1. A method for recommending appropriate music to a user, comprising the steps of:receiving input from the user including mood, activity, and musical preferences; processing the input using an artificial intelligence model to determine appropriate music recommendations; presenting the user with the recommended music; and adjusting the recommendations based on user feedback.

2. The method of claim 1, wherein the artificial intelligence model is trained using a machine learning algorithm.

3. The method of claim 1, wherein the artificial intelligence model considers the user's listening history.

4. The method of claim 1, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.

5. The method of claim 1, wherein the user interface provides an option for the user to adjust the recommended music manually.

6. A system for recommending appropriate music to a user, comprising:a computer processor is configured to:receive input from the user including mood, activity, and musical preferences; process the input using an artificial intelligence model to determine appropriate music recommendations; present the user with the recommended music; and adjusting the recommendations based on user feedback; and a user interface for displaying the recommended music and providing an option for the user to adjust the recommendations manually.

7. The system of claim 6, wherein the artificial intelligence model is trained using a machine learning algorithm.

8. The system of claim 6, wherein the artificial intelligence model considers the user's listening history.

9. The system of claim 7, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.

10. The system of claim 7, further comprising a feedback mechanism to improve the accuracy of the artificial intelligence model by receiving user feedback on the effectiveness of the recommended music and adjusting the model based on the feedback. ARTIFICIAL INTELLIGENCE BASED APPROPRIATE MUSIC RECOMMENDATION Abstract The current disclosure may further comprise a method for proposing suitable music to a user, wherein the user's mood, activities, and musical preferences are taken into account. In certain implementations, the input is run via an AI recommendation engine to find appropriate tunes. Providing the user with the suggested tunes is another possible embodiment. In certain embodiments, the suggestions may be modified after receiving input from users. , Claims:Claims :

1. A method for recommending appropriate music to a user, comprising the steps of:receiving input from the user including mood, activity, and musical preferences; processing the input using an artificial intelligence model to determine appropriate music recommendations; presenting the user with the recommended music; and adjusting the recommendations based on user feedback.

2. The method of claim 1, wherein the artificial intelligence model is trained using a machine learning algorithm.

3. The method of claim 1, wherein the artificial intelligence model considers the user's listening history.

4. The method of claim 1, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.

5. The method of claim 1, wherein the user interface provides an option for the user to adjust the recommended music manually.

6. A system for recommending appropriate music to a user, comprising:a computer processor is configured to:receive input from the user including mood, activity, and musical preferences; process the input using an artificial intelligence model to determine appropriate music recommendations; present the user with the recommended music; and adjusting the recommendations based on user feedback; and a user interface for displaying the recommended music and providing an option for the user to adjust the recommendations manually.

7. The system of claim 6, wherein the artificial intelligence model is trained using a machine learning algorithm.

8. The system of claim 6, wherein the artificial intelligence model considers the user's listening history.

9. The system of claim 7, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.

10. The system of claim 7, further comprising a feedback mechanism to improve the accuracy of the artificial intelligence model by receiving user feedback on the effectiveness of the recommended music and adjusting the model based on the feedback.

Description:ARTIFICIAL INTELLIGENCE BASED APPROPRIATE MUSIC RECOMMENDATION
Field of the Invention
[0001] The invention relates to the field of music recommendation systems, particularly to the use of artificial intelligence (AI) algorithms to recommend appropriate music based on individual preferences, mood, and context. The technology involves analyzing various factors such as music preference, mood, and activity to provide personalized music recommendations to users.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Artificial intelligence (AI) has revolutionized the way we interact with technology, and music is no exception. With the advent of AI, music recommendation systems have evolved significantly. Music recommendation systems are designed to suggest songs that match a user's listening habits and preferences, enabling users to discover new music that they may not have otherwise encountered. Music recommendation systems have been around for decades, but traditional methods of recommending music were based on simple metrics such as genre, artist, and album.
[0004] These systems did not consider the user's listening history, preferences, or contextual factors such as time of day or mood. With AI-based music recommendation systems, however, the focus has shifted towards more personalized recommendations, based on user-specific data. AI-based music recommendation systems use machine learning algorithms to analyse large volumes of data, including user listening histories, playlists, social media activity, and other contextual factors. By analysing this data, AI algorithms can identify patterns and preferences in a user's listening habits and generate personalized recommendations.
[0005] Some of the popular AI-based music recommendation systems currently in use “Spotify” that is one of the most popular music streaming platforms, and it uses AI-based recommendation algorithms to suggest new songs to users. The system considers factors such as user listening history, playlists, and social media activity to generate personalized recommendations. Similarly, “pandora” is a radio-style music streaming platform that uses AI algorithms to create personalized radio stations for users. The system analyses user preferences based on user feedback and generated new stations based on the user's preferences. “Apple Music” uses AI-based algorithms to recommend new music to users based on their listening history, playlists, and other contextual factors.
[0006] Amazon Music uses a combination of AI algorithms and user feedback to generate personalized recommendations for users. In addition to these major players, there are also several startups and smaller companies that are using AI-based algorithms to create personalized music recommendations. AI-based music recommendation systems can provide users with personalized recommendations based on their individual preferences and listening history.
[0007] AI-based music recommendation systems can help users discover new music that they may not have otherwise encountered. Improved User Experience - By providing users with personalized recommendations, AI-based music recommendation systems can improve the overall user experience. By providing users with personalized recommendations and helping them discover new music, AI-based music recommendation systems can increase user engagement and retention. Few prior arts are listed below.
[0008] WO2021168563A1 (By: LUCID) A method, system, and medium for affective music recommendation and composition. A listener's current affective state and target affective state are identified, and an audio stream, such as a music playlist, is generated with the intent of effecting a controlled trajectory of the listener's affective state from the current state to the target state. The audio stream is generated by a machine learning system trained using data from the listener and/or other users indicating the effectiveness of specific audio segments, or audio segments having specific features, in effecting the desired affective trajectory. The audio stream is presented to the user as an auditory stimulus. The machine learning system may be updated based on the affective state changes induced in the listener after exposure to the auditory stimulus. Over time, the machine learning system gains a robust understanding of the relationship between music and human affect, and thus the machine learning system may also be used to compose, master, and/or adapt music configured to induce specific affective responses in listeners.
[0009] US10055411B2 (By: IBM) A method and system implemented for generating a music recommendation intended to surprise and delight a user using data and computer analytics. The method and system collects and categorizes musical preferences using past preferences, user demographics, social media data and psychological variables which include the Big-5 personality traits of the user to generate a profile containing preferred musical parameters. The method and system categorize music based on different dimensions and compare music with a user generated profile containing preferred music parameters. The method and system may search for additional music across two vectors, parameters that closely resemble the preferred musical parameters, and outlying parameters that extend outside the preferred parameters, in order to locate and present musical recommendations that have similarities to the preferred music of the user, but variable enough to surprise and delight the user.
[00010] CA3169171A1 (By: LUCID) A method, system, and medium for affective music recommendation and composition. A listener's current affective state and target affective state are identified, and an audio stream, such as a music playlist, is generated with the intent of effecting a controlled trajectory of the listener's affective state from the current state to the target state. The audio stream is generated by a machine learning system trained using data from the listener and/or other users indicating the effectiveness of specific audio segments, or audio segments having specific features, in effecting the desired affective trajectory. The audio stream is presented to the user as an auditory stimulus. The machine learning system may be updated based on the affective state changes induced in the listener after exposure to the auditory stimulus. Over time, the machine learning system gains a robust understanding of the relationship between music and human affect, and thus the machine learning system may also be used to compose, master, and/or adapt music configured to induce specific affective responses in listeners.
[00011] AI-based music recommendation systems rely on user data to generate personalized recommendations. However, there are concerns about data privacy, and users may be hesitant to share their data with these systems. AI-based music recommendation systems can sometimes create a "filter bubble" where users are only exposed to music that fits within their existing preferences. This can lead to a lack of diversity in the music that users are exposed to. AI-based algorithms can sometimes create biased recommendations based on factors such as race, gender, and geography. There is a risk that AI-based music recommendation systems could perpetuate these biases. Thus, a further development in this field of technology is required.
Summary
[00012] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00013] The invention relates to the field of music recommendation systems, particularly to the use of artificial intelligence (AI) algorithms to recommend appropriate music based on individual preferences, mood, and context. The technology involves analyzing various factors such as music preference, mood, and activity to provide personalized music recommendations to users.
[00014] Embodiments of the present disclosure may include a method for recommending appropriate music to a user, including the steps of receiving input from the user including mood, activity, and musical preferences. Embodiments may also include processing the input using an artificial intelligence model to determine appropriate music recommendations. Embodiments may also include presenting the user with the recommended music. Embodiments may also include adjusting the recommendations based on user feedback.
[00015] In some embodiments, the artificial intelligence model may be trained using a machine learning algorithm. In some embodiments, the artificial intelligence model considers the user's listening history. In some embodiments, the recommended music may be selected based on a combination of mood, activity, and musical preferences. In some embodiments, the user interface provides an option for the user to adjust the recommended music manually.
[00016] Embodiments of the present disclosure may also include a system for recommending appropriate music to a user, wherein the system including a computer processor that may be configured to receive input from the user including mood, activity, and musical preferences. Embodiments may also include process the input using an artificial intelligence model to determine appropriate music recommendations. Embodiments may also include presenting the user with the recommended music. Embodiments may also include adjusting the recommendations based on user feedback. Embodiments may also include a user interface for displaying the recommended music and providing an option for the user to adjust the recommendations manually.
[00017] In some embodiments, the artificial intelligence model may be trained using a machine learning algorithm. In some embodiments, the recommended music may be selected based on a combination of mood, activity, and musical preferences. In some embodiments, the system may include a feedback mechanism to improve the accuracy of the artificial intelligence model by receiving user feedback on the effectiveness of the recommended music and adjusting the model based on the feedback. In some embodiments, the artificial intelligence model considers the user's listening history.
Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 is a flowchart illustrating a method for recommending appropriate music to a user, according to some embodiments of the present disclosure.
[00020] FIG. 2 is a block diagram illustrating a system for recommending appropriate music to a user, according to some embodiments of the present disclosure.
Detailed Description
[00021] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00022] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00023] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00024] The invention relates to the field of music recommendation systems, particularly to the use of artificial intelligence (AI) algorithms to recommend appropriate music based on individual preferences, mood, and context. The technology involves analyzing various factors such as music preference, mood, and activity to provide personalized music recommendations to users.
[00025] The flowchart that can be found in Figure 1 illustrates a method that can be used to recommend appropriate music to a user. The method illustrated in Figure 1, which is in accordance with particular applications of the present disclosure, can be found here. The method may, in some implementations, include the step of receiving information from the user at position 110. The user's preferences regarding their mood, activity, and musical tastes could be included in this input. The method may, at step 120, include the process of analysing the input by means of a model that is powered by artificial intelligence in order to come up with appropriate recommendations for musical pieces. In stage 130 of the method, the user can be presented with the music that was suggested for them to listen to. It is possible for the process to include, at step 140, the modification of the suggestions in accordance with the feedback obtained from users.
[00026] One of the many ways that the artificial intelligence model can be implemented is by training it with the assistance of a machine learning algorithm. In some implementations, the artificial intelligence model may be able to take into account the user's previous listening activity. In some implementations, the music that is recommended to the user may be selected based on a combination of their current mental state, the activity that they are participating in, and their musical preferences. The user interface of some implementations may include a setting that provides the user with the ability to directly modify the music that is suggested to them. This may be the case in some instances.
[00027] As a block diagram, the system 200 for recommending appropriate music to a user is depicted in FIG. 2, which also provides a description of the system in accordance with various aspects of the current disclosure. It's possible that the system 200 will have a computer processor 210 that's been programmed to carry out a specific set of operations in some implementations. The user interface 218, a musical preference 216, a mood 212, and an activity 214 are all possible components that could be included in the computer processor 210. The user interface 218 is the component that is in charge of displaying the recommended tracks and providing the user with the option to manually alter the recommendations. The computer processor 210 utilize a model that is driven by artificial intelligence to analyse the data that you provided in order to generate music recommendations that are pertinent to your needs. It would be helpful if you could give the user the music that was suggested. Changing the suggestions in light of the feedback received from users of the site.
[00028] An artificial intelligence model may, in some implementations, be educated with the help of a machine learning algorithm. This may occur in certain circumstances. It is possible that the music that is suggested to the user is selected based on a combination of their current state of mind, the activity that they are participating in, and their musical preferences. A feedback mechanism for the purpose of improving the accuracy of the artificial intelligence model might be included in the system 200 in some implementations. This would be done so that the model could learn from its mistakes. This is accomplished by first requesting feedback from users regarding the usefulness of the music that is recommended, and then, after receiving this feedback, modifying the model in accordance with the information that was gathered. The user's listening history might be taken into account by the artificial intelligence model in some implementations of the system.
[00029] The present invention relates to a system for recommending appropriate music to a user is designed to provide personalized music recommendations based on the user's mood, activity, and musical preferences. The system comprises a computer processor that is configured to receive input from the user, process the input using an artificial intelligence model, and present the user with the recommended music. The system also includes a user interface for displaying the recommended music and allowing the user to adjust the recommendations manually.
[00030] In an embodiment, the computer processor receive input from the user, which may include information about the user's current mood, activity, and musical preferences. The user may provide this input through a variety of means, such as selecting from a list of activities or moods, entering search terms, or selecting from a list of musical genres or artists. When a user interacts with the system, they may provide input regarding their current mood, activity, and musical preferences. The user may indicate their mood as being happy, sad, relaxed, or energetic, for example. The activity may range from working out to studying to relaxing, and the user may also indicate their preferred music genre or artists.
[00031] Referring to the preceding embodiment, additionally, the user may provide input about their current location, time of day, or weather conditions, which can also influence the music recommendations provided by the system. All this information is used by the system to make appropriate music recommendations that align with the user's current state and preferences. For instance, if a user indicates that they are in a happy mood and engaged in a workout, the system may recommend high-energy, upbeat music with a fast tempo to match the user's mood and activity. Conversely, if a user indicates that they are feeling sad and want to relax, the system may recommend slower, more calming music to help improve the user's mood. If a user indicates that they prefer classical music, the system may recommend pieces from composers such as Bach, Mozart, or Beethoven.
[00032] In an embodiment, the computer processor then processes this input using an artificial intelligence model to determine appropriate music recommendations. The model may consider a variety of factors, including the user's past listening habits, the popularity of the music, and the user's stated preferences. The model may also consider external factors, such as the time of day or weather conditions, in making its recommendations. For instance, the model may analyse the user's listening history to identify patterns in the

types of music they enjoy. If the user has listened to a lot of indie rock and alternative music in the past, the model may recommend similar artists or genres.
[00033] Referring to the preceding embodiment, the model may consider the popularity of certain songs or artists to make recommendations. For example, if a new hit song is trending on streaming platforms and has received positive reviews, the model may recommend it to the user. The model may consider the user's stated preferences, such as favourite genres or artists, to make recommendations. For example, if the user has indicated a preference for electronic dance music (EDM), the model may recommend EDM playlists or artists. The model may adjust its recommendations based on the time of day. For example, in the morning it may suggest upbeat and energetic music to help the user start their day, while in the evening it may suggest more relaxing and mellow music to help the user wind down. The model may also consider the weather conditions to make recommendations. For example, on a rainy day, the model may suggest soothing and relaxing music to complement the cosy and comfortable atmosphere.
[00034] In an embodiment, once the model has generated a list of recommended music, the system presents the user with the options, which may be sorted by relevance or popularity. The user interface may display information about each recommended song, such as the artist, album, and track title, as well as a preview of the song or a link to play the full song. For example, if the system recommends a particular type of music for a user's workout but the user finds it not energetic enough, the user can provide feedback indicating that the recommendation was not effective. The system can then use this feedback to adjust the artificial intelligence model to better account for the user's preferences and adjust its recommendations accordingly. Additionally, the system may also ask for explicit feedback from the user, such as a rating or thumbs up/down, for each recommended song or playlist. This feedback can then be used to train the artificial intelligence model to better understand the user's preferences and make more accurate recommendations in the future.
[00035] Referring to the preceding embodiment, the system also includes the ability to adjust the recommendations based on user feedback. For example, the user may indicate that they did not enjoy a particular song or that the recommended music does not match their current mood or activity. The system can use this feedback to adjust the recommendations and provide more accurate and relevant music suggestions over time. In yet another epitome of illustration, if the system recommends a playlist for a user's morning routine and the user thumbs up a particular song, the system can use this information to better understand the user's musical preferences for that time of day and adjust future recommendations accordingly.
[00036] Referring to the preceding embodiment, thus, the user interface allows the user to manually adjust the recommendations, such as by selecting specific artists or genres or creating custom playlists. The system can incorporate these manual adjustments into future recommendations, further personalizing the music suggestions for the user. the user has a particular affinity towards a specific genre of music, say classical music. However, the artificial intelligence model is not able to pick up on this preference from the user's past listening habits.
[00037] Referring to the preceding embodiment, the user interface can provide the user with the option to manually adjust the recommendations by selecting the "Classical" genre. The system can then incorporate this preference into future recommendations and suggest more classical music to the user.
[00038] Alternatively, the user may be in the mood for a specific artist or song that is not being recommended by the artificial intelligence model. In this case, the user interface can provide the user with the option to search for and add the desired artist or song to their playlist.
[00039] Referring to the preceding embodiment, the system can then learn from this manual adjustment and incorporate it into future recommendations for the user. Another way the user interface can allow for manual adjustments is by creating custom playlists. For example, if the user is planning to go for a run, they may want to create a playlist specifically for their workout. The user interface can provide the user with the option to create custom playlists and add or remove songs as they see fit. The system can then consider these custom playlists as a part of the user's listening habits and make recommendations based on the content of these playlists.
[00040] A method for recommending appropriate music to a user may be included among the embodiments of the present disclosure. This method may include the steps of receiving input from the user regarding their preferences regarding musical genres, activities, and moods. In other embodiments, the input data can be processed through the lens of an artificial intelligence model, which then generates recommendations for suitable musical works. It is possible that some embodiments will involve providing the user with the recommended music. Altering the recommendations according to the comments and suggestions of users is another possible embodiment.
[00041] There are a few different ways that the artificial intelligence model can be implemented, but one of them involves training it with a machine learning algorithm. The user's listening history is taken into consideration by the artificial intelligence model in some implementations. The recommended music can be chosen in some implementations according to a combination of the user's current state of mind, the activity they are engaged in, and their musical preferences. In some implementations, the user interface includes a setting that gives the user the ability to manually adjust the music that is recommended.
[00042] The present disclosure may also include embodiments of a system for recommending suitable music to a user. Such a system may comprise a computer processor that is configured to receive input from the user regarding their musical preferences, as well as their state of mind, activities, and types of music they enjoy listening to. Additionally, embodiments might involve processing the input through the lens of an artificial intelligence model in order to come up with relevant music recommendations. Presenting the user with the recommended music is another option that can be included in embodiments. Altering the recommendations according to the comments and suggestions of users is another possible embodiment. A user interface for displaying the recommended music and providing the option for the user to manually adjust the recommendations may also be included in some embodiments of the invention.
[00043] There are a few different ways that the artificial intelligence model can be implemented, but one of them involves training it with a machine learning algorithm. The recommended music can be chosen in some implementations according to a combination of the user's current state of mind, the activity they are engaged in, and their musical preferences. The system may, in some implementations, include a feedback mechanism for the purpose of improving the accuracy of the artificial intelligence model. This is accomplished by soliciting feedback from users regarding the usefulness of the music that is recommended and then modifying the model in accordance with that feedback. The user's listening history is taken into consideration by the artificial intelligence model in some implementations.
[00044] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be cconstrued as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00045] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00046] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00047] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00048] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A method for recommending appropriate music to a user, comprising the steps of:receiving input from the user including mood, activity, and musical preferences; processing the input using an artificial intelligence model to determine appropriate music recommendations; presenting the user with the recommended music; and adjusting the recommendations based on user feedback.
2. The method of claim 1, wherein the artificial intelligence model is trained using a machine learning algorithm.
3. The method of claim 1, wherein the artificial intelligence model considers the user's listening history.
4. The method of claim 1, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.
5. The method of claim 1, wherein the user interface provides an option for the user to adjust the recommended music manually.
6. A system for recommending appropriate music to a user, comprising:a computer processor is configured to:receive input from the user including mood, activity, and musical preferences; process the input using an artificial intelligence model to determine appropriate music recommendations; present the user with the recommended music; and adjusting the recommendations based on user feedback; and a user interface for displaying the recommended music and providing an option for the user to adjust the recommendations manually.
7. The system of claim 6, wherein the artificial intelligence model is trained using a machine learning algorithm.
8. The system of claim 6, wherein the artificial intelligence model considers the user's listening history.
9. The system of claim 7, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.
10. The system of claim 7, further comprising a feedback mechanism to improve the accuracy of the artificial intelligence model by receiving user feedback on the effectiveness of the recommended music and adjusting the model based on the feedback.

ARTIFICIAL INTELLIGENCE BASED APPROPRIATE MUSIC RECOMMENDATION
Abstract
The current disclosure may further comprise a method for proposing suitable music to a user, wherein the user's mood, activities, and musical preferences are taken into account. In certain implementations, the input is run via an AI recommendation engine to find appropriate tunes. Providing the user with the suggested tunes is another possible embodiment. In certain embodiments, the suggestions may be modified after receiving input from users. , Claims:Claims
I/We Claim:
1. A method for recommending appropriate music to a user, comprising the steps of:receiving input from the user including mood, activity, and musical preferences; processing the input using an artificial intelligence model to determine appropriate music recommendations; presenting the user with the recommended music; and adjusting the recommendations based on user feedback.
2. The method of claim 1, wherein the artificial intelligence model is trained using a machine learning algorithm.
3. The method of claim 1, wherein the artificial intelligence model considers the user's listening history.
4. The method of claim 1, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.
5. The method of claim 1, wherein the user interface provides an option for the user to adjust the recommended music manually.
6. A system for recommending appropriate music to a user, comprising:a computer processor is configured to:receive input from the user including mood, activity, and musical preferences; process the input using an artificial intelligence model to determine appropriate music recommendations; present the user with the recommended music; and adjusting the recommendations based on user feedback; and a user interface for displaying the recommended music and providing an option for the user to adjust the recommendations manually.
7. The system of claim 6, wherein the artificial intelligence model is trained using a machine learning algorithm.
8. The system of claim 6, wherein the artificial intelligence model considers the user's listening history.
9. The system of claim 7, wherein the recommended music is selected based on a combination of mood, activity, and musical preferences.
10. The system of claim 7, further comprising a feedback mechanism to improve the accuracy of the artificial intelligence model by receiving user feedback on the effectiveness of the recommended music and adjusting the model based on the feedback.