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 Amendment
2. In response to the office action mailed on 12/11/2025, applicant filed an amendment on 01/23/2026, amending claims 1 and 11-13. The pending claims are 1-20.
Response to Arguments
3. Applicant's arguments filed on 01/23/2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art Novitasari does not teach “adding a particular short segment to a processing queue, and wherein, if speech is not detected, declining to add the particular segment to the processing queue, reducing unnecessary processing” and “filtering out non-speech segments to reduce computational waste, focusing resources on relevant audio data and minimizing latency”.
Applicant asserts that the VAD output in Novitasari influences feature representations within the encoder but does not control admission of audio segments into downstream processing. The examiner notes that “a processing queue”, as claimed, does not specify whether the processing queue is within the encoder or outside of the encoder. Furthermore, applicant is referred to Novitasari’s paragraph [0071], wherein said, the VAD integrated system 100 can include the VAD model 204 integrated at a pre-encoder position (see, e.g., FIG. 2A) and/or a post-encoder position (see, e.g., FIG. 2B); and [0080]- [0081], wherein the VAD model receives short segments of speech only, non-speech speech only, and speech and non-speech segments.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the language, of paragraph [0026] of Novitasari, describes the concept of excluding non-speech content from ASR processing. However, the paragraph does not describe how such removal is implemented, ….) are not recited in the rejected claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Furthermore, applicant is referred to Novitasari’s paragraph [0071]- [0072].
Applicant argues that the reference does not identify computational waste or latency as problem to be addressed.
The claims recite “filtering out non-speech segments to reduce computational waste, focusing resources on relevant audio data and minimizing latency”, the examiner notes even though, reducing computational waste, focusing resources on relevant audio data and minimizing latency is claimed as an intended result, Novitasari teaches maintaining performance by extracting/processing necessary speech parts and removing unnecessary non-speech parts and long silence regions from input audio signals during inference, improving robustness of speech recognition in noisy conditions ([0030]- [0031], Fig. 7, [0080]).
As per the rest of the claims, and combinations of prior art reference, applicant has no further arguments beside the ones mentioned above. Therefore, all the combinations of prior art reference mentioned above are valid, and all other claims are rejected for the same reasons as set above.
Claim Rejections - 35 USC § 102
4. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 7-9, and 13-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Novitasari (US 2024/0038221).
As per claims 1 and 13, Novitasari teaches continuously capturing an audio data and segment it into short segments ([0026]- [0029], [0071], [0101], Figs. 2A and 2B, capturing audio signals and segmenting the segments into a sequence of frames);
implementing a pre-trained enterprise-grade voice activity detection (“VAD”) system on each of the short segments ([0026], wherein said, the automatic speech recognition (ASR) systems can be deployed together with a voice activity detection (VAD) system to run ASR on the voiced acoustic signals), wherein, if speech is detected, then adding a particular short segment to a processing queue, and wherein, if speech is not detected, declining to add the particular segment to the processing queue, reducing unnecessary processing (in addition to [0026]- [0028], the ASR system can maintain ASR performance by extracting/processing necessary speech parts and removing unnecessary non-speech parts and long silence regions from input audio signals during inference, see paragraph [0071], wherein said, the VAD integrated system 100 can include the VAD model 204 integrated at a pre-encoder position (see, e.g., FIG. 2A) and/or a post-encoder position (see, e.g., FIG. 2B); and [0080]- [0081], wherein the VAD model receives short segments of speech only, non-speech speech only, and speech and non-speech segments ); and
filtering out non-speech segments to reduce computational waste, focusing resources on relevant audio data and minimizing latency ([0026]- [0028], maintaining performance by extracting/processing necessary speech parts and removing unnecessary non-speech parts and long silence regions from input audio signals during inference and improving robustness of speech recognition in noisy conditions [0030]- [0031] and Fig. 7).
As per claims 2 and 14, Novitasari teaches applying VAD again to queued audio to eliminate any residual at least one noise and silence, refining the audio data further ([0026]- [0028], the ASR system can maintain ASR performance by extracting/processing necessary speech parts and removing unnecessary non-speech parts and long silence regions from input audio signals during inference); and
stitching together cleaned audio segments to form a coherent audio stream without gaps, wherein this refined, continuous audio stream is more representative of natural speech, improving the accuracy and effectiveness of subsequent machine learning processes (removing unnecessary non-speech parts and long silence regions and concatenating speech frames [0071]- [0073]).
As per claims 3 and 15, Novitasari teaches organizing the coherent audio stream into segments and pad them to uniform lengths to fit the expected input format for the transcription model; and enhancing an efficiency of deep learning models by reducing variability in input data [0071]- [0073], wherein said, the VAD model 204 can predict the sequence of voice activity class v=(v.sub.1, . . . , v.sub.T) of length T from a speech frame sequence x with the same length. The VAD integration system 100 can concatenate features between the VAD output probability p(v|x) and the ASR feature of the corresponding speech frame.
As per claims 7-9, system claims 7-9 and method claims 1-3 are related as apparatus and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 7-9 are similarly rejected under the same rationale as applied above with respect to method claims 1-3. Furthermore, Novitasari teaches one or more processors; and memory storing thereon instructions, as claimed ([0111]- [0114]).
Claim Rejections - 35 USC § 103
5. 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.
Claims 4-6, 10-12, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Novitasari (US 2024/0038221) in view of Hiray (US 20240221721).
As per claim 4, Novitasari teaches transforming the input data into a transcribed text ([0095] text decoded by standard RNN-T of the speech recognition system). Novitasari may not explicitly disclose automatically detecting the language of the transcribed text, facilitating targeted translation processes; and translating the transcribed text into the desired language as a translated text using a robust language model from open-source libraries, supporting multiple language pairs, wherein the multiple language pair is an identifier that describes a combination of multiple languages as used in the translation process; and converting the translated text back into speech to provide auditory feedback, enhancing accessibility for users who may not be able to read text conveniently. Hiray in the same field of endeavor teaches a multi-language translation system (“MHLTS”) that detects and translates between different spoken languages in real-time, and automatically adapt to users speaking different languages during the presentation, conversation, or conference, and that convert, translate, and/or transcribe the audio from each spoken language to text or audio of a desired target language ([0012]). Converting the translated text back into speech is well known in the and suggested by paragraph [0012], wherein said, transcribing the audio from each spoken language to text or audio of a desired target language, and necessarily disclosed by the process of paragraph [0013], wherein said the MLTS allows the conference participants to speak in different native languages (e.g., different languages they are most comfortable with that other participants may not understand), and translates the spoken dialog to one or more target languages selected by each conference participant. Therefore, it would have been obvious at the time the application was filed to use the above features of Hiray with the system of Novitasari, in order to provide further technological benefit of a multilingual conferencing solution for large numbers of participants speaking different languages ([0016]).
As per claim 5, Novitasari may not explicitly disclose processing audio data without waiting for long recordings to end to enable live translation and responsive voice-activation. Hiray in the same field of endeavor teaches a multi-language translation system for detecting and translating between different spoken languages in real-time as one or more users switch between the different languages during an online or computer-hosted presentation, conversation, or conference ([0012]), providing a real-time transcription of the dialog as it is spoken ([0014]), and [0016], wherein said the system performs a real-time translation and/or transcription of the dialog within each conference. Therefore, it would have been obvious at the time the application was filed to use the above features of Hiray with the system of Novitasari, in order to provide further technological benefit of a multilingual conferencing solution for large numbers of participants speaking different languages ([0016]).
As per claim 6, Novitasari may not explicitly disclose wherein each short segment is optimized to fall between 250ms and 500ms to allow a system to handle audio data almost instantaneously. Hiray in the same field of endeavor teaches processing speech segments that are 300 ms (0.3 second), see paragraph [0023]. Therefore, it would have been obvious at the time the application was filed to use the above length feature of Hiray with the system of Novitasari, in order to improve accuracy, make it easier to correct errors, and enhance efficiency.
As per claims 10-12, system claims 10-12 and method claims 4-6 are related as apparatus and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 10-12 are similarly rejected under the same rationale as applied above with respect to method claims 4-6. Furthermore, Novitasari teaches one or more processors; and memory storing thereon instructions, as claimed ([0111]- [0114]).
As per claims 16-20, the claims are similarly rejected under the same rationale as applied above with respect to claims 4-6.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDELALI SERROU whose telephone number is (571)272-7638. The examiner can normally be reached M-F 9 Am - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre-Louis Desir can be reached at 571-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ABDELALI SERROU/Primary Examiner, Art Unit 2659
Prosecution Insights