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 2/24/2026 and 6/17/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Response to Amendments
Objections to claims 6-9 are withdrawn in response to Applicant’s amendments.
Response to Arguments
Applicant’s arguments, see pages 7-11, filed 4/30/2026, with respect to claims 6-9, 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 6 and 8-9 are rejected under 35 U.S.C. 103 as unpatentable over Akama (US 20210358461 A1, filed 10/10/2019), hereinafter Akama, in view of Brunner et al. (MIDI-VAE: "Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer," Published 09/20/2018, retrieved from Instant Application file wrapper), hereinafter Brunner, and further in view of Roberts et al. (A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music, Nov 11, 2019, retrieved August 14, 2026 from https://arxiv.org/pdf/1803.05428), hereinafter Roberts.
Regarding claim 6, Akama teaches a music processing system (Akama ¶0220: "Information devices such as the information processing apparatus 100 according to each embodiment described above are implemented by, for example, a computer 1000"), comprising: musical-piece generating means which generates a musical piece (Akama ¶0041: "In the embodiment, the information processing apparatus 100 generates a learned model for extracting features of content, and generates new content based on the generated learned model. In the embodiment, the content is constituted by digital data of a predetermined format such as music (song), an image, and a moving image. In the example of FIG. 1, the information processing apparatus 100 is used to process a song as an example of the content.") by using a learning model which performs machine learning (Akama ¶0042: "The learned model according to the embodiment has an encoder that extracts a feature quantity from data constituting content, and a decoder that reconstitutes the content from the extracted feature quantity. For example, the information processing apparatus 100 learns an encoder by unsupervised learning such as a variational auto encoder (VAE) and generative adversarial networks (GANs).") on the basis of input data including musical piece data in which a musical score of a musical piece (Akama ¶0049: "The song 30 is constituted by, for example, a symbol string (digital data) indicating a pitch, a sound length, and a rest. As an example, the pitch is a pitch that expresses a frequency indicating a pitch of a sound in predetermined steps (for example, 128 steps and the like). In addition, the sound length expresses how long the reproduced sound is maintained. In addition, the rest expresses a timing at which the reproduction of the sound stops.") constituted by one channel or more of melodies and one channel or more of chords is described (Akama ¶0144: "For example, in the above embodiment, the example has been illustrated in which the information processing apparatus 100 extracts a chord constituent sound of the song 30, but the information processing apparatus 100 may extract not only the chord constituent sound but also a constituent sound of a melody or a constituent sound of a drum.") and configuration information (Akama ¶0090: "In the example illustrated in FIG. 5, the song data storage unit 122 has items such as 'song ID', 'partial data ID', 'pitch information', 'sound length rest information', 'chord information', and 'rhythm information'.") indicating attributes of elements constituting the musical piece of the musical piece data (Akama ¶0092: "The 'chord information' indicates a type of chords included in the partial data, the constituent sound of the chord, the switching of the chords in the bar, and the like. The 'rhythm information' indicates a beat or a tempo of a bar, a position of a strong beat, a position of a weak beat, and the like."); and the musical-piece generating means accepts an input of an operation parameter (c) (Akama ¶0155: "For example, the information processing apparatus 100 can change the song 65 to images of the entire song illustrated in the graph 64 according to the user's request. As described above, the information processing apparatus 100 can generate new content so as to adjust a blend ratio of the feature quantity.") for operating a style of a musical piece to be generated (Akama ¶149: "Further, the information processing apparatus 100 may extract sounds for each musical instrument constituting a song or for each musical instrument group. In addition, the information processing apparatus 100 may extract a style feature quantity or the like in which a correlation between features of a certain layer is calculated when features of a certain song are learned by a deep neural network (DNN). Further, the information processing apparatus 100 may extract self-similarity or the like in the song.") together with the input data (Akama ¶0107: "Further, the acquisition unit 133 may acquire arbitrary data from the information processing terminal used by the user. For example, the acquisition unit 133 acquires data constituting a song. Then, the acquisition unit 133 may input the acquired data to the learned model (in this case, inputs the same data to the first encoder 50 and the second encoder 55, respectively), and acquire the feature quantities output from each encoder.").
Akama does not explicitly disclose that the musical-piece generating means has: an encoder which outputs an average vector (µ) and a distribution vector (σ) of a latent variable (z) corresponding to input data by using the learning model on the basis of the input data; latent-variable processing means which generates the latent variable (z) by processing the average vector (µ), the distribution vector (σ); a decoder which outputs output data in the same format as that of input data according to the latent variable generated by the latent-variable processing means by using the learning model; and the latent-variable processing means processes the average vector (µ), the distribution vector (σ), by mixing in a noise coefficient (ε) according to the formula: z = µ + εσ, wherein the noise coefficient (ε) is represented by a matrix N (0,I) in a Variational Autoencoder (VAE) framework, and wherein I is set to the operation parameter (c) to determine the style of the musical piece to be generated.
However, Brunner teaches that the musical-piece generating means has: an encoder which outputs an average vector (µ) and a distribution vector (σ) of a latent variable (z) corresponding to input data (Brunner § 3.2: "For each input sample (i.e., a piece of length nB beats), the pitch, velocity and instrument rolls are passed through their respective encoders, implemented as RNNs. The output of the three encoders is concatenated and passed through several fully connected layers, which then predict σz and µz, the parameters of the approximate posterior qθ(z|x) = N(µz,σz).") by using the learning model on the basis of the input data (Brunner § 3.2: "MIDI-VAE is based on the standard VAE [20] with a hyperparameter β to weigh the Kullback-Leibler divergence in the loss function (as in [17]). A VAE consists of an encoder qθ(z|x), a decoder pφ(x|z) and a latent variable z, where q and p are usually implemented as neural networks parameterized by θ and φ."); latent-variable processing means which generates the latent variable (z) by processing the average vector (µ), the distribution vector (σ) (Brunner § 3.2: "The output of the three encoders is concatenated and passed through several fully connected layers, which then predict σz and µz, the parameters of the approximate posterior qθ(z|x) = N(µz,σz).2 Using the reparameterization trick [20], a latent vector z is sampled from this distribution as z ∼ N(µz,σz ∗ ) where ∗ stands for element-wise multiplication."); and a decoder which outputs output data in the same format as that of input data according to the latent variable generated by the latent-variable processing means by using the learning model (Brunner § 3.2: "This shared latent vector is then fed into three parallel fully connected layers, from which the three decoders try to reconstruct the pitch, velocity and instrument rolls."); and wherein I is set to the operation parameter (c) (Brunner § 3.2: "ε is sampled from an isotropic Gaussian distribution N(0,σ ∗I), where we treat σ as a hyperparameter (see Section 4.2 for more details)." Brunner places an adjustable scalar σε on I in the VAE reparameterization-noise distribution.) to determine the style of the musical piece to be generated (Brunner § 3.3: "In order to change a song’s style from Si to Sj, we pass the song through the encoder to get z, swap the values of dimensions zi style and zj style, and pass the modified latent vector through the de coder. As style we choose the music genre (e.g., Jazz, Pop or Classic) or individual composers (Bach or Mozart).").
Furthermore, Roberts teaches that the latent-variable processing means processes the average vector (µ), the distribution vector (σ), by mixing in a noise coefficient (ε) according to the formula: z = µ + εσ (Roberts § 2.1: "Naively computing the gradient through the ELBO is infeasible due to the sampling operation used to obtain z. In the common case where p(z) is a diagonal-covariance Gaussian, this can be circumvented by replacing z ~ N(µ, σ I) with ε ~ N(0, I), z = µ + σ ⊙ ε), wherein the noise coefficient (ε) is represented by a matrix N (0,I) in a Variational Autoencoder (VAE) framework (Roberts § 2.1: "In the common case where p(z) is a diagonal-covariance Gaussian, this can be circumvented by replacing z ~ N(µ, σ I) with ε ~ N(0, I), z = µ + σ ⊙ ε).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the music processing system of Akama by adding the average vector and distribution vector processing of Brunner and the latent variable processing of Roberts to more effectively perform a neural style transfer to complete musical compositions (Brunner abstract).
Regarding claim 8, Akama teaches a music processing program (Akama ¶0220: "Information devices such as the information processing apparatus 100 according to each embodiment described above are implemented by, for example, a computer 1000") characterized by causing a computer to function as: musical-piece generating means which generates a musical piece (Akama ¶0041: "In the embodiment, the information processing apparatus 100 generates a learned model for extracting features of content, and generates new content based on the generated learned model. In the embodiment, the content is constituted by digital data of a predetermined format such as music (song), an image, and a moving image. In the example of FIG. 1, the information processing apparatus 100 is used to process a song as an example of the content.") by using a learning model which performs machine learning (Akama ¶0042: "The learned model according to the embodiment has an encoder that extracts a feature quantity from data constituting content, and a decoder that reconstitutes the content from the extracted feature quantity. For example, the information processing apparatus 100 learns an encoder by unsupervised learning such as a variational auto encoder (VAE) and generative adversarial networks (GANs).") on the basis of learning data having musical piece data for learning in which a musical score of a musical piece (Akama ¶0049: "The song 30 is constituted by, for example, a symbol string (digital data) indicating a pitch, a sound length, and a rest. As an example, the pitch is a pitch that expresses a frequency indicating a pitch of a sound in predetermined steps (for example, 128 steps and the like). In addition, the sound length expresses how long the reproduced sound is maintained. In addition, the rest expresses a timing at which the reproduction of the sound stops.") constituted by one channel or more of melodies of and one channel or more of chords is described (Akama ¶0144: "For example, in the above embodiment, the example has been illustrated in which the information processing apparatus 100 extracts a chord constituent sound of the song 30, but the information processing apparatus 100 may extract not only the chord constituent sound but also a constituent sound of a melody or a constituent sound of a drum."); and the musical-piece generating means accepts an input of an operation parameter (c) (Akama ¶0155: "For example, the information processing apparatus 100 can change the song 65 to images of the entire song illustrated in the graph 64 according to the user's request. As described above, the information processing apparatus 100 can generate new content so as to adjust a blend ratio of the feature quantity.") for operating a style of a musical piece to be generated (Akama ¶0149: "Further, the information processing apparatus 100 may extract sounds for each musical instrument constituting a song or for each musical instrument group. In addition, the information processing apparatus 100 may extract a style feature quantity or the like in which a correlation between features of a certain layer is calculated when features of a certain song are learned by a deep neural network (DNN). Further, the information processing apparatus 100 may extract self-similarity or the like in the song.") together with the input data (Akama ¶0107: "Further, the acquisition unit 133 may acquire arbitrary data from the information processing terminal used by the user. For example, the acquisition unit 133 acquires data constituting a song. Then, the acquisition unit 133 may input the acquired data to the learned model (in this case, inputs the same data to the first encoder 50 and the second encoder 55, respectively), and acquire the feature quantities output from each encoder.").
Akama does not explicitly disclose that the musical-piece generating means has: an encoder which outputs an average vector (µ) and a distribution vector (σ) of a latent variable (z) corresponding to input data by using the learning model on the basis of the input data; latent-variable processing means which generates the latent variable (z) by processing the average vector (µ) and a distribution vector (σ); and a decoder which outputs output data in the same format as that of input data according to the latent variable generated by the latent-variable processing means by using the teaming model; and the latent-variable processing means processes the average vector (µ), the distribution vector (σ), by mixing in a noise coefficient (ε) according to the formula: z = µ + εσ, wherein the noise coefficient (ε) is represented by a matrix N (0,I) in a Variational Autoencoder (VAE) framework, and wherein I is set to the operation parameter (c) to determine the style of the musical piece to be generated.
However, Brunner teaches that the musical-piece generating means has: an encoder which outputs an average vector (µ) and a distribution vector (σ) of a latent variable (z) corresponding to input data (Brunner § 3.2: "For each input sample (i.e., a piece of length nB beats), the pitch, velocity and instrument rolls are passed through their respective encoders, implemented as RNNs. The output of the three encoders is concatenated and passed through several fully connected layers, which then predict σz and µz, the parameters of the approximate posterior qθ(z|x) = N(µz,σz).") by using the learning model on the basis of the input data (Brunner § 3.2: "MIDI-VAE is based on the standard VAE [20] with a hyperparameter β to weigh the Kullback-Leibler divergence in the loss function (as in [17]). A VAE consists of an encoder qθ(z|x), a decoder pφ(x|z) and a latent variable z, where q and p are usually implemented as neural networks parameterized by θ and φ."); latent-variable processing means which generates the latent variable (z) by processing the average vector (µ), the distribution vector (σ) (Brunner § 3.2: "The output of the three encoders is concatenated and passed through several fully connected layers, which then predict σz and µz, the parameters of the approximate posterior qθ(z|x) = N(µz,σz).2 Using the reparameterization trick [20], a latent vector z is sampled from this distribution as z ∼ N(µz,σz ∗ ) where ∗ stands for element-wise multiplication."); and a decoder which outputs output data in the same format as that of input data according to the latent variable generated by the latent-variable processing means by using the learning model (Brunner § 3.2: "This shared latent vector is then fed into three parallel fully connected layers, from which the three decoders try to reconstruct the pitch, velocity and instrument rolls."); and wherein I is set to the operation parameter (c) (Brunner § 3.2: "ε is sampled from an isotropic Gaussian distribution N(0,σ ∗I), where we treat σ as a hyperparameter (see Section 4.2 for more details)." Brunner places an adjustable scalar σε on I in the VAE reparameterization-noise distribution.) to determine the style of the musical piece to be generated (Brunner § 3.3: "In order to change a song’s style from Si to Sj, we pass the song through the encoder to get z, swap the values of dimensions zi style and zj style, and pass the modified latent vector through the de coder. As style we choose the music genre (e.g., Jazz, Pop or Classic) or individual composers (Bach or Mozart).").
Furthermore, Roberts teaches that the latent-variable processing means processes the average vector (µ), the distribution vector (σ), by mixing in a noise coefficient (ε) according to the formula: z = µ + εσ (Roberts § 2.1: "Naively computing the gradient through the ELBO is infeasible due to the sampling operation used to obtain z. In the common case where p(z) is a diagonal-covariance Gaussian, this can be circumvented by replacing z ~ N(µ, σ I) with ε ~ N(0, I), z = µ + σ ⊙ ε), wherein the noise coefficient (ε) is represented by a matrix N (0,I) in a Variational Autoencoder (VAE) framework (Roberts § 2.1: "In the common case where p(z) is a diagonal-covariance Gaussian, this can be circumvented by replacing z ~ N(µ, σ I) with ε ~ N(0, I), z = µ + σ ⊙ ε).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the music processing program of Akama by adding the average vector and distribution vector processing of Brunner and the latent variable processing of Roberts to more effectively perform a neural style transfer to complete musical compositions (Brunner abstract).
Regarding claim 9, Akama teaches a music processing method performed by a music processing system (Akama ¶0220: "Information devices such as the information processing apparatus 100 according to each embodiment described above are implemented by, for example, a computer 1000"), characterized in that the music processing system includes musical-piece generating means (Akama ¶0042: "The learned model according to the embodiment has an encoder that extracts a feature quantity from data constituting content, and a decoder that reconstitutes the content from the extracted feature quantity."); the musical-piece generating means generates a musical piece (Akama ¶0041: "In the embodiment, the information processing apparatus 100 generates a learned model for extracting features of content, and generates new content based on the generated learned model. In the embodiment, the content is constituted by digital data of a predetermined format such as music (song), an image, and a moving image. In the example of FIG. 1, the information processing apparatus 100 is used to process a song as an example of the content.") by using a learning model which performs machine learning (Akama ¶0042: "The learned model according to the embodiment has an encoder that extracts a feature quantity from data constituting content, and a decoder that reconstitutes the content from the extracted feature quantity. For example, the information processing apparatus 100 learns an encoder by unsupervised learning such as a variational auto encoder (VAE) and generative adversarial networks (GANs).") on the basis of learning data having musical piece data for learning in which a musical score of a musical piece (Akama ¶0049: "The song 30 is constituted by, for example, a symbol string (digital data) indicating a pitch, a sound length, and a rest. As an example, the pitch is a pitch that expresses a frequency indicating a pitch of a sound in predetermined steps (for example, 128 steps and the like). In addition, the sound length expresses how long the reproduced sound is maintained. In addition, the rest expresses a timing at which the reproduction of the sound stops.") constituted by one channel or more of melodies and one channel or more of chords is described (Akama ¶0144: "For example, in the above embodiment, the example has been illustrated in which the information processing apparatus 100 extracts a chord constituent sound of the song 30, but the information processing apparatus 100 may extract not only the chord constituent sound but also a constituent sound of a melody or a constituent sound of a drum."); and the musical-piece generating means accepts an input of an operation parameter (c) (Akama ¶0155: "For example, the information processing apparatus 100 can change the song 65 to images of the entire song illustrated in the graph 64 according to the user's request. As described above, the information processing apparatus 100 can generate new content so as to adjust a blend ratio of the feature quantity.") for operating a style of a musical piece to be generated (Akama ¶0149: "Further, the information processing apparatus 100 may extract sounds for each musical instrument constituting a song or for each musical instrument group. In addition, the information processing apparatus 100 may extract a style feature quantity or the like in which a correlation between features of a certain layer is calculated when features of a certain song are learned by a deep neural network (DNN). Further, the information processing apparatus 100 may extract self-similarity or the like in the song.") together with the input data (Akama ¶0107: "Further, the acquisition unit 133 may acquire arbitrary data from the information processing terminal used by the user. For example, the acquisition unit 133 acquires data constituting a song. Then, the acquisition unit 133 may input the acquired data to the learned model (in this case, inputs the same data to the first encoder 50 and the second encoder 55, respectively), and acquire the feature quantities output from each encoder.").
Akama does not explicitly disclose that the musical-piece generating means has: an encoder which outputs an average vector (µ) and a distribution vector (σ) of a latent variable (z) corresponding to input data by using the learning model on the basis of the input data; latent-variable processing means which generates the latent variable (z) by processing the average vector (µ), the distribution vector (σ); a decoder which outputs output data in the same format as that of input data according to the latent variable generated by the latent-variable processing means by using the learning model; and the latent-variable processing means processes the average vector (µ), the distribution vector (σ), by mixing in a noise coefficient (ε) according to the formula: z = µ + εσ, wherein the noise coefficient (ε) is represented by a matrix N (0,I) in a Variational Autoencoder (VAE) framework, and wherein I is set to the operation parameter (c) to determine the style of the musical piece to be generated.
However, Brunner teaches that the musical-piece generating means has: an encoder which outputs an average vector (µ) and a distribution vector (σ) of a latent variable (z) corresponding to input data (Brunner § 3.2: "For each input sample (i.e., a piece of length nB beats), the pitch, velocity and instrument rolls are passed through their respective encoders, implemented as RNNs. The output of the three encoders is concatenated and passed through several fully connected layers, which then predict σz and µz, the parameters of the approximate posterior qθ(z|x) = N(µz,σz).") by using the learning model on the basis of the input data (Brunner § 3.2: "MIDI-VAE is based on the standard VAE [20] with a hyperparameter β to weigh the Kullback-Leibler divergence in the loss function (as in [17]). A VAE consists of an encoder qθ(z|x), a decoder pφ(x|z) and a latent variable z, where q and p are usually implemented as neural networks parameterized by θ and φ."); latent-variable processing means which generates the latent variable (z) by processing the average vector (µ), the distribution vector (σ) (Brunner § 3.2: "The output of the three encoders is concatenated and passed through several fully connected layers, which then predict σz and µz, the parameters of the approximate posterior qθ(z|x) = N(µz,σz).2 Using the reparameterization trick [20], a latent vector z is sampled from this distribution as z ∼ N(µz,σz ∗ ) where ∗ stands for element-wise multiplication."); and a decoder which outputs output data in the same format as that of input data according to the latent variable generated by the latent-variable processing means by using the learning model (Brunner § 3.2: "This shared latent vector is then fed into three parallel fully connected layers, from which the three decoders try to reconstruct the pitch, velocity and instrument rolls."); and wherein I is set to the operation parameter (c) (Brunner § 3.2: "ε is sampled from an isotropic Gaussian distribution N(0,σ ∗I), where we treat σ as a hyperparameter (see Section 4.2 for more details)." Brunner places an adjustable scalar σε on I in the VAE reparameterization-noise distribution.) to determine the style of the musical piece to be generated (Brunner § 3.3: "In order to change a song’s style from Si to Sj, we pass the song through the encoder to get z, swap the values of dimensions zi style and zj style, and pass the modified latent vector through the de coder. As style we choose the music genre (e.g., Jazz, Pop or Classic) or individual composers (Bach or Mozart).").
Furthermore, Roberts teaches that the latent-variable processing means processes the average vector (µ), the distribution vector (σ), by mixing in a noise coefficient (ε) according to the formula: z = µ + εσ (Roberts § 2.1: "Naively computing the gradient through the ELBO is infeasible due to the sampling operation used to obtain z. In the common case where p(z) is a diagonal-covariance Gaussian, this can be circumvented by replacing z ~ N(µ, σ I) with ε ~ N(0, I), z = µ + σ ⊙ ε), wherein the noise coefficient (ε) is represented by a matrix N (0,I) in a Variational Autoencoder (VAE) framework (Roberts § 2.1: "In the common case where p(z) is a diagonal-covariance Gaussian, this can be circumvented by replacing z ~ N(µ, σ I) with ε ~ N(0, I), z = µ + σ ⊙ ε).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the music processing method performed by a music processing system of Akama by adding the average vector and distribution vector processing of Brunner and the latent variable processing of Roberts to more effectively perform a neural style transfer to complete musical compositions (Brunner abstract).
Claim 7 is rejected under 35 U.S.C. 103 as unpatentable over Akama in view of Brunner, and further in view of Simon, Roberts, and Aoki et al. (JP 2002202779 A, July 19, 2002), hereinafter Aoki.
Regarding claim 7, Akama (in view of Brunner and further in view of Roberts) teaches a music processing system comprising the features of claim 6.
Akama (in view of Brunner and further in view of Roberts) does not explicitly disclose shaping means which shapes the generated musical piece generated by the musical-piece generating means to a musically harmonized content.
However, Aoki suggests shaping means which shapes the generated musical piece generated by the musical-piece generating means (Aoki ¶0009: "the generated melody is evaluated, and the melody of the melody is evaluated based on the evaluation result. It is characterized by comprising melody correcting means for appropriately correcting the rhythm or the pitch.") to a musically harmonized content (Aoki ¶0008: "the melody generating means corrects the pitch obtained by the pitch calculating means to a note on a scale. It is characterized by comprising high-correction means.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the music processing system of Akama (as modified by Brunner and Roberts) by adding the shaping of Aoki to automatically correct a generated melody (Aoki ¶0004).
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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