IDENTIFICATION OF USERS OR USER GROUPS BASED ON PERSONALITY PROFILES

Abstract

Identification of Users or User Groups Based on Personality Profiles The disclosure relates to a method for determining a user or user group. The method comprises obtaining an identification of one or more media items for a user or user group; obtaining a set of media content descriptors for each of the identified one or more media items, the set of media content descriptors comprising features including semantic descriptors for the respective media item, the semantic descriptors comprising at least one emotional descriptor for the respective media item; determining a set of aggregated media content descriptors for the entirety of the identified one or more media items based on the respective media content descriptors of the individual media items; and mapping the set of aggregated media content descriptors to a personality profile of the user or user group, wherein the personality profile comprises a plurality of personality scores for elements of the profile, the personality scores calculated from aggregated features of the set of aggregated media content descriptors; wherein a personality profile is determined for each of a plurality of users or user groups, the method further comprising: comparing the personality profiles of the plurality of users or user groups with a target personality profile and determining at least one user or user group having the best matching personality profile.

Claims

1. Method for determining a user or user group, comprising: obtaining an identification of one or more media items for a user or user group; obtaining a set of media content descriptors for each of the identified one or more media items, the set of media content descriptors comprising features including semantic descriptors for the respective media item, the semantic descriptors comprising at least one emotional descriptor for the respective media item; determining a set of aggregated media content descriptors for the entirety of the identified one or more media items based on the respective media content descriptors of the individual media items; and mapping the set of aggregated media content descriptors to a personality profile of the user or user group, wherein the personality profile comprises a plurality of personality scores for elements of the profile, the personality scores calculated from aggregated features of the set of aggregated media content descriptors; wherein a personality profile is determined for each of a plurality of users or user groups, the method further comprising: comparing the personality profiles of the plurality of users or user groups with a target personality profile and determining at least one user or user group having the best matching personality profile.

2. Method of claim 1, wherein the media items comprise musical portions and preferably are pieces of music that have been presented to a user or user group.

3. Method of claim 1, wherein the identification of one or more media items comprises a playlist of the user or user group.

4. Method of claim 1, wherein the identification of one or more media items comprises a short-term media consumption history of the user and the personality profile characterizes the current mood of the user.

5. Method of claim 1, 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.

6. Method of claim 1, wherein the set of media content descriptors for a media item is determined based on an artificial intelligence model that determines one or more semantic descriptors and/or emotional descriptors for the media item; wherein the one or more semantic descriptors comprise at least one of genres, voice presence, voice gender, vocal pitch, musical moods, and rhythmic moods.

7. (canceled)

8. Method of claim 1, wherein segments of a media item are analyzed and the set of media content descriptors for the media item is determined based on the results of the analysis for the segments; wherein the step of 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; wherein the step of determining a set of aggregated media content descriptors comprises calculating aggregated numerical features from respective numerical features of the identified media items; wherein the personality profile is based on a personality scheme that defines a number of personality scores for profile elements that represent personality traits.

9-11. (canceled)

12. Method of claim 1, wherein 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; wherein the mapping rule is learned by a machine learning technique.

13. (canceled)

14. Method of claim 1, wherein a personality score of the personality profile is determined based on weighted aggregated numerical features of the identified media items.

15. Method of claim 1, wherein a personality score of the personality profile is determined based on the presence or the absence of an aggregated feature of the identified media items.

16. Method of claim 1, wherein the comparing of profiles is based on matching profile elements and selecting personality profiles of users or user groups having same or similar elements as the target personality profile.

17. Method of claim 1, wherein the comparing of profiles 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; ranking the personality profiles of the users according to their matching scores.

18. (canceled)

19. Method of claim 1, wherein the comparing of profiles depends on the respective context or environment of the users or user groups.

20. Method of claim 1, wherein the target personality profile corresponds to a target user group or to a product or brand profile.

21. Method of claim 20, wherein the target personality profile is generated from a product or brand profile by mapping elements of the product or brand profile to personality scores of the personality profile; wherein a personality score of the target personality profile is determined based on a mapping rule that defines how the personality score is computed from the elements of the product or brand profile; wherein the mapping rule is learned by a machine learning technique.

22-23. (canceled)

24. Method of claim 1, wherein a media item corresponding to the target personality profile is selected for presentation to the at least one determined user or user group.

25. Method of claim 1, wherein an electronic message is automatically generated for the at least one determined user or user group and the generated message electronically transmitted to the user or user group; wherein the electronic message comprises information on a product or brand associated with the target personality profile.

26. (canceled)

27. Method of claim 1, wherein an identification of the at least one determined user or user group is transmitted to a database server; wherein the identified one or more media items correspond to recently consumed media items and the personality profiles of the users characterize the current mood of the users, and wherein the comparing the personality profiles of the users with a target personality profile is performed in real time; wherein the determining of the personality profiles and the comparing with the target profile is performed repeatedly, in particular after a number of media items have been provided to a user or user group.

28.-29. (canceled)

30. Computing device comprising a memory and a processor, configured to perform the method of claim 1.

Description

BRIEF DESCRIPTION OF FIGURES

[0034] Example embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings in which:

[0035] FIG. 1 schematically illustrates the operation of an embodiment of the present disclosure;

[0036] FIG. 2a illustrates the generation of semantic descriptors from audio files;

[0037] FIG. 2b illustrates the generation of semantic descriptors by an audio content analysis unit;

[0038] FIG. 3a illustrates the mapping of mood content descriptors to the E-I (extraversion—introversion) personality score of the MBTI personality scheme;

[0039] FIG. 3b illustrates the mapping of mood content descriptors to the openness personality score of the OCEAN personality scheme;

[0040] FIG. 4a illustrates an example for the graphical presentation of a personality profile of the MBTI personality scheme;

[0041] FIG. 4b illustrates an example for the graphical presentation of a personality profile of the OCEAN personality scheme;

[0042] FIG. 5 shows an embodiment for a method to determine a user or user group; and

[0043] FIG. 6 illustrates the mapping from attributes of a product profile to mood content descriptors.

DETAILED DESCRIPTION

[0044] According to a broad aspect of the present disclosure, characteristics of media items such as pieces of music are determined by a personality profiling engine for generating a personality profile or an emotional profile corresponding to the analyzed media items. This allows a variety of new applications (also called ‘use cases’ in this disclosure) to enable classification, search, recommendation and targeting of media items or media users. For example, personality profiles or emotional profiles may be employed for recommending similar media items or displaying advertising a media user might be interested in.

[0045] For example, if the input to the personality profiling engine is a short-term music listening history of a user, a personality profile characterizing the mood of the music listener can be determined from the recently played music of the user. If the input is a long-term music listening history, it is possible to determine the general personality profile of the music listener. One can even compute the difference between the long-term personality profile and the current mood of the user and determine if the user is in an exceptional situation.

[0046] The personality profile generated by the personality profiling engine allows to detect e.g. a music listener's emotional signature, focusing on the moods, feelings and values that define humans' multi-layered personalities. This allows addressing, e.g., the following questions: Is the listener self-aware or spiritual? Does he/she like exercising or travelling?

[0047] In an audio example, one can find similar sounding music tracks based on the emotional descriptors and/or semantic descriptors of an audio file. A media similarity engine using generated emotional profiles may leverage machine learning or artificial intelligence (AI) to match and find musically and/or emotionally similar tracks. Such media similarity engine can listen to and comprehend music in a similar way people do, then searches millions of music tracks for particular acoustic or emotional patterns, matching the requirements to find the music that is needed within seconds. Based on the generated profiles, one can search e.g. for instrumental or vocal tracks only, or according to other semantic criteria, such as genres, tempo, moods, or low- vs. high-pitched voice.

[0048] The basis for the proposed technology is the personality profiling engine that performs tagging of media items with media content descriptors based on audio analysis and/or artificial intelligence, e.g. deep learning algorithms, neural networks, etc. The personality profiling engine may leverage AI to enrich metadata, tagging media tracks with weighted moods, emotions and musical attributes such as genre, key and tempo (in beats per minute—bpm). The personality profiling engine may analyze moods, genres, acoustic attributes and contextual situations in media items (e.g. a music track (song)) and obtain weighted values for different “tags” within these categories. The personality profiling engine may analyze a media catalogue and tag each media item within the catalogue with corresponding metadata. Media items may be tagged with media content descriptors e.g. regarding [0049] acoustic attributes (bpm, key, energy . . . ); [0050] moods/rhythmic moods; [0051] genres; [0052] vocal attributes (instrumental, high-pitched voice, low-pitched voice); and [0053] contextual situation.

[0054] Within the moods category for tagging music from an “emotional” perspective, the personality profiling engine may output, for example, values for up to 35 “complex moods” which may be classified taxonomy-wise within 18 sub-families of moods that are structured into 6 main families. The 6 main families and 18 sub-families comprise all human emotions. The applied level of detail in the taxonomy of moods can be refined arbitrarily, i.e. the 35 “complex moods” can be further sub-divided if needed or further “complex moods” added.

[0055] FIG. 1 schematically illustrates the operation of an embodiment of the present disclosure, for generating personality profiles and determining similarities in profiles to make various recommendations such as for similar media items or matching users or user groups. A personality profiling engine 10 receives one or more media files 21 from a media database 20. For retrieving the media items from the database 20, the media files are identified in a media list 30 provided to the personality profiling engine 10. The media list 30 may be a playlist of a user retrieved from a playlist database that stores the most recent media items that a user has played and user-defined playlists that represent the user's media preferences.

[0056] The media files 21 are analyzed to determine media content descriptors 43 comprising acoustic descriptors, semantic descriptors and/or emotional descriptors for the audio content. Some media content descriptors 43 are determined by an audio content analysis unit 40 comprising an acoustic analysis unit 41 that analyses the acoustic characteristics of the audio content, e.g. by producing a frequency-domain representation such as a spectrogram of the audio content, and analyzing the time-frequency plane with methods to compute acoustic characteristics such as the tempo (bpm) or key. The spectrogram may be transformed according to a perspective and/or logarithmic scale, e.g. in the form of a Log-Mel-Spectrogram. Media content descriptors may be stored in a media content descriptor database 44.

[0057] The audio content analysis unit 40 of the personality profiling engine 10 further comprises an artificial intelligence unit 42 that uses an artificial intelligence model to determine media content descriptors 43 such as emotional descriptors and/or semantic descriptors for the audio content. The artificial intelligence unit 42 may operate on any appropriate representation of the audio content such as the time-domain representation, the frequency-domain representation of the audio content (e.g. a Log-Mel-Spectrogram as mentioned above) or intermediate features derived from the audio waveform and/or the frequency-domain representation as generated by the acoustic analysis unit 41. The artificial intelligence unit 42 may generate, e.g., mood descriptors for the audio content that characterize the musical and/or rhythmical moods of the audio content. These AI models may be trained on proprietary large-scale expert data.

[0058] FIG. 2a illustrates an example for the generation of semantic descriptors from audio files by an audio content analysis unit. In embodiments, the audio file samples are optionally segmented into chunks of audio and converted in to a frequency representation such as a Log-Mel-Spectrogram. The audio content analysis unit 40 then applies various audio analysis techniques to extract low and/or mid and/or high-level semantic descriptors from the spectrogram.

[0059] FIG. 2b further illustrates an example for the generation of semantic descriptors by the audio content analysis unit 40. While FIG. 2a illustrates a direct audio content analysis by traditional signal processing methods, FIG. 2b shows a neural-network powered audio content analysis, which has to learn from “groundtruth” data (“prior knowledge”) first. Audio files are converted to a spectrogram and one or more neural networks are applied to generate media content descriptors 43 such as moods, genres and situations for the audio file. The neural networks are trained for this task based on large-scale expert data (large and detailed “groundtruth” media annotations for supervised neural network training). In an example for the generation of semantic descriptors by the artificial intelligence unit 42, spectrogram data for audio files are fed as input to neural networks that generate, as output, semantic descriptors. In embodiments, one or more convolutional neural networks are used to generate e.g. descriptors for genres, rhythmic moods, voice family. Other network configurations and combinations of networks can be used as well.

[0060] A mapping unit 50 maps the media content descriptors 43 for the audio file to a media personality profile 61, by applying mapping rules 51 received from a mapping rule database 52. The mapping rules 51 may define which media content descriptor(s) is/are used for computing a profile score (i.e. the value for a profile attribute), and which weight to be applied to a media content descriptor. The mapping rules 51 may be represented as a matrix that link media content descriptors and profile attributes, and providing the media content descriptor weights. The generated personality profile 61 may be provided to the media similarity engine 70 for determining similar profiles, or stored in a profile database 60 for later usage.

[0061] In case a personality profile for a group of media items is generated, the media content descriptors 43 for the individual media items in the group are generated (or retrieved from the media content descriptor database 44) and aggregated media content descriptors are generated for the entire group of media items. Aggregation of numerical media content descriptors may be implemented by calculating the average value of the respective media content descriptor for the group of media items. Other aggregation algorithms such as Root-Mean-Square (RMS) may be used as well. The mapping unit 50 then operates on the aggregated media content descriptors (e.g. an emotional profile) and generates a personality profile for the entire group of media items.

[0062] The media similarity engine 70 can receive profiles directly from the personality profiling engine 10 or from the profile database 60, as shown in FIG. 1. The media similarity engine 70 compares profiles to determine similarities in profiles by matching profile elements or based on a similarity search as disclosed below. Once similar profiles 71 to a target profile are determined, corresponding media items or users may be determined and respective recommendations made. For example, one or more media items matching a playlist of a user may be determined and automatically played on the user's terminal device. Other use cases are set out in this disclosure.

[0063] As mentioned before, the personality profiling engine can use machine learning or deep learning techniques for determining emotional descriptors and semantic descriptors of media items. The training may be based on a database composed of a large number of data points in order to learn relations to analyze a person's music tastes and listening habits. The algorithm can retrieve the psych-emotional portrait of a user and complement existing demographic and behavioral statistics to create a complete and evolutive user profile. The output of the personality profiling engine is psychologically-motivated user profiles (“personality profiles”) for users from analyzing their music (playlists or listening history).

[0064] The personality profiling engine can derive the personality profile of a user from a smaller or larger number of media items. If based e.g. on the last 10 or more music items played by the user on a streaming service, the engine can compute a short term (“instant”) profile of the user (reflecting the “current mood of a music listener”). If (a larger number of) music items represent the longer-term listening history or favorite playlists of the user, the engine can compute the inherent personality profile of the user.

[0065] The personality profiling engine may use advanced machine learning and deep learning technologies to understand the meaningful content of music from the audio signal, looking beyond simple textual language and labels to achieve a human-like level of comparison. By capturing the musically essential information from the audio signal, algorithms can learn to understand rhythm, beats, styles, genres and moods in music. The generated profiles may be applied for music or video streaming service, digital or linear radio, advertising, product targeting, computer gaming, label, library, publisher, in-store music provider or sync agency, voice assistants/smart assistants, smart homes, etc.

[0066] The personality profiling engine may apply advanced deep learning technologies to understand the meaningful content of music from audio to achieve a human-like level of comparison. The algorithm can analyze and predict relevant moods, genres, contextual situations and other key attributes, and assign weighted relevancy scores (%).

[0067] The media similarity engine can be applied for recommendation, music targeting and audio-branding tasks. It can be used for music or video streaming service, digital or linear radio, fast-moving consumer goods (FMCG), also known as consumer-packaged goods (CPG), advertiser, creative agency, dating company, in-store music provider or in e-commerce.

[0068] The personality engine may be configured to generate a personality profile based on a group of media items associated with a user by performing the following method. In a first step, a group listing comprising an identification of one or more media items is obtained, e.g. in form of a playlist defined by a user. Next, a set of media content descriptors for each of the identified one or more media items of the group is generated or retrieved from a database of previously analyzed media items. The set of media content descriptors comprises at least one of: acoustic descriptors, semantic descriptors and emotional descriptors of the respective media item. The method then comprises determining a set of aggregated media content descriptors for the entire group of the identified one or more media items (i.e. the user's emotional profile) based on the respective media content descriptors of the individual media items. Finally, the set of aggregated media content descriptors is mapped to the personality profile for the group of media items. The scores of the profile elements are calculated from the aggregated features of the set of aggregated media content descriptors.

[0069] In example embodiments, the personality profiling engine is applied to determine the mood of a media user. For example, the mood of a music listener is determined based on the input: “short-term music listening history”; or the general personality profile of a music listener is determined from the input: long-term music listening history. In further use cases, a person's personality profile may be related to other person's personality profiles, to determine persons of similar profiles (e.g. matching people, recommending people with similar profiles products (e-commerce) or suggesting people to connect with other people (friending, dating, social networks . . . )) for that particular moment.

[0070] The personality profiling engine may further be used for adapting media items such as music (e.g. current playlist and/or suggestions or other forms of entertainment (film, . . . ) or environments such as smart home) a) to the person's current mood and/or b) with the intent to change the person's mood (intent either explicitly expressed by the person, or implicit change intent triggered by system, e.g. for product recommendation, or optimizing (increasing) a user's retention on a platform).

[0071] The personality profiling engine can be used to compute the difference between the long-term personality profile and the current (mood) profile of a user, in order to determine how different a user's current mood is from his/her general personality. This is useful, for example, for adapting a recommendation in the short-term “deviation” of the user's general personality profile into a certain musical direction (depending on a certain listening context, time of the day, user's mood etc.); and for determining the display of an advertising (ad) that would normally fit a user's personality profile but not in this moment because the current mood profile of the current listening situation deviates. In both cases the recommendation or the ad placement may adapt to the user's individual situation at the moment.

[0072] The basis for these embodiments is the personality profiling engine which analyses a group of media items identified by a provided list. For example, audio tracks in a group of music songs (from digital audio files) are analyzed. The analysis may be e.g. through the application of audio content analysis and/or machine learning (e.g. deep learning) methods. The personality profiling engine may apply: [0073] Algorithms for low-, mid- and high-level feature extraction from audio. Examples for low-level features are audio waveform/spectrogram related features (or “descriptors”), mid-level features (or “descriptors”) are “fluctuations”, “energy” etc. and high-level features are semantic descriptors and emotional descriptors like genres or moods or key). [0074] Acoustic waveform and spectrogram analysis to analyze acoustic attributes such as tempo (beats per minute), key, mode, duration, spectral energy, rhythm presence and the like. [0075] Neural Network/Deep learning based models to analyze from audio input (e.g. via log Mel-frequency spectrograms, extracted from various segments of an audio track), high-level descriptors such as genres, moods, rhythmic moods and voice presence (instrumental or vocal), and vocal attributes (e.g. low-pitched or high-pitched voice). The neural network/deep learning models may have been trained on a large-scale training dataset comprising (hundreds of) thousands of annotated examples of the aforementioned categories tagged by expert musicologists. For example, deep learning convolutional neural networks may be used but other types of neural networks (such as recurrent neural networks) or other machine learning approaches or any mix of those may be used as an alternative. In embodiments, one model is trained for each category group of moods, genres, rhythmic moods, voice presence/vocal attributes. An alternative is to train one common model altogether, or e.g. one model for moods and rhythmic moods together, or even one model per each mood or genre itself.

[0076] The audio analysis may be performed on several temporal positions of the audio file (e.g. 3 times 15 seconds for first, middle and last part of a song) or also on the full audio file.

[0077] The output may be stored on segment level or audio track (song) level (e.g. aggregated from segments). The subsequent procedures may also be applied on segment level (e.g. to get the list of moods (or mood scores) per each segment; e.g. applicable for longer audio recordings such as classical music, DJ mixes, or podcasts or in the case of audio tracks with changing genres or moods). The personality profiling engine may store all derived music content descriptors with the predicted values or % values in one or more databases for further use (see below).

[0078] The output of the audio content analysis are media (e.g. music) content descriptors (also named audio features or musical features) from the input audio such as: [0079] tempo: e.g. 135 bpm [0080] key and mode: e.g. F# minor [0081] spectral energy: e.g. 67% (100% is determined by the maximum on a catalog of tracks) [0082] rhythm presence: e.g. 55% (100% is determined by the maximum on a catalog of tracks) [0083] genres: as a list of categories (each with a % value between 0 and 100, independent of others), e.g. Pop 80%, New Wave 60%, Electro Pop 33%, Dance Pop 25% [0084] moods: as a list of moods contained in the music (each with a % value between 0 and 100, independent of others), e.g. Dreaming 70%, Cerebral 60%, Inspired 40%, Bitter 16% [0085] rhythmic moods: as a list of moods contained in the music (each with a % value between 0 and 100, independent of others), e.g. Flowing 67%, Lyrical 53% [0086] vocal attributes: either instrumental (0 or 100%), or any combination of low-pitched and/or high-pitched voice between 50 and 100%

[0087] In an embodiment, the audio content analysis outputs: [0088] from the audio feature extraction: 14 mid- and high-level features+52 low-level (spectral) features; and [0089] from the deep learning model: 67 genres, 35 moods (+24 through aggregation to sub-families and families, see below), 5 rhythmic moods, 3 vocal attributes.

[0090] Optionally, a subsequent post-processing on the values is performed, e.g. giving some of the genre, mood or other categories a higher or lower weight, by applying so-called adjustment factors. Adjustment factors adapt the machine-predicted values so that they become closer to human perception. The adjustment factors may be determined by experts (e.g. musicologists) or learned by machine learning; they may be defined by one factor per each semantic descriptor or emotional descriptor, or by a non-linear mapping from different machine-predicted values to adjusted output values.

[0091] Furthermore, optionally an aggregation may be performed of music content descriptors to create values for a group or “family” of music content descriptors, usually along a taxonomy: In an example, the 35 moods predicted by the deep learning model are aggregated to their 18 parent “sub-families” of moods and 6 “main families”, forming 59 moods in total (along a taxonomy of moods).

[0092] The analysis may be performed on song-level for a set of music songs, delivered in the form of audio (compressed or uncompressed, in various digital formats). For the generation of personality profiles, music content descriptors of multiple songs and their values may be aggregated for a group of multiple songs (usually referred to as “playlist”).

[0093] In some embodiments (use cases), the current mood of a listener is determined. In other use cases, the long-term personality profile of the listener is determined by the personality profiling engine. In both cases, the input is a list of music songs and the output is a user's personality profile (along one or more personality profile schemes). In order to determine the mood of a music listener, the input is the last few recently listened songs. These songs allow to get an idea of the current mood profile of the user. For determining the general (long-term) personality profile of a music listener, the input is (usually a larger set of) songs that represent the (longer-term) history of the user.

[0094] The generation of personality profiles may be based on characteristics of the music a user listens to, comprising for example (but not limited to): moods, genres, voice presence, vocal attributes, key, bpm, energy and other acoustic attributes (=“musical content descriptors”, “audio features” or “music features”). This may be determined per each song's music content characteristics.

[0095] In embodiments, an aggregation is done from n songs' music content descriptors to aggregated content descriptors i.e. an emotional profile of a user, e.g. as an average of the numeric (%) values of each of the songs in the set (playlist), or applying more complex aggregation procedures, such as median, geometric mean, RMS (root mean square) or various forms of weighted means.

[0096] In embodiments, songs in a user's playlist or a user's listening history may have been pre-analyzed to extract the music content descriptors, which may contain numeric values (e.g. in the range of 0-100% for each value). For each content descriptor (e.g. mood “sensibility”), the root mean squared (RMS) of all the individual songs' “sensibility” values may be computed and stored. The output of this aggregation will be a set of music content descriptors having the same number of descriptors (attributes) as each song has. This aggregated music content descriptor (emotional profile) will be used in the second stage of the personality profile engine to determine the user's personality profile.

[0097] Once the aggregated value for each music content descriptor has been calculated, a personality profile is generated. For example, a mapping is performed from the elements in the emotional profile (which represent music content descriptors aggregated for n songs) to one or more personality profile(s). The mapping translates moods, genres, style, etc. to psych-emotional user characteristics (personality traits). The mapping is performed from said musical content descriptors to the scores of the personality profile (including personality traits/human characteristics). Rules may be defined to map from music content descriptors and their values to one or more types of personality profiles defined by personality profile schemes.

[0098] The output of the personality profile engine is a range of numeric output parameters, called personality profile attributes and scores, describing the personality profile of a user.

[0099] A personality profile may be defined according to various personality profile schemes such as: [0100] MBTI (Myers-Briggs type indicator) [0101] Ego Equilibrium [0102] OCEAN (also known as Big Five personality traits) [0103] Enneagram

[0104] Each of these personality profile schemes is composed by personality attributes, for instance “extraversion” or “openness” and assigned scores (values) such as 51% or 88% (concrete examples are given below).

[0105] For all of these schemes, a mapping from music content descriptors to profile scores and vice versa may be used. FIG. 3a illustrates the mapping of mood content descriptors to the EI personality score of the MBTI personality scheme.

[0106] The mapping may apply a matrix like in the example shown in FIG. 3a. Either the presence (% of a mood or other music content descriptor) or the absence (100−% of the mood or other music content descriptor) may be relevant to compute a score (value) within a personality profile scheme.

[0107] Each scheme can have a number of “scores” that it computes, e.g. MBTI scheme computes 4 scores: EI, SN, TF, JP. For each score, one or more mapping rules may be defined, which affect how the score will be computed from the aggregated music content descriptors. For example, the score is equal to the sum of the values computed by the matrix divided by the number of values taken into account (i.e. a regular averaging mechanism).

[0108] For instance, the mood (comprised in the music content descriptors) “Withdrawal” is used in the EI calculation as part of the MBTI scheme. FIG. 3a illustrates an example for a rule matrix applied for the EI calculation from the moods section of the music content descriptors. The rule matrix shows how the presence of a mood or its absence can be used for calculating the EI score. Other music content descriptors may be included in the calculation in a similar manner.

[0109] In embodiments, the EI calculation comprises 17 rules incorporating 17 values from the music content descriptors. These rules follow psychological recipes, e.g. the rules within the group of “metal” define psychologically “closed shoulders”, while the rules within the group “wood” define “open shoulders”.

[0110] Similar computations may be made for other profiling matrixes, like OCEAN.

[0111] As mentioned, an MBTI personality profile has the following scores: EI, TF, JP, SN. Below is an example of representation of a MBTI personality profile and its scores:

TABLE-US-00001 “mbti”:{“name”:“INTJ”,“sources”:{ “EI”: 33.66403316629877, “SN”: 42.419498057065084, “TF”: 57.82423612828757, “JP”: 61.02633025243475}}

[0112] Depending on the score value, a basic score classification may be made. The classification may be based on comparing score values with specific threshold values. For example, the EI score in the MBTI scheme represents the balance between extraversion (E) and introversion (I) of the user. EI below 50% means introversion, while EI above 50% means extraversion. Thus, if EI<50% a user may be assigned to the I (introversion) class, otherwise he is assigned to the E (extraversion) class. The other MBTI scores may be classified in a similar way.

[0113] The scores are defined as opposites on each axis, (E-I, S-N, T-F, J-P). In each pair of letters, the value determines which side of the trait the person is, decided by <50% or >50%. To deduct the letters from above example, usually for <50% the right letter of a letter pair is taken, for =>50% the left letter.

[0114] The results of scores for a generated profile may be further classified in general personality types, e.g. based on the basic classification results for the profile scores. For example, the following general personality types may be derived from the basic score classification results: [0115] ESTJ: extraversion (E), sensing (S), thinking (T), judgment (J) [0116] INFP: introversion (I), intuition (N), feeling (F), perception (P)

[0117] The profile in above example is classified as INTJ personality type. The classification of the 4-dimensional space of profile scores (EI, TF, JP, SN) into personality types allows a 2-dimensional arrangement of the personality traits in squares having a meaningful representation.

[0118] FIG. 4a shows a graphical representation of a personality profile according to the MBTI scheme where the classification result (INTJ) for a user's profile can be indicated in color. This diagram provides for an intuitive representation of the user's profile along the different psychological dimensions. A person classified as “INTJ” is interpreted as a “Mastermind, Scientist”. Additional personality traits associated with this MBTI type may be output on the user interface.

[0119] In the OCEAN personality profile scheme, the following scores for the “Big Five” mindsets are defined: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism. FIG. 3b illustrates the mapping of mood content descriptors to the openness personality score of the OCEAN personality scheme. Here is an example of a representation of an OCEAN personality profile and its scores:

TABLE-US-00002 “ocean”:{ “agreeableness”: 51.10149671582637, “conscientiousness”: 73.42223321884429, “extraversion”: 33.66403316629877, “neuroticism”: 50.21693055551433, “openness”: 39.72017677623826}

[0120] FIG. 4b shows a graphical representation of a personality profile according to the OCEAN scheme. This diagram provides for an intuitive representation of the user's profile along the different psychological dimensions.

[0121] In some embodiments, the personality profile can optionally be enriched or associated with additional person-related parameters characterizing from additional sources (e.g. age, sex and/or biological signals of the human body via body sensors (smart watch, sports tracking devices, emotion sensors, etc.)). Optionally the personality profile can also be enriched or associated with additional parameters characterizing the context and environment of the person (location, day of time, weather, other people in the vicinity).

[0122] In embodiments, the personality profiling engine and the media similarity engine are configured to determine a user or group of users for a specific target personality profile and select the best matching user (user group) for the target profile. The personality profiling engine may analyze one or more media items associated with a user or user group for its content in terms of acoustical attributes, genres, styles, moods, etc. It then generates a description of the user or user group (in the form of a personality profile). After a personality profile is determined for each of a plurality of users or user groups, the personality profiling engine compares the personality profiles of the plurality of users or user groups with the target personality profile and determines at least one user or user group having the best matching personality profile(s) with regards to the target profile. The target profile may be specified by a personality profile following a personality profile scheme such as MBTI, OCEAN, Enneagram, Ego-Equilibrium, or others, similar to the definition of user profiles. The profile may optionally be enriched by person-related parameters (such as age, sex, etc.).

[0123] In more detail, the audio in a set of music songs associated with a user is analyzed to derive its music content descriptors including semantic descriptors and/or emotional descriptors. Optionally, aggregation of said descriptors (using different methods) for a number of tracks (which can represent an album or an artist) is performed and the user's emotional profile is determined, e.g. by computing the average of the moods and/or other descriptors of multiple songs (possibilities: mean, RMS or weighted average, etc.). Then a mapping is performed from musical content descriptors to a personality profile as described above. The system then outputs and stores profiles for a plurality of users, defined by one of the different personality profile schemes. The profiles may be provided in numeric form, e.g. floating-point numbers for different profile scores within the mentioned schemes.

[0124] The personality profiles for users or user groups are generated as disclosed above and specified by one or more personality profiles following schemes such as MBTI, OCEAN, Enneagram, Ego-Equilibrium, or others, as described above. In addition. demographic parameters for the users may be added.

[0125] A search (e.g. similarity search, or exact score matching) can be performed in the personality profiles space between the target profile and personality profiles for each individual user (or user group). Then, the personality profiles that best match the target personality profile are identified. In that respect, the personality profile scores for different personality profile schemes may be pre-computed for the users. The best match for a target profile is then found by a similarity search between the defined target profile scores and each user's profile scores. Different options for the similarity search will be described next.

[0126] The term “similarity search” shall comprise a range of mechanisms for searching large spaces of objects (here profiles) based on the similarity between any pair of objects (e.g. profiles). Nearest neighbor search and range queries are examples of similarity search. The similarity search may rely upon the mathematical notion of metric space, which allows the construction of efficient index structures in order to achieve scalability in the search domain. Alternatively, non-metric spaces, such as Kullback-Leibler divergence or Embeddings learned e.g. by neural networks may be used in the similarity search. Nearest neighbor search is a form of proximity search and can be expressed as an optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the dissimilarity function values. In the present case, the (dis)similarity of profiles is the metric for the search.

[0127] The search for the best matching user for a target profile may be performed in the personality profiles space by comparing the target profile with the personality profiles of the users, or in the content descriptor set space by comparing a target content descriptor set with media content descriptor sets corresponding to users. In the latter case, the target content descriptor set may be derived from the target profile or from a product or brand profile.

[0128] For the comparison of profiles, this search may be performed by: [0129] matching of elements of the profiles (depending on which elements of a profile are present or not); [0130] matching of values of attributes (scores) of the profiles (numeric search); [0131] searching ranges of such values (e.g. score “Respect” is between 75% and 100%); [0132] vector-based matching and similarity computation: computing how “close” (similar in terms of numeric distance) values of a target profile and a personality profile are, by comparing the elements of their numeric profiles (e.g. using a distance measure, such as Euclidean distance, Manhattan distance, Cosine distance, or other methods such as Kullback-Leibler divergence, etc.); [0133] machine learning based learned similarity, where a machine or deep learning algorithm learns a similarity function based on examples provided to the algorithm; this learned similarity function can then be permanently used in an embodiment.

[0134] In embodiments, the media similarity engine may use one or more of a user's personality profile, the user's current situation or context and the current mood of the user for searching the user with the best match to the target profile. For example, a user's listening history is analyzed by the personality profiling engine, as described above. In this way, the user's personality profile and/or the emotional profile of a music listener (including his/her mood) is determined. Next, the media similarity engine may be configured to determine and find users best fitting the target profile, based on the person's (long-term) personal music listening history and/or personality profile and/or (short-term) mood profile and/or personality profile, a weighted mix between short-term and long-term personality profile, and optionally user context and environment information. The context and environment of the person can be determined by other numeric factors, e.g. measured from a mobile or other personal user device where location data, weather data, movement data, body signal data etc. can be derived. This may be performed instantly, during a user is listening in a listening session. For this, the users' personality profiles, generated via mapping from media content descriptor sets as explained above, are compared with the target profile. For example, a similarity search is performed between the user's personality profiles and the target profile, and thus the best matching user profiles (and corresponding users) are determined (and possibly ranked according to their matching score).

[0135] An embodiment of a method 100 to determine a user or user group that matches a target personality profile is shown in FIG. 5. The method starts in step 110 with obtaining, for a user or user group, an identification of a group of media items comprising one or more media items. The identification of the group of media items may be a playlist or media consumption history of the user or user group.

[0136] For example, the identification of one or more media items comprises a short-term media consumption history of the user (or user group) and the personality profile characterizes the current mood of the user (or user group). A set of media content descriptors for each of the identified one or more media items is obtained in step 120. The media content descriptors comprise features characterizing acoustic descriptors, semantic descriptors and/or emotional descriptors of the respective media item and may be calculated directly from the media item or retrieved from a database. Details on the generation of media content descriptors are provided above.

[0137] A set of aggregated media content descriptors for the entire group of the identified one or more media items is determined in step 130 based on the respective media content descriptors of the individual media items. For example, if the one or more identified media items correspond to a playlist, a set of aggregated media content descriptors is determined for the playlist. If only one media item is identified, the set of aggregated media content descriptors may be determined from segments of the media item. In step 140 the set of aggregated media content descriptors (e.g. a user's emotional profile) is then mapped to a personality profile that is defined according to a personality scheme as explained above. The mapping may be based on mapping rules. The generated personality profile of the media items for the user (or user group) is provided to the media similarity engine in step 150. The above process is repeated for a plurality of users or user groups and personality profiles are generated for each further user or user group. This way a plurality of personality profiles is generated, each associated with its corresponding user or user group and characterizing the user or user group in terms of the applied personality scheme.

[0138] In step 160 the personality profiles of the users or user groups are compared with a target personality profile and at least one user or user group having the best matching personality profile is determined. The target personality profile corresponds to a target user group or to a product or brand profile. The at least one user or user group having the best matching personality profile is/are selected in step 170. In step 180 a new media item corresponding to the target personality profile is selected for presentation to the at least one determined user or user group. For example, an electronic message comprising the new media item is automatically generated for the at least one determined user or user group and the generated message electronically transmitted to the user or user group. The electronic message (or the new media item) may comprise information on the product or brand associated with the target personality profile.

[0139] In an embodiment, the media similarity engine is configured to select an individual (user) by matching his/her personality profile with a product or a personality profile of a target group. In this embodiment, an advertising customer or brand that uses the disclosed system first defines a target group by setting the score values within a certain personality profile scheme (schemes such as MBTI, OCEAN, Enneagram, Ego-Equilibrium, or others) or defines a brand/product profile with attributes of a brand or product that describe it in a psychological, emotional or marketing-like way, thereby providing a target “personality profile”.

[0140] The system may have already (pre-)analyzed some users' music tastes (listening history, favorite tracks/albums/artists) to profile the users. The media similarity engine then finds individuals that match the given product profile or profile of a target group. The output is a list of users (e.g. by user IDs) fitting a brand/product profile or specified target group. The identified individuals can then be targeted with specific advertisements.

[0141] In embodiments, the selection of an individual (user) by matching his/her personality profile with a personality profile of a target group is based on the definition of a target group: a target group may be defined by setting the values of a target profile within a certain personality profile scheme (such as MBTI, OCEAN, Enneagram, Ego-Equilibrium, or others).

[0142] The mapping of a target group to individuals may be based on a similarity search of the (defined) personality profile of the target group to the personality profiles of a set of users (e.g. pre-computed, determined based on their listening habits). Comparing profiles by similarity search and generation of matching scores has been explained above. Individuals corresponding to the personality profiles may be ranked based on the matching scores of their profiles. A threshold for the matching score may be applied to select the best matching group of individuals.

[0143] In other embodiments, the selection of an individual (user) by matching his/her personality profile with a product profile may be based on the definition of a product profile: an advertising customer or a brand customer defines which kind of emotions they provide with each product in each advertisement.

[0144] In an example for generating a product profile, marketing experts define product attributes and values, similarly as a target group is specified with the attributes in a personality profile (e.g. MBTI attributes and % values (“scores”)). Thus, a product profile may comprise, like a personality profile, attributes and scores defined by % values. These product attributes may be grouped into different groups. E.g. in an embodiment, 3 such groups (also called “appraisals”) are “Evocation of the brand”, “Symbolic of the product” and “Use of the product”. Each of the 3 groups may have the same or different elements (attributes), and a product profile is defined by setting % values for those attributes.

[0145] In an example embodiment, each of the 3 groups (appraisals) can be defined by one of a number of terms (e.g. “25 positive emotions” commonly used in marketing), and assigning a % value to it: sympathy, kindness, respect, love, admiration, dreaminess, lust, desire, worship, euphoria, joy, amusement, hope, anticipation, surprise, energized, courage, pride, confidence, inspiration, enchantment, fascination, relief, relaxation, satisfaction.

[0146] In another embodiment, only the attribute terms associated with the product and no associated values are defined. A choice of one word in each of the 3 appraisal groups forming the product profile will allow to define the corresponding musical content descriptors (in an embodiment mainly moods) needed to fit the product's target group. For example, finding individuals to advertise a new Harley Davidson motorbike could be performed by defining the following 3 attributes (one per appraisal group, respectively): [0147] Evocation of the brand: respect [0148] Symbolic of the product: amusement [0149] Use of the product: satisfaction

[0150] There are two ways for the mapping of a product profile to individuals' personality profiles: [0151] a) Application of mapping rules from the attributes and scores that define a product profile to a personality profile (such as MBTI, etc.). This allows to derive a target personality profile that is compared to the (pre-computed) personality profiles of individuals using a similarity search as explained above. The mapping rules from a product profile to personality profile elements may be manually defined or learned by a machine learning algorithm similar to the mapping rules from content descriptors to personality profile elements as disclosed above. [0152] b) Mapping from defined product profile to corresponding music content descriptors by specifying a set of mapping rules. Emotional profiles of users (individuals) or user groups are computed by aggregation of musical content descriptors as described before. A similarity search is performed in the space of music content descriptors in order to find the best-matching users or user groups based on their (ad hoc or pre-computed) emotional profiles which are represented by values within the music content descriptor space.

[0153] FIG. 6 illustrates the mapping from attributes of a product profile to mood content descriptors. It shows rules for mapping the product attribute “Sympathy” to moods such as “sentimental”, “cool” and “friendly”, etc. In this example, the attribute “Sympathy” requires the moods “sentimental” and “innocent” to be near 50%, while “cool”, “friendly” and “warm-hearted” are required to be close to 100%. There are similar but different rules for other appraisals (groups of product attributes). In a similar way, the relations between product attributes “Kindness” and “Respect” and mood content descriptors are shown.

[0154] Thus, only user (or user group) emotional profiles with those mood criteria closely fulfilled will be considered candidates for a match. Depending on the similarity approach chosen, a closer numerical match will lead to a higher relevance score in the output of matching users (or user groups).

[0155] Similar to the mapping from content descriptors to personality profiles explained above, mapping rules define how a (aggregated) media content descriptor can be used to search for matching users. A mapping rule defines which and how a product profile attribute and its value contributes to a media content descriptor. Again, a mapping rule may be learned by a machine learning technique.

[0156] Using the personality profile engine described above, the system is able to build a database of users which can be searched by emotional profile or by personality profile (both derived from music content descriptors, e.g. moods). Once the system determined the product profile, finding the list of users with a personality profile aligned with a product profile can be performed by mapping or similarity search as described above.

[0157] When the best matching users have been identified, an advertisement may be pushed first to users best-aligned with the product profile or target group of the brand, respectively. The system may output the list of identified users to target, e.g. by user identifier plus a matching score value of how well that user fits the brand or product.

[0158] In an embodiment, the media similarity engine is configured for real-time selection of an individual by matching his/her current mood with a product profile or a personality profile of a target group. In this case, a brand defines a target group by a target personality profile. The target group may be defined by personality profile schemes such as MBTI, OCEAN, Enneagram, Ego-Equilibrium, or others. The system finds individuals having a short-term personality profile (a.k.a. “momentary user mood profile”) at this moment that fits the given target profile. The brand can then target the individuals with specific advertisement.

[0159] In this embodiment, the system analyses in real-time the current user's media consumption (e.g. the last 10 music tracks) to profile the user at this moment and assign him/her into a target group. The definition of a brand's target group or a product profile and the mapping to music and persons (listeners) is done in the same way as described above. An advertisement is pushed to users aligned with the target group of the brand/product. While listening to music, a person is selected to receive individually targeted advertising or exposed to a particular branding or e-commerce campaign that best matches the current short-term personality profile (a.k.a. momentary user mood profile) of the person.

[0160] In this scenario, the user profiles are computed in “real-time”, meaning using only a small number (e.g. 10) of the last tracks that the user has listened to. Using the personality profiling engine described above, the system computes a user profile in a recent short-term timeframe. By doing this computation for all the users, the system stores in a database on a regular basis (e.g. every 10 tracks listened) all the “real-time” user's profiles. Once the system knows the product or brand profile (as described above), finding a group of users with a profile aligned to the product/brand profile can be done as described above. The system outputs the list of users that the brand should target now, because of the mood alignment between the brand or product and the user.

[0161] It should be noted that the apparatus (device, system) features described above correspond to respective method features that may however not be explicitly described, for reasons of conciseness. The disclosure of the present document is considered to extend also to such method features. In particular, the present disclosure is understood to relate to methods of operating the devices described above, and/or to providing and/or arranging respective elements of these devices.

[0162] It should also to be noted that the disclosed example embodiments can be implemented in many ways using hardware and/or software configurations. For example, the disclosed embodiments may be implemented using dedicated hardware and/or hardware in association with software executable thereon. The components and/or elements in the figures are examples only and do not limit the scope of use or functionality of any hardware, software in combination with hardware, firmware, embedded logic component, or a combination of two or more such components implementing particular embodiments of this disclosure.

[0163] It should further be noted that the description and drawings merely illustrate the principles of the present disclosure. Those skilled in the art will be able to implement various arrangements that, although not explicitly described or shown herein, embody the principles of the present disclosure and are included within its spirit and scope. Furthermore, all examples and embodiment outlined in the present disclosure are principally intended expressly to be only for explanatory purposes to help the reader in understanding the principles of the proposed method. Furthermore, all statements herein providing principles, aspects, and embodiments of the present disclosure, as well as specific examples thereof, are intended to encompass equivalents thereof.

Glossary

[0164] The following terminology is used throughout the present document.

Media

[0165] Media comprises all types of media items that can be presented to a user such as audio (in particular music) and video (including an incorporated audio track). Further, pictures, series of pictures, slides and graphical representations are examples of media items.

Media Content Descriptors

[0166] Media content descriptors (a.k.a. “features”) are computed by analyzing the content of media items. Music content descriptors (a.k.a. “music features”) are computed by analyzing digital audio—either segments (excerpts) of a song or the entirety of a song. They are organized into music content descriptor sets, which comprise moods, genres, situations, acoustic attributes (key, tempo, energy, etc.), voice attributes (voice presence, voice family, voice gender (low- or high-pitched voice)), etc. Each of them comprises a range of descriptors or features. A feature is defined by a name and either a floating point or % value (e.g. bpm: 128.0, energy: 100%).

Music

[0167] Music is one example for a media item and refers to audio data comprising tones or sounds, occurring in single line (melody) or multiple lines (harmony), and sounded by one or more voices or instruments, or both. A media content descriptor for a music item is also called a music content descriptor or musical profile.

Emotional Profile

[0168] An emotional profile comprises one or more sets of media or music content descriptors related to moods or emotions and can be determined for a number of media items, in which case they are the aggregation of the content descriptors of the individual media items. They are typically derived by aggregating media/music content descriptors from a set of media items related to (e.g. consumed by) the persons or individuals. They comprise the same elements as the media/music content descriptors with the values determined by the aggregation of individual content descriptors (depending on the aggregation method used).

Person (User, Individual): Emotional Profile and Personality Profile

[0169] A person (also called user or individual) is characterized by an emotional profile or a personality profile. An emotional profile is characterized by the elements of the media content descriptors (see above). Whereas, a personality profile comprises a number of different elements with % values: A personality profile's element is a weighted element within a personality profile scheme (defined by a name or attribute and % value, e.g. MBTI: “EI: 51%”). Personality profiles are defined by a personality profile scheme such as MBTI, OCEAN, Enneagram, etc. and may relate to: [0170] a user's mood (instant, short term)—i.e. a personality profile interpreted as a short-term emotional status of the user (also called mood profile of the user); or [0171] the user's personality type (long-term)—i.e. a personality profile derived from a long-term observation of the user's media consumption behavior.

Target Group

[0172] A target group describes a group of persons. It is specified as one or a combination of “personality profile(s)”. Optionally, it may be enriched by person-related parameters (such as age, sex, etc.).

Product

[0173] A product profile comprises attributes of a product that describe it in a psychological, emotional or marketing-like way. Attributes may be associated with a % value of importance.

Brand

[0174] Product profiles may relate to brands. A brand profile comprises attributes of a brand that describe it in a psychological, emotional or marketing-like way. Attributes may be associated with a % value of importance.

Mapping

[0175] Mapping refers to a set of rules that are implemented algorithmically and transform a profile from one entity (e.g. media item, music) to another (e.g. person, product, or brand) (or vice-versa). For example, mapping is applied between a set of content descriptors (emotional profile) and a personality profile according to a personality profile scheme.

Similarity Search

[0176] A similarity search is an algorithmic procedure that computes a similarity, proximity or distance between two or more “profiles” of any kind (emotional profiles, personality profiles, product profiles etc.). The output is a ranked list of profile items having matching scores: a value that indicates of how well the profiles match.