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 .
DETAILED ACTION
Claims 1-20 are pending.
This action is in response to the application filed on May 20, 2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 6, 8, 10,12, 14 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As per claims 6, 8, 10,12, 14 and 16:
Claims 6, 8, 10,12, 14 and 16 rejected under 35 U.S.C. 101 because Mathematical Formulas or Equations.
A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping and Mathematical Calculations. A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation.
Step 1: Statutory Category:
No, The claims that recite a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping, The claims that recite a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation, therefore, is a Judicial Exception Recited.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. As per MPEP 2106,04(a)(2) III “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)”. Accordingly, the claim recites an abstract idea.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Thaler (US 20060224533 A1).
With respect to claims 1 and 17, Thaler teaches
training a first neural network based on a training data set ([0008] neural-network based data analysis tool that utilizes a self-training artificial neural network);
receiving a deletion request, an insertion request, or a modification request from a network wherein the deletion request indicates a removal of at least one first element from the training data set, the insertion request indicates an inclusion of at least one second element to be added into the training data set and the modification request indicates a modification of at least one third element of the training data set ([0057] to remove this tag and children from the ANNML project, as training of the network with this tag block present will result in another search for an optimal architecture [0125] it may be desirable to remove this tag and its children from the ANNML project, the network with this tag block present will result in another search for an optimal architecture removed during the training and used for generalization);
generating a second neural network equivalent to or approximated to the first neural network by embedding the training data set into the first neural network and removing the at least one first element from the training data set to obtain a first data set according to the deletion request and embedding the first data set into the first neural network to generate the equivalent or approximated second neural network ([0019] artificial neural network-based data analysis system that includes at least a first pair of a training input and a corresponding training output; a first, untrained, artificial neural network that produces at least one output when at least one input is supplied to the first artificial neural network; and a comparator portion that compares an actual output pattern generated by the first artificial neural network as result of said training input pattern being supplied to the first artificial neural network with the corresponding training output),
inserting the at least one second element into the training data set to obtain a second data set according to the insertion request and embedding the second data set into the first neural network to generate the equivalent second neural network or updating the training data set by the at least one third element to obtain a third data set according to the modification request and embedding the third data set into the first neural network to generate the equivalent second neural network ([0020] artificial neural network-based data analysis system including at least a first pair of a training input pattern and a corresponding training output pattern; a first, untrained, artificial neural network; and a first algorithm that generates an architecture, learning rate, and a momentum for the first artificial neural network randomly or systematically; at least a second, untrained artificial neural network that trains approximately simultaneously with or sequentially after the first artificial neural network; a second architecture, learning rate, and second momentum associated with the second artificial neural network which is generated randomly or systematically by the first algorithm; a comparator algorithm that compares an actual output pattern generated by either of the networks as a result of the training input pattern being supplied to either network with the corresponding training output pattern and produces an output error based on a calculation of a cumulative learning error; a third artificial neural network that receives and trains on the architectures, learning rates, momentums, and learning errors associated with the first and second artificial neural networks).
With respect to claims 2 and 18, Thaler teaches the first data set includes a minimum data set to be embedded into the first neural network to generate the second neural network equivalent to the first neural network ([0132] The format of the input file is a tab-delimited text file. A double tab is used to separate the input data from the target output data. Each training set must be on its own line. Blank lines are not allowed. Labels for the input must exist on the first line of the file and are tab-delimited in the same manner as the input training data. network with two inputs and one output would have training data in the following format).
With respect to claims 3 and 19, Thaler teaches second neural network is approximated to the first neural network when the first data set is smaller than the minimum data set to be embedded into the first neural network to generate the second neural network equivalent to the first neural network ([0132] The format of the input file is a tab-delimited text file. A double tab is used to separate the input data from the target output data. Each training set must be on its own line. Blank lines are not allowed).
With respect to claims 4 and 20, Thaler teaches second neural network is approximated to the first neural network when the first data set is smaller than the minimum data set to be embedded into the first neural network to generate the second neural network equivalent to the first neural network ([0132] The format of the input file is a tab-delimited text file. A double tab is used to separate the input data from the target output data. Each training set must be on its own line. Blank lines are not allowed).
With respect to claim 5, Thaler teaches trained weight matrix W in a layer in the first neural network is a multiplication of a sparse matrix A and the transpose of matrix U, where matrix U is the output of the previous layer derived by using the training data set as the input to the first neural network ([0132] The format of the input file is a tab-delimited text file. A double tab is used to separate the input data from the target output data).
With respect to claim 6, Thaler teaches training data set into the first neural network to generate an equivalent second neural network, further includes: calculating the sparse matrix A based on the following formula: generating the equivalent second neural network according to the calculated sparse matrix A ([0032] FIG. 10 is a diagram of the general operation of another embodiment in which a first, hetero-associative, artificial neural network and a second, auto-associative, artificial neural network train together).
With respect to claim 7, Thaler teaches deletion request, a trained weight matrix W in a layer in the first neural network is a multiplication of a sparse matrix A' and the transpose to the matrix H, where matrix H is the output of the previous layer obtained using H0 as inputs to the first neural network where H0 is the second data set derived after deleting the at least one first element from the training set under the deletion request ([0032] FIG. 10 is a diagram of the general operation of another embodiment in which a first, hetero-associative, artificial neural network and a second, auto-associative, artificial neural network train together).
With respect to claim 8, Thaler teaches second data set into the first neural network to generate the equivalent second neural network, further includes: calculating the sparse matrix A' based on the following formula: and generating the equivalent second neural network according to the calculated sparse matrix A ([0032] FIG. 10 is a diagram of the general operation of another embodiment in which a first, hetero-associative, artificial neural network and a second, auto-associative, artificial neural network train).
With respect to claim 9, Thaler teaches to respond to the insertion request, a trained weight matrix W in a layer in the first neural network is a multiplication of matrix B' and the transpose of the matrix V' where matrix V' is the output of the previous layer obtained using V'0 as inputs to the first neural network where V'0 is the second data set derived after insertion the at least one second element to the training set under the insertion request ([0032] FIG. 10 is a diagram of the general operation of another embodiment in which a first, hetero-associative, artificial neural network and a second, auto-associative, artificial neural network train together).
With respect to claim 10, Thaler teaches second data set into the first neural network to generate the equivalent second neural network, further includes: calculating the sparse matrix B' based on the following formula: and generating the equivalent second neural network according to the calculated sparse matrix B' ([0034] FIG. 12 is a diagram of a target-seeking embodiment of the present invention including a series of training networks and a master network).
With respect to claim 11, Thaler teaches trained weight matrix W in a layer in the first neural network is a multiplication of matrix A'' and the transpose matrix of K where matrix K is the output of the previous layer obtained using K0 as inputs to the first neural network where K0 is the third data set derived after modifying the at least one third element in the training set under the modification request ([0034] FIG. 12 is a diagram of a target-seeking embodiment of the present invention including a series of training networks and a master network).
With respect to claim 12, Thaler teaches third data set into the first neural network to generate the second neural network, further includes: calculating the sparse matrix A'' based on the following formula: and generating the equivalent second neural network according to the calculated sparse matrix A'' ([0034] FIG. 12 is a diagram of a target-seeking embodiment of the present invention including a series of training networks and a master network).
With respect to claim 13, Thaler teaches trained weight matrix W in a layer in the first neural network is a multiplication of matrix B and the transpose matrix of V where matrix V is the output of the previous layer obtained using V0 as inputs to the first neural network where V0 is the minimum data set ([0165] FIG.10, a second artificial neural network, advantageously an auto-associative network, which may train simultaneously with the first network One of the outputs of the second, auto-associative network is a set of learning parameters).
With respect to claim 14, Thaler teaches first data set into the first neural network to generate the equivalent second neural network, further includes: calculating the sparse matrix B based on the following formula: and generating the equivalent second neural network according to the calculated sparse matrix Bi ([0165] FIG.10, a second artificial neural network, advantageously an auto-associative network, which may train simultaneously with the first network One of the outputs of the second, auto-associative network is a set of learning parameters).
With respect to claim 15, Thaler teaches first data set is smaller than the minimum data set, any trained weight matrix W in a layer in the first neural network is approximated by a multiplication of matrix C and the transpose matrix of S where matrix S is the output of the previous layer obtained using S0 as inputs to the first neural network where S0 is the first data set ([0165] FIG.10, a second artificial neural network, advantageously an auto-associative network, which may train simultaneously with the first network One of the outputs of the second, auto-associative network is a set of learning parameters).
With respect to claim 16, Thaler teaches first data set is smaller than the minimum data set, any trained weight matrix W in a layer in the first neural network is approximated by a multiplication of matrix C and the transpose matrix of S where matrix S is the output of the previous layer obtained using S0 as inputs to the first neural network where S0 is the first data set ([0165] FIG.10, a second artificial neural network, advantageously an auto-associative network, which may train simultaneously with the first network One of the outputs of the second, auto-associative network is a set of learning parameters).
Conclusion
The prior arty made of record and not relied upon is considered pertinent to applicant’s disclosure.
Yu et al (US 20210334644 A1) NEURAL NETWORK TRAINING TECHNIQUE.
Considered for teaching for systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained based, at least in part, on inferencing output from one or more second neural networks. Training neural networks to perform image processing tasks can use significant amounts of time and computing resources and may require manual intervention. Techniques and procedures for training neural networks can be improved
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISAAC M WOO/ Primary Examiner, Art Unit 2163
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