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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 October 7, 2025 has been entered.
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 September 5, 2025. Claims 1 to 5, 7 to 10, 14, 19, 20, 22 to 25, 28, and 29 are currently pending with the application.
Claim Objections
Claims 1 and 29 are objected to because of the following informalities:
Claim 1 recites the limitation “the best matching content descriptor set” in line 21, which appears to contain a typographical error, and that it should read “the best matching media content descriptor set”. Appropriate correction is required.
Claim 29 recites the limitation “agreeableness, and/or neuroticism” in line 2. It is not clear whether the Applicant intends the claim to mean “and” or “or”, therefore, for purposes of clarity and consistency, such deficiencies must be resolved. Appropriate correction is required.
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 5, 7 to 10, 14, 19, 20, 22 to 25, 28, and 29 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 limitations “the target profile corresponds to an individual user or a user group” in line 5 and “wherein the target profile … characterizes a current mood of the user or the user group” in line 9, and further “the target profile corresponds to a product or a brand” in line 9. These limitations are not clear. More specifically, it is not clear how a target profile can correspond a user or user group, and characterize a current mood of the user or the user group, and also, at the same time, correspond to a product or a brand. These deficiencies render the claim indefinite.
Claim 1 further recites “determining a user or user group” in line 23. It is not clear whether the “a user or user group” recited in line 23 refers to the same “user or a user group” elements recited in line 6. Furthermore, it is not clear what the intention of the limitation is. These deficiencies also render the claim indefinite.
Same rationale applies to claim 28, since it recites the same limitations as claim 1, and to claims 2 to 5, 7 to 10, 14, 19, 20, 22 to 25, and 29, since they inherit the same deficiencies, by virtue of their dependencies.
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 5, 7 to 10, 14, 19, 20, 22 to 25, and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 28 recite a target profile based on a personality scheme, and determined based on consumption history, mapping a target profile, searching for content, and determining a user or user group.
The limitation of a target profile based on a personality scheme, which specifically recites “the target profile corresponds to an individual user or a user group and is based on a personality scheme that defines a number of profile scores for target profile elements that represent personality traits”, 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 at least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by at least one processor” language, “corresponds”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, correlating a user or group to a target profile, based on a scheme defining scores for elements representing personality traits. The limitation of determined based on consumption history, which specifically recites “the target profile is determined based on a short-term media consumption history of the user or the user group and characterizes a current mood of the user or the user group, and wherein the target profile corresponds to a product or a brand”, 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 the least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the at least one processor” language, “determined”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, reading information of user consumption history, and determining a profile that represents a current mood of the user.
The limitation of mapping a target profile, which specifically recites “mapping the target profile to a set of target content descriptors having a plurality of features, the mapping by applying at least one mapping rule that defines how a feature of a target content descriptor set is computed from profile scores”, 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 the least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the least one processor” language, “mapping”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, writing down a list of features in a sheet of paper, applying a rule specifying how to determine features from profile scores. The limitation of searching for content, which specifically recites “searching for at least one media content descriptor set having the best matching content descriptor set with respect to the target content descriptor set”, 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 the least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the at least one processor” language, “searching”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, comparing descriptor sets to target descriptor sets and mentally determining a best match.
The limitation of determining a user or user group, which specifically recites “determining a user or user group associated with the best matching media content descriptor set”, 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 the least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the at least one processor” language, “determining”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, comparing descriptor sets to target descriptor sets and mentally determining a user or user group associated with the best match. 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 claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “obtaining a target profile having a plurality of profile scores”, “the target profile is stored in a database communicatively coupled with the at least one processor”, “target content descriptors stored in the database”, “obtaining a plurality of media content descriptor sets, each media content descriptor set associated with a media item or a group of media items and having features comprising semantic descriptors for the respective media item or group of media items, the semantic descriptors comprising at least one emotional descriptor for the media item or group of media items”, at least one processor of a computing device, a memory and processor (claim 28). The “obtaining” limitations amount to data gathering steps, and represent insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data (See MPEP 2106.05(g)).
Continuing with the analysis of the additional elements, the limitations “the target profile is stored in a database communicatively coupled with the at least one processor” and “target content descriptors stored in the database” amount to data storing steps, which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). The at least one processor of a computing device, memory and processor in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is 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 activities identified above, which include the data gathering and data storing steps, are 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)). The claims are not patent eligible.
Claims 2, 3, 9, 10, 19, 20, 22, and 23 are processes that can be performed in the human mind, and therefore, are merely elaborating on the abstract idea, which do not amount to significantly more.
Claims 4, 5, 8, and 24 are dependent on claims 1, 4, and 23, and include all the limitations of claim 1. Therefore, claims 4, 5, 8, and 24 recite the same abstract idea of claim 1. The additional limitations recited by the claims are recited at a high-level of generality, and amounts to no more than mere instructions to apply the exception using generic computer components, because it does no more than invoking computers or other machinery merely as a tool to perform an existing process (i.e., determining based on an artificial intelligence model). Additional elements that invoke computers, computer components, or other machinery in its ordinary capacity, merely as a tool, or simply add a general-purpose computer or computer components after the fact to an abstract idea, do not integrate a judicial exception into a practical application nor provide significantly more.
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 limitation of “wherein the obtaining a plurality of media content descriptor sets comprises retrieving a media content descriptor set for a media item or a group of media items from a database”, which amounts to data gathering steps, which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)), and is 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)). Therefore, the limitations do not amount to significantly more than the abstract idea.
Claim 25 is dependent on claim 18, and includes all the limitations of claim 1. Therefore, claim 25 recites the same abstract idea of claim 1. The claims recites the additional limitations “wherein the search of best matching content descriptor sets depends on context or environment of the users or user groups associated with the media content descriptor sets; wherein a media item corresponding to the target profile is selected for presentation to the user or user group; wherein an electronic message comprising information on the product or brand is automatically generated for the user or user group and the generated message electronically transmitted to the user or user group”, where the search limitation is further elaborating on the abstract idea. The limitation of automatically generating a message 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”. Further, the selecting and transmitting limitations 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, these additional limitations do not amount to significantly more than the abstract idea. Same rationale applies to claim 14, since it recites similar limitations.
Claim 29 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 29 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the personality traits include openness, conscientiousness, extraversion, agreeableness, and/or neuroticism”, which is 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; 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)).
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 5, 7 to 10, 12 to 14, 19, 20, 22 to 25, and 28 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, 7, 9, 10, 14, 19, 20, 22, 23, 25, 28, and 29 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 PINILLA et al. (U.S. Publication No. 2021/0365998) hereinafter Pinilla.
As to claim 1:
Bach discloses:
A method for determining a best matching media content descriptor set, comprising:
obtaining, by at least one processor of a computing device, a target profile having a plurality of profile scores, wherein the target profile is stored in a database communicatively coupled with the at least one processor [Paragraph 0109 teaches collecting profiles from users in a user group or room; 500, Fig. 5, Collect profiles from users in a group or room, where the user profiles represent the target profile; Paragraph 0042 teaches collection profile can be used for performing matching operations in a database, where this database may comprise collection profiles of other users], wherein the target profile corresponds to an individual user or a user group and is based on a personality scheme that defines a number of profile scores for target profile elements that represent personality traits [Paragraph 0109 teaches collecting profiles from users in a user group or room; 500, Fig. 5, Collect profiles from users in a group or room, where the user profiles represent the target profile; 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], and wherein the target profile is determined based on a short-term media consumption history of the user or the user group and characterizes a current mood of the user or the user group [Paragraph 0011 teaches creating a raw collection profile without information on a user behavior logged by the profile creator or information on a music taste, and weighting the raw collection profile using weights derived from the information on the music taste or user behavior to obtain the collection profile, therefore, the target profile is determined on a short-term consumption history of the user and characterizes a current mood of the user, and is based on the weights; Paragraph 0050 teaches generating a user-taste-adapted collection profile; Paragraph 0120 teaches modifying the user’s collection profile to generate a modified profile that conforms to the user’s current mood, based on a modification of his or her own collection, hence, from a short-term media consumption history], and wherein the target profile corresponds to a product or a brand [Paragraph 0108 teaches based on the matching operation, information is sent or received, and the information can relate to any product or service];
mapping, by the at least one processor, the target profile to a set of target content descriptors stored in the database and having a plurality of features, by applying at least one mapping rule that defines how a feature of a target content descriptor set is computed from the number of profile scores [Paragraph 0055 teaches collection profiles are calculated for all items, where each item corresponding features, and where the values for each feature are averaged to generate a vector that comprises the average value corresponding to each feature for the collection profile; Paragraph 0063 teaches features in the music DNA (profile) can be weighted in order to account for user taste, therefore, representing rules of how to compute values of features from respective scores; Paragraph 0064 teaches a result of a database matching operation; Paragraph 0110 teaches calculating an average profile from the obtained user profiles, therefore, computing the features for the target profile by averaging the values of individual features corresponding to each user profile, which represents the mapping rule; 502, Fig. 5, Calculate average profile];
obtaining, by the at least one processor, from the database, a plurality of media content descriptor sets, each media content descriptor set associated with a media item or a group of media items and having features comprising semantic descriptors for the respective media item or group of media items, the semantic descriptors comprising at least one emotional descriptor for the media item or group of media items [Paragraph 0107 teaches the collection profile is compared to a plurality of different feature vectors of media data items to find a media data item having a matching feature vector; Paragraph 0110 teaches searching audio pieces to find matches to the average profile, based on a collection of feature vectors corresponding to audio pieces; Paragraph 0087 teaches features include genre, tempo, percussiveness, music color, mood, etc., therefore, including semantic descriptors comprising emotional descriptors corresponding to each media item];
searching, by the at least one processor, from the plurality of media content descriptor sets, for at least one media content descriptor set having the best matching content descriptor set with respect to the target content descriptor set [Paragraph 0066 teaches performing a matching operation in a database, and obtaining a distance for each media item to determine the media items that match with the profile; Paragraph 0110 teaches based on the average profile, audio pieces are searched in a database to find matches to the average profile, where a match is performed by comparing the average profile to a collection of feature vectors corresponding to audio pieces in an audio database, in order to find matching audio pieces; Paragraph 0059 teaches determining a match between the music DNA (profile) and a media data item having a feature vector which results in the smallest distance among all other feature vectors in the set; Paragraph 0061 teaches when the selection requires that only the best matching media file be extracted, the media data item having the smallest distance will be selected]; and
determining, by the at least one processor, a user or user group [Paragraph 0107 teaches using the collection profile in order to find a user or a user group having a matching collection profile].
Bach does not appear to expressly disclose determining, a user or user group associated with the best matching media content descriptor set.
Pinilla discloses:
determining, a user or user group associated with the best matching media content descriptor set [Paragraph 0014 teaches brand personality profiles are matched with audience personality profiles; Paragraph 0035 teaches comparing and matching audience profile elements with the brand profile elements and with the media asset profile elements].
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, a user or user group associated with the best matching media content descriptor set, as taught by Pinilla [Paragraph 0014], because both applications are directed to characterization of media items for improved content identification and recommendations; determining users or groups associated with media content descriptor sets by matching personality traits and profile elements provides additional insights into brand and content acceptance and enhances the ability to match content and brands and audiences (See Pinilla Para [0011]).
Claim 28 recites same limitations as recited in claim 1, therefore, it is similarly rejected since 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:
wherein a feature of a media content descriptor set 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 7:
Bach discloses:
wherein the obtaining a plurality of media content descriptor sets comprises retrieving a media content descriptor set for a media item or a group of media items from a database [Paragraph 0041 teaches data items have been already associated with metadata containing their respective features, and feature extractor may need to only parse and evaluate a metadata portion of a media data item to extract the different features of the items; Paragraph 0110 teaches audio pieces are searched in a database].
As to claim 9:
Bach discloses:
wherein the search of best matching content descriptor sets is based on matching the
target content descriptor set with media content descriptor sets having same or similar features as the target content descriptor set [Paragraph 0107 teaches using the 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 for media data items; Paragraph 0061 teaches locating matching items, and selecting the best matching data item].
As to claim 10:
Bach discloses:
wherein the search of best matching content descriptor sets is based on a similarity search where corresponding features of content descriptor sets are compared and matching scores indicating the similarity of respective pairs of content descriptor sets 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 further comprising ranking, by the at least one processor, the media content descriptor sets 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 14:
Bach discloses:
the target profile is determined based on a media consumption history or a playlist of the user or the user group [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 0033 teaches the music DNA of a user is modified in accordance with the current user’s taste; Paragraph 0048 teaches collection profile/music DNA is calculated based on corresponding features of different audio files of a user’s collection; Paragraph 0049 teaches taking into account the user’s usage behavior to calculate the profile];
a media item corresponding to the best matching media content descriptor set is selected for playback or recommendation to the user or the user group [Paragraph 0110 teaches the selected matching audio pieces for the user group or the room are played or streamed; Paragraph 0124 teaches the result of the matching operation is used to play the matching result for the user’s own enjoyment];
information associated with a media item corresponding to the best matching media content descriptor set 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; Paragraph 0124 teaches the result of the matching operation is used to play the matching result for the user’s own enjoyment];
the searching of content descriptor sets depends on context or environment of the user or user group corresponding to the target profile [Paragraph 0112 teaches calculating a modified profile based on a specific day in a week, which will render different matching results; Paragraph 0118 teaches generating a user’s modified DNA based on a geographic location of the user, which will render different search results].
As to claim 19:
Bach discloses:
wherein each media content descriptor set is associated with a user or a user group [Paragraph 0052 teaches generation of collection profile/user DNA based on the songs of a user’s collection].
As to claim 20:
Bach discloses:
wherein a media content descriptor set comprises aggregated features that characterize a group of media items that have been presented to the user or user group, in particular media items identified in a playlist associated with the user or user group [Paragraph 0052 teaches generation of collection profile/user DNA based on the songs of a user’s collection]; wherein a user personality profile is provided for the user or user group associated with the media content descriptor set; and wherein the personality profile comprises a plurality of personality scores for elements of the profile that represent personality traits of the user or user group based on a personality scheme [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; 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 22:
Bach discloses:
wherein the user personality profile is generated by mapping features of the associated media content descriptor set to personality scores, the user personality profile characterizing a user’s personality or a user’s mood [Paragraph 0120 teaches generating a modified profile to conform the user’s current mood; Paragraph 0128 teaches collection profiles or DNA can be mapped to moods, which can be a combination of different features].
As to claim 23:
Bach discloses:
wherein the mapping of features of the media content descriptor set to personality scores is based on a mapping rule that defines how a personality score is computed from features of the media content descriptor set associated with the user [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 25:
Bach discloses:
wherein the search of best matching content descriptor sets depends on context or environment of the users or user groups associated with the media content descriptor sets [Paragraph 0112 teaches calculating a modified profile based on a specific day in a week, which will render different matching results; Paragraph 0118 teaches generating a user’s modified DNA based on a geographic location of the user, which will render different search results]; wherein a media item corresponding to the target profile is selected for presentation to the user or user group [Paragraph 0119 teaches the result will be the rendering of a media item]; and wherein an electronic message comprising information on the product or brand is automatically generated for the user or user group and the generated message electronically transmitted to the user or user group [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; Paragraph 0124 teaches the result of the matching operation is used to play the matching result for the user’s own enjoyment; Paragraph 0132 teaches user DNA can be applied to user groups to provide several unique services, such as automated music recommendation or self-pushing files in a peer-to-peer scenario].
As to claim 29:
Bach as modified by Pinilla discloses:
the personality traits include openness, conscientiousness, extraversion, agreeableness, and/or neuroticism [Paragraph 0016 teaches identifying personality traits including openness, conscientiousness, extroversion, agreeableness, and emotional range].
Claims 4 and 5 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 PINILLA et al. (U.S. Publication No. 2021/0365998) hereinafter Pinilla, and further in view of Fuzell-Casey et al. (U.S. Publication No. 2018/0139268) hereinafter Fuzell-Casey.
As to claim 4:
Bach discloses all the limitations as set forth in the rejections of claim 1 above, but does not appear to expressly disclose wherein a feature of a media content descriptor set for a media item is determined based on an artificial intelligence model that determines a semantic descriptor for the media item.
Fuzell-Casey discloses:
wherein a feature of a media content descriptor set for a media item is determined based on an artificial intelligence model that determines a semantic descriptor for the media item [Paragraph 0063 teaches a machine learning system, such as support vector machine, or other classifier, is trained to identify moods in songs by using rhythm, texture, and pitch; Paragraph 0064 teaches using the SVM to classify the songs].
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 a feature of a media content descriptor set for a media item based on an artificial intelligence model that determines a semantic descriptor for the media item, as taught by Fuzell-Casey [Paragraph 0063, 0064], because both applications are directed to characterization of media items for improved content identification; by incorporating an artificial intelligence model to determine features of the media items enables classify a large number of songs into specified moods classes in a more efficient way, allowing the user to perform further customizations of songs, and improving thereby the user’s experience (See Fuzell-Casey Para [0064]).
As to claim 5:
Bach discloses:
wherein a semantic descriptor comprises one of genres, voice presence, voice gender, musical moods, and rhythmic moods [Paragraph 0087 teaches features include genre,
subgenre, vocal detection, aggressiveness, mood, etc.].
Bach does not appear to expressly disclose wherein an emotional descriptor is determined by the artificial intelligence model.
Bach as modified by Fuzell-Casey discloses:
wherein an emotional descriptor is determined by the artificial intelligence model [Paragraph 0063 teaches a machine learning system, such as support vector machine, or other classifier, is trained to identify moods in songs].
Claims 8 and 24 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 PINILLA et al. (U.S. Publication No. 2021/0365998) hereinafter Pinilla, and further in view of Knight et al. (U.S. Publication No. 2018/0049688) hereinafter Knight.
As to claim 8:
Bach discloses all the limitations as set forth in the rejections of claim 1 above, but does not appear to expressly disclose wherein a mapping rule is learned by a machine learning technique.
Knight discloses:
wherein a mapping rule is learned by a machine learning technique [Paragraph 0184 teaches generating rules for identifying emotion of media, and training a mood model creator; Paragraph 0185 teaches mood model can be implemented as an artificial neural network; Paragraph 0213 teaches determining personality traits of a user based on emotions or mood of media, mood-based music features in a user playlist, and media access history, where this information can be stored in a user profile].
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 Knight [Paragraph 0184, 0185, 0213], because both applications are directed to characterization of media items for improved content identification; incorporating an artificial intelligence model enables the mood of media to be identified more quickly and accurately, which results in a reduction of processing and memory requirements of media recommendation systems, while improving the relevancy of the recommendations of media provided to the users (See Knight [0213], [0258]).
As to claim 24:
Bach discloses all the limitations as set forth in the rejections of claim 23 above, but does not appear to expressly disclose wherein the mapping rule is learned by a machine learning technique.
Knight discloses:
wherein the mapping rule is learned by a machine learning technique [Paragraph 0184 teaches generating rules for identifying emotion of media, and training a mood model creator; Paragraph 0185 teaches mood model can be implemented as an artificial neural network; Paragraph 0213 teaches determining personality traits of a user based on emotions or mood of media, mood-based music features in a user playlist, and media access history, where this information can be stored in a user profile].
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 Knight [Paragraph 0184, 0185, 0213], because both applications are directed to characterization of media items for improved content identification; incorporating an artificial intelligence model enables the mood of media to be identified more quickly and accurately, which results in a reduction of processing and memory requirements of media recommendation systems, while improving the relevancy of the recommendations of media provided to the users (See Knight [0213], [0258]).
Response to Arguments
The following is in response to arguments filed on September 5, 2025. Arguments have been carefully and respectfully considered.
Claim Rejections - 35 USC § 101
Applicant’s have been fully and respectfully considered, but are not persuasive.
In regards to claim 1, Applicant argues that “Amended independent claim 1 recites features that are performed by “at least one processor of a computing device” that is “communicatively coupled” with “a database.” Accordingly, the claimed features of amended independent claim 1 cannot be practically performed in a human mind or with the aid of pen and paper”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that adding a “computer-aided” limitation, computer components, or database recited at a high-level of generality without significantly more, to a claim covering an abstract concept, is insufficient to render a claim eligible where the claims are silent as to how the computer aids the method, the extent to which a computer aids the method, or the significance of the computer to the performance of the method, and amounts to merely saying “apply-it”.
In order for a machine 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 argues that “amended independent claim 1 recites features that are directed to further improve a personality profile or an emotional profile based upon analysis of characteristics of media content, which enables a variety of new applications including recommending similar media content to a user”, and further that “the database includes a catalogue having large quantity of media content, and going through each media content and the media content’s metadata searching for the most appropriate tag as claimed "searching ... from the plurality of media content descriptor sets, for at least one media content descriptor set having the best matching content descriptor set with respect to the target content descriptor set" is impossible to perform in a human mind as alleged by the Office due to sheer volume of the content to search”.
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. Based on the preceding argument, it appears that the improvement is related to “improve a personality profile”, and “which enables a variety of new applications including recommending similar media content to a user”. However, it is not clear what the technical improvement is, nor its correlation with the claim limitations.
Furthermore, and as mentioned above, merely including generic computer components recited at a high level of generality to perform the abstract idea, amounts to saying “apply-it”, and do not amount to significantly more. In order for a machine 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)). Moreover, “Requiring the use of software to tailor information and provide it to the user on a generic computer” is an example of computer components that function solely as an obvious mechanism for permitting a solution to be achieved more quickly, and do not amount to significantly more. Therefore, 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 § 102
Arguments have been carefully 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