervised learning. The combination of supervised and unsupervised learning, while not unknown in the prior art, would require impermissible hindsight when considered with all other claimed factors.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCHYLER S SANKS whose telephone number is (571)272-6125. The examiner can normally be reached 06:30 - 15:30 Central Time, M-F.
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/SCHYLER S SANKS/Primary Examiner, Art Unit 2129