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 .
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Foreign Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in US on December 24, 2024. It is noted, however, that applicant has not filed a certified copy of the foreign patent application number JP 2022-107675 as required by 37 CFR 1.55.
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.
Claims 2, 15, 17 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 2 recites “the user” in line 6 of claim 2, but “the user” herein has an insufficient antecedent basis for the limitation in claim 2 and causes confusing because it is unclear what it is referred to and it is unclear what it is, e.g., it is unclear whether it is referred to claimed “performer” or “performers” in claim 1-2, etc., and thus, renders claim indefinite. Claims 15, 17 are rejected for the at least similar reasons as described in claim 2 above because claims 15, 17 recited the similar deficient limitation as recited in claim 2.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-8, 10, 14, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hamada et al (US 20190174247 A1, hereinafter Hamada) and in view of reference Lee Su-Ji (KR 102402113 B1, English translation and original versions attached herein and applied in the following cited paragraphs, hereinafter Lee).
Claim 1: Hamada teaches a method of processing sound (title and abstract, ln 1-15, method steps in figs. 7-10 and executed by a computer with software program, para 133), the method comprising:
arranging objects of a plurality of performers in a virtual space (guitar 20-1, trumpet 20-2, and clarinet 20-3 as sound sources and performed by performers, arranged in displayed virtual space of figs. 11(b) by MS-1, MS-2, MS-3, respectively, para 116-118);
receiving a plurality of sound signals respectively corresponding to the plurality of performers (sound data regarding each sound source stored in the information storage section 60 for mixing process section 50 that is connected to the information storage section 60 in fig. 2, para 45 and regenerating sound data by adjusting the levels of the sound source data undergone the effect process using the gain for each sound source, para 76);
obtaining, using a model (a dial hardware for rotating action, para 50 or 3D model data with the information storage section 60 storing applicable parameter information used in the mixing process, para 46), sound volume adjustment parameters respectively for the plurality of performers (the sound volume value adjusted through rotation action applied on the dial on the sound sources associated with the sound source setting sections 20, para 50, or metadata recorded recording levels, sound effects set at the time of recording, para 46); and
adjusting and mixing sound volumes respectively of the plurality of sound signals based on the sound volume adjustment parameters obtained using the model (through the mixing process section 50, setting or changing the sound volume, para 50 and for generating and displaying mixing parameters, para 51 and regarding each sound source, para 66 and outputting the sound signals generated by the mixing process section 50 to sound output section 91 to discriminate the sound output from the sound sources indicated by the sound source setting sections 20, para 65).
However, Hamada does not explicitly teach wherein the model is a trained model being trained to learn a relationship between each sound signal, among the plurality of sound signals, that corresponds to each performer of the plurality of performers and each sound volume adjustment parameter, among the sound volume adjustment parameters, that corresponds to the each sound signal; and the sound volume adjustment parameters are obtained using the trained model.
Lee teaches an analogous field of endeavor by disclosing a method of processing sound (title and abstract, ln 1-18, method steps in fig. 3 and executed by an orchestra ensemble system 100 in fig. 1), the method comprising:
arranging objects of a plurality of performers in a virtual space (receiving, via an input unit 110, performance positions or change of the position of the players on the virtual stage from player terminals connected to the orchestra ensemble system 100, para 9, p.5, para 3, p.9);
receiving a plurality of sound signals respectively corresponding to the plurality of performers (providing ensembled music to listener 300 through the providing unit 130 of the orchestra 100 at S340, para 8, p.7 and the music played by the player at player terminal 200, para 4-5, p.5 and thus, the orchestra 100 inherently receives the music played by the players 200);
obtaining, using a trained model (pre-learned learning model through AI, para 9-10, p.5), sound volume adjustment parameters respectively for the plurality of performers (the performance volume is determined by the pre-learned learning model operated by the controller 120, para 3, p.7 and applied to the music of the instrument through the equalizer, and sync, para 10, p.5), the trained model being trained to learn a relationship between each sound signal, among the plurality of sound signals, that corresponds to each performer of the plurality of performers (music from the type of the instrument and played by the player or musician, para 4, p.5 and to be applied with music volume of the instrument, para 9, p.5 or learned the volume of the performance to be heard at the virtual audience location through AI learning, para 4, p.7, i.e., the audio signal outputted from the output unit 130 of the orchestra 100) and each sound volume adjustment parameter, among the sound volume adjustment parameters, that corresponds to the each sound signal (music volume per the instrument, para 10, p.5, para 1, p.6 and re-determining the performance volume by adjusting the equalizer, volume, and sync in response to a change of performance position, para 2, p.6 or learned the volume, sync, and equalizer of the instrument according to the performance position on the virtual stage, para 6, p.7); and adjusting and mixing sound volumes respectively of the plurality of sound signals (adjusting the equalizer, volume, and the sync so that playing volume of the corresponding player is heard while the performance location is changed, para 7-9, p.7 and through the music transmission, para 11, p.7 and according to determined volume of the performance to be heard at the virtual audience location through AI, para 4, p.7) based on the sound volume adjustment parameters obtained using the trained model (learned volume of the instrument and performance volume to be heard at the listener terminal, para 7, p.9 and based on the determination of the control unit, para 1, p.10) for benefits of improving the performance of the listeners’ experience (by virtually and adaptively providing the realistic experiences to listeners, para 2 of abstract and with improved players’ skills, para 1, p.8).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the trained model and wherein the trained model is trained to learn the relationship between each sound signal, among the plurality of sound signals, that corresponds to each performer of the plurality of performers and each sound volume adjustment parameter, among the sound volume adjustment parameters, that corresponds to the each sound signal; and the sound volume adjustment parameters are obtained using the trained model, as taught by Lee, to the model applied in the method or processing sound, as taught by Hamada, for the benefits discussed above.
Claim 14 has been analyzed and rejected according to claim 1 above and the combination of Hamada and Lee further teaches a sound processing apparatus (Hamada, a computer to implement the methods and functions with memories and program codes, para 13-14, and Lee, orchestra ensemble system 100 with player terminals 200 and listener terminals 300 in fig. 1 and by using a computer, para 1, p.4) comprising a processor (Hamada and Lee, the computer used for the execution of the method discussed above, a processor for the computer is inherency) to perform the method as recited in claim 1 (discussed in claim 1 above).
Claim 16 has been analyzed and rejected according to claims 1, 14 above, and wherein the combination of Hamada and Lee further teaches a non-transitory computer-readable storage medium storing a program (Hamada, memories stored with program codes, para 13-14) which, when executed by at least one processor, causes the at least one processor (Hamada and Lee, the computer and thus, the processor inherently disclosed and discussed in claim 14 above) to perform the method steps of claim 1 (Hamada and Lee, the method steps discussed in claim 1 above and Hamada and Lee, implemented by at one processor of the computer discussed in claim 14 above).
Claim 2: the combination of Hamada and Lee further teaches, according to claim 1 above, the method further comprising:
arranging, in the virtual space, a plurality of sound volume adjustment interfaces respectively corresponding to the objects of the plurality of performers (Hamada, manually setting the mixing parameters and listening parameters, para 86, and dial for rotations through operation section 21 to change sound volume or sound effects on the sound sources, para 50 and e.g., the volume is setting per sound source based shape of the specific sound source modified, para 54 and the setting is individually displayed in fig. 12, para 117);
receiving the plurality of sound signals respectively corresponding to the plurality of performers (Hamada, sound data regarding each sound source stored in the information storage section 60, para 45 and for mixing process section 50 connected to the information storage section 60 in fig. 2 and Lee, providing ensembled music to listener 300 through the providing unit 130 of the orchestra 100 at S340, para 8, p.7 and the music played by the player at player terminal 200, para 4-5, p.5 and thus, the orchestra 100 inherently receives the music played by the players 200 and discussed in claim 1 above);
receiving, from the user, sound volume adjustment parameters respectively for the plurality of performers and respectively corresponding to the plurality of sound volume adjustment interfaces (Hamada, at ST8, parameters set or changed by the sound source setting sections 20, e.g., 20-1, 20-2, 20-3, para 88, and the sound source setting sections 20 per sound source has functions to set sound source including sound volume, para 42, and each has dial for setting the volume per sound source, para 50 and Lee, inputs from player terminals 200 and listener terminal 300, para 3, p.5); and
generating the trained model trained to learn a relationship between the each sound signal corresponding to the each performer and each sound volume adjustment parameter, among the sound volume adjustment parameters received from the user, that corresponds to the each sound signal (Hamada, the manually receiving sound volume per sound source, positions and locations of sound sources and audience through sound source setting sections 20 and discussed above, and Lee, learning model learns the volume, sync, and equalizer of the instrument as sound source, and level of the performance volume heard at the virtual audience position selected by the listener using AI, i.e., the learning model from AI to have relationship among the instrument types, the locations/positions, the equalizer, sync, volume, etc., para 6, p.7).
Claim 3: the combination of Hamada and Lee further teaches, according to claim 1 above, wherein the trained model is trained to learn a relationship between the each sound signal and an effect parameter of effect processing performed on the each sound signal (Hamada, sound processing or effects are set with the sound volume set by the sound source setting sections 20 for each sound source, para 42 or based on metadata, para 46 and Lee, the learning model learns not just the volume of the instrument, but sync, the equalizer, etc., i.e., effect parameter of the effect processing applied to each of instrument played, the last paragraph of p.5, para 6, p.7 and para 7, p.9), and the method also comprises obtaining, using the trained model, effect parameters respectively for the plurality of performers (Hamada, sound effect applied to the sound source and displayed 222 as reverb sound effect for guitar in fig. 12, para 117, and Lee, the equalizer and sync as sound effect settings are adjusted while the sound source changed location, para 7-8, p.7), and performing the effect processing on the plurality of sound signals based on the effect parameters obtained using the trained model (Lee, so that the listener can hear the adjusted sound effect by adjusting equalizer, and the sync, and as well the playing volume by which ensembled music heard by the listener, para 9-10, p.7).
Claim 4: the combination of Hamada and Lee further teaches, according to claim 3 above, further comprising: receiving information regarding a plurality of audio appliances respectively used by the plurality of performers (Hamada, types of instruments are assigned to the sound source setting sections 20, para 117 and Lee, receiving type of instruments by the orchestra from the player terminals connected to the orchestra ensemble system, para 3, p.5, last para of p.9 and then used by the pre-learn model for obtaining parameters such as sound source volume and the volume of the sound to be heard and discussed above); and obtaining the effect parameters respectively for the plurality of performers based on the received information (Hamada, the sound source setting section 20 to obtain sound effect parameter, and Lee, the performance volume is determined by the pre-learned learning model S330 upon at least the type of instrument, para 3, p.7).
Claim 5: the combination of Hamada and Lee further teaches, according to claim 1 above, the method further comprising: obtaining information regarding a plurality of audio appliances respectively used by the plurality of performers (Hamada, information on 20-1, 20-2, 20-3 as individual sound source setting section 20 is given as guitar, trumpet, and clarinet, para 116 and Lee, receiving the type of instrument by the orchestra system from the music player terminals, para 3, p.5 and the discussion in claims 3-4 above); and adjusting the sound volumes respectively of the plurality of sound signals based on the obtained information (Hamada, the sound effect parameters set by the individual sound source setting section 20s, para 42 and Lee, the performance volume is determined in response to at least the received type of instrument, para 3, p.7).
Claim 6: the combination of Hamada and Lee further teaches, according to claim 1 above, the method further comprising: obtaining first position information regarding positions respectively of the objects of the plurality of performers (Hamada, sound source position is set via the sound source setting sections 20, para 42, and Lee, the performance position on the virtual stage obtained from the player terminals 200, para 3, p.5) and second position information regarding a position of a viewer (Hamada, through the listening setting section 30, to set a listening point position, para 43, and Lee, the virtual audience seat location obtained from the listeners’ terminals 300, para 3, p.5); and adjusting the sound volumes respectively of the plurality of sound signals based on the first position information and the second position information (Hamada, sound effects or sound processing are set by operating the dial and associated with listening setting section 30, para 50, and associated with sound source setting section 20, para 50, and Lee, the performance volume, equalizer, sync, are determined in response to the performance position on the virtual stage and virtual audience position of the listener, last para of p.5).
Claim 7: the combination of Hamada and Lee further teaches, according to claim 1 above, wherein the user comprises a performer (Hamada, user who operated operation section 21 at the sound source setting section 20 in fig. 3 and the element 21 receives user’s operations such as setting and changing of mixing parameters, para 50, and Lee, the user using the terminal plays a musical instrument, i.e., the player terminal 200).
Claim 8: the combination of Hamada and Lee further teaches, according to claim 1 above, wherein the sound volume adjustment parameters obtained using the trained model (discussed in claim 1 above) are used in a reception-side appliance configured to mix the plurality of received sound signals (Hamada, the listener terminals 300 as the reception-side appliance, in fig. 4 and the listening setting control section 35 in fig. 4, transmits the setting parameter information inputted from operation section 31 to the mixing process section 50, para 61 and Lee, the orchestra 100 as the reception-side connected to the listeners’ terminals 300 in fig. 1 and for submission of audio signals to each of the listener terminal in fig. 1).
Claim 10: the combination of Hamada and Lee further teaches, according to claim 1 above, wherein the plurality of sound signals respectively corresponding to the plurality of performers are received via a network (Hamada, via communication media networks, para 14 and Lee, terminals 200, 300, are inter-connected together with the wire or wireless through the orchestra ensemble system 100 in fig. 1, para 6, p.5 and Lee, player terminals 200 and listener terminals 300 are connected together through wire or wireless link, para 6, p.5).
Claim 15 has been analyzed and rejected according to claims 14, 2 above.
Claim 17 has been analyzed and rejected according to claims 16, 2 above.
Claim 18 has been analyzed and rejected according to claims 2, 3 above.
Claim 19 has been analyzed and rejected according to claims 2, 5 above.
Claim 20 has been analyzed and rejected according to claims 3, 5 above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Hamada (above) and in view of references Lee (above) and Reiss et al. (US 20150117685 A1, hereinafter Reiss).
Claim 9: the combination of Hamada and Lee further teaches, according to claim 1 above, wherein the sound volume adjustment parameters obtained using the trained model are respectively used in a plurality of appliances respectively used by the plurality of performers (Hamada, 20-1 guitar, 20-2 trumpet, and 20-3 clarinet, para 116 and Lee, the learning model determines the listening volume, para 1, p.6, and at the player terminals 300, the performers played Geumbu, Seokbu, Sabu, Jukbu, Apobu, Tobu, and Hyukbu, etc., para 10, p.6), except explicitly teaching wherein the plurality of appliances are configured to adjust the sound volumes respectively of the plurality of sound signals based on the sound volume adjustment parameters, and a reception-side appliance is configured to receive and mix the plurality of sound signals whose sound volumes have been adjusted by the plurality of appliances.
Reiss teaches an analogous field of endeavor by disclosing a method (title and abstract, ln 1-10 and fig. 1) and wherein sound volume adjustment parameters (perceptual loudness parameters determined for all channels in fig. 2) obtained using a trained model (the combination of modules 202, 203 in considering perceptual loudness in loudness extraction and loudness optimization in fig. 2, para 69, or sub-grouping to achieve optimal mix of the audio signals 101 by source recognition that is based on machine learning algorithms, para 50-51, e.g., recognizing type of instrument, para 95) are respectively used in a plurality of appliances respectively used by the plurality of performers (output signals from variety of instruments in fig. 1-4), the plurality of appliances are configured to adjust the sound volumes respectively of the plurality of sound signals based on the sound volume adjustment parameters (the tracks representing audio signals from different instruments have their loudness altered by having gains applied to increase or decrease a signal level, para 69), and a reception-side appliance (combiners 311, 312 with mastering 316 in fig. 3 or multi-track subgroup with processors element 300c in fig. 4) is configured to receive and mix the plurality of sound signals whose sound volumes have been adjusted by the plurality of appliances (mixing the processed audio signals of the tracks and providing audio output to stereo out 318 in figs. 3, para 94, or 403 in fig. 4, para 95-96) for benefits of improving the audio signal processing efficiency (by using sub-grouping of the channels or tracks, para 51, and by improving perceived sound quality in reduce the sibilant sound effects, para 76, by improving the sound quality in spatial balance analyzed, para 88, e.g., by analyzing the ratio of the peak, etc., para 90).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied wherein the plurality of appliances are configured to adjust the sound volumes respectively of the plurality of sound signals based on the sound volume adjustment parameters, and a reception-side appliance is configured to receive and mix the plurality of sound signals whose sound volumes have been adjusted by the plurality of appliances, as taught by Reiss, to the sound volume adjustment parameters obtained using the trained model being respectively used in the plurality of appliances respectively used by the plurality of performers in the method, as taught by the combination of Hamada and Lee, for the benefits discussed above.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Hamada (above) and in view of references Lee (above) and Wang et al. (TW 1717596 B, English translation and original attached herein and the English translation version applied below while paragraphs cited under Wang).
Claim 11: the combination of Hamada and Lee further teaches, according to claim 1 above, the trained model being trained and the sound volume adjustment parameters (discussed in claim 1 above), except explicitly teaching receiving the sound volume adjustment parameters from a first information processor of a first user; training a predetermined model using the sound volume adjustment parameters to generate the trained model; transmitting the trained model to a second information processor of a second user; and obtaining, using the second information processor, the sound volume adjustment parameters using the trained model.
Wang teaches an analogous field of endeavor by disclosing a method (title and abstract, ln 1-9 and method steps in fig. 3) and wherein receiving sound volume adjustment parameters from a first information processor of a first user (system volume s1 from pre-adjusted system record and as training data 420 of the user data model in fig. 4, para 2, p.6 and with respect to one of Internet streaming platforms, BACKGROUND-ART, p.2); training a predetermined model using the sound volume adjustment parameters to generate the trained model (user data model trained in fig. 4 and to generate system volume s2 in fig. 4, para 2, p.6); transmitting the trained model to a second information processor of a second user (e.g., from user A in fig. 5, para 5-6, p.6, to user B with laptop in fig. 7, para 4-5, p.7, and the current volume loudness exceeds the tolerance range, para 3, p.6, with different platforms, BACKGROUND-ART, p.2); and obtaining, using the second information processor, the sound volume adjustment parameters using the trained model (obtaining system volume s2 as output 440 of the training user data model of fig. 4 corresponding to different situations of the user data in fig. 4, para 3-4, p.5) for benefits of
Improving user’s experiences (by dynamically and automatically adjusting sound volume according to the time and the location, para 1, p.6, and the audio content such as music vs. advertisement, para 2, p.7) to achieve a high quality of sound (corresponding to different situations, BACKGROUND-ART, p.2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied receiving the sound volume adjustment parameters from the first information processor of the first user; training the predetermined model using the sound volume adjustment parameters to generate the trained model; transmitting the trained model to the second information processor of the second user; and obtaining, using the second information processor, the sound volume adjustment parameters using the trained model, as taught by Wang, to the trained model being trained with the sound volume adjustment parameters in the method, as taught by the combination of Hamada and Lee, for the benefits discussed above.
Claim 12: the combination of Hamada, Lee, and Wang further teaches, according to claim 11 above, the method further comprising: receiving the sound volume adjustment parameters from the first information processor or the second information processor (Hamada, and Lee, discussed in claim 1 above, and Wang, s1, s2 as output of the training user data model and discussed in claim 11 above); and re-training the trained model using the received sound volume adjustment parameters (Wang, re-training is performed if the current volume loudness exceeds the tolerance range through the user data model, s1, or s2, or sx can be the input 420 of training in fig. 4, para 6, p.3).
Claim 13: the combination of Hamada, Lee, and Wang further teaches, according to claim 11 above, central service unit (Hamada, mixing processing section in fig. 6, and Lee, the orchestra system 100 in fig. 1, and Wang, a volume control system 530) and applications (Hamada, music production, para 75, and Lee, music practice for musician, para 4, p.4, Wang, watch YouTubeTM), except explicitly teaching performing, using a server, billing processing for the second user; and performing, using the server, compensation payment processing for the first user.
An Official Notice is taken that service about billing processing and compensation payment provided by a server is notoriously well-known in the art for benefits of being easier and convenient by using natural language processing including speech, audio signal processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the billing processing and the compensation payment provided by the server, as taught by the well-known in the art, to the central service unit in the method, as taught by the combination of Hamada, Lee, and Wang for the benefits discussed above.
Conclusion
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/LESHUI ZHANG/
Primary Examiner,
Art Unit 2695
Prosecution Insights