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
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 12/11/2014 and 12/5/2025. 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 1-2, 6-7, 9-11, 15-17 and 21 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 three sets of claims: a system in claims 1-7 (i.e., a process), a non-transitory computer-readable storage medium in claims 9 and 11-16 (i.e., a manufacture) and a device in claims 10 and 17-21 (i.e., a manufacture). 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 “obtaining a speech recognition model based on calculating a loss function”. Claim 1 recites the following (with emphasis added):
Claim 1: A method for training a speech recognition model, comprising:
constructing an initial speech recognition model, wherein the initial speech recognition model comprises a first network having a first initial parameter and a second network having a second initial parameter;
fixing the second initial parameter, calculating a contrastive learning loss function based on an unlabeled data set, and performing self-supervised training on the first network according to the contrastive learning loss function to adjust the first initial parameter to a first intermediate parameter;
fixing the first intermediate parameter, calculating a first joint loss function based on a labeled data set, and performing training on the second network according to the first joint loss function to adjust the second initial parameter to a second intermediate parameter; and
calculating a second joint loss function based on the labeled data set, and performing training on the first network and the second network according to the second joint loss function to adjust the first intermediate parameter and the second intermediate parameter to obtain a target speech recognition model.
The bold portions of claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2, 6-7, and substantially also encompassed by claims 9, 11, 15-16 and 10, 17, 21.
Claims 1, 9, and 10 recite the steps to obtaining a speech recognition model based on calculating a loss function when 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. storage medium or processors in claim 9) or a computer model (language model 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 natural language prompt to generate output data of the trained natural language model. Using unlabed and labeled data to a train machine-learning or speech recognition model is a generic feature of speech recognition process, which does not represent a technological improvement. The using of the computer and speck recognition modeling 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 recognition 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 processor, storage medium, speech recognition model and input data set (in claim 1 and 9-10). 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 1-2 and 6-7 are not patent eligible.
Claims 9, 11, 15-16 and 10, 17, 21 recite similar limitations of claims 1-2 and 6-7, thus are abstract idea and not patent eligible.
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) 1, 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al (US 20240013777 A1) in view of TANG (CN 112086087 A).
Regarding claim 1, Lu discloses a method [e.g. FIG. 1] for training a speech recognition model [e.g. 200; training a speech recognition model], comprising:
constructing an initial speech recognition model [e.g. FIG. 1-3; ASR model], wherein the initial speech recognition model comprises a first network [e.g. network 210; encoder network] having a first initial parameter [e.g. parameter of the model] and a second network having a second initial parameter [the prediction network 220; e.g. decoder];
fixing the second initial parameter [e.g. FIG. 4; [0041]; the concept of the parameters are unchanged if the model is not being trained], calculating a contrastive learning loss function based on an unlabeled data set [e.g. loss function calculation for self-supervised (or unsupervised) machine learning techniques to process the corpus of unlabeled training data], and performing self-supervised training [e.g. self-supervised machine training] on the first network according to the contrastive learning loss function to adjust the first initial parameter to a first intermediate parameter [e.g. FIG. 2-4; pre-training speech recognition model by unlabeled data set; updating the parameters of ASR];
fixing the first intermediate parameter [e.g. FIG. 4; [0041]; the concept of the parameters are unchanged if the model (first for the pre-training of the network) is not being trained], calculating a first joint loss function based on a labeled data set [e.g. loss function calculation for supervised machine learning techniques to process the corpus of labeled training data], and performing training on the second network according to the first joint loss function to adjust the second initial parameter to a second intermediate parameter [e.g. FIG. 2-4; supervised training speech recognition model by labeled data set; updating the parameters of ASR].
It is noted that Lu differs to the present invention in that Lu fails to explicitly disclose the detail of calculating the loss function.
However, TANG teaches the well-known concept of training a speech recognition model [e.g. FIG. 3; training voice recognition model] comprising constructing an initial speech recognition model [e.g. FIG. 3A; training primary network of the voice recognition model], wherein the initial speech recognition model comprises a first network [e.g. page 6; a primary coding network] having a first initial parameter [e.g. network parameter] and a second network [e.g. a secondary coding network] having a second initial parameter [e.g. network parameter];
calculating a contrastive learning loss function based on an unlabeled data set [e.g. FIG. 3A; S305; page 8-10; calculating contrast coding loss rate of the first voice unit; a first voice sequence without label text], and performing self-supervised training on the first network according to the contrastive learning loss function to adjust the first initial parameter to a first intermediate parameter [e.g. S309; training coding network and updating the parameters];
calculating a first joint loss function based on a labeled data set [e.g. FIG. 3A; S305-311; page 12-13; using the predicted text and the second voice sequence of the marked text calculating loss rate], and performing training on the second network according to the first joint loss function [using the second voice sequence marked with text to train the voice recognition model] to adjust the second initial parameter to a second intermediate parameter [e.g. updating the parameters of the network]; and
calculating a second joint loss function based on the labeled data set [e.g. FIG. 3A; S305-311; page 12-13; using the predicted text and the second voice sequence of the marked text calculating loss rate; using the second voice sequence marked with text to train the voice recognition model], and performing training on the first network and the second network according to the second joint loss function to adjust the first intermediate parameter and the second intermediate parameter [e.g. if the loss rate is not less than the preset threshold, adjusting the network parameter of the primary coding network and the decoding network according to the loss rate, returning the step of inputting the second voice sequence into the primary coding network to output the predicted text in the decoding network; ] to obtain a target speech recognition model [e.g. the final trained voice recognition model].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the speech recognition system disclosed by Lu to exploit the well-known training speech recognition technique taught by TANG as above, in order to provide reduced cost of marked data for training coding network [See TANG; abstract].
Regarding claim 10, this is an apparatus that includes same limitation as in claim 1 above, the rejection of which are incorporated herein.
Regarding claim 9, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 1 above, the rejection of which are incorporated herein.
Claim(s) 2, 7, 11, 16-17 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al (US 20240013777 A1) in view of TANG (CN 112086087 A) and WANG et al (US 20220148571 A1).
Regarding claim 2, Lu and TANG further disclose the first network and the second network [e.g. Lu: FIG. 1-3; TANG: FIG. 2-3], but Lu and TANG fail to disclose the detail of the first network.
However, WANG teaches the well-known concept of the first network comprises a convolutional neural network module and a convolutional enhancement module [e.g. FIG. 3-4; [0034]; a convolution neural network; enhancement model with CNN].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the speech recognition system disclosed by Lu to exploit the well-known training speech recognition technique taught by TANG and WANG as above, in order to provide reduced cost of marked data for training coding network [See TANG; abstract] and improved accuracy of speech recognition [See WANG; [0177]].
Regarding claim 7, Lu, TANG and WANG further disclose obtaining audio sample data based on a preset audio sampling rate [e.g. WANG: [0055]; sampling rate of 16KHZ], and dividing the audio sample data into first audio samples and second audio samples [e.g. WANG: frame length]; obtaining the unlabeled data set by calculating audio feature matrices of the first audio samples [e.g. Lu: FIG. 2-3; TANG: FIG. 2-3; WANG: FIG. 4-6; feature matrix]; and obtaining the labeled data set according to calculated audio feature matrices of the second audio samples and an obtained text labeling result of the second audio samples [e.g. Lu: FIG. 2-3; TANG: FIG. 2-3; WANG: FIG. 4].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the speech recognition system disclosed by Lu to exploit the well-known training speech recognition technique taught by TANG and WANG as above, in order to provide reduced cost of marked data for training coding network [See TANG; abstract] and improved accuracy of speech recognition [See WANG; [0177]].
Regarding claim 17 and 21, this is an apparatus that includes same limitation as in claim 2 and 7 above, the rejection of which are incorporated herein.
Regarding claim 11 and 16, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 2 and 7 above, the rejection of which are incorporated herein.
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al (US 20240013777 A1) in view of TANG (CN 112086087 A) and LIN et al (US 20210183391 A1).
Regarding claim 6, Lu, TANG and WANG further disclose the first network and the second network [e.g. Lu: FIG. 1-3; TANG: FIG. 2-3], but Lu and TANG fail to disclose the detail of the second network.
However, LIN teaches the well-known concept of a network for speech recognition comprises a feature deformation module [e.g. FIG. 5-6; [0030]; tracking deformation of the context of thew utterance].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the speech recognition system disclosed by Lu to exploit the well-known training speech recognition technique taught by TANG and LIN as above, in order to provide reduced cost of marked data for training coding network [See TANG; abstract] and recognizing speech using depth information [See LIN; [0003-0004]].
Regarding claim 15, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 6 above, the rejection of which are incorporated herein.
Allowable Subject Matter
Claim 3-5, 12-14 and 18-20 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.
Kingsbury (US 9390370 B2).
WANG et al (US 20230031733 A1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHUBING REN whose telephone number is (571)272-2788. The examiner can normally be reached Monday-Friday 9am-5pm.
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, Richemond Dorvil can be reached at 571-272-7602. 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.
/ZHUBING REN/Primary Examiner, Art Unit 2658
Prosecution Insights