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 .
Response to Amendment
Receipt is acknowledged of applicant’s amendment filed April 17, 2026. Claims 1-11 are pending and an action on the merits is as follows.
Objection to the specification has been withdrawn.
Applicant's arguments with respect to claims have been considered but are moot in view of the new ground(s) of rejection.
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Jancsy (US 2020/0074876 A1) in view of Klapuri et al. (US 9,767,705 B1).
Claims 1 and 5: Jancsy discloses a musical piece generation device and method executed by a computer, comprising: an electronic controller including at least one processor, the electronic controller being configured to execute a plurality of modules including a data acquisition module configured to acquire target musical piece data selected by a student indicating at least a part (segment) of a musical piece, a parameter acquisition module configured to acquire by measuring a value of a difficulty level parameter, a generation module configured to generate from the target musical piece data and the value of the difficulty level parameter, new musical piece data indicating at least a part of a new musical piece obtained by changing a difficulty level of the musical piece to a difficulty level specified by the difficulty level parameter (page 3 paragraph [0022]), and an output module configured to output the new musical piece data that has been generated (page 6 paragraph [0025]). The new musical piece data is generated by using a trained generative model (Markov model) (page 7 paragraph [0026]). The part of the musical piece is repeatedly presented to the student at tempos and durations to match the measured difficulty level (ability) of the student (page 3 paragraph [0022]). This reference fails to disclose the generated new musical piece data to be the target musical piece data with some notes and/or rhythms changed according to the changed difficulty level.
However Klapuri et al. teaches a musical piece generation device and method, where a generated new musical piece data (simpler/harder version of the song) is a target musical piece data with some notes and/or rhythms changed according to the changed difficulty level until the new musical piece data reaches the level of the original musical piece data (original song) (column 15 line 63 through column 16 line 6).
Given the teachings of Klapuri et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the musical piece generation device and method disclosed in Jancsy with providing the generated new musical piece data to be the target musical piece data with some notes and/or rhythms changed according to the changed difficulty level. Doing so would allow a user “to perform the simplified versions of the song and she can increase the difficulty characteristic level step-by-step to keep the level of challenge suitable, strengthening her skills along the way while performing those gradually harder versions of the song, until she has reached the level of the original song or is satisfied with the simplified version that she has reached so far” as taught in Klapuri et al. (column 15 line 67 through column 16 line 6).
Claims 2 and 6: Jancsy modified by Klapuri et al. discloses a musical piece generation device and method where the new musical piece is generated by changing a difficulty level of the musical piece, as stated above. The value of the difficulty level parameter is disclosed in Jancsy to indicate a width of a performance sound range corresponding to pitches that are the same or different of the new musical piece (page 8 paragraph [0030]).
Claims 3 and 7: Jancsy modified by Klapuri et al. discloses a musical piece generation device and method where the new musical piece is generated by changing a difficulty level of the musical piece, as stated above. The value of the difficulty level parameter is disclosed in Jancsy to indicate a maximum number of simultaneously operated operators in a musical instrument used to play a chord (page 9 paragraph [0036]), as is known in the art.
Claims 4 and 8: Jancsy modified by Klapuri et al. discloses a musical piece generation device and method as stated above, where the target musical piece data is disclosed in Jancsy to include an input token sequence (segment) arranged to indicate at least the part of the musical piece, and the new musical piece data include an output token sequence that is output from the trained generative model and arranged to indicate at least the part of the new musical piece (page 9 paragraph [0039]).
Claim 9: Jancsy discloses a model generation method executed by a computer comprising: acquiring a plurality of training datasets each of which includes a combination of training data (musical score) and correct answer data (musical score model), the training data including training musical piece data that indicate at least a part (segment) of a musical piece and including a difficulty level parameter for training, the correct answer data including new training musical piece data indicating at least a part of a new musical piece generated by changing a difficulty level of the musical piece of the training musical piece data to a difficulty level specified by the difficulty level parameter (page 3 paragraph [0022]). The part of the musical piece is repeatedly presented to the student at tempos and durations to match the measured difficulty level (ability) of the student (page 3 paragraph [0022]). Machine learning of a generative model is executed by using the plurality of training datasets that have been acquired, the machine learning being configured by training the generative model via Markov model such that, with respect to each of the training datasets, musical piece data, which are generated by the generative model from the training musical piece data and a value of the difficulty level parameter that are included in the training data (page 7 paragraph [0026]), match the new training musical piece data included in the correct answer data in order to compare a student’s performance audio model to a reference audio model to (page 6 paragraph [0025]). This reference fails to disclose the new training musical piece data to be the training musical piece data with some notes and/or rhythms changed according to the changed difficulty level.
However Klapuri et al. teaches a model generation method, where a generated new training musical piece data (simpler/harder version of the song) is a training musical piece data with some notes and/or rhythms changed according to the changed difficulty level until the new musical piece data reaches the level of the original musical piece data (original song) (column 15 line 63 through column 16 line 6).
Given the teachings of Klapuri et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model generation method disclosed in Jancsy with providing the new training musical piece data to be the training musical piece data with some notes and/or rhythms changed according to the changed difficulty level. Doing so would allow a user “to perform the simplified versions of the song and she can increase the difficulty characteristic level step-by-step to keep the level of challenge suitable, strengthening her skills along the way while performing those gradually harder versions of the song, until she has reached the level of the original song or is satisfied with the simplified version that she has reached so far” as taught in Klapuri et al. (column 15 line 67 through column 16 line 6).
Claim 10: Jancsy modified by Klapuri et al. discloses a model generation method where the new musical piece is generated by changing a difficulty level of the musical piece, as stated above. The value of the difficulty level parameter is disclosed in Jancsy to indicate a width of a performance sound range corresponding to pitches that are the same or different of the new musical piece (page 8 paragraph [0030]).
Claim 11: Jancsy modified by Klapuri et al. discloses a model generation method where the new musical piece is generated by changing a difficulty level of the musical piece, as stated above. The value of the difficulty level parameter is disclosed in Jancsy to indicate a maximum number of simultaneously operated operators in a musical instrument used to play a chord (page 9 paragraph [0036]), as is known in the art.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER UHLIR whose telephone number is (571)270-3091. The examiner can normally be reached M-F 8:30-4.
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/Christopher Uhlir/Primary Examiner, Art Unit 3619 June 24, 2026
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