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 .
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 103 in regards to claims 1, 11 and 18 have been considered but are moot due to new grounds of rejection necessitated by amendments. See detailed rejection below.
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 (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 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) 1-4, 9-13 and 16-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Min et al. (US 11,475,878).
Claims 1, 11 and 18,
Min teaches a portable communication device comprising: a display; a microphone; memory, comprising one or more storage media, storing instructions; and at least one processor communicatively coupled with the display, the microphone, and the memory ([Fig. 1] “The electronic device 100 is illustrated as a smartphone in Fig. 1” and may be “a wearable device, a tablet PC, a laptop computer … a PDA and a portable multimedia player”; [Fig. 34] “The outputter 1200 … includes a display 1210”, “The A/V inputter 1600… may include… a microphone 1620”; “The memory 1700 may store programs for processing and control operations of the processor 1300”; “the electronic device 100… may include processor 100… may include TTS model generation module 1740 in the memory 1700”),
wherein the instructions, when executed by the at least one processor individually or collectively, cause the portable communication device to ([col. 10 lines 36-46] “the TTS model generation module 1740 may be implemented as at least one software module… including instructions”):
receive a request for setting a personalized audio configuration associated with an audio-related function ([col. 24 lines 53-59] [col. 26 lines 5-8] [Figs. 19 and 23] “a TTS model, i.e., a target model, for providing a TTS service by using a voice similar to a target voice desired by a user… may be generated by executing an application”; “the electronic device 100 may display a user interface for selecting a menu such as ‘Input Voice’ 191 or ‘Open Voice File’ 192”; “the electronic device 100 may select a method of generating a TTS model that best matches a user-desired priority, according to a priority set based on a user input”),
display, via the display, one or more specified texts based at least in part on the request ([col. 24 line 64 to col. 25 line 8] [Fig. 20] “when ‘Input Voice’ 191 is selected based on a user input,” the device obtains the user’s voice; “the electronic device 100 may display a screen 201 for inducing an utterance of the user”; “the electronic device 100 may provide certain sentence examples to be ready by the user.”),
receive one or more voice inputs of a user each corresponding to a respective text of the one or more specified texts ([col. 8 line 65 to col. 9 line 4] [Fig. 20] [col. 25 lines 5-8] “the electronic device 100 may provide certain sentence examples to be ready by the user, and obtain an uttered voice of the user through the microphone 1620.”; Min Also teaches corresponding audio/text training data: “audio data WAV2 based on a utterance of the specific speaker B, and text data TEXT2 corresponding to the audio data WAV2 and including text having the same meaning as the audio data WAV2.”),
select a first acoustic model based at least in part on at least one voice input of the one or more voice inputs ([Fig. 10] [col. 18 lines 40-44] [col. 13 line 27 to col. 14 line 14] [col. 18 line 55-65] [col. 24 line 64 to col. 65 line 8] Min teaches selection of the pre-trained model based on features of target voice data: “the electronic device 100 may select a pre-trained model to learn target voice data, from among one or more pre-trained models stored in the memory 1700, based on data features of the target voice data.”; The target voice data is obtained from utterance input; Min teaches that data features include “acoustic features” and “speaker features”),
generate a second acoustic model corresponding to a phonetic feature of the user based at least in part on training the first acoustic model using the at least one voice input ([col. 7 lines 29-34] [col. 8 line 50 to col. 9 line 4] [col. 10 lines 1-7] “the electronic device 100 may generate a target model by additionally training the model #1… by using, as training data, audio data WAV2 based on an utterance of the specific speaker B, and text data TEXT2 corresponding to the audio data WAV2”; see Fig. 2; “a ‘target model’ may refer to a trained model generated by additionally training a pre-trained model… based on voice data based on an utterance of a specific speaker… by using the pre-trained model as an initial state”; “the target model may output an audio signal converted to a voice similar to that of the specific speaker B”; Min teaches phonetic/acoustic characteristics: “pronunciation, prosody, intonation, sound quality”), and
perform the audio-related function using the second acoustic model instead of the first acoustic model ([col. 17 lines 4-18] “the electronic device 100 may generate output data obtained by converting input text into an audio signal, by using the generated target model.”; “the electronic device 100 may generate the output data… by inputting the obtained input text to the target model”; “the electronic device 100 may output the generated output data”), and
wherein the portable communication device is configured to provide, as at least part of the audio-related function, a speech output corresponding to a text-to-speech (TTS) function using the second acoustic model ([col. 6 lines 53-56] [col. 9 lines 51-55] [col. 32 lines 51-53] “The electronic device 100 may provide a text-to-speech (TTS) service. TTS is a speech synthesis technology for converting text into audio and outputting the audio.”; “a ‘pre-trained mode’ or a ‘target model’ may be a TTS model for obtaining certain text as input data and generating output data obtained by converting the input data into an audio signal.”; “the sound outputter 1230 may output an audio signal generated by a target model.”),
the speech output having the phonetic feature of the user ([col. 6 line 64 to col. 7 line 3] [col. 7 lines 29-55] [col. 9 lines 11-22] [col. 17 lines 19-22] “The electronic device 100 may output an audio signal 15 with a voice similar to that of the speaker B by using the model trained based on the voice data of the speaker B”; The generated model outputs audio “similar to a voice of the specific speaker B and having a low pronunciation error rate.”; The device output audio “with a voice similar to that of the specific speaker learned by the target model”; Min defines the relevant phonetic/acoustic properties as “pronunciation, prosody, intonation, sound quality”).
Claims 2, 12 and 19,
Min further teaches the portable communication device of The portable communication device of wherein the instructions that, when executed by the at least one processor individually or collectively, further cause the portable communication device to: select the first acoustic model from a plurality acoustic models including the first acoustic model and a third acoustic model ([Fig. 4] [col. 10 lines 52-59] Min teaches a plurality of stored pre-trained models: “the pre-trained model storage 1701 may store one or more pre-trained models (e.g., pre-trained models #1, #2 and #3)”; Min further teaches that those model are distinct voice/TTS models: “the pre-trained models may be models which have pre-learned voice data based on utterances of speakers having different genders, tones, intonations, and pronunciations”; [col. 11 lines 57-63] [Fig. 4] Min then taches selecting a model from those models: “the pre-trained model determination module 1704 may select a pre-trained model to learn the target voice data, from among the one or more pre-trained models stored in the pre-trained model storage 1701, based on the data features of the target voice data analyzed by the data feature analysis model 1702”; [Fig. 10] [col. 18 lines 40-44] Min further teaches the same selection in the Fig. 10; “the electronic device 100 may select a pre-trained model to learn target voice data, from among one or more pre-trained models stored in the memory 1700, based on data features of the target voice data”; Min also teaches that Fig. 12 stores data according to each pre-trained model ID: “Fig. 12 shows an example of data stored according to an identifier (ID) of each pre-trained model”; [col. 18 line 66 to col. 19 line 5] [Figs. 12-13] Min teaches that selection is based on comparing the target voice data with the stored model: “the electronic device 100 may select a pre-trained model… based on comparison between the analyzed data features of the target voice data… and the data features of the voice data pre-learned by the pre-trained models.”; claim 15 also recites that the processor is configured to “select the pre-trained model from among one or more pre-trained models stored in the memory to learn the target voice data”).
Claim 3,
Min further teaches the portable communication device of claim 2, wherein the memory stores an acoustic model database including the first acoustic model and the third acoustic model ([col. 10 lines 36-63] [Figs. 33-34] Min teaches that the device memory include the TTS generation module: “the memory 1700… of the electronic device 100 may include the TTS model generation module 1740”; Min then teaches that this module include a stored model repository: “the TTS model generation module 1740 may include a pre-trained model storage 1701, a data feature analysis module 1702, a learning step number determination module 1703, a pre-trained model determination module 1704, a pre-trained model generation module 1705, and a training module 1706”; see Fig. 4; Min identifies these models as voice/acoustic TTS models: “the pre-trained models may be models which have pre-learned voice data based on utterances of speakers having different genders, tones, intonations, and pronunciations”; Min further teaches that the storage stores data for those models: “the pre-trained model storage 1701 may store voice data pre-learned by the pre-trained models (e.g., the pre-trained models #1, #2 and #3)”; Min’s database/table teaching is reinforced by Fig. 12: “Fig. 12 shows an example of data stored according to an identifier (ID) of each pre-trained model”; Min states that the stored model-ID data includes acoustic and speaker features: “according to the ID of each pre-trained model, data on acoustic features (e.g. FS and BW), speaker features (e.g., Gend, Lang, F0/Pitch, and Tempo), and the number of pre-learned steps of voice data pre-learned by the pre-trained model may be stored”; Min also provides that modules may be implemented as “data, a database, data structures, tables, arrays or variables”).
Claims 4 and 13,
Min further teaches the portable communication device of The portable communication device of wherein the instructions that, when executed by the at least one processor individually or collectively, further cause the portable communication device to: as part of the selection of the first acoustic model ([col. 10 line 47 to col 11 line 9] [Fig. 4] Min teaches a model-selection architecture: Min states: “the TTS model generation module 1740, a data feature analysis module 1702… and a pre-trained model determination module 1704”; Min teaches that the data feature analysis module obtains the voice input and analyzes features: “the data feature analysis module 1702 may obtain target voice data based on an utterance input of a specific speaker”; Min further states: “the pre-trained model determination module 1704 may select a pre-trained model to learn the target voice data, from among the one or more pre-trained models stored in the pre-trained model storage 1701, based on the data features of the target voice data analyzed by the data feature analysis module 1702”),
extract one or more acoustic characteristics from the at least one voice input ([col. 13 line 27 to col. 14 line 55] [Fig. 5] Min teaches obtaining the voice input as target voice data: “in operation S501, the electronic device 100 may obtain target voice data on an utterance input of a specific speaker”; Min further teaches that “the electronic device 100 may obtain the target voice data based on an utterance input of the specific speaker through a microphone 1620”; see Fig. 34; Min defines acoustic characteristics/features of that voice data: “data features may be features of voice data and include at least one of a data amount, acoustic features, speaker features or content features”; Min states that “the acoustic features may be features of an audio signal include in voice data, and refer to features related to sound quality”; Min gives examples: “the acoustic features may include sampling rate (FS), a bandwidth (BW), a signal-to-noise ratio (SNR) and a reverberation time (RT), but are not limited thereto”; Min further teaches actual analysis of acoustic feature from obtained target voice data: “Fig. 13 shows an example of data features and the number of learning steps of target voice data WAV2. For example, acoustic features (e.g. FS and BW) and speaker features… of the obtained target voice data WAV2 may be analyzed by the electronic device 100”; see Fig. 13; Min also teaches calculating feature values: “the electronic device 100 may calculate 1001111011001111… as a data feature value of the target voice data WAV2 with reference to the prestored table of Fig. 6”).
Claims 9 and 16,
Min further teaches the portable communication device of claim 1, further comprising: a microphone, wherein the instructions that, when executed by the at least one processor individually or collectively, further cause the portable communication device to: receive the at least one voice input via the microphone while at least one of the one or more specified texts is displayed ([col. 34 lines 37 to 56] [Fig. 34] Min teaches the electronic device includes a microphone; Min states “The A/V inputter 1600 is used to input audio signals or video signals, and may include, for example, a camera 1610 and a microphone 1620”; Min further states: “The microphone 1620 receives an external sound signal and processes the same into electrical voice data”; Min also states: “According to an embodiment of the disclosure, the microphone 1620 may receive a sound signal based on utterance of a specific speaker”).
Claims 10 and 17,
Min further teaches the portable communication device of claim 1, wherein the instructions that, when executed by the at least one processor individually or collectively, further cause the portable communication device to: receive the at least one voice input from a voice data file provided by the user ([Fig. 19] [col. 24 lines 60-67] Min teaches a user-interface option for opening a voice file; Min states “Referring to FIG. 19, according to an embodiment of the disclosure, to obtain target voice data, the electronic device 100 may display a user interface for selecting a menu such as ‘Input Voice’ 191 or ‘Open Voice File’ 192”).
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) 5-6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Min et al. (US 11,475,878) and further in view of Zhao et al. (US 2010/0312563).
Claims 5 and 14,
Min further teaches the portable communication device of The portable communication device of wherein the instructions that, when executed by the at least one processor individually or collectively, further cause the portable communication device to: perform the training of the first acoustic model ([col. 8 line 65 to col. 9 line 4] [col. 16 line 66 to col. 17 line 3] Min teaches training the selected pre-trained model: “generate a target model by additionally training the model #1… by using, as training data, audio data WAV2… and text data TEXT2 corresponding to the audio wave WAV2”; see Fig. 2; Min also teaches training the determined pre-trained model at).
based at least in part on a determination that the at least one voice input corresponds to respective one or more of the one or more specified texts by a specified validity ([col. 8 line 65 to col. 9 line 4] Min taches correspondence between audio and text but does not teach determine validity: “text data TEXT 2 corresponding to the audio data WAV2 and including text having the same meaning as the audio data WAV2”).
The difference between the prior art and the claimed invention is that Min does not explicitly teach based at least in part on a determination that the at least one voice input corresponds to respective one or more of the one or more specified texts by a specified validity.
Zhao teaches based at least in part on a determination that the at least one voice input corresponds to respective one or more of the one or more specified texts by a specified validity ([0021] Zhao teaches a confidence score and threshold: “a basic confidence score may be used,” and sentences with “large mismatch, compared to a threshold, may be discarded… The remaining sentences may be retained.”; Zhao’s claim 1 similarly recites “retaining a sentence having a degree of matching higher than a threshold.”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Min with teachings of Zhao by modifying the electronic device and operating method as taught by Min to include the one or more specified texts by a specified validity as taught by Zhao for the benefit of generating custom voice font data usable by the TTS component (Zhao [Abstract]).
Claim 6,
Min further teaches the portable communication device of claim 1, wherein the instructions that, when executed by the at least one processor individually or collectively, further cause the portable communication device to: display an additional text ([Fig. 20] [col. 25 lines 1-22] Min teaches displaying text prompts/sentence examples for user utterance and further teaches displaying a screen to include additional voice input when more data is needed: Min states “the electronic device 100 may display a screen 201 for inducing an utterance of the user,” and may provide certain sentence examples to be read by the user” ),
receive an additional voice input from the user corresponding to the additional text ([col. 25 lines 5-9] Min teaches receiving uttered voice corresponding to displayed sentence example: “the electronic device 100 may provide certain sentence examples to be read by the user, and obtain an uttered voice of the user through the microphone 1620”; Min also teaches obtaining additional target voice data when necessary: “the electronic device 100 may additionally obtain target voice data” by displaying a UI asking whether other recorded voice data is available or by “including a direct utterance input of a target speaker when possible”), and
The difference between the prior art and the claimed invention is that Min does not explicitly teach based at least in part on a determination that the at least one voice input does not correspond to respective one or more of the one or more specified texts by a specified validity, determine whether the specified validity is met further based on the additional voice input before the training of the first acoustic model.
Zhao teaches based at least in part on a determination that the at least one voice input does not correspond to respective one or more of the one or more specified texts by a specified validity ([0021] Zhao teaches automatically verifying whether recorded voice audio corresponds to the text script using speech recognition and a confidence/threshold comparison: Zhao states: “Verification component 124 may user techniques based on speech recognition technology to analyze the voice audio data 104 and scripts 106 with pronunciation”; Zhao further states: “a basic confidence score may be used,” and “the sentences in scripts 106 may be ordered by the degree of matching between the recognized speech from the voice data 104 and the corresponding text from the script.”; Zhao then teaches the non-correspondence determination by threshold: “the sentences with large mismatch, compared to a threshold may be discarded from the sentence pool and will not be used further”),
determine whether the specified validity is met further based on the additional voice input before the training of the first acoustic model ([0036] [Fig. 5] Zhao teaches verifying voice/audio data against the corresponding text before training and retraining only data meeting the threshold; Zhao states: “the logic flow 500 may automatically verify the voice audio data and the rich script at block 506”; Zhao then teaches that “the sentences having a higher than threshold degree of matching between the recognized speech from the voice audio data and the script text may be retained for further processing”; [0037] Zhao further teaches training only after verification: “the logic flow 500 may train a custom voice font from the retained sentences of verified voice data and the rich script at block 508”; [0021] Zhao also states that “the sentences with large mismatch, compared to a threshold, may be discarded… and will not be used further,” while “the remaining sentences may be retrained”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Min with teachings of Zhao by modifying the electronic device and operating method as taught by Min to include based at least in part on a determination that the at least one voice input does not correspond to respective one or more of the one or more specified texts by a specified validity, determine whether the specified validity is met further based on the additional voice input before the training of the first acoustic model as taught by Zhao for the benefit of generating custom voice font data usable by the TTS component (Zhao [Abstract]).
Claim(s) 8, 15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Min et al. (US 11,475,878) and further in view of Li (US 10,410,621).
Claims 8, 15 and 20,
Min further teaches the portable communication device of claim 1, wherein the instructions that, when executed by the at least one processor individually or collectively, further cause the portable communication device to: select a third acoustic model over the first acoustic model based at least in part on at least one voice input corresponding to another user ([Fig. 4] [col. 10 lines 53-59] Min teaches storing plural pre-trained models and selecting one based on target voice data features; “the pre-trained model storage 1701 may store one or more pre-trained models (e.g., pre-trained models #1, #2, and #3)”; Min further teaches that those models are trained on speakers with different voice properties: “the pre-trained models may be models which have pre-learned voice data based on utterances of speakers having different genders, tones, intonations, and pronunciations”; [col. 11 lines 4-15] Min teaches obtaining voice input of the other speaker/user as target voice data: “the data feature analysis module 1702 may obtain target voice data based on an utterance input of a specific speaker,” and the target voice data is “voice data obtained based on an utterance of a specific speaker who has a voice to be used to provide a TTS service”; [col. 11 lines 57-63] Min teaches selecting the model based on those voice-data features: “the pre-trained model determination module 1704 may select a pre-trained model to learn the target voice data, from among the one or more pre-trained models stored in the pre-trained model storage 1701, based on the data features of the target voice data analyzed by the data feature analysis module 1702”; [Fig. 10] [col. 18 lines 40-44] Min’s Fig. 10 embodiment similarly states that the device “may select a pre-trained model to learn target voice data, from among one or more pre-trained models stored in the memory 1700, based on data features of the target voice data”; Min provides an example, see [col. 18 lines 45-49]);
The difference between the prior art and the claimed invention is that Min does not explicitly teach generate a fourth acoustic model corresponding to a phonetic feature of the other user based at least in part on training the third acoustic model using the at least one voice input corresponding to the other user; and perform the audio-related function for the other user using the fourth acoustic model instead of the first acoustic model, the second acoustic model or the third acoustic model.
Li teaches generate a fourth acoustic model corresponding to a phonetic feature of the other user based at least in part on training the third acoustic model using the at least one voice input corresponding to the other user ([col. 6 lines 4-6] Li teaches obtaining speech data of a target user: “speech data of a target user is obtained” and “the speech data includes speech features of the target user”; [col. 6 lines 16-19] Li teaches that the target user reads recording text to obtain speech data: “ the recording text is pre-designed according to index such as phone coverage and prosody coverage, and then is provided to the target user for reading, to obtain speech data of the target user”; [col. 6 line 45 to col. 7 line 5] Li teaches training a target-user acoustic model from a reference acoustic model and the target-user speech data: “a first target user acoustic model is trained according to the reference acoustic model and the speech data”; Li further explains that “the first target user acoustic model can be trained using the speech data of the target user”; [col. 7 lines 35-52] Li also teaches updating neural-network parameters “to obtain the first target user acoustic model having speech features of the target user”); and
perform the audio-related function for the other user using the fourth acoustic model instead of the first acoustic model, the second acoustic model or the third acoustic model ([col. 8 lines 43-56] Li teaches applying multiple generated acoustic models to speech synthesis and using selected target-user acoustic model; Li states: “after the multiple target user acoustic models are generated… the multiple acoustic models can be applied to the speech synthesis system”; Li further teaches that “the user can selectively select a personalized acoustic model used in the speech synthesis system according to his apparatus situation or his will, and the speech synthesis system can perform speech synthesis according to the acoustic model selected by the user”; [Fig. 3] [S304] [col. 8 line 57 to col. 9 line 36] Li then teaches using the selected first target-user acoustic model for speech synthesis: “the user selects a first target user acoustic model for speech synthesis” and “step S304, the result of phonetic notation, the prosodic features, and context features of the text to be synthesized are input to the first target user acoustic model… to generate an acoustic parameter sequence of the text to be synthesized”; [col. 9 lines 37-67] Li teaches generating the output speech: “a speech synthesis result of the text to be synthesized is generated according to the acoustic parameter sequence” and “a speech synthesis result of the text to be synthesized is generated according to the acoustic parameter sequence”; Li confirms that the resulting speech includes the target user’s feature: “the speech synthesis result of the speech synthesis system includes the features of the target user”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Min with teachings of Li by modifying the electronic device and operating method as taught by Min to include generate a fourth acoustic model corresponding to a phonetic feature of the other user based at least in part on training the third acoustic model using the at least one voice input corresponding to the other user; and perform the audio-related function for the other user using the fourth acoustic model instead of the first acoustic model, the second acoustic model or the third acoustic model as taught by Li for the benefit of training a personalized acoustic model with high accuracy and results (Li [Background]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
Prosecution Insights