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.
Response to Arguments
Applicant’s arguments, see pages 8-13, filed 2/2/2026, with respect to claims 1-20, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 9-10, 11-13, 16, and 19-20 are rejected under 35 U.S.C. 103 as unpatentable over Huo et al. (US 20200066240 A1, filed June 6, 2019), hereinafter Huo, in view of Jancsy (US 20200074876 A1, filed August 27, 2019).
Regarding claim 1, Huo teaches an arrangement generation method executed by a computer, the arrangement generation method comprises: acquiring target musical piece data that include performance information (Huo ¶0017: "the method for music generation may include steps of receiving any length of input (110); recognizing pitches and rhythm of the input (120)") that indicates a melody (Huo ¶0018: "The step of recognizing pitches and rhythm of the input (120) is a signal processing of the input, wherein the frame of a generated music is generated in this step including an initial short melody") and a chord of at least a part of a musical piece (Huo ¶0029: "After the frame of the generated music is generated, the sound input is processing through a deep learning system (200) to generate a first segment of a full music (130) and segments other than the first segment to complete a full music (140) in sequence. Furthermore, each of the two steps (130) (140) is completed through the deep learning system (200) including steps of extracting music instrument digital interface (MIDI) from the music input (201); extracting score information from the MIDI (202); extracting a main melody from the MIDI (203); extracting a chord progression from the MIDI (204)"), and include meta information that indicates characteristics of at least the part of the musical piece (Huo ¶0126: "the system of the present invention is configured to accept different inputs in the same time such as user humming (1101) and metadata (1102), wherein the metadata includes genre and user's mood."); generating, from the target musical piece data (Huo ¶0029: "Furthermore, each of the two steps (130) (140) is completed through the deep learning system (200) including steps of extracting music instrument digital interface (MIDI) from the music input (201)"), by using a generative model trained by machine learning (Huo ¶0029: "After the frame of the generated music is generated, the sound input is processing through a deep learning system (200) to generate a first segment of a full music (130) and segments other than the first segment to complete a full music (140) in sequence."), arrangement data in which the performance information is arranged in accordance with the meta information (Huo ¶0126: "the steps of generating a first segment of a full music include receiving any length of input (110); recognizing pitches and rhythm of the input (120); generating music progression form metadata (170); generating a first segment of a full music (130); generating segments other than first segment to complete the full music (140); generating connecting notes, chords and beats between two segments of the full music and handling anacrusis (150); and generating instrument accompaniment for the full music (160)"); and outputting the arrangement data (Huo ¶0118: "As shown in FIG. 7, the step of generating instrument accompaniment for the full music (160) is processing after the connecting notes, chords and beats and handling anacrusis is generated for the full music, wherein the data representations of generating instrument accompaniment for the full music (Equation 16) is shown as below.").
Huo does not explicitly disclose the meta information including difficulty level information that indicates a degree of difficulty of playing the musical piece as a condition of arrangement; and both the performance information and the difficulty level information being provided as inputs to the generative model to generate the arrangement data by using the generative model.
However, Jancsy teaches the meta information including difficulty level information that indicates a degree of difficulty of playing the musical piece as a condition of arrangement (Jancsy ¶0022: "the music generator 135 may be configured to generate a music segment score as a function of a difficulty level." A music segment score comprises metadata.); and both the performance information and the difficulty level information being provided as inputs to the generative model to generate the arrangement data by using the generative model (Janscy ¶0025: "The method continues at step 425 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment difficulty level to generate a music segment score at the selected music segment difficulty level. The method continues at step 430 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment score to create a reference audio model of the music segment score, and the method continues as described Supra.").
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 arrangement generation method of Huo by adding the difficulty level of Jancsy to provide practice music customized by artificial intelligence configured to generate music tailored to a music student's proficiency and preference (Jancsy ¶0008).
Regarding claim 2, Huo (in view of Jancsy) teaches n arrangement generation method comprising the features of claim 1 as discussed above.
Jancsy further teaches that in the generating of the arrangement data, the arrangement data that correspond to the degree of difficulty indicated by the difficulty level information are generated from the target musical piece data by using the generative model (Jancsy ¶0025: "The method continues at step 425 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment difficulty level to generate a music segment score at the selected music segment difficulty level.").
Regarding claim 3, Huo (in view of Jancsy) teaches an arrangement generation method comprising the features of claim 1 as discussed above.
Huo further teaches that the meta information includes style information that indicates a musical style of the musical piece as a condition of arrangement (Huo ¶0126: "the system of the present invention is configured to accept different inputs in the same time such as user humming (1101) and metadata (1102), wherein the metadata includes genre and user's mood." Genre comprises style information.), and in the generating of the arrangement data, the arrangement data that correspond to the musical style indicated by the style information are generated from the target musical piece data by using the generative model (Huo ¶0126: "the steps of generating a first segment of a full music include receiving any length of input (110); recognizing pitches and rhythm of the input (120); generating music progression form metadata (170)).
Regarding claim 6, Huo (in view of Jancsy) teaches an arrangement generation method comprising the features of claim 1 as discussed above.
Huo further teaches that the performance information includes beat information that indicates a rhythm of at least the part of the musical piece (Huo ¶0029: "extracting a main melody from the MIDI (203); extracting a chord progression from the MIDI (204); extracting a beat pattern from the MIDI (205); extracting a music progression from the MIDI (206)").
Regarding claim 9, Huo (in view of Jancsy) teaches an arrangement generation method comprising the features of claim 1 as discussed above.
Huo further teaches that in the acquiring of the target musical piece data, a plurality of pieces of the target musical piece data each corresponding to each of a plurality of parts that are obtainable by dividing one musical piece are acquired (Huo ¶0032: "As shown in FIG. 7, the step of generating instrument accompaniment for the full music (160) is processing after the connecting notes, chords and beats and handling anacrusis is generated for the full music."), and in the generating of the arrangement data, a plurality of pieces of the arrangement data are generated by performing the generating of the arrangement data with respect to each of the plurality of pieces of the target musical piece data (Huo ¶0003: "generating a first segment of a full music; generating segments other than the first segment to complete the full music; generating connecting notes, chords and beats of the segments of the full music and handling anacrusis; and generating instrument accompaniment for the full music."), and the plurality of pieces of the arrangement data are integrated to generate the arrangement data that correspond to the musical piece (Huo ¶0126: "generating music progression form metadata (170); generating a first segment of a full music (130); generating segments other than first segment to complete the full music (140); generating connecting notes, chords and beats between two segments of the full music and handling anacrusis (150); and generating instrument accompaniment for the full music (160)").
Regarding claim 10, Huo (in view of Jancsy) teaches an arrangement generation method comprising the features of claim 1 as discussed above.
Huo further teaches generating musical score data by using the arrangement data that have been generated (Huo ¶0118: "As shown in FIG. 7, the step of generating instrument accompaniment for the full music (160) is processing after the connecting notes, chords and beats and handling anacrusis is generated for the full music, wherein the data representations of generating instrument accompaniment for the full music (Equation 16) is shown as below").
Regarding claim 11, Huo teaches an arrangement generation device comprising: an electronic controller including at least one processor (Huo ¶0004-0005: "Techniques for sound extractions are employed in sound processing and several data representations… the sound input is processing through a deep learning system."), the electronic controller being configured to execute a plurality of modules including a target data acquisition module configured to acquire target musical piece data that include performance information (Huo ¶0017: "the method for music generation may include steps of receiving any length of input (110); recognizing pitches and rhythm of the input (120)") that indicates a melody (Huo ¶0018: "The step of recognizing pitches and rhythm of the input (120) is a signal processing of the input, wherein the frame of a generated music is generated in this step including an initial short melody") and a chord of at least a part of a musical piece (Huo ¶0029: "After the frame of the generated music is generated, the sound input is processing through a deep learning system (200) to generate a first segment of a full music (130) and segments other than the first segment to complete a full music (140) in sequence. Furthermore, each of the two steps (130) (140) is completed through the deep learning system (200) including steps of extracting music instrument digital interface (MIDI) from the music input (201); extracting score information from the MIDI (202); extracting a main melody from the MIDI (203); extracting a chord progression from the MIDI (204)") and include meta information that indicates characteristics of at least the part of the musical piece (Huo ¶0126: "the system of the present invention is configured to accept different inputs in the same time such as user humming (1101) and metadata (1102), wherein the metadata includes genre and user's mood."), an arrangement generation module configured to generate, from the target musical piece data (Huo ¶0029: "Furthermore, each of the two steps (130) (140) is completed through the deep learning system (200) including steps of extracting music instrument digital interface (MIDI) from the music input (201)"), by using a generative model trained by machine learning (Huo ¶0029: "After the frame of the generated music is generated, the sound input is processing through a deep learning system (200) to generate a first segment of a full music (130) and segments other than the first segment to complete a full music (140) in sequence."), arrangement data in which the performance information is arranged in accordance with the meta information (Huo ¶0126: "the steps of generating a first segment of a full music include receiving any length of input (110); recognizing pitches and rhythm of the input (120); generating music progression form metadata (170); generating a first segment of a full music (130); generating segments other than first segment to complete the full music (140); generating connecting notes, chords and beats between two segments of the full music and handling anacrusis (150); and generating instrument accompaniment for the full music (160)"), and an output module configured to output the arrangement data (Huo ¶0118: "As shown in FIG. 7, the step of generating instrument accompaniment for the full music (160) is processing after the connecting notes, chords and beats and handling anacrusis is generated for the full music, wherein the data representations of generating instrument accompaniment for the full music (Equation 16) is shown as below.").
Huo does not explicitly disclose the meta information including difficulty level information that indicates a degree of difficulty of playing the musical piece as a condition of arrangement; and both the performance information and the difficulty level information being provided as inputs to the generative model to generate the arrangement data by using the generative model.
However, Jancsy teaches the meta information including difficulty level information that indicates a degree of difficulty of playing the musical piece as a condition of arrangement (Jancsy ¶0022: "the music generator 135 may be configured to generate a music segment score as a function of a difficulty level." A music segment score comprises metadata.); and both the performance information and the difficulty level information being provided as inputs to the generative model to generate the arrangement data by using the generative model (Janscy ¶0025: "The method continues at step 425 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment difficulty level to generate a music segment score at the selected music segment difficulty level. The method continues at step 430 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment score to create a reference audio model of the music segment score, and the method continues as described Supra.").
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 arrangement generation device of Huo by adding the difficulty level of Jancsy to provide practice music customized by artificial intelligence configured to generate music tailored to a music student's proficiency and preference (Jancsy ¶0008).
Regarding claim 12, Huo (in view of Jancsy) teaches an arrangement generation device comprising the features of claim 11 as discussed above.
Jancsy further teaches that the arrangement generation module is configured to generate the arrangement data that correspond to the degree of difficulty indicated by the difficulty level information from the target musical piece data by using the generative model (Jancsy ¶0025: "The method continues at step 425 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment difficulty level to generate a music segment score at the selected music segment difficulty level.").
Regarding claim 13, Huo (in view of Jancsy) teaches an arrangement generation device comprising the features of claim 11 as discussed above.
Huo further teaches that the meta information includes style information that indicates a musical style of the musical piece as a condition of arrangement (Huo ¶0126: "the system of the present invention is configured to accept different inputs in the same time such as user humming (1101) and metadata (1102), wherein the metadata includes genre and user's mood." Genre comprises style information.), and the arrangement generation module is configured to generate the arrangement data that correspond to the musical style indicated by the style information from the target musical piece data by using the generative model (Huo ¶0126: "the steps of generating a first segment of a full music include receiving any length of input (110); recognizing pitches and rhythm of the input (120); generating music progression form metadata (170)).
Regarding claim 16, Huo (in view of Jancsy) teaches an arrangement generation device comprising the features of claim 11 as discussed above.
Huo further teaches that the performance information includes beat information that indicates a rhythm of at least the part of the musical piece (Huo ¶0029: "extracting a main melody from the MIDI (203); extracting a chord progression from the MIDI (204); extracting a beat pattern from the MIDI (205); extracting a music progression from the MIDI (206)").
Regarding claim 19, Huo (in view of Jancsy) teaches an arrangement generation device comprising the features of claim 11 as discussed above.
Jancsy further teaches that the electronic controller is further configured to execute a musical score generation module configured to generate musical score data by using the arrangement data that have been generated (Huo ¶0118: "As shown in FIG. 7, the step of generating instrument accompaniment for the full music (160) is processing after the connecting notes, chords and beats and handling anacrusis is generated for the full music, wherein the data representations of generating instrument accompaniment for the full music (Equation 16) is shown as below").
Jancsy further teaches outputting of the arrangement data is configured by outputting of the musical score data (Jancsy ¶0025: "In some embodiments, an exemplary sight reading trainer 132 design may display a musical score on a device 105 screen for the user to play. In various embodiments, the musical score may scroll across the screen in time with the music.").
Regarding claim 20, Huo teaches a non-transitory computer readable medium storing a generation program that causes a computer to execute a process (Huo abstract: "A method and apparatus for music generation"), the process comprising: acquiring target musical piece data that include performance information (Huo ¶0017: "the method for music generation may include steps of receiving any length of input (110); recognizing pitches and rhythm of the input (120)") that indicates a melody (Huo ¶0018: "The step of recognizing pitches and rhythm of the input (120) is a signal processing of the input, wherein the frame of a generated music is generated in this step including an initial short melody") and a chord of at least a part of a musical piece (Huo ¶0029: "After the frame of the generated music is generated, the sound input is processing through a deep learning system (200) to generate a first segment of a full music (130) and segments other than the first segment to complete a full music (140) in sequence. Furthermore, each of the two steps (130) (140) is completed through the deep learning system (200) including steps of extracting music instrument digital interface (MIDI) from the music input (201); extracting score information from the MIDI (202); extracting a main melody from the MIDI (203); extracting a chord progression from the MIDI (204)"), and include meta information that indicates characteristics of at least the part of the musical piece (Huo ¶0126: "the system of the present invention is configured to accept different inputs in the same time such as user humming (1101) and metadata (1102), wherein the metadata includes genre and user's mood."); generating, from the target musical piece data (Huo ¶0029: "Furthermore, each of the two steps (130) (140) is completed through the deep learning system (200) including steps of extracting music instrument digital interface (MIDI) from the music input (201)"), by using a generative model trained by machine learning (Huo ¶0029: "After the frame of the generated music is generated, the sound input is processing through a deep learning system (200) to generate a first segment of a full music (130) and segments other than the first segment to complete a full music (140) in sequence."), arrangement data in which the performance information is arranged in accordance with the meta information (Huo ¶0126: "the steps of generating a first segment of a full music include receiving any length of input (110); recognizing pitches and rhythm of the input (120); generating music progression form metadata (170); generating a first segment of a full music (130); generating segments other than first segment to complete the full music (140); generating connecting notes, chords and beats between two segments of the full music and handling anacrusis (150); and generating instrument accompaniment for the full music (160)"); and outputting the arrangement data (Huo ¶0118: "As shown in FIG. 7, the step of generating instrument accompaniment for the full music (160) is processing after the connecting notes, chords and beats and handling anacrusis is generated for the full music, wherein the data representations of generating instrument accompaniment for the full music (Equation 16) is shown as below.").
Huo does not explicitly disclose the meta information including difficulty level information that indicates a degree of difficulty of playing the musical piece as a condition of arrangement; and both the performance information and the difficulty level information being provided as inputs to the generative model to generate the arrangement data by using the generative model.
However, Jancsy teaches the meta information including difficulty level information that indicates a degree of difficulty of playing the musical piece as a condition of arrangement (Jancsy ¶0022: "the music generator 135 may be configured to generate a music segment score as a function of a difficulty level." A music segment score comprises metadata.); and both the performance information and the difficulty level information being provided as inputs to the generative model to generate the arrangement data by using the generative model (Janscy ¶0025: "The method continues at step 425 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment difficulty level to generate a music segment score at the selected music segment difficulty level. The method continues at step 430 with the processor 305 running the Music Generator (depicted by FIG. 5) as a function of the music segment score to create a reference audio model of the music segment score, and the method continues as described Supra.").
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 non-transitory computer readable medium of Huo by adding the difficulty level of Jancsy to provide practice music customized by artificial intelligence configured to generate music tailored to a music student's proficiency and preference (Jancsy ¶0008).
Claims 4, 7, 14, and 17 are rejected under 35 U.S.C. 103 as unpatentable over Huo in view of Jancsy, and further in view of MuseNet (April 25, 2019, stored April 25, 2019 by WayBack Machine and retrieved 10/26/2025, https://web.archive.org/web/20190425234508/https://openai.com/blog/musenet/), hereinafter Musenet.
Regarding claim 4, Huo (in view of Jancsy) teaches an arrangement generation method comprising the features of claim 3 as discussed above.
Huo (in view of Jancsy) does not explicitly disclose that the style information includes arranger information for specifying an arranger.
However, MuseNet suggests that the style information includes arranger information for specifying an arranger (MuseNet, Composer and instrumentation tokens: "During training time, these composer and instrumentation tokens were prepended to each sample, so the model would learn to use this information in making note predictions. At generation time, we can then condition the model to create samples in a chosen style." MuseNet, figure caption: "Here we use t-SNE to create a 2-D map of the cosine similarity of various musical composer and style embeddings." Composers such as those disclosed by MuseNet can also be arrangers of their own music and that of other composers.).
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 arrangement generation method of Huo (as modified by Jancsy) by adding the arranger information of MuseNet to create samples in a chosen style (MuseNet, Composer and instrumental tokens).
Regarding claim 7, Huo (in view of Jancsy) teaches an arrangement generation method comprising the features of claim 1 as discussed above.
Huo (in view of Jancsy) does not explicitly disclose that the generating of the arrangement data includes generating an input token sequence that corresponds to the target musical piece data, and generating an output token sequence that corresponds to the arrangement data by inputting a token included in the input token sequence to the generative model and executing computation of the generative model.
However, MuseNet suggests that the generating of the arrangement data includes generating an input token sequence that corresponds to the target musical piece data (MuseNet, Dataset: "We experimented with several different ways to encode the MIDI files into tokens suitable for this task."), and generating an output token sequence that corresponds to the arrangement data by inputting a token included in the input token sequence to the generative model and executing computation of the generative model (MuseNet, Dataset: "The transformer is trained on sequential data: given a set of notes, we ask it to predict the upcoming note.").
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 arrangement generation method of Huo (as modified by Jancsy) by adding the token sequence of MuseNet to condition on arranger information (MuseNet, Embeddings).
Regarding claim 14, Huo (in view of Jancsy) teaches an arrangement generation device comprising the features of claim 13 as discussed above.
Huo (in view of Jancsy) does not explicitly disclose that the style information includes arranger information for specifying an arranger.
However, MuseNet suggests that the style information includes arranger information for specifying an arranger (MuseNet, Composer and instrumentation tokens: "During training time, these composer and instrumentation tokens were prepended to each sample, so the model would learn to use this information in making note predictions. At generation time, we can then condition the model to create samples in a chosen style." MuseNet, figure caption: "Here we use t-SNE to create a 2-D map of the cosine similarity of various musical composer and style embeddings." Composers such as those disclosed by MuseNet can also be arrangers of their own music and that of other composers.).
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 arrangement generation device of Huo (as modified by Jancsy) by adding the arranger information of MuseNet to create samples in a chosen style (MuseNet, Composer and instrumental tokens).
Regarding claim 17, Huo (in view of Jancsy) teaches an arrangement generation device comprising the features of claim 11 as discussed above.
Huo (in view of Jancsy) does not explicitly disclose that the arrangement generation module is configured to generate an input token sequence that corresponds to the target musical piece data, and generate an output token sequence that corresponds to the arrangement data by inputting a token included in the input token sequence to the generative model and executing computation of the generative model.
However, MuseNet suggests that the arrangement generation module is configured to generate an input token sequence that corresponds to the target musical piece data (MuseNet, Dataset: "We experimented with several different ways to encode the MIDI files into tokens suitable for this task."), and generate an output token sequence that corresponds to the arrangement data by inputting a token included in the input token sequence to the generative model and executing computation of the generative model (MuseNet, Dataset: "The transformer is trained on sequential data: given a set of notes, we ask it to predict the upcoming note.").
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 arrangement generation device of Huo (as modified by Jancsy) by adding the token sequence of MuseNet to condition on arranger information (MuseNet, Embeddings).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as unpatentable over Huo in view of Jancsy, and further in view of Kishi (US 20220406283 A1, Effective Filing Date November 26, 2019).
Regarding claim 5, Huo (in view of Jancsy) teaches an arrangement generation method comprising the features of claim 1 as discussed above.
Huo (in view of Jancsy) does not explicitly disclose that the meta information includes composition information that indicates a musical instrument composition of the musical piece as a condition of arrangement, and in the generating of the arrangement data, the arrangement data that correspond to the musical instrument composition indicated by the composition information are generated from the target musical piece data by using the generative model.
However, Kishi teaches that the meta information includes composition information (Kishi ¶0123: "The learning model information meta information is information such as tempo of music, genre, atmosphere such as light and dark, structure of music such as 1st verse, 2nd verse, and chorus, chord progression, scale, and a church mode." ¶0126: "For example, the composition model information meta information may store various types of additional information related to the composition model.") that indicates a musical instrument composition of the musical piece as a condition of arrangement (Kishi ¶0132: "The music information is created by the producer using a music creation-related application installed in the user terminal 300, that is, the automatic composition function, and includes feature amounts related to music such as a chord progression, a melody, a bass progression, and a drum sound progression." A drum sound progression indicates a musical instrument composition.), and in the generating of the arrangement data, the arrangement data that correspond to the musical instrument composition indicated by the composition information are generated (Kishi ¶0138: "The music information is created by the producer using a music creation-related application installed in the user terminal 300, that is, the automatic composition function, and includes feature amounts related to music such as a chord progression, a melody, a bass progression, and a drum sound progression.") from the target musical piece data by using the generative model (Kishi ¶0138: "The composition unit 237 may compose music using various existing music generation algorithms. For example, the composition unit 237 may use a music generation algorithm using a Markov chain or may use a music generation algorithm using deep learning.").
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 arrangement generation method of Huo (as modified by Jancsy) by adding the meta information of Kishi to receive provision of music information that matches the features of the music (Kishi ¶0005).
Regarding claim 15, Huo (in view of Jancsy) teaches an arrangement generation device comprising the features of claim 11 as discussed above.
Huo (in view of Jancsy) does not explicitly disclose that the meta information includes composition information that indicates a musical instrument composition of the musical piece as a condition of arrangement, and the arrangement generation module is configured to generate the arrangement data that correspond to the musical instrument composition indicated by the composition information from the target musical piece data by using the generative model.
However, Kishi teaches that the meta information includes composition information (Kishi ¶0123: "The learning model information meta information is information such as tempo of music, genre, atmosphere such as light and dark, structure of music such as 1st verse, 2nd verse, and chorus, chord progression, scale, and a church mode." ¶0126: "For example, the composition model information meta information may store various types of additional information related to the composition model.") that indicates a musical instrument composition of the musical piece as a condition of arrangement (Kishi ¶0132: "The music information is created by the producer using a music creation-related application installed in the user terminal 300, that is, the automatic composition function, and includes feature amounts related to music such as a chord progression, a melody, a bass progression, and a drum sound progression." A drum sound progression indicates a musical instrument composition.), and the arrangement generation module is configured to generate the arrangement data that correspond to the musical instrument composition indicated by the composition information (Kishi ¶0138: "The music information is created by the producer using a music creation-related application installed in the user terminal 300, that is, the automatic composition function, and includes feature amounts related to music such as a chord progression, a melody, a bass progression, and a drum sound progression.") from the target musical piece data by using the generative model (Kishi ¶0138: "The composition unit 237 may compose music using various existing music generation algorithms. For example, the composition unit 237 may use a music generation algorithm using a Markov chain or may use a music generation algorithm using deep learning.").
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 arrangement generation device of Huo (as modified by Jancsy) by adding the meta information of Kishi to receive provision of music information that matches the features of the music (Kishi ¶0005).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as unpatentable over Aoki in view of Morris, and further in view of MuseNet and Bretan et al. (US 20210049989 A1, filed December 5, 2019), hereinafter Bretan.
Regarding claim 8, Huo (in view of Jancsy ad further in view of MuseNet) teaches an arrangement generation method comprising the features of claim 7 as discussed above.
MuseNet further teaches that the input token sequence is configured such that after a token that corresponds to the meta information is arranged (Musenet, Composer and instrumentation tokens: "We created composer and instrumentation tokens to give more control over the kinds of samples MuseNet generates. During training time, these composer and instrumentation tokens were prepended to each sample, so the model would learn to use this information in making note predictions."), tokens that correspond to the performance information are arranged in chronological order (MuseNet, Dataset: "The transformer is trained on sequential data: given a set of notes, we ask it to predict the upcoming note."), and in the generating of the output token sequence, tokens that constitute the output token sequence are sequentially generated by inputting tokens included in the input token sequence to the generative model in order from a beginning (MuseNet, introduction: "MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files.").
Huo (in view of Jancsy and further in view of MuseNet) does not explicitly disclose that the generative model is configured to have a recursive structure, and repeatedly executing the computation of the generative model.
However, Bretan teaches that the generative model is configured to have a recursive structure (Bretan ¶0087: "As shown here, the output generated by the at least one recurrent neural network 706 for at least one set of input data can be provided in a feed-forward manner for use in processing additional sets of input data."), and repeatedly executing the computation of the generative model (Bretan ¶0086: "The embeddings 704 are provided to at least one recurrent neural network (RNN) 706, which processes the embeddings 704 to produce derived embeddings 708. The derived embeddings 708 represent embeddings in the latent space that are generated based on the musical seed represented by the input data 702 a-702 n. The derived embeddings 708 can then be decoded (similar to the generation operation 512 described above) to produce the derived musical content, which can be played to one or more users.").
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 arrangement generation method of Huo (as modified by Jancsy and MuseNet) by adding the recursive structure of Bretan to generate different portions of the derived musical content that are generally consistent with each other (Bretan ¶0087).
Regarding claim 18, Huo (in view of Jancsy and further in view of MuseNet) teaches an arrangement generation device comprising the features of claim 17 as discussed above.
MuseNet further teaches that the input token sequence is configured such that after a token that corresponds to the meta information is arranged (Musenet, Composer and instrumentation tokens: "We created composer and instrumentation tokens to give more control over the kinds of samples MuseNet generates. During training time, these composer and instrumentation tokens were prepended to each sample, so the model would learn to use this information in making note predictions."), tokens that correspond to the performance information are arranged in chronological order (MuseNet, Dataset: "The transformer is trained on sequential data: given a set of notes, we ask it to predict the upcoming note."), the arrangement generation module is configured to sequentially generate tokens that constitute the output token sequence by inputting tokens included in the input token sequence to the generative model in order from a beginning (MuseNet, introduction: "MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files.").
Huo (in view of Jancsy and further in view of MuseNet) does not explicitly disclose that the generative model is configured to have a recursive structure, and repeatedly executing the computation of the generative model.
However, Bretan teaches that the generative model is configured to have a recursive structure (Bretan ¶0087: "As shown here, the output generated by the at least one recurrent neural network 706 for at least one set of input data can be provided in a feed-forward manner for use in processing additional sets of input data."), and repeatedly executing the computation of the generative model (Bretan ¶0086: "The embeddings 704 are provided to at least one recurrent neural network (RNN) 706, which processes the embeddings 704 to produce derived embeddings 708. The derived embeddings 708 represent embeddings in the latent space that are generated based on the musical seed represented by the input data 702 a-702 n. The derived embeddings 708 can then be decoded (similar to the generation operation 512 described above) to produce the derived musical content, which can be played to one or more users.").
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 arrangement generation device of Huo (as modified by Jancsy and MuseNet) by adding the recursive structure of Bretan to generate different portions of the derived musical content that are generally consistent with each other (Bretan ¶0087).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PHILIP G SCOLES/
Examiner, Art Unit 2837
/DEDEI K HAMMOND/Supervisory Patent Examiner, Art Unit 2837
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