DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 18, 2026 has been entered.
Response to Arguments
Applicants argue that the prior art cited fails to teach the claims as amended. Applicants’ arguments are persuasive, but are moot in view of new grounds of rejection.
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) 1-3, 6-7, 9-12, 15, 17 and 48-51 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (PGPUB 2021/0142783), hereinafter reference as Kim in view of Kaneko et al. (USPN 202/0057913), hereinafter referenced Kaneko.
Regarding claims 1 and 17, Kim discloses a method and system, hereinafter referenced as a method of modifying a speech signal generated by a text-to- speech synthesiser, the method comprising:
receiving a text signal (input text; p. 0124-0126);
generating a speech signal from the text signal (Text-to-speech; p. 0119-0123);
deriving a control feature vector, wherein the control feature vector represents modifications to the speech signal (vector; p. 0129-0138);
inputting the control feature vector in the text-to-speech synthesiser, wherein the text-to-speech synthesiser is configured to generate a modified speech signal using the control feature vector (synthesis; p. 0135-0136); and
outputting the modified speech signal wherein:
the text-to-speech synthesiser comprises a first model configured to generate the speech signal, and a controllable model configured to generate the modified speech signal (change speech style; p. 0102, 0112, 0122); and
the controllable model is trained using speech signals generated by the first model (train; p. 0059), but does not specifically teach that the controllable neural network model is distinct from the first model.
Kaneko discloses a method wherein a first neural network model is generated based on the audio model components and the second neural network model comprises a modified audio signal, which is two distinct models (fig. 3 with p. 0042, 0195), to assist with storing, organizing and maintaining acoustic data.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to assist with reconstructing the audio signal having diversity for the attributes.
Regarding claims 2 and 48, Kim discloses a method wherein deriving the control feature vector comprises:
analysing the speech signal (analyze data; p. 0143)
obtaining a first feature vector from the analysed speech signal (vector; p. 0129-0138);
obtaining a user input (user input; p. 0063, 0074-0076); and
modifying the first feature vector using the user input to obtain the control feature vector (vector; p. 0129-0138).
Regarding claims 3 and 49, Kim discloses a method wherein the user input comprises a reference speech signal (reference speech; p. 0102).
Regarding claims 6 and 50, it is interpreted and rejected for similar reasons as set forth above. In addition, Kim discloses a method comprising an encoder module, a decoder module, and an attention module linking the encoder module to the decoder module (p. 0126-0128, 0131, 0135-0136).
Regarding claims 7 and 51, Kim discloses a method wherein the first feature vector is inputted at the decoder module (decoder; p. 0126-0128, 0131, 0135-0136).
Regarding claim 9, Kim discloses a method wherein the first feature vector represents one of the properties of pitch or intensity (p. 0133-0136).
Regarding claim 10, Kim discloses the method further comprising deriving a second feature vector, wherein the second feature vector represents features of the generated speech signal that are used to generate the modified speech signal (change speech style; p. 0102, 0112, 0122); and
inputting the second feature vector in the text-to-speech synthesiser, wherein the second feature vector is obtained from the analysed speech signal (synthesis; p. 0135-0136).
Regarding claim 11, it is interpreted and rejected for similar reasons as set forth above. In addition, Kim discloses a method wherein:
the controllable model comprises an encoder module, a decoder module, and an attention module linking the encoder module to the decoder module (p. 0126-0128, 0131, 0135-0136), and
the second feature vector is inputted at the decoder module of the controllable model (p. 0126-0128, 0131, 0135-0136).
Regarding claim 12, it is interpreted and rejected for similar reasons as set forth above. In addition, Kim discloses a method wherein a representation of the speech signal is inputted at the encoder module of the controllable model (p. 0126-0128, 0131, 0135-0136).
Regarding claim 15, it is interpreted and rejected for similar reasons as set forth above. In addition, Kim discloses a method wherein the first model comprises an encoder module, a decoder module, and an attention module linking the encoder module to the decoder module (p. 0126-0128, 0131, 0135-0136).
Claim(s) 8, 13-14, 16 and 52 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Kaneko and in further view of Arik et al. (PGPUB 2018/0336880), hereinafter referenced as Arik.
Regarding claims 8 and 52, it is interpreted and rejected for similar reasons as set forth above, however, Kim and Kaneko disclose a method as described above, but does not specifically teach wherein the first feature vector is modified by a pre net before being inputted at the decoder module of the controllable model.
Arik discloses a method wherein the first feature vector is modified by a pre net before being inputted at the decoder module of the controllable model (p. 0110), to assist with modeling and data analysis.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to improve computer performances, features and uses.
Regarding claim 13, it is interpreted and rejected for similar reasons as set forth above. In addition, Kim discloses a method wherein the method further comprises deriving a modified alignment from the user input, wherein the modified alignment indicates modifications to a timing of the speech signal (p. 0052-0065, 0111).
Regarding claim 14, it is interpreted and rejected for similar reasons as set forth above. In addition, Kim discloses a method wherein the modified alignment is inputted at the attention module of the controllable model (p. 0107-0110).
Regarding claim 16, it is interpreted and rejected for similar reasons as set forth above. In addition, Kim discloses a method the method further comprising:
inputting the third feature vector in the encoder module of the controllable model (p. 0126-0128, 0131, 0135-0136). Furthermore, Arik discloses a method comprising deriving a third feature vector from the attention module of the first model, wherein the third feature vector corresponds to a timing of phonemes of the received text signal (p. 0052-0065, 0111).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This information has been detailed in the PTO 892 attached (Notice of References Cited).
Xu et al. teaches modifying signals using neural networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAKIEDA R JACKSON whose telephone number is (571)272-7619. The examiner can normally be reached Mon - Fri 6:30a-2:30p.
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/JAKIEDA R JACKSON/ Primary Examiner, Art Unit 2657
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