DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 22 January 2025, 15 August 2025, 13 November 2025, and 09 April 2026, respectively, are 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 § 102
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.
Claim(s) 1 and 5-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by CN 115116443, hereinafter referred to as Chen et al.
Regarding claim 1 (Original), Chen et al. discloses a data processing system for a speech recognition model, comprising:
a cloud-side device (Chen et al. - terminal 400 (i.e., terminal-side devices), a network 300, and a server 200. See also, Chen et al., Highlight 1.), configured to:
acquire a sample set, wherein the sample set comprises a plurality of sample pairs, and the sample pair comprises sample speech data and sample Chinese text (Chen et al., Highlights 2-4. The terminal 400 is used for obtaining an original audio sample, generating a model training instruction carrying the original audio sample, and sending the model training instruction to the server 200. The server 200 is used for performing generalization processing on the original audio sample to obtain a plurality of audio samples carrying a first text tag corresponding to the original audio sample, wherein an embodiment relates to predicting a Chinese text corresponding to an audio.);
encode, by using an encoder, the sample speech data to obtain a speech feature of the sample speech data, wherein the encoder is pre-trained based on performing a Chinese pronunciation unit predicting task on pre-training speech data (Chen et al., Highlights 5-8. Phoneme prediction is performed on each audio sample by means of an acoustic sub-model (i.e., an encoder) to obtain a phoneme sequence corresponding to each audio sample. The acoustic sub-model also needs to be pretrained.);
input the speech feature into a decoder to obtain predicted Chinese text, wherein the decoder is pre-trained based on performing a text prediction task on a pre-training Chinese pronunciation unit (Chen et al., Highlights 9-11. Text conversion is performed on each phoneme sequence by means of a conversion sub-model (i.e., a decoder) to obtain a conversion text corresponding to each audio sample. An error between each conversion text and the first text tag is obtained, and model parameters of the speech recognition model are updated on the basis of the obtained error, so as to obtain a trained speech recognition model.);
pre-train a model comprising the encoder and the decoder based on the predicted Chinese text and the sample Chinese text, and in a case where a pre-training stop condition is met, acquire a model parameter of a speech recognition model obtained by pre-training (Chen et al., Highlights 12-13.);
wherein the cloud-side device is further configured to send the model parameter of the speech recognition model obtained by pre-training to an end-side device (Chen et al., Highlight 14. The trained speech recognition model is sent to the terminal 400.);
the end-side device, configured to perform speech recognition on to-be-recognized speech data by using the speech recognition model to obtain target text corresponding to the to-be-recognized speech data (Chen et al., Highlight 14. The terminal 400 is further used for obtaining an audio to be recognized, performing speech recognition on said audio by means of the trained speech recognition model to obtain a convention text corresponding to said audio, and outputting the convention text.).
As to claim 9, method claim 9 and system claim 1 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 9 is similarly rejected under the same rationale as applied above with respect to system claim.
As to claim 10, method claim 10 and system claim 1 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 10 is similarly rejected under the same rationale as applied above with respect to system claim.
As to claim 15, device claim 15 and system claim 1 are related as system and device of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 15 is similarly rejected under the same rationale as applied above with respect to system claim. And, Chen et al., Highlights 15-17, teach memory and processor.
As to claim 18, CRM claim 18 and system claim 1 are related as system and CRM of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 18 is similarly rejected under the same rationale as applied above with respect to system claim. And, Chen et al., Highlights 15-17, teach memory and processor.
The additional features of claims 5-8 and 11-12 are taught by Chen et al., Highlights 2-13.
Regarding claim 13 (Currently Amended), Chen et al. discloses a computing device, comprising:
a memory and a processor (Chen et al., Highlights 15-16.);
wherein the memory is configured to store computer-executable instructions, the processor is configured to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the data processing method for the speech recognition model according to claim 9
Regarding claim 14 (Currently Amended), Chen et al. discloses a non-transitory computer-readable storage medium, having computer-executable instructions stored thereon, and when the computer-executable instructions are executed by a processor, the steps of the data processing method for the speech recognition model according to claim 9 .
Claim Rejections – 35 USC § 103
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) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over CN 115116443, hereinafter referred to as Chen et al., in view of CN 114373481, hereinafter referred to as Li et al.
Claims 2 and 4 are additionally directed to pronunciation training using masking,
which is disclosed by Li et al., Highlights 1 and 2. Li et al. benefits Chen et al. by performing masking on phoneme sequences and training on a pronunciation error detection model, thereby improving the robustness of the pronunciation error detection model. Therefore, it would be obvious for one skilled in the art to combine the teachings of Chen et al. with those of Li et al. to improve the training of the speech recognition model of Chen et al.
Regarding claim 3 (Original), Chen et al., as modified by Li et al., discloses the data processing system according to claim 2, wherein the cloud-side device is specifically configured to:
extract a spectral feature of the first pre-training speech data (Chen et al., Highlights 9-11. It is known in the art that speech data, in particular phonemes comprise spectral features.);
input the spectral feature of the first pre-training speech data into the encoder to obtain the first speech feature corresponding to the first pre-training speech data (Chen et al., Highlights 9-11. It is known in the art that speech data, in particular phonemes comprise spectral features.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE L THOMAS-HOMESCU whose telephone number is (571)272-0899. The examiner can normally be reached Mon-Fri 8-6.
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/ANNE L THOMAS-HOMESCU/Primary Examiner, Art Unit 2656
