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 .
DETAILED ACTION
Claim Rejections - 35 USC § 112
1. 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.
Claim 1 recites the limitation "the array" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
2. 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.
3. Claims 1-4, 13, 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Olsen (US Patent 4,268,,822) in view of Alfonso (US Patent 12,210,095).
As to claim 1, Olsen teaches a system comprising: a plurality of resonator circuits wherein different resonator circuits are tuned to generate different output frequencies (col. 1, lines 33-42 - a series of equal duration square wave trains, each of a different frequency, are sequentially generated and transmitted over a two wire line to a remotely located group of parallel resonant LC circuits whose inputs are serially connected. A separate two position micro-switch is connected to each LC circuit to provide for switching in additional capacitance to the LC circuits); an excitation signal that when applied to the array caused one or more resonator circuits in the plurality of resonator circuits to output a signal at an associated frequency (col. 1, lines 55-59 - a process control system utilizes a two wire line to transmit a DC control signal to an operator, such as a valve, simultaneously with a series of AC excitation signals to a group of parallel resonant LC circuits; claim 1 - signal-generating means coupled to said two-wire line at said central stations for producing a plurality of a-c excitation signals having different frequencies); a separate two position micro-switch is connected to each LC circuit to provide for switching in additional capacitance to the LC circuits; each individual LC circuit is designed to respond to only one of the generated frequencies (col. 1, lines 38-42).
Olsen does not explicitly discuss an acoustic effects module for applying one or more acoustic effects to selected frequencies from a frequency spectrum.
Alfonso teaches the acoustic characteristics are values that are added to the point definition, and may specify how different sound frequencies are affected when colliding with or coming into contact with the point cloud points that are defined with those acoustic characteristics. The sound effects defined by the acoustic characteristics may include specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties (col. 13, lines 11-22).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Alfonso into Olsen for the purpose of applying acoustic effects by specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties.
As to claims 2 and 16, Alfonso teaches the device of claim 1 and the method of claim 15, wherein the one or more acoustic effects is selected from one or more of phase advance, an amplitude level, and a decay interval (col. 13, lines 11-22 - the acoustic characteristics are values that are added to the point definition, and may specify how different sound frequencies are affected when colliding with or coming into contact with the point cloud points that are defined with those acoustic characteristics. The sound effects defined by the acoustic characteristics may include specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties).
As to claim 3, Olsen and Alfonso do not explicitly discuss the device of claim 1, wherein the one or more acoustic effects comprising a set of parameters comprising an input to a resonator circuit of the plurality of circuits. However, Alfonso teaches the sound effects defined by the acoustic characteristics may include specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties are values that are added to the point definition, and may specify how different sound frequencies are affected when colliding with or coming into contact with the point cloud points that are defined with those acoustic characteristics (col. 13, lines 11-22); and input component 940 may include a mechanism that permits an operator to input information to device 900, such as a keyboard, a keypad, a button, a switch, etc. (col. 16, lines 24-26). Olsen teaches group of parallel resonant LC circuits whose inputs are serially connected (col. 2, lines 46-47). It would have been obvious to modify Alfonso’s system that permits to input information to a resonator circuit to apply acoustic effects parameters to selected frequency generated by the resonator circuit.
As to claim 4, Alfonso teaches the device of claim 1, further comprising: input port for receiving a user input device (col. 16, lines 24-26 - input component 940 may include a mechanism that permits an operator to input information to device 900, such as a keyboard, a keypad, a button, a switch, etc.).
As to claim 13, Olsen teaches a method for producing and audio output from a plurality of resonator circuits comprising: receiving at the plurality of resonator circuits (col. 1, lines 33-42 - a series of equal duration square wave trains, each of a different frequency, are sequentially generated and transmitted over a two wire line to a remotely located group of parallel resonant LC circuits whose inputs are serially connected. A separate two position micro-switch is connected to each LC circuit to provide for switching in additional capacitance to the LC circuits; col. 4, lines 28-33 – a signal generator 70 generates in a sequential manner equal duration square wave trains, each of a different frequency (i.e., f.sub.1, f.sub.2 . . . ) The square wave trains are transmitted through the resistor 18 and in turn over the two wire line 22 to a remotely located group of serially connected parallel resonant LC circuits); producing, from the plurality of resonator circuits, an acoustic signal based on the excitation signal (col. 1, lines 55-59 - a process control system utilizes a two wire line to transmit a DC control signal to an operator, such as a valve, simultaneously with a series of AC excitation signals to a group of parallel resonant LC circuits; claim 1 - signal-generating means coupled to said two-wire line at said central stations for producing a plurality of a-c excitation signals having different frequencies); a separate two position micro-switch is connected to each LC circuit to provide for switching in additional capacitance to the LC circuits; each individual LC circuit is designed to respond to only one of the generated frequencies (col. 1, lines 38-42).
Olsen does not explicitly discuss an acoustic effects module for applying one or more acoustic effects to selected number of the plurality of resonator circuits and producing an acoustic signal based the applied acoustic effect.
Alfonso teaches the acoustic characteristics are values that are added to the point definition, and may specify how different sound frequencies are affected when colliding with or coming into contact with the point cloud points that are defined with those acoustic characteristics. The sound effects defined by the acoustic characteristics may include specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties (col. 13, lines 11-22).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Alfonso into Olsen for the purpose of applying acoustic effects by specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties.
As to claim 15, Olsen teaches the method of claim 14, further comprising: a plurality of resonator circuits wherein different resonator circuits are tuned to generate different output frequencies (col. 1, lines 33-42 - a series of equal duration square wave trains, each of a different frequency, are sequentially generated and transmitted over a two wire line to a remotely located group of parallel resonant LC circuits whose inputs are serially connected. A separate two position micro-switch is connected to each LC circuit to provide for switching in additional capacitance to the LC circuits); an excitation signal that when applied to the array caused one or more resonator circuits in the plurality of resonator circuits to output a signal at an associated frequency (col. 1, lines 55-59 - a process control system utilizes a two wire line to transmit a DC control signal to an operator, such as a valve, simultaneously with a series of AC excitation signals to a group of parallel resonant LC circuits; claim 1 - signal-generating means coupled to said two-wire line at said central stations for producing a plurality of a-c excitation signals having different frequencies); a separate two position micro-switch is connected to each LC circuit to provide for switching in additional capacitance to the LC circuits; each individual LC circuit is designed to respond to only one of the generated frequencies (col. 1, lines 38-42). Alfonso teaches the acoustic characteristics are values that are added to the point definition, and may specify how different sound frequencies are affected when colliding with or coming into contact with the point cloud points that are defined with those acoustic characteristics. The sound effects defined by the acoustic characteristics may include specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties (col. 13, lines 11-22).
4. Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Olsen (US Patent 4,268,,822) and Alfonso (US Patent 12,210,095) in view of Elson (US Patent 10,410,614).
As to claim 5, Olsen and Alfonso do not explicitly discuss the device of claim 4, wherein the user input is a musical keyboard.
Elson teaches receiving commands via a keyboard note key (e.g., when an instrument has a keyboard) (col. 19, lines 45-47); receiving a command from the remote receiver (which in turn received the command from the local receiver), the remote instrument, which may be a computing device acting as a synthesizer or coupled to a digital keyboard, piano, guitar or other instrument (col. 8, lines 24-28).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Elson into Olsen and Alfonso for the purpose of receiving commands via a keyboard note key.
As to claim 6, Elson teaches the device of claim 4, wherein the user input is a musical instrument digital interface (MIDI) controller (col. 5, lines 24-28 - the musical instrument may generate instrument digital commands (e.g., MIDI commands). The digital recording may be in the form of stored instrument digital commands (e.g., MIDI commands) or a digital recording of analog music; col. 6, lines 8-22).
As to claim 7, Elson teaches receiving commands via a keyboard note key (e.g., when an instrument has a keyboard), the commands may also be received via other input mechanisms, such as, by way of non-limiting example, instrument strings (e.g., when an instrument has strings), via a resonator blown into by a user or air pump (such as an instrument that has a wind component, such as a trumpet, clarinet, oboe, flute, etc.), or via a percussive element (e.g., which an instrument has a percussive component) (col. 19, lines 45-53); and When the user provides an input or activates a control, a corresponding computing system may perform a corresponding operation (e.g., store the user input, process the user input, provide a response to the user input, etc.) (col. 45, line 67 through col. 46, line 3). Alfonso teaches the acoustic characteristics are values that are added to the point definition, and may specify how different sound frequencies are affected when colliding with or coming into contact with the point cloud points that are defined with those acoustic characteristics. The sound effects defined by the acoustic characteristics may include specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties (col. 13, lines 11-22). It would have been obvious to incorporate the teachings of Alfonso into the teachings of Elson to apply one or more sound effects to selected frequency of the user input in order to provide user opportunity to select different frequencies with different sound effects as desired.
5. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Olsen (US Patent 4,268,,822) and Alfonso (US Patent 12,210,095) in view of Gou et al. (2024/0347054).
As to claim 8, Olsen and Alfonso do not explicitly discuss the device of claim 1, further comprising an artificial intelligence (AI) network in communication with the acoustic effects module.
Gou teaches a series of training texts 510 and their standard pronunciation audio 512 are input into the aforementioned data preprocessing system 110, to be converted into training phonetic symbols 514 and training audio data 516 by the phonetic symbol generation system 112 (including the artificial intelligence model 124) ... The language model construction system 506 trains the language model 502 based on the training phonetic symbols 514. The acoustic model construction system 508 trains the acoustic model 504 based on the training phonetic symbols 514 as well as the training audio data 516. Under this design, the language model 502 may be well-trained by the language model construction system 506 based on the high-accuracy training phonetic symbols 514. Similarly, the acoustic model 504 the acoustic model may be well-trained to have better acoustic simulation effects by the construction system 508 based on the high-accuracy training phonetic symbols ([0064]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Gou into the teachings of Olsen and Alfonso for the purpose of having a well-trained acoustic model for better acoustic simulation effects.
6. Claims 9-12 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Olsen, Alfonso, and Gou in view of Reams (2004/0013272).
As to claim 9, Olsen, Alfonso, and Gou do not explicitly discuss the device of claim 8, wherein the AI network stores a library of models, a model providing inputs to the acoustic effects module for applying acoustic effects to frequencies selected by the AI network.
Reams teaches the spectral frequency bands can be selected corresponding to a number of artificial intelligence processing systems, such as neural network systems that are used to learn response characteristics for spectral frequency band gain settings … bandwidth reallocation can be selected based on predetermined selections from a library of characteristic sets, or other suitable processes can be performed ([0079]). Method 800 begins at 802 where spectral frequency bands are selected, the spectral frequency bands can be selected corresponding to a number of artificial intelligence processing systems, such as neural network systems that are used to learn response characteristics for spectral frequency band gain settings, these spectral frequency bands can be set to a user selected frequency range, to concentrate frequency bands in areas in which human hearing is most sensitive. Likewise, where any suitable number of frequency bands and corresponding artificial intelligence processing systems can be used, the spectral frequency bands can be selected based on the characteristics of the audio data, aesthetic qualities, or other suitable characteristics ([0079]), and adaptive gain settings are selected for each band, the adaptive gain settings can include one or more inputs, modifiers, conditionals, or other suitable characteristics that are used to control the generation of spectral gain characteristics. For example, adaptive gain settings can include threshold levels, response times, maximum gain settings, transfer functions settings, target levels, or other suitable adaptive gain settings. Bands can be used for each variable that are accessible absolutely or cumulatively, and can further include inputs based on other spectral frequency bands, such as gain levels or settings ([0080]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Reams into the teachings Olsen, Alfonso, and Gou for the purpose of performing bandwidth allocation selected based on predetermined selections from a library of characteristics set or other suitable processes can be performed.
As to claims 10 and 17, Reams teaches the audio device of claim 8 and the method of claim 15, wherein the AI network is trained with audio samples having labels indicating if the audio samples contain a pleasing sound to a human ear ([0037] - Sample selection system 204 allows the user to mark and select portions of the sample audio data having suitable aesthetic characteristics. In one exemplary embodiment, spectral shaping system 106, image management system 108, and other suitable systems can include artificial intelligence systems, such as one or more neural networks or other suitable artificial intelligence systems, that can be trained using the sample audio data so as to provide improved processing control of the target audio data, sample selection system 204 tracks the number of data points required (which corresponds to the time length of the sample audio data), and allows the user to determine whether the audio sample has sufficient length to allow such artificial intelligence systems to be trained; and [0038] - if a 10-second sample is required in order to train certain artificial intelligence components, then sample selection system 204 can allow the user to listen to the sample audio data to determine whether the aesthetic characteristics are desirable while viewing a graphical display that indicates whether the sample has sufficient length, if there are only certain portions of the audio sample that have the aesthetic characteristics desired by the user, sample selection system 204 allows the user to mark those portions to determine which portions have an acceptable length, to assign a relative ranking to each portion, or to perform other suitable processes).
As to claims 11 and 19, Reams teaches the audio device of claim 8 and the method of claim 15, wherein the AI network is trained to contain models that improve the aesthetic qualities of music and using the sample audio data so as to provide improved processing control of the target audio, emulate a particular musical instrument ([0037] - other suitable systems can include artificial intelligence systems, such as one or more neural networks or other suitable artificial intelligence systems, that can be trained using the sample audio data so as to provide improved processing control of the target audio data without operator intervention, sample selection system 204 tracks the number of data points required (which corresponds to the time length of the sample audio data), and allows the user to determine whether the audio sample has sufficient length to allow such artificial intelligence systems to be trained and [0091] - allowing audio image data characteristics to be generated from sample audio data, such as to reproduce the aesthetic qualities of the sample audio data. Method 900 also allows audio image data characteristics to be set or modified by user so as to provide aesthetic qualities not found in sample audio data, or for other suitable purposes. Method 900 can be used to minimize masking of perceptual queues so as to improve the aesthetic qualities of music by decreasing listener fatigue, masking or other undesirable characteristics of the music; [0004] - Audio data processing thus focuses on several concepts: spectral shaping and audio image. Spectral shaping refers to the settings of equalization bands, whereas audio image refers to the three-dimensional characteristic of stereophonic audio data as heard by a listener. For example, a listener in a room with two loudspeakers, one emitting stereophonic left channel signals and the other emitting stereophonic right channel signals, may seem to hear sound coming from the side of the room, the back of the room, or locations other than the two loudspeakers. The ability to present three-dimensional aesthetic qualities to music is referred to as the audio image of the audio data).
As to claims 12 and 20, Reams teaches the audio device of claim 8 and the method of claim 15, wherein the AI network is trained to contain models that emulate a particular acoustic/audio space ([0037] - other suitable systems can include artificial intelligence systems, such as one or more neural networks or other suitable artificial intelligence systems, that can be trained using the sample audio data so as to provide improved processing control of the target audio data without operator intervention, sample selection system 204 tracks the number of data points required (which corresponds to the time length of the sample audio data), and allows the user to determine whether the audio sample has sufficient length to allow such artificial intelligence systems to be trained; and [0093] - The audio data can be received at a studio, an audio data processor, can be received by a listener, or other suitable audio data reception points can be established).
As to claim 18, Reams teaches the method of claim 17, further comprising: producing the audio signal output from a model of the AI network, the model trained to produce an audio sample that is pleasing to the human ear ([0038] - a 10-second sample is required in order to train certain artificial intelligence components, then sample selection system 204 can allow the user to listen to the sample audio data to determine whether the aesthetic characteristics are desirable).
7. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Olsen and Alfonso in view of Reams (2004/0013272) and Andersen et al. (2013/0095759).
As to claim 14, Alfonso teaches the method of claim 13, further comprising the acoustic characteristics are values that are added to the point definition, and may specify how different sound frequencies are affected when colliding with or coming into contact with the point cloud points that are defined with those acoustic characteristics. The sound effects defined by the acoustic characteristics may include specifying an amount by which certain frequencies are absorbed or reflected, adjustments to apply to different frequencies, adding echo, reverberation, distortion, changing tone, timbre adjustments, amplitude adjustments, loudness adjustments, and/or specifying other changes to the sound or sound properties (col. 13, lines 11-22). Olsen and Alfonso do not explicitly discuss in a model of an artificial intelligence (AI) network, selecting the selected number of the plurality of resonator circuits; providing the selected number of resonator circuits to an acoustic effects module.
Reams teaches the spectral frequency bands can be selected corresponding to a number of artificial intelligence processing systems, such as neural network systems that are used to learn response characteristics for spectral frequency band gain settings … bandwidth reallocation can be selected based on predetermined selections from a library of characteristic sets, or other suitable processes can be performed ([0079]). Method 800 begins at 802 where spectral frequency bands are selected, the spectral frequency bands can be selected corresponding to a number of artificial intelligence processing systems, such as neural network systems that are used to learn response characteristics for spectral frequency band gain settings, these spectral frequency bands can be set to a user selected frequency range, to concentrate frequency bands in areas in which human hearing is most sensitive. Likewise, where any suitable number of frequency bands and corresponding artificial intelligence processing systems can be used, the spectral frequency bands can be selected based on the characteristics of the audio data, aesthetic qualities, or other suitable characteristics ([0079]), and adaptive gain settings are selected for each band, the adaptive gain settings can include one or more inputs, modifiers, conditionals, or other suitable characteristics that are used to control the generation of spectral gain characteristics. For example, adaptive gain settings can include threshold levels, response times, maximum gain settings, transfer functions settings, target levels, or other suitable adaptive gain settings. Bands can be used for each variable that are accessible absolutely or cumulatively, and can further include inputs based on other spectral frequency bands, such as gain levels or settings ([0080]).
Andersen teaches the fundamental frequency corresponds to the resonance frequency of the selected resonator circuit of the reader and data carrier ([0029]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Reams and Andersen into the teachings Olsen and Alfonso for the purpose of performing bandwidth allocation selected based on the selected resonator circuit and predetermined selections from a library of characteristics set or other suitable processes can be performed.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUYNH H NGUYEN whose telephone number is (571)272-7489. The examiner can normally be reached Monday-Thursday 7:30AM-5:30PM.
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/QUYNH H NGUYEN/Primary Examiner, Art Unit 2693
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