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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 27, 2026 has been entered.
Response to Amendment
This Office Action has been issued in response to Applicant’s Communication of amended application S/N 18/945,772 filed on April 27, 2026. Claims 1, 3 to 5, 7 to 13, 15 to 17, 19, and 20 are currently pending with the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 to 5, 7 to 13, 15 to 17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 13, and 17 recite determining a candidate object, obtaining information, determining the candidate object as a target object, a set of feature values, probabilities, and that a classification result satisfies a condition.
The limitation of determining candidate object, which specifically recites “determining a candidate object for a music content”, 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, “determining”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying a candidate attribute associated with a song. The limitation of obtaining information, which specifically recites “obtaining music information of the music content and object information of the candidate object, wherein at least one of the music information and the object information comprises a plurality of types of 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, “obtaining”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, reading information associated with the song and the previously identified attribute from a sheet of paper.
The limitation of determining the candidate object as a target object, which specifically recites “determining, based on the music information and the object information, the candidate object as a target object”, 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, “determining”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, reading information associated with the song and the previously determined attribute and determining the identified attribute as a target object. Same rationale applies to the limitations “determining, based on a matching result of the music information and the object information in an object information database, a set of feature values for the candidate object”, “determining that a classification result of the set of feature values satisfies a condition, the classification result comprising an association probability between the music content and the candidate object”, “determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result”, “determining the candidate object as the target object”, since, as presently presented, and as previously mentioned, nothing in the claim element precludes the steps from practically being performed in a human mind, and making determinations based on information can be performed in the human mind.
The limitation of “determining a plurality of association probabilities between the music content and a plurality of candidate objects based on a plurality of sets of feature values”, 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 classification model”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a classification model” language, “determining”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, reading information associated with the song and candidate objects, probabilities that the objects are associated, based on feature values. The limitation of “filtering, based on a threshold condition, the plurality of association probabilities, and sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities”, 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, “filtering”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, narrowing down the previously determined probabilities based on a threshold, and writing the remaining probabilities in the sheet of paper, in a sequence descending order. 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 – “detecting a user click on a control of a target object; displaying a music content associated with the target object”, “performing at least one of the following: in an event that the music content is a song, displaying a play page of the song comprising the target object; and in an event that the music content is an album, displaying an album page comprising the target object and at least one song in the album” (claim 13), and a processor (claim 17). The limitations “detecting a user click on a control of a target object; displaying a music content associated with the target object”, and “performing at least one of the following: in an event that the music content is a song, displaying a play page of the song comprising the target object; and in an event that the music content is an album, displaying an album page comprising the target object and at least one song in the album” amount to data-gathering and data-presentation steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The processor in these steps is 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 presentation 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))). The claims are not patent eligible.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitations of “the object information comprises an object name of the candidate object, and determining the set of feature values for the candidate object comprises: determining a number of matching results for the object name in the object information database; and determining, based on the number of matching results for the object name, a feature value for the object name of the candidate object”, which are steps that can be performed in the human mind with the aid of pen and paper, and therefore are further elaborating on the abstract idea, therefore, the claim does not amount to significantly more than the abstract idea. Same rationale applies to claims 5, 7 to 9, 11 and 12, since they recite determination limitations that are similarly further elaborating on the abstract idea.
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 music information comprises at least one of a language, a genre, and a release time of the music content”, 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)).
Claim 10 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim recites the additional limitation of “determining that the classification result of the set of feature values satisfies a condition, updating, based on the music information and the object information, the object information database, wherein updating, based on the music information and the object information, the object information database comprises: determining that the object information database does not containing an object profile for the target object, and adding, based on the music information and the object information, an object profile for the target object into the object information database; or determining that the object information database contains an object profile for the target object, and updating, based on the music information and the object information, the object profile for the target object in the object information database”, where the determining steps can be performed in the human mind, and where the adding and updating steps amount to data storing 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)(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.
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, 19, and 20, since they recite similar limitations.
Claims 1, 3 to 5, 7 to 13, 15 to 17, 19, and 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, 3 to 5, 7 to 13, 15 to 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jahan (U.S. Publication No. 2022/0067086), in view of Hristova et al. (U.S. Publication No. 2024/0232257) hereinafter Hristova, and further in view of Fan (U.S. Publication No. 2023/0326185).
As to claim 1:
Jahan discloses:
A method for determining an object associated with music, comprising:
determining a candidate object for a music content [Paragraph 0020 teaches audio files corresponding to audio content are received, and identifying metadata values of the audio content];
obtaining music information of the music content and object information of the candidate object, wherein at least one of the music information and the object information comprises a plurality of types of information [Paragraph 0019 teaches searching content repository for metadata of audio recordings, and standardizing the metadata; Paragraph 0021 teaches determining if the distinctive metadata values for the audio files match one of the metadata values in their standardized format from the content repository]; and
determining, based on the music information and the object information, the candidate object as a target object [Paragraph 0021 teaches determining a match between the audio files and the identified metadata values in their standardized format]; wherein the method further comprises:
determining, based on a matching result of the music information and the object information in an object information database, a set of feature values for the candidate object [Paragraph 0019 teaches responsive to finding an audio recording that matches one of the unique identifiers in the content repository, retrieving a non-standardized representation of metadata for the audio recording, in other words, determining a set of feature values]; and
determining that a classification result of the set of feature values satisfies a condition [Paragraph 0021 teaches responsive to determining a match between any of the corresponding audio files and one of the identified metadata values in their standardized format, matching the audio file to the identified metadata values, where the condition to satisfy is the match];
determining the candidate object as a target object [Paragraph 0021 teaches determining a match between the audio files and the identified metadata values in their standardized format].
Jahan does not appear to expressly disclose the classification result comprising an association probability between the music content and the candidate object; determining, by a classification model, a plurality of association probabilities between the music content and a plurality of candidate objects based on a plurality of sets of feature values; filtering, based on a threshold condition, the plurality of association probabilities, and sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities; determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result.
Hristova discloses:
the classification result comprises an association probability between the music content and the candidate object [Paragraph 0087 teaches determining a pairwise similarity distance between the media items];
determining, by a classification model, a plurality of association probabilities between the music content and a plurality of candidate objects based on a plurality of sets of feature values [Paragraph 0064 teaches generating pairwise similarity distances, by determining a set of attributes for each media item in a plurality of selected media items, selected based on features of the media items; Paragraph 0065 teaches media items are associated with a plurality of attributes; Paragraph 0087 teaches determining a pairwise similarity distance between the media items];
filtering, based on a threshold condition, the plurality of association probabilities [Paragraph 0087 teaches determining media content items that do not satisfy a threshold similarity distance in a cluster, the similarity distance between the media content items exceeds a threshold similarity distance and thus does not satisfy the threshold similarity distance, and not including the respective media content items];
determining, based on a result of a plurality of the filtered association probabilities, the candidate object [Paragraph 0069 teaches when a pair of content items have a similarity distance that satisfies a threshold, the items are assigned to a same cluster; Paragraph 0072 teaches a cluster corresponds to an artist, where based on the metadata of the content items, they are associated with the corresponding cluster].
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 Jahan, by the classification result comprising an association probability between the music content and the candidate object; determining, by a classification model, a plurality of association probabilities between the music content and a plurality of candidate objects based on a plurality of sets of feature values; filtering, based on a threshold condition, the plurality of association probabilities; determining, based on the result of the plurality of the filtered association probabilities, the candidate object, as taught by Hristova [Paragraph 0072, 0069, 0087], because both applications are directed to retrieval, analysis and reconciliation of content metadata; determining similarity based on threshold values improves the accuracy of the identifications.
Neither Jahan, nor Hristova appear to expressly disclose sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities; determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result.
Fan discloses:
sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities [Paragraph 0064 teaches ranking the candidate objects by their recognition probabilities in a descending order of the recognition probabilities];
determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result [Paragraph 0064 teaches selecting the candidate object with the recognition probabilities ranking at the top positions, e.g., top 3 and top 5].
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 Jahan, by sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities; determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result, as taught by Fan [Paragraph 0064], because the applications are directed to retrieval and analysis of content; selecting the content of the top ranked probabilities enables to provide the content to the user that will be most relevant with higher accuracy.
As to claim 3:
Jahan discloses:
the object information comprises an object name of the candidate object [Paragraph 0098 teaches identifying an artist name in the list of returned metadata], and determining the set of feature values for the candidate object comprises:
determining a number of matching results for the object name in the object information database [Paragraph 0098 teaches determining if the return track name metadata matches the featuring artist name listed in track name metadata]; and
determining, based on the number of matching results for the object name, a feature value for the object name of the candidate object [Paragraph 0098 teaches if the artist name matches the featuring artist in track name metadata, the artist is set as the featuring artist].
As to claim 4:
Jahan discloses:
the music information comprises at least one of a language, a genre, and a release time of the music content [Paragraph 0073 teaches metadata for audio content include genre].
As to claim 5:
Jahan discloses:
determining an auxiliary object for the music content, wherein the auxiliary object comprises at least one of an artist, a lyricist, and a composer [Paragraph 0098 teaches
standardize retrieved artist metadata];
obtaining auxiliary object information of the auxiliary object [Paragraph 0098 teaches retrieving artist metadata]; and
determining, based on a matching result of the auxiliary object information, the music information and the object information in the object information database, the set of feature values for the candidate object [Paragraph 0098 teaches determining if the metadata matches the featuring artist name; Paragraph 0099 teaches determining if a remix artist is listed in track name metadata of the song, and determining if the artist name matches the featuring artist, then set the artist as the remixer artist].
As to claim 7:
The combination of Jahan and Hristova discloses:
a training process of the classification model comprises: obtaining a set of sample feature values corresponding to a set of label weights, wherein the set of label weights is determined based on an impact of sample feature values in the set of sample feature values on a label association probability; determining, by the classification model, a set of training weights and a training association probability based on the set of sample feature values; determining a first loss between the set of label weights and the set of training weights, and a second loss between the label association probability and the training association probability; and adjusting, based on the first loss and the second loss, parameters of the classification mode [Paragraph 0061 teaches the machine learning algorithm is trained using supervised learning (e.g., with labels attached to the inputs indicating the similarity of the inputs), where adjustment of classification parameters based on loss is included in the training of machine learning models].
As to claim 8:
Jahan as modified by Hristova discloses:
determining that none of the plurality of association probabilities satisfies the threshold condition; and determining that the music content does not contain a corresponding object [Paragraph 0069 teaches upon determination that a pairwise similarity distance for a pair of content items does not satisfy the threshold similarity distance, the content items are separated into distinct clusters].
As to claim 9:
Jahan as modified by Hristova discloses:
obtaining a first identifier corresponding to the target object; determining that a second identifier corresponding to the music content is different from the first identifier; and determining that the target object contains an error [Paragraph 0004 teaches identifying media items that are likely to be mismatched or misattributed based on dissimilarity; Paragraph 0064 teaches determining attributes for the media items including artist name associated with the media items; Paragraph 0072 teaches determining that content item does not correspond to artist A].
As to claim 10:
Jahan discloses:
the object information database is divided into a plurality of object profiles based on a plurality of objects [Paragraph 0021 teaches the plurality of audio files can be matched and linked to the identified metadata values in their standardized format to digitally reconstruct the digital audio catalog, where the profiles are represented by the audio files records in the catalog], and the method further comprises:
determining that the classification result of the set of feature values satisfies a condition; and updating, based on the music information and the object information, the object information database; wherein updating, based on the music information and the object information [Paragraph 0021 teaches responsive to determining a match between any of the corresponding audio files and one of the identified metadata values in their standardized format, the corresponding audio file is linked to the metadata values in their standardized format], the object information database comprises:
determining that the object information database not contains an object profile for the target object, and adding, based on the music information and the object information, an object profile for the target object into the object information database; or determining that the object information database containing an object profile for the target object, and updating, based on the music information and the object information, the object profile for the target object in the object information database [Paragraph 0021 teaches responsive to determining a match between any of the corresponding audio files and one of the identified metadata values in their standardized format, the corresponding audio file is linked to the metadata values in their standardized format].
As to claim 11:
Jahan discloses:
obtaining maintenance information of the object profile for the target object, wherein the maintenance information is determined based on at least one of a sampling detection result of the object information database and user feedback information [Paragraph 0103 teaches comparing media content to determine if there are any inconsistencies amongst the metadata];
determining, based on the maintenance information, that the music information and the object information in the object profile for the target object contain an error [Paragraph 0103 teaches comparing media content to determine if there are any inconsistencies amongst the metadata, and flagging inconsistencies]; and
correcting the object profile for the target object [Paragraph 0103 teaches inconsistencies are flagged and presented to a user for manual correction].
As to claim 12:
Jahan discloses:
the music content comprises a song and an album [Paragraph 0071 teaches media content includes metadata and media files of audio or audio recordings, where the audio can be an audio track or an album; Paragraph 0073 teaches metadata for audio content includes artist, genre, album name, song title, etc., therefore, comprising a song and an album].
Jahan does not appear to expressly disclose determining a first object of the song and a second object of the album; determining that the first object is the same as the second object; and determining that the song belongs to the album; and adding the song into the album.
Hristova discloses:
determining a first object of the song and a second object of the album; determining that the first object is the same as the second object; and determining that the song belongs to the album; and adding the song into the album [Paragraph 0080 teaches obtaining a plurality of media items, and a plurality of attributes of the media item; Paragraph 0073 teaches determining that the content item belongs to a distinct cluster, and grouping the content item into the corresponding cluster; Paragraph 0081 teaches attributes include similarities, e.g., similarity between an album artist name and a track artist name, and obtaining features including artist name similarity between 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 Jahan, by determining that the first object is the same as the second object; and determining that the song belongs to the album; and adding the song into the album, as taught by Hristova [Paragraph 0073, 0080, 0081], because both applications are directed to retrieval, analysis and reconciliation of content metadata; determining that the objects are the same enables the correction or identification of inconsistencies, improving thereby the user’s experience.
As to claim 13:
Jahan discloses:
A method for displaying an object associated with music, comprising:
determining a candidate object for a music content [Paragraph 0020 teaches audio files corresponding to audio content are received, and identifying metadata values of the audio content];
obtaining music information of the music content and object information of the candidate object, wherein at least one of the music information and the object information comprises a plurality of types of information [Paragraph 0019 teaches searching content repository for metadata of audio recordings, and standardizing the metadata; Paragraph 0021 teaches determining if the distinctive metadata values for the audio files match one of the metadata values in their standardized format from the content repository]; and
determining, based on the music information and the object information, the candidate object as a target object [Paragraph 0021 teaches determining a match between the audio files and the identified metadata values in their standardized format]; wherein the method further comprises:
determining, based on a matching result of the music information and the object information in an object information database, a set of feature values for the candidate object [Paragraph 0019 teaches responsive to finding an audio recording that matches one of the unique identifiers in the content repository, retrieving a non-standardized representation of metadata for the audio recording, in other words, determining a set of feature values]; and
determining that a classification result of the set of feature values satisfies a condition [Paragraph 0021 teaches responsive to determining a match between any of the corresponding audio files and one of the identified metadata values in their standardized format, matching the audio file to the identified metadata values, where the condition to satisfy is the match];
determining the candidate object as a target object [Paragraph 0021 teaches determining a match between the audio files and the identified metadata values in their standardized format].
Jahan does not appear to expressly disclose detecting a user click on a control of a target object; displaying a music content associated with the target object; the classification result comprising an association probability between the music content and the candidate object; determining, by a classification model, a plurality of association probabilities between the music content and a plurality of candidate objects based on a plurality of sets of feature values; filtering, based on a threshold condition, the plurality of association probabilities, and sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities; determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result; determining a user click on a control of the music content; and performing at least one of the following: in an event that the music content is a song, displaying a play page of the song comprising the target object; and in an event that the music content is an album, displaying an album page comprising the target object and at least one song in the album.
Hristova discloses:
detecting a user click on a control of a target object; displaying a music content associated with the target object [Paragraph 0022 teaches receiving requests to play music, podcasts, movies, videos, or other media items, or playlists thereof];
the classification result comprises an association probability between the music content and the candidate object [Paragraph 0087 teaches determining a pairwise similarity distance between the media items];
determining, by a classification model, a plurality of association probabilities between the music content and a plurality of candidate objects based on a plurality of sets of feature values [Paragraph 0064 teaches generating pairwise similarity distances, by determining a set of attributes for each media item in a plurality of selected media items, selected based on features of the media items; Paragraph 0065 teaches media items are associated with a plurality of attributes; Paragraph 0087 teaches determining a pairwise similarity distance between the media items];
filtering, based on a threshold condition, the plurality of association probabilities [Paragraph 0087 teaches determining media content items that do not satisfy a threshold similarity distance in a cluster, the similarity distance between the media content items exceeds a threshold similarity distance and thus does not satisfy the threshold similarity distance, and not including the respective media content items];
determining, based on a result of a plurality of the filtered association probabilities, the candidate object [Paragraph 0069 teaches when a pair of content items have a similarity distance that satisfies a threshold, the items are assigned to a same cluster; Paragraph 0072 teaches a cluster corresponds to an artist, where based on the metadata of the content items, they are associated with the corresponding cluster];
determining a user click on a control of the music content [Paragraph 0036 teaches a user interface module that receives commands or inputs from a user via the user interface (e.g., from the input devices) and provides outputs for playback or display on the user interface]; and performing at least one of the following: in an event that the music content is a song, displaying a play page of the song comprising the target object; and in an event that the music content is an album, displaying an album page comprising the target object and at least one song in the album [Paragraph 0022 teaches receiving requests to play music, podcasts, movies, videos, or other media items, or playlists thereof; Paragraph 0024 allowing a user to request for playback at the electronic device, and present media content, e.g., control playback of music tracks, playlists, videos, etc.; Paragraph 0039 teaches displaying a shelf that includes representations of playlists, albums, tracks, podcasts, audiobooks, or other media content for the user].
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 Jahan, by detecting a user click on a control of a target object; displaying a music content associated with the target object; the classification result comprising an association probability between the music content and the candidate object; determining, by a classification model, a plurality of association probabilities between the music content and a plurality of candidate objects based on a plurality of sets of feature values; filtering, based on a threshold condition, the plurality of association probabilities; determining, based on the result of the plurality of the filtered association probabilities, the candidate object; determining a user click on a control of the music content; and performing at least one of the following: in an event that the music content is a song, displaying a play page of the song comprising the target object; and in an event that the music content is an album, displaying an album page comprising the target object and at least one song in the album, as taught by Hristova [Paragraph 0022, 0024, 0036, 0039, 0072, 0069, 0087], because both applications are directed to retrieval, analysis and reconciliation of content metadata; determining similarity based on threshold values improves the accuracy of the identifications.
Neither Jahan, nor Hristova appear to expressly disclose sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities; determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result.
Fan discloses:
sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities [Paragraph 0064 teaches ranking the candidate objects by their recognition probabilities in a descending order of the recognition probabilities];
determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result [Paragraph 0064 teaches selecting the candidate object with the recognition probabilities ranking at the top positions, e.g., top 3 and top 5].
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 Jahan, by sorting a plurality of the filtered association probabilities in descending order of values of the association probabilities to obtain a sorted result of the plurality of the filtered association probabilities; determining, based on the sorted result of the plurality of the filtered association probabilities, the candidate object corresponding to an association probability satisfying a sorting condition, wherein the association probability satisfying the sorting condition comprises: at least one association probability in top positions of the sorted result, as taught by Fan [Paragraph 0064], because the applications are directed to retrieval and analysis of content; selecting the content of the top ranked probabilities enables to provide the content to the user that will be most relevant with higher accuracy.
Same rationale applies to claims 15 to 17, 19, and 20, since they recite similar limitations, and are therefore, similarly rejected.
Response to Arguments
The following is in response to arguments filed on April 27, 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 problem addressed by amended claim 1 is how to accurately matching the correct target object with the music content when one music content corresponds to a plurality of candidate objects”, and further that “significantly improving the accuracy and efficiency of association between music and associated objects”.
In response to the preceding argument, Examiner respectfully points out 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. Based on Applicant’s arguments, and in view of the claims as presently presented, it appears that the improvement is directed to matching of data, however, it is not clear how data matching and association constitute an improvement in the technology. It is important to keep in mind that an improvement in the judicial exception itself is not an improvement in technology. For example, providing an accurate association of data even when the data includes additional elements (or multiple candidate objects), could enhance the accuracy of the association, but does not improve computers or technology. Additionally, the limitations as presently presented, are limitations that can practically be performed in the human mind. Therefore, and as further described in the 101 rejections above, the claims are directed to an abstract idea under the “Mental Processes” grouping of abstract ideas, without significantly more. 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
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
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