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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/03/2026 was filed after the mailing date of the application on 10/25/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement filed 07/01/2025; 10/25/2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered.
Claim Objections
Claims 1-8 are objected to because of the following informalities: claim recites “… the method…” instead of the “... the computer-implemented method”. Appropriate correction is required.
Claims 18-24 are objected to because of the following informalities: the claim recites “The computer-readable storage medium…” should be rewritten as “The non-transitory computer-readable storage medium…”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13, 15, 21, 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 13, 15, 21, 23 recite the limitation “the method”. There is insufficient antecedent basis for this limitation in the claims.
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.
Claim(s) 1-2, 9-10, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engi et al (US 2022/0417364 A1) in view of Vaughn et al (US 2018/0336000 A1).
Regarding claim 1, Engi et al disclose a computer-implemented method to generate a machine-learning model for a meeting room environment (Engi et al; Para [0040]), the method comprising: implementing an audio optimization and control (“AOC”) operating system on a processing core communicably coupled to one or more acoustic sensors and an audio-visual system, the processing core being configured to optimize and control audio functionality of the audio-visual system (Engi et al; Fig 2; audio optimization process 248; Para [0040][0043]); capturing a first set of acoustic signals using the one or more acoustic sensors (Engi et al; Para [0085]; microphone interpreted as acoustic sensor); but do not expressly disclose contextualizing the first set of acoustic signals; classifying the first set of acoustic signals; designating, based on one or both of the contextualization or classification, at least one of the first set of acoustic signals as a target signal; and training, based on the target signal, at least one machine-learning model to identify the target signal, thereby generating the machine-learning model for the meeting room environment. However, in the same field of endeavor, Vaughn et al disclose a method further comprising: contextualizing the first set of acoustic signals (Vaughn et al; Para [0028][0051]); classifying the first set of acoustic signals (Vaughn et al; Para [0028][0051]); designating, based on one or both of the contextualization or classification, at least one of the first set of acoustic signals as a target signal (Vaughn et al; Para [0055]-[0057]; compare identified and classified context to selected action interpreted as target signal); and training, based on the target signal, at least one machine-learning model to identify the target signal, thereby generating the machine-learning model for the meeting room environment (Vaughn et al; Para [0056][0057]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]).
Regarding claim 2, Engi et al in view of Vaughn et al disclose the computer-implemented method as defined in claim 1, but do not expressly disclose further comprising: implementing the machine-learning model on the processing core; capturing, using the one or more acoustic sensors, a second set of acoustic signals; matching, using the processing core, at least one of the signals in the second set of acoustic signals to the target signal; processing the matched signal to thereby optimize acoustics of the audio-visual system. However, in the same field of endeavor, Vaughn et al disclose a method further comprising: implementing the machine-learning model on the processing core (Vaughn et al; Para [0026][0155]-[0157]); capturing, using the one or more acoustic sensors, a second set of acoustic signals (Vaughn et al; Para [0067]); matching, using the processing core, at least one of the signals in the second set of acoustic signals to the target signal (Vaughn et al; Para [0067]); processing the matched signal to thereby optimize acoustics of the audio-visual system (Vaughn et al; Para [0072][0080]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]).
Regarding claim 9, Engi et al disclose a system for generating a machine-learning model for a meeting room environment (Engi et al; Para [0040]), the system comprising: one or more acoustic sensors (Engi et al; Para [0085]; microphone interpreted as acoustic sensor); and an audio optimization and control (“AOC”) processing core communicably coupled to the one or more acoustic sensors and an audio-visual system, the AOC processing core having an AOC operating system executable thereon to optimize and control audio functionality of the audio-visual system (Engi et al; Fig 2; audio optimization process 248; Para [0040][0043]), wherein the AOC processing core is configured to perform operations comprising: capturing a first set of acoustic signals using the one or more acoustic sensors (Engi et al; Para [0085]; microphone interpreted as acoustic sensor); but do not expressly disclose contextualizing the first set of acoustic signals; classifying the first set of acoustic signals; designating, based on one or both of the contextualization or classification, at least one of the first set of acoustic signals as a target signal; and training, based on the target signal, at least one machine-learning model to identify the target signal, thereby generating the machine-learning model for the meeting room environment. However, in the same field of endeavor, Vaughn et al disclose a system further comprising: contextualizing the first set of acoustic signals (Vaughn et al; Para [0028][0051]); classifying the first set of acoustic signals (Vaughn et al; Para [0028][0051]); designating, based on one or both of the contextualization or classification, at least one of the first set of acoustic signals as a target signal (Vaughn et al; Para [0055]-[0057]; compare identified and classified context to selected action interpreted as target signal); and training, based on the target signal, at least one machine-learning model to identify the target signal, thereby generating the machine-learning model for the meeting room environment (Vaughn et al; Para [0056][0057]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]).
Regarding claim 10, Engi et al in view of Vaughn et al disclose the system as defined in claim 9, but do not expressly disclose further comprising: implementing the machine-learning model on the processing core; capturing, using the one or more acoustic sensors, a second set of acoustic signals; matching, using the processing core, at least one of the signals in the second set of acoustic signals to the target signal; and processing the matched signal to thereby optimize acoustics of the audio-visual system. However, in the same field of endeavor, Vaughn et al disclose a method further comprising: implementing the machine-learning model on the processing core (Vaughn et al; Para [0026][0155]-[0157]); capturing, using the one or more acoustic sensors, a second set of acoustic signals (Vaughn et al; Para [0067]); matching, using the processing core, at least one of the signals in the second set of acoustic signals to the target signal (Vaughn et al; Para [0067]); processing the matched signal to thereby optimize acoustics of the audio-visual system (Vaughn et al; Para [0072][0080]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]).
Regarding claim 17, Engi et al disclose a non-transitory computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations (Engi et al; Para [0037]) comprising: implementing an audio optimization and control (“AOC”) operating system on a processing core communicably coupled to one or more acoustic sensors and an audio-visual system, the processing core being configured to optimize and control audio functionality of the audio-visual system (Engi et al; Fig 2; audio optimization process 248; Para [0040][0043]); capturing a first set of acoustic signals using the one or more acoustic sensors (Engi et al; Para [0085]; microphone interpreted as acoustic sensor); but do not expressly disclose contextualizing the first set of acoustic signals; classifying the first set of acoustic signals; designating, based on one or both of the contextualization or classification, at least one of the first set of acoustic signals as a target signal; and training, based on the target signal, at least one machine-learning model to identify the target signal, thereby generating the machine-learning model for the meeting room environment. However, in the same field of endeavor, Vaughn et al disclose a method further comprising: contextualizing the first set of acoustic signals (Vaughn et al; Para [0028][0051]); classifying the first set of acoustic signals (Vaughn et al; Para [0028][0051]); designating, based on one or both of the contextualization or classification, at least one of the first set of acoustic signals as a target signal (Vaughn et al; Para [0055]-[0057]; compare identified and classified context to selected action interpreted as target signal); and training, based on the target signal, at least one machine-learning model to identify the target signal, thereby generating the machine-learning model for the meeting room environment (Vaughn et al; Para [0056][0057]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]).
Regarding claim 18, Engi et al in view of Vaughn et al disclose the computer-readable storage medium as defined in claim 17, but do not expressly disclose further comprising: implementing the machine-learning model on the processing core; capturing, using the one or more acoustic sensors, a second set of acoustic signals; matching, using the processing core, at least one of the signals in the second set of acoustic signals to the target signal; processing the matched signal to thereby optimize acoustics of the audio-visual system. However, in the same field of endeavor, Vaughn et al disclose a method further comprising: implementing the machine-learning model on the processing core (Vaughn et al; Para [0026][0155]-[0157]); capturing, using the one or more acoustic sensors, a second set of acoustic signals (Vaughn et al; Para [0067]); matching, using the processing core, at least one of the signals in the second set of acoustic signals to the target signal (Vaughn et al; Para [0067]); processing the matched signal to thereby optimize acoustics of the audio-visual system (Vaughn et al; Para [0072][0080]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]).
Claim(s) 3, 8, 11, 16, 19, 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engi et al (US 2022/0417364 A1) in view of Vaughn et al (US 2018/0336000 A1) and further in view of Jaeger et al (US 2022/0092470 A1) and further in view of Loether (US 2015/0230025 A1).
Regarding claim 3, Engi et al in view of Vaughn et al disclose the computer-implemented method as defined in claim 1, but do not expressly disclose wherein: the first set of acoustic signals originate from inside the meeting room environment; or the first set of acoustic signals originate from outside the meeting room environment. However, in the same field of endeavor, Loether discloses a method wherein: the first set of acoustic signals originate from inside the meeting room environment; or the first set of acoustic signals originate from outside the meeting room environment (Loether; Para [0070]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the acoustic data taught by Loether as audio data for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize system performance and intelligibility (Loether; Para [0066]).
Regarding claim 8, Engi et al in view of Vaughn et al disclose the computer-implemented method as defined in claim 1, but do not expressly disclose wherein: the first set of acoustic signals are captured while the meeting room environment is empty; or the first set of acoustic signals are captured while the meeting room environment is occupied. However, in the same field of endeavor, Loether discloses a method wherein: the first set of acoustic signals are captured while the meeting room environment is empty; or the first set of acoustic signals are captured while the meeting room environment is occupied (Loether; Para [0070]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the acoustic data taught by Loether as audio data for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize system performance and intelligibility (Loether; Para [0066]).
Regarding claim 11, Engi et al in view of Vaughn et al disclose the system as defined in claim 9, but do not expressly disclose wherein: the first set of acoustic signals originate from inside the meeting room environment; or the first set of acoustic signals originate from outside the meeting room environment. However, in the same field of endeavor, Loether discloses a system wherein: the first set of acoustic signals originate from inside the meeting room environment; or the first set of acoustic signals originate from outside the meeting room environment (Loether; Para [0070]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the acoustic data taught by Loether as audio data for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize system performance and intelligibility (Loether; Para [0066]).
Regarding claim 16, Engi et al in view of Vaughn et al disclose the system as defined in claim 9, but do not expressly disclose wherein: the first set of acoustic signals are captured while the meeting room environment is empty; or the first set of acoustic signals are captured while the meeting room environment is occupied. However, in the same field of endeavor, Loether discloses a system wherein: the first set of acoustic signals are captured while the meeting room environment is empty; or the first set of acoustic signals are captured while the meeting room environment is occupied (Loether; Para [0070]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the acoustic data taught by Loether as audio data for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize system performance and intelligibility (Loether; Para [0066]).
Regarding claim 19, Engi et al in view of Vaughn et al disclose the computer-readable storage medium as defined in claim 17, but do not expressly disclose wherein: the first set of acoustic signals originate from inside the meeting room environment; or the first set of acoustic signals originate from outside the meeting room environment. However, in the same field of endeavor, Loether discloses a system wherein: the first set of acoustic signals originate from inside the meeting room environment; or the first set of acoustic signals originate from outside the meeting room environment (Loether; Para [0070]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the acoustic data taught by Loether as audio data for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize system performance and intelligibility (Loether; Para [0066]).
Regarding claim 24, Engi et al in view of Vaughn et al disclose the computer-readable storage medium as defined in claim 17, but do not expressly disclose wherein: the first set of acoustic signals are captured while the meeting room environment is empty; or the first set of acoustic signals are captured while the meeting room environment is occupied. However, in the same field of endeavor, Loether discloses a system wherein: the first set of acoustic signals are captured while the meeting room environment is empty; or the first set of acoustic signals are captured while the meeting room environment is occupied (Loether; Para [0070]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the acoustic data taught by Loether as audio data for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize system performance and intelligibility (Loether; Para [0066]).
Claim(s) 4, 12, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engi et al (US 2022/0417364 A1) in view of Vaughn et al (US 2018/0336000 A1) and further in view of Deetz et al (US 2018/0277133 A1) and further in view of Ding et al (US 2013/0297547 A1).
Regarding claim 4, Engi et al in view of Vaughn et al disclose the computer-implemented method as defined in claim 1, but do not expressly disclose wherein: sensor data is obtained to contextualize the first set of acoustic signals; and the sensor data comprises at least one of: a direction of arrival of acoustic signals; a time or date of the acoustic signals; meeting room environment reservation details; a position of a door or window; or state of a heating, ventilation and air conditioning system. However, in the same field of endeavor, Deetz et al disclose a method wherein: sensor data is obtained to contextualize the first set of acoustic signals (Deetz; Para [0037]); and the sensor data comprises at least one of: a direction of arrival of acoustic signals (Deetz; Para [0031][0045]); a time or date of the acoustic signals (Deetz; Para [0037]); meeting room environment reservation details (Deetz; Para [0037][0055]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the context identification taught by Deetz as context detection for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize the audio signal processing (Deetz et al; Para [0010]). Furthermore, in the same field of endeavor, Ding et al disclose a method wherein: wherein: sensor data is obtained to contextualize the first set of acoustic signals (Ding et al; Para [0005]); and the sensor data comprises at least one of: meeting room environment reservation details; a position of a door or window; or state of a heating, ventilation and air conditioning system (Ding et al; Para [0046]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the context identification taught by Ding as context identification for the audio optimizer in the method taught by Engi. The motivation to do so would have been to output a highest value context (Ding et al; Para [0023]).
Regarding claim 12, Engi et al in view of Vaughn et al disclose the system as defined in claim 9, but do not expressly disclose wherein: sensor data is obtained to contextualize the first set of acoustic signals; and the sensor data comprises at least one of: a direction of arrival of acoustic signals; a time or date of the acoustic signals; meeting room environment reservation details; a position of a door or window; or state of a heating, ventilation and air conditioning system. However, in the same field of endeavor, Deetz et al disclose a system wherein: sensor data is obtained to contextualize the first set of acoustic signals (Deetz; Para [0037]); and the sensor data comprises at least one of: a direction of arrival of acoustic signals (Deetz; Para [0031][0045]); a time or date of the acoustic signals (Deetz; Para [0037]); meeting room environment reservation details (Deetz; Para [0037][0055]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the context identification taught by Deetz as context detection for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize the audio signal processing (Deetz et al; Para [0010]). Furthermore, in the same field of endeavor, Ding et al disclose a system wherein: wherein: sensor data is obtained to contextualize the first set of acoustic signals (Ding et al; Para [0005]); and the sensor data comprises at least one of: meeting room environment reservation details; a position of a door or window; or state of a heating, ventilation and air conditioning system (Ding et al; Para [0046]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the context identification taught by Ding as context identification for the audio optimizer in the method taught by Engi. The motivation to do so would have been to output a highest value context (Ding et al; Para [0023]).
Regarding claim 20, Engi et al in view of Vaughn et al disclose the computer-readable storage medium as defined in claim 17, but do not expressly disclose wherein: sensor data is obtained to contextualize the first set of acoustic signals; and the sensor data comprises at least one of: a direction of arrival of acoustic signals; a time or date of the acoustic signals; meeting room environment reservation details; a position of a door or window; or state of a heating, ventilation and air conditioning system. However, in the same field of endeavor, Deetz et al disclose a system wherein: sensor data is obtained to contextualize the first set of acoustic signals (Deetz; Para [0037]); and the sensor data comprises at least one of: a direction of arrival of acoustic signals (Deetz; Para [0031][0045]); a time or date of the acoustic signals (Deetz; Para [0037]); meeting room environment reservation details (Deetz; Para [0037][0055]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the context identification taught by Deetz as context detection for the audio optimizer in the method taught by Engi. The motivation to do so would have been to optimize the audio signal processing (Deetz et al; Para [0010]). Furthermore, in the same field of endeavor, Ding et al disclose a system wherein: wherein: sensor data is obtained to contextualize the first set of acoustic signals (Ding et al; Para [0005]); and the sensor data comprises at least one of: meeting room environment reservation details; a position of a door or window; or state of a heating, ventilation and air conditioning system (Ding et al; Para [0046]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the context identification taught by Ding as context identification for the audio optimizer in the method taught by Engi. The motivation to do so would have been to output a highest value context (Ding et al; Para [0023]).
Claim(s) 5, 13, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engi et al (US 2022/0417364 A1) in view of Vaughn et al (US 2018/0336000 A1) and further in view of Jaeger et al (US 2022/0092470 A1).
Regarding claim 5, Engi et al in view of Vaughn et al disclose the computer-implemented method as defined in claim 1, but do not expressly disclose wherein: one or more machine learning models are trained using the target signal; and the method further comprises evaluating a performance of the one or more machine learning models. However, in the same field of endeavor, Vaughn et al disclose a method wherein: one or more machine learning models are trained using the target signal (Vaughn et al; Para [0056]-[0057]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]). Moreover, in the same field of endeavor, Jaeger et al disclose a method further comprises evaluating a performance of the one or more machine learning models (Jaeger et al; Para [0071]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the model evaluation taught by Jaeger as model evaluation for the audio optimizer in the method taught by Engi. The motivation to do so would have been to execute without exceeding the available time budget (Jaeger; Para [0045]).
Regarding claim 13, Engi et al in view of Vaughn et al disclose the system as defined in claim 9, but do not expressly disclose wherein: one or more machine learning models are trained using the target signal; and the method further comprises evaluating a performance of the one or more machine learning models. However, in the same field of endeavor, Vaughn et al disclose a method wherein: one or more machine learning models are trained using the target signal (Vaughn et al; Para [0056]-[0057]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]). Moreover, in the same field of endeavor, Jaeger et al disclose a method further comprises evaluating a performance of the one or more machine learning models (Jaeger et al; Para [0071]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the model evaluation taught by Jaeger as model evaluation for the audio optimizer in the method taught by Engi. The motivation to do so would have been to execute without exceeding the available time budget (Jaeger; Para [0045]).
Regarding claim 21, Engi et al in view of Vaughn et al disclose the computer-readable storage medium as defined in claim 17, but do not expressly disclose wherein: one or more machine learning models are trained using the target signal; and the method further comprises evaluating a performance of the one or more machine learning models. However, in the same field of endeavor, Vaughn et al disclose a method wherein: one or more machine learning models are trained using the target signal (Vaughn et al; Para [0056]-[0057]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning taught by Vaughn as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to accept contextual input from a variety of digital and analog systems (Vaughn et al; Para [0065]). Moreover, in the same field of endeavor, Jaeger et al disclose a method further comprises evaluating a performance of the one or more machine learning models (Jaeger et al; Para [0071]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the model evaluation taught by Jaeger as model evaluation for the audio optimizer in the method taught by Engi. The motivation to do so would have been to execute without exceeding the available time budget (Jaeger; Para [0045]).
Claim(s) 6, 14, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engi et al (US 2022/0417364 A1) in view of Vaughn et al (US 2018/0336000 A1) and further in view of Jaeger et al (US 2022/0092470 A1) and further in view of Peters et al (US 2014/0161270 A1).
Regarding claim 6, Engi et al in view of Vaughn et al and further in view of Jaeger et al disclose the computer-implemented method as defined in claim 5, but do not expressly disclose wherein the evaluation comprises at least one of: evaluating the performance of the one or more machine learning models in a same meeting room environment; or evaluating the performance of the one or more machine learning models in a different meeting room environment. However, in the same field of endeavor, Peters et al disclose a method wherein the evaluation comprises at least one of: evaluating the performance of the one or more machine learning models in a same meeting room environment (Peters et al; Para [0017]-[0019]); or evaluating the performance of the one or more machine learning models in a different meeting room environment (Peters et al; Para [0017]-[0019]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the model evaluation taught by Peters as model evaluation for the audio optimizer in the method taught by Engi. The motivation to do so would have been to provide more accurate estimation of the room (Peters et al; Para [0019]).
Regarding claim 14, Engi et al in view of Vaughn et al and further in view of Jaeger et al disclose the system as defined in claim 13, but do not expressly disclose wherein the evaluation comprises at least one of: evaluating the performance of the one or more machine learning models in a same meeting room environment; or evaluating the performance of the one or more machine learning models in a different meeting room environment. However, in the same field of endeavor, Peters et al disclose a method wherein the evaluation comprises at least one of: evaluating the performance of the one or more machine learning models in a same meeting room environment (Peters et al; Para [0017]-[0019]); or evaluating the performance of the one or more machine learning models in a different meeting room environment (Peters et al; Para [0017]-[0019]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the model evaluation taught by Peters as model evaluation for the audio optimizer in the method taught by Engi. The motivation to do so would have been to provide more accurate estimation of the room (Peters et al; Para [0019]).
Regarding claim 22, Engi et al in view of Vaughn et al and further in view of Jaeger et al disclose the computer-readable storage medium as defined in claim 21, but do not expressly disclose wherein the evaluation comprises at least one of: evaluating the performance of the one or more machine learning models in a same meeting room environment; or evaluating the performance of the one or more machine learning models in a different meeting room environment. However, in the same field of endeavor, Peters et al disclose a method wherein the evaluation comprises at least one of: evaluating the performance of the one or more machine learning models in a same meeting room environment (Peters et al; Para [0017]-[0019]); or evaluating the performance of the one or more machine learning models in a different meeting room environment (Peters et al; Para [0017]-[0019]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the model evaluation taught by Peters as model evaluation for the audio optimizer in the method taught by Engi. The motivation to do so would have been to provide more accurate estimation of the room (Peters et al; Para [0019]).
Claim(s) 7, 15, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engi et al (US 2022/0417364 A1) in view of Vaughn et al (US 2018/0336000 A1) and further in view of Jaeger et al (US 2022/0092470 A1) and further in view of Perez Rua et al (US 2021/0125026 A1).
Regarding claim 7, Engi et al in view of Vaughn et al and further in view of Jaeger et al disclose the computer-implemented method as defined in claim 5, but do not expressly disclose wherein: one of the machine learning models is a large machine learning model operating on a cloud platform; one of the machine learning models is a small machine learning model operating on a local platform; and the method further comprises using the large machine learning model to evaluate the small machine learning model. However, in the same field of endeavor, Perez Rua et al disclose a method wherein: one of the machine learning models is a large machine learning model operating on a cloud platform (Perez Rua et al; Para [0024]); one of the machine learning models is a small machine learning model operating on a local platform (Perez Rua et al; Para [0024]); and the method further comprises using the large machine learning model to evaluate the small machine learning model (Perez Rua et al; Para [0025]-[0026]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning model taught by Perez Rua et al as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to enable this additional, personalized functionality (Perez Rua et al; Para [0024]).
Regarding claim 15, Engi et al in view of Vaughn et al and further in view of Jaeger et al disclose the system as defined in claim 13, but do not expressly disclose wherein: one of the machine learning models is a large machine learning model operating on a cloud platform; one of the machine learning models is a small machine learning model operating on a local platform; and the method further comprises using the large machine learning model to evaluate the small machine learning model. However, in the same field of endeavor, Perez Rua et al disclose a method wherein: one of the machine learning models is a large machine learning model operating on a cloud platform (Perez Rua et al; Para [0024]); one of the machine learning models is a small machine learning model operating on a local platform (Perez Rua et al; Para [0024]); and the method further comprises using the large machine learning model to evaluate the small machine learning model (Perez Rua et al; Para [0025]-[0026]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning model taught by Perez Rua et al as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to enable this additional, personalized functionality (Perez Rua et al; Para [0024]).
Regarding claim 23, Engi et al in view of Vaughn et al and further in view of Jaeger et al disclose the computer-readable storage medium as defined in claim 21, but do not expressly disclose wherein: one of the machine learning models is a large machine learning model operating on a cloud platform; one of the machine learning models is a small machine learning model operating on a local platform; and the method further comprises using the large machine learning model to evaluate the small machine learning model. However, in the same field of endeavor, Perez Rua et al disclose a method wherein: one of the machine learning models is a large machine learning model operating on a cloud platform (Perez Rua et al; Para [0024]); one of the machine learning models is a small machine learning model operating on a local platform (Perez Rua et al; Para [0024]); and the method further comprises using the large machine learning model to evaluate the small machine learning model (Perez Rua et al; Para [0025]-[0026]). It would have been obvious to one of the ordinary skills in the art before the effective filing date of the application to use the machine learning model taught by Perez Rua et al as machine learning for the audio optimizer in the method taught by Engi. The motivation to do so would have been to enable this additional, personalized functionality (Perez Rua et al; Para [0024]).
Conclusion
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/KUASSI A GANMAVO/Examiner, Art Unit 2692
/CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692
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