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 .
DETAIL ACTION
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 12/11/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-5 and 7-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses two sets of claims: aa apparatus in claims 1-6 and 8-13 (i.e., a manufacture) and a method in claims 7 (i.e., a process). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significant more. The instant invention is broadly directed to “updating a model parameter for a speech recognition model by a loss calculation”. Claim 1 recites the following (with emphasis added):
Claim 1: A speech recognition model learning apparatus comprising:
processing circuitry configured to:
execute a first voice conversion processing that converts an auxiliary feature amount, which is a feature amount sequence of a voice of a target speaker, into an auxiliary intermediate feature amount, using a first multilayer neural network;
execute a second voice conversion processing that receives, as inputs, the auxiliary intermediate feature amount and a mixed sound feature amount which is a feature amount sequence of voices of a plurality of speakers and converts the auxiliary intermediate feature amount and the mixed sound feature amount into a target speaker intermediate feature amount that is an intermediate feature amount sequence of the target speaker using a second multilayer neural network;
execute a symbol conversion processing that converts a symbol feature amount that is a symbol sequence of the target speaker into an intermediate character feature amount that is a feature amount of corresponding continuous values, using a third multilayer neural network;
execute an estimation processing that receives, as inputs, the target speaker intermediate feature amount and the intermediate feature amount sequence and calculates an output probability distribution of a two-dimensional matrix for label estimation using a neural network;
execute a loss calculation processing that receives, as inputs, a correct symbol that is a symbol sequence of the target speaker corresponding to correct data and an output probability distribution and calculates a loss corresponding to an error of the output probability distribution; and
execute an update processing that updates model parameters of the first voice conversion processing, the second voice conversion processing, the symbol conversion processing, and the estimation processing using the loss.
The bold portions of claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-5 and 8-12, and substantially also encompassed by claims 7.
Claims 1 and 7 recite the steps to perform feature conversion, estimation, loss calculation, and parameter updating by the use of neural networks. These limitations, when given their broadest reasonable interpretation, are directed to certain performing of organizing human activity and mental processes, which is abstract idea.
Prong Two
This judicial exception is not integrated into a practical application because mere instruction to implement on computers (i.e. processing circuitry in claim 1) or a computer model (speech recognition model and neural networks here in claim 1), or merely using computers as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment for field of use is not considered integration into a practical application. Claim 1 recites using multiple neural networks to perform feature conversion, estimation, loss calculation, and parameter updating. Using feature conversion, estimation, loss calculation, and parameter updating to train a speech recognition model is a generic feature of speech data process, which does not represent a technological improvement. The using of the computer and audio data feature process does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. The claims are drafted in a result-oriented fashion, without the requisite specificity needed to provide a nonabstract technological solution. The computing system and speech data process are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the abstract idea as presented.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a processing circuitry, speech recognition model and neural networks (in claim 1). These additional elements are merely conventional computer and computer model. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing a mental process). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus, the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claims 1-5 and 8-12 are not patent eligible.
Claims 7 recite similar limitations of claims 1, thus are abstract idea and not patent eligible.
Allowable Subject Matter
Claims 6 and 13 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: the closest prior arts of AIHARA, Hamada, and HOMMA do not teach the limitations of the claims. Specifically, none of the cited prior art teaches a speech recognition model learning apparatus comprising processing circuitry to execute a first voice conversion processing using a first multilayer neural network that converts an auxiliary feature amount which is a feature amount sequence of a voice of a target speaker into an auxiliary intermediate feature amount; execute a second voice conversion processing including receiving the auxiliary intermediate feature amount and a mixed sound feature amount which is a feature amount sequence of voices of a plurality of speakers and using a second multilayer neural network to convert the auxiliary intermediate feature amount and the mixed sound feature amount into a target speaker intermediate feature amount that is an intermediate feature amount sequence of the target speaker; execute a symbol conversion processing using a third multilayer neural network to convert a symbol feature amount that is a symbol sequence of the target speaker into an intermediate character feature amount that is a feature amount of corresponding continuous values; execute an estimation processing including receiving the target speaker intermediate feature amount and the intermediate feature amount sequence and using a neural network to calculate an output probability distribution of a two-dimensional matrix for label estimation; execute a loss calculation processing that receives a correct symbol that is a symbol sequence of the target speaker corresponding to correct data and an output probability distribution and calculates a loss corresponding to an error of the output probability distribution; and execute an update processing that using the loss to update model parameters of the first voice conversion processing, the second voice conversion processing, the symbol conversion processing, and the estimation processing; and execute an inversion processing, wherein the inversion processing generates a second auxiliary feature amount using the auxiliary feature amount and an inversion coefficient, and generates a second correct symbol using the correct symbol and the inversion coefficient, the first voice conversion processing replaces a sequence used for conversion from the auxiliary feature amount to a second auxiliary feature amount, the loss calculation processing replaces a sequence used for calculation from the correct answer symbol to a second correct symbol, in a case where the second voice conversion processing cannot find the second auxiliary feature amount in the mixed sound feature amount, the second voice conversion processing outputs a fact that the second auxiliary feature amount cannot be found, and the estimation processing outputs a symbol indicating a non-target speaker as a result of the output probability distribution Y in a case where the input of the fact is received. Hence, none of the cited prior art, either singularly or in combination, fail to anticipate or render the above limitations obvious.
Conclusion
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/ZHUBING REN/Primary Examiner, Art Unit 2658
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