DETAILED ACTION
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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 Patent No. 11,468,893 B2.
Although the conflicting claims are not identical, they are not patentably distinct from each other because claims in the continuations are broader than the ones in patent, broad claims in the continuation application are rejected previously patented narrow claims. For example, claim 1 of the present invention is the same as claim 1 of Patent No. 11,468,893 B2 except that “whether to continue providing, for output at the corresponding computing device of the user.” Therefore, claim 1 of the present invention is broader than the patented claim 1.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4, 7-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S Pub. No. 2012/0101820 A1 to Ljolje in view of U.S Pub. No. 2018/0301151 A1 to Mont-Reynaud et al. (hereinafter “Mont-Reynaud”).
Regarding claim 1, Ljolje teaches a method implemented by one or more processors, the method comprising:
monitoring a conversation between a user and a bot at a computing device (paragraphs [0003] and [0047]; a multi-state barge-in-model and its use in handling barge-in speech in a spoken dialog with an automated system); and
while monitoring the conversation between the user and the bot at the computing
device (Abstract and paragraph [0047]; presenting a prompt to the user from a spoken dialog system (902), receiving an audio speech input from the user during the presentation of the prompt (904), accumulating this audio speech input from the user (906), applying a non-speech component having at least two one-Hidden Markov Models (HMMs) to the audio speech input (908), applying a speech component having at least five 3-state HMMs to the audio speech input from the user wherein each of the five 3-state HMMs represent a different phonetic category (910), determining whether the audio speech input is a barge-in speech input from the user (912), and if the audio speech input is determined to be the barge-in speech input from the user, terminating the presentation of the prompt (914)):
receiving, from the user, a user utterance that interrupts synthesized
speech being rendered by the bot (Abstract, paragraphs [0007] and [0047]; presenting a prompt to the user from a spoken dialog system (902), receiving an audio speech input from the user during the presentation of the prompt (904), accumulating this audio speech input from the user (906), applying a non-speech component having at least two one-Hidden Markov Models (HMMs) to the audio speech input (908), applying a speech component having at least five 3-state HMMs to the audio speech input from the user wherein each of the five 3-state HMMs represent a different phonetic category (910), determining whether the audio speech input is a barge-in speech input from the user (912), and if the audio speech input is determined to be the barge-in speech input from the user, terminating the presentation of the prompt (914));
in response to receiving the user utterance that interrupts the synthesized
speech being rendered by the bot, classifying the received user utterance as a
given type of interruption, the given type of interruption being one of multiple
disparate types of interruptions, the multiple disparate types of interruptions
including at least: a non-meaningful interruption, a non-critical meaningful
interruption, and a critical meaningful interruption (Abstract, paragraphs [0037], [0047]- [0048]; wherein each of the five 3-state HMMs represent a different phonetic category (910), determining whether the audio speech input is a barge-in speech input from the user (912), and if the audio speech input is determined to be the barge-in speech input from the user, terminating the presentation of the prompt (914)).
However, Ljolje does not explicitly teach determining, based on the given type of interruption, whether to continue providing, for output at the computing device or an additional computing device, the synthesized speech of the bot that was being provided when the user utterance that interrupts the synthesized speech the bot is received.
In the same field of endeavor, Mont-Reynaud discloses determining, based on the given type of interruption, whether to continue providing, for output at the computing device or an additional computing device, the synthesized speech of the bot that was being provided when the user utterance that interrupts the synthesized speech the bot is received (Abstract, paragraphs [0015], [0072]- [0082] virtual Agents engage and disengage with users intelligently. Users can tell agents to remain engaged without requiring a wakeword. Engaged states can support modal dialogs and barge-in. Users can cause disengagement explicitly. Disengagement can be conditional based on timeout, change of user, or environmental conditions. Engagement can be one-time or recurrent. Recurrent states can be attentive or locked. Locked states can be unconditional or conditional, including being reserved to support user continuity).
At the time of the effective filing date of the invention, it would have been obvious to a person of ordinary skilled in the art to modify Ljolje’s teaching with a feature of determining, based on the given type of interruption, whether to continue providing, for output at the computing device or an additional computing device, the synthesized speech of the bot that was being provided when the user utterance that interrupts the synthesized speech the bot is received as taught by Mont-Reynaud in order to improve dialog naturalness and efficiency (paragraph [0002]; Mont-Reynaud).
Regarding claim 2, Ljolje teaches the method of claim 1, wherein the given type of interruption is the non-meaningful interruption, and wherein classifying the received user utterance as the non-meaningful interruption comprises:
processing audio data corresponding to the received user utterance or a
transcription corresponding to the received user utterance to determine that the
received user utterance includes one or more of: background noise, affirmation words
or phrases, or filler words or phrases; and classifying the received user utterance as the non-meaningful interruption based on determining that the received user utterance includes one or more of: background noise, affirmation words or phrases, or filler words or phrases (Abstract, paragraphs [0037], [0047]- [0048]; wherein each of the five 3-state HMMs represent a different phonetic category (910), determining whether the audio speech input is a barge-in speech input from the user (912), and if the audio speech input is determined to be the barge-in speech input from the user, terminating the presentation of the prompt (914)).
Regarding claim 3, Ljolje teaches the method of claim 2, wherein determining whether to continue providing the synthesized speech of the bot comprises:
determining to continue providing the synthesized speech of the bot based on
classifying the received user utterance as the non-meaningful interruption (Abstract, paragraphs [0037], [0047]- [0048]; wherein each of the five 3-state HMMs represent a different phonetic category (910), determining whether the audio speech input is a barge-in speech input from the user (912), and if the audio speech input is determined to be the barge-in speech input from the user, terminating the presentation of the prompt (914)).
Regarding claim 4, Ljolje teaches the method of claim 1, wherein the given type of interruption is the non-critical meaningful interruption, and wherein classifying the received user utterance as the non-critical meaningful interruption comprises:
processing audio data corresponding to the received user utterance or a
transcription corresponding to the received user utterance to determine that the
received user utterance includes a request for information that is known by the bot, and
that is yet to be provided; and classifying the received user utterance as the non-critical meaningful interruption based on determining that the received user utterance includes the request for the information that is known by the bot, and that is yet to be provided (Abstract, paragraphs [0037], [0047]- [0048]; wherein each of the five 3-state HMMs represent a different phonetic category (910), determining whether the audio speech input is a barge-in speech input from the user (912), and if the audio speech input is determined to be the barge-in speech input from the user, terminating the presentation of the prompt (914)).
Regarding claim 7, Ljolje teaches the method of claim 1, wherein the given type of interruption is the critical meaningful interruption, and wherein classifying the received user utterance as the critical meaningful interruption comprises:
processing audio data corresponding to the received user utterance or a
transcription corresponding to the received user utterance to determine that the
received user utterance includes a request for the bot to repeat the synthesized speech
or a request to place the bot on hold; and classifying the received user utterance as the non-critical meaningful interruption based on determining that the received user utterance includes the request for the bot to repeat the synthesized speech or the request to place the bot on hold (Abstract and paragraphs [0006] and [0047]- [0048]; AD application tries to detect every non-speech segment within a continuous utterance, for example, a short pause. Another application, most commonly encountered in automatic speech recognition (ASR) applications is the problem of endpointing. This is important when detecting the beginning and the end of an utterance, the ASR system is relied on to internally determine if there are any utterance internal pauses).
Regarding claim 8, Ljolje teaches the method of claim 7, wherein determining whether to continue providing the synthesized speech of the bot comprises:
providing, for output, a remainder portion of a current word or term of the
synthesized speech of the bot; and after providing, for output, the remainder portion of the current word or term, ceasing to provide, for output, the synthesized speech of the bot (paragraphs [0026], [0037] and [0049]; model views different HMMs as the phoneme inventory, and any phoneme sequence is a valid alternative pronunciation, as long as the model uses only speech HMMs for the speech "word", and non-speech HMMs for the non-speech "word". The other approach is to think of the HMMs as the words, where any word sequence of the speech "words" during the speech segment is valid, and similarly any sequence of non-speech "words" is valid during the non-speech segment. In practice, this makes little difference as the training process ends up doing the same steps).
Regarding claim 9, Ljolje teaches the method of claim 1, wherein classifying the received user utterance as the given type of interruption comprises: processing audio data corresponding to the received user utterance or a transcription corresponding to the received user utterance using a machine learning model to determine the given type of interruption (paragraphs [0026] and [0049]; ASR module 102 may analyze speech input and may provide a transcription of the speech input as output. SLU module 104 may receive the transcribed input and may use a natural language understanding model to analyze the group of words that are included in the transcribed input to derive a meaning from the input).
Regarding claim 10, Ljolje teaches the method of claim 1, wherein the bot is implemented locally at the computing device (paragraphs [0024]- [0025]; a spoken dialogue system having acceptable accuracy rates for the domain of information and conversation associated with the enterprise. Accordingly, as used herein, the term "the system" will refer to any computer device or devices that are programmed to function and process the steps of the method).
Regarding claim 11, Ljolje teaches the method of claim 1, wherein the bot is implemented remotely from the computing device (paragraphs [0024]- [0025]; a spoken dialogue system having acceptable accuracy rates for the domain of information and conversation associated with the enterprise. Accordingly, as used herein, the term "the system" will refer to any computer device or devices that are programmed to function and process the steps of the method).
Claims 12-15 and 18-19 a system claims correspond to method claims 1-4 and 7-8. Therefore, claims 12-15 and 18-19 have been analyzed and rejected based on method claims 1-4 and 7-8.
Claim 20 is a non-transitory claim correspond to method claim 1. Therefore, claim 20 has been analyzed and rejected based on method claim 1.
Allowable Subject Matter
Claims 5-6 and 16-17 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
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AKELAW TESHALE
Primary Examiner
Art Unit 2694
/AKELAW TESHALE/Primary Examiner, Art Unit 2694
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