DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action has been issued in response to Applicant’s Communication of amended application S/N 18/035,175 filed on April 3, 2026. Claims 1 to 3, 5, 7, 9, 13, 15 to 17, 22, 24, 29, and 32 are currently pending with the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 to 3, 5, 7, 9, 13, 15 to 17, 22, 24, 29, and 32 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the real-time computer calculated target personality profile” in line 3 at page 3. There is insufficient antecedent basis for this limitation in the claim.
Same rationale applies to claim 32, since it recites similar limitations, and to claims 2, 3, 5, 7, 9, 13, 15 to 17, 22, 24, 29, since they inherit the same deficiencies by virtue of their dependency.
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.
Claim 1 to 3, 5, 9, 15, 16, 22, 24, 29, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over BACH et al. (U.S. Publication No. 2012/0296908) hereinafter Bach, and further in view of Fuzell-Casey et al. (U.S. Publication No. 2020/0228596) hereinafter Fuzell.
As to claim 1:
Bach discloses:
A method for automatic playback of matching media items [Paragraph 0132 teaches user DNA can be applied to user groups to provide automated music recommendation], comprising:
obtaining a set of media content descriptors comprising features including semantic descriptors for a respective media item of the identified one or more media items, the semantic descriptors comprising at least one emotional descriptor for the respective media item [Paragraph 0041 teaches extracting the at least two different features of a media item from associated metadata, and supplying the features to create collection profiles, therefore, pre-analyzed media content descriptors; Paragraph 0087 teaches features include genre, subgenre, vocal detection, aggressiveness, mood, etc., therefore, semantic descriptors and emotional descriptors of the media items];
determining, using real-time computer processing, a set of aggregated media content descriptors for an entirety of the identified one or more media items based on the set of media content descriptors of each of the identified one or more media items [Paragraph 0041 teaches combining the extracted features for the plurality of media items of the collection; Paragraph 0048 teaches corresponding features of the different audio files are combined; Paragraph 0055 teaches corresponding features for all items in the collection are added and an average is calculated for each individual features of the plurality of features];
mapping, using real-time computer processing, the set of aggregated media content descriptors to a target personality profile of a user, wherein the target personality profile comprises a plurality of personality scores for elements of the target personality profile, the personality scores calculated by the real-time computer from aggregated features of the set of aggregated media content descriptors [Paragraph 0015 teaches utilizing a person-specific collection of media data items, or a collection of different media data items owned by a certain user, for the purpose of characterizing the owner of the collection; Paragraph 0019 teaches the collection profile is a quantitative fingerprint derived from content features and represents the content of a media data collection, and is an average over each separate feature, therefore, being a vector of several averaged different features; Paragraph 0032 teaches a music DNA of a user, for providing him with matching media items related to her or his media data taste; Paragraph 0048 teaches user’s music DNA or collection profile is based on features of the different audio files, weighted in accordance with the user’s taste; Paragraph 0128 teaches collection profiles can be mapped to moods, where moods can be calculated by a combination of different features, by using a rule for combining certain components of a collection profile vector or features in a certain way, which results in a certain number, and where this number is then mapped to a certain mood, where the mood represents the personality profile of the collection].
providing the real-time computer calculated target personality profile, wherein the target personality profile corresponds to the identified one or more media items [Paragraph 0048 teaches user’s music DNA or collection profile is based on features of the different audio files, weighted in accordance with the user’s taste];
providing a personality profile for each of a plurality of second media items [Paragraph 0016 teaches music DNA individually characterizes the collection of different media data items; Paragraph 0055 teaches raw music DNA or raw collection profile, corresponding to the features of the items in a collection, therefore, a personality profile for the items];
comparing the personality profile for each of the plurality of second media items with the target personality profile and determining at least one second media item of the plurality of second media items having a best matching personality profile [Paragraph 0042 teaches performing a matching operation of the user’s collection profile (user’s music DNA) with profiles of audio data items to locate a matching audio data item which best matches the user’s collection profile; Paragraph 0060 teaches comparing the user’s music DNA to a plurality of media items or different music DNAs, to find a matching item; Paragraph 0061 teaches locating matching items based on distance between the user’s music DNA and the items, where the best matching media file will be extracted; Paragraph 0107 teaches using the collection profile in order to find a media data item having a matching feature vector, by comparing the collection profile to a plurality of different feature vectors (profiles) for media data items]; and
selecting at least one of the determined second media item of the plurality of second media items for playback [Paragraph 0119 teaches the result will be the rendering of a media item; Paragraph 0124 teaches the result of the matching operation is used to play the matching result for the user’s own enjoyment].
Bach does not appear to expressly disclose obtaining an identification of one or more media items, wherein each identified media item is real-time computer analyzed to generate a spectrogram for each identified media item; obtaining a set of media content descriptors for each of the identified one or more media items by analyzing each real-time computer-generated spectrogram with a real-time computer program.
Fuzell discloses:
obtaining an identification of one or more media items, wherein each identified media item is real-time computer analyzed to generate a spectrogram for each identified media item [Paragraph 0078 teaches once the track has been input, it may be analyzed to determine the scores, hence, analyzed in real-time; Paragraph 0096 teaches a music identification code may be obtained from the metadata file associated with the music; Paragraph 0095 teaches generate static representation of the track based on the low-level sampled frequency data, which may be a static visual representation, such as a spectrogram or Mel-spectrogram];
obtaining a set of media content descriptors for each of the identified one or more media items by analyzing each real-time computer-generated spectrogram with a real-time computer program [Paragraph 0029 teaches using spectrograms to determine qualities of RTP and further determine moods of the tracks, hence, obtaining content descriptors by analyzing the spectrogram; Paragraph 0095 teaches utilize the spectrograms in a neural network to determine RTP scores].
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 Bach, by obtaining an identification of one or more media items, wherein each identified media item is real-time computer analyzed to generate a spectrogram for each identified media item; obtaining a set of media content descriptors for each of the identified one or more media items by analyzing each real-time computer-generated spectrogram with a real-time computer program, as taught by Fuzell [Paragraph 0029, 0078, 0095, 0096], because both applications are directed to analysis of music content for generation of profiles based on extracted content features; generating spectrograms and obtaining features from the generated spectrograms improves recognition performance (See Fuzell Para [0067]).
Claim 32 is similarly rejected, since it recites the same limitations as claim 1, therefore, same rationale applies.
As to claim 2:
Bach discloses:
the media items comprise musical portions and preferably are pieces of music [Paragraph 0061 teaches media data items are music items; Paragraph 0077 teaches generating a profile from the user’s music collection].
As to claim 3:
Bach discloses:
the identification of one or more media items comprises a playlist of a user or user group [Paragraph 0077 teaches generating a profile from the user’s music collection].
As to claim 5:
Bach discloses:
the one or more identified media items correspond to an album or an artist [Paragraph 0130 teaches comparing user DNA (profile) and link to songs, artists, etc.], and wherein the set of media content descriptors for a media item comprises one or more acoustic descriptors of the media item that are determined based on an acoustic analysis of the media item [Paragraph 0082 teaches content information can include features for tempo, beats per minute, percussiveness, etc.; Paragraphs 0093-0100 teach extraction and generation of audio features, by decoding the audio material, splitting into frequency bands and short-time frames, calculation of features for each frame such as modulation spectrum, noise likeness, etc., distinguishing chorus, verse, intro, and solos, calculating high-level features, and embedding the extracted data into the audio file, hence, the acoustic descriptors are determined based on an acoustic analysis of the media item].
As to claim 9:
Bach discloses:
wherein segments of a media item are analyzed and the set of media content descriptors for the media item is determined based on results of the analysis for the segments [Paragraphs 0093-0100 teach extraction and generation of acoustic features, by splitting the audio material into frequency bands and short-time frames, and analyzing and calculating features for each frame];
wherein obtaining a set of media content descriptors for each of the identified one or more media items comprises retrieving the set of media content descriptors for a media item from a database [Paragraph 0041 teaches data items have been already associated with metadata containing their respective features, and feature extractor can only parse and evaluate a metadata portion of a media data item to extract the different features of the items];
wherein determining a set of aggregated media content descriptors comprises calculating aggregated numerical features from respective numerical features of the identified media items [Paragraph 0041 teaches all the extracted features of each data item are weighted and the weighed extracted features of the plurality of the data items are combined; Paragraph 0055 teaches corresponding features for all items are added and an average is calculated, i.e., adding and averaging feature F1 for all the items, feature F2, and so on]; and
wherein the personality profile is based on a personality scheme that defines a number of personality scores for profile elements that represent personality traits [Paragraph 0128 teaches collection profiles or DNA can be mapped to moods, which can be a combination of different features, where a rule for combining certain components of a collection profile vector or features, in a certain way, results in a certain number, and where this number is then mapped to a certain mood].
As to claim 15:
Bach discloses:
a personality score of the personality profile is determined based on weighted aggregated numerical features of the identified media items [Paragraph 0015 teaches creates the collection profile by combining the weighted extracted features of the plurality of media data items to obtain a quantitative collection profile; Paragraph 0041 teaches combining the weighted extracted features for the plurality of media data items of the collection to obtain a quantitative profile].
As to claim 16:
Bach discloses:
wherein a personality score of the personality profile is determined based on presence or absence of an aggregated feature of the identified media items [Paragraph 0052 teaches analyzing tags of the songs for similar features, where outliers are excluded; Paragraph 0077 teaches features can be excluded or included from the profile].
As to claim 22:
Bach discloses:
wherein the comparing of personality profiles of second media items is based on a similarity search where corresponding scores of profiles are compared and matching scores indicating the similarity of respective pairs of profiles are computed [Paragraph 0066 teaches performing a matching operation and obtaining a distance for each media items representing how well the media items match, hence, a score]; and the method further comprising ranking the personality profiles of the media items according to their matching scores [Paragraph 0066 teaches a result list of a matching operation is a sorted list, where the media item having the smallest distance will be the first media item and where the media item having the second to smallest distance will be the second media item, and so on].
As to claim 24:
Bach discloses:
wherein the target personality profile corresponds to a group of users or an individual user [Paragraph 0042 teaches performing matching of the profile to collection profiles of other users, to locate best matches].
As to claim 28:
Bach discloses:
wherein information associated with at least one of the at least one media item having the best matching personality profile is provided to the user or to a user device associated with the user [Paragraph 0108 teaches based on the matching operation, information related to a product/service determined in response to the collection profile matching operation is sent or received].
As to claim 29:
Bach discloses:
wherein the comparing the personality profile for each of the plurality of second media items with the target personality profile and the determining the at least one second media item of the plurality of second media items having the best matching personality profile is performed repeatedly [Paragraph 0088 teaches performing automated playlist generation]; and wherein the personality profiles are generated on a server platform [Paragraph 0042 teaches collection profiles can be stored, and used to perform matching operations in a database that comprises collection profiles of other users or audio data items], and the method further comprising:
transmitting an identification of one or more preferred media items for the user from a user device associated with the user to the server platform [Paragraph 0044 teaches collection profiles can be stored in a collection profile storage]; and
receiving a representation of at least one determined media item at the user device [Paragraph 0110 teaches the selected audio pieces are played or streamed]; wherein the identification of one or more preferred media items for the user is stored on the server platform and the personality profiles are generated on the server platform [Paragraph 0049 teaches profile refinement based on the statistics of the usage behavior of the user, where the profile changes with the changing user habits, by taking into account media data items which are often used, and that have been used more recently; Paragraph 0076 teaches the result list can be stored for later use; Paragraph 0109 teaches collection profile information generator is a second entity, and collets profiles from users in a certain Internet user group; Paragraph 0110 teaches the second entity information handler calculates an average profile among the profiles from the users received, hence, the profiles are generated in the server platform], the method further comprising:
transmitting, by the at least one processor, a representation of at least one determined media item to the user device associated with the user [Paragraph 0076 teaches the result list can be played; Paragraph 0124 teaches the result of the matching operation is used to play the matching result for the user’s own enjoyment].
Claims 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over BACH et al. (U.S. Publication No. 2012/0296908) hereinafter Bach, in view of Fuzell-Casey et al. (U.S. Publication No. 2020/0228596) hereinafter Fuzell, and further in view of Herberger et al. (U.S. Publication No. 2012/0023403) hereinafter Herberger.
As to claim 7:
Bach discloses:
wherein the one or more semantic descriptors comprise at least one of genres, voice presence, voice gender, vocal pitch, musical moods, and rhythmic moods [Paragraph 0087 teaches features include genre, subgenre, vocal detection, aggressiveness, mood, etc.].
Bach does not appear to expressly disclose the set of media content descriptors for a media item is determined based on an artificial intelligence model that determines one or more semantic descriptor or emotional descriptors for the media item.
Herberger discloses:
the set of media content descriptors for a media item is determined based on an artificial intelligence model that determines one or more semantic descriptor or emotional descriptors for the media item [Paragraph 0039 teaches musical characteristics of songs have been calculated via an artificial intelligence methodology].
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 Bach, by determining the set of media content descriptors for a media item based on an artificial intelligence model that determines one or more semantic descriptor or emotional descriptors for the media item, as taught by Herberger [Paragraph 0039], because both applications are directed to analysis of music content for generation of profiles based on extracted content features; by implementing an artificial intelligence model, flexibility of the system can be increased, while improving the accuracy of the operations.
As to claim 13:
Bach discloses:
a personality score of the personality profile is determined based on a mapping rule that defines how the personality score is computed from the set of aggregated media content descriptors [Paragraph 0128 teaches calculating a profile score (number) by combining different features, based on a rule for combining certain components of a collection profile vector, and further mapping the score (number) to a mood (profile)].
Bach does not appear to expressly disclose wherein the mapping rule is learned by a machine learning technique.
Herberger discloses:
the mapping rule is learned by a machine learning technique [Paragraph 0038 teaches aspect classification will produce a histogram which illustrates, per temporal unit, to which class the currently analyzed music item belongs to, where the aspects are attributes of the audio content that describe its musical characteristics, and where a number of different classes might be connected to each aspect; Paragraph 0039 teaches the song classes are defined in terms of calculated musical characteristics and obtained via an artificial intelligence methodology].
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 Bach, by incorporating a mapping rule learned by a machine learning technique, as taught by Herberger [Paragraph 0038, 0039], because both applications are directed to analysis of music content for generation of profiles based on extracted content features; by implementing an artificial intelligence model, flexibility of the system can be increased, while improving the accuracy of the operations.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over BACH et al. (U.S. Publication No. 2012/0296908) hereinafter Bach, in view of Fuzell-Casey et al. (U.S. Publication No. 2020/0228596) hereinafter Fuzell, and further in view of Brust et al. (U.S. Publication No. 2015/0356261) hereinafter Brust.
As to claim 17:
Bach discloses:
providing the personality profile comprises displaying a graphical representation of the personality profile or transmitting the personality profile to a database server [Paragraph 0042 teaches the collection profile of the user can be stored for later used, or can be transmitted to a different entity, or used to perform matching operations in a database comprising collection profiles of other users or audio data items, hence, transmitting the profile to a database server].
Bach does not appear to expressly disclose wherein the personality profile is classified in one of a plurality of personality types.
Brust discloses:
wherein the personality profile is classified in one of a plurality of personality types [Paragraph 0022 teaches profile includes information such as personality type; Paragraph 0037 teaches mapping data to a personality type; Paragraph 0038 teaches personality types can include caregiver, colleague, competitor, authoritarian, optimist, skeptic, fatalist, activist, driver, analytical, amiable, expressive, or combinations thereof].
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 Bach, by incorporating a personality profile classified in one of a plurality of personality types, as taught by Brust [Paragraph 0022, 0037, 0038], because both applications are directed to analysis of data for generation of profiles; by incorporating personality types enables to provide more relevant content dynamically to the users (See Brust Para [0022]).
Response to Arguments
The following is in response to arguments filed on April 3, 2023. Applicant arguments have been carefully and respectfully considered.
Claim Rejections - 35 USC § 101
Applicant’s arguments have been carefully and respectfully considered. In view of claim amendments, and arguments, rejections under 35 USC § 101 are hereby withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments have been carefully and respectfully considered, but are moot in view of new grounds of rejections, as necessitated by the amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL PEREZ-ARROYO whose telephone number is (571)272-8969. The examiner can normally be reached Monday - Friday, 8:00am - 5:30pm, Alt Friday, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
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