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, see pages 10-12 of Applicant correspondence, filed 05/11/2026, with respect to 2-21 have been fully considered and are persuasive. The 35 U.S.C. 101 of claims 2-21 has been withdrawn.
Applicant's arguments filed 05/11/2026 have been fully considered but they are not persuasive. The Obviousness Double Patenting rejection is not overcome by Applicant response. The Obviousness Double Patenting rejection is maintained.
Applicant’s arguments, see pages 10-12 of Applicant response, filed 05/11/2026, with respect to claims 2-21 have been fully considered and are persuasive. The 35 U.S.C. 102(a2) rejection of claims 2-21 has been withdrawn.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 2-21 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,039,082. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 2-21 of the instant application are similar in scope and content of the patented claims 1-20 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 2-21 are to be found in patented claims 1-20 (as the application claims 2-21 fully encompasses patented claims 1-20). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-20 of the patent is in effect a “species” of the “generic” invention of the application claims 2-21. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 2-21 is anticipated by claims 1-20 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/744,592
Patent No: 12,039,982
2. (Currently Amended) A computer-implemented method comprising: obtaining an input sequence of audio data comprising an audio segment sequence characterizing an audio segment spoken by at least one speaker; rocessing, using a neural network configured to perform one or more tasks comprising joint automatic speech recognition - speaker diarization (ASR-SD),an input comprising the input sequence of the audio data comprising the audio segment sequence to generate an output sequence defining a sequence of output symbols, sequence of output symbols in the output sequence comprises a plurality of text symbols and at least one speaker label symbol selected from [[the]] a plurality of speaker label symbols that each identify a different speaker from a set of possible speakers; and determining, from the output sequence, a transcription of the audio segment that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word.
1. A computer-implemented method comprising: obtaining an audio segment sequence characterizing an audio segment, the audio segment sequence comprising a plurality of audio frames; mapping, using a joint automatic speech recognition-speaker diarization (ASR-SD) neural network, the audio segment sequence to an output sequence comprising a respective output symbol for each of a plurality of time steps, wherein, for each of the time steps, the output symbol for the time step in the output sequence is selected from a set of output symbols that includes (i) a plurality of text symbols, (ii) a plurality of speaker label symbols, each speaker label symbol identifying a different speaker from a set of possible speakers, and (iii) a blank symbol, wherein the respective output symbols in the output sequence comprise a plurality of text symbols and at least one speaker label symbol selected from the plurality of speaker label symbols; and determining, from the output sequence, a transcription of the audio segment data that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word.
3. (Currently Amended) The method of claim 2, wherein the output sequence comprises a respective output symbol at each of a plurality of time steps, and wherein the neural network comprises a transcription neural network, and wherein processing the input sequence to generate a respective encoded representation [[of]] for each of the plurality of time steps.
2. The method of claim 1, wherein the joint ASR-SD neural network comprises a transcription neural network, and wherein mapping the audio segment sequence comprises: processing the audio segment sequence using the transcription neural network, wherein the transcription neural network is configured to process the audio segment data to generate a respective encoded representation of each of the plurality of time steps.
4. (Currently Amended) The method of claim 3, wherein each output symbol of the sequence of output symbols is selected from a set of output symbols that includes (i) a set of text symbols, (ii) a set of speaker label symbols, and (iii) a blank symbol, wherein the neural network further comprises a prediction neural network, and wherein processing the input comprises, for each time step: identifying a current output symbol for the time step, and processing the current output symbol for the time step using the prediction neural network, wherein the prediction neural network is configured to process the current output symbol to generate a prediction representation for the time step conditioned on any non-blank output symbols that have already been included at any earlier time steps in the output sequence.
3. The method of claim 2, wherein the joint ASR-SD neural network further comprises a prediction neural network, and wherein mapping the audio segment sequence comprises, for each time step: identifying a current output symbol for the time step, and processing the current output symbol for the time step using the prediction neural network, wherein the prediction neural network is configured to process the current output symbol to generate a prediction representation for the time step conditioned on any non-blank output symbols that have already been included at any earlier time steps in the output sequence.
5. (Currently Amended) The method of claim 4, wherein the neural network comprises a joint neural network and a softmax output layer, and wherein rocessing the input comprises, for each time step: processing the encoded representation for the time step and the prediction representation for the time step to generate a respective logit for each of the output symbols in the set of output symbols; and processing the logits for the output symbols using the softmax output layer to generate a probability distribution over the output symbols in the set of output symbols.
4. The method of claim 3, wherein the joint ASR-SD neural network comprises a joint neural network and a softmax output layer, and wherein mapping the audio segment sequence comprises, for each time step: processing the encoded representation for the time step and the prediction representation for the time step to generate a respective logit for each of the output symbols in the set of output symbols; and processing the logits for the output symbols using the softmax output layer to generate a probability distribution over the output symbols in the set of output symbols.
6. (Currently Amended) The method of claim 5, wherein rocessing the input comprises, for each time step: selecting an output symbol from the set of output symbols using the probability distribution.
5. The method of claim 4, wherein mapping the audio segment sequence comprises, for each time step: selecting an output symbol from the set of output symbols using the probability distribution.
7. (Currently Amended) The method of claim 2, wherein the plurality of text symbols comprise symbols representing one or more of phonemes, morphemes, or characters.
6. The method of claim 1, wherein the text symbols represent phonemes, morphemes, or characters.
8. (Currently Amended) The method of claim 2, wherein determining, from the output sequence, [[a]] the transcription of the audio segment [[data]] that identifies (i) the words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word comprises: identifying words represented by the text symbols defined by the output sequence; and for each identified word: identifying a speaker label symbol that immediately follows the text symbols representing the word in the output sequence; and identifying the word as having been spoken by a speaker represented by the identified speaker label.
7. The method of claim 1, wherein determining, from the output sequence, a transcription of the audio segment data that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word comprises: identifying words represented by the text symbols in the output sequence; and for each identified word: identifying a speaker label symbol that immediately follows the text symbols representing the word in the output sequence; and identifying the word as having been spoken by a speaker represented by the identified speaker label.
9. (Currently Amended) The method of claim 2, wherein the set of possible speakers is a set of possible speaking roles in a conversation, and wherein each speaker label symbol identifies a different speaking role from the set of possible speaking roles.
8. The method of claim 1, wherein the set of possible speakers is a set of possible speaking roles in a conversation, and wherein each speaker label symbol identifies a different speaking role from the plurality of possible speaking roles.
10. (Currently Amended) One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: obtaining an input sequence of audio data comprising an audio segment sequence characterizing an audio segment spoken by at least one speaker; rocessing, using a neural network configured to perform one or more tasks comprising joint automatic speech recognition -speaker diarization (ASR-SD),an input comprising the input sequence of the audio data comprising the audio segment sequence to generate an output sequence defining a sequence of output symbols, sequence of output symbols in the output sequence comprises a plurality of text symbols and at least one speaker label symbol selected from [[the]] a plurality of speaker label symbols that each identify a different speaker from a set of possible speakers; and determining, from the output sequence, a transcription of the audio segment that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word.
9. One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: obtaining an audio segment sequence characterizing an audio segment, the audio segment sequence comprising a plurality of audio frames; mapping, using a joint automatic speech recognition-speaker diarization (ASR-SD) neural network, the audio segment sequence to an output sequence comprising a respective output symbol for each of a plurality of time steps, wherein, for each of the time steps, the output symbol for the time step in the output sequence is selected from a set of output symbols that includes (i) a plurality of text symbols, (ii) a plurality of speaker label symbols, each speaker label symbol identifying a different speaker from a set of possible speakers, and (iii) a blank symbol, wherein the respective output symbols in the output sequence comprise a plurality of text symbols and at least one speaker label symbol selected from the plurality of speaker label symbols; and determining, from the output sequence, a transcription of the audio segment data that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word.
11. (Currently Amended) The computer-readable storage media of claim 10, wherein the output sequence comprises a respective output symbol at each of a plurality of time steps, and wherein the neural network comprises a transcription neural network, and wherein processing the input sequence to generate a respective encoded representation [[of]]for each of the plurality of time steps.
10. The computer-readable storage media of claim 9, wherein the joint ASR-SD neural network comprises a transcription neural network, and wherein mapping the audio segment sequence comprises: processing the audio segment sequence using the transcription neural network, wherein the transcription neural network is configured to process the audio segment data to generate a respective encoded representation of each of the plurality of time steps.
12. (Currently Amended) The computer-readable storage media of claim 11, wherein each output symbol of the sequence of output symbols is selected from a set of output symbols that includes (i) a set of text symbols, (ii) a set of speaker label symbols, and (iii) a blank symbol, wherein the neural network further comprises a prediction neural network, and wherein processing the input comprises, for each time step: identifying a current output symbol for the time step, and processing the current output symbol for the time step using the prediction neural network, wherein the prediction neural network is configured to process the current output symbol to generate a prediction representation for the time step conditioned on any non-blank output symbols that have already been included at any earlier time steps in the output sequence.
11. The computer-readable storage media of claim 10, wherein the joint ASR-SD neural network further comprises a prediction neural network, and wherein mapping the audio segment sequence comprises, for each time step: identifying a current output symbol for the time step, and processing the current output symbol for the time step using the prediction neural network, wherein the prediction neural network is configured to process the current output symbol to generate a prediction representation for the time step conditioned on any non-blank output symbols that have already been included at any earlier time steps in the output sequence.
13. (Currently Amended) The computer-readable storage media of claim 12, wherein the neural network comprises a joint neural network and a softmax output layer, and whereinrocessing the input comprises, for each time step: processing the encoded representation for the time step and the prediction representation for the time step to generate a respective logit for each of the output symbols in the set of output symbols; and processing the logits for the output symbols using the softmax output layer to generate a probability distribution over the output symbols in the set of output symbols.
12. The computer-readable storage media of claim 11, wherein the joint ASR-SD neural network comprises a joint neural network and a softmax output layer, and wherein mapping the audio segment sequence comprises, for each time step: processing the encoded representation for the time step and the prediction representation for the time step to generate a respective logit for each of the output symbols in the set of output symbols; and processing the logits for the output symbols using the softmax output layer to generate a probability distribution over the output symbols in the set of output symbols.
14. (Currently Amended) A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining an input sequence of audio data comprising an audio segment sequence characterizing an audio segment spoken by at least one speaker; rocessing, using a neural network configured to perform one or more tasks comprising joint automatic speech recognition -speaker diarization (ASR-SD),an input comprising the input sequence of the audio data comprising the audio segment sequence to generate an output sequence defining a sequence of output symbols, sequence of output symbols in the output sequence comprises a plurality of text symbols and at least one speaker label symbol selected from [[the]] a plurality of speaker label symbols that each identify a different speaker from a set of possible speakers; and determining, from the output sequence, a transcription of the audio segment that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word.
13. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining an audio segment sequence characterizing an audio segment, the audio segment sequence comprising a plurality of audio frames; mapping, using a joint automatic speech recognition-speaker diarization (ASR-SD) neural network, the audio segment sequence to an output sequence comprising a respective output symbol for each of a plurality of time steps, wherein, for each of the time steps, the output symbol for the time step in the output sequence is selected from a set of output symbols that includes (i) a plurality of text symbols, (ii) a plurality of speaker label symbols, each speaker label symbol identifying a different speaker from a set of possible speakers, and (iii) a blank symbol, wherein the respective output symbols in the output sequence comprise a plurality of text symbols and at least one speaker label symbol selected from the plurality of speaker label symbols; and determining, from the output sequence, a transcription of the audio segment data that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word.
15. (Currently Amended) The system of claim 14, wherein the output sequence comprises a respective output symbol at each of a plurality of time steps, and wherein the neural network comprises a transcription neural network, and wherein processing the input sequence to generate a respective encoded representation [[of]]for each of the plurality of time steps.
14. The system of claim 13, wherein the joint ASR-SD neural network comprises a transcription neural network, and wherein mapping the audio segment sequence comprises: processing the audio segment sequence using the transcription neural network, wherein the transcription neural network is configured to process the audio segment data to generate a respective encoded representation of each of the plurality of time steps.
16. (Currently Amended) The system of claim 15, wherein each output symbol of the sequence of output symbols is selected from a set of output symbols that includes (i) a set of text symbols, (ii) a set of speaker label symbols, and (iii) a blank symbol, wherein the neural network further comprises a prediction neural network, and wherein processing the input comprises, for each time step: identifying a current output symbol for the time step, and processing the current output symbol for the time step using the prediction neural network, wherein the prediction neural network is configured to process the current output symbol to generate a prediction representation for the time step conditioned on any non-blank output symbols that have already been included at any earlier time steps in the output sequence.
15. The system of claim 14, wherein the joint ASR-SD neural network further comprises a prediction neural network, and wherein mapping the audio segment sequence comprises, for each time step: identifying a current output symbol for the time step, and processing the current output symbol for the time step using the prediction neural network, wherein the prediction neural network is configured to process the current output symbol to generate a prediction representation for the time step conditioned on any non-blank output symbols that have already been included at any earlier time steps in the output sequence.
18. The system of claim 17, wherein mapping the audio segment sequence comprises, for each time step: selecting an output symbol from the set of output symbols using the probability distribution.
17. The system of claim 16, wherein mapping the audio segment sequence comprises, for each time step: selecting an output symbol from the set of output symbols using the probability distribution.
18. (Currently Amended) The system of claim 17, wherein rocessing the input comprises, for each time step: selecting an output symbol from the set of output symbols using the probability distribution.
18. The system of claim 13, wherein the text symbols represent phonemes, morphemes, or characters.
20. (Currently Amended) The system of claim 14, wherein determining, from the output sequence, [[a]] the transcription of the audio segment [[data]] that identifies (i) the words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word comprises: identifying words represented by the text symbols defined by the output sequence; and for each identified word: identifying a speaker label symbol that immediately follows the text symbols representing the word in the output sequence; and identifying the word as having been spoken by a speaker represented by the identified speaker label.
19. The system of claim 13, wherein determining, from the output sequence, a transcription of the audio segment data that identifies (i) words spoken in the audio segment and (ii) for each of the spoken words, the speaker from the set of possible speakers that spoke the word comprises: identifying words represented by the text symbols in the output sequence; and for each identified word: identifying a speaker label symbol that immediately follows the text been spoken by a speaker represented by the identified speaker label.
21. (Currently Amended) The system of claim 14, wherein the set of possible speakers is a set of possible speaking roles in a conversation, and wherein each speaker label symbol identifies a different speaking role from the set of possible speaking roles.
20. The system of claim 13, wherein the set of possible speakers is a set of possible speaking roles in a conversation, and wherein each speaker label symbol identifies a different speaking symbols representing the word in the output sequence; and identifying the word as having role from the plurality of possible speaking roles.
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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/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658
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