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 1-10 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.
Claim 1 recites the limitation "the number", “the …… layers” in ln 5, and the “the …… quantum neural network structure”. There is insufficient antecedent basis for this limitation in the claim. Appropriate corrections are needed.
Claim 8 has similar problem. Appropriate corrections are needed.
The other dependent claims are rejected to on the same ground and for not resolving the issues.
Allowable Subject Matter
The following is an examiner’s statement of reasons for allowance:
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
CN113128665A discloses a quantum feedforward neural network construction method applied to facial expression recognition, which belongs to the field of quantum machine learning. First, the basic structure of a quantum neuron is constructed, and then a corresponding quantum feedforward neural network is formed from the neuron model. Model, the input layer, output layer and intermediate hidden layer of the model are all represented by the form of quantum state, so that it can use some unique properties of quantum computing to improve its learning ability in the calculation process, and finally use the improved quantum search algorithm as The learning algorithm for this network model. The quantum feedforward neural network model constructed by the present invention can greatly save data storage resources through quantum superposition, and can simultaneously perform parallel computing on multiple samples of the data set through quantum parallelism, thereby improving the training rate. Compared with the classical search algorithm It has the ability of quadratic acceleration, so the algorithm used as the quantum feedforward neural network will have more advantages in the training of the neural network.
US 20210005183 A1 discloses a method for operating a neural network includes receiving an input sequence at an encoder. The input sequence is encoded to produce a set of hidden representations. Attention-heads of the neural network calculate attention weights based on the hidden representations. A context vector is calculated for each attention-head based on the attention weights and the hidden representations. Each of the context vectors correspond to a portion of the input sequence. An inference is output based on the context vectors.
However, none of the cited prior arts discloses “sampling method for quantum neural network structure optimization, comprising the steps of: initializing structural parameters and constraint vectors of a quantum computer; sampling in the constraint vector according to the constraint vector and a constraint rule to construct a quantum gate sublayer; constructing a single-layer quantum neural network structure according to the quantum gate sublayer until the number of the single-layer quantum neural network structure layers reaches a specified value to form a quantum neural network structure; and outputting the quantum neural network structure.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 12614014 B1 Grouping cells in cell library based on iterative clustering
US 12530535 B2 Intelligent prediction of next step sentences from a communication session
US 12456041 B2 AI system and operation method of AI system
US 12340794 B2 Language identification classifier trained using encoded audio from encoder of pre-trained speech-to-text system
US 11556782 B2 Structure-preserving attention mechanism in sequence-to-sequence neural models
US 10558893 B2 Systems and methods for recognizing characters in digitized documents
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANK F HUANG whose telephone number is (571)272-0701. The examiner can normally be reached Monday-Friday, 8:30 am - 6:00 pm (Eastern Time), Federal Alternative First Friday Off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jay Patel can be reached at (571)272-2988.. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FRANK F HUANG/Primary Examiner, Art Unit 2485
