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 Objections
Claims 1, 9 and 17 should be objected to:
Regarding Clam 1, on line 2, after “computer processor ”, insert “,”.
Regarding Claim 9, on line 3, after “computer processor”, insert “,“.
Regarding Claim 17, on line 5, after “computer processor”, insert “,”.
.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims -3, 9-11 and 17-19are rejected under 35 U.S.C. §112(b) as being indefinite.
Basis for Rejection
Regarding claims 1-3, 9-11 and 17-19, the term “as a function of” is unclear and renders the claims indefinite.
For purposes of examination, the Examiner interprets:
“as a function of” as based on a computed relationship, mapping, or algorithmic transformation derived from one or more musical characteristics.
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 as a function of 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 as a function of 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 as a function of 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 as a function of 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 as a function of 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 as a function of 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 as a function of 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 as a function of 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 as a function of 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US20180047373 teaches techniques for processing and modifying musical performance data, including analysis and transformation of digital musical signals (e.g., MIDI or audio representations) to alter performance characteristics such as timbre, style, or performance attributes. The reference further discloses applying signal processing operations and/or learned models to modify musical content based on extracted features, which is relevant to the claimed modification of a MIDI characteristic as a function of a learned music characteristic.
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