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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 19, 2026 has been entered.
Response to Amendment
This Office Action has been issued in response to Applicant’s Communication of amended application S/N 19/018,854 filed on June 19, 2026. Claims 1 to 4, and 6 to 20 are currently pending with the application.
Claim Objections
Claim 13 is objected to because of the following informalities:
Claim 13 recites the limitations “retrieving the one or moresubset of the plurality of content items” in line 9, and line 2 at page 6, which appear to contain a typographical error, and that should read “retrieving the subset of the plurality of content items”.
Appropriate corrections are required.
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 4, and 6 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 and 10 recite determining search features, generating an explanation, identifying keywords, determining themes, and claim 13 recites filtering content items.
The limitation of determining search features, which specifically recites “based on the query, determining one or more search features comprising terms, moods, intents, ratings, themes, brands, entities, categories, classifications, criteria, characteristics, properties, values, or some combination thereof”, 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 “using the artificial intelligence agent executing one more computer-implemented models”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “using the artificial intelligence agent executing one more computer-implemented models” language, “determining”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying attributes included in a question.
The limitation of generating an explanation, which specifically recites “generating an explanation for retrieving the one or more content items” by “identify one or more keywords in the one or more search features, determine, based on the one or more keywords, one or more themes, and generate, using at least the one or more themes, the explanation including one or more reasons or rationale for retrieving the one or more content items and, for each content item of the one or more content items, a portion of text from the content item that is associated with at least one of the one or more themes”, 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 “using the one more computer-implemented models”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “using the one more computer-implemented models” language, “generating”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying keywords in the search features, deciding a theme, and writing down a rationale for selecting the content item, which is the explanation, as well as writing down a portion of lyric or text matching the theme or features.
Finally, the limitation of filtering content items, which specifically recites “filtering the plurality of content items to retrieve the subset of the plurality of content items”, 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, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, “filtering”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, writing down a list of items that is a smaller selection or a subset of items from an original list of items. 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, via an artificial intelligence agent, a query input by a user”, “using the artificial intelligence agent executing one more models”, “retrieving, using the one or more search features, one or more content items relevant to the one or more search features from a data store storing a plurality of content items and metadata associated with the plurality of content items”, “wherein the metadata includes at least text associated with the plurality of content items”, “transmitting the one or more content items to a computing device to cause the one or more content items to be presented on a user interface of the computing device”, “wherein the one or more content items represent one or more classified objects within the data store, and the one or more content items are playable via a media player included in the user interface when selected”, “and wherein the explanation is presented on the user interface with the one or more content items”, “receiving, from an artificial intelligence agent executing one or more models, a query for a subset of a plurality of content items, wherein the query specifies one or more features associated with metadata of the subset of the plurality of content items”, “retrieve the subset of the plurality of content items by selecting, from a data store, the subset of the plurality of content items associated with metadata matching the one or more features”, “the one or more computer-implemented models are trained to”, and “transmitting, to the artificial intelligence agent, the subset of the plurality of content items to cause the artificial intelligence agent to present the subset of the plurality of content items on a user interface of a computing device”.
Continuing with the analysis, the limitations “receiving, via an artificial intelligence agent, a query input by a user”, “retrieving, using the one or more search features, one or more content items relevant to the one or more search features from a data store storing a plurality of content items and metadata associated with the plurality of content items”, “transmitting the one or more content items to a computing device to cause the one or more content items to be presented on a user interface of the computing device”, “receiving, from an artificial intelligence agent executing one or more models, a query for a subset of a plurality of content items, wherein the query specifies one or more features associated with metadata of the subset of the plurality of content items”, “retrieve the subset of the plurality of content items by selecting, from a data store, the subset of the plurality of content items associated with metadata matching the one or more features”, and “transmitting, to the artificial intelligence agent, the subset of the plurality of content items to cause the artificial intelligence agent to present the subset of the plurality of content items on a user interface of a computing device” amount to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)).
The “limitation using the artificial intelligence agent executing one more models”, and “the one or more computer-implemented models are trained to” are 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 limitations “wherein the metadata includes at least text associated with the plurality of content items”, and “wherein the one or more content items represent one or more classified objects within the data store, and the one or more content items are playable via a media player included in the user interface when selected” are tying the abstract idea to a field of use by further specifying the target data, and is simply an attempt to limit the application of the abstract idea to a particular technological environment.
The limitation “and wherein the explanation is presented on the user interface with the one or more content items” amounts to data presentation steps, considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). 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 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); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93); Gran et al. (U.S. Publication No. 2014/0052770), Para [0006] “A typical media playback device will allow the user to select media objects for individual playback”). 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 “at least one content item of the one or more content items is loaded in the media player of the user interface and playback of the at least one content item is initiated”, which amount to data gathering and data presentation steps, and 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); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93)). Therefore, the limitations do not amount to significantly more than the abstract idea. Same rationale applies to claims 3, 4, 11, 12, 15, 18, and 19, since they are similarly directed to limitations that amount to data gathering and presentation steps, and further elaborating on the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “receiving the plurality of content items; extracting, using the one more computer-implemented models, the metadata from the plurality of content items; indexing the metadata; and storing the metadata associated with the plurality of content items in the data store”, which amounts to data gathering and storing steps, and 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); (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)). Therefore, does not amount to significantly more than the abstract idea. Same rationale applies to claim 16, since it recites similar storing limitations.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitations of “the text is lyrics and the content item is a song”, 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 claims 9, 14, 17, and 20, since they are similarly directed to limitations merely indicate a field of use or technological environment in which to apply the judicial exception.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein the one or more computer-implemented models comprise one or more machine learning models”, 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.
Claims 1 to 4, and 6 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 3, 6, 8 to 13, 15, and 17 to 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jacobson et al. (U.S. Publication No. 2023/0205809) hereinafter Jacobson, in view of VARSHNEY et al. (U.S. Publication No. 2025/0225190) hereinafter Varshney, and further in view of Gorrepati (U.S. Publication No. 2017/0293618).
As to claim 1:
Jacobson discloses:
A computer-implemented method comprising:
receiving, via an artificial intelligence agent, a query input by a user [Paragraph 0015 teaches receiving a user query; Paragraph 0043 teaches receiving a user query; Paragraph 0077 teaches providing a voice assistant that performs various voice-based interactions with the user; Paragraph 0117 teaches user command interpretation server that includes a natural language understanding application; Paragraph 0122 teaches receiving a user query, as an utterance that the user can speak as a voice request];
based on the query, determining, using the artificial intelligence agent executing one or more computer-implemented models, one or more search features comprising terms, moods, intents, ratings, themes, brands, entities, categories, classifications, criteria, characteristics, properties, values, or some combination thereof [Paragraph 0015 teaches identifying at least one descriptor from the user query; Paragraph 0122 teaches processing and analyzing the utterance of the user query to identify descriptive terms; Paragraph 0126 teaches using natural language understanding system for processing the user query; Paragraph 0128 teaches performing automated speech recognition on the utterance data, using neural networks or models];
retrieving, using the one or more search features, one or more content items relevant to the one or more search features from a data store storing a plurality of content items and metadata associated with the plurality of content items, wherein the metadata includes at least text associated with the plurality of content items [Paragraph 0015 teaches identifying one or more media content items associated with the descriptor; Paragraph 0043 teaches processing the user query and identifying media content in response to the user query, and retrieving the identified media content; Paragraph 0086 teaches media data store that stores media content items, metadata, etc.; Paragraph 0091 teaches metadata includes cultural metadata, which is text-based information, including any text information that may be used to describe, rank, or interpret music; Paragraph 0093 teaches metadata includes explicit metadata, as numerical, text, pictorial, and other information associated with the songs or tracks; Paragraph 0123 teaches performing descriptive media content search based on the query, and identifying media content that is most relevant to the descriptive terms, e.g., descriptors, in the user query]; and
transmitting the one or more content items to a computing device to cause the one or more content items to be presented on a user interface of the computing device, wherein the one or more content items represent one or more classified objects within the data store, and the one or more content items are playable via a media player included in the user interface when selected [Paragraph 0043 teaches media playback device receives the media content that was identified, and plays the media content generating media output; Paragraph 0069 teaches storing metadata about media content items such as title, artist name, album name, length, genre, mood, era, etc., therefore, objects have been classified, at least by identifying genre; Paragraph 0071 teaches media playback engine operates to play media content to the user; Paragraph 0124 teaches providing the identified media content to the media playback device].
Jacobson does not appear to expressly disclose generating, using the one or more computer-implemented models, an explanation for retrieving the one or more content items, wherein the one or more computer-implemented models are trained to: identify one or more keywords in the one or more search features, determine, based on the one or more keywords, one or more themes, and generate, using at least the one or more themes, the explanation including one or more reasons or rationale for retrieving the one or more content items and, for each content item of the one or more content items, a portion of text from the content item that is associated with at least one of the one or more themes, and wherein the explanation is presented on the user interface with the one or more content items.
Varshney discloses:
generating, using the one or more computer-implemented models, an explanation for retrieving the one or more content items [Paragraph 0017 teaches the language model can provide an explanation of why the selected candidate item recommendation was chosen], wherein the one or more computer-implemented models are trained to:
identify one or more keywords in the one or more search features, determine, based on the one or more keywords, one or more themes [Paragraph 0039 teaches generating query tags including preference tags or filter tags, specifying information related to items that the user prefers included in the query terms, and determining based on the terms/tags, attributes of the items; Paragraph 0040 teaches determining attributes based on the query, e.g., movie, hence, determining themes based on the keywords of the query], and generate, using at least the one or more themes, the explanation including one or more reasons or rationale for retrieving the one or more content items [Paragraph 0017 teaches the language model can provide reasons for the selection in terms of the items; Paragraph 0042 teaches selects a recommended item from the set of candidate items using the LLM and based on the question and the one or more attributes of each item in the set of candidate items; Paragraph 0043 teaches generating a recommendation including an explanation of why the recommended movie is being recommended, e.g., a description of the aspects of the recommended movie (“The Building Electra Movie”) that are similar to aspects of the movies indicated as being preferred in the question]; and
wherein the explanation is presented on the user interface with the one or more content items [Paragraph 0017 teaches providing reasons for the selection in terms of the items included in the candidate item recommendations; Paragraph 0043 teaches presenting the recommended item to the user, where the recommendation includes an explanation of why the recommended items are being recommended, therefore, the explanation is also presented in the interface with the content items].
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 Jacobson, by generating, using the one or more computer-implemented models, an explanation for retrieving the one or more content items, wherein the one or more computer-implemented models are trained to: identify one or more keywords in the one or more search features, determine, based on the one or more keywords, one or more themes, and generate, using at least the one or more themes, the explanation including one or more reasons or rationale for retrieving the one or more content items, and wherein the explanation is presented on the user interface with the one or more content items, as taught by Varshney [Paragraph 0017, 0039, 0040, 0042, 0043], because both applications are directed to identification of media; by providing an explanation of the rationale for retrieving the items improves the user’s experience by providing additional information to the user as well as an engaging experience (See Varshney Para [0002]).
Neither Jacobson nor Varshney appear to expressly disclose generate for each content item of the one or more content items, a portion of text from the content item that is associated with at least one of the one or more themes.
Gorrepati discloses:
generate for each content item of the one or more content items, a portion of text from the content item that is associated with at least one of the one or more themes [Paragraph 0021 teaches search results may include matches within transcripts, where relevant sections of the transcripts are shown to the user; Paragraph 0042 teaches displaying one or more relevant portions of the one or more transcripts sufficiently matching the request].
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 Jacobson, by generate for each content item of the one or more content items, a portion of text from the content item that is associated with at least one of the one or more themes, as taught by Gorrepati [Paragraph 0021, 0042], because the applications are directed to identification of media; by generating and providing relevant text of the content item enables the user to determine if the results are relevant to their inquiry, thereby improving the user’s experience (See Gorrepati Para [0016]).
As to claim 2:
Jacobson discloses:
wherein at least one content item of the one or more content items is loaded in the media player of the user interface and playback of the at least one content item is initiated [Paragraph 0043 teaches media playback device operates to play the media content and generate the media output; Paragraph 0124 teaches providing the identified media content to the media playback device].
As to claim 3:
Jacobson discloses:
classifying the plurality of content items into one or more sets of content items based on one or more features of the plurality of content items satisfying one or more feature thresholds [Paragraph 0187 teaches selecting the media content items based on the relevance score, where the selected media content items match the search keywords, and have the highest relevance scores, and where the number of media content items is determined based on, i.e., a relevance score threshold]; and
transmitting the sets of content items to the computing device to cause the sets of content items to be presented on the user interface of the computing device [Paragraph 0043 teaches media playback device operates to play the media content and generate the media output; Paragraph 0124 teaches providing the identified media content to the media playback device].
As to claim 6:
Jacobson discloses:
prior to receiving the query: receiving the plurality of content items [Paragraph 0138 teaches descriptive search database, used to conduct a descriptive search, can be built and stored prior to receiving the user query];
extracting, using the one more computer-implemented models, the metadata from the plurality of content items [Paragraph 0041 teaches identifying additional descriptive information for media content items to be searched, to set up a descriptive search database, analyzing a variety of data associated with the content items, to generate descriptive terms, e.g., tokens for the media content, where the descriptive search database can be generated with the descriptive terms that map to media content items];
indexing the metadata [Paragraph 0172 teaches descriptive search database includes an inverted index structure that maps descriptive terms to the media content items]; and
storing the metadata associated with the plurality of content items in the data store [Paragraph 0172 teaches descriptive search database includes an inverted index structure that maps descriptive terms to the media content items].
As to claim 8:
Jacobson discloses:
the one or more computer-implemented models comprise one or more machine learning models [Paragraph 0118 teaches using a natural language understanding algorithm; Paragraph 0143 teaches descriptive term lexicon can be at least partially automatically created and updated using, for example, machine learning technology].
As to claim 9:
Jacobson discloses:
the metadata is extracted from the text of the plurality of content items and is classified as one or more track names, artist names, genres, lyrics languages, lyrics bodies of text, meanings, moods, themes, entities, explicit flags, ratings, ratings descriptions, religious flags, religious categories, harassment flags, harassment scores, hate flags, hate scores, sexual flags, or some combination thereof [Paragraph 0088 teaches media content metadata includes one or more of title, artist name, album name, length, genre, mood, era, etc.; Paragraph 0155 teaches descriptive text includes words, phrases, or sentences that characterize the content items, and can be obtained from any text information that may be used to describe, rank, or interpret the associated music; Paragraph 0156 descriptive text can be analyzed to identify descriptive terms].
As to claim 13:
Jacobson discloses:
A computer-implemented method comprising:
receiving, from an artificial intelligence agent executing one or more models, a query for a subset of a plurality of content items, wherein the query specifies one or more features associated with metadata of the subset of the plurality of content items [Paragraph 0015 teaches receiving a user query including at least one descriptor; Paragraph 0043 teaches receiving a user query; Paragraph 0077 teaches providing a voice assistant that performs various voice-based interactions with the user; Paragraph 0117 teaches user command interpretation server that includes a natural language understanding application; Paragraph 0122 teaches receiving a user query, as an utterance that the user can speak as a voice request; Paragraph 0126 teaches using natural language understanding system for processing the user query; Paragraph 0183 teaches descriptors can be used to perform other types of search, such as entity-focused search using media content metadata];
filtering the plurality of content items to retrieve the subset of the plurality of content items by selecting, from a data store, the subset of the plurality of content items associated with metadata matching the one or more features [Paragraph 0014 teaches media content items may be identified from the plurality of playlists based in part on the metadata; Paragraph 0015 teaches identifying one or more media content items associated with the descriptor; Paragraph 0043 teaches processing the user query and identifying media content in response to the user query; Paragraph 0091 teaches metadata includes cultural metadata, which is text-based information, including any text information that may be used to describe, rank, or interpret music; Paragraph 0093 teaches metadata includes explicit metadata, as numerical, text, pictorial, and other information associated with the songs or tracks; Paragraph 0123 teaches performing descriptive media content search based on the query, and identifying media content that is most relevant to the descriptive terms, e.g., descriptors, in the user query; Paragraph 0190 teaches search for media content items having information from the metadata that matches the descriptors]; and
transmitting, to the artificial intelligence agent, the subset of the plurality of content items to cause the artificial intelligence agent to present the subset of the plurality of content items on a user interface of a computing device, wherein the one or more content items represent one or more classified objects within the data store, and the one or more content items are playable via a media player included in the user interface when selected [Paragraph 0043 teaches media playback device receives the media content that was identified, and plays the media content generating media output; Paragraph 0069 teaches storing metadata about media content items such as title, artist name, album name, length, genre, mood, era, etc., therefore, objects have been classified, at least by identifying genre; Paragraph 0071 teaches media playback engine operates to play media content to the user; Paragraph 0124 teaches providing the identified media content to the media playback device].
Jacobson does not appear to expressly disclose generating, using the one or more computer-implemented models, an explanation for retrieving the one or more subset of the plurality of content items, wherein the one or more computer-implemented models are trained to: identify one or more keywords in the one or more search features, determine, based on the one or more keywords, one or more themes, and generating, using at least the one or more themes, the explanation including one or more reasons or rationale for retrieving the one or more subset of the plurality of content items and including within the explanation, a portion of text from the subset that is associated with the one or more features for each content item of the subset of the plurality of content items; present the content items and the explanation.
Varshney discloses:
generating, using the one or more computer-implemented models, an explanation for retrieving the one or more subset of the plurality of content items [Paragraph 0017 teaches the language model can provide an explanation of why the selected candidate item recommendation was chosen], wherein the one or more computer-implemented models are trained to:
identify one or more keywords in the one or more search features, determine, based on the one or more keywords, one or more themes [Paragraph 0039 teaches generating query tags including preference tags or filter tags, specifying information related to items that the user prefers included in the query terms, and determining based on the terms/tags, attributes of the items; Paragraph 0040 teaches determining attributes based on the query, e.g., movie, hence, determining themes based on the keywords of the query], and generate, using at least the one or more themes, the explanation including one or more reasons or rationale for retrieving the one or more subset of the plurality of content items [Paragraph 0017 teaches the language model can provide reasons for the selection in terms of the items; Paragraph 0042 teaches selects a recommended item from the set of candidate items using the LLM and based on the question and the one or more attributes of each item in the set of candidate items; Paragraph 0043 teaches generating a recommendation including an explanation of why the recommended movie is being recommended, e.g., a description of the aspects of the recommended movie (“The Building Electra Movie”) that are similar to aspects of the movies indicated as being preferred in the question]; and
present the content items and the explanation [Paragraph 0017 teaches providing reasons for the selection in terms of the items included in the candidate item recommendations; Paragraph 0043 teaches presenting the recommended item to the user, where the recommendation includes an explanation of why the recommended items are being recommended, therefore, the explanation is also presented in the interface with the content items].
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 Jacobson, by generating, using the one or more computer-implemented models, an explanation for retrieving the one or more content items, wherein the one or more computer-implemented models are trained to: identify one or more keywords in the one or more search features, determine, based on the one or more keywords, one or more themes, and generate, using at least the one or more themes, the explanation including one or more reasons or rationale for retrieving the one or more content items, present the content items and the explanation, as taught by Varshney [Paragraph 0017, 0039, 0040, 0042, 0043], because both applications are directed to identification of media; by providing an explanation of the rationale for retrieving the items improves the user’s experience by providing additional information to the user as well as an engaging experience (See Varshney Para [0002]).
Neither Jacobson nor Varshney appear to expressly disclose including within the explanation, a portion of text from the subset that is associated with the one or more features for each content item of the subset of the plurality of content items.
Gorrepati discloses:
including within the explanation, a portion of text from the subset that is associated with the one or more features for each content item of the subset of the plurality of content items [Paragraph 0021 teaches search results may include matches within transcripts, where relevant sections of the transcripts are shown to the user; Paragraph 0042 teaches displaying one or more relevant portions of the one or more transcripts sufficiently matching the request].
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 Jacobson, by including within the explanation, a portion of text from the subset that is associated with the one or more features for each content item of the subset of the plurality of content items, as taught by Gorrepati [Paragraph 0021, 0042], because the applications are directed to identification of media; by generating and providing relevant text of the content item enables the user to determine if the results are relevant to their inquiry, thereby improving the user’s experience (See Gorrepati Para [0016]).
As to claim 17:
Jacobson discloses:
the one or more features comprise a rating, an artist, an album, a mood, a moderation category, an explicit flag, or some combination thereof [Paragraph 0049 teaches user query includes one or more descriptive terms (also referred to herein as descriptors) that can be used as one or more keywords to identify media content associated with the keywords, i.e., “play relaxing jazz for tonight”; Paragraph 0050 teaches identify media content associated with at least one of the descriptive terms of the user query, such as “relaxing,” “tonight,” “relaxing jazz,” or “jazz”].
As to claim 18:
Jacobson discloses:
including, within the subset of the plurality of content items, a portion of text from the subset that is associated with the one or more features for each content item of the subset of the plurality of content items [Paragraph 0088 teaches media content metadata includes one or more of title, artist name, album name, length, genre, mood, era, etc.; Paragraph 0155 teaches descriptive text includes words, phrases, or sentences that characterize the content items, and can be obtained from any text information that may be used to describe, rank, or interpret the associated music; Paragraph 0156 descriptive text can be analyzed to identify descriptive terms].
Same rationale applies to claims 10 to 12, 15, 19, and 20, since they recite similar limitations, and are therefore, similarly rejected.
Claims 4, 7, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jacobson et al. (U.S. Publication No. 2023/0205809) hereinafter Jacobson, in view of VARSHNEY et al. (U.S. Publication No. 2025/0225190) hereinafter Varshney, in view of Gorrepati (U.S. Publication No. 2017/0293618), and further in view of Wold (U.S. Publication No. 2021/0357451).
As to claim 4:
Jacobson discloses:
transmitting the set of content items to the computing device to cause the set of content items to be presented on the user interface of the computing device [Paragraph 0043 teaches media playback device operates to play the media content and generate the media output; Paragraph 0124 teaches providing the identified media content to the media playback device].
Jacobson does not appear to expressly disclose generating a set of content items by matching a plurality of artists associated with the plurality of content items, wherein the matching is based on one or more features of the plurality of content items satisfying a feature threshold.
Wold discloses:
generating a set of content items by matching a plurality of artists associated with the plurality of content items, wherein the matching is based on one or more features of the plurality of content items satisfying a feature threshold [Paragraph 0031 teaches determining metadata similarity between content items, where the metadata may include data such as a name of a performer, a name of a band, etc.; Paragraph 0030 teaches determining whether combined similarity values meet or exceed a threshold; Paragraph 0066 teaches combined similarity value or score may be computed and compared to a threshold; Paragraph 0117 teaches thresholds on particular features may be used].
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 Jacobson, by generating a set of content items by matching a plurality of artists associated with the plurality of content items, wherein the matching is based on one or more features of the plurality of content items satisfying a feature threshold, as taught by Wold [Paragraph 0030, 0031, 0066, 0117], because both applications are directed to identification of media, including songs; by matching a plurality of artists associated with the plurality of content items, based on one or more features satisfying a feature threshold enables to greatly improve the accuracy of media identification (See Wold Para [0021]).
As to claim 7:
Jacobson discloses:
the content item is a song [Paragraph 0084 teaches media content includes music or other audio, video, etc.; Paragraph 0091 teaches content media is tracks or songs].
Jacobson does not appear to expressly disclose the text is lyrics.
Wold discloses:
the text is lyrics [Paragraph 0123 teaches determining lyrical content of the media item].
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 Jacobson, by incorporating the text is lyrics, as taught by Wold [Paragraph 0123], because both applications are directed to identification of media, including songs; by incorporating the lyrics of the content, and further using the lyrics as an identifying feature, the accuracy of content identification is greatly improved, while also reducing an overall false positive rate (See Wold Para [0021, 0024]).
As to claim 16:
Jacobson discloses:
prior to receiving the query: receiving the plurality of content items [Paragraph 0138 teaches descriptive search database, used to conduct a descriptive search, can be built and stored prior to receiving the user query];
extracting, using the one more computer-implemented models, the metadata from the plurality of content items [Paragraph 0041 teaches identifying additional descriptive information for media content items to be searched, to set up a descriptive search database, analyzing a variety of data associated with the content items, to generate descriptive terms, e.g., tokens for the media content, where the descriptive search database can be generated with the descriptive terms that map to media content items];
indexing the metadata associated with the plurality of content items [Paragraph 0172 teaches descriptive search database includes an inverted index structure that maps descriptive terms to the media content items]; and
storing the metadata associated with the plurality of content items in the data store [Paragraph 0172 teaches descriptive search database includes an inverted index structure that maps descriptive terms to the media content items].
Jacobson does not appear to expressly disclose wherein the metadata associated with the plurality of content items comprises at least text representing lyrics of the plurality of content items.
Wold discloses:
wherein the metadata associated with the plurality of content items comprises at least text representing lyrics of the plurality of content items [Paragraph 0043 teaches storage stores media content items, metadata index, lyrical content of media content items, etc.; Paragraph 0123 teaches determining lyrical content of the media item].
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 Jacobson, by incorporating metadata associated with the plurality of content items comprising at least text representing lyrics of the plurality of content items, as taught by Wold [Paragraph 0123], because both applications are directed to identification of media, including songs; by incorporating the lyrics of the content, and further using the lyrics as an identifying feature, the accuracy of content identification is greatly improved, while also reducing an overall false positive rate (See Wold Para [0021, 0024]).
Same rationale applies to claim 14, since it recites similar limitations, and is therefore, similarly rejected.
Response to Arguments
The following is in response to arguments filed on June 19, 2026. Arguments have been carefully and respectfully considered.
Claim Rejections - 35 USC § 101
Applicant’s arguments have been carefully and respectfully considered, but are not persuasive.
In regards to claim 1, Applicant argues that “the features of claim 1 do not recite a mental process”, and further that “These limitations are not reasonably performed in the human mind as a practical matter. They require trained model execution within a computerized retrieval pipeline that converts a natural-language query into machine-usable search features, uses those features against datastore metadata, and generates an explanation tied to retrieved content items”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that, as presently presented, the broadest reasonable interpretation of the claims can be performed in the human mind, as further described in the rejections above, and that the model and computer components are used as a tool. Examiner respectfully points out that merely invoking computers, machinery, or algorithms as a tool to perform an existing process, does not integrate a judicial exception into a practical application. It is noted that adding a “computer-implemented model” limitation to a claim covering an abstract concept, without significantly more, is insufficient to render a claim eligible where the claims are silent as to how the model aids the method, the extent to which a model aids the method, or the significance of the model to the performance of the method. In order for a machine or algorithm to add significantly more, it must “play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly”. (See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); See MPEP 2106.05(f)(II)(v) Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)).
In regards to claim 1, Applicant further argues that “the claims integrate that idea into a practical application”, more specifically that “the Specification confirms that this architecture addresses a technical problem in computerized content search by using an artificial-intelligence-based system to automate metadata extraction, indexing, classification, storage, and retrieval of content items based on desired metadata features”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully points out that it is not clear what is the technical problem being addressed. Moreover, Examiner respectfully points out that mental processes remain unpatentable even when automated (See CyberSource, 654 F.3d 1375 (“That purely mental processes can be unpatentable, even when performed by a computer [as a tool], was precisely the holding of the Supreme Court in Gottschalkv. Benson [409 U.S. 63, 67 (1972)]”).
In regards to claim 1, Applicant further argues that “functions such as computer-implemented models being trained to generate an explanation for retrieving content items and the one or more content items are playable via a media player included in the user interface when selected cannot be practically performed in the human mind”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that as presently presented, the models and computer components are additional elements, and are recited at a high level of generality, such that it amounts to apply-it. The content items being playable via a media player upon selection of the user, is also an additional limitation, and amount to data presentation, and is considered as well-understood, routine and conventional, therefore, do not amount to significantly more, nor integrate the exception into a practical application (See MPEP 2106.05(d)(II)(v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93); Gran et al. (U.S. Publication No. 2014/0052770), Para [0006] “A typical media playback device will allow the user to select media objects for individual playback”).
In regards to claim 1, Applicant further argues that “the claims, read as a whole and in view of the Specification, recite a particular interaction among trained model execution, indexed metadata, datastore retrieval, generated explanations, and on-screen presentation of playable content items. That interaction improves computerized search and selection by enabling natural-language query processing, feature-based retrieval from structured metadata, and user- consumable explanations for retrieved results”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that it is not clear, from the Applicant’s argument, what is the specific improvement in the functioning of a computer, or the improvement to another technology or technical field, that is achieved with the claimed invention. Furthermore, it is also not apparent from the Applicant’s argument, how such improvement correlate with the claim language as presently presented. The computer elements recited in the claims, considered individually, or as a whole, are used merely as a tool, and therefore, do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea without significantly more, under the “Mental Processes” grouping of abstract ideas, as further detailed in the rejections above. 101 Rejections are hereby sustained.
Claim Rejections - 35 USC § 103
Applicant’s arguments have been fully and respectfully considered, but are moot in view of new grounds of rejections, as necessitated by the amendments.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
Prosecution Insights