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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 19/040,448 filed on January 29, 2025. Claims 1 to 20 are currently pending with the application.
Priority
The instant application claims priority from provisional Application No. 63/626,752, filed on January 30, 2024. Applicant’s claim for the benefit of the prior-filed application under 35 U.S.C. 119(e), 120, 121, or 365(c), or 386(c) is acknowledged.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 to 3, 7, 10, and 14 to 19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 to 5, 7, and 8 of copending Application No. 19/040,453. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the instant application are anticipated by the claims in the copending Application. This is a provisional nonstatutory double patenting rejection.
Following mapping of claims 1 to 3, 5, 7, and 10 of Instant Application to claims 1 to 5, 7, and 8 of the copending application. Similar mapping applies to claims 14 to 19 of instant application, since they recite similar limitations.
Instant Application
Application No. 19/040,453
1. A method of automated generation of descriptive tags for a music segment, the method comprising: receiving at least one of basic metadata information and lyric information for the music segment; generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, the first prompt including a first request for first context information based on the at least one of the basic metadata information and the lyric information; generating the first context information by providing the first prompt as an input to the computer-implemented machine-learning language model; generating a second prompt for the computer-implemented machine-learning language model based on the first context information, the second prompt including a second request to generate a plurality of tags based on the first context information; generating the plurality of tags by providing the second prompt as an input to the computer-implemented machine-learning language model; and modifying electronic data of a queryable electronic database to retrievably associate the plurality of tags with the music segment.
1. A method of automated generation of contextually-relevant images for a music segment, the method comprising: receiving at least one of basic metadata information and lyric information for the music segment; generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, the first prompt including a first request for context information based on the at least one of the basic metadata information and the lyric information; receiving the context information from the computer-implemented machine- learning language model in response to the first prompt; generating a second prompt for the computer-implemented machine-learning language model based on the context information, the second prompt including a second request to generate a third prompt for a computer- implemented machine-learning image-generation model including a third request to generate an image descriptive of the music segment; generating the third prompt by providing the second prompt as an input to the computer-implemented machine-learning language model; and generating the image descriptive of the music segment by providing the third prompt as an input to the computer-implemented machine-learning image generation model.
8. The method of claim 1, and further comprising modifying data of an image database to store the image and to retrievably associate the image with an identifier for the music segment.
2. The method of claim 1, and further comprising: generating a database query based on the at least one of the basic metadata information and the lyric information; querying a first database with the database query; and receiving database data from the first database in response to the database query; wherein generating the first prompt comprises generating the first prompt based on the database data and the at least one of basic metadata information and lyric information.
4. The method of claim 1, and further comprising: generating a first database query based on the at least one of the basic metadata information and the lyric information; querying a first database with the first database query; and receiving first database data from the first database in response to the first database query; wherein generating the first prompt comprises generating the first prompt based on the first database data and the at least one of basic metadata information and lyric information.
3. The method of claim 1, and further comprising: generating a first database query based on the first context information; querying a first database with the database query; and receiving first database data from the first database in response to the database query; wherein generating the second prompt comprises generating the second prompt based on the first database data and the first context information.
5. The method of claim 4, and further comprising: generating a second database query based on the context information; querying the first database with the second database query; and receiving second database data from the first database in response to the second database query; wherein generating the second prompt comprises generating the first prompt based on the second database data and context information.
5. The method of claim 4, wherein the second prompt also includes the at least one of the basic metadata information and the lyric information.
2. The method of claim 1, wherein the generating the second prompt comprises generating the second prompt based on the context information and the at least one of the basic metadata information and the lyric information.
7. The method of claim 6, and further comprising: generating a third prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, the first prompt including a third request for second context information based on the at least one of the basic metadata information and the lyric information; and2 generating the second context information by providing the third prompt as an input to the computer-implemented machine-learning language model; wherein the second prompt is further based on the second context information and the second request is to generate the plurality of tags based on the first context information, the first database data, and the second context information.
7. The method of claim 1, wherein the context information is artist context information, and further comprising: generating a fourth prompt for the computer-implemented machine-learning language model based on the at least one of the basic metadata information 2 and the lyric information, the fourth prompt including a fourth request for historical context information based on the at least one of the basic metadata information and the lyric information; and receiving the historical context information from the computer-implemented machine-learning language model in response to the first prompt; wherein generating the second prompt comprises generating the second prompt based on the artist context information and the historical context information.
10. The method of claim 9, wherein the first context information is historical context information and the second context information is artist context information.
3. The method of claim 1, wherein the context information comprises at least one of artist context information and historical context information.
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 to 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 14, and 17 recite generating a first and second prompt, context information, and a plurality of tags.
The limitation of generating a first prompt, which specifically recites “generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, the first prompt including a first request for first context information based on the at least one of the basic metadata information and the lyric information”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by a processor” (in claim 17), nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “generating”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, writing down a question or prompt requesting context information based on either metadata or lyric information.
The limitation of “generating the first context information”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by a processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “generating”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, writing down context information.
The limitation of generating a second prompt, which specifically recites “generating a second prompt for the computer-implemented machine-learning language model based on the first context information, the second prompt including a second request to generate a plurality of tags based on the first context information”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by a processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “generating”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, writing down a question or prompt requesting tags related to the context information.
Finally, the limitation of “generating the plurality of tags”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by a processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “generating”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, writing down tags related to the context information. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – “receiving at least one of basic metadata information and lyric information for the music segment”, “providing the first prompt as an input to the computer-implemented machine-learning language model”, “providing the second prompt as an input to the computer-implemented machine-learning language model”, “modifying electronic data of a queryable electronic database to retrievably associate the plurality of tags with the music segment”, a queryable electronic database, a server, a processor, and at least one memory. The limitations “receiving at least one of basic metadata information and lyric information for the music segment”, “providing the first prompt as an input to the computer-implemented machine-learning language model”, and “providing the second prompt as an input to the computer-implemented machine-learning language model” amount to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)).
Continuing the analysis of the additional limitations, “modifying electronic data of a queryable electronic database to retrievably associate the plurality of tags with the music segment” amounts to data storing steps, and which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). The queryable electronic database, server, processor, and at least one memory in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activity identified above, which include the data gathering and data storing steps, is recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Mm., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). The claims are not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitations of “generating a database query based on the at least one of the basic metadata information and the lyric information; querying a first database with the database query; and receiving database data from the first database in response to the database query; wherein generating the first prompt comprises generating the first prompt based on the database data and the at least one of basic metadata information and lyric information”. The generating limitation can be performed in the human mind with the aid of pen and paper, and therefore is further elaborating on the abstract idea. The querying is recited at a high-level of generality, with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, and is equivalent to merely saying “applying it”. The receiving limitation amounts to data gathering steps, which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d) (II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claim does not amount to significantly more than the abstract idea. Same rationale applies to claims 3, 6 to 9, 12, and 13, since they recite similar limitations.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitations of “the first database query is also based on the at least one of the basic metadata information and the lyric information”, which can be performed in the human mind with the aid of pen and paper, and therefore is further elaborating on the abstract idea. The claim does not amount to significantly more. Same rationale applies to claim 5.
Claim 10 is dependent on claim 9 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim recites the additional limitations of “the first context information is historical context information and the second context information is artist context information”, which is tying the abstract idea to a field of use by further specifying the target data, and which is simply an attempt to limit the application of the abstract idea to a particular technological environment; merely indicating a field of use or technological environment in which to apply the judicial exception does not meaningfully limit the claim (See MPEP 2106.05(h)). Same rationale applies to claim 11.
Claim 12 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 12 recites the same abstract idea of claim 1. The claim recites the additional limitation of “supporting unstructured data assets through object tables”, which is recited at a high-level of generality, with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, and is equivalent to merely saying “applying it”, therefore, does not integrate the judicial exception into a practical application nor amount to significantly more.
Additionally, the claims do not include a requirement of anything other than conventional, generic computer technology for executing the abstract idea, and therefore, do not amount to significantly more than the abstract idea.
Same rationale applies to claims 15, 16, and 18 to 20 since they recite similar limitations.
Claims 1 to 20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 to 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bariki et al. (U.S. Publication No. 2025/0217340) hereinafter Bariki, and further in view of Mondlock et al (U.S. Patent No. 12,079,570) hereinafter Mondlock.
As to claim 1:
Bariki discloses:
A method of automated generation of descriptive tags for a music segment, the method comprising:
receiving at least one of basic metadata information and lyric information for the music segment [Paragraph 0016 teaches obtaining metadata associated with the content; Paragraph 0056 teaches obtaining metadata associated with the digital medium];
generating a first request for first context information based on the at least one of the basic metadata information and the lyric information [Paragraph 0055 teaches obtaining a social media tag associated with the digital medium];
generating a second prompt for the computer-implemented machine-learning language model based on the first context information, the second prompt including a second request to generate a plurality of tags based on the first context information [Paragraph 0049 teaches providing a prompt, the audio, metadata, to an artificial intelligence, where the prompt requests multiple tags associated with the digital content; Paragraph 0056 teaches providing the social media tags, audio, metadata, and prompt to the artificial intelligence to obtain the multiple tags];
generating the plurality of tags by providing the second prompt as an input to the computer-implemented machine-learning language model [Paragraph 0049 teaches providing the prompt, the audio, metadata, to the artificial intelligence to generate the tags; Paragraph 0056 teaches providing the social media tags, audio, metadata, and prompt to the artificial intelligence to obtain the multiple tags]; and
modifying electronic data of a queryable electronic database to retrievably associate the plurality of tags with the music segment [Paragraph 0016 teaches the database storing the content is configured to support a traditional search using the metadata associated with the content; Paragraph 0017 teaches storing the multiple new tags in the database by adding the multiple tags to the metadata associated with the content, which enables a more accurate search of the content stored in the database by searching using the new metadata tags; Paragraph 0050 teaches storing the multiple tags in the database by adding the multiple tags to the metadata associated with the digital content].
Bariki does not appear to expressly disclose generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information; generating the first context information by providing the first prompt as an input to the computer-implemented machine-learning language model.
Mondlock discloses:
generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, the first prompt including a first request for first context information [Column 9, lines 1 to 6 teach LLM interface module may transmit, for each relevant text chunk and relevant data chunk, a prompt that includes the user query and the relevant text chunk and/or relevant data chunk to the LLM service];
generating the first context information by providing the first prompt as an input to the computer-implemented machine-learning language model [Column 9, lines 1 to 6 teach receive relevant information from the relevant text chunk and/or relevant data chunk from the LLM, the relevant data chunk representing the context information].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Bariki, by generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, the first prompt including a first request for first context information; generating the first context information by providing the first prompt as an input to the computer-implemented machine-learning language model, as taught by Mondlock [Column 6], because both applications are directed to improvements in generative artificial intelligence systems; obtaining additional relevant (contextual) information to be included in the prompts enables LLMs to provide improve responses (See Mondlock [Col 1]).
As to claim 2:
Bariki discloses:
generating a database query based on the at least one of the basic metadata information and the lyric information [Paragraph 0058 teaches the database can store additional information associated with the media content, which can provide additional description of the media content];
querying a first database with the database query [Paragraph 0058 teaches gathering the tags (the additional information associated with the media content) from the database]; and
receiving database data from the first database in response to the database query [Paragraph 0058 teaches gathering the tags (the additional information associated with the media content) from the database];
wherein generating the first prompt comprises generating the first prompt based on the database data and the at least one of basic metadata information and lyric information [Paragraph 0058 teaches providing the tags as input to the large language model, in addition to the audio, transcript, etc.].
As to claim 3:
Bariki discloses:
generating a database query based on the first context information [Bariki - Paragraph 0058 teaches the database can store additional information associated with the media content, which can provide additional description of the media content];
querying a first database with the database query [Paragraph 0058 teaches gathering the tags (the additional information associated with the media content) from the database]; and
receiving database data from the first database in response to the database query [Paragraph 0058 teaches gathering the tags (the additional information associated with the media content) from the database];
wherein generating the second prompt comprises generating the second prompt based on the database data and the first context information [Paragraph 0058 teaches providing the tags as input to the large language model, in addition to the audio, transcript, etc.].
As to claim 4:
Bariki discloses:
the first database query is also based on the at least one of the basic metadata information and the lyric information [Paragraph 0056 teaches obtaining from the database, metadata associated with the digital content; Paragraph 0058 teaches obtaining additional metadata, e.g., additional tags].
As to claim 5:
Bariki discloses:
the second prompt also includes the at least one of the basic metadata information and the lyric information [Paragraph 0040 teaches the prompt can request multiple tags based on the audio, the closed captions, title, etc., hence, the prompt also includes the basic metadata information].
As to claim 6:
Bariki discloses:
generating a second database query based on the at least one of the basic metadata information and the lyric information [Bariki - Paragraph 0058 teaches the database can store additional information associated with the media content, which can provide additional description of the media content];
querying the first database with the second query [Paragraph 0058 teaches gathering the tags (the additional information associated with the media content) from the database]; and receiving second database data from the first database in response to the database query [Paragraph 0058 teaches gathering the tags (the additional information associated with the media content) from the database];
wherein generating the first prompt comprises generating the first prompt based on the second database data and the at least one of basic metadata information and lyric information [Paragraph 0040 teaches the prompt can request multiple tags based on the audio, the closed captions, title, etc.; Paragraph 0058 teaches providing the tags as input to the large language model, in addition to the audio, transcript, etc.].
As to claim 10:
Bariki discloses:
the first context information is historical context information and the second context information is artist context information [Paragraph 0092 teaches obtaining a history of user actions associated with the digital content; Paragraph 0072 teaches obtaining from a database, a performer associated with the digital content].
As to claim 11:
Bariki discloses:
the basic metadata information includes at least one of an artist name, a song name, an album name, a genre descriptor, and a release date [Paragraph 0047 teaches metadata can include a title or a genre associated with the digital content].
As to claim 12:
Bariki discloses:
receiving a natural-language request from a user device [Paragraph 0052 teaches receiving a natural language query from a user];
generating a fifth database query based on the natural-language request [Paragraph 0052 teaches searching the database based on the natural language query, hence, generating a query];
querying the queryable electronic database with the natural-language request [Paragraph 0052 teaches searching the database based on the natural language query, hence, generating a query];
retrieving the music segment, by the queryable electronic database and in response to querying the queryable electronic database, based on a similarity between the fifth database query and the plurality of tags [Paragraph 0052 teaches obtaining multiple results based on a match between the natural language query and the new metadata (newly created tags), to obtain sorted results]; and
electronically transmitting the retrieved music segment to the user device [Paragraph 0052 teaches presenting the sorted results to the user].
As to claim 14:
Bariki discloses:
A method of automated generation of descriptive tags for a music segment, the method comprising:
receiving at least one of basic metadata information and lyric information for the music segment [Paragraph 0016 teaches obtaining metadata associated with the content; Paragraph 0056 teaches obtaining metadata associated with the digital medium];
generating a first request for first historical context information based on the at least one of the basic metadata information and the lyric information [Paragraph 0092 teaches obtaining a history of user actions associated with the digital content];
generating a second request for artist context information based on the at least one of the basic metadata information and the lyric information [Paragraph 0072 teaches obtaining from a database, a performer associated with the digital content];
generating a third prompt for the computer-implemented machine-learning language model based on the historical context information and the artist context information, the third prompt including a third request to generate a plurality of tags based on the historical context information and the artist context information [Paragraph 0045 teaches determining the tags based on the history; Paragraph 0049 teaches providing a prompt, the audio, metadata, to an artificial intelligence, where the prompt requests multiple tags associated with the digital content; Paragraph 0056 teaches providing the social media tags, audio, metadata, and prompt to the artificial intelligence to obtain the multiple tags];
receiving the plurality of tags from the computer-implemented machine-learning language model in response to the third prompt[Paragraph 0049 teaches providing the prompt, the audio, metadata, to the artificial intelligence to generate the tags; Paragraph 0056 teaches providing the social media tags, audio, metadata, and prompt to the artificial intelligence to obtain the multiple tags]; and
modifying electronic data of a queryable electronic database to retrievably associate the plurality of tags with the music segment [Paragraph 0016 teaches the database storing the content is configured to support a traditional search using the metadata associated with the content; Paragraph 0017 teaches storing the multiple new tags in the database by adding the multiple tags to the metadata associated with the content, which enables a more accurate search of the content stored in the database by searching using the new metadata tags; Paragraph 0050 teaches storing the multiple tags in the database by adding the multiple tags to the metadata associated with the digital content].
Bariki does not appear to expressly disclose generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information; generating the context information by providing the first prompt as an input to the computer-implemented machine-learning language model; generating a second prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information; generating the context information by providing the second prompt as an input to the computer-implemented machine-learning language model.
Mondlock discloses:
generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information; generating a second prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information [Column 9, lines 1 to 6 teach LLM interface module may transmit, for each relevant text chunk and relevant data chunk, a prompt that includes the user query and the relevant text chunk and/or relevant data chunk to the LLM service];
generating the context information by providing the first prompt as an input to the computer-implemented machine-learning language model; generating the context information by providing the first prompt as an input to the computer-implemented machine-learning language model [Column 9, lines 1 to 6 teach receive relevant information from the relevant text chunk and/or relevant data chunk from the LLM, the relevant data chunk representing the context information].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Bariki, by generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information; generating the context information by providing the first prompt as an input to the computer-implemented machine-learning language model; generating a second prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information; generating the context information by providing the second prompt as an input to the computer-implemented machine-learning language model, as taught by Mondlock [Column 6], because both applications are directed to improvements in generative artificial intelligence systems; obtaining additional relevant (contextual) information to be included in the prompts enables LLMs to provide improve responses (See Mondlock [Col 1]).
Same rationale applies to claims 7 to 9, 13, and 15 to 20, since they recite similar limitations, and are therefore, similarly rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL PEREZ-ARROYO whose telephone number is (571)272-8969. The examiner can normally be reached Monday - Friday, 8:00am - 5:30pm, Alt Friday, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
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