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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 8/14/2023 and 9/28/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) are being considered by the examiner.
Election/Restrictions
Claims 19-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 6/24/2026.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-7, 9, 11, 13-14, and 17-18 are rejected under 35 U.S.C. 103 as unpatentable over Yamada (US 5063820 A, November 12, 1991) in view of Lee et al. ("Neural networks for simultaneous classification and parameter estimation in musical instrument control," 1992, retrieved September 4, 2026 from https://www.spiedigitallibrary.org/conference-proceedings-of-spie/1706/1/Neural-networks-for-simultaneous-classification-and-parameter-estimation-in-musical/10.1117/12.139949.short), hereinafter Lee.
Regarding claim 1, Yamada teaches an information processing system comprising: at least one memory that stores a program (Yamada col. 8, lines 29-33: "Also, a plurality of analyzed algorithms can be stored in a memory, so that the touch data can be analyzed by selecting respective algorithms. Moreover, a method using artificial intelligence can be used to analyze the touch data."); and at least one processor that executes the program (Yamada col. 3, line 64 - col. 4, line 3: "The performance analyzer 7 outputs addresses to the performance information memories M1 and M2 to control the writing and reading of touch data V, and then analyzes the touch data stored in the performance information memory M1 by using the touch data stored in the performance information memory M2, to rewrite the touch conversion table 3 in accordance with the result of the analysis.") to: acquire input data (Yamada col. 7, lines 50-57: "Accordingly, in the electronic musical instrument, touch data V is always written into the performance information memory M1. When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis.") that includes habit data indicative of a playing habit of a user in playing a musical instrument (Yamada col. 8, lines 52-55: "The characteristic curve shown in FIG. 3 and the number of occurrences of data shown in FIG. 5 can be displayed on a display apparatus to evaluate a habit and/or a performance of a player, or the like."); and correct, using the generated correction data, at least one first intensity characteristic (Yamada col. 5, lines 50-57: "Assuming that the difference E.sub.n is positive, the inclination of characteristic curve is increased, as shown by a broken line. While the difference E.sub.n is negative, the inclination of the characteristic curve is decreased, as shown by a chain line. In such cases, the magnitude of the inclination is determined by the compensation value X.sub.n which is calculated by equation (2) or (3).") representative of a relationship between: a playing intensity in playing the musical instrument by the user (Yamada col. 1, lines 13-19: "Conventional types of electronic musical instrument as disclosed in, for example, Japanese Patent Publication No. 53-5545, detect key-velocity of a key depressed by a player, and converts the key-velocity into tone volume control data based on a touch conversion table stored in a memory. The tone volume control data then controls volumes of musical tones."); and a sound intensity of a musical sound output in response to playing of the musical instrument (Yamada col. 1, lines 13-19: "Conventional types of electronic musical instrument as disclosed in, for example, Japanese Patent Publication No. 53-5545, detect key-velocity of a key depressed by a player, and converts the key-velocity into tone volume control data based on a touch conversion table stored in a memory. The tone volume control data then controls volumes of musical tones.").
Yamada does not explicitly disclose: generate correction data by inputting the acquired input data into at least one trained model that learns a relationship between training input data and training correction data.
However, Lee teaches: generate correction data by inputting the acquired input data into at least one trained model (Lee § 6.1: "In parallel with the classifier, a group of parameter estimation modules runs for each class. These estimation modules process data in the same manner as the classification module; raw input passes through a preprocessor and then flows through a neural network. The difference between the two types of modules is that estimator uses the neural network as a multidimensional function approximator and the output of the module as a control vector.") that learns a relationship between training input data and training correction data (Lee § 4.2: "MAXNet has a companion program, MACNet, which runs on the Macintosh and UNIX machines to facilitate training. Both code objects have identical capabilities. The most efficient way to use MAXNet is to setup a data acquisition harness in MAX and collect the data into a training file. Then use MACNet to learn the input/output mapping and save the weights file. Finally, attach MAXNet to the acquisition harness, read in the weights, and then compute.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the information processing system of Yamada by adding the trained models of Lee to employ a method using artificial intelligence to analyze the touch data (Yamada col. 8, lines 31-33).
Regarding claim 2, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 1 as discussed above.
Yamada further teaches that the at least one processor further executes the program to set a second intensity characteristic by correcting the at least one first intensity characteristic using the correction data (Yamada col. 7, lines 52-59: "When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis. As a result, the rewritten touch conversion table 3 is set in the most suitable for a player automatically.").
Regarding claim 3, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 2 as discussed above.
Yamada further teaches that the at least one first intensity characteristic is provided in advance (Yamada col. 2, lines 3-13: "performance operation means for outputting performance information in response to a performance by a player; converting means for converting said performance information into musical tone control data determining a characteristic of said musical tone in accordance with a predetermined characteristic of conversion; storage means for storing said performance information; and analyzing means for analyzing said performance information stored in said storage means and for changing said characteristic of conversion in accordance with the analysis result."), and the second intensity characteristic reflects the playing habit of the user (Yamada col. 7, lines 52-59: "When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis. As a result, the rewritten touch conversion table 3 is set in the most suitable for a player automatically.").
Regarding claim 6, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 1 as discussed above.
Lee further teaches that the input data includes user playing data indicative of a time series of notes played by the user (Lee § 6.3.1: "We used the MDI keyboard as the segmentor (segmentation is resolved when pitch and note onset are determined) and to provide pitch and a velocity measure. Because each timbre had different velocity response curves, we used estimator modules to match and rescale the volume of the bank to the liking of the user").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the information processing system of Yamada by adding the time series of notes of Lee to use one gesture to represent a sequence of notes (Lee § 2).
Regarding claim 7, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 1 as discussed above.
Yamada further teaches that the at least one first intensity characteristic comprises a plurality of first intensity characteristics, each first intensity characteristic of the plurality of first intensity characteristics corresponding to a different tone (Yamada col. 3, lines 7-16: "The touch conversion table 3 is used to convert touch data V supplied from the keyboard circuit 1, into tone volume control data D, which is then supplied to the musical tone generator 4. The touch conversion table 3 comprises a conversion table, each section of which corresponds to one of the tone colors. Each section of the conversion table is selected by its corresponding tone color code TC selected by the tone color switching circuit 2 so as to convert touch data V into tone volume control data D."), and the at least one processor further executes the program to correct, using the correction data, a first intensity characteristic that corresponds to a tone selected by the user from among the plurality of first intensity characteristics (Yamada col. 7, lines 52-59: "When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis. As a result, the rewritten touch conversion table 3 is set in the most suitable for a player automatically.").
Regarding claim 9, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 1 as discussed above.
Lee further teaches that the at least one trained model comprises a plurality of trained models (Lee § 6.1: "In parallel with the classifier, a group of parameter estimation modules runs for each class. These estimation modules process data in the same manner as the classification module; raw input passes through a preprocessor and then flows through a neural network. The difference between the two types of modules is that estimator uses the neural network as a multidimensional function approximator and the output of the module as a control vector."), each trained model of the plurality of trained models corresponding to a different tone, and the at least one processor further executes the program to generate the correction data (Lee § 6.3.1: "Because each timbre had different velocity response curves, we used estimator modules to match and rescale the volume of the bank to the liking of the user. The user controlled the composite timbre of the sound with one hand and the triggered the note with the other."), using a trained model that corresponds to a tone selected by the user from among the plurality of trained models (Lee § 6.3.1: "Because each timbre had different velocity response curves, we used estimator modules to match and rescale the volume of the bank to the liking of the user. The user controlled the composite timbre of the sound with one hand and the triggered the note with the other.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the information processing system of Yamada by adding the plurality of trained models of Lee to scale the sound to the liking of the user (Lee § 6.3.1).
Regarding claim 11, Yamada teaches an electronic musical instrument comprising: at least one memory that stores a program (Yamada col. 8, lines 29-33: "Also, a plurality of analyzed algorithms can be stored in a memory, so that the touch data can be analyzed by selecting respective algorithms. Moreover, a method using artificial intelligence can be used to analyze the touch data."); at least one processor that executes the program (Yamada col. 3, line 64 - col. 4, line 3: "The performance analyzer 7 outputs addresses to the performance information memories M1 and M2 to control the writing and reading of touch data V, and then analyzes the touch data stored in the performance information memory M1 by using the touch data stored in the performance information memory M2, to rewrite the touch conversion table 3 in accordance with the result of the analysis.") to: acquire input data (Yamada col. 7, lines 50-57: "Accordingly, in the electronic musical instrument, touch data V is always written into the performance information memory M1. When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis.") that includes habit data indicative of a playing habit of a user in playing the electronic musical instrument (Yamada col. 8, lines 52-55: "The characteristic curve shown in FIG. 3 and the number of occurrences of data shown in FIG. 5 can be displayed on a display apparatus to evaluate a habit and/or a performance of a player, or the like."); and correct, using the generated correction data, at least one first intensity characteristic (Yamada col. 5, lines 50-57: "Assuming that the difference E.sub.n is positive, the inclination of characteristic curve is increased, as shown by a broken line. While the difference E.sub.n is negative, the inclination of the characteristic curve is decreased, as shown by a chain line. In such cases, the magnitude of the inclination is determined by the compensation value X.sub.n which is calculated by equation (2) or (3).") representative of a relationship between: a playing intensity in playing the electronic musical instrument by the user (Yamada col. 1, lines 13-19: "Conventional types of electronic musical instrument as disclosed in, for example, Japanese Patent Publication No. 53-5545, detect key-velocity of a key depressed by a player, and converts the key-velocity into tone volume control data based on a touch conversion table stored in a memory. The tone volume control data then controls volumes of musical tones."); and a sound intensity of a musical sound output in response to playing of the electronic musical instrument (Yamada col. 1, lines 13-19: "Conventional types of electronic musical instrument as disclosed in, for example, Japanese Patent Publication No. 53-5545, detect key-velocity of a key depressed by a player, and converts the key-velocity into tone volume control data based on a touch conversion table stored in a memory. The tone volume control data then controls volumes of musical tones."); and set a second intensity characteristic by correcting the at least one first intensity characteristic (Yamada col. 7, lines 52-59: "When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis. As a result, the rewritten touch conversion table 3 is set in the most suitable for a player automatically."); a playing device configured to receive playing input by the user (Yamada col. 2, lines 54-61: "The keyboard circuit 1 outputs key-on signal KON to a musical tone generator 4 and a performance analyzer 7, key-code KC to the musical tone generator 4, and touch data V to a touch conversion table 3 and a performance information memory M1, in which the key-on signal KON indicates a key depressed, the key-code KC indicates a key code of the key depressed, and the touch data V indicates a speed of the key in depressing."); and a playback controller configured to control a playback system to play back a musical sound dependent on the received playing input using the second intensity characteristic (Yamada col. 3, lines 38-45: "Returning to FIG. 2, the musical tone generator 4 generates a musical tone signal which comprises a tone pitch of key-code KC supplied from the keyboard circuit 1, a tone volume of tone volume control data D supplied from the touch conversion table 3, and a tone color of tone color code TC supplied from the tone color switching circuit 2. This musical tone signal is output to a speaker 5.").
Yamada does not explicitly disclose: generate correction data by inputting the acquired input data into at least one trained model that learns a relationship between training input data and training correction data.
However, Lee teaches: generate correction data by inputting the acquired input data into at least one trained model (Lee § 6.1: "In parallel with the classifier, a group of parameter estimation modules runs for each class. These estimation modules process data in the same manner as the classification module; raw input passes through a preprocessor and then flows through a neural network. The difference between the two types of modules is that estimator uses the neural network as a multidimensional function approximator and the output of the module as a control vector.") that learns a relationship between training input data and training correction data (Lee § 4.2: "MAXNet has a companion program, MACNet, which runs on the Macintosh and UNIX machines to facilitate training. Both code objects have identical capabilities. The most efficient way to use MAXNet is to setup a data acquisition harness in MAX and collect the data into a training file. Then use MACNet to learn the input/output mapping and save the weights file. Finally, attach MAXNet to the acquisition harness, read in the weights, and then compute.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the electronic musical instrument of Yamada by adding the trained models of Lee to employ a method using artificial intelligence to analyze the touch data (Yamada col. 8, lines 31-33).
Regarding claim 13, Yamada teaches a computer-implemented information processing method (Yamada col. 3, line 64 - col. 4, line 3: "The performance analyzer 7 outputs addresses to the performance information memories M1 and M2 to control the writing and reading of touch data V, and then analyzes the touch data stored in the performance information memory M1 by using the touch data stored in the performance information memory M2, to rewrite the touch conversion table 3 in accordance with the result of the analysis.") comprising: acquiring input data (Yamada col. 7, lines 50-57: "Accordingly, in the electronic musical instrument, touch data V is always written into the performance information memory M1. When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis.") that includes habit data indicative of a playing habit of a user in playing a musical instrument (Yamada col. 8, lines 52-55: "The characteristic curve shown in FIG. 3 and the number of occurrences of data shown in FIG. 5 can be displayed on a display apparatus to evaluate a habit and/or a performance of a player, or the like."); and correcting, using the generated correction data, at least one first intensity characteristic (Yamada col. 5, lines 50-57: "Assuming that the difference E.sub.n is positive, the inclination of characteristic curve is increased, as shown by a broken line. While the difference E.sub.n is negative, the inclination of the characteristic curve is decreased, as shown by a chain line. In such cases, the magnitude of the inclination is determined by the compensation value X.sub.n which is calculated by equation (2) or (3).") representative of a relationship between: a playing intensity in playing the musical instrument by the user (Yamada col. 1, lines 13-19: "Conventional types of electronic musical instrument as disclosed in, for example, Japanese Patent Publication No. 53-5545, detect key-velocity of a key depressed by a player, and converts the key-velocity into tone volume control data based on a touch conversion table stored in a memory. The tone volume control data then controls volumes of musical tones."); and a sound intensity of a musical sound output in response to playing of the musical instrument (Yamada col. 1, lines 13-19: "Conventional types of electronic musical instrument as disclosed in, for example, Japanese Patent Publication No. 53-5545, detect key-velocity of a key depressed by a player, and converts the key-velocity into tone volume control data based on a touch conversion table stored in a memory. The tone volume control data then controls volumes of musical tones.").
Yamada does not explicitly disclose: generating correction data by inputting the acquired input data into at least one trained model that learns a relationship between training input data and training correction data.
However, Lee teaches: generating correction data by inputting the acquired input data into at least one trained model (Lee § 6.1: "In parallel with the classifier, a group of parameter estimation modules runs for each class. These estimation modules process data in the same manner as the classification module; raw input passes through a preprocessor and then flows through a neural network. The difference between the two types of modules is that estimator uses the neural network as a multidimensional function approximator and the output of the module as a control vector.") that learns a relationship between training input data and training correction data (Lee § 4.2: "MAXNet has a companion program, MACNet, which runs on the Macintosh and UNIX machines to facilitate training. Both code objects have identical capabilities. The most efficient way to use MAXNet is to setup a data acquisition harness in MAX and collect the data into a training file. Then use MACNet to learn the input/output mapping and save the weights file. Finally, attach MAXNet to the acquisition harness, read in the weights, and then compute.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the computer-implemented information processing method of Yamada by adding the trained models of Lee to employ a method using artificial intelligence to analyze the touch data (Yamada col. 8, lines 31-33).
Regarding claim 14, Yamada (in view of Lee) teaches a computer-implemented information processing method comprising the features of claim 13 as discussed above.
Yamada further teaches setting a second intensity characteristic by correcting the at least one first intensity characteristic using the correction data (Yamada col. 7, lines 52-59: "When a thousand occurrences of touch data V with respect to one tone color is written into the performance information memory M1, all touch data is read out for analysis. The touch conversion table 3 is then rewritten in accordance with the result of the analysis. As a result, the rewritten touch conversion table 3 is set in the most suitable for a player automatically.").
Regarding claim 17, Yamada (in view of Lee) teaches a computer-implemented information processing method comprising the features of claim 13 as discussed above.
Lee further teaches that the input data includes user playing data indicative of a time series of notes played by the user (Lee § 6.3.1: "We used the MDI keyboard as the segmentor (segmentation is resolved when pitch and note onset are determined) and to provide pitch and a velocity measure. Because each timbre had different velocity response curves, we used estimator modules to match and rescale the volume of the bank to the liking of the user").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the computer-implemented information processing method of Yamada by adding the time series of notes of Lee to use one gesture to represent a sequence of notes (Lee § 2).
Regarding claim 18, Yamada (in view of Lee) teaches a computer-implemented information processing method comprising the features of claim 13 as discussed above.
Yamada further teaches that the at least one first intensity characteristic comprises a plurality of first intensity characteristics, each first intensity characteristic of the plurality of first intensity characteristics corresponding to a different tone (Yamada col. 3, lines 7-16: "The touch conversion table 3 is used to convert touch data V supplied from the keyboard circuit 1, into tone volume control data D, which is then supplied to the musical tone generator 4. The touch conversion table 3 comprises a conversion table, each section of which corresponds to one of the tone colors. Each section of the conversion table is selected by its corresponding tone color code TC selected by the tone color switching circuit 2 so as to convert touch data V into tone volume control data D."), and the correcting corrects, using the generated correction data, a first intensity characteristic that corresponds to a tone selected by the user, from among the plurality of first intensity characteristics (Starr ¶0094: "Other processing parameters may be selected through the user interface, such as muting zones other than a designated zone, transposing keys in a zone, adjusting key pressure for activation, selecting fixed velocity or touch-sensitive key response, selection of velocity-response curve for the zone, and a unison setting to give all keys in a selected zone the same MIDI pitch value. Thus, the response to the pressing of the keys may be set and adjusted to provide various zones having different function and operation and sound.").
Claims 4-5 and 15-16 are rejected under 35 U.S.C. 103 as unpatentable over Yamada in view of Lee, and further in view of Worrall et al. (US 20070234878 A1, October 11, 2007), hereinafter Worrall.
Regarding claim 4, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 1 as discussed above.
Yamada further teaches that the habit data is indicative of at least one playing characteristic (Yamada col. 8, lines 1-7: "In the above embodiment, touch data V is recorded for every tone color, but this touch data can be recorded independent of the tone color. That is, the touch data V can be recorded for, e.g. every player of the keyboard, or every group of keys of the keyboard, or every keyboard in the case of an electronic musical instrument having a plurality of keyboards.").
Yamada (in view of Lee) does not explicitly disclose at least one playing characteristic for an operator used in playing a piece of music by the user from among a plurality of operators.
However, Worrall teaches at least one playing characteristic for an operator used in playing a piece of music by the user from among a plurality of operators (Worrall ¶0152: "Hand and/or finger positioning information for a keyboard instrument may indicate which finger(s) of the left hand is/are to be placed on which key(s), and which finger(s) of the right hand is/are to be placed on which key(s) to play a given note or sequential string of notes; and so on.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the information processing system of Yamada (as modified by Lee) by adding the operator of from among of plurality of operators of Worrall to render the musical composition easier to perform on the instrument for the skill level of a given performer (Worrall ¶0042).
Regarding claim 5, Yamada (in view of Lee and further in view of Worrall) teaches an information processing system comprising the features of claim 4 as discussed above.
Worrall further teaches that the at least one playing characteristic includes a combination of the operator and a finger of the user (Worrall ¶0226: "For example, for a string instrument, like a guitar, this information would indicate which finger of the left hand is on which string and at which fret; for a keyboard instrument, like a piano, or a wind instrument, like a clarinet, this information would indicate which hand and which finger is pressing which key.").
Regarding claim 15, Yamada (in view of Lee) teaches a computer-implemented information processing method comprising the features of claim 13 as discussed above.
Yamada further teaches that the habit data is indicative of at least one playing characteristic (Yamada col. 8, lines 1-7: "In the above embodiment, touch data V is recorded for every tone color, but this touch data can be recorded independent of the tone color. That is, the touch data V can be recorded for, e.g. every player of the keyboard, or every group of keys of the keyboard, or every keyboard in the case of an electronic musical instrument having a plurality of keyboards.").
Yamada (in view of Lee) does not explicitly disclose at least one playing characteristic for an operator used in playing a piece of music by the user from among a plurality of operators.
However, Worrall teaches at least one playing characteristic for an operator used in playing a piece of music by the user from among a plurality of operators (Worrall ¶0152: "Hand and/or finger positioning information for a keyboard instrument may indicate which finger(s) of the left hand is/are to be placed on which key(s), and which finger(s) of the right hand is/are to be placed on which key(s) to play a given note or sequential string of notes; and so on.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the computer-implemented information processing method of Yamada (as modified by Lee) by adding the operator of from among of plurality of operators of Worrall to render the musical composition easier to perform on the instrument for the skill level of a given performer (Worrall ¶0042).
Regarding claim 16, Yamada (in view of Lee and further in view of Worrall) teaches a computer-implemented information processing method comprising the features of claim 15 as discussed above.
Worrall further teaches that the at least one playing characteristic includes a combination of the operator and a finger of the user (Worrall ¶0226: "For example, for a string instrument, like a guitar, this information would indicate which finger of the left hand is on which string and at which fret; for a keyboard instrument, like a piano, or a wind instrument, like a clarinet, this information would indicate which hand and which finger is pressing which key.").
Claim 8 is rejected under 35 U.S.C. 103 as unpatentable over Yamada in view of Lee, and further in view of Starr (US 20080271594 A1, November 6, 2008).
Regarding claim 8, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 1 as discussed above.
Yamada (in view of Lee) does not explicitly disclose that the at least one first intensity characteristic comprises a plurality of first intensity characteristics, each first intensity characteristic of the plurality of first intensity characteristics corresponding to a different music genre, and the at least one processor further executes the program to correct, using the correction data, a first intensity characteristic that corresponds to a music genre selected by the user from among the plurality of first intensity characteristics.
However, Starr teaches that the at least one first intensity characteristic comprises a plurality of first intensity characteristics, each first intensity characteristic of the plurality of first intensity characteristics corresponding to a different music genre (Starr ¶0094: "The electronic musical instrument 800 can store multiple arrangements of control settings to give the instrument multiple "sounds" or personalities. For example, it may be desired to select different control settings to produce better sound for different musical genres, or to accommodate different playing techniques or styles, or to suit particular songs."), and the at least one processor further executes the program to correct, using the correction data, a first intensity characteristic that corresponds to a music genre selected by the user from among the plurality of first intensity characteristics (Starr ¶0094: "Other processing parameters may be selected through the user interface, such as muting zones other than a designated zone, transposing keys in a zone, adjusting key pressure for activation, selecting fixed velocity or touch-sensitive key response, selection of velocity-response curve for the zone, and a unison setting to give all keys in a selected zone the same MIDI pitch value. Thus, the response to the pressing of the keys may be set and adjusted to provide various zones having different function and operation and sound.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the information processing system of Yamada (as modified by Lee) by adding the intensity characteristics and genre settings of Starr to select different control settings to produce better sound for different musical genres (Starr ¶0094).
Claim 10 is rejected under 35 U.S.C. 103 as unpatentable over Yamada in view of Lee, and further in view of Ackerman et al. (US 20180322854 A1, November 8, 2018), hereinafter Ackerman.
Regarding claim 10, Yamada (in view of Lee) teaches an information processing system comprising the features of claim 1 as discussed above.
Yamada (in view of Lee) does not explicitly disclose that the at least one trained model comprises a plurality of trained models, each trained model of the plurality of trained models corresponding to a different music genre, and the at least one processor further executes the program to generate the correction data, using a trained model that corresponds to a music genre selected by the user from among the plurality of trained models.
However, Ackerman teaches that the at least one trained model comprises a plurality of trained models, each trained model of the plurality of trained models corresponding to a different music genre (Ackerman ¶0033: "The automated song generation system 104 can create melody prediction models that are unique and different across genres of music using corpuses that each include songs of a specific genre of music. For example, the automated song generation system 104 can create a melody prediction model for classical music using a song corpus that only includes classical songs and create another melody prediction model for modern popular music using a song corpus that only includes modern popular music songs."), and the at least one processor further executes the program to generate the correction data, using a trained model that corresponds to a music genre selected by the user from among the plurality of trained models (Ackerman ¶0033: "This can allow a user to select a specific genre of music and create songs that are tailored to the specific genre of music through a melody prediction model created for the specific genre of music.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the information processing system of Yamada (as modified by Lee) by adding the plurality of models of Ackerman to help to ensure that the prediction models are tailored to different genres of music (Ackerman ¶0032).
Claim 12 is rejected under 35 U.S.C. 103 as unpatentable over Yamada in view of Lee, and further in view of Brosh et al. (US 4580478 A, April 8, 1986), hereinafter Brosh, and Hastings et al. (US 20130055879 A1, March 7, 2013), hereinafter Hastings.
Regarding claim 12, Yamada (in view of Lee) teaches an electronic musical instrument comprising the features of claim 11 as discussed above
Yamada (in view of Lee) does not explicitly disclose that the playing device includes: an operator that is displaced when played by the user; a signal generator that includes a first coil that receives a periodic reference signal; and a detectable portion disposed on the operator, the detectable portion includes a second coil that generates an induced current caused by electromagnetic induction due to a magnetic field generated in the first coil in response to supply of the periodic reference signal to the first coil, and the signal generator is configured to output a detection signal with a level dependent on a distance between the first coil and the second coil.
However, Brosh teaches that the playing device includes: an operator that is displaced when played by the user (Brosh col. 2, lines 37-54: "A musical key normally consists of a top member 11 which is linked to a hinge 12 on one side and loaded by a spring 13 at the hinge end. A moving sensor board 14 is mounted on the underside of the key member 11, while a static sensor board 15 is mounted underneath the key on a fixed surface 16… As the key number 11 is depressed, the board 14 which moves in the direction of arrow 17 moves closer to the static board or stationary board 15 providing a continuous output signal as will be described."); a signal generator that includes a first coil that receives a periodic reference signal; and a detectable portion disposed on the operator (Brosh col. 2, lines 60-66: "The moving coil which is analogous to coil 14 of FIG. 1 consists of a shorted coil arrangement. The static coil as 15 of FIG. 1 consists of a first coil 25 designated as a drive coil which is positioned adjacent to a second coil 24 designated as a sense coil. The drive coil 25 is energized by means of a suitable source 26 such as a square or sinewave generator.").
Furthermore, Hastings teaches that the detectable portion includes a second coil (Hastings ¶0043: "Referring to FIG. 2, the reactive element preferably comprises a passive tuned resonant circuit which comprises an inductive coil 8, a capacitive element 9 and optionally a means of connecting 11 read-out electronics to the read-out point 10.") that generates an induced current caused by electromagnetic induction due to a magnetic field generated in the first coil in response to supply of the periodic reference signal to the first coil (Hastings ¶0054: "In the case where said passive tuned resonant circuit is connected to the read-out electronics FIG. 2B, the oscillating current in the inductive coil 1 of the active tuned resonant circuit FIG. 1 induces a corresponding current in the inductive coil 8 of said passive tuned resonant circuit which allows a voltage to be measured by said read-out electronics."), and the signal generator is configured to output a detection signal with a level dependent on a distance between the first coil and the second coil (Hastings ¶0052: "The amplitude of the recovered signal thus depends directly on the mutual separation of the inductive coil of the active tuned resonant circuit 1 and the reactive element 20, thus an embodiment of the invention can itself be a circuit element in an electronic musical effect circuit.").
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the information processing system of Yamada (as modified by Lee) by adding the first coil of Brosh and the second coil of Hastings to sense key movements without requiring electrical connections to the moving element, thus improving robustness and reliability (Hastings ¶0057).
Conclusion
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/PHILIP G SCOLES/
Examiner, Art Unit 2837
/DEDEI K HAMMOND/Supervisory Patent Examiner, Art Unit 2837
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