Notice of Pre-AIA or AIA Status
1. 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 § 112
2. 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.
3. Claims 19, 26, 28, 32, and 39 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 19, 28, and 32, the claims recite in part a limitation for generating chord progressions based on harmonic analysis of successful musical compositions. The term “successful” in the aforementioned claims is a relative term which renders the claim indefinite. The term “successful” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding claims 26 and 39, the claims recite in part a limitation for providing copyright clearance verification for generated content. The only prior mention of any sort of content in the aforementioned claims or the independent claims from which they depend is the term “musical content”, for example as subjected to steps for generating, analyzing, and integrating in independent claim 1. The Examiner asserts that there is not a strict and proper antecedent basis agreement between the “content” of claims 26 and 39 and the “musical content” of claims 17 and 30, and hence it raise the question and therefore the doubt as to whether the content subjected to copyright clearance verification in these aforementioned dependent claims is some or all of the musical content established in the aforementioned independent claims.
If it is the case that Applicants wish these differently-termed content instances to be related, then the Examiner recommends clarifying amendments that provide strict antecedent basis agreement between the recitations of the term as found in the different claims. The Applicants having done that, the Examiner will withdraw the present grounds of rejection.
If it is the case that Applicants do not wish to logically link these recitations, then Applicants should clarify that on the record and provide an indication of support in the specification. The Applicants having done that, the Examiner will reconsider the present grounds of rejection.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. 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.
7. Claims 17-19, 21-28, 30-32, and 34-47 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2023/0259327 (“Balassanian”) in view of CN 114530137 A (“Zhang”).
Regarding claim 17, BALASSANIAN teaches A method for AI powered songwriting tools ([0041]: “The present disclosure generally relates to systems for generating custom music content by selecting and combining audio tracks based on various parameters. In various embodiments, machine learning algorithms (including neural networks such as deep learning neural networks) are configured to generate and customize music content to particular users. In some embodiments, users may create their own control elements and the computing system may be trained to generate output music content according to a user's intended functionality of a user-defined control element.”) comprising:
receiving MIDI data representing a musical composition ([0043], [0087], [0089]-[0090], [0093], [0132], [0171], and [0174] make clear that the music being subject to analysis, extraction, characterization, etc. as part of the machine-learned approach is inclusive of being maintained in the MIDI format) through an interface protocol (FIG. 21 and related discussion in [0290] make clear that the modular components for composition and analysis for example may be partitionable across different devices, e.g. a server and a client, in which case transfer of information between the different devices would be understood to be governed by communication/transfer protocols known in the state of the art, such that music (e.g., FIG. 21 element 2112) stored/kept/used in one device (FIG. 21 element 2110) is transmittable/communicable to another device (FIG. 21 element 2120) (see, e.g., [0285] for a clear statement of that music content transfer/communication between devices)) compatible with a digital audio workstation (FIG. 8’s elements 820-830 and FIG. 21’s element 2120 constitute at least portions of a user-facing song/music composition/generation software/GUI (as further clarified in at least [0067] and [0122]-[0125]), which the Examiner equates with the recited ‘digital audio workstation’, which [0290] indicates could be implemented in either a server or a client device);
determining one or more creative parameters ... of a user request ([0041] as was cited above, referring to a user’s ability to define and set parameters (for elaboration, see also [0045], [0121]-[0130], [0141], and [0144]), where the user’s ability to personalize song/music composition/generation through their selection/definition and setting of parameters of many types constitutes the user’s capability to define a request for how the generated song/music will be made, sound, etc.);
analyzing the MIDI data to identify one or more of a chord sequence, a melodic contour, or a melodic motif ([0232]-[0235] discussing a learned capability to grasp/recognize “musical themes” and more specifically to implement rhythm elements and melody elements, explicitly including melodic contours);
generating musical content based at least in part on the analyzed MIDI data and the one or more creative parameters using one or more of a recurrent neural network (RNN) or a transformer architecture (regarding music generation based on user-provided parameters, see [0041], [0045], [0121]-[0130], [0141], and [0144], where the machine-learned framework as trained and as operative is arrived at through the analysis of music ([0076]-[0083], for example), and where the learning and processing pipeline as applied to music to train the models involves music information/data as represented via MIDI format (as cited above per the receiving limitation: [0043], [0087], [0089]-[0090], [0093], [0132], [0171], and [0174]), and where the inference aspects of the trained ML framework to generate music involves a RNN per [0078], e.g. based on patterns in the music in a training stage and also patterns in the music as being presently generated);
analyzing one or more chord progressions of the generated musical content for one or more of musical coherence or originality using at least one harmonic analysis module ([0118] and [0120] discussing music selection via a rules-based approach such that harmony and rhythm rule coherence is evaluated such that the selected music via this approach is understood to help build the generated music composition in a manner that features that coherence (see also [0152] and [0327]));
assessing one or more generated melodies using one or more melodic analysis components ([0092] discussing the evaluation of melodies as characterized by an image representation for existing music, and [0146] where generated music is being evaluated and modified to change the melody in a manner that implies an assessment of melody as is and melody as desired, and [0301]-[0302] discussing an arrangement portion of the music generation/composition aspect, where the arrangement is created and evaluated in part to create a melody in manner that comports with established/defined rules); and
integrating the generated musical content with a digital audio workstation and songwriting software ([0121], [0140], and [0149] discussing aspects of the taught framework that are user-facing such that the music as being generated is subject to playback and modification in a manner that clearly integrates the generated music with the user experience, where the user may be a DJ, a user, a composer, etc.).
As discussed above, Balassanian teaches the determination and use of creative parameters in pursuit of its AI/ML-driven music composition/generation framework. That said, Balassanian does not teach that the determining one or more creative parameters is by performing natural language processing of a user request. Rather, the Examiner relies upon ZHANG to teach what Balassanian does not teach, see e.g., Zhang’s comparable machine-learning driven framework for personalized music generation, and specifically see page 2’s heading Contents of the Invention, under which there is mention of music-making based on word/language inputs (“extracting the emotional words from the sentence to be processed”) where the language extraction and processing is a form of natural language processing as recited. See also the detailed discussion for step S10 as provided in relation to FIG. 2 (e.g., page 5’s 6th paragraph), for elaborative detail.
The Examiner notes that music generation per Zhang also features steps for initial music generation based on such inputs as discussed above, and that the music as initially generated is then evaluated for chord-based coherence. See, e.g., page 6 discussing steps S30-S50, which the Examiner believes also reads especially on the limitation for analyzing (as discussed above with respect to Balassanian) of chord progressions in the generated music.
Balassanian and Zhang both relate to taking user inputs for processing by a machine-learning framework to then generate personalized music for the user. In particular, both references contemplate that such inputs, among many aspects, may be expressive of mood and emotionality, for example. Hence, they are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art to extend Balassanian’s input aspect to go beyond its contemplation of classic GUI implementations (e.g., sliders, knobs, etc.) to also encompass input in other ways otherwise known and established in the state of the art, such as a language-type input as Zhang teaches, with a reasonable expectation of success, particularly when the inputs Balassanian contemplates are inclusive of emotion, mood, or tone expressions which some users may feel are more intuitively or simply expressed with a language input, per Zhang, as opposed to a numerical or measurable style input per Balassanian.
Regarding claim 18, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for analyzing meter patterns within existing musical databases (per [0285], loop library is available for selecting and using music components/elements from for the framework’s learned music composition/generation, and where the music that is subject to searching and selection may be characterized and selected based on “beat timing” (Balassanian: [0042], [0044], [0078], [0182], [0184], [0186], [0191], and [0292])). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 19, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for generating chord progressions based on harmonic analysis of successful musical compositions (Balassanian: [0118] and [0120] discussing music selection via a rules-based approach such that harmony and rhythm rule coherence is evaluated such that the selected music via this approach is understood to help build the generated music composition in a manner that features that coherence (see also [0152] and [0327]); and Zhang: page 6 discussing steps S30-S50). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 21, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for analyzing musical structure patterns including verse-chorus arrangements and bridge sections (Balassanian: [0076] discussing analysis by DNNs of music content for structure, including the detection of verses and choruses, and more generally sections, and where a bridge is a well-known section in music composition, and the Examiner reasons that if a DNN can explicitly identify through analysis sections, including verse and chorus sections, then it would be obvious to identify through analysis other well-known sections such as a bridge – particularly, since [0300] and [0311] explicitly detail that a section as understood for arranging purposes by the same framework is a bridge). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 22, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for generating instrumental arrangements based on genre-specific orchestration patterns (Balassanian: [0053] teaches the specification of combining different instruments together by the music generator framework in a rules-driven approach, i.e., a multi-instrument arrangement, which can be coded for use by the framework as discussed per [0082], [0084], [0107], [0109], and [0137] for example, and where genre is generally configurable by a user ([0045], [0122], and [0144]) and it would be obvious for the instrument combination rule codification as discussed just prior per [0053] to encompass different rules / instrument combinations for say different types of music, e.g. different genres). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 23, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for providing automated mixing and mastering suggestions based on genre conventions (Balassanian: [0284] discussing mixing and mastering the generated music output, and could be understood to be influenced by the user’s feedback as mentioned in the same paragraph, of which genre is one such configurable element that a user can adjust ([0045], [0122], and [0144])). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 24, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for analyzing tempo and rhythm patterns to ensure musical coherence (Balassanian: [0327] (and [0118]) teaching that “harmony and rhythm coherence are tested in the output music content”, and [0051] discussing a regulated approach to changes in tempo such that the tempo changes are made with respect to time in a manner that is mindful of specified attributes such as variety, energy, etc.). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 25, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for performing at least one of generating drum patterns and percussion arrangements tailored to specific musical styles (Balassanian: [0148] discussing rhythm and drum are configurable by a user, and the settings might be associated with a particular artist and hence that artist’s sound or style as expressed via an artist pack for that artist (e.g., as further expanded upon per [0302])), generating alternative versions of compositions with different emotional tones (Balassanian: [0144] discussing the ability for a user to configure a mood setting which can change the key of the music being played, which the Examiner equates with creating a different version for a different mood), or generating demo recordings with synthesized instruments. The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 26, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for providing copyright clearance verification for generated content (Balassanian: [0041] discussing a feature to record playback/usage of music content, the music content having legal rights, and the recording of this information is obviously intended so that the playback and the rights to perform playback can be checked or validated or interrogated). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 27, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for providing personalized songwriting assistance based on individual artist preferences and historical output (Balassanian: [0082] discussing a predictive element provided by the music generation framework such that a prediction is provided as to what a DJ or a composer might do next, and per [0083] the feature is tailored to a particular artist such that it can approximate what that particular artist would choose, and additionally/alternatively, [0135]-[0138] provides for a similar predictive aspect personalized for a user based on the framework being trained for that user based on inputs/preferences provided by that user). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 28, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references teach the additional limitations for analyzing successful song structures within specific genres to inform composition suggestions (Balassanian: [0083] discussing the framework’s learned ability to reproduce or approximate a DJ or composer’s decision making in song generation, i.e., what might be considered experts or professionals in the trade (e.g., “successful” a recited), such that the predictive element arrived at is akin to a “composition suggestion” as recited based on the belief that the next move/action is what the DJ/composer would do). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 30, the claim includes the same or similar limitations as claim 17 discussed above, and is therefore rejected under the same rationale.
Regarding claim 31, the claim includes the same or similar limitations as claim 18 discussed above, and is therefore rejected under the same rationale.
Regarding claim 32, the claim includes the same or similar limitations as claim 19 discussed above, and is therefore rejected under the same rationale.
Regarding claim 34, the claim includes the same or similar limitations as claim 21 discussed above, and is therefore rejected under the same rationale.
Regarding claim 35, the claim includes the same or similar limitations as claim 22 discussed above, and is therefore rejected under the same rationale.
Regarding claim 36, the claim includes the same or similar limitations as claim 23 discussed above, and is therefore rejected under the same rationale.
Regarding claim 37, the claim includes the same or similar limitations as claim 24 discussed above, and is therefore rejected under the same rationale.
Regarding claim 38, the claim includes the same or similar limitations as claim 25 discussed above, and is therefore rejected under the same rationale.
Regarding claim 39, the claim includes the same or similar limitations as claim 26 discussed above, and is therefore rejected under the same rationale.
Regarding claim 40, the claim includes the same or similar limitations as claim 27 discussed above, and is therefore rejected under the same rationale.
Regarding claim 41, the claim includes the same or similar limitations as claim 28 discussed above, and is therefore rejected under the same rationale.
Regarding claim 42, Balassanian in view of Zhang teach the system of claim 30, as discussed above. The aforementioned references teach the additional limitations wherein the instructions are configured such that the one or more processors are configured to implement one or more of the harmonic analysis module, the melodic analysis module, or the integration interface (Balassanian’s FIG. 21 showing different modules that provide the breadth of functions as discussed above in relation to claim 1, for example, and hence are inclusive of each of harmonic analysis, melodic analysis, and integration interfacing as was mapped above per claim 1). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 43, Balassanian in view of Zhang teach the system of claim 30, as discussed above. The aforementioned references teach the additional limitations wherein the instructions are configured such that the one or more processors are configured to generate real-time feedback for artists regarding content quality (Balassanian: the feedback from users as discussed per [0139]-[0140], [0284], and [0297]). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 44, Balassanian in view of Zhang teach the system of claim 30, as discussed above. The aforementioned references teach the additional limitations wherein the instructions are configured such that the one or more processors are configured to generate creative content suggestions to provide real-time creative assistance (Balassanian: [0148]-[0149], [0254], and [0277]-[0280]). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 45, Balassanian in view of Zhang teach the system of claim 30, as discussed above. The aforementioned references teach the additional limitations further comprising a sentiment analysis module configured to determine emotional content and mood of musical content (Balassanian: mood is a configurable aspect of music generation ([0045], [0050], [0122], [0144], [0150], [0290]), as is attitude ([0158]), and these aspects of the music being generated may be configured in view of goals ([0157]-[0160]), and similarly these types of features or features that influence these broader categories for mood/attitude are extractable from existing music as part of the framework’s training/learning – see [0291]-[0292]). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 46, Balassanian in view of Zhang teach the system of claim 30, as discussed above. The aforementioned references teach the additional limitations further comprising a topic modeling module configured to identify thematic patterns using one or more machine learning algorithms (Balassanian: [0275] and [0283] discussing how theme can be associated with music/content). The motivation for combining the references is as discussed above in relation to claim 17.
Regarding claim 47, Balassanian in view of Zhang teach the system of claim 30, as discussed above. The aforementioned references teach the additional limitations wherein the instructions are configured such that the one or more processors are configured to receive approval from one or more of artists or music labels to use an artist's existing music (Balassanian: [0273] discussing the selling of rights to content, which the Examiner reasons involves the providing of rights/approval in exchange for payment). The motivation for combining the references is as discussed above in relation to claim 17.
8. Claims 20 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Balassanian in view of Zhang and further in view of U.S. Patent Application Publication No. 2025/0191558 (“Silverstein”).
Regarding claim 20, Balassanian in view of Zhang teach the method of claim 17, as discussed above. The aforementioned references, particularly Balassanian, contemplate that many users might use the platform and access the same library-stored content to perform the same type of music generation work/task. However, that is not the same as the further limitation for providing real-time collaboration features for multiple artists working on a same composition. Rather, the Examiner relies upon SILVERSTEIN to teach what Balassanian etc. otherwise lack, see e.g., Silverstein’s [0968] discussing a particular mode of operation for a comparable AI/ML-driven music generation framework in which different users can actively collaborate on a common music project.
Balassanian and Zhang both relate to taking user inputs for processing by a machine-learning framework to then generate personalized music for the user. Silverstein is similar. Hence, the references as aforementioned are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art to extend Balassanian’s framework to encompass work on a composition based on a user’s inputs to a situation/scenario where the inputs come from more than one user, with a reasonable expectation of success, such that the user feedback elements that Balassanian already contemplate can be expanded so that the feedback is more directly provided in terms of active collaborative input with more than just one user in the loop.
Regarding claim 33, the claim includes the same or similar limitations as claim 20 discussed above, and is therefore rejected under the same rationale.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure:
US 20140259097 A1
US 20220309131 A1
US 12537998 B1
US 20250371511 A1
US 20240386078 A1
CN 118132797 A
IN 202311028636 A
JP 2004013493 A
WO 2019121577 A1
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHOURJO DASGUPTA whose telephone number is (571) 272-7207. The examiner can normally be reached M-F 8am-5pm CST.
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/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144
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