Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . See 35 U.S.C. § 100 (note).
Art Rejections
Obviousness
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 1–5 and 7–12 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of US Patent Application Publication 2022/0383847 (effectively filed 25 May 2021) (“Chen”) and US Patent Application Publication 20230071695 (effectively filed 06 September 2021) (“Chang”).
Claim 6 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Chen; Chang; US Patent Application Publication 2016/0084268 (published 24 March 2016) (“Cocks”) and US Patent Application Publication 2005/0097910 (published 12 May 2005) (“Nakajima”).
Claim 1 is drawn to “a method for actively controlling sound emissions of a flow machine.” The following table illustrates the correspondence between the claimed method and the Chen reference.
Claim 1
The Chen Reference
“1. A method for actively controlling sound emissions of a flow machine having an electric motor of a fan or a turbomachine, comprising:
The Chen reference similarly describes a method for cancelling noise produced by fans 131–13N powered by a motor. Chen at Abs., ¶¶ 2–5, 49.
“recording a sound signal generated from superimposition of a sound emission from the flow machine with at least one counter-sound signal using at least one receiver at at least one receiver position and transmitting the sound signal to a control unit, wherein the control unit has an artificial intelligence,
Chen’s method uses a noise cancellation module 15C. Id. at ¶ 54, FIG.6. The module includes microphones 401–40S, loudspeakers 411–41U and a noise cancellation engine 60. Id.
Chen’s priority document describes microphones corresponding to microphones 401–40S as reference microphones or error microphones. Chen II at ideas 5, 6. As explained in detail below, based on the teachings of Chen and Chang, it would have been obvious to implement Chen’s microphones 401–40S as a mix of reference and noise microphones.
Modified to implement a feedback ANC, at least one of microphones 401–40S would record an error signal
a
resulting from the superposition of sound from a fan 131 and sound produced by one of loudspeakers 41. Id. at ¶ 54, FIG.6. An analog front end (AFE) 6540 and a constant-time-to-constant-angle (CT to CA) interpolator 67 processes the audio
a
from the microphones and provides it as a signal
y
to ANC circuitry 680. Id. ANC circuitry 680 includes an artificial neural network (ANN), or artificial intelligence, 730. Id. at ¶ 56, FIG.7A.
“generating a control signal for at least one actuator by the artificial intelligence taking into consideration the sound signal so that the actuator produces a counter-sound signal which cooperates with the sound emission of the flow machine so that a sound load at least in the region of the receiver position or the receiver positions is reduced,
ANN 730 analyzes signals
y
to generate control signals, or anti-noise (AN) signals, for a set of loudspeakers 411–41U. Id. at ¶¶ 55, 57, FIG.7A. The output of the AN signals by the loudspeakers effectively cancels the sound produced by a corresponding fan to create a quiet zone. See id. at ¶¶ 46, 61, 62, FIGs.4, 8A, 8B.
“wherein at least two measured state values of the flow machine describing a current operating state of the flow machine are transmitted to the control unit, wherein the control signal is generated by the artificial intelligence taking into consideration the state values.”
Chen’s NN generates the AN signals by considering multiple simultaneously measured state values, including a CA signal
y
s
from an error microphone, as described above; a CA signal
y
1 from a reference microphone and an
A
C
signal derived from a fan tach signal
F
G
. Chen at ¶¶ 54, 56. Signal
y
1
is a measured state value describing a current operating state of a flow machine because it describes the amount of acoustic noise being generated by Chen’s fans. Signal
A
C
is a measured state value describing a current operating state of a flow machine because it describes the current phase of Chen’s fans based on the speed of the fans. Id. at ¶¶ 40, 46, 49, 55.1
Table 1
Chen’s priority document does not clearly identify the microphones in ideas 5 and 6 (which correspond to Chen’s FIG.6) as reference microphones or error microphones. One of ordinary skill in the art of noise cancellation would have immediately recognized microphones 401–40S as microphones used for ANC. One of ordinary skill would have also known that in the context of ANC, the microphones may be configured either as reference microphones, error microphones or a combination of both. Examples are seen in the Chen priority document ideas 1–3, where microphones operate either as error microphones (ideas 1 and 2) to provide feed back control or as reference microphones (idea 3) to provide feed forward control. The inclusion of multiple microphones 401–40S in ideas 5 and 6 further suggests configuring some microphones as reference microphones and some microphones as error microphones. This type of joint feed forward and feed back ANC is further taught and suggested by the Chang reference. Chang at ¶¶ 11, FIG.1. Accordingly, it would have been obvious for one of ordinary skill to configure at least one of microphones 401–40S as an error microphone to record the superposition of acoustic noise generated by a fan and an acoustic noise cancellation signal produced by one of loudspeakers 411–41U. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claim.
Claim 2 depends on claim 1, and further requires the following:
“wherein at least one of a time signal
The
A
N
signal is generated by the NN to so to cancel the noise from the fan. One of ordinary skill in the art would have understood from the art-accepted meaning of the term anti-noise (AN) and active noise cancellation (ANC) that this means that
A
N
will cause the speakers to output sound that is a phase-inverted replica of the noise. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claim.
Claim 3 depends on claim 1, and further requires the following:
“wherein the artificial intelligence uses a reinforcement learning method in order to generate the control signal.”
The Chen reference’s priority document does not specify the type of learning used to train an artificial neural network ANN. The Lee reference, however, teaches and suggests training an ANN to cancel engine noise, which is similar in concept to cancelling fan noise. Lee at ¶¶ 9, 23–29, 123–126, FIG.4. In particular, Lee identifies the use of reinforcement learning in order to train the ANN. Id. This would have reasonably suggested similarly training Chen’s ANN using reinforcement learning. For the foregoing reasons, the combination of the Chen, the Chang and the Lee references makes obvious all limitations of the claim.
Claim 4 depends on claim 1, and further requires the following:
“wherein the artificial intelligence is trained beforehand.”
The Chen reference’s priority document does not specify the type of learning used to train an artificial neural network ANN. The Lee reference, however, teaches and suggests training an ANN to cancel engine noise, which is similar in concept to cancelling fan noise. Lee at ¶¶ 9, 23–29, 123–126, FIG.4. In particular, Lee identifies the use of supervised learning with provided labels (that are not available in normal use) in order to train the ANN before its use. Id. This would have reasonably suggested similarly training Chen’s ANN using supervised learning with predetermined labels that are not present in actual use. For the foregoing reasons, the combination of the Chen, the Chang and the Lee references makes obvious all limitations of the claim.
Claim 5 depends on claim 1, and further requires the following:
“wherein at least one state value is the measurement value of a corresponding sensor.
As shown in the obviousness rejection of claim 1, the prior art suggests providing an ANN with sensor values, such as sensor values from a reference microphone. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claim.
Claim 6 depends on claim 1, and further requires the following:
“wherein the at least two measured state values are:
“a motor speed of the flow machine and a speed of an impeller anemometer, or
“a motor speed of the flow machine and a signal of a hot-wire anemometer, or
“a motor speed of the flow machine and a motor current of the flow machine, or
“a motor current of the flow machine and a speed of an impeller anemometer, or
“a motor current of the flow machine and a signal from a hot-wire anemometer, or
“a motor speed of the flow machine and a differential pressure upstream and downstream of the flow machine, or
“a motor current of the flow machine and a differential pressure upstream and downstream of the flow machine.”
Chen’s NN generates the AN signals by considering multiple simultaneously measured state values, including a CA signal
y
s
from an error microphone, as described above; a CA signal
y
1 from a reference microphone and an
A
C
signal derived from a fan tach signal
F
G
. Chen at ¶¶ 54, 56. Signal
y
1
is a measured state value describing a current operating state of a flow machine because it describes the amount of acoustic noise being generated by Chen’s fans. Signal
A
C
is a measured state value describing a current operating state of a flow machine because it describes the current phase of Chen’s fans based on the speed of the fans. Id. at ¶¶ 40, 46, 49, 55. Chen does not describe measuring any of the claimed state values.
Similar to Chen’s anti-noise feature for a fan, the Cocks reference, however, describes a blower system with an anti-noise feature. Cocks at Abs., ¶¶ 2, 21–22, FIG.2. Cocks describes generating an anti-noise vibration by considering feedback 60 from multiple sensors. Id. at ¶¶ 21–22, FIG.2. For example, Cocks describes feedback 60 as including measuring temperature, acceleration, strain, air flow, audio and pressure. Id. Cocks further describes measuring motor feedback 56, such as speed, or RPMs. Id. at ¶¶ 21, 32. Using feedback signals 56 and 60, Cocks derives a control signal for its motor to vibrate in a way that produces an anti-noise signal. Id.
Read in light of Chen’s anti-noise system, Cocks reasonably suggests the value in considering other measured state variables beyond those produced by and used by Chen. For example, Cocks teaches and suggests that in addition to audio measurements, it would be beneficial to measure motor speed, air flow and pressure in order to dynamically update a noise cancellation estimate. This reasonably suggests modifying Chen’s system so that its neural network is trained on and is input with additional measured state values, including motor speed and air flow (i.e., a signal derived from any conventional anemometer, such as a hot-wire anemometer or impeller anemometer as is well-known in the art and worthy of Official notice). See Nakajima at ¶ 64 (describing the use of a hot-wire anemometer for detecting air velocity in a blower system). For the foregoing reasons, the combination of the Chen, the Chang and the Cocks references makes obvious all limitations of the claim.
Claim 7 depends on claim 1, and further requires the following:
“wherein at least one of a microphone is used as the receiver
Similarly, Chen describes providing microphones 401–40S and loudspeakers 411–41U. Chen at ¶ 54, FIG.6. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claim.
Claim 8 depends on claim 1, and further requires the following:
“wherein the actuator excites a component of the flow machine in order to emit sound.”
Claim 12 depends on claim 8, and further requires the following:
“wherein the actuator excites the component of the flow machine via at least one of:
“a piezo-actuator;
“modulation of an excitation current; and
“an excitation voltage in an electric motor with a suitable, superimposed excitation signal.”
Claims 8 and 12 are addressed together. Chen’s fan system, or flow machine, similarly describes a loudspeaker, or actuator, that by definition includes a transducer of some type to convert electrical energy into acoustic energy and a diaphragm of some type to interface with air for producing sound. Chen excites the loudspeaker diaphragm to create sound by modulating an excitation current applied to the transducer of the loudspeaker. See Chen at ¶¶ 46, 61, 62, FIGs.4, 8A, 8B. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claims.
Claim 9 is drawn to “a system, for actively controlling sound emissions.” The following table illustrates the correspondence between the claimed system and the Chen reference.
Claim 9
The Chen Reference
“9. A system, for actively controlling sound emissions comprising:
“a flow machine having an electric motor of a fan or a turbomachine,
The Chen reference similarly describes a system and method for cancelling noise produced by fans 131–13N powered by a motor. Chen at Abs., ¶¶ 2–5, 49.
“at least one receiver for detecting a sound signal at at least one receiver position, wherein the sound signal is generated from the superimposition of a sound emission generated by the flow machine and at least one counter-sound signal,
“a control unit, and
Chen’s method uses a noise cancellation module 15C. Id. at ¶ 54, FIG.6. The module includes microphones 401–40S, loudspeakers 411–41U and a noise cancellation engine 60. Id.
Chen’s priority document describes microphones corresponding to microphones 401–40S as reference microphones or error microphones. Chen II at ideas 5, 6. As explained in detail below, based on the teachings of Chen and Chang, it would have been obvious to implement Chen’s microphones 401–40S as a mix of reference and noise microphones.
Modified to implement a feedback ANC, at least one of microphones 401–40S would record an error signal
a
resulting from the superposition of sound from a fan 131 and sound produced by one of loudspeakers 41. Id. at ¶ 54, FIG.6. An analog front end (AFE) 6540 and a constant-time-to-constant-angle (CT to CA) interpolator 67 processes the audio
a
from the microphones and provides it as a signal
y
to ANC circuitry 680. Id. ANC circuitry 680 includes an artificial neural network (ANN), or artificial intelligence, 730. Id. at ¶ 56, FIG.7A.
“at least one actuator, wherein the control unit has an artificial intelligence, wherein the artificial intelligence controls the actuator taking into consideration the sound signal detected and taking into consideration at least two measured state values of the flow machine describing a current operating state of the flow machine so that it produces a counter-sound signal which cooperates with the sound emission of the flow machine so that a sound load at least in the region of the receiver position or the receiver positions is reduced.”
ANN 730 analyzes signals
y
to generate control signals, or anti-noise (AN) signals, for a set of loudspeakers 411–41U. Id. at ¶¶ 55, 57, FIG.7A. The output of the AN signals by the loudspeakers effectively cancels the sound produced by a corresponding fan to create a quiet zone. See id. at ¶¶ 46, 61, 62, FIGs.4, 8A, 8B
Chen’s NN generates the AN signals by considering multiple simultaneously measured state values, including a CA signal
y
s
from an error microphone, as described above; a CA signal
y
1 from a reference microphone and an
A
C
signal derived from a fan tach signal
F
G
. Chen at ¶¶ 54, 56. Signal
y
1
is a measured state value describing a current operating state of a flow machine because it describes the amount of acoustic noise being generated by Chen’s fans. Signal
A
C
is a measured state value describing a current operating state of a flow machine because it describes the current phase of Chen’s fans based on the speed of the fans. Id. at ¶¶ 40, 46, 49, 55.
Table 2
Chen’s priority document does not clearly identify the microphones in ideas 5 and 6 (which correspond to Chen’s FIG.6) as reference microphones or error microphones. One of ordinary skill in the art of noise cancellation would have immediately recognized microphones 401–40S as microphones used for ANC. One of ordinary skill would have also known that in the context of ANC, the microphones may be configured either as reference microphones, error microphones or a combination of both. Examples are seen in the Chen priority document ideas 1–3, where microphones operate either as error microphones (ideas 1 and 2) to provide feed back control or as reference microphones (idea 3) to provide feed forward control. The inclusion of multiple microphones 401–40S in ideas 5 and 6 further suggests configuring some microphones as reference microphones and some microphones as error microphones. This type of joint feed forward and feed back ANC is further taught and suggested by the Chang reference. Chang at ¶¶ 11, FIG.1. Accordingly, it would have been obvious for one of ordinary skill to configure at least one of microphones 401–40S as an error microphone to record the superposition of acoustic noise generated by a fan and an acoustic noise cancellation signal produced by one of loudspeakers 411–41U. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claim.
Claim 10 depends on claim 9, and further requires the following:
“wherein the control unit is one of an integral component of the flow machine and a separate control module.”
Chen describes combining a fan 131–13N and an ANC module 15 as a integral unit in a computer, laptop or graphic card. Chen at ¶¶ 3, 36, FIG.1. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claims.
Claim 11 is drawn to “an apparatus, for actively controlling sound emissions of a flow machine.” The following table illustrates the correspondence between the claimed apparatus and the Chen reference.
Claim 11
The Chen Reference
“11. An apparatus, for actively controlling sound emissions of a flow machine comprising:
The Chen reference similarly describes a system and method for cancelling noise produced by fans 131–13N powered by a motor. Chen at Abs., ¶¶ 2–5, 49.
“at least one receiver for detecting a sound signal at at least one receiver position, wherein the sound signal is generated from superimposition of a sound emission produced by a flow machine and at least one counter-sound signal,
“a control unit and
Chen’s method uses a noise cancellation module 15C. Id. at ¶ 54, FIG.6. The module includes microphones 401–40S, loudspeakers 411–41U and a noise cancellation engine 60. Id.
Chen’s priority document describes microphones corresponding to microphones 401–40S as reference microphones or error microphones. Chen II at ideas 5, 6. As explained in detail below, based on the teachings of Chen and Chang, it would have been obvious to implement Chen’s microphones 401–40S as a mix of reference and noise microphones.
Modified to implement a feedback ANC, at least one of microphones 401–40S would record an error signal
a
resulting from the superposition of sound from a fan 131 and sound produced by one of loudspeakers 41. Id. at ¶ 54, FIG.6. An analog front end (AFE) 6540 and a constant-time-to-constant-angle (CT to CA) interpolator 67 processes the audio
a
from the microphones and provides it as a signal
y
to ANC circuitry 680. Id. ANC circuitry 680 includes an artificial neural network (ANN), or artificial intelligence, 730. Id. at ¶ 56, FIG.7A.
“at least one actuator, wherein the control unit has an artificial intelligence, wherein the artificial intelligence controls the actuator taking into consideration the sound signal detected and taking into consideration at least two measured state values of the flow machine describing a current operating state of the flow machine so that the actuator produces a counter-sound signal which cooperates with the sound emission of the flow machine so that a sound load at least in the region of the receiver position or the receiver positions is reduced.”
ANN 730 analyzes signals
y
to generate control signals, or anti-noise (AN) signals, for a set of loudspeakers 411–41U. Id. at ¶¶ 55, 57, FIG.7A. The output of the AN signals by the loudspeakers effectively cancels the sound produced by a corresponding fan to create a quiet zone. See id. at ¶¶ 46, 61, 62, FIGs.4, 8A, 8B
Chen’s NN generates the AN signals by considering multiple simultaneously measured state values, including a CA signal
y
s
from an error microphone, as described above; a CA signal
y
1 from a reference microphone and an
A
C
signal derived from a fan tach signal
F
G
. Chen at ¶¶ 54, 56. Each signal is measured from a device associated with fans 131–13N. Signal
y
1
is a measured state value describing a current operating state of a flow machine because it describes the amount of acoustic noise being generated by Chen’s fans. Signal
A
C
is a measured state value describing a current operating state of a flow machine because it describes the current phase of Chen’s fans based on the speed of the fans. Id. at ¶¶ 40, 46, 49, 55.
Table 3
Chen’s priority document does not clearly identify the microphones in ideas 5 and 6 (which correspond to Chen’s FIG.6) as reference microphones or error microphones. One of ordinary skill in the art of noise cancellation would have immediately recognized microphones 401–40S as microphones used for ANC. One of ordinary skill would have also known that in the context of ANC, the microphones may be configured either as reference microphones, error microphones or a combination of both. Examples are seen in the Chen priority document ideas 1–3, where microphones operate either as error microphones (ideas 1 and 2) to provide feed back control or as reference microphones (idea 3) to provide feed forward control. The inclusion of multiple microphones 401–40S in ideas 5 and 6 further suggests configuring some microphones as reference microphones and some microphones as error microphones. This type of joint feed forward and feed back ANC is further taught and suggested by the Chang reference. Chang at ¶¶ 11, FIG.1. Accordingly, it would have been obvious for one of ordinary skill to configure at least one of microphones 401–40S as an error microphone to record the superposition of acoustic noise generated by a fan and an acoustic noise cancellation signal produced by one of loudspeakers 411–41U. For the foregoing reasons, the combination of the Chen and the Chang references makes obvious all limitations of the claim.
Summary
Claims 1–5 and 7–12 are rejected under at least one of 35 U.S.C. §§ 102 and 103 as being unpatentable over the cited prior art. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Response to Applicant’s Arguments
Applicant’s Reply (11 June 2026) has substantively amended all the claims. This Office action has been updated accordingly.
Applicant’s Reply at includes comments pertaining to the rejections presented in the Non-Final Rejection (23 March 2026). Those comments have been considered, but are not persuasive.
The claim term “measured state values of the flow machine describing a current operating state of the flow machine” is defined in the Specification at p. 5 ll. 16–20 as “all values which represent or describe the current operating state of the flow machine, and consequently also represent or describe the occurring sound emissions. This may also include components which cooperate with the flow machine.” This broad definition includes signals from a reference microphone that cooperates with the flow machine to record the sounds being produced by the fan and thus describe the operating state by recording the sounds produced by the machine. It also includes the AC signal indicating the phase of the machine’s fans since that is a direct measure of the machine’s operation. Applicant’s comments do not show with any particularity how the above understanding of Chen differs materially from the claim as properly construed. Accordingly, all the rejections will be maintained.
Additional Citations
The following table lists additional documents identified as being relevant to the subject matter disclosed and claimed in this Application. While this Office action does not rely on these documents, Applicant is advised to consider them carefully in preparing a response.
Citation
Relevance
US 2022/0136728
Describes at ¶¶ 47, 69, 70, FIG.20A using a trained neural network to control a blower to achieve desired flow rate based on non-linear relationships with motor speed and current draw.
US 2022/0114997
Describes at ¶¶ 40–42 that inputting all factors related to noise production into an ANN will improve modeling and cancellation (i.e., better resulting SNR).
US 2021/0241751
Describes at ¶ 19 various sources of noise in different rotating machines, including fluid dynamic interactions and high-speed air flow through ducts.
US 2017/0113684
Describes at ¶ 39 considering multiple state variables to improve ANC, including vehicle speed, rotational speed of an engine, other descriptions of engine status and speed of a ventilation fan.
Table 4
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 C.F.R. § 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 C.F.R. § 1.17(a)) pursuant to 37 C.F.R. § 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 WALTER F BRINEY III whose telephone number is (571)272-7513. The examiner can normally be reached M-F 8 am-4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carolyn Edwards can be reached at 571-270-7136. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Walter F Briney III/
Walter F Briney IIIPrimary ExaminerArt Unit 2692
7/22/2026
1 This application is based on the broad definition of “state values of the flow machine” provided in the Spec. at p.5, ll. 16–22. That definition includes all values that represent or describe the current operating state of the flow machine, and consequently also represent or describe the occurring sound emissions. This may also include components which cooperate with the flow machine.
Prosecution Insights