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 .
Election/Restrictions
Applicant’s election without traverse of claims of claims 1 – 24 in the reply filed on 08/04/26 is acknowledged.
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.
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Claims 1 – 24 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 2, 4 – 9, 11 – 17, 18 – 20 of copending Application No. 18/585,587 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1 - 24 of the instant application are similar in scope and content of the claims of the copending application.
It would have been obvious to an artisan at the time the invention was made to use the teaching of claims 1, 2, 4 – 9, 11 – 17, 18 – 20 of the '587' copending application as a general teaching for detecting oral commands using LLM, to perform method as claimed in the present invention. The instant claims obviously encompass the claimed invention of the '587' copending application and differ only in the method steps. The extent that the instant claims are broaden and therefore generic to claimed invention of '587' copending application [species], In re Goodman 29 USPQ 2d 2010 CAFC 1993, states that a generic claim cannot be issued without a terminal disclaimer, if a species claim has been previously been claimed in a co- pending application. And since the structure is as recited, the method step is obtained and therefore, obvious.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Here is a comparison between claim 4 of the instant application and claim 1 of the copending application.
Instant Application 18/927,569
Copending Application 18/585,587
Comparison
4. A computer-implemented method, comprising:
1. A computer-implemented method, comprising:
Same
implementing an audio, video and control ("AVC") operating system on an AVC processing core communicably coupled to one or more peripheral devices, the AVC processing core being configured to manage and control functionality of the peripheral devices,
implementing an audio, video and control ("AVC") operating system on an AVC processing core communicably coupled to one or more peripheral devices, the AVC processing core being configured to manage and control functionality of audio, video and control features of the peripheral devices;
Similar
wherein the AVC operating system is adapted to perform actions using oral commands detected within audio signals, the oral commands corresponding to the actions; detecting, using a large language model ("LLM") module communicably coupled to the AVC processing core, one or more audio signals obtained from a user;
detecting, using a large language model ("LLM") module communicably coupled to the AVC processing core, one or more oral commands issued from a user; and
Similar
inferring, using the LLM module, one or more oral commands from the audio signals; and performing actions on the peripheral devices or AVC processing core which correspond to the oral commands.
performing actions on the peripheral devices or AVC processing core which correspond to the oral commands,
Similar
wherein the LLM module executes a taught command set to perform actions on the peripheral devices, the taught command set being taught by the user.
wherein the LLM module executes a taught command set to perform actions on the peripheral devices, the taught command set being taught by the user.
Same
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.
Claims 1 – 24 are rejected under 35 U.S.C. 103 as being unpatentable over Nighman et al. (US PAP 2023/0115674)in view of Papayiannis et al. (US PAP 2025/0104693).
As per Claims 1, 9 and 17, Nighman et al. discloses a computer-implemented method, a system and a non- transitory computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations, comprising (title, abstract, Multi-source audio processing systems and methods; para 0025, the techniques described herein relate to a non-transitory computer readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations):
implementing an audio, video and control ("AVC") operating system on an AVC processing core communicably coupled to one or more peripheral devices, the AVC processing core being configured to manage and control functionality of the peripheral devices (“Some modern systems incorporate integrated audio, video, and control (AV&C) capability to provide an integrated system architecture. An example of such a system is the QSC® Q-SYS™ Ecosystem provided by QSC, LLC, which provides a scalable software-based platform… the second network channel 139 can transmit command information for controlling the deployed devices 110 and/or audio and video content for playback by the deployed devices 110. With respect to audio and video content, the second network channel 139 can transmit to the deployed devices 110, without limitation”; Paragraphs 74 – 76, 88);
wherein the AVC operating system is adapted to perform actions using oral commands detected within audio signals, the oral commands corresponding to the actions (“a detailed view of the speech source processor 306 according to certain embodiments. The speech source processor 306 receives signals corresponding the speech point sources 322 from the source classifier 304… The speech source processor 306 can include a voice command processing engine 332 configured to process the supplemented point speech source stream 322, e.g., to perform an AI “wake word” scan on the speech sources, and select and map sources for voice command services.”; Paragraphs 117 - 120);
detecting, using a language model communicably coupled to the AVC processing core, one or more audio signals obtained from a user (“the voice command processing engine 332 can identify utterances of pre-determined wake words, extract commands from speech uttered subsequent to the wake word for controlling a voice command service, such as a cloud-based voice command service (e.g., Amazon Alexa, Siri, etc.), and/or embed corresponding metadata to the point speech source stream 322. The voice command processing engine 332 can additionally format the point speech source stream 322 for compatibility with an external voice command service.”; Paragraphs 120 - 123),
inferring, using the [speech source processor] module, one or more oral commands from the audio signals (“the voice command processing engine 332 can identify utterances of pre-determined wake words, extract commands from speech uttered subsequent to the wake word for controlling a voice command service, such as a cloud-based voice command service… The speech source processor 306 can also include a natural language processor (NLP) 348, which can be configured to combine rule-based human language modeling with machine learning to process and "understand" the speech source content. The NLP 348 can perform a variety of NLP tasks including speech-to-text, part of speech tagging, word sense disambiguation, named entity recognition, emotion or sentiment analysis, or the like.”; Paragraphs 120 – 123); and
performing actions on the peripheral devices or AVC processing core which correspond is to the oral commands (“The speech source processor 306 can additionally include a voice communications processing engine 336. The voice communications processing engine 336 can also filter out certain other speech content. As an example, where a conference call user utters a wake word followed by a command (e.g., "Alexa, turn up the volume."), the voice command processing engine 336 may exclude the utterance from voice data sent to the network channel 139 for broadcast to the other users via one or more of the loudspeakers 150, third party devices 180, or other deployed devices 110”; Paragraphs 120 -123).
However, Nighman et al. do not specifically teach detecting, using a large language model ("LLM") module, one or more audio signals obtained from a user; inferring, using the LLM module, data from the audio signals.
Papayiannis et al. disclose that the system may be configured to process the ASR data or other input text data using one or more language models (e.g., one or more large language models (LLMs)) to generate a natural language response to the user input and prosody information corresponding to one or more voice characteristics associated with the natural language response (paragraph 18).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to use a LLM to detect oral command as taught by Papayiannis et al. in Nighman et al., because that would help provide a system capable of using LLM(s) to generate a natural language response to a use (paragraph 22).
As per Claims 2, 10 and 18, Nighman et al., in view of Papayiannis et al. further disclose analyzing, using the LLM, the audio signals to determine which oral commands the audio signals most closely match; and performing actions on the peripheral devices or AVC processing core which correspond to the matched oral commands (“The speech samples can be compared against multiple voice prints to determine best match, such as the voice prints stored in memory in the voice fingerprint repository 359, which can be stored in the storage 121, network storage 131, or other appropriate location. In some embodiments, the voice biometrics engine 357 can implement text-dependent recognition, such as by using a common pass phrase or other prompt to enroll and verify talkers, or text-independent recognition, where speakers are identified with a speech recognition algorithm that does not rely on a talker's conscious knowledge or prompts for enrollment.”; Nighman et al., paragraphs 117 – 123; see also “process the ASR data or other input text data using one or more language models (e.g., one or more large language models (LLMs)) to generate a natural language response to the user input”; Papayiannis et al. paragraphs 18 - 22).
As per Claims 3, 11 and 19, Nighman et al., in view of Papayiannis et al. further disclose the LLM module executes a preconfigured command set to perform actions on the peripheral devices (Nighman et al., paragraphs 117 – 123; Papayiannis et al. paragraphs 18 – 22).
As per Claims 4, 12 and 20, Nighman et al., in view of Papayiannis et al. further disclose the LLM module executes a taught command set to perform actions on the peripheral devices, the taught command set being taught by the user (“The voice biometries engine 330 can assign unique acoustic speech signatures to each speaker. For instance, the voice biometries engine 330 can perform enrollment and verification phases [user taught] to record and extract a number of features from a voice print for different talkers and then compare speech samples in the separated sources in the point speech source stream 322 against the previously created voice print.”; Nighman et al., paragraphs 118 – 123; see also Papayiannis et al. paragraph 140).
As per Claims 5, 6, 13, 14, 21, 22, Nighman et al., in view of Papayiannis et al. further disclose the taught command set is obtained from the user via a web interface; the taught command set is obtained from the user via a listening device (“The first network channel 137 communicates data from the deployed devices 110 to the processing core 120. For instance, the first network channel 137 can transmit to the processing core 120, without limitation: streams of raw or processed audio data from the microphones 140; streams of raw or processed video data captured by the cameras 160; touch screen or other user interface input data or other control data from the control devices 170; and video and audio data streams from the party devices 180…The voice biometrics engine 330 can assign unique acoustic speech signatures to each speaker. For instance, the voice biometrics engine 330 can perform enrollment and verification phases [user taught] to record and extract a number of features from a voice print for different talkers and then compare speech samples in the separated sources in the point speech source stream 322 against the previously created voice print.”; Nighman et al., paragraphs 87, 118 – 123).
As per Claims 7, 15 and 23, Nighman et al., in view of Papayiannis et al. further disclose the AVC processing core communicates the taught command set to one or more secondary AVC processing cores, thereby teaching the secondary AVC processing cores the taught command set (“The services 114 can be in communication with the processing core 120 and/or deployed devices 110 via the Internet or another wide or local area network, and can include audio processing services such as cloud/edge voice command processing services, transcription services, or artificial intelligence processing…The speech source processor 306 can include a voice command processing engine 332 configured to process the supplemented point speech source stream 322, e.g., to perform an AI “wake word” scan on the speech sources, and select and map sources for voice command services.”; paragraphs 25, 77, 120 – 123).
As per Claims 8, 16 and 24, Nighman et al., in view of Papayiannis et al. further disclose the LLM module is accessed from a cloud service, local network service, or on the AVC processing core (“The services 114 can be in communication with the processing core 120 and/ or deployed devices 110 via the Internet or another wide or local area network, and can include audio processing services such as cloud/edge voice command processing services, transcription services, or artificial intelligence processing”; Nighman et al., paragraph 77; Papayiannis et al. paragraphs 18 -22).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Qiu et al. teach an LLM designed to process, understand, and generate multi-modal data including audio, text, image, and/or video. Fan et al. teach natural language processing using LLM model. Mechlowicz teaches Large Language Model-Based AI-Powered Interactive Doll.
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/LEONARD SAINT-CYR/ Primary Examiner, Art Unit 2658
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