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 .
Information Disclosure Statement
The filed information disclosure statement (IDS) is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-8, 13-16, 25, and 27-29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sporer (US 2022/0159403).
As per claim 1, Sporer teaches an audio device (Fig. 2) comprising: an electro-acoustic transducer for providing an audio output (Fig. 2, signal generator 150);
a set of microphones for detecting ambient sounds (Fig. 2, [0027], at least two received microphone signals of a hearing environment); and
a processor coupled with the electro-acoustic transducer and the set of microphones (Fig. 2), the processor configured to:
evaluate microphone signals from the set of microphones to identify classes of sound sources in the ambient sounds (Fig. 2, [0065], wherein the system includes an audio type classifier 130 for allocating an audio signal type to the audio source signal portion of each of the one or more audio sources); and
adjust output of at least one class of the ambient sounds relative to another class of ambient sounds based on a user input (Fig. 2, [0066], wherein the system includes a signal portion modifier 140 for varying the audio source signal portion of at least one audio source of the one or more audio sources depending on the audio signal type of the audio source signal portion of the at least one audio source so as to obtain a modified audio signal portion of the at least one audio source).
As per claim 2, Sporer teaches wherein the processor is configured to identify at least one of the following classes of sound sources: i) nearby voice, ii) alerts and sirens, iii) nearby transit, iv) out loud music, v) background sounds, vi) nature and animals ([0119], wherein the processor is configured to identify fire alarms) .
As per claim 3, Sporer teaches wherein the processor is configured to operate in a plurality of modes including two or more of: a) quiet mode, b) aware mode, c) safety mode, d) atmosphere mode, e) voice boost mode, or f) custom mode, wherein the processor is configured to automatically select one of the plurality of modes based on at least one of a contextual indicator or a usage indictor ([0047], [0092], [0221]- [0226], wherein said, People whose partner snores are disturbed in their nightly rest and have problems sleeping. The hearable provides relief, since it suppresses snoring sounds, ensures nightly rest, and provides domestic peace. At the same time, the hearable lets other sounds pass (a baby crying, alarms sounds, etc.) so that the user is not fully isolated acoustically from the outside world).
As per claim 4, Sporer teaches wherein adjusting the output includes separating at least two of the identified classes of sound source ([0084]- [0091] audio source and sound separation).
As per claim 5, Sporer teaches wherein the processor is configured to provide at least three interface options for sound source selection ([0092], [0119], wherein said, the user can modify the auditory scene with a user interface as illustrated in FIG. 9 or with any type of interaction such as speech control, gestures, sight direction, etc.).
As per claim 6, Sporer teaches wherein the interface options include a full manual control, whereby the user adjusts a plurality of classes of ambient sounds on a per-class basis (Fig. 9, wherein a user interface allows the user to amplify, reduce, or modify defined acoustical objects).
As per claim 7, Sporer teaches wherein the interface options include a modes-based control, whereby predefined mixes of class-based settings are provided to the user for selection ([0119], The user can modify the auditory scene with a user interface as illustrated in FIG. 9).
As per claim 8, Sporer teaches wherein the interface options include a natural language (NL) based control mode, whereby the at least one class of sound sources is selected by a user natural language command ([0119], speech control and speech recognition [0238]).
As per claim 13, Sporer teaches wherein the processor is configured to differentiate between user selection of ambient acoustic signals that include music from music playback at the audio device command ([0197]- [0198], [0210], wherein audio reproduction is adapted based on a user selection
As per claim 14, Sporer teaches wherein the user input is provided via a voice command ([0119], speech control).
As per claim 15, Sporer teaches wherein the user input is provided via a user profile command ([0074], the system may include a user interface 160 for selecting the previously learned user scenario from a group of two or more previously learned user scenarios).
As per claim 16, Sporer teaches wherein the user input is a default user input at startup of the audio device ([0237], wherein sounds and conversations of the passengers are suppressed acoustically by default (in other word, the user input is the default input)).
As per claim 25 Sporer teaches method of interfacing with a large language model (LLM) for sound source classification (Fig. 7), the method comprising: training the LLM by: capturing microphone signals including ambient sounds from a set of microphones at an audio device (Fig. 2, [0027], at least two received microphone signals of a hearing environment. See also [0189]- [0192]);
detecting classes of sound sources in the ambient sounds (Fig. 2, [0065], [0068], wherein the system includes an audio type classifier 130 for allocating an audio signal type to the audio source signal portion of each of the one or more audio sources); and
providing the microphone signals and sound source classifications to the LLM to aid in future classification of ambient sounds (Fig. 7, [0012], [0157]- [0160 (Fig. 7 and [0189]- [0196]).
As per claim 27, Sporer teaches providing contextual usage cues for the audio device with the sound source classifications and microphone signals ([0119], [0159]).
As per claim 28, Sporer teaches receiving audio device settings values from the LLM based on the user inputs (Fig. 7, [0012], [0157]- [0160 (Fig. 7 and [0189]- [0196]). Sporer may not explicitly disclose sending natural language (NL) prompts to the LLM associated with detected user inputs. Altaf in the same field of endeavor teaches a natural language processing system that performs classifications of NLP text, as derived from an audio speech signal. For instance, the computer may perform deepfake detection by applying a machine-learning architecture for deepfake or fraud detection, trained to detect deepfakes (e.g., machine-based utterances or audio data) based on textual content derived from speech signals. The NLP processes include Large Language Model (LLM) identification or classification ([0019], [0105], [0246]). Therefore, it would have been obvious at the time the application was filed to use the above features of Altaf with the system of Sporer, in order to identify the at least one class of sound sources based on the natural language input, as claimed. This would enhance the accuracy of audio recognition tasks.
As per claim 29, Sporer teaches wherein the user inputs include at least one of: i) contextual cues inferring user intent based on operation of the audio device, or ii) a user selection (Fig. 7 and [0238]).
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.
Claims 9-12, 17-24, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Sporer (US 2022/0159403) in view of Altaf (US 2024/0363103).
As per claim 17, Spore teaches an audio device (Fi. 2) comprising: an electro-acoustic transducer for providing an audio output (Fig. 2, signal generator 150);
a set of microphones for detecting ambient sounds (Fig. 2, [0027], at least two received microphone signals of a hearing environment); and
a processor coupled with the electro-acoustic transducer and the set of microphones (Fig. 2), the processor configured to:
receive a user command to adjust a control function at the audio device ([0119], receiving a speech command to control the auditory scene);
convert the user command into a natural language input ([0012], [0119], [0238], performing speech recognition on speech control commands);
provide input to a machine learning (ML) model and receive a formatted response indicating the control function from the ML model ]) for identifying the control function based on the natural language input (Fig. 7, [0012], [0157]- [0160 (Fig. 7 and [0189]- [0196]); and execute the control function at the audio device based on the formatted response ([0159], [0181], performing the variation of the audio source signal portion of the at least one audio source of the one or more audio sources depending on at least one of the one or more room acoustics parameters). Sporer may not explicitly disclose providing the natural language input to a machine learning (ML) model for identifying the control function based on the natural language input. Altaf in the same field of endeavor teaches a natural language processing system that performs classifications of NLP text, as derived from an audio speech signal. For instance, the computer may perform deepfake detection by applying a machine-learning architecture for deepfake or fraud detection, trained to detect deepfakes (e.g., machine-based utterances or audio data) based on textual content derived from speech signals. The NLP processes include Large Language Model (LLM) identification or classification ([0019], [0105], [0246]). Therefore, it would have been obvious at the time the application was filed to use the above features of Altaf with the system of Sporer, in order to identify the at least one class of sound sources based on the natural language input, as claimed. This would enhance the accuracy of audio recognition tasks.
As per claim 9, Sporer teaches wherein in the NL based control mode, the processor is configured to: convert the user natural language command into a natural language input ([0012], [0119], [0238], performing speech recognition on speech control commands), and providing input to a machine learning (ML) model ([0157]- [0160]). Sporer may not explicitly disclose providing the natural language input to a machine learning (ML) model for identifying the at least one class of sound sources based on the natural language input. Altaf in the same field of endeavor teaches a natural language processing system that performs classifications of NLP text, as derived from an audio speech signal. For instance, the computer may perform deepfake detection by applying a machine-learning architecture for deepfake or fraud detection, trained to detect deepfakes (e.g., machine-based utterances or audio data) based on textual content derived from speech signals. The NLP processes include Large Language Model (LLM) identification or classification ([0019], [0105], [0246]). Therefore, it would have been obvious at the time the application was filed to use the above features of Altaf with the system of Sporer, in order to identify the at least one class of sound sources based on the natural language input, as claimed. This would enhance the accuracy of audio recognition tasks.
As per claims 10 and 18, Sporer teaches wherein the processor is further configured to provide at least one of the following to the ML model: audio device context data about usage of the audio device, or a set of controllable attributes for the audio device ([0156]- [0159]).
As per claims 11 and 19, Sporer teaches wherein the set of controllable attributes are defined in terms of an application programming interface (API) (Fig. 9)
As per claims 12 and 20, Sporer may not explicitly disclose wherein the ML model includes a large language model (LLM). Altaf in the same field of endeavor teaches wherein the ML model includes a large language model (LLM) ([0019], [0179]). Therefore, it would have been obvious at the time the application was filed to use the above features of Altaf with the system of Sporer, in order to include a large language model (LLM) as a part of the ML, as claimed. This would enhance the accuracy of audio recognition tasks.
As per claim 21, Sporer teaches wherein the control function is selected from: audio class selection from ambient noise, playback control functions, transport control functions, active noise reduction (ANR) control functions, connectivity control functions, playback source control functions, or audio setting control functions ([0119], audio setting control function).
As per claim 22, Sporer teaches wherein the ML model is run at a device separate from the audio device ([0192], the machine learning block 131 may be part of the device of the user, or it may be part of a separate device).
As per claim 23 Sporer teaches wherein a version of the ML model is run locally at the audio device ([0192], the machine learning block 131 may be part of the device of the user, or it may be part of a separate device). Sporer may not explicitly disclose wherein the version of the ML model run locally at the audio device is a lightweight version of the ML model. However, the feature of having computing components/devices that are connected to other components/devices either physically and/or over a network for performing computing operations is well known in the art. Therefore, it would have been obvious at the time the application was filed for the system of Sporer in view of Altaf to have a version of the ML model run locally at the audio device is a lightweight version of the remote ML model, as claimed. This pairing of a lighter local model with a heavy remote model creates a hybrid ML architecture that saves on network transmission costs.
As per claim 24 Sporer teaches wherein the user command includes a sound source class selection, and wherein the processor is further configured to: evaluate microphone signals from the set of microphones to identify classes of sound sources in the ambient sounds; and adjust output of at least one class of the ambient sounds relative to another class of ambient sounds based on the sound source class selection (Fig. 2, [0027], [0065], [0066]).
As per claim 26, Sporer may not explicitly disclose providing natural language (NL) prompts to the LLM associated with the sound source classifications. Altaf in the same field of endeavor teaches a natural language processing system that performs classifications of NLP text, as derived from an audio speech signal. For instance, the computer may perform deepfake detection by applying a machine-learning architecture for deepfake or fraud detection, trained to detect deepfakes (e.g., machine-based utterances or audio data) based on textual content derived from speech signals. The NLP processes include Large Language Model (LLM) identification or classification ([0019], [0105], [0246]). Therefore, it would have been obvious at the time the application was filed to use the above features of Altaf with the system of Sporer, in order to identify the at least one class of sound sources based on the natural language input, as claimed. This would enhance the accuracy of audio recognition tasks.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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.
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/ABDELALI SERROU/Primary Examiner, Art Unit 2659 05/29/2026
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