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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Huo et al. (US 2020/0066240) in view of that which is well-known in the art.
In terms of claim 1, Huo et al. teaches an information processing method (see Figure 1) comprising: generating a output tracks (130, 140, 150, 160) (see Figure 7, generating multiple output accompaniment tracks; paragraph [0061], more than one melody track generated) by using an input track including a plurality of first information elements (110, 120) provided over a certain period or a certain section, and a learned model (200) (see Figure 2), wherein each output track includes a first track that is a same track as the input track or a changed track (130, 140), and a second track including a plurality of second information elements (150, 160) provided over the certain period or the certain section, and the learned model (200) is a learned model generated by using training data so as to output output data corresponding to the output track when input data corresponding to the first track is input.
Huo et al. fails to explicitly teach the use of an encoder or decoder. Huo et al. does however teach the learned model extracting key input features according to the characteristics of input sounds (see paragraph [0018]), and then generating the output data accordingly (see Figures 2 and 3).
It is known in the art that many components and applications of a deep learning system involve encoding, or transforming raw musical data into a structured, machine-learnable format, similar to that taught by Huo et al. (see references cited above). In deep learning, an encoder often compresses the input into a latent representation, while often working in tandem with a decoder, which reconstructs the output from the latent space. Deep learning models like RNNs, LSTMs, Transformers, and diffusion models are used to generate or process music. In these systems: The encoder might process a sequence of notes, chords, or audio frames into a latent vector that captures the musical structure. The decoder then generates the next musical element based on that latent representation.
Therefore, for the reasons outlined above, it would have been obvious, to one of ordinary skill in the art, at the time of the effective filing date, that the operations of the deep learning system (200) of Huo et al. could be considered as encoder and decoder operations (see Figures 2 and 3, and paragraphs [0004], [0005] and [0018]).
Still further, if the Applicant disagrees with the Examiner’s interpretation of Huo’s multiple “output tracks” (i.e. melody, accompaniment, etc.), then the method of generating a track, or full piece of music/song, can be replicated or duplicated to generate multiple output tracks or songs. It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to duplicate the method steps of Huo, since it has been held that mere duplication of the essential working parts or steps, of a device or method, involves only routine skill in the art. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).
As for claim 2, Huo et al. teaches a changed first track (see paragraph [0061]: “the deep learning system (200) is configured to get one track which is most likely to be the main melody of the music to generate. However, it is also possible for the deep learning system (200) to extract more than one main melody from a MIDI file.”, paragraph [0075]: “The deep learning system (200) is adapted to assume a chord change can only happen at a downbeat. The deep learning system (200) is adapted to detect whether there is a chord change for each downbeat and identify which chord is changed when detecting a chord change so as to generate the adjusted chord progression.”; these reference show generating a track in which a part of the input track has been changed, by changing a part of the plurality of first information elements included in the input track). Therefore, obviousness stands for the reasons cited above.
As for claims 3-6, Huo et al. teaches the tracks based on data corresponding to the identification of time signatures (see equation 2, paragraphs [0018] and [0028], and claim 2) and beat patterns (see claim 3). Therefore, obviousness stands for the reasons cited above.
As for claims 7 and 8, Huo et al. teaches that sound information includes at least one pitch (see claims 1 and 2), and the learning system further includes steps of extracting a music instrument digital interface (MIDI) from the music input (see claim 3). Therefore, obviousness stands for the reasons cited above.
In terms of claims 9 and 10, the same reasoning applied in the rejection of method claim 1, mutatis mutandis, applies to the subject-matter of device claim 9 and program claim 10, given the apparatus is considered inseparable from the method of using the apparatus, and the method is inseparable from the instructions for implementing the method.
Response to Arguments
Applicant’s amendments and arguments, filed 06/30/2026, have been fully considered and are persuasive. Therefore, the previous 35 USC 102 rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Huo et al. and that which is well-known in the art.
As outlined above, the operations of the deep learning system (200) of Huo et al. can be considered encoding and decoding operations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christina Schreiber whose telephone number is (571)272-4350. The examiner can normally be reached M-F 7-4 PM.
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/CHRISTINA M SCHREIBER/Primary Examiner, Art Unit 2837 07/07/2026
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