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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Suh (US 20200265307 A1).
Regarding claims 1 and 9, Suh discloses a neural network construction apparatus comprises a neural network construction method, comprising: generating parameters of a target neural network based on a parameter generation network (120-130 of fig. 1, 210 and 220 of fig. 2, [0005], [0008], [0010], [0018], [0067]-[0069]),
wherein input of the parameter generation network comprises information about a relative number of a neuron in the target neural network, the relative number of the neuron represents a relative location of the neuron at a first neural network layer, and the first neural network layer is a layer at which the neuron in the target neural network is located ([0017], [0061], [0066], and [0082]); and
constructing the target neural network based on the parameters of the target neural network (130 and 140 of fig. 1, 210 of fig. 2, [0065], [0066]).
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) 2, 5, 6, 10, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suh (US 20200265307 A1) in view of Ma et al. (US 20210201155 A1).
Regarding claims 2 and 10, Suh discloses the method according to claim 1, Suh does not disclose wherein before the generating parameters of a target neural network based on a parameter generation network, the method further comprises: obtaining N parameter generation networks, wherein N is determined based on a quantity M of parameter categories of the target neural network and a quantity L of hidden layers of the target neural network, and N, M and L are positive integers.
Ma teaches wherein before the generating parameters of a target neural network based on a parameter generation network, the method further comprises: obtaining N parameter generation networks, wherein N is determined based on a quantity M of parameter categories of the target neural network and a quantity L of hidden layers of the target neural network, and N, M and L are positive integers (fig. 1 and 2).
Taking the teachings of Suh and Ma together as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of hidden layers parameters of Ma into the generating parameters of Suh to automatically adjust the network structure during training using a grey relation analysis-based algorithm.
Regarding claims 5 and 13, Suh and Ma disclose the method according to claim 2, Ma further discloses wherein before the generating parameters of a target neural network based on a parameter generation network, the method further comprises: determining the quantity L of hidden layers of the target neural network; and determining a quantity of neurons at each of the L hidden layers based on a scale of a processing task of the target neural network ([0015], [0040], and [0041]).
Regarding claims 6 and 14, Suh and Ma disclose the method according to claim 5, Ma further discloses wherein before the generating parameters of a target neural network based on a parameter generation network, the method further comprises: training the parameter generation network based on training data of the processing task of the target neural network ([0006] and [0010] to [0014]).
Allowable Subject Matter
Claims 3-4, 7-8, 11-12, 15-16 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cheung et al. (US 20200410365 A1) discloses the training data 120 is referred to as unsupervised training data because the training network inputs in the training data 120 are either (i) not associated with any labels for any machine learning tasks or (ii) the labels are not used in updating the values of the parameters of the base neural network 110 during the training. In other words, ground truth or target, i.e., known, outputs for any machine learning task for which the numeric representations will be used are either not available or not used when determining the updates to the neuron parameter values.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUNG T VO whose telephone number is (571)272-7340. The examiner can normally be reached Monday-Friday 6:30 AM - 5:00 PM.
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, Brian Pendleton can be reached at 571-272-7527. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
TUNG T. VO
Primary Examiner
Art Unit 2425
/TUNG T VO/Primary Examiner, Art Unit 2425
Prosecution Insights