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
This office action is responsive to applicant’s remarks received on 5/29/2026. Claims 1-20 remain pending.
Response to Arguments
Applicant's arguments filed 5/29/2026 have been fully considered but they are not persuasive.
A: Applicant’s Remarks
For applicant’s remarks “See Applicant Arguments/Remarks Made in an Amendment” filed on 5/29/2026.
A: Examiner’s Response
Applicant argues that “even under the Office Action's stated interpretation, the cited references do not teach or suggest the claimed invention as a whole, and the proposed combination does not provide a sufficient evidentiary or technical basis for
concluding obviousness”.
Examiner understands Applicant’s arguments but respectfully disagree. As set forth in the prior Office action, the rejection is based on the combined teachings of Aharonson and Lenz and includes articulated reason for combining their respective teachings. Aharonson teaches machine-learning-based analysis of musical characteristics, including notes, chords, technique, rhythm, transition complexity, and tempo (Aharonson, col. 162, lines 52-62, 66-67). Lenz teaches receiving and processing MIDI data representing musical characteristics, including pitch, intensity, control parameters, and tempo (Lenz, ¶¶[0009], [0026],[0037]) and further teaches processing musical note data into “rich MIDI data” (Lenz, ¶[0041]).
As explained in the prior Office action, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the MIDI input/data handling of the US’736 in the ML-based music-processing environment of US’898 because doing so would have predictably enabled computer implementation of the learned musical features in standard MIDI form. The Office action further explained that it would have been obvious to use Aharonson’s learned characteristics as control inputs to Lenz’s MIDI processing/conversion technique because both references computationally process musical performance information and the combination would have predictably produced MIDI information reflecting the learned musical characteristics.
Accordingly, the rejection does not rely merely upon the individual presence of isolated claim elements in the prior art. Rather, the rejection identifies the respective teachings of Aharonson and Lenz and provides an articulated reason with rationale underpinning for combining those teachings to arrive at the claimed invention as a whole. Therefore, Applicant’s argument does not overcome the rejection.
Applicant argues that Aharonson is directed to adaptive instruction for learning to play a musical instrument and “does not disclose receiving output from a machine learning algorithm trained to learn a music characteristic of a work of music then using that learned output to modify a MIDI characteristic of a MIDI track.” Applicant further argues that Aharonson’s analysis or assessment of a user’s performance is materially different from the claimed use of machine-learning output as an input for transforming a MIDI track based on learned characteristics of a work of music
Examiner understands Applicant’s arguments but respectfully disagrees. As set forth in the prior Office Action, Aharonson teaches machine-learning-based analysis of musical characteristics. In particular, Aharonson teaches that “[t]he user ability is a measurement of the characteristics of what the user played so far and the ability that the user already knows – such as notes, chords, technique, rhythm, and more, “ and further teaches and assessment comprising a “comparison of computed features using AI learning” and scoring features including “note/chord knowledge, rhythm, transition complexity, tempo, and more” (Aharonson, col. 163, lines 57-62, 66-67). Thus, Aharonson was relied upon for receiving output from machine-learning-based analysis corresponding to musical characteristics including notes, chords, rhythm, technique, transition complexity, and tempo.
The rejection did rely upon Aharonson alone as expressly teaching the claimed MIDI input and modification. Rather, the prior Office Action expressly acknowledged that Aharonson does not explicitly disclose receiving the recited MIDI track and relied upon Lenz for the MIDI teachings. Accordingly, Applicant’s argument that Aharonson alone does not disclose the entire claimed machine-learning/MIDI processing arrangement does not address the combined teachings upon which the rejection under 35 U.S.C. § 103 is based.
Applicant further argues that Lenz “does not disclose a machine learning algorithm training to learn a music characteristic of a work of music, nor does it disclose modifying a MIDI characteristic of a MIDI track based on such learned music characteristic”, and that Lenz at most teaches capture, comparison, tempo handling, and MIDI conversion in an instructional setting.
Examiner understands Applicant’s arguments but respectfully disagrees. The rejection does not rely upon Lenz for teaching the machine-learning-based determination of musical characteristics. Lenz is relied upon for the MIDI processing teachings. Specifically, Lenz teaches that a “MIDI-enabled instrument 101 is connected” and is “capable of passing MIDI data,” and that software receives input from the MIDI-enabled musical instrument as a player plays (Lenz, ¶[0009]). Lenz additionally teaches that “the data is fed into a conversion module 702 which converts the raw note data into rich MIDI data: (Lenz, ¶[0041]) and that “a tempo setting is included as part of the MIDI input” and changes in tempo are indicated in MIDI input (Lenz, ¶[0037]).
Thus, the rejection relies upon Aharonson for the machine-learning-based musical characteristics and Lenz for receiving and processing musical information represented as MIDI data. Applicant’s argument that Lenz does not itself teach machine learning does not overcome the rejections because Lenz was not relied upon for that limitation.
Applicant further argues that, even if Aharonson and Lenz were combined, “the result still would not be the claimed invention” because the claims require a specific relationship among “(i) output from a machine !earning algorithm that is trained to learn a music characteristic of a work of music, (ii) a received MIDI track having a MIDI characteristic, and (iii) modification of the MIDI characteristic based on the learned music characteristic.” Applicant argues that neither reference teaches using learned characteristics of a work of music as control inputs for modifying MIDI characteristics of a received MIDI track.
Applicant argues that neither reference individually provides the recited relationship and further addresses the proposed combination, the Examiner respectfully disagrees that the combined teachings fail to suggest that relationship. As explained in the prior Office action, Aharonson teaches determining musical characteristics including notes, chords, rhythm, technique, transition complexity, and tempo using AI/machine-learning analysis (Aharonson, so. 163, lines 57-62, 66-67). Lenz teaches receiving MIDI information containing musical characteristics, including pitch, intensity, control parameters, and tempo (Lenz, ¶¶[0009], [0026], [0037]), and processing musical note data into “rich MIDI data” (Lenz, ¶[0041]).
The prior Office action further explained that it would have been obvious to one of ordinary skill in the art to use Aharonson’s learned musical characteristics as control inputs to Lenz’s MIDI processing/conversion technique because both references computationally process musical performance information such a combination would have predictably produced MIDI information reflecting the learned musical characteristics. Thus, the claimed relationship results from the combination of Aharonson’s machine-learning-derived musical characteristics with Lenz’s MIDI processing teachings. A rejection under 35 U.S.C. § 103 does not require that the reference individually disclose the claimed combination where the Examiner has provided a reason why one of ordinary skill in the art would have combined their respective teachings.
Applicant further argues that claim 2, 10, and 18 require “integrating or substituting a style of a musician associated with the work of music into the MIDI track, “ and that the cited portions of Aharonson and Lenz do not disclose or suggest performing such modification within the claimed machine-learning-based MIDI modification framework.
Examiner understands Applicant’s arguments but respectfully disagrees. As explained with respect to the independent claims, the claimed MIDI modification framework is rendered obvious by the combined teachings of Aharonson and Lenz. Aharonson further teaches “using different musical instruments, either as system generated sound or as being played by the cooperatively playing users,” and teaches that system-generated music of a musical instrument that is identical to, or similar to, one that is actually played by one of the actual players” (Aharonson, col. 163, lines 2-17). Thus, Aharonson teaches incorporating or substituting generated musical material corresponding to an instrument associated with an actual player into the resulting music. When considered with Aharonson’s machine-learning-based determination of musical characteristics and Lenz’s MIDI processing teachings discussed above, the combined references suggest integrating or substituting musician-associated musical characteristics into the MIDI track as set forth in claims 2, 10 and 18.
Applicant further argues that claims 3, 11, and 19 require “randomizing one or more instruments on the MIDI track” and that general disclosures concerning accompaniment, instrument substitution, or MIDI handling do not teach the claimed modification.
Examiner understands Applicant’s arguments but respectfully disagrees. Aharonson teaches the use of “different musical instruments” and further teaches replacement of system-generated simulated music corresponding to an instrument identical or similar to an instrument actually played by a player (Aharonson, col. 163, lines 2-17). Thus, Aharonson teaches varying and substituting the instrumentation used in generated musical playback. In view of Lenz’s teachings of representing and processing musical characteristics using MIDI data, it would have been obvious to implement Aharonson’s variation or substitution of instruments through the MIDI processing framework of Lenz to provided variation in the instrumentation of the resulting MIDI track. Accordingly, Applicant’s argument does not overcome the combined teachings relied upon in the rejection.
Applicant further argues that claims 4-8, 12-16, and 20 should be allowed because their rejection depends upon the same allegedly deficient combination applied to the independent claims, and that the additional limitations concerning a sole musician or plurality of musicians, raw or composite music tracks, audio music data, and representative music and MIDI characteristics do not remedy the asserted absence of the machine-learning-based MIDI modification framework.
Examiner understands Applicant’s arguments but respectfully disagrees. Applicant’s arguments regarding claims 4-8, 12-16, and 20 is based on the asserted deficiency of the Aharonson and Lenz combination discussed above. As explained above, the Examiner maintains that the combined teachings of Aharonson and Lenz render obvious the recited machine-learning-based MIDI modification framework. Applicant has not separately identified a deficiency in the prior-art teachings relied upon for the additional limitations of claims 4-8, 12-16, and 20. Accordingly, Applicant’s arguments do not overcome the rejection of these claims for the reasons set forth above and in the prior Office action.
Applicant further argues that the rejection “is premised on an improper hindsight reconstruction of Applicant’s disclosure rather than on teachings or suggestions actually present in Aharonson and Lenz”.
Examiner understands Applicant’s arguments but respectfully disagrees. The combination is not based upon Applicant’s disclosure, but upon the respective teaching of the cited references and the articulated reason for combining those teachings. Aharonson reaches machine-learning-based determination of musical characteristics including notes, chords, rhythm, technique, transition complexity, and tempo (Aharonson, col. 163, lines 57-62, 66-67), while Lenz teaches receiving and computationally processing musical information in MIDI form, including pitch, intensity, control parameters, and tempo, and converting musical note information into rich MIDI data (Lenz, ¶[0009],¶[0026], ¶[0037], and ¶[0041]). As previously explained, one of ordinary skill in the art would have found it obvious to use Aharonson’s learned musical characteristics in Lenz’s MIDI processing technique to implement those musical characteristics in a standardized computer-processable musical representation and thereby predictably produce MIDI information reflecting the learned musical characteristics. Therefore, the Examiner’s conclusion of obviousness in based upon the teachings of the cited references and an articulated reason for combining those teachings, rather than impermissible hindsight.
Any judgement on obviousness is necessarily a reconstruction based upon hindsight reasoning; however, so long as the reconstruction takes into account only knowledge within the level of ordinary skill in the art at the time of the claimed invention was made and does not include knowledge gleaned only from Applicant’s disclosure, such a reconstruction is proper.
Accordingly, Applicant’s arguments are not persuasive, and the rejection under 35 U.S.C. § 103 is maintained.
Claim Objections
Regarding Claims 1, 9 and 17, Applicant has amended claims to correct the minor informalities identified in the prior Office action. The previous claim objections to claims 1, 9, and 17 are withdrawn.
Claim Rejections - 35 USC § 112
Regarding Claims 1-3 , 9-11, and 17-19, the limitation has been amended to recite "based on", thereby resolving the indefiniteness identified in the prior Office action. The previous claim rejection under 35 U.S.C.§112(b) of Claims 1-3, 9-11, and 17-19 is withdrawn in light of the applicant's amendments.
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 1-8 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 11893898 (Aharonson), hereinafter US‘898, in view of US 20100313736 (Lenz), hereinafter US’736.
Regarding Claim 1 US‘898 discloses ‘A process (US’898, col. 163, lines 26-28 : “software modules for performing one of more of the flow charts, steps, or methods herein is described in FIG. 23”) comprising:
receiving into a computer processor, output from a machine learning algorithm, the machine learning algorithm trained to learn a music characteristic of work of music (US’898, col.163, lines 57-62: "The user ability is a measurement of the characteristics of what the user played so far and the ability that the user already knows—such as notes, chords, technique, rhythm, and more. The assessment can be a score or comparison of computed features using Al learning ... [and] ... scoring various features including note/chord knowledge, rhythm, transition complexity, tempo, and more (US’898, col.163, lines 66-67)", receiving output from a machine-learning-based analysis of a work of music, where the learned output corresponds to music characteristics such as notes, chords, rhythm, technique, transition complexity, and tempo);
US‘898 does not explicitly disclose ‘receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track.
However, US’736 discloses ‘receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track (US’736, ¶[0026]:"MIDI-enabled instrument 101 is connected ... capable of passing MIDI data ... Software 110 ... receives input from the MIDI-enabled musical instrument 101 as a player plays." ¶[0009]: "MIDI-enabled keyboard transmits event messages such as the pitch and intensity of musical notes ... control signals for parameters such as volume, vibrato, and panning ... and clock signals to set the tempo”, receiving a MIDI track/input into a computer, and further teaches that MIDI carries characteristics such as pitch, intensity/velocity, control parameters, and tempo information).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the MIDI input/data handling of the US’736 in the ML-based music-processing environment of US’898 because doing so would have predictably enabled computer implementation of the learned musical features in standard MIDI form.
US’898 (in view of US’736) further discloses ‘and modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music (US’898, col.163, lines 57-62: “The user ability is a measurement of the characteristics ... such as notes, chords, technique, rhythm, and more ... [and] ... scoring various features including note/chord knowledge, rhythm, transition complexity, tempo, and more."; US’736 ¶[0041]: "the data is fed into a conversion module 702 which converts the raw note data into rich MIDI data"; US’736 ¶[0037]: "a tempo setting is included as part of the MIDI input ... [and] changes in tempo are indicated in a MIDI input", learned music characteristics (e.g., notes, chords, rhythm, transition complexity, tempo). The secondary reference teaches modifying/enriching MIDI data, including tempo-related MIDI information, based on processed musical note data. Together they teach modifying a MIDI characteristic as a function of a learned music characteristic).
It would have been obvious to use US’898’s learned musical characteristics as control inputs to US’736’s MIDI-conversion/modification pipeline, because both references seek to process musical performance information computationally and the combination would have predictably produced modified MIDI output reflecting learned music characteristics.
Regarding Claim 2, US’898 (in view of US’736) teaches ‘The process of claim 1 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music comprises integrating or substituting a style of a musician associated with the work of music into the MIDI track (US’898, col.163, lines 2-17: "using different musical instruments, either as system generated sound or as being played by the cooperatively playing users ... any system generated simulated music ... may be replaced with a system generated music of a musical instrument that is identical to, or similar to, one that is actually played by one of the actual players", substituting a generated musical part with one made identical or similar to an actual player’s instrument/part in the work, which reads on integrating or substituting the musician-associated style into the resulting musical track).
Regarding Claim 3, US’898 (in view of US’736) teaches ‘The process of claim 1 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music comprises randomizing one or more instruments on the MIDI track (US’898, col.163, lines 2-17: "using different musical instruments ... any system generated simulated music ... may be replaced with a system generated music", varying/reassigning instrument parts among available instruments and system-generated parts within the musical arrangement, which at minimum suggests selecting among instruments on the track).
Regarding Claim 4, US’898 (in view of US’736) teaches ‘The process of claim 1 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the work of music is generated by a sole musician (US’898, col. 176, line 59: "The user may be playing alone ...").
Regarding Claim 5, US’898 (in view of US’736) teaches ‘The process of claim 1 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the work of music is generated by a plurality of musicians (US’898, col.162 lines 23-38: "the example of the arrangement 180 involved multiple users ... a single musical piece can be played together (jammed) by 5 people ..."; [see also col. 176 lines 59-60]: "The user may be playing ... in a jam session together with others").
Regarding Claim 6, US’898 (in view of US’736) teaches ‘The process of claim 1 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the work of music comprises a raw data track of music of a sole musician, or the work of music comprises a composite music track including the music of the sole musician and music of other musicians (US’898, col. 177, lines 2-12 : "the sound, including the user's playing, the played BGM, environmental noises and optionally playing by other users, may be recorded ... the component of the user's playing may be recognized ..."; US’736, ¶[0035]: "The audio signal is passed to an audio conversion module 401, which converts the audio stream into a MIDI output ... enables any musical instrument, or a vocal part, to be practiced and trained ..." US’898 teaches both a composite recording containing multiple musicians/BGM and extraction of the individual user’s playing, which corresponds to a composite track and a raw/isolated track of a sole musician. US’736 teaches processing audio music data into MIDI form).
Regarding Claim 7, US’898 (in view of US’736) teaches ‘The process of claim 1 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the music characteristic and the MIDI characteristic comprise one or more of notes, chord progressions, key changes, attack and sustain patterns, transition patterns, voicing techniques, timings and rhythms (US’898, col.128, lines 6-15: "the extracted features ... may include notes or chords difficulty, ... transition between notes/chords difficulty, defined Tempo ... [and] rhythmic difficulty ..."; lines 4394-4396: "notes, chords, technique, rhythm ... tempo ...". US’736, ¶[0009]: "pitch and intensity of musical notes ... volume, vibrato, and panning ... and clock signals to set the tempo", cited characteristics read on notes, chord-related content, transition patterns, timings/rhythms, and expressive patterns reflected in MIDI note/control/tempo data).
Regarding Claim 8, US’898 (in view of US’736) teaches ‘The process of claim 1 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the music data comprise audio music data (US’898, col. 154, line 53 & col. 155 line 1:"For each musical instrument involved ... an audio file ... .mp3, .wav ..."; US’736, ¶[0035]: "an audio input device ... capture a player's music play ... The audio signal is passed to an audio conversion module 401 ...").
Regarding Claim 17, US’898 discloses ‘A system comprising:
a computer processor, (US’898, col.184, line 24: "Computing device 29500 may comprise);
and a computer memory coupled to the computer processor: wherein the computer processor and computer memory are operable for (US’898, col. 184, line 26: “storage devices ... program code, executable by processor "):
receiving into a computer processor, output from a machine learning algorithm, the machine learning algorithm trained to learn a music characteristic of work of music (US’898, col. 163, lines 57-62: "The user ability is a measurement of the characteristics of what the user played so far and the ability that the user already knows—such as notes, chords, technique, rhythm, and more. The assessment can be a score or comparison of computed features using Al learning ... [and] ... scoring various features including note/chord knowledge, rhythm, transition complexity, tempo, and more (US’898, col.163, lines 66-67).", receiving output from a machine-learning-based analysis of a work of music, where the learned output corresponds to music characteristics such as notes, chords, rhythm, technique, transition complexity, and tempo);
US‘898 does not explicitly disclose ‘receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track.
However, US’736 discloses ‘receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track.
It would have been obvious to use the MIDI input/data handling of the US’736 in the ML-based music-processing environment of US’898 because doing so would have predictably enabled computer implementation of the learned musical features in standard MIDI form.
US’898 (in view of US’736) further discloses ‘and modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music (US’898, col.163, lines 57-62: “The user ability is a measurement of the characteristics ... such as notes, chords, technique, rhythm, and more ...”; US’736 ¶[0041]: "the data is fed into a conversion module 702 which converts the raw note data into rich MIDI data"; US’736 ¶[0037]: "a tempo setting is included as part of the MIDI input ... [and] changes in tempo are indicated in a MIDI input", learned music characteristics (e.g., notes, chords, rhythm, transition complexity, tempo). The secondary reference teaches modifying/enriching MIDI data, including tempo-related MIDI information, based on processed musical note data. Together they teach modifying a MIDI characteristic as a function of a learned music characteristic).
It would have been obvious to use US’898’s learned musical characteristics as control inputs to US’736’s MIDI-conversion/modification pipeline, because both references seek to process musical performance information computationally and the combination would have predictably produced modified MIDI output reflecting learned music characteristics.
Regarding Claim 18, US’898 (in view of US’736) teaches ‘The system of claim 17 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music comprises integrating or substituting a style of a musician associated with the work of music into the MIDI track (US’898, col.163, lines 12-17: "any system generated simulated music ... may be replaced with a system generated music of a musical instrument that is identical to, or similar to, one that is actually played by one of the actual players.", substituting generated musical content with content corresponding to an actual player’s instrument/part, which reads on integrating or substituting the musician-associated style into the track).
Regarding Claim 19, US’898 (in view of US’736) teaches ‘The system of claim 17 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music comprises randomizing one or more instruments on the MIDI track (US’898, col. 163, lines 2-14: "using different musical instruments ... any system generated simulated music ... may be replaced ...", varying/reassigning instrument parts among available instruments and generated parts in the musical arrangement).
Regarding Claim 20, US’898 (in view of US’736) teaches ‘The system of claim 17 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the music characteristic and the MIDI characteristic comprise one or more of notes, chord progressions, key changes, attack and sustain patterns, transition patterns, voicing techniques, timings and rhythms. (US’898, col.128, lines 7-15: “notes or chords ... transition ... Tempo ... rhythmic difficulty" and "notes, chords, technique, rhythm"[US’898, col. 163, lines 59-60], ; US’736, ¶[0009]: "pitch and intensity of musical notes ... control signals ... and clock signals to set the tempo", US’898 teaches notes/chords, transition-related features, tempo, and rhythm as music characteristics, while Us’736 teaches corresponding MIDI note/control/tempo characteristics. Together they teach representative recited music and MIDI characteristics).
Claims 9–16 are rejected under 35 U.S.C. §103 as being unpatentable over US’898 in view of US’736. Claims 9–16 correspond respectively to claims 1–8, but are drafted in terms of a non-transitory machine-readable medium comprising instructions that, when executed, perform the recited steps.
Regarding Claim 9, A non-transitory machine-readable medium (US’898, col. 184, lines 37-40: “device such as a CD, a DVD… Flash device”) comprising
instructions that when executed by a computer processor executes a process (US’898, col.184, lines 42-45: “may be implemented as one or more sets of interrelated computer instructions, executed for example by any of processors 29504 and/or by 45 another processor”) comprising:
receiving into the computer processor, output from a machine learning algorithm, the machine learning algorithm trained to learn a music characteristic of work of music;
receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track;
and modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music. (Claim 9 corresponds to claim 1)
Regarding Claim 10, US’898 (in view of US’736) teaches ‘The non-transitory machine-readable medium of claim 9 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music comprises integrating or substituting a style of a musician associated with the work of music into the MIDI track. (Claim 10 corresponds to claim 2)
Regarding Claim 11, US’898 (in view of US’736) teaches ‘The non-transitory machine-readable medium of claim 9 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the modifying the MIDI characteristic of the MIDI track based on the music characteristic of the work of music comprises randomizing one or more instruments on the MIDI track. (Claim 11 corresponds to claim 3)
Regarding Claim 12, US’898 (in view of US’736) teaches ‘The non-transitory machine-readable medium of claim 9 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the work of music is generated by a sole musician. (Claim 12 corresponds to claim 4)
Regarding Claim 13, US’898 (in view of US’736) teaches ‘The non-transitory machine-readable medium of claim 9 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the work of music is generated by a plurality of musicians. (Claim 13 corresponds to claim 5)
Regarding Claim 14, US’898 (in view of US’736) teaches ‘The non-transitory machine-readable medium of claim 9 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the work of music comprises a raw data track of music of a sole musician, or the work of music comprises a composite music track including the music of the sole musician and music of other musicians. (Claim 14 corresponds to claim 6)
Regarding Claim 15, US’898 (in view of US’736) teaches ‘The non-transitory machine-readable medium of claim 9 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the music characteristic and the MIDI characteristic comprise one or more of notes, chord progressions, key changes, attack and sustain patterns, transition patterns, voicing techniques, timings and rhythms. (Claim 15 corresponds to claim 7)
Regarding Claim 16, US’898 (in view of US’736) teaches ‘The non-transitory machine-readable medium of claim 9 as discussed above.
US’898 (in view of US’736) further discloses ‘wherein the music data comprise audio music data. (Claim 16 corresponds to claim 8)
Conclusion
THIS ACTION IS MADE FINAL. 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 NICOLE K GILLESPIE whose telephone number is (571)482-4187. The examiner can normally be reached Monday-Friday 7:30-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dedei K Hammond can be reached at (571)270-3819. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NICOLE K GILLESPIE/ Examiner, Art Unit 2837
/DEDEI K HAMMOND/ Supervisory Patent Examiner, Art Unit 2837
Prosecution Insights