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
Receipt is acknowledged of applicant’s amendment filed May 19, 2026. Claims 4 and 5 have been canceled without prejudice. Claims 1-3 and 6-22 are pending and an action on the merits is as follows.
Objection to the specification has been withdrawn.
Rejection of 6 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph has been withdrawn.
Applicant's arguments with respect to claims have been considered and are addressed below.
Claim Objections
Claims 15, 18 and 21 are objected to because the following elements lack proper antecedent basis in the claim(s):
Claim 15 lines 3-4: “the updated style parameter”
Claim 18 lines 2-3: “the feature amount (of the vocal characteristic element)”
Claim 21 lines 2-3: “the elements extracted”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3 and 6-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor, at the time the application was filed, had possession of the claimed invention.
Claim 1 includes the limitation “feature amounts of each of a plurality of elements forming the selected generated piece of music information are individually displayed in an editable manner … such that each feature amount is independently editable”. However applicant’s originally filed disclosure does not properly describe multiple feature amounts to be individually displayed so that they are each editable. Although the specification describes editing individual feature amounts (page 21 paragraph [0060]), the specification does not describe multiple feature amounts to be displayed such that each are independently editable. It is unclear how the process determines when to generate a new piece of music information having updated feature amounts if a user is able to edit multiple feature amounts of the selected piece of music information, e.g. after a predetermined number of edits have been made, or after a predetermined amount of time has passed. Therefore this limitation is considered new matter.
Claims 9 and 10 include the limitation “displaying, in an editable manner by the interface, feature amounts of each of a plurality of elements forming a selected generated piece of music information individually such that each feature amount is independently editable”. However applicant’s originally filed disclosure does not properly describe multiple feature amounts to be individually displayed so that they are each editable. Although the specification describes editing individual feature amounts (page 21 paragraph [0060]), the specification does not describe multiple feature amounts to be displayed such that each are independently editable. It is unclear how the process determines when to generate a new piece of music information having updated feature amounts if a user is able to edit multiple feature amounts of the selected piece of music information, e.g. after a predetermined number of edits have been made, or after a predetermined amount of time has passed. Therefore this limitation is considered new matter.
Claim 11 includes the limitation “wherein the interface comprises a first display region presenting the different pieces of music information for selection, a second display region presenting the feature amounts of each of the plurality of elements of the selected generated piece in the editable manner”. However applicant’s originally filed disclosure does not properly describe these separate display regions. Applicant’s specification describes a style information list 280a in a first region shown in FIG. 8 in which style information is selectable (page 19 paragraph [0055]). This figure further shows a second region presenting what appears to be feature amounts of a plurality of elements. However the shown feature amounts are not described to be editable in the second region, nor are they editable based on a selected style information. The descriptions of FIGS. 3-7, 9, 10, 21 and 22 further do not provide a first display region presenting different pieces of music information for selection and a second region presenting feature amounts of elements of the selected generated piece in an editable manner. Therefore this limitation is considered new matter.
Claim 12 includes the limitation “wherein the feature amounts include at least one of … timbre, or key signature of an element forming the music information”. The claims previously describe the feature amounts to be independently editable. However applicant’s originally filed disclosure does not properly describe an editable feature amount to include a timbre. The application describes arbitrary instrument information to be set (page 36 paragraph [0114]), but does not describe timbre to be editable such that it is included as a feature amount. Therefore this limitation is considered new matter.
Claim 13 includes the limitation “the plurality of pieces of music information created using the interface are created by a plurality of different users”. However applicant’s originally filed disclosure does not properly describe the plurality of pieces of music information to be created by a plurality of different users using a single interface. Applicant’s specification describes a plurality of different users providing a plurality of pieces of music information as a composition contest (page 46 paragraph [0156]), but does not describe the different users to utilize the same interface. It is unclear whether the different users are to use the interface simultaneously, or at different times to submit each piece of music information. Therefore this limitation is considered new matter.
This claim further includes the limitation “the machine learning model generates the new music information based on patterns identified across the plurality of pieces of music information from the plurality of users”. However the claim previously describes the plurality of pieces of music information to be created by the plurality of users. Applicant’s originally filed disclosure does not properly describe a plurality of pieces of music information created by the plurality of different users to be processed using machine learning. According to the specification, a general user posts score information of a composition contest involving a plurality of different users, and a new style information is generated using the score information of the general user, not the plurality of pieces of music information from the plurality of users (page 46 paragraph [0156]). Therefore this limitation is considered new matter.
Claim 14 includes the limitation “wherein editing a part of elements forming the music information includes selecting a time span within the music information”. However applicant’s originally filed disclosure does not properly describe a time span within the music information to be selected. Although applicant’s specification describes inputting time-series feature amounts based on acquired style information changes (page 31 paragraph [0097]), the specification is silent as to a time span being selected of the music information. It is unclear how applicant intends a time span to be selected, e.g., via entering numbers on the interface, dragging starting and ending markers on a line of musical notion, etc. Therefore this limitation is considered new matter.
This claim further includes the limitation “replacing the selected time span with newly generated music information”. However applicant’s originally filed disclosure does not properly describe newly generated music information to replace a selected time span of the music information. Applicant’s specification describes inputting time-series feature amounts based on acquired style information changes (page 31 paragraph [0097]), but the specification does not describe a selected time span of the music information to be replaced. Therefore this limitation is considered new matter.
Claim 16 includes the limitation “relearning the machine learning model includes generating a model trained on a selected subset of the plurality of pieces of music information created using the interface”. However applicant’s originally filed disclosure does not properly describe the machine learning model to be relearned based on a selected subset of pieces of music information. Applicant’s specification describes the composition model to be relearned using acquired style information from the interface (page 31 paragraph [0096]), and separately describes the learning model to be relearned based on created pieces of music information (page 41 paragraph [0131]), but the specification does not describe the machine learning model to be relearned based on a selected subset of pieces of music information. It is unclear whether the relearning of the machine learning model is to be performed automatically anytime a piece of music information is selected, or only upon selection of a certain number of pieces of music information. Therefore this limitation is considered new matter.
This claim further includes the limitation “wherein the trained model is stored in association with an identifier”. However applicant’s originally filed disclosure does not properly describe a trained model to be stored in association with an identifier. Applicant’s specification is silent as to storing a trained model, and further does not associate a stored trained model with an identifier. Therefore this limitation is considered new matter.
Claim 17 includes the limitation “wherein the machine learning model is one of a plurality of stored models, and … receiving a selection of one of the plurality of stored models prior to generating the new music information”. However applicant’s originally filed disclosure does not properly describe multiple models to be stored, nor selection of a stored model prior to generating new music information. Applicant’s specification describes only a single model as a composition model being generated by the machine learning unit (page 31 paragraph [0096]), and does not reference other types of models. The specification is also silent as to the composition model being stored, and selection of different models before new music information is generated. It is unclear whether a user is able to edit an established model to create a new model, new models are automatically generated based on results of a previous iteration of a model, or if a plurality of models are initially stored in a program for selection. It is further unclear whether a user is to perform the selection of a specific model, or if the selection is performed automatically based on inputted data. Therefore this limitation is considered new matter.
Claim 18 includes the limitation “the elements forming the music information include a vocal characteristic element, and wherein the feature amount of the vocal characteristic element specifies a vocal style or identity used in generating the music information”. However applicant’s originally filed disclosure does not properly describe a vocal characteristic element included in the music information, nor does it describe a feature amount of vocal characteristic element to specify a vocal style or identity. Applicant’s specification only describes a function of detecting a voice input, e.g., via a microphone (page 33 paragraph [0102]), but does not describe the music information to include a vocal characteristic element, nor a feature amount of the vocal characteristic element to specify a vocal style or identity. It is unclear whether applicant intends the vocal style or identity to be based on a note range of the vocal characteristic element, a timbre of the vocal characteristic element, or a rhythm of the vocal characteristic element. Therefore this limitation is considered new matter.
Claim 19 includes the limitation “the machine learning model is made accessible to a plurality of users”. However applicant’s originally filed disclosure does not properly describe a plurality of users being able to access the machine learning model. Although applicant’s specification describes a plurality of users participating in a composition contest in which evaluation information and ranking can be provided (page 46 paragraph [0156]), the specification does not describe the plurality of users to be able to access the same machine learning model. It is unclear whether a same machine learning model is to be downloaded or shared to different remote interfaces of the different users, respectively, or if the same machine learning model is to be installed on each interface as factory preset data. Therefore this limitation is considered new matter.
This claim further includes the limitation “a user other than a user who created the machine learning model may select the machine learning model to generate new music information”. However applicant’s originally filed disclosure does not properly describe such a feature. Applicant’s specification describes a system administrator app which has different authority with respect to other users (page 45 paragraph [0147]), but does not describe what authority a user is able to have with respect to a user who created a machine learning model. The application is silent as to only allowing other users to select a machine learning model generated by someone else. Therefore this limitation is considered new matter.
Claim 22 includes the limitation “the new piece of music information having the updated feature amount is generated by rearranging existing music information included in the selected piece of music information in accordance with the updated feature amount”. However applicant’s originally filed disclosure does not properly describe generating new piece of music information by rearranging existing music information included in the selected piece of music information. Although applicant’s specification describes generating new piece of music information from a selected piece of music information, the specification is silent about rearranging existing music information included in the selected piece of music information in accordance with updated feature amount to generate the new piece of music information. Therefore this limitation is considered new matter.
Claims 2, 3, 6-8, 15, 20 and 21 depend from claim 1 and therefore inherit all claimed limitations. These claims then also contain the limitations considered as new matter.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6-10 and 12-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 9 and 10 recite a step of generating music information by editing a part of elements forming the music information, generating new music information on a basis of characteristics of a plurality of pieces of music information created, selectively displaying different generated pieces of music information, displaying feature amounts of each of a plurality of elements forming a selected generated piece of music information in a case where a selection of a piece of music information is received, and generating and adding to the different pieces of music information displayed, a new piece of music information having an updated feature amount corresponding to a selected element in a case where a feature amount of a selected one of the elements forming the selected piece of music information is updated. The claimed limitations are directed to a process, which under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components and processes, e.g. a musician mentally changing each of a specific note of a read musical phrase before performance of the musical phrase based on remembering previously made changes to other musical phrases that sounded acceptable. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components and processes, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The only additional elements described in the claims are an interface and a machine learning model, which are both described at a high-level of generality, such that they amount to no more than mere components and instructions to apply the exception using a generic computer component and known processing algorithm to perform the process. Neither are described to require any unique components or steps in order to achieve applicant’s claimed limitations, e.g. it is common for an interface to display selectable data and provide a means to edit displayed data for output. It is also well known that machine learning models are trained according to various parameters and provide new versions of received inputs in accordance with its training. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the interface which generates the music information, displays pieces of music information, allows selection of pieces of music information, and displays feature amounts of a plurality of elements to be edited, is considered a generic computer component without requiring any additional specific elements, and the machine learning model to generate new music information is generically claimed without providing additional unique steps or features of the machine learning model. These steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The dependent claims 2, 3, 6-8 and 12-22 only further define the abstract idea without significantly more, e.g. mapping the music information to a multidimensional space as in claim 8, creating a plurality of pieces of music information by a plurality of different users as in claim 13, and making the machine learning model accessible to a plurality of users are not described to require any unique component or process and therefore do not amount to significantly more than the judicial exception. The claims are not patent eligible.
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 1-3, 8-13, 15-17, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kolen et al. (US 10,657,934 B1) in view of Song et al. (US 2006/0230910 A1).
Claims 1, 9 and 10: Kolen et al. discloses an information processing method for generating music information, an information processing program for causing a computer to function as an interface control unit, and an information processing device including an interface control unit for providing an interface capable of generating music information (musical phrase) according elements (set of constraints) (column 8 lines 10-17) by editing (updating) a part of elements (individual constraint) forming the music information (column 12 lines 9-13), and generating new music information by a machine learning model (column 8 lines 12-17) that is trained (updated) on a basis of characteristics of a plurality of pieces of music information created using the interface (column 14 lines 51-58).
Different pieces of music information generated by the machine learning model are selectively displayed (presented) by the interface and a selection of a piece of music information is received from the different pieces of music information (column 4 lines 36-42). Corresponding feature amounts (notes) of each of a plurality of elements forming the selected piece of music information (column 14 lines 6-7) are individually displayed in a viewable manner by the interface, as shown in FIG. 1D. In a case where a feature amount of a selected element forming the selected piece of music information is updated, e.g. from ‘B’ to ‘B flat’ (132), new piece of music information having an updated feature amount corresponding to the selected element is generated and added to the different pieces of musical information displayed by the interface (column 12 lines 56-66, column 14 lines 7-12), as shown in FIGS. 1B-1D. This reference fails to disclose the feature amounts to be individually displayed in an editable manner by the interface such that each feature amount is independently editable.
However Song et al. teaches an information processing method, an information processing program, and an information processing device, where feature amounts (notes) are individually displayed in an editable manner by an interface such that each feature amount is independently editable (page 3 paragraph [0046]).
Given the teachings of Song et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing method disclosed in Kolen et al. with providing the feature amounts to be individually displayed in an editable manner by the interface such that each feature amount is independently editable. Doing so would allow a “user [to] input a self-composed melody” by “[adjusting] the pitch and duration information of the sound” as taught in Song et al. (page 3 paragraph [0046]).
Claim 2: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where the machine learning model is disclosed in Kolen et al. to be relearned on a basis of the plurality of pieces of music information (column 14 lines 51-58).
Claim 3: Kolen et al. modified by Song et al. discloses an information processing method where an interface is provided, as stated above. Fixed feature amounts (characteristics) for a part of elements are disclosed in Kolen et al. to be acquired that correspond to certain generalities associated with that part of elements (column 2 lines 34-45), and a plurality of feature amounts for other elements included in the plurality of pieces of music information are acquired for learning a user’s preference and updating the machine learning model (column 14 lines 51-64).
Claim 8: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where a point (note) corresponding to the music information is disclosed in Kolen et al. to be mapped in a multidimensional space of user interface on a basis of a feature amount (scale and key) included in the music information, and the mapped point is displayed, as shown in FIG. 1C (column 12 lines 56-66).
Claim 11: Kolen et al. modified by Song et al. discloses an information processing method where the feature amounts to be individually displayed in an editable manner by the interface, as stated above. The interface is disclosed in Kolen et al. to present different pieces of music information for selection (column 4 lines 40-46). The interface is shown in FIG. 1D to include a display region (upper half) containing text, and a second display region (lower half) presenting the feature amounts of each of the plurality of elements of the selected generated piece in the editable manner, and a playback control (selectable option 146) associated with each of the different pieces of music information when selected (column 14 lines 40-43). These references fail to disclose the interface to include a first display region to present the different pieces of music information.
However it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the interface to include a first display region to present the different pieces of music information, since it has been held that rearranging parts of an invention involves only routine skill in the art. In re Japikse, 86 USPQ 70. Doing so would allow a user to review and temporarily edit each piece of music information for accurate comparison between pieces of music information before selection of a piece of music information is made.
Claim 12: Kolen et al. modified by Song et al. discloses an information processing method where the feature amounts include notes of an element forming the music information, as stated above. The feature amounts are shown in FIG. 1D of Kolen et al. to include a pitch of the note.
Claim 13: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where the plurality of pieces of music information created using the interface are disclosed in Kolen et al. to be created by a plurality of different users, and wherein the machine learning model generates the new music information based on patterns identified across the plurality of pieces of music information from the plurality of users (particular constraints that are generally followed) (column 13 lines 38-42).
Claim 15: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where the feature amounts of elements are disclosed in Kolen et al. to include a style parameter specifying one or more sonic characteristics of the music information, and wherein the new music information is generated reflecting the updated style parameter (column 2 lines 47-54).
Claim 16: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where relearning the machine learning model is disclosed in Kolen et al. to include generating a model trained on a selected subset corresponding to style of the plurality of pieces of music information created using the interface (column 2 lines 47-54). The trained model then would be stored in association with an identifier in order to properly select the appropriate style, as is recognized in the art.
Claim 17: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where the machine learning model is disclosed in Kolen et al. to be one of a plurality of stored models, and further includes receiving a selection of one of the plurality of stored models prior to generating the new music information (column 5 lines 3-7).
Claim 20: Kolen et al. modified by Song et al. discloses an information processing method where the different pieces of music information are selectively displayed by the interface, as stated above. The pieces of music information are displayed together with associated metadata comprising a comment (132) associated with each piece of music information, as shown in FIG. 1C of Kolen et al, and the selection of a piece of music information is received based on the associated metadata (column 12 line 60 through column 13 line 2).
Claim 21: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where the machine learning model is disclosed in Kolen et al. to be relearned on a basis of time-series feature amounts of each of the elements extracted from the plurality of pieces of music information, such that the machine learning model learns a relationship between the feature amounts of the elements across time within each piece of music information (column 13 lines 38-46).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kolen et al. (US 10,657,934 B1) modified by Song et al. (US 2006/0230910 A1) as applied to claim 1 above, further in view of Gartland-Jones (US 2005/0076772 A1).
Claim 6: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where a feature amount of an element forming the selected piece of music information is disclosed in Kolen et al. to be specified, e.g. from ‘B’ to ‘B flat’ (132), the specified feature amount is arranged and generated as the new music information (column 12 lines 56-66), as shown in FIGS. 1B-1D. These references fail to disclose a range to be specified for a feature amount of an element forming the selected piece of music information such that a feature amount included in the range is arranged and generated as the new music information.
However Gartland-Jones teaches an information processing method for generating music information, where a range (pitch range) is specified for a feature amount (notes) of an element forming a piece of music information (phrase) such that a feature amount included in the range is arranged and generated as a new music information (pages 18-19 paragraph [0263]).
Given the teachings of Gartland-Jones, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing method disclosed in Kolen et al. as modified by Song et al. with providing a range to be specified for a feature amount of an element forming the selected piece of music information such that a feature amount included in the range is arranged and generated as the new music information. Doing so would allow the generated music information to be in a comfortable playing range of the user, allowing the user to play along with the generated music information.
Claim 14: Kolen et al. modified by Song et al. discloses an information processing method as stated above, where editing a part of elements forming the music information is disclosed in Kolen et al. to include selecting a time span (measures) within the music information (column 12 lines 33-41). These references fail to disclose the selected time span to be replaced with newly generated music information.
However Gartland-Jones teaches an information processing method for generating music information, where a time span of music information (initial music) is replaced with a time span of a newly generated (modified section) music information (music produced) (page 16 paragraph [0228]).
Given the teachings of Gartland-Jones, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing method disclosed in Kolen et al. as modified by Song et al. with providing the selected time span to be replaced with newly generated music information. Doing so would allow a user to replace any portion of the music information with newly generated music information “should the user particularly like the way in which the music produced … has evolved” as taught in Gartland-Jones (page 16 paragraph [0228]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kolen et al. (US 10,657,934 B1) modified by Song et al. (US 2006/0230910 A1) as applied to claim 1 above, further in view of Kumar et al. (US 10,991,349 B2).
Claim 7: Kolen et al. modified by Song et al. discloses an information processing method as stated above, but fails to disclose that every time new music information is generated, the new music information to be stored in association with identification information of a user.
However Kumar et al. teaches an information processing method for generating music information, where new music information that is generated is stored in a storage module in association with (corresponding to) each of different users with identification information (profile) of each user (column 10 line 66 through column 11 line 2).
Given the teachings of Kumar et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing method disclosed in Kolen et al. as modified by Song et al. with providing that every time new music information is generated, the new music information to be stored in association with identification information of a user. Doing so would allow different music information created for different users to be stored as taught in Kumar et al. (column 11 lines 1-2) and accessed later (column 9 lines 8-9) based on user authentication (column 9 lines 40-43).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kolen et al. (US 10,657,934 B1) modified by Song et al. (US 2006/0230910 A1) as applied to claim 1 above, further in view of Balassanian et al. (US 11,635,936 B2).
Claim 18: Kolen et al. modified by Song et al. discloses an information processing method as stated above, but fails to disclose the elements forming the music information to include a vocal characteristic element, and the feature amount of the vocal characteristic element to specify a vocal style or identity used in generating the music information.
However Balassanian et al. teaches an information processing method for generating music information, where elements forming music information include a vocal characteristic element, and feature amount of the vocal characteristic element specifies a vocal identity (type) used in generating the music information (column 23 line 66 through column 24 line 6).
Given the teachings of Balassanian et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing method disclosed in Kolen et al. as modified by Song et al. with providing the elements forming the music information to include a vocal characteristic element, and the feature amount of the vocal characteristic element to specify a vocal style or identity used in generating the music information. Doing so would allow a user to edit additional characteristics about the music information, including attitude, as taught in Balassanian et al. (column 23 line 66 through column 24 line 6).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Kolen et al. (US 10,657,934 B1) modified by Song et al. (US 2006/0230910 A1) as applied to claim 1 above, further in view of Balassanian et al. herein referred to as Edward et al. (US 12,205,565 B2).
Claim 19: Kolen et al. modified by Song et al. discloses an information processing method as stated above, but fails to disclose the machine learning model to be made accessible to a plurality of users, and wherein a user other than a user who created the machine learning model may select the machine learning model to generate new music information.
However Edward et al. teaches an information processing method for generating music information, where control elements created by a user is made accessible to a plurality of users, and a user other than the user who created the control element may select the control element to generate new music information (column 12 lines 42-46).
Given the teachings of Edward et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing method disclosed in Kolen et al. as modified by Song et al. with providing the machine learning model to be made accessible to a plurality of users, and wherein a user other than a user who created the machine learning model may select the machine learning model to generate new music information. Doing so would allow remote users to collaborate together on the same music information.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Kolen et al. (US 10,657,934 B1) modified by Song et al. (US 2006/0230910 A1) as applied to claim 1 above, further in view of Gozzi (US 9,672,800 B2).
Claim 22: Kolen et al. modified by Song et al. discloses an information processing method as stated above, but fails to disclose the new piece of music information having the updated feature amount to be generated by rearranging existing music information included in the selected piece of music information in accordance with the updated feature amount.
However Gozzi teaches an information processing method for generating music information, where a new piece of music information (new musical composition) is generated by rearranging existing music information (segments) included in a piece of music information (music performance data) (column 7 lines 9-13).
Given the teachings of Gozzi, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing method disclosed in Kolen et al. as modified by Song et al. with providing the new piece of music information having the updated feature amount to be generated by rearranging existing music information included in the selected piece of music information in accordance with the updated feature amount. Doing so would give a user more options of editing the piece of music information, thereby allowing generation of a new piece of music information “that may be entirely new and unique” as taught in Gozzi (column 24 lines 5-7).
Response to Arguments
Applicant's arguments filed May 19, 2026 have been fully considered but they are not persuasive.
Applicant states on pages 11 and 12 of the response that “the guidance expressly clarifies that claim limitations that ‘cannot be practically performed in the human mind’ should not be grouped into the mental process exception” and “no human musician can simultaneously process a machine learning model’s generative output across a plurality of parametric feature amounts of multiple music elements while dynamically presenting them through a responsive interface”. However the interface and machine learning model describe “additional elements” of the abstract idea. The abstract idea described in the independent claims corresponds to generating music information by editing a part of elements forming the music information, generating new music information on a basis of characteristics of a plurality of pieces of music information created, selectively displaying different generated pieces of music information, displaying feature amounts of each of a plurality of elements forming a selected generated piece of music information in a case where a selection of a piece of music information is received, and generating and adding to the different pieces of music information displayed, a new piece of music information having an updated feature amount corresponding to a selected element in a case where a feature amount of a selected one of the elements forming the selected piece of music information is updated. The limitations are directed to a process, which under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components and processes. The generic computer components and processes correspond to the claimed interface and machine learning model. The interface is described to be used to generate data by editing a part of elements and to display different pieces of music information and feature amounts of a plurality of elements in an editable manner. The machine learning model is described to generate new music information that is trained according to parameters. It is well known that interfaces are used to display selectable data and provide a means to edit displayed data for output, and that machine learning models are trained according to various parameters and provide new versions of received inputs in accordance with its training. Although the processes involve data which correspond to music editing and generation, the interface and machine learning model are generically described according to their common uses. “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016). It has been held that "‘claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept”. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Therefore the generically claimed interface and machine learning model does not integrate the judicial exception into a practical application or provide significantly more. The claims then are directed to a mental process.
Applicant further states that the “specific human-computer interaction model … constitutes a concrete, computer-implemented technical improvement in the field of AI-assisted music generation tools…. This is not merely applying an abstract idea using generic components; it is a specific technical interaction architecture that achieves a result not achievable by prior art composition tools”. However the claims do not describe specific improvements to a computer itself, but instead describe the interface and machine learning model as generic tools for performing the abstract idea, e.g. generating data and displaying editable data. “A claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more”. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §2106.05(f)(2). Since the claims do not describe the interface nor the machine learning model as containing improved computing capabilities, the elements are not integrated into a practical application.
Additionally, applicant states on page 13 that “the machine learning model is not claimed as a generic processor performing conventional functions”. However as shown above, it is well known that machine learning models are trained according to various parameters and provide new versions of received inputs in accordance with its training. Said parameters are either preset or user selectable, as is known in the art. The claims only describe the machine learning model in terms of it being used to generate music information according to its training and do not describe improvements to the machine learning model itself. Therefore the machine learning model is recited as a generic computer component used to implement the abstract idea. “Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. It should be noted that the determination whether a claim contains patent-eligible subject matter is a separate and distinct determination as to whether the limitations are novel over the prior art. The additional elements recited in the claims then do not amount to significantly more than the judicial exception.
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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/Christopher Uhlir/Primary Examiner, Art Unit 3619 August 18, 2026
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