System

Abstract

A system is provided.SOLUTION: A system comprising: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression to determine the mood of the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, and location information; means for playing back the generated music playlist; and means for controlling music by voice commands and touch operations.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Modern in-car music entertainment systems are unable to take into account various factors such as the user's preferences, mood of the day, current weather, and location information. As a result, users must manually select music, which may compromise driving safety. Furthermore, pre-prepared playlists are not individually optimized, which can lead to reduced user satisfaction. A system that solves these issues and makes the user's driving experience more personalized and safer is needed. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression to determine the user's mood for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, and location information; means for playing the generated music playlist; and means for controlling music with voice commands and touch operations. Furthermore, the system further includes means for updating a user profile based on the user's emotional data and environmental data, and means for displaying the generated music playlist on an in-vehicle system, thereby improving safety and the music experience while driving.

[0006] "User's music preferences" refers to a particular user's personal preferences for music genres, artists, styles of music, and the like.

[0007] "Past play history" refers to a record of music that a user has previously played, including the number of times a particular song has been played and its rating.

[0008] "Real-time data" refers to data that is obtained instantly based on the current time, and includes changing information such as weather, location information, and traffic volume.

[0009] "Weather" refers to the current weather conditions, including the state of the natural environment, such as sunny, rainy, snowy, and windy.

[0010] "Location Information" refers to a user's current geographic location, including data obtained through GPS or other location acquisition means.

[0011] "Traffic volume" refers to the volume or volume of vehicle traffic on a particular road or area, including congestion and congestion.

[0012] "User's voice and facial expression" refers to voice and facial expression, and is data used to determine the user's emotions and mood by analyzing these.

[0013] "Determining mood" means analyzing the user's emotional state and psychological condition to identify whether the user is relaxed, tired, energetic, or the like.

[0014] An "optimal music playlist" refers to a list of songs that best suit a user's current situation and preferences, including song selections personalized to each individual user.

[0015] "Voice command" refers to a method in which a user gives instructions to a system by voice, including specific instructions such as "play the next song" or "add this song to my favorites."

[0016] "Touch control" refers to the way in which a user controls music playback and other functions by operating a touchscreen.

[0017] A "user profile" refers to a collection of information related to a particular user, including elements such as music preferences, past playback history, and emotional data.

[0018] "Generating" means that the system uses algorithms and databases to create a specific result. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention is a system that combines various data to individually recommend music to optimize the user's music experience. The operation method and program processing of this system are described below.

[0041] System configuration

[0042] The system of the present invention consists of the following major components:

[0043] 1. User profile management module: Manages users' music preferences and past playing history.

[0044] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[0045] 3. Sentiment analysis module: Analyzes the user's voice and facial expressions to determine their mood for the day.

[0046] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[0047] 5. User interface: Users can easily control music with voice or touch.

[0048] Program processing

[0049] 1. Managing your user profile

[0050] The server processes the music data the user has listened to in the past and their ratings, and updates the user profile. For example, if the user has a tendency to like a particular genre, that information is stored.

[0051] 2. Environmental data collection

[0052] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[0053] 3. Acquiring Emotion Data

[0054] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, then applies an emotion analysis algorithm to determine their emotional state for the day and sends that data to the server. For example, if the user is tired, it will report that data.

[0055] 4. Running the Music Recommendation Engine

[0056] The server integrates user profile, environmental data, and emotional data to generate an optimal playlist using a music recommendation engine. For example, if it's a clear morning and the user is in a good mood, it will recommend up-tempo pop music.

[0057] 5. Providing and Controlling Playlists

[0058] The device displays the playlist received from the server on the user interface and automatically starts playback. The user can then use voice commands or touch controls to play, skip, or add songs to favorites.

[0059] Specific examples

[0060] Case 1: Morning commute (sunny)

[0061] First, the user gets into their car in the morning and connects the in-car system to their smartphone. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends these to the server. At the same time, the server analyzes the user's smile and cheerful voice and reports that they are in a "cheerful mood." The server uses this data to create a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," it performs the additional process according to the instruction.

[0062] Case 2: Returning home (rainy day, tired)

[0063] In the evening, the user gets into their car after work and connects their smartphone. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the server analyzes the user's tired face and calm voice and reports that they are "tired." Based on this data, the server generates a playlist of calming acoustic music and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[0064] As a result, this system individually optimizes the user's driving experience while providing safe and comfortable music entertainment.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] When a user gets into the car, they connect their smartphone to the in-car system, which activates the entire system.

[0068] Step 2:

[0069] The device collects real-time location information (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is obtained through the environmental data acquisition module.

[0070] Step 3:

[0071] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion analysis module. The emotion analysis algorithm analyzes the user's mood and emotions, determining their state, for example, "relaxed" or "tired."

[0072] Step 4:

[0073] The device sends the data (environmental data, emotional data) collected in steps 2 and 3 to the server, including the current location, weather, traffic conditions, and the user's mood.

[0074] Step 5:

[0075] The server updates the user profile based on the received data, which includes the user's music preferences, past playback history, and emotional data, all of which are updated based on the latest information.

[0076] Step 6:

[0077] The server runs a music recommendation engine based on the updated user profile, environmental data, and emotional data. For example, a playlist of up-tempo pop music is generated for a sunny, cheerful morning. The generated playlist is then sent to the device.

[0078] Step 7:

[0079] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[0080] Step 8:

[0081] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[0082] Step 9:

[0083] Every time the user makes a change, the device sends that information (new favorite songs, changes to the playlist) to the server, updating the user profile accordingly, which makes future playlists even more accurate.

[0084] Through the above processing steps, the system can provide the optimal music experience according to the user's individual preferences and circumstances.

[0085] Example 1

[0086] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0087] In today's information society, it is becoming increasingly important to individually optimize each user's music experience. Conventional music recommendation systems only recommend music based on a user's preferences and past playback history, but there is a growing demand for music recommendations that appropriately reflect a variety of factors, such as the user's real-time situation and emotional state. Therefore, it is necessary to realize a more accurate music recommendation system that can respond to the user's individual situation and emotions.

[0088] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0089] In this invention, the server includes a means for managing a user's music preferences and past playback history, a means for acquiring real-time data such as weather, location information, and traffic volume, and a means for analyzing the user's voice and facial expression to determine their mood for the day. This provides a means for using a generative model to generate an optimal music playlist based on the user's preferences, mood, weather, and location information, and enables music control via voice commands and touch operations. This enables a more personalized music experience that takes into account the user's real-time situation and emotional state.

[0090] "User's music preferences" refers to information about the user's tastes accumulated based on the music the user has listened to in the past and their ratings of that music.

[0091] "Playback history" is history data of music that the user has played in the past, and includes information such as specific dates and times and the number of times the music has been played.

[0092] "Weather data" is data that indicates the current weather conditions, and includes environmental information such as whether it is sunny, rainy, wind speed, and temperature.

[0093] "Location information" refers to information about the user's current location and travel route obtained by GPS or the like.

[0094] "Traffic volume data" refers to data that indicates real-time traffic conditions, including congestion information and road congestion levels.

[0095] "User's voice and facial expression" refers to the user's voice and facial expression, which are information used to determine the user's emotional state by analyzing them.

[0096] "Mood" refers to the user's emotional or mental state, and includes happiness, sadness, fatigue, and the like.

[0097] A "generative model" is an algorithm or AI model that generates optimal music playlists based on various collected data.

[0098] A "music playlist" is a list of multiple songs that are played in a specified order, and is generated to suit the user's preferences and mood.

[0099] A "voice command" is an input method that allows a user to give instructions to a device by voice.

[0100] "Touch operation" is an input method in which a user issues instructions to a device by operating a touch screen.

[0101] "Electronic devices" are devices with a user interface, including smartphones, tablets, in-vehicle systems, etc.

[0102] "Emotion data" is data relating to the emotional state of the user obtained by analyzing the user's voice and facial expression.

[0103] "Environmental data" refers to real-time environmental information surrounding the user, such as weather, location information, and traffic volume.

[0104] A "user profile" is individual setting information generated based on a user's past behavior and preferences.

[0105] The present invention is a system for individually optimizing a user's music experience, and is implemented using the following major hardware and software:

[0106] System Configuration

[0107] 1. User Profile Management Module

[0108] The server stores the user's past music playback history and rating data, and updates the user profile based on this information. It also records the user's preferences for genres and artists in detail and stores them in a profile database.

[0109] 2. Environmental data acquisition module

[0110] The device connects to the user's smartphone or in-car system to collect real-time information such as current location, weather data, traffic volume, etc. For example, it uses a GPS sensor and weather API to obtain this data and send it to a server.

[0111] 3. Emotion Data Acquisition Module

[0112] The device uses a built-in camera and microphone to capture the user's facial expressions and voice. This data is processed by an emotion analysis algorithm, and the user's emotional state is transmitted to the server. For example, the camera analyzes the user's smile and the microphone analyzes the tone of the voice.

[0113] 4. Music recommendation engine

[0114] The server combines user profiles, environmental data, and emotional data to generate an optimal music playlist using a generative AI model, which includes algorithms for selecting music based on weather and the user's mood.

[0115] 5. User Interface

[0116] The device displays the playlist sent from the server, and the user can control the music by voice or touch operation, such as playing, skipping, adding to favorites, etc. The user interface has an intuitive and easy-to-use design.

[0117] Specific examples

[0118] Case 1: Morning commute (sunny)

[0119] When a user gets into their car in the morning, they connect their smartphone to the in-car system. The device collects weather data (clear), location information (home), and traffic conditions (smooth), and sends this information to the server.

[0120] At the same time, the device captures the user's smile and cheerful voice, and uses an emotion analysis algorithm to determine that the user is in a "cheerful mood," then sends the data to the server.

[0121] The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. When the user issues a voice command such as "Add this song to my favorites," the device adds it to the playlist accordingly.

[0122] Case 2: Returning home (rainy day, tired)

[0123] In the evening, the user gets into the car after work and connects the smartphone. The device collects weather data (rain), location information (workplace), and traffic conditions (traffic jam), and sends them to the server.

[0124] The device also captures tired facial expressions and calm voices, determines whether the user is "tired" using an emotion analysis algorithm, and sends the data to a server.

[0125] The server uses this data to generate a playlist of calming acoustic music and sends it to the device. When the user issues a voice command such as "play the next song," the device plays the next song as requested.

[0126] Prompt Sentence Examples

[0127] By inputting the following prompt sentences into the generative AI model, the optimal music playlist can be generated.

[0128] Create the perfect music playlist for your users, taking into account their preferred music genres, current weather, location, and emotional state. Specific situations include:

[0129] User Preferences: Rock and Pop

[0130] Current weather: Sunny

[0131] Location: On the way from home to work

[0132] Emotional state: cheerful and lively

[0133] Suggest music playlist content to be recommended.

[0134] As described above, the present invention individually optimizes the user's driving experience and provides safe and comfortable music entertainment.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: Manage your user profile

[0137] The server collects the user's past music playback history and rating data, and updates the user profile based on this.

[0138] Input: User play history and rating data

[0139] Data processing: Statistical analysis of play counts and ratings

[0140] Output: Updated user profile

[0141] Specific operation: For example, the server analyzes the playback history for the past month and stores the most played genres and artists in a profile database.

[0142] Step 2: Collect environmental data

[0143] The terminal works in conjunction with smartphones and in-vehicle systems to collect current location information, weather data, and traffic volume data, and transmits it to a server.

[0144] Input: GPS data, weather API information, traffic information API data

[0145] Data processing: location information acquisition, real-time updates of weather and traffic information

[0146] Output: Collected environmental data

[0147] Specific operation: The device obtains location information using the GPS sensor, collects current weather data using the weather API, and obtains traffic volume data using the traffic information API.

[0148] Step 3: Obtaining emotion data

[0149] The device uses a camera and microphone to capture the user's facial expressions and voice, and then uses an analysis algorithm to determine their emotional state and send the results to a server.

[0150] Input: Video data captured by the camera, audio data recorded by the microphone

[0151] Data processing: Emotion analysis using facial recognition and tone of voice analysis

[0152] Output: Parsed emotional state data

[0153] Specific operation: The device captures the user's facial expression with a camera and determines whether they are smiling. It also analyzes the tone of their voice recorded by a microphone to determine whether they are in good spirits.

[0154] Step 4: Running the Music Recommendation Engine

[0155] The server integrates user profiles, environmental data, and emotional data, and generates an optimal music playlist using a generative AI model.

[0156] Input: User profile, real-time environmental data, emotional state data

[0157] Data processing: Integrating various data and recommending music using AI models

[0158] Output: Generated music playlist

[0159] Specific operation: The server runs an algorithm that prioritizes up-tempo pop music based on data indicating that it is a clear morning and the user is in a cheerful mood.

[0160] Step 5: Providing and Controlling Playlists

[0161] The device displays the playlist received from the server on the user interface and automatically plays music. The user can use voice commands or touch operations to play, skip, add to favorites, etc.

[0162] Input: Generated music playlist, user voice commands or touch actions

[0163] Data processing: display in the user interface and processing of user commands

[0164] Output: Music playback control according to user instructions

[0165] Specific behavior: The device will display the playlist and start playing. If the user issues a voice command such as "Play the next song," the device will play the next song. If the user issues a voice command such as "Add this song to my favorites," the device will add the song to the favorites list.

[0166] (Application example 1)

[0167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0168] In recent years, there has been a demand for improving the in-car user experience. In particular, providing highly personalized entertainment contributes greatly to customer satisfaction in self-driving vehicles. However, conventional music recommendation systems have difficulty comprehensively considering user preferences, emotional state, and environmental data, making it impossible to achieve high-quality personalization. The purpose of this invention is to solve these problems and provide users with the optimal music experience.

[0169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0170] In this invention, the server includes means for managing a user's music preferences and past playback history, means for acquiring real-time data such as weather, location information, and traffic volume, and means for analyzing the user's voice and facial expression to determine the user's mood for the day. This makes it possible to generate an optimal music playlist based on the user's preferences, mood, weather, location information, and traffic conditions, and a highly personalized music experience can be realized by means for playing the generated music playlist, means for controlling music with voice commands and touch operations, means for inputting a user profile, environmental data, and emotional data into an AI model to generate a music playlist, and means for displaying the generated playlist on the infotainment system and accepting operations with voice commands.

[0171] A "user profile" is a collection of information including a user's music preferences and past playback history.

[0172] "Environmental data" refers to real-time information such as weather, location, and traffic volume.

[0173] "Emotion data" is data that indicates the mood and emotional state of the user on that day, analyzed from the user's voice and facial expression.

[0174] A "music playlist" is a list of selected music tracks in a playback order.

[0175] An "AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate optimal results.

[0176] An "infotainment system" is a system installed in a vehicle that provides information and entertainment.

[0177] A "voice command" is an instruction given by a user to operate the system by voice.

[0178] "Touch operation" refers to an operation method performed by touching a screen or panel with a finger.

[0179] The present invention is a system for recommending music to a user based on a personalized music recommendation system that combines a user profile, environmental data, and emotional data to optimize the user's music experience. Specific embodiments of the present invention are described below.

[0180] System configuration

[0181] The system of the present invention consists of the following major components:

[0182] 1. User profile management module: This module manages users' music preferences and past playback history. It uses information stored in the database server to accurately grasp users' tastes.

[0183] 2. Environmental Data Acquisition Module: This module collects real-time data such as weather, location information, traffic volume, etc. Data collection uses, for example, OpenWeatherMap or Google (registered trademark) Maps API.

[0184] 3. Sentiment Analysis Module: This module uses the in-car camera and microphone to capture the user's facial expressions and voice, and analyzes their emotional state. It uses Face++ for facial recognition and Google Speech Recognition for voice recognition.

[0185] 4. Music recommendation engine: Integrates user profiles, environmental data, and emotional data to generate optimal music playlists using AI models. For example, we use Tensorflow (registered trademark) and PyTorch to build AI models.

[0186] 5. User Interface: Users can easily control music with voice commands and touch. This interface is integrated with the vehicle's infotainment system.

[0187] Program processing

[0188] The server collects and manages user profiles, environmental data, and emotional data, and runs the music recommendation engine. For example, the user profile management module processes the music data that users have listened to in the past and their ratings, and stores them in a database.

[0189] The device collects real-time weather, location, and traffic information through the environmental data acquisition module and sends it to the server.The emotion analysis module uses the in-car camera and microphone to capture the user's facial expressions and voice, analyzes the data, and identifies the user's emotional state, which is then sent to the server.

[0190] The server runs a music recommendation engine based on the collected user profile, environmental data, and emotional data, and uses AI models to generate an optimal music playlist, which is then sent to the device and displayed on the infotainment system.

[0191] Users control the playlist with voice commands or touch gestures, such as "play the next song" or "add this song to my favorites," and the system responds.

[0192] Specific examples

[0193] Case 1: Morning commute (sunny)

[0194] A user gets into a self-driving car in the morning and connects to the system. The in-car camera captures the user's smile, and the microphone analyzes their cheerful voice. Environmental data, such as location (home), weather data (sunny), and traffic conditions (smooth), are collected and sent to the server. The server uses this data to create a playlist of up-tempo pop music, sends it to the infotainment system, and begins playback. When the user issues a voice command such as "Add this song to my favorites," the system follows the instructions and performs additional processing.

[0195] Example prompts for generative AI models

[0196] Consider the user's music profile, location, weather, traffic conditions, and emotional state to generate the best music playlist based on the following information:

[0197] Profile: Pop, Rock

[0198] Location: Home

[0199] Weather: Sunny

[0200] Traffic Condition: Smooth

[0201] Emotion: Cheerful mood

[0202] Example of generated playlist: "Song A", "Song B", "Song C"

[0203] In this way, the system can provide the high-quality music experience that users desire.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] The device retrieves the user's music preferences and past playback history from a database. The user ID is required as input, and user profile information (music preferences, ratings, playback history) is obtained as output. Specifically, the device queries the database, and the user profile management module returns the information.

[0207] Step 2:

[0208] The device collects real-time data such as location information, weather, and traffic volume. It receives the current location, weather API, and traffic information API URLs as input, and obtains the current location, weather conditions, and traffic conditions as output. Specifically, it obtains location information from the GPS sensor, sends HTTP requests to the weather and traffic information APIs, and analyzes the obtained data.

[0209] Step 3:

[0210] The device uses an in-car camera and microphone to capture the user's facial expressions and voice and perform emotion analysis. It requires camera feed and audio data as input, and obtains the user's emotional state (e.g., cheerful, tired, etc.) as output. Specifically, the device inputs the images and audio data captured by the camera into the emotion analysis module, which then obtains the analysis results.

[0211] Step 4:

[0212] The server runs a music recommendation engine based on user profile, environmental data, and emotional data to generate an optimal music playlist. It integrates user profile, location information, weather, traffic conditions, and emotional state data as input, and obtains a music playlist as output. Specifically, it inputs this data into an AI model and generates an optimal playlist using a recommendation algorithm.

[0213] Step 5:

[0214] The server sends the generated music playlist to the terminal. The server requires the playlist data as input and obtains the playlist sent to the terminal as output. The specific operation is to send the playlist to the terminal via the network.

[0215] Step 6:

[0216] The device displays the received playlist on the infotainment system and starts playback. The input is the received playlist data, and the output is the music displayed on the screen and played through the car speakers. The specific operation is to display the playlist and control playback using the infotainment system's API.

[0217] Step 7:

[0218] Users can use voice commands or touch operations to play music, skip, add to favorites, etc. Voice commands and touch operation data are required as input, and the output is the music control results according to the user's instructions. Specifically, the voice recognition system analyzes the command, and the infotainment system executes the corresponding operation.

[0219] Through this series of processes, users can enjoy a highly personalized music experience in their autonomous vehicles.

[0220] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0221] The present invention is a music recommendation system that combines an emotion engine that recognizes the user's emotions, thereby generating an optimal music playlist based on the driver's emotional state, providing a personalized music experience in real time.

[0222] System configuration

[0223] The system of the present invention consists of the following major components:

[0224] 1. User profile management module: Manages users' music preferences and past playing history.

[0225] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[0226] 3. Emotion engine: Analyzes the user's voice and facial expressions to recognize their mood and emotions for the day.

[0227] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[0228] 5. User interface: Users can easily control music with voice or touch.

[0229] Program processing

[0230] 1. Managing your user profile

[0231] The server updates the user profile based on the music data the user has listened to in the past and their ratings. For example, if the user has a tendency to like a particular genre, the server stores that information.

[0232] 2. Environmental data collection

[0233] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[0234] 3. Acquisition and Analysis of Emotion Data

[0235] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine, which analyzes the user's mood and emotions and determines their state, such as "relaxed" or "tired."

[0236] 4. Running the Music Recommendation Engine

[0237] The server integrates the user profile, environmental data, and emotional data and generates an optimal playlist using a music recommendation engine. For example, it recommends an up-tempo pop playlist for a cheerful, sunny morning. The generated playlist is then sent to the device.

[0238] 5. Providing and Controlling Playlists

[0239] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[0240] 6. Interactive Operation

[0241] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform actions such as playing or pausing music, skipping to the next song, or adding music to favorites.

[0242] Specific examples

[0243] Case 1: Morning commute (sunny)

[0244] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends them to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device follows the instruction and adds the song to the favorites list.

[0245] Case 2: Returning home (rainy day, tired)

[0246] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[0247] These specific examples enable the system to better personalize the user's driving experience and provide safe and comfortable music entertainment.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] When the user gets into the car, they connect their smartphone to the in-car system, which activates the system.

[0251] Step 2:

[0252] The device activates the environmental data acquisition module to collect real-time information on the current location (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is then sent to the server as soon as it is collected.

[0253] Step 3:

[0254] The device activates the camera and microphone inside the car to capture the user's facial expressions and voice, and this data is sent to the emotion engine.

[0255] Step 4:

[0256] The emotion engine analyzes the received data and determines the user's mood and emotions. For example, if the user's face looks relaxed, it will recognize the user as "relaxed" and send the result of the determination to the server.

[0257] Step 5:

[0258] The server integrates the received environmental data, emotional data, and user profile information to update the user profile management module, taking into account past playback history and music preferences.

[0259] Step 6:

[0260] The server runs a music recommendation engine based on the updated user profile and collected data. For example, it generates an up-tempo pop playlist for a sunny, cheerful morning. The generated playlist is then sent to the device.

[0261] Step 7:

[0262] The device displays the received playlist on the user interface and starts playing the music, which is then provided through the car's audio system.

[0263] Step 8:

[0264] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[0265] Step 9:

[0266] Every time the user operates the music, the device sends the operation data to the server and updates the user profile accordingly, which increases the accuracy of playlist generation from the next time onwards.

[0267] As a result, this system can individually optimize the user's driving experience and provide safe and comfortable music entertainment.

[0268] Example 2

[0269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] Conventional music recommendation systems only recommend music based on a user's music preferences and past playback history, and do not take into account the user's current emotional state or environmental conditions (weather, location information, traffic volume, etc.), making it difficult to provide an optimal music experience for the user's mood and situation.In addition, users are only able to control music in a limited way, requiring intuitive operation.

[0271] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0272] In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression using a camera and microphone to determine the user's mood and emotions for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, location information, traffic volume, and emotional state; means for playing the generated music playlist; and means for controlling music with voice commands and touch operations. This enables optimal music recommendations that take into account the user's current emotional state and environmental conditions, providing a more intuitive and interactive music experience.

[0273] "Means for managing users' music preferences and past playback history" refers to a system that records and analyzes the music that users have played in the past and their ratings, thereby understanding the users' music preferences.

[0274] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to sensors and data acquisition systems for collecting current weather, user location information, and traffic conditions in real time.

[0275] "Means for analyzing the user's voice and facial expressions using a camera and microphone to determine the user's mood and emotions for that day" refers to a system that uses a camera and microphone installed in the vehicle to capture the user's facial expressions and voice, and analyzes them to determine the user's emotional state.

[0276] "Means for generating optimal music playlists based on user preferences, mood, weather, location information, traffic volume, and emotional state" refers to algorithms and systems that integrate and analyze multiple collected data to generate music playlists that are most suitable for users.

[0277] The "means for playing the generated music playlist" is a system for automatically transmitting the optimal playlist to a playback device and playing music.

[0278] "A means for controlling music with voice commands and touch operations" refers to an interface and system that allows users to intuitively control music playback, stopping, changing songs, etc. through voice instructions or a touch screen.

[0279] This invention is a music recommendation system that recognizes user emotions and generates and plays optimal music playlists based on the driver's emotional state. The system consists of the following main components: a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface.

[0280] 1. User Profile Management Module

[0281] The server manages the user's music preferences and past playback history. This module understands the user's tastes by accumulating and analyzing data such as the songs the user has listened to in the past, their ratings, and the number of times they have played the songs. For example, if a user likes rock music, this preference is saved in their profile and reflected in the next music recommendation.

[0282] 2. Environmental data acquisition module

[0283] The device connects to the user's smartphone or in-car system to obtain real-time data such as current location, weather, and traffic volume. This is achieved using hardware and software such as a GPS sensor, weather information API, and traffic information API. The collected data is sent to a server in real time.

[0284] 3. Emotion Engine

[0285] The device uses an in-car camera and microphone to capture the user's voice and facial expressions. The emotion engine recognizes the user's mood and emotional state by analyzing the user's facial muscle movements with the camera and the tone of voice with the microphone. For example, if the user is smiling, it will be determined that the user is in a "cheerful mood."

[0286] 4. Music recommendation engine

[0287] The server combines user profile data, location information, weather, traffic conditions, and emotional state, and inputs them into a music recommendation engine. This engine has an algorithm that generates a music playlist that best suits the user's current state. For example, it might recommend up-tempo pop music for a cheerful, sunny morning, or calm acoustic music for a tired evening. The generated playlist is then sent to the device.

[0288] 5. User Interface

[0289] The device displays the received playlist and automatically plays music. The user can control the playlist using voice commands (e.g., "Play next song") or touch interactions (e.g., using the touchscreen to select songs). For example, the user can say, "Add this song to my favorites," and the song will be added to the favorites list.

[0290] Specific examples

[0291] Case 1: Morning commute (sunny)

[0292] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device starts playing the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device adds the song to the favorites list.

[0293] Case 2: Returning home (rainy day, tired)

[0294] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[0295] Specific prompt examples

[0296] "Describe a music recommendation system that recognizes user emotions and generates music playlists. The system consists of a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface. Explain the function and processing flow of each module in detail, and provide examples of specific cases and prompt sentences."

[0297] The present invention allows the user to enjoy the most suitable music experience according to the mood and situation at the time, making driving even more comfortable.

[0298] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0299] Step 1:

[0300] Managing User Profiles

[0301] The server manages the user's past music playing history and ratings.

[0302] Input: User play history data and rating data

[0303] Specific behavior:

[0304] The server accesses a database to obtain the music that the user has played in the past and its rating data.

[0305] Based on the acquired data, the system analyzes trends such as users' favorite genres, artists, and songs.

[0306] Output: User profile data reflecting the user's music preferences

[0307] Step 2:

[0308] Environmental data collection

[0309] The device collects real-time environmental data from the user's smartphone or in-vehicle system and transmits it to a server.

[0310] Input: GPS data, weather data, traffic data

[0311] Specific behavior:

[0312] The device uses GPS to obtain its current location information.

[0313] To obtain weather information, a weather information API is accessed via the Internet.

[0314] Use the traffic information API to obtain traffic volume data.

[0315] The collected data is sent to the server.

[0316] Output: Environment data sent to the server

[0317] Step 3:

[0318] Acquiring and analyzing emotion data

[0319] The device uses the in-car camera and microphone to analyze the user's voice and facial expressions, and transmits emotional data to the server.

[0320] Input: Camera video data, audio data

[0321] Specific behavior:

[0322] The camera captures the user's facial expressions and analyzes the movement of facial muscles.

[0323] The microphone collects the user's voice and analyzes the tone and intonation of the voice.

[0324] Camera data and audio data are input into an emotion engine to determine the user's emotional state.

[0325] The determined emotion data is transmitted to the server.

[0326] Output: Emotion data sent to the server

[0327] Step 4:

[0328] Running a music recommendation engine

[0329] The server runs a music recommendation engine based on the integrated data to generate an optimal playlist.

[0330] Input: User profile data, environmental data, emotional data

[0331] Specific behavior:

[0332] The server integrates the user profile data, the environmental data, and the emotional data.

[0333] The integrated data is input into a music recommendation engine, which runs an algorithm to generate an optimal music playlist.

[0334] The generated playlist is sent to the terminal.

[0335] Output: Music playlist sent to device

[0336] Step 5:

[0337] Playlist provision and control

[0338] The terminal displays the playlist received from the server on the user interface and starts automatic playback.

[0339] Input: Music playlist data

[0340] Specific behavior:

[0341] The terminal displays the playlist received from the server.

[0342] Music is automatically played through the car's speakers.

[0343] Manage playback progress.

[0344] Output: Music being played

[0345] Step 6:

[0346] Interactive Operation

[0347] Users control the music using voice commands and touch gestures.

[0348] Input: Voice command, touch operation

[0349] Specific behavior:

[0350] The user issues voice commands such as "play next song" or "add this song to my favorites."

[0351] The device recognizes the voice command and performs the corresponding operation.

[0352] The user uses touch to change songs or stop playback.

[0353] Output: Music playback controlled by user instructions

[0354] (Application example 2)

[0355] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0356] There is a need to more individually optimize the music experience for passengers in autonomous vehicles and recommend music that matches their emotional state in real time. However, conventional music recommendation systems are based only on the user's static profile information and past playback history, and have the problem of not being able to reflect the user's real-time emotional state or current environmental data (weather, traffic conditions, etc.). This makes it difficult for passengers in autonomous vehicles to receive the optimal music experience that matches their mood at any given time.

[0357] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression to determine the user's mood for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, and location information; means for playing the generated music playlist; means for controlling music with voice commands and touch operations; means for integrating user emotional data and environmental data in an autonomous vehicle to generate and play an optimal music playlist; and means for recognizing a user's voice command and performing operations such as playing the next song or adding to a favorites list. This enables passengers in autonomous vehicles to enjoy an optimal music experience in real time.

[0358] The "means for managing the user's music preferences and past playback history" refers to a means for recording data on music that the user has listened to in the past and their ratings, and for grasping the user's music preferences.

[0359] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to means for collecting the latest information such as current weather conditions, the user's current location, and traffic flow.

[0360] The "means for analyzing the user's voice and facial expression to determine the mood of the day" is a means for analyzing the user's voice and facial expression, identifying the user's emotional state, and estimating the mood of the day.

[0361] The "means for generating an optimal music playlist" is a means for comprehensively analyzing data such as the user's preferences, mood, weather, and location information to create the most suitable music list.

[0362] The "means for playing the generated music playlist" refers to a means for starting playback to provide the music in the generated playlist to the user.

[0363] "Means for controlling music with voice commands and touch operations" refers to means for a user to perform operations such as playing, stopping, and skipping songs through voice instructions and touch operations.

[0364] The "means for integrating user emotional data and environmental data to generate and play an optimal music playlist" is a means for integrating driver emotional information and real-time environmental information, and automatically creating and playing an optimal music playlist based on that information.

[0365] "Means for performing actions such as next song or adding to favorites list" means means for a user to perform actions such as playing the next song or adding a specific song to a favorites list through voice commands.

[0366] The present invention is a music recommendation system that recognizes the user's emotions in an autonomous vehicle and provides an optimal music playlist based on the emotions. This system is composed of several main components. The role of each component and a specific embodiment are described below.

[0367] System configuration

[0368] 1. User Profile Management Module:

[0369] The server updates the user profile based on the music data the user has listened to in the past and their ratings. This profile serves as a database to understand the user's musical preferences. Specifically, if the user tends to like a particular genre or artist, that information is saved and analyzed.

[0370] 2. Environmental Data Acquisition Module:

[0371] The device connects with the autonomous vehicle's sensors and smartphone to collect real-time weather data, location information, traffic volume, etc. For example, if the current weather is sunny and traffic is smooth, that information is sent to the server.

[0372] 3. Emotion Engine:

[0373] The device uses the in-car camera and microphone to capture the user's facial expressions and voice to obtain emotional data. The emotion engine analyzes this data to determine the user's emotional state, such as "relaxed" or "tired." This analysis is performed using emotion analysis software such as Microsoft® Azure® Cognitive Services.

[0374] 4. Music recommendation engine:

[0375] The server integrates user profiles, environmental data, and emotional data to generate optimal music playlists. This recommendation engine runs in a cloud environment such as Amazon AWS Lambda. The generated playlists are optimized for the user's emotions and the current environment.

[0376] 5. User Interface:

[0377] The device displays the playlist as it plays and allows control via voice commands or touch gestures, for example, when the user says "play the next song," it skips to the next song using a voice recognition framework like Google Speech-to-Text.

[0378] Specific use cases

[0379] Case 1: Morning commute (sunny)

[0380] The user gets into the car and it starts driving automatically.

[0381] The device collects weather data (sunny), location information (home), and traffic conditions (smooth) and sends them to the server.

[0382] The emotion engine analyzes the user's smile and cheerful voice and determines that they are in a "cheerful mood."

[0383] The server generates an up-tempo playlist based on this data and sends it to the device.

[0384] When the device plays the playlist and the user issues a voice command such as "Add this song to my favorites," the device follows the instructions and adds the song to the favorites list.

[0385] Case 2: Returning home (rainy day, tired)

[0386] The user finishes work and gets into the car.

[0387] The device collects weather data (rain), location information (company), and traffic conditions (traffic jams) and sends them to the server.

[0388] The emotion engine analyzes the user's tired face and calm voice and determines that they are tired.

[0389] The server uses this data to generate a playlist of calming acoustic music and sends it to the device.

[0390] When the device plays a playlist and the user issues a voice command such as "play the next song," the device will follow the instruction and play the next song.

[0391] Prompt Sentence Examples

[0392] Implement a system that recognizes emotions through a camera and microphone while the user is in the car, and recommends music based on real-time weather and location information. Meet the following requirements:

[0393] 1. User Profile Management

[0394] 2. Real-time collection of environmental data

[0395] 3. Emotion Recognition Engine Integration

[0396] 4. Playlist generation by recommendation engine

[0397] 5. Interactive operation via voice commands

[0398] Through the above embodiments, the system of the present invention can more individually optimize the user's driving experience and provide safe and comfortable music entertainment to passengers in autonomous vehicles.

[0399] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0400] Step 1:

[0401] The user enters the car and begins operating the autonomous vehicle. The device establishes a connection with the user's smartphone and in-vehicle system. This connection transmits the user's past music playback history and ratings to a server, which populates the user profile. The server uses this data to update the user's music preferences.

[0402] Input: User's music playback history, rating data

[0403] Output: Updated user profile

[0404] Step 2:

[0405] The device uses the autonomous vehicle's built-in sensors and internet connection to collect real-time weather data, location information, and traffic volume, which is then sent to a server.

[0406] Input: Weather data, location information, traffic

[0407] Output: Collected environmental data

[0408] Step 3:

[0409] The emotion engine captures the user's facial expressions and voice through the car's camera and microphone. It then uses emotion analysis software, such as Microsoft Azure Cognitive Services, to determine the user's emotional state. For example, if the user is smiling while speaking, the emotional state is recognized as "cheerful."

[0410] Input: User's facial expression data, voice data

[0411] Output: Parsed emotion data

[0412] Step 4:

[0413] The server integrates user profiles, environmental data, and emotional data, and uses a music recommendation engine to generate optimal music playlists. Utilizing cloud environments such as Amazon AWS Lambda, the server comprehensively analyzes information from multiple data sources and selects music that matches the user's mood.

[0414] Input: User profile, environmental data, emotional data

[0415] Output: Optimized music playlist

[0416] Step 5:

[0417] The generated music playlist is sent to the terminal and displayed on the user interface, and the terminal activates a function to play music based on the playlist, allowing the user to listen to the music through the car speakers.

[0418] Input: Generated music playlist

[0419] Output: Music being played

[0420] Step 6:

[0421] Users can control music through the car's voice command system or touch controls. For example, if a user says, "Play the next song," the device will recognize the command using voice recognition software like Google Speech-to-Text and play the next song. Users can also add a song to their "favorites" list, which will be carried out.

[0422] Input: User voice commands, touch actions

[0423] Output: Music playback control and favorites update

[0424] Through these steps, the system can provide optimal music in real time based on the user's emotions and environmental data, making the user's driving experience even more comfortable.

[0425] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0426] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0427] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0431] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0432] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0433] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0434] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0435] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0436] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0437] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0438] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0439] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0440] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0441] The present invention is a system that combines various data to individually recommend music to optimize the user's music experience. The operation method and program processing of this system are described below.

[0442] System configuration

[0443] The system of the present invention consists of the following major components:

[0444] 1. User profile management module: Manages users' music preferences and past playing history.

[0445] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[0446] 3. Sentiment analysis module: Analyzes the user's voice and facial expressions to determine their mood for the day.

[0447] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[0448] 5. User interface: Users can easily control music with voice or touch.

[0449] Program processing

[0450] 1. Managing your user profile

[0451] The server processes the music data the user has listened to in the past and their ratings, and updates the user profile. For example, if the user has a tendency to like a particular genre, that information is stored.

[0452] 2. Environmental data collection

[0453] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[0454] 3. Acquiring Emotion Data

[0455] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, then applies an emotion analysis algorithm to determine their emotional state for the day and sends that data to the server. For example, if the user is tired, it will report that data.

[0456] 4. Running the Music Recommendation Engine

[0457] The server integrates user profile, environmental data, and emotional data to generate an optimal playlist using a music recommendation engine. For example, if it's a clear morning and the user is in a good mood, it will recommend up-tempo pop music.

[0458] 5. Providing and Controlling Playlists

[0459] The device displays the playlist received from the server on the user interface and automatically starts playback. The user can then use voice commands or touch controls to play, skip, or add songs to favorites.

[0460] Specific examples

[0461] Case 1: Morning commute (sunny)

[0462] First, the user gets into their car in the morning and connects the in-car system to their smartphone. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends these to the server. At the same time, the server analyzes the user's smile and cheerful voice and reports that they are in a "cheerful mood." The server uses this data to create a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," it performs the additional process according to the instruction.

[0463] Case 2: Returning home (rainy day, tired)

[0464] In the evening, the user gets into their car after work and connects their smartphone. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the server analyzes the user's tired face and calm voice and reports that they are "tired." Based on this data, the server generates a playlist of calming acoustic music and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[0465] As a result, this system individually optimizes the user's driving experience while providing safe and comfortable music entertainment.

[0466] The processing flow will be explained below.

[0467] Step 1:

[0468] When a user gets into the car, they connect their smartphone to the in-car system, which activates the entire system.

[0469] Step 2:

[0470] The device collects real-time location information (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is obtained through the environmental data acquisition module.

[0471] Step 3:

[0472] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion analysis module. The emotion analysis algorithm analyzes the user's mood and emotions, determining their state, for example, "relaxed" or "tired."

[0473] Step 4:

[0474] The device sends the data (environmental data, emotional data) collected in steps 2 and 3 to the server, including the current location, weather, traffic conditions, and the user's mood.

[0475] Step 5:

[0476] The server updates the user profile based on the received data, which includes the user's music preferences, past playback history, and emotional data, all of which are updated based on the latest information.

[0477] Step 6:

[0478] The server runs a music recommendation engine based on the updated user profile, environmental data, and emotional data. For example, a playlist of up-tempo pop music is generated for a sunny, cheerful morning. The generated playlist is then sent to the device.

[0479] Step 7:

[0480] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[0481] Step 8:

[0482] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[0483] Step 9:

[0484] Every time the user makes a change, the device sends that information (new favorite songs, changes to the playlist) to the server, updating the user profile accordingly, which makes future playlists even more accurate.

[0485] Through the above processing steps, the system can provide the optimal music experience according to the user's individual preferences and circumstances.

[0486] Example 1

[0487] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] In today's information society, it is becoming increasingly important to individually optimize each user's music experience. Conventional music recommendation systems only recommend music based on a user's preferences and past playback history, but there is a growing demand for music recommendations that appropriately reflect a variety of factors, such as the user's real-time situation and emotional state. Therefore, it is necessary to realize a more accurate music recommendation system that can respond to the user's individual situation and emotions.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0490] In this invention, the server includes a means for managing a user's music preferences and past playback history, a means for acquiring real-time data such as weather, location information, and traffic volume, and a means for analyzing the user's voice and facial expression to determine their mood for the day. This provides a means for using a generative model to generate an optimal music playlist based on the user's preferences, mood, weather, and location information, and enables music control via voice commands and touch operations. This enables a more personalized music experience that takes into account the user's real-time situation and emotional state.

[0491] "User's music preferences" refers to information about the user's tastes accumulated based on the music the user has listened to in the past and their ratings of that music.

[0492] "Playback history" is history data of music that the user has played in the past, and includes information such as specific dates and times and the number of times the music has been played.

[0493] "Weather data" is data that indicates the current weather conditions, and includes environmental information such as whether it is sunny, rainy, wind speed, and temperature.

[0494] "Location information" refers to information about the user's current location and travel route obtained by GPS or the like.

[0495] "Traffic volume data" refers to data that indicates real-time traffic conditions, including congestion information and road congestion levels.

[0496] "User's voice and facial expression" refers to the user's voice and facial expression, which are information used to determine the user's emotional state by analyzing them.

[0497] "Mood" refers to the user's emotional or mental state, and includes happiness, sadness, fatigue, and the like.

[0498] A "generative model" is an algorithm or AI model that generates optimal music playlists based on various collected data.

[0499] A "music playlist" is a list of multiple songs that are played in a specified order, and is generated to suit the user's preferences and mood.

[0500] A "voice command" is an input method that allows a user to give instructions to a device by voice.

[0501] "Touch operation" is an input method in which a user issues instructions to a device by operating a touch screen.

[0502] "Electronic devices" are devices with a user interface, including smartphones, tablets, in-vehicle systems, etc.

[0503] "Emotion data" is data relating to the emotional state of the user obtained by analyzing the user's voice and facial expression.

[0504] "Environmental data" refers to real-time environmental information surrounding the user, such as weather, location information, and traffic volume.

[0505] A "user profile" is individual setting information generated based on a user's past behavior and preferences.

[0506] The present invention is a system for individually optimizing a user's music experience, and is implemented using the following major hardware and software:

[0507] System Configuration

[0508] 1. User Profile Management Module

[0509] The server stores the user's past music playback history and rating data, and updates the user profile based on this information. It also records the user's preferences for genres and artists in detail and stores them in a profile database.

[0510] 2. Environmental data acquisition module

[0511] The device connects to the user's smartphone or in-car system to collect real-time information such as current location, weather data, traffic volume, etc. For example, it uses a GPS sensor and weather API to obtain this data and send it to a server.

[0512] 3. Emotion Data Acquisition Module

[0513] The device uses a built-in camera and microphone to capture the user's facial expressions and voice. This data is processed by an emotion analysis algorithm, and the user's emotional state is transmitted to the server. For example, the camera analyzes the user's smile and the microphone analyzes the tone of the voice.

[0514] 4. Music recommendation engine

[0515] The server combines user profiles, environmental data, and emotional data to generate an optimal music playlist using a generative AI model, which includes algorithms for selecting music based on weather and the user's mood.

[0516] 5. User Interface

[0517] The device displays the playlist sent from the server, and the user can control the music by voice or touch operation, such as playing, skipping, adding to favorites, etc. The user interface has an intuitive and easy-to-use design.

[0518] Specific examples

[0519] Case 1: Morning commute (sunny)

[0520] When a user gets into their car in the morning, they connect their smartphone to the in-car system. The device collects weather data (clear), location information (home), and traffic conditions (smooth), and sends this information to the server.

[0521] At the same time, the device captures the user's smile and cheerful voice, and uses an emotion analysis algorithm to determine that the user is in a "cheerful mood," then sends the data to the server.

[0522] The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. When the user issues a voice command such as "Add this song to my favorites," the device adds it to the playlist accordingly.

[0523] Case 2: Returning home (rainy day, tired)

[0524] In the evening, the user gets into the car after work and connects the smartphone. The device collects weather data (rain), location information (workplace), and traffic conditions (traffic jam), and sends them to the server.

[0525] The device also captures tired facial expressions and calm voices, determines whether the user is "tired" using an emotion analysis algorithm, and sends the data to a server.

[0526] The server uses this data to generate a playlist of calming acoustic music and sends it to the device. When the user issues a voice command such as "play the next song," the device plays the next song as requested.

[0527] Prompt Sentence Examples

[0528] By inputting the following prompt sentences into the generative AI model, the optimal music playlist can be generated.

[0529] Create the perfect music playlist for your users, taking into account their preferred music genres, current weather, location, and emotional state. Specific situations include:

[0530] User Preferences: Rock and Pop

[0531] Current weather: Sunny

[0532] Location: On the way from home to work

[0533] Emotional state: cheerful and lively

[0534] Suggest music playlist content to be recommended.

[0535] As described above, the present invention individually optimizes the user's driving experience and provides safe and comfortable music entertainment.

[0536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0537] Step 1: Manage your user profile

[0538] The server collects the user's past music playback history and rating data, and updates the user profile based on this.

[0539] Input: User play history and rating data

[0540] Data processing: Statistical analysis of play counts and ratings

[0541] Output: Updated user profile

[0542] Specific operation: For example, the server analyzes the playback history for the past month and stores the most played genres and artists in a profile database.

[0543] Step 2: Collect environmental data

[0544] The terminal works in conjunction with smartphones and in-vehicle systems to collect current location information, weather data, and traffic volume data, and transmits it to a server.

[0545] Input: GPS data, weather API information, traffic information API data

[0546] Data processing: location information acquisition, real-time updates of weather and traffic information

[0547] Output: Collected environmental data

[0548] Specific operation: The device obtains location information using the GPS sensor, collects current weather data using the weather API, and obtains traffic volume data using the traffic information API.

[0549] Step 3: Obtaining emotion data

[0550] The device uses a camera and microphone to capture the user's facial expressions and voice, and then uses an analysis algorithm to determine their emotional state and send the results to a server.

[0551] Input: Video data captured by the camera, audio data recorded by the microphone

[0552] Data processing: Emotion analysis using facial recognition and tone of voice analysis

[0553] Output: Parsed emotional state data

[0554] Specific operation: The device captures the user's facial expression with a camera and determines whether they are smiling. It also analyzes the tone of their voice recorded by a microphone to determine whether they are in good spirits.

[0555] Step 4: Running the Music Recommendation Engine

[0556] The server integrates user profiles, environmental data, and emotional data, and generates an optimal music playlist using a generative AI model.

[0557] Input: User profile, real-time environmental data, emotional state data

[0558] Data processing: Integrating various data and recommending music using AI models

[0559] Output: Generated music playlist

[0560] Specific operation: The server runs an algorithm that prioritizes up-tempo pop music based on data indicating that it is a clear morning and the user is in a cheerful mood.

[0561] Step 5: Providing and Controlling Playlists

[0562] The device displays the playlist received from the server on the user interface and automatically plays music. The user can use voice commands or touch operations to play, skip, add to favorites, etc.

[0563] Input: Generated music playlist, user voice commands or touch actions

[0564] Data processing: display in the user interface and processing of user commands

[0565] Output: Music playback control according to user instructions

[0566] Specific behavior: The device will display the playlist and start playing. If the user issues a voice command such as "Play the next song," the device will play the next song. If the user issues a voice command such as "Add this song to my favorites," the device will add the song to the favorites list.

[0567] (Application example 1)

[0568] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0569] In recent years, there has been a demand for improving the in-car user experience. In particular, providing highly personalized entertainment contributes greatly to customer satisfaction in self-driving vehicles. However, conventional music recommendation systems have difficulty comprehensively considering user preferences, emotional state, and environmental data, making it impossible to achieve high-quality personalization. The purpose of this invention is to solve these problems and provide users with the optimal music experience.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0571] In this invention, the server includes means for managing a user's music preferences and past playback history, means for acquiring real-time data such as weather, location information, and traffic volume, and means for analyzing the user's voice and facial expression to determine the user's mood for the day. This makes it possible to generate an optimal music playlist based on the user's preferences, mood, weather, location information, and traffic conditions, and a highly personalized music experience can be realized by means for playing the generated music playlist, means for controlling music with voice commands and touch operations, means for inputting a user profile, environmental data, and emotional data into an AI model to generate a music playlist, and means for displaying the generated playlist on the infotainment system and accepting operations with voice commands.

[0572] A "user profile" is a collection of information including a user's music preferences and past playback history.

[0573] "Environmental data" refers to real-time information such as weather, location, and traffic volume.

[0574] "Emotion data" is data that indicates the mood and emotional state of the user on that day, analyzed from the user's voice and facial expression.

[0575] A "music playlist" is a list of selected music tracks in a playback order.

[0576] An "AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate optimal results.

[0577] An "infotainment system" is a system installed in a vehicle that provides information and entertainment.

[0578] A "voice command" is an instruction given by a user to operate the system by voice.

[0579] "Touch operation" refers to an operation method performed by touching a screen or panel with a finger.

[0580] The present invention is a system for recommending music to a user based on a personalized music recommendation system that combines a user profile, environmental data, and emotional data to optimize the user's music experience. Specific embodiments of the present invention are described below.

[0581] System configuration

[0582] The system of the present invention consists of the following major components:

[0583] 1. User profile management module: This module manages users' music preferences and past playback history. It uses information stored in the database server to accurately grasp users' tastes.

[0584] 2. Environmental Data Acquisition Module: This module collects real-time data such as weather, location information, traffic volume, etc. It uses, for example, OpenWeatherMap and Google Maps APIs to collect data.

[0585] 3. Sentiment Analysis Module: This module uses the in-car camera and microphone to capture the user's facial expressions and voice, and analyzes their emotional state. It uses Face++ for facial recognition and Google Speech Recognition for voice recognition.

[0586] 4. Music recommendation engine: Integrates user profiles, environmental data, and emotional data to generate optimal music playlists using AI models. For example, TensorFlow and PyTorch can be used to build AI models.

[0587] 5. User Interface: Users can easily control music with voice commands and touch. This interface is integrated with the vehicle's infotainment system.

[0588] Program processing

[0589] The server collects and manages user profiles, environmental data, and emotional data, and runs the music recommendation engine. For example, the user profile management module processes the music data that users have listened to in the past and their ratings, and stores them in a database.

[0590] The device collects real-time weather, location, and traffic information through the environmental data acquisition module and sends it to the server.The emotion analysis module uses the in-car camera and microphone to capture the user's facial expressions and voice, analyzes the data, and identifies the user's emotional state, which is then sent to the server.

[0591] The server runs a music recommendation engine based on the collected user profile, environmental data, and emotional data, and uses AI models to generate an optimal music playlist, which is then sent to the device and displayed on the infotainment system.

[0592] Users control the playlist with voice commands or touch gestures, such as "play the next song" or "add this song to my favorites," and the system responds.

[0593] Specific examples

[0594] Case 1: Morning commute (sunny)

[0595] A user gets into a self-driving car in the morning and connects to the system. The in-car camera captures the user's smile, and the microphone analyzes their cheerful voice. Environmental data, such as location (home), weather data (sunny), and traffic conditions (smooth), are collected and sent to the server. The server uses this data to create a playlist of up-tempo pop music, sends it to the infotainment system, and begins playback. When the user issues a voice command such as "Add this song to my favorites," the system follows the instructions and performs additional processing.

[0596] Example prompts for generative AI models

[0597] Consider the user's music profile, location, weather, traffic conditions, and emotional state to generate the best music playlist based on the following information:

[0598] Profile: Pop, Rock

[0599] Location: Home

[0600] Weather: Sunny

[0601] Traffic Condition: Smooth

[0602] Emotion: Cheerful mood

[0603] Example of generated playlist: "Song A", "Song B", "Song C"

[0604] In this way, the system can provide the high-quality music experience that users desire.

[0605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0606] Step 1:

[0607] The device retrieves the user's music preferences and past playback history from a database. The user ID is required as input, and user profile information (music preferences, ratings, playback history) is obtained as output. Specifically, the device queries the database, and the user profile management module returns the information.

[0608] Step 2:

[0609] The device collects real-time data such as location information, weather, and traffic volume. It receives the current location, weather API, and traffic information API URLs as input, and obtains the current location, weather conditions, and traffic conditions as output. Specifically, it obtains location information from the GPS sensor, sends HTTP requests to the weather and traffic information APIs, and analyzes the obtained data.

[0610] Step 3:

[0611] The device uses an in-car camera and microphone to capture the user's facial expressions and voice and perform emotion analysis. It requires camera feed and audio data as input, and obtains the user's emotional state (e.g., cheerful, tired, etc.) as output. Specifically, the device inputs the images and audio data captured by the camera into the emotion analysis module, which then obtains the analysis results.

[0612] Step 4:

[0613] The server runs a music recommendation engine based on user profile, environmental data, and emotional data to generate an optimal music playlist. It integrates user profile, location information, weather, traffic conditions, and emotional state data as input, and obtains a music playlist as output. Specifically, it inputs this data into an AI model and generates an optimal playlist using a recommendation algorithm.

[0614] Step 5:

[0615] The server sends the generated music playlist to the terminal. The server requires the playlist data as input and obtains the playlist sent to the terminal as output. The specific operation is to send the playlist to the terminal via the network.

[0616] Step 6:

[0617] The device displays the received playlist on the infotainment system and starts playback. The input is the received playlist data, and the output is the music displayed on the screen and played through the car speakers. The specific operation is to display the playlist and control playback using the infotainment system's API.

[0618] Step 7:

[0619] Users can use voice commands or touch operations to play music, skip, add to favorites, etc. Voice commands and touch operation data are required as input, and the output is the music control results according to the user's instructions. Specifically, the voice recognition system analyzes the command, and the infotainment system executes the corresponding operation.

[0620] Through this series of processes, users can enjoy a highly personalized music experience in their autonomous vehicles.

[0621] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0622] The present invention is a music recommendation system that combines an emotion engine that recognizes the user's emotions, thereby generating an optimal music playlist based on the driver's emotional state, providing a personalized music experience in real time.

[0623] System configuration

[0624] The system of the present invention consists of the following major components:

[0625] 1. User profile management module: Manages users' music preferences and past playing history.

[0626] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[0627] 3. Emotion engine: Analyzes the user's voice and facial expressions to recognize their mood and emotions for the day.

[0628] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[0629] 5. User interface: Users can easily control music with voice or touch.

[0630] Program processing

[0631] 1. Managing your user profile

[0632] The server updates the user profile based on the music data the user has listened to in the past and their ratings. For example, if the user has a tendency to like a particular genre, the server stores that information.

[0633] 2. Environmental data collection

[0634] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[0635] 3. Acquisition and Analysis of Emotion Data

[0636] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine, which analyzes the user's mood and emotions and determines their state, such as "relaxed" or "tired."

[0637] 4. Running the Music Recommendation Engine

[0638] The server integrates the user profile, environmental data, and emotional data and generates an optimal playlist using a music recommendation engine. For example, it recommends an up-tempo pop playlist for a cheerful, sunny morning. The generated playlist is then sent to the device.

[0639] 5. Providing and Controlling Playlists

[0640] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[0641] 6. Interactive Operation

[0642] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform actions such as playing or pausing music, skipping to the next song, or adding music to favorites.

[0643] Specific examples

[0644] Case 1: Morning commute (sunny)

[0645] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends them to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device follows the instruction and adds the song to the favorites list.

[0646] Case 2: Returning home (rainy day, tired)

[0647] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[0648] These specific examples enable the system to better personalize the user's driving experience and provide safe and comfortable music entertainment.

[0649] The processing flow will be explained below.

[0650] Step 1:

[0651] When the user gets into the car, they connect their smartphone to the in-car system, which activates the system.

[0652] Step 2:

[0653] The device activates the environmental data acquisition module to collect real-time information on the current location (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is then sent to the server as soon as it is collected.

[0654] Step 3:

[0655] The device activates the camera and microphone inside the car to capture the user's facial expressions and voice, and this data is sent to the emotion engine.

[0656] Step 4:

[0657] The emotion engine analyzes the received data and determines the user's mood and emotions. For example, if the user's face looks relaxed, it will recognize the user as "relaxed" and send the result of the determination to the server.

[0658] Step 5:

[0659] The server integrates the received environmental data, emotional data, and user profile information to update the user profile management module, taking into account past playback history and music preferences.

[0660] Step 6:

[0661] The server runs a music recommendation engine based on the updated user profile and collected data. For example, it generates an up-tempo pop playlist for a sunny, cheerful morning. The generated playlist is then sent to the device.

[0662] Step 7:

[0663] The device displays the received playlist on the user interface and starts playing the music, which is then provided through the car's audio system.

[0664] Step 8:

[0665] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[0666] Step 9:

[0667] Every time the user operates the music, the device sends the operation data to the server and updates the user profile accordingly, which increases the accuracy of playlist generation from the next time onwards.

[0668] As a result, this system can individually optimize the user's driving experience and provide safe and comfortable music entertainment.

[0669] Example 2

[0670] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0671] Conventional music recommendation systems only recommend music based on a user's music preferences and past playback history, and do not take into account the user's current emotional state or environmental conditions (weather, location information, traffic volume, etc.), making it difficult to provide an optimal music experience for the user's mood and situation.In addition, users are only able to control music in a limited way, requiring intuitive operation.

[0672] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0673] In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression using a camera and microphone to determine the user's mood and emotions for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, location information, traffic volume, and emotional state; means for playing the generated music playlist; and means for controlling music with voice commands and touch operations. This enables optimal music recommendations that take into account the user's current emotional state and environmental conditions, providing a more intuitive and interactive music experience.

[0674] "Means for managing users' music preferences and past playback history" refers to a system that records and analyzes the music that users have played in the past and their ratings, thereby understanding the users' music preferences.

[0675] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to sensors and data acquisition systems for collecting current weather, user location information, and traffic conditions in real time.

[0676] "Means for analyzing the user's voice and facial expressions using a camera and microphone to determine the user's mood and emotions for that day" refers to a system that uses a camera and microphone installed in the vehicle to capture the user's facial expressions and voice, and analyzes them to determine the user's emotional state.

[0677] "Means for generating optimal music playlists based on user preferences, mood, weather, location information, traffic volume, and emotional state" refers to algorithms and systems that integrate and analyze multiple collected data to generate music playlists that are most suitable for users.

[0678] The "means for playing the generated music playlist" is a system for automatically transmitting the optimal playlist to a playback device and playing music.

[0679] "A means for controlling music with voice commands and touch operations" refers to an interface and system that allows users to intuitively control music playback, stopping, changing songs, etc. through voice instructions or a touch screen.

[0680] This invention is a music recommendation system that recognizes user emotions and generates and plays optimal music playlists based on the driver's emotional state. The system consists of the following main components: a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface.

[0681] 1. User Profile Management Module

[0682] The server manages the user's music preferences and past playback history. This module understands the user's tastes by accumulating and analyzing data such as the songs the user has listened to in the past, their ratings, and the number of times they have played the songs. For example, if a user likes rock music, this preference is saved in their profile and reflected in the next music recommendation.

[0683] 2. Environmental data acquisition module

[0684] The device connects to the user's smartphone or in-car system to obtain real-time data such as current location, weather, and traffic volume. This is achieved using hardware and software such as a GPS sensor, weather information API, and traffic information API. The collected data is sent to a server in real time.

[0685] 3. Emotion Engine

[0686] The device uses an in-car camera and microphone to capture the user's voice and facial expressions. The emotion engine recognizes the user's mood and emotional state by analyzing the user's facial muscle movements with the camera and the tone of voice with the microphone. For example, if the user is smiling, it will be determined that the user is in a "cheerful mood."

[0687] 4. Music recommendation engine

[0688] The server combines user profile data, location information, weather, traffic conditions, and emotional state, and inputs them into a music recommendation engine. This engine has an algorithm that generates a music playlist that best suits the user's current state. For example, it might recommend up-tempo pop music for a cheerful, sunny morning, or calm acoustic music for a tired evening. The generated playlist is then sent to the device.

[0689] 5. User Interface

[0690] The device displays the received playlist and automatically plays music. The user can control the playlist using voice commands (e.g., "Play next song") or touch interactions (e.g., using the touchscreen to select songs). For example, the user can say, "Add this song to my favorites," and the song will be added to the favorites list.

[0691] Specific examples

[0692] Case 1: Morning commute (sunny)

[0693] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device starts playing the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device adds the song to the favorites list.

[0694] Case 2: Returning home (rainy day, tired)

[0695] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[0696] Specific prompt examples

[0697] "Describe a music recommendation system that recognizes user emotions and generates music playlists. The system consists of a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface. Explain the function and processing flow of each module in detail, and provide examples of specific cases and prompt sentences."

[0698] The present invention allows the user to enjoy the most suitable music experience according to the mood and situation at the time, making driving even more comfortable.

[0699] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0700] Step 1:

[0701] Managing User Profiles

[0702] The server manages the user's past music playing history and ratings.

[0703] Input: User play history data and rating data

[0704] Specific behavior:

[0705] The server accesses a database to obtain the music that the user has played in the past and its rating data.

[0706] Based on the acquired data, the system analyzes trends such as users' favorite genres, artists, and songs.

[0707] Output: User profile data reflecting the user's music preferences

[0708] Step 2:

[0709] Environmental data collection

[0710] The device collects real-time environmental data from the user's smartphone or in-vehicle system and transmits it to a server.

[0711] Input: GPS data, weather data, traffic data

[0712] Specific behavior:

[0713] The device uses GPS to obtain its current location information.

[0714] To obtain weather information, a weather information API is accessed via the Internet.

[0715] Use the traffic information API to obtain traffic volume data.

[0716] The collected data is sent to the server.

[0717] Output: Environment data sent to the server

[0718] Step 3:

[0719] Acquiring and analyzing emotion data

[0720] The device uses the in-car camera and microphone to analyze the user's voice and facial expressions, and transmits emotional data to the server.

[0721] Input: Camera video data, audio data

[0722] Specific behavior:

[0723] The camera captures the user's facial expressions and analyzes the movement of facial muscles.

[0724] The microphone collects the user's voice and analyzes the tone and intonation of the voice.

[0725] Camera data and audio data are input into an emotion engine to determine the user's emotional state.

[0726] The determined emotion data is transmitted to the server.

[0727] Output: Emotion data sent to the server

[0728] Step 4:

[0729] Running a music recommendation engine

[0730] The server runs a music recommendation engine based on the integrated data to generate an optimal playlist.

[0731] Input: User profile data, environmental data, emotional data

[0732] Specific behavior:

[0733] The server integrates the user profile data, the environmental data, and the emotional data.

[0734] The integrated data is input into a music recommendation engine, which runs an algorithm to generate an optimal music playlist.

[0735] The generated playlist is sent to the terminal.

[0736] Output: Music playlist sent to device

[0737] Step 5:

[0738] Playlist provision and control

[0739] The terminal displays the playlist received from the server on the user interface and starts automatic playback.

[0740] Input: Music playlist data

[0741] Specific behavior:

[0742] The terminal displays the playlist received from the server.

[0743] Music is automatically played through the car's speakers.

[0744] Manage playback progress.

[0745] Output: Music being played

[0746] Step 6:

[0747] Interactive Operation

[0748] Users control the music using voice commands and touch gestures.

[0749] Input: Voice command, touch operation

[0750] Specific behavior:

[0751] The user issues voice commands such as "play next song" or "add this song to my favorites."

[0752] The device recognizes the voice command and performs the corresponding operation.

[0753] The user uses touch to change songs or stop playback.

[0754] Output: Music playback controlled by user instructions

[0755] (Application example 2)

[0756] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0757] There is a need to more individually optimize the music experience for passengers in autonomous vehicles and recommend music that matches their emotional state in real time. However, conventional music recommendation systems are based only on the user's static profile information and past playback history, and have the problem of not being able to reflect the user's real-time emotional state or current environmental data (weather, traffic conditions, etc.). This makes it difficult for passengers in autonomous vehicles to receive the optimal music experience that matches their mood at any given time.

[0758] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression to determine the user's mood for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, and location information; means for playing the generated music playlist; means for controlling music with voice commands and touch operations; means for integrating user emotional data and environmental data in an autonomous vehicle to generate and play an optimal music playlist; and means for recognizing a user's voice command and performing operations such as playing the next song or adding to a favorites list. This enables passengers in autonomous vehicles to enjoy an optimal music experience in real time.

[0759] The "means for managing the user's music preferences and past playback history" refers to a means for recording data on music that the user has listened to in the past and their ratings, and for grasping the user's music preferences.

[0760] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to means for collecting the latest information such as current weather conditions, the user's current location, and traffic flow.

[0761] The "means for analyzing the user's voice and facial expression to determine the mood of the day" is a means for analyzing the user's voice and facial expression, identifying the user's emotional state, and estimating the mood of the day.

[0762] The "means for generating an optimal music playlist" is a means for comprehensively analyzing data such as the user's preferences, mood, weather, and location information to create the most suitable music list.

[0763] The "means for playing the generated music playlist" refers to a means for starting playback to provide the music in the generated playlist to the user.

[0764] "Means for controlling music with voice commands and touch operations" refers to means for a user to perform operations such as playing, stopping, and skipping songs through voice instructions and touch operations.

[0765] The "means for integrating user emotional data and environmental data to generate and play an optimal music playlist" is a means for integrating driver emotional information and real-time environmental information, and automatically creating and playing an optimal music playlist based on that information.

[0766] "Means for performing actions such as next song or adding to favorites list" means means for a user to perform actions such as playing the next song or adding a specific song to a favorites list through voice commands.

[0767] The present invention is a music recommendation system that recognizes the user's emotions in an autonomous vehicle and provides an optimal music playlist based on the emotions. This system is composed of several main components. The role of each component and a specific embodiment are described below.

[0768] System configuration

[0769] 1. User Profile Management Module:

[0770] The server updates the user profile based on the music data the user has listened to in the past and their ratings. This profile serves as a database to understand the user's musical preferences. Specifically, if the user tends to like a particular genre or artist, that information is saved and analyzed.

[0771] 2. Environmental Data Acquisition Module:

[0772] The device connects with the autonomous vehicle's sensors and smartphone to collect real-time weather data, location information, traffic volume, etc. For example, if the current weather is sunny and traffic is smooth, that information is sent to the server.

[0773] 3. Emotion Engine:

[0774] The device uses the in-car camera and microphone to capture the user's facial expressions and voice to obtain emotional data. The emotion engine analyzes this data to determine the user's emotional state, such as "relaxed" or "tired." This analysis is performed using emotion analysis software such as Microsoft Azure Cognitive Services.

[0775] 4. Music recommendation engine:

[0776] The server integrates user profiles, environmental data, and emotional data to generate optimal music playlists. This recommendation engine runs in a cloud environment such as Amazon AWS Lambda. The generated playlists are optimized for the user's emotions and the current environment.

[0777] 5. User Interface:

[0778] The device displays the playlist as it plays and allows control via voice commands or touch gestures, for example, when the user says "play the next song," it skips to the next song using a voice recognition framework like Google Speech-to-Text.

[0779] Specific use cases

[0780] Case 1: Morning commute (sunny)

[0781] The user gets into the car and it starts driving automatically.

[0782] The device collects weather data (sunny), location information (home), and traffic conditions (smooth) and sends them to the server.

[0783] The emotion engine analyzes the user's smile and cheerful voice and determines that they are in a "cheerful mood."

[0784] The server generates an up-tempo playlist based on this data and sends it to the device.

[0785] When the device plays the playlist and the user issues a voice command such as "Add this song to my favorites," the device follows the instructions and adds the song to the favorites list.

[0786] Case 2: Returning home (rainy day, tired)

[0787] The user finishes work and gets into the car.

[0788] The device collects weather data (rain), location information (company), and traffic conditions (traffic jams) and sends them to the server.

[0789] The emotion engine analyzes the user's tired face and calm voice and determines that they are tired.

[0790] The server uses this data to generate a playlist of calming acoustic music and sends it to the device.

[0791] When the device plays a playlist and the user issues a voice command such as "play the next song," the device will follow the instruction and play the next song.

[0792] Prompt Sentence Examples

[0793] Implement a system that recognizes emotions through a camera and microphone while the user is in the car, and recommends music based on real-time weather and location information. Meet the following requirements:

[0794] 1. User Profile Management

[0795] 2. Real-time collection of environmental data

[0796] 3. Emotion Recognition Engine Integration

[0797] 4. Playlist generation by recommendation engine

[0798] 5. Interactive operation via voice commands

[0799] Through the above embodiments, the system of the present invention can more individually optimize the user's driving experience and provide safe and comfortable music entertainment to passengers in autonomous vehicles.

[0800] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0801] Step 1:

[0802] The user enters the car and begins operating the autonomous vehicle. The device establishes a connection with the user's smartphone and in-vehicle system. This connection transmits the user's past music playback history and ratings to a server, which populates the user profile. The server uses this data to update the user's music preferences.

[0803] Input: User's music playback history, rating data

[0804] Output: Updated user profile

[0805] Step 2:

[0806] The device uses the autonomous vehicle's built-in sensors and internet connection to collect real-time weather data, location information, and traffic volume, which is then sent to a server.

[0807] Input: Weather data, location information, traffic

[0808] Output: Collected environmental data

[0809] Step 3:

[0810] The emotion engine captures the user's facial expressions and voice through the car's camera and microphone. It then uses emotion analysis software, such as Microsoft Azure Cognitive Services, to determine the user's emotional state. For example, if the user is smiling while speaking, the emotional state is recognized as "cheerful."

[0811] Input: User's facial expression data, voice data

[0812] Output: Parsed emotion data

[0813] Step 4:

[0814] The server integrates user profiles, environmental data, and emotional data, and uses a music recommendation engine to generate optimal music playlists. Utilizing cloud environments such as Amazon AWS Lambda, the server comprehensively analyzes information from multiple data sources and selects music that matches the user's mood.

[0815] Input: User profile, environmental data, emotional data

[0816] Output: Optimized music playlist

[0817] Step 5:

[0818] The generated music playlist is sent to the terminal and displayed on the user interface, and the terminal activates a function to play music based on the playlist, allowing the user to listen to the music through the car speakers.

[0819] Input: Generated music playlist

[0820] Output: Music being played

[0821] Step 6:

[0822] Users can control music through the car's voice command system or touch controls. For example, if a user says, "Play the next song," the device will recognize the command using voice recognition software like Google Speech-to-Text and play the next song. Users can also add a song to their "favorites" list, which will be carried out.

[0823] Input: User voice commands, touch actions

[0824] Output: Music playback control and favorites update

[0825] Through these steps, the system can provide optimal music in real time based on the user's emotions and environmental data, making the user's driving experience even more comfortable.

[0826] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0827] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0828] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0829] [Third embodiment]

[0830] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0831] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0832] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0833] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0834] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0835] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0836] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0837] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0838] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0839] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0840] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0841] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0842] The present invention is a system that combines various data to individually recommend music to optimize the user's music experience. The operation method and program processing of this system are described below.

[0843] System configuration

[0844] The system of the present invention consists of the following major components:

[0845] 1. User profile management module: Manages users' music preferences and past playing history.

[0846] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[0847] 3. Sentiment analysis module: Analyzes the user's voice and facial expressions to determine their mood for the day.

[0848] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[0849] 5. User interface: Users can easily control music with voice or touch.

[0850] Program processing

[0851] 1. Managing your user profile

[0852] The server processes the music data the user has listened to in the past and their ratings, and updates the user profile. For example, if the user has a tendency to like a particular genre, that information is stored.

[0853] 2. Environmental data collection

[0854] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[0855] 3. Acquiring Emotion Data

[0856] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, then applies an emotion analysis algorithm to determine their emotional state for the day and sends that data to the server. For example, if the user is tired, it will report that data.

[0857] 4. Running the Music Recommendation Engine

[0858] The server integrates user profile, environmental data, and emotional data to generate an optimal playlist using a music recommendation engine. For example, if it's a clear morning and the user is in a good mood, it will recommend up-tempo pop music.

[0859] 5. Providing and Controlling Playlists

[0860] The device displays the playlist received from the server on the user interface and automatically starts playback. The user can then use voice commands or touch controls to play, skip, or add songs to favorites.

[0861] Specific examples

[0862] Case 1: Morning commute (sunny)

[0863] First, the user gets into their car in the morning and connects the in-car system to their smartphone. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends these to the server. At the same time, the server analyzes the user's smile and cheerful voice and reports that they are in a "cheerful mood." The server uses this data to create a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," it performs the additional process according to the instruction.

[0864] Case 2: Returning home (rainy day, tired)

[0865] In the evening, the user gets into their car after work and connects their smartphone. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the server analyzes the user's tired face and calm voice and reports that they are "tired." Based on this data, the server generates a playlist of calming acoustic music and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[0866] As a result, this system individually optimizes the user's driving experience while providing safe and comfortable music entertainment.

[0867] The processing flow will be explained below.

[0868] Step 1:

[0869] When a user gets into the car, they connect their smartphone to the in-car system, which activates the entire system.

[0870] Step 2:

[0871] The device collects real-time location information (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is obtained through the environmental data acquisition module.

[0872] Step 3:

[0873] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion analysis module. The emotion analysis algorithm analyzes the user's mood and emotions, determining their state, for example, "relaxed" or "tired."

[0874] Step 4:

[0875] The device sends the data (environmental data, emotional data) collected in steps 2 and 3 to the server, including the current location, weather, traffic conditions, and the user's mood.

[0876] Step 5:

[0877] The server updates the user profile based on the received data, which includes the user's music preferences, past playback history, and emotional data, all of which are updated based on the latest information.

[0878] Step 6:

[0879] The server runs a music recommendation engine based on the updated user profile, environmental data, and emotional data. For example, a playlist of up-tempo pop music is generated for a sunny, cheerful morning. The generated playlist is then sent to the device.

[0880] Step 7:

[0881] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[0882] Step 8:

[0883] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[0884] Step 9:

[0885] Every time the user makes a change, the device sends that information (new favorite songs, changes to the playlist) to the server, updating the user profile accordingly, which makes future playlists even more accurate.

[0886] Through the above processing steps, the system can provide the optimal music experience according to the user's individual preferences and circumstances.

[0887] Example 1

[0888] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0889] In today's information society, it is becoming increasingly important to individually optimize each user's music experience. Conventional music recommendation systems only recommend music based on a user's preferences and past playback history, but there is a growing demand for music recommendations that appropriately reflect a variety of factors, such as the user's real-time situation and emotional state. Therefore, it is necessary to realize a more accurate music recommendation system that can respond to the user's individual situation and emotions.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0891] In this invention, the server includes a means for managing a user's music preferences and past playback history, a means for acquiring real-time data such as weather, location information, and traffic volume, and a means for analyzing the user's voice and facial expression to determine their mood for the day. This provides a means for using a generative model to generate an optimal music playlist based on the user's preferences, mood, weather, and location information, and enables music control via voice commands and touch operations. This enables a more personalized music experience that takes into account the user's real-time situation and emotional state.

[0892] "User's music preferences" refers to information about the user's tastes accumulated based on the music the user has listened to in the past and their ratings of that music.

[0893] "Playback history" is history data of music that the user has played in the past, and includes information such as specific dates and times and the number of times the music has been played.

[0894] "Weather data" is data that indicates the current weather conditions, and includes environmental information such as whether it is sunny, rainy, wind speed, and temperature.

[0895] "Location information" refers to information about the user's current location and travel route obtained by GPS or the like.

[0896] "Traffic volume data" refers to data that indicates real-time traffic conditions, including congestion information and road congestion levels.

[0897] "User's voice and facial expression" refers to the user's voice and facial expression, which are information used to determine the user's emotional state by analyzing them.

[0898] "Mood" refers to the user's emotional or mental state, and includes happiness, sadness, fatigue, and the like.

[0899] A "generative model" is an algorithm or AI model that generates optimal music playlists based on various collected data.

[0900] A "music playlist" is a list of multiple songs that are played in a specified order, and is generated to suit the user's preferences and mood.

[0901] A "voice command" is an input method that allows a user to give instructions to a device by voice.

[0902] "Touch operation" is an input method in which a user issues instructions to a device by operating a touch screen.

[0903] "Electronic devices" are devices with a user interface, including smartphones, tablets, in-vehicle systems, etc.

[0904] "Emotion data" is data relating to the emotional state of the user obtained by analyzing the user's voice and facial expression.

[0905] "Environmental data" refers to real-time environmental information surrounding the user, such as weather, location information, and traffic volume.

[0906] A "user profile" is individual setting information generated based on a user's past behavior and preferences.

[0907] The present invention is a system for individually optimizing a user's music experience, and is implemented using the following major hardware and software:

[0908] System Configuration

[0909] 1. User Profile Management Module

[0910] The server stores the user's past music playback history and rating data, and updates the user profile based on this information. It also records the user's preferences for genres and artists in detail and stores them in a profile database.

[0911] 2. Environmental data acquisition module

[0912] The device connects to the user's smartphone or in-car system to collect real-time information such as current location, weather data, traffic volume, etc. For example, it uses a GPS sensor and weather API to obtain this data and send it to a server.

[0913] 3. Emotion Data Acquisition Module

[0914] The device uses a built-in camera and microphone to capture the user's facial expressions and voice. This data is processed by an emotion analysis algorithm, and the user's emotional state is transmitted to the server. For example, the camera analyzes the user's smile and the microphone analyzes the tone of the voice.

[0915] 4. Music recommendation engine

[0916] The server combines user profiles, environmental data, and emotional data to generate an optimal music playlist using a generative AI model, which includes algorithms for selecting music based on weather and the user's mood.

[0917] 5. User Interface

[0918] The device displays the playlist sent from the server, and the user can control the music by voice or touch operation, such as playing, skipping, adding to favorites, etc. The user interface has an intuitive and easy-to-use design.

[0919] Specific examples

[0920] Case 1: Morning commute (sunny)

[0921] When a user gets into their car in the morning, they connect their smartphone to the in-car system. The device collects weather data (clear), location information (home), and traffic conditions (smooth), and sends this information to the server.

[0922] At the same time, the device captures the user's smile and cheerful voice, and uses an emotion analysis algorithm to determine that the user is in a "cheerful mood," then sends the data to the server.

[0923] The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. When the user issues a voice command such as "Add this song to my favorites," the device adds it to the playlist accordingly.

[0924] Case 2: Returning home (rainy day, tired)

[0925] In the evening, the user gets into the car after work and connects the smartphone. The device collects weather data (rain), location information (workplace), and traffic conditions (traffic jam), and sends them to the server.

[0926] The device also captures tired facial expressions and calm voices, determines whether the user is "tired" using an emotion analysis algorithm, and sends the data to a server.

[0927] The server uses this data to generate a playlist of calming acoustic music and sends it to the device. When the user issues a voice command such as "play the next song," the device plays the next song as requested.

[0928] Prompt Sentence Examples

[0929] By inputting the following prompt sentences into the generative AI model, the optimal music playlist can be generated.

[0930] Create the perfect music playlist for your users, taking into account their preferred music genres, current weather, location, and emotional state. Specific situations include:

[0931] User Preferences: Rock and Pop

[0932] Current weather: Sunny

[0933] Location: On the way from home to work

[0934] Emotional state: cheerful and lively

[0935] Suggest music playlist content to be recommended.

[0936] As described above, the present invention individually optimizes the user's driving experience and provides safe and comfortable music entertainment.

[0937] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0938] Step 1: Manage your user profile

[0939] The server collects the user's past music playback history and rating data, and updates the user profile based on this.

[0940] Input: User play history and rating data

[0941] Data processing: Statistical analysis of play counts and ratings

[0942] Output: Updated user profile

[0943] Specific operation: For example, the server analyzes the playback history for the past month and stores the most played genres and artists in a profile database.

[0944] Step 2: Collect environmental data

[0945] The terminal works in conjunction with smartphones and in-vehicle systems to collect current location information, weather data, and traffic volume data, and transmits it to a server.

[0946] Input: GPS data, weather API information, traffic information API data

[0947] Data processing: location information acquisition, real-time updates of weather and traffic information

[0948] Output: Collected environmental data

[0949] Specific operation: The device obtains location information using the GPS sensor, collects current weather data using the weather API, and obtains traffic volume data using the traffic information API.

[0950] Step 3: Obtaining emotion data

[0951] The device uses a camera and microphone to capture the user's facial expressions and voice, and then uses an analysis algorithm to determine their emotional state and send the results to a server.

[0952] Input: Video data captured by the camera, audio data recorded by the microphone

[0953] Data processing: Emotion analysis using facial recognition and tone of voice analysis

[0954] Output: Parsed emotional state data

[0955] Specific operation: The device captures the user's facial expression with a camera and determines whether they are smiling. It also analyzes the tone of their voice recorded by a microphone to determine whether they are in good spirits.

[0956] Step 4: Running the Music Recommendation Engine

[0957] The server integrates user profiles, environmental data, and emotional data, and generates an optimal music playlist using a generative AI model.

[0958] Input: User profile, real-time environmental data, emotional state data

[0959] Data processing: Integrating various data and recommending music using AI models

[0960] Output: Generated music playlist

[0961] Specific operation: The server runs an algorithm that prioritizes up-tempo pop music based on data indicating that it is a clear morning and the user is in a cheerful mood.

[0962] Step 5: Providing and Controlling Playlists

[0963] The device displays the playlist received from the server on the user interface and automatically plays music. The user can use voice commands or touch operations to play, skip, add to favorites, etc.

[0964] Input: Generated music playlist, user voice commands or touch actions

[0965] Data processing: display in the user interface and processing of user commands

[0966] Output: Music playback control according to user instructions

[0967] Specific behavior: The device will display the playlist and start playing. If the user issues a voice command such as "Play the next song," the device will play the next song. If the user issues a voice command such as "Add this song to my favorites," the device will add the song to the favorites list.

[0968] (Application example 1)

[0969] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0970] In recent years, there has been a demand for improving the in-car user experience. In particular, providing highly personalized entertainment contributes greatly to customer satisfaction in self-driving vehicles. However, conventional music recommendation systems have difficulty comprehensively considering user preferences, emotional state, and environmental data, making it impossible to achieve high-quality personalization. The purpose of this invention is to solve these problems and provide users with the optimal music experience.

[0971] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0972] In this invention, the server includes means for managing a user's music preferences and past playback history, means for acquiring real-time data such as weather, location information, and traffic volume, and means for analyzing the user's voice and facial expression to determine the user's mood for the day. This makes it possible to generate an optimal music playlist based on the user's preferences, mood, weather, location information, and traffic conditions, and a highly personalized music experience can be realized by means for playing the generated music playlist, means for controlling music with voice commands and touch operations, means for inputting a user profile, environmental data, and emotional data into an AI model to generate a music playlist, and means for displaying the generated playlist on the infotainment system and accepting operations with voice commands.

[0973] A "user profile" is a collection of information including a user's music preferences and past playback history.

[0974] "Environmental data" refers to real-time information such as weather, location, and traffic volume.

[0975] "Emotion data" is data that indicates the mood and emotional state of the user on that day, analyzed from the user's voice and facial expression.

[0976] A "music playlist" is a list of selected music tracks in a playback order.

[0977] An "AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate optimal results.

[0978] An "infotainment system" is a system installed in a vehicle that provides information and entertainment.

[0979] A "voice command" is an instruction given by a user to operate the system by voice.

[0980] "Touch operation" refers to an operation method performed by touching a screen or panel with a finger.

[0981] The present invention is a system for recommending music to a user based on a personalized music recommendation system that combines a user profile, environmental data, and emotional data to optimize the user's music experience. Specific embodiments of the present invention are described below.

[0982] System configuration

[0983] The system of the present invention consists of the following major components:

[0984] 1. User profile management module: This module manages users' music preferences and past playback history. It uses information stored in the database server to accurately grasp users' tastes.

[0985] 2. Environmental Data Acquisition Module: This module collects real-time data such as weather, location information, traffic volume, etc. It uses, for example, OpenWeatherMap and Google Maps APIs to collect data.

[0986] 3. Sentiment Analysis Module: This module uses the in-car camera and microphone to capture the user's facial expressions and voice, and analyzes their emotional state. It uses Face++ for facial recognition and Google Speech Recognition for voice recognition.

[0987] 4. Music recommendation engine: Integrates user profiles, environmental data, and emotional data to generate optimal music playlists using AI models. For example, TensorFlow and PyTorch can be used to build AI models.

[0988] 5. User Interface: Users can easily control music with voice commands and touch. This interface is integrated with the vehicle's infotainment system.

[0989] Program processing

[0990] The server collects and manages user profiles, environmental data, and emotional data, and runs the music recommendation engine. For example, the user profile management module processes the music data that users have listened to in the past and their ratings, and stores them in a database.

[0991] The device collects real-time weather, location, and traffic information through the environmental data acquisition module and sends it to the server.The emotion analysis module uses the in-car camera and microphone to capture the user's facial expressions and voice, analyzes the data, and identifies the user's emotional state, which is then sent to the server.

[0992] The server runs a music recommendation engine based on the collected user profile, environmental data, and emotional data, and uses AI models to generate an optimal music playlist, which is then sent to the device and displayed on the infotainment system.

[0993] Users control the playlist with voice commands or touch gestures, such as "play the next song" or "add this song to my favorites," and the system responds.

[0994] Specific examples

[0995] Case 1: Morning commute (sunny)

[0996] A user gets into a self-driving car in the morning and connects to the system. The in-car camera captures the user's smile, and the microphone analyzes their cheerful voice. Environmental data, such as location (home), weather data (sunny), and traffic conditions (smooth), are collected and sent to the server. The server uses this data to create a playlist of up-tempo pop music, sends it to the infotainment system, and begins playback. When the user issues a voice command such as "Add this song to my favorites," the system follows the instructions and performs additional processing.

[0997] Example prompts for generative AI models

[0998] Consider the user's music profile, location, weather, traffic conditions, and emotional state to generate the best music playlist based on the following information:

[0999] Profile: Pop, Rock

[1000] Location: Home

[1001] Weather: Sunny

[1002] Traffic Condition: Smooth

[1003] Emotion: Cheerful mood

[1004] Example of generated playlist: "Song A", "Song B", "Song C"

[1005] In this way, the system can provide the high-quality music experience that users desire.

[1006] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1007] Step 1:

[1008] The device retrieves the user's music preferences and past playback history from a database. The user ID is required as input, and user profile information (music preferences, ratings, playback history) is obtained as output. Specifically, the device queries the database, and the user profile management module returns the information.

[1009] Step 2:

[1010] The device collects real-time data such as location information, weather, and traffic volume. It receives the current location, weather API, and traffic information API URLs as input, and obtains the current location, weather conditions, and traffic conditions as output. Specifically, it obtains location information from the GPS sensor, sends HTTP requests to the weather and traffic information APIs, and analyzes the obtained data.

[1011] Step 3:

[1012] The device uses an in-car camera and microphone to capture the user's facial expressions and voice and perform emotion analysis. It requires camera feed and audio data as input, and obtains the user's emotional state (e.g., cheerful, tired, etc.) as output. Specifically, the device inputs the images and audio data captured by the camera into the emotion analysis module, which then obtains the analysis results.

[1013] Step 4:

[1014] The server runs a music recommendation engine based on user profile, environmental data, and emotional data to generate an optimal music playlist. It integrates user profile, location information, weather, traffic conditions, and emotional state data as input, and obtains a music playlist as output. Specifically, it inputs this data into an AI model and generates an optimal playlist using a recommendation algorithm.

[1015] Step 5:

[1016] The server sends the generated music playlist to the terminal. The server requires the playlist data as input and obtains the playlist sent to the terminal as output. The specific operation is to send the playlist to the terminal via the network.

[1017] Step 6:

[1018] The device displays the received playlist on the infotainment system and starts playback. The input is the received playlist data, and the output is the music displayed on the screen and played through the car speakers. The specific operation is to display the playlist and control playback using the infotainment system's API.

[1019] Step 7:

[1020] Users can use voice commands or touch operations to play music, skip, add to favorites, etc. Voice commands and touch operation data are required as input, and the output is the music control results according to the user's instructions. Specifically, the voice recognition system analyzes the command, and the infotainment system executes the corresponding operation.

[1021] Through this series of processes, users can enjoy a highly personalized music experience in their autonomous vehicles.

[1022] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1023] The present invention is a music recommendation system that combines an emotion engine that recognizes the user's emotions, thereby generating an optimal music playlist based on the driver's emotional state, providing a personalized music experience in real time.

[1024] System configuration

[1025] The system of the present invention consists of the following major components:

[1026] 1. User profile management module: Manages users' music preferences and past playing history.

[1027] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[1028] 3. Emotion engine: Analyzes the user's voice and facial expressions to recognize their mood and emotions for the day.

[1029] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[1030] 5. User interface: Users can easily control music with voice or touch.

[1031] Program processing

[1032] 1. Managing your user profile

[1033] The server updates the user profile based on the music data the user has listened to in the past and their ratings. For example, if the user has a tendency to like a particular genre, the server stores that information.

[1034] 2. Environmental data collection

[1035] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[1036] 3. Acquisition and Analysis of Emotion Data

[1037] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine, which analyzes the user's mood and emotions and determines their state, such as "relaxed" or "tired."

[1038] 4. Running the Music Recommendation Engine

[1039] The server integrates the user profile, environmental data, and emotional data and generates an optimal playlist using a music recommendation engine. For example, it recommends an up-tempo pop playlist for a cheerful, sunny morning. The generated playlist is then sent to the device.

[1040] 5. Providing and Controlling Playlists

[1041] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[1042] 6. Interactive Operation

[1043] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform actions such as playing or pausing music, skipping to the next song, or adding music to favorites.

[1044] Specific examples

[1045] Case 1: Morning commute (sunny)

[1046] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends them to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device follows the instruction and adds the song to the favorites list.

[1047] Case 2: Returning home (rainy day, tired)

[1048] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[1049] These specific examples enable the system to better personalize the user's driving experience and provide safe and comfortable music entertainment.

[1050] The processing flow will be explained below.

[1051] Step 1:

[1052] When the user gets into the car, they connect their smartphone to the in-car system, which activates the system.

[1053] Step 2:

[1054] The device activates the environmental data acquisition module to collect real-time information on the current location (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is then sent to the server as soon as it is collected.

[1055] Step 3:

[1056] The device activates the camera and microphone inside the car to capture the user's facial expressions and voice, and this data is sent to the emotion engine.

[1057] Step 4:

[1058] The emotion engine analyzes the received data and determines the user's mood and emotions. For example, if the user's face looks relaxed, it will recognize the user as "relaxed" and send the result of the determination to the server.

[1059] Step 5:

[1060] The server integrates the received environmental data, emotional data, and user profile information to update the user profile management module, taking into account past playback history and music preferences.

[1061] Step 6:

[1062] The server runs a music recommendation engine based on the updated user profile and collected data. For example, it generates an up-tempo pop playlist for a sunny, cheerful morning. The generated playlist is then sent to the device.

[1063] Step 7:

[1064] The device displays the received playlist on the user interface and starts playing the music, which is then provided through the car's audio system.

[1065] Step 8:

[1066] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[1067] Step 9:

[1068] Every time the user operates the music, the device sends the operation data to the server and updates the user profile accordingly, which increases the accuracy of playlist generation from the next time onwards.

[1069] As a result, this system can individually optimize the user's driving experience and provide safe and comfortable music entertainment.

[1070] Example 2

[1071] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1072] Conventional music recommendation systems only recommend music based on a user's music preferences and past playback history, and do not take into account the user's current emotional state or environmental conditions (weather, location information, traffic volume, etc.), making it difficult to provide an optimal music experience for the user's mood and situation.In addition, users are only able to control music in a limited way, requiring intuitive operation.

[1073] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1074] In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression using a camera and microphone to determine the user's mood and emotions for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, location information, traffic volume, and emotional state; means for playing the generated music playlist; and means for controlling music with voice commands and touch operations. This enables optimal music recommendations that take into account the user's current emotional state and environmental conditions, providing a more intuitive and interactive music experience.

[1075] "Means for managing users' music preferences and past playback history" refers to a system that records and analyzes the music that users have played in the past and their ratings, thereby understanding the users' music preferences.

[1076] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to sensors and data acquisition systems for collecting current weather, user location information, and traffic conditions in real time.

[1077] "Means for analyzing the user's voice and facial expressions using a camera and microphone to determine the user's mood and emotions for that day" refers to a system that uses a camera and microphone installed in the vehicle to capture the user's facial expressions and voice, and analyzes them to determine the user's emotional state.

[1078] "Means for generating optimal music playlists based on user preferences, mood, weather, location information, traffic volume, and emotional state" refers to algorithms and systems that integrate and analyze multiple collected data to generate music playlists that are most suitable for users.

[1079] The "means for playing the generated music playlist" is a system for automatically transmitting the optimal playlist to a playback device and playing music.

[1080] "A means for controlling music with voice commands and touch operations" refers to an interface and system that allows users to intuitively control music playback, stopping, changing songs, etc. through voice instructions or a touch screen.

[1081] This invention is a music recommendation system that recognizes user emotions and generates and plays optimal music playlists based on the driver's emotional state. The system consists of the following main components: a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface.

[1082] 1. User Profile Management Module

[1083] The server manages the user's music preferences and past playback history. This module understands the user's tastes by accumulating and analyzing data such as the songs the user has listened to in the past, their ratings, and the number of times they have played the songs. For example, if a user likes rock music, this preference is saved in their profile and reflected in the next music recommendation.

[1084] 2. Environmental data acquisition module

[1085] The device connects to the user's smartphone or in-car system to obtain real-time data such as current location, weather, and traffic volume. This is achieved using hardware and software such as a GPS sensor, weather information API, and traffic information API. The collected data is sent to a server in real time.

[1086] 3. Emotion Engine

[1087] The device uses an in-car camera and microphone to capture the user's voice and facial expressions. The emotion engine recognizes the user's mood and emotional state by analyzing the user's facial muscle movements with the camera and the tone of voice with the microphone. For example, if the user is smiling, it will be determined that the user is in a "cheerful mood."

[1088] 4. Music recommendation engine

[1089] The server combines user profile data, location information, weather, traffic conditions, and emotional state, and inputs them into a music recommendation engine. This engine has an algorithm that generates a music playlist that best suits the user's current state. For example, it might recommend up-tempo pop music for a cheerful, sunny morning, or calm acoustic music for a tired evening. The generated playlist is then sent to the device.

[1090] 5. User Interface

[1091] The device displays the received playlist and automatically plays music. The user can control the playlist using voice commands (e.g., "Play next song") or touch interactions (e.g., using the touchscreen to select songs). For example, the user can say, "Add this song to my favorites," and the song will be added to the favorites list.

[1092] Specific examples

[1093] Case 1: Morning commute (sunny)

[1094] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device starts playing the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device adds the song to the favorites list.

[1095] Case 2: Returning home (rainy day, tired)

[1096] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[1097] Specific prompt examples

[1098] "Describe a music recommendation system that recognizes user emotions and generates music playlists. The system consists of a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface. Explain the function and processing flow of each module in detail, and provide examples of specific cases and prompt sentences."

[1099] The present invention allows the user to enjoy the most suitable music experience according to the mood and situation at the time, making driving even more comfortable.

[1100] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1101] Step 1:

[1102] Managing User Profiles

[1103] The server manages the user's past music playing history and ratings.

[1104] Input: User play history data and rating data

[1105] Specific behavior:

[1106] The server accesses a database to obtain the music that the user has played in the past and its rating data.

[1107] Based on the acquired data, the system analyzes trends such as users' favorite genres, artists, and songs.

[1108] Output: User profile data reflecting the user's music preferences

[1109] Step 2:

[1110] Environmental data collection

[1111] The device collects real-time environmental data from the user's smartphone or in-vehicle system and transmits it to a server.

[1112] Input: GPS data, weather data, traffic data

[1113] Specific behavior:

[1114] The device uses GPS to obtain its current location information.

[1115] To obtain weather information, a weather information API is accessed via the Internet.

[1116] Use the traffic information API to obtain traffic volume data.

[1117] The collected data is sent to the server.

[1118] Output: Environment data sent to the server

[1119] Step 3:

[1120] Acquiring and analyzing emotion data

[1121] The device uses the in-car camera and microphone to analyze the user's voice and facial expressions, and transmits emotional data to the server.

[1122] Input: Camera video data, audio data

[1123] Specific behavior:

[1124] The camera captures the user's facial expressions and analyzes the movement of facial muscles.

[1125] The microphone collects the user's voice and analyzes the tone and intonation of the voice.

[1126] Camera data and audio data are input into an emotion engine to determine the user's emotional state.

[1127] The determined emotion data is transmitted to the server.

[1128] Output: Emotion data sent to the server

[1129] Step 4:

[1130] Running a music recommendation engine

[1131] The server runs a music recommendation engine based on the integrated data to generate an optimal playlist.

[1132] Input: User profile data, environmental data, emotional data

[1133] Specific behavior:

[1134] The server integrates the user profile data, the environmental data, and the emotional data.

[1135] The integrated data is input into a music recommendation engine, which runs an algorithm to generate an optimal music playlist.

[1136] The generated playlist is sent to the terminal.

[1137] Output: Music playlist sent to device

[1138] Step 5:

[1139] Playlist provision and control

[1140] The terminal displays the playlist received from the server on the user interface and starts automatic playback.

[1141] Input: Music playlist data

[1142] Specific behavior:

[1143] The terminal displays the playlist received from the server.

[1144] Music is automatically played through the car's speakers.

[1145] Manage playback progress.

[1146] Output: Music being played

[1147] Step 6:

[1148] Interactive Operation

[1149] Users control the music using voice commands and touch gestures.

[1150] Input: Voice command, touch operation

[1151] Specific behavior:

[1152] The user issues voice commands such as "play next song" or "add this song to my favorites."

[1153] The device recognizes the voice command and performs the corresponding operation.

[1154] The user uses touch to change songs or stop playback.

[1155] Output: Music playback controlled by user instructions

[1156] (Application example 2)

[1157] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1158] There is a need to more individually optimize the music experience for passengers in autonomous vehicles and recommend music that matches their emotional state in real time. However, conventional music recommendation systems are based only on the user's static profile information and past playback history, and have the problem of not being able to reflect the user's real-time emotional state or current environmental data (weather, traffic conditions, etc.). This makes it difficult for passengers in autonomous vehicles to receive the optimal music experience that matches their mood at any given time.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression to determine the user's mood for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, and location information; means for playing the generated music playlist; means for controlling music with voice commands and touch operations; means for integrating user emotional data and environmental data in an autonomous vehicle to generate and play an optimal music playlist; and means for recognizing a user's voice command and performing operations such as playing the next song or adding to a favorites list. This enables passengers in autonomous vehicles to enjoy an optimal music experience in real time.

[1160] The "means for managing the user's music preferences and past playback history" refers to a means for recording data on music that the user has listened to in the past and their ratings, and for grasping the user's music preferences.

[1161] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to means for collecting the latest information such as current weather conditions, the user's current location, and traffic flow.

[1162] The "means for analyzing the user's voice and facial expression to determine the mood of the day" is a means for analyzing the user's voice and facial expression, identifying the user's emotional state, and estimating the mood of the day.

[1163] The "means for generating an optimal music playlist" is a means for comprehensively analyzing data such as the user's preferences, mood, weather, and location information to create the most suitable music list.

[1164] The "means for playing the generated music playlist" refers to a means for starting playback to provide the music in the generated playlist to the user.

[1165] "Means for controlling music with voice commands and touch operations" refers to means for a user to perform operations such as playing, stopping, and skipping songs through voice instructions and touch operations.

[1166] The "means for integrating user emotional data and environmental data to generate and play an optimal music playlist" is a means for integrating driver emotional information and real-time environmental information, and automatically creating and playing an optimal music playlist based on that information.

[1167] "Means for performing actions such as next song or adding to favorites list" means means for a user to perform actions such as playing the next song or adding a specific song to a favorites list through voice commands.

[1168] The present invention is a music recommendation system that recognizes the user's emotions in an autonomous vehicle and provides an optimal music playlist based on the emotions. This system is composed of several main components. The role of each component and a specific embodiment are described below.

[1169] System configuration

[1170] 1. User Profile Management Module:

[1171] The server updates the user profile based on the music data the user has listened to in the past and their ratings. This profile serves as a database to understand the user's musical preferences. Specifically, if the user tends to like a particular genre or artist, that information is saved and analyzed.

[1172] 2. Environmental Data Acquisition Module:

[1173] The device connects with the autonomous vehicle's sensors and smartphone to collect real-time weather data, location information, traffic volume, etc. For example, if the current weather is sunny and traffic is smooth, that information is sent to the server.

[1174] 3. Emotion Engine:

[1175] The device uses the in-car camera and microphone to capture the user's facial expressions and voice to obtain emotional data. The emotion engine analyzes this data to determine the user's emotional state, such as "relaxed" or "tired." This analysis is performed using emotion analysis software such as Microsoft Azure Cognitive Services.

[1176] 4. Music recommendation engine:

[1177] The server integrates user profiles, environmental data, and emotional data to generate optimal music playlists. This recommendation engine runs in a cloud environment such as Amazon AWS Lambda. The generated playlists are optimized for the user's emotions and the current environment.

[1178] 5. User Interface:

[1179] The device displays the playlist as it plays and allows control via voice commands or touch gestures, for example, when the user says "play the next song," it skips to the next song using a voice recognition framework like Google Speech-to-Text.

[1180] Specific use cases

[1181] Case 1: Morning commute (sunny)

[1182] The user gets into the car and it starts driving automatically.

[1183] The device collects weather data (sunny), location information (home), and traffic conditions (smooth) and sends them to the server.

[1184] The emotion engine analyzes the user's smile and cheerful voice and determines that they are in a "cheerful mood."

[1185] The server generates an up-tempo playlist based on this data and sends it to the device.

[1186] When the device plays the playlist and the user issues a voice command such as "Add this song to my favorites," the device follows the instructions and adds the song to the favorites list.

[1187] Case 2: Returning home (rainy day, tired)

[1188] The user finishes work and gets into the car.

[1189] The device collects weather data (rain), location information (company), and traffic conditions (traffic jams) and sends them to the server.

[1190] The emotion engine analyzes the user's tired face and calm voice and determines that they are tired.

[1191] The server uses this data to generate a playlist of calming acoustic music and sends it to the device.

[1192] When the device plays a playlist and the user issues a voice command such as "play the next song," the device will follow the instruction and play the next song.

[1193] Prompt Sentence Examples

[1194] Implement a system that recognizes emotions through a camera and microphone while the user is in the car, and recommends music based on real-time weather and location information. Meet the following requirements:

[1195] 1. User Profile Management

[1196] 2. Real-time collection of environmental data

[1197] 3. Emotion Recognition Engine Integration

[1198] 4. Playlist generation by recommendation engine

[1199] 5. Interactive operation via voice commands

[1200] Through the above embodiments, the system of the present invention can more individually optimize the user's driving experience and provide safe and comfortable music entertainment to passengers in autonomous vehicles.

[1201] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1202] Step 1:

[1203] The user enters the car and begins operating the autonomous vehicle. The device establishes a connection with the user's smartphone and in-vehicle system. This connection transmits the user's past music playback history and ratings to a server, which populates the user profile. The server uses this data to update the user's music preferences.

[1204] Input: User's music playback history, rating data

[1205] Output: Updated user profile

[1206] Step 2:

[1207] The device uses the autonomous vehicle's built-in sensors and internet connection to collect real-time weather data, location information, and traffic volume, which is then sent to a server.

[1208] Input: Weather data, location information, traffic

[1209] Output: Collected environmental data

[1210] Step 3:

[1211] The emotion engine captures the user's facial expressions and voice through the car's camera and microphone. It then uses emotion analysis software, such as Microsoft Azure Cognitive Services, to determine the user's emotional state. For example, if the user is smiling while speaking, the emotional state is recognized as "cheerful."

[1212] Input: User's facial expression data, voice data

[1213] Output: Parsed emotion data

[1214] Step 4:

[1215] The server integrates user profiles, environmental data, and emotional data, and uses a music recommendation engine to generate optimal music playlists. Utilizing cloud environments such as Amazon AWS Lambda, the server comprehensively analyzes information from multiple data sources and selects music that matches the user's mood.

[1216] Input: User profile, environmental data, emotional data

[1217] Output: Optimized music playlist

[1218] Step 5:

[1219] The generated music playlist is sent to the terminal and displayed on the user interface, and the terminal activates a function to play music based on the playlist, allowing the user to listen to the music through the car speakers.

[1220] Input: Generated music playlist

[1221] Output: Music being played

[1222] Step 6:

[1223] Users can control music through the car's voice command system or touch controls. For example, if a user says, "Play the next song," the device will recognize the command using voice recognition software like Google Speech-to-Text and play the next song. Users can also add a song to their "favorites" list, which will be carried out.

[1224] Input: User voice commands, touch actions

[1225] Output: Music playback control and favorites update

[1226] Through these steps, the system can provide optimal music in real time based on the user's emotions and environmental data, making the user's driving experience even more comfortable.

[1227] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1228] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1229] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1230] [Fourth embodiment]

[1231] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1232] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1233] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1234] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1235] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1237] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1238] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1239] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1240] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1241] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1242] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1243] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1244] The present invention is a system that combines various data to individually recommend music to optimize the user's music experience. The operation method and program processing of this system are described below.

[1245] System configuration

[1246] The system of the present invention consists of the following major components:

[1247] 1. User profile management module: Manages users' music preferences and past playing history.

[1248] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[1249] 3. Sentiment analysis module: Analyzes the user's voice and facial expressions to determine their mood for the day.

[1250] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[1251] 5. User interface: Users can easily control music with voice or touch.

[1252] Program processing

[1253] 1. Managing your user profile

[1254] The server processes the music data the user has listened to in the past and their ratings, and updates the user profile. For example, if the user has a tendency to like a particular genre, that information is stored.

[1255] 2. Environmental data collection

[1256] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[1257] 3. Acquiring Emotion Data

[1258] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, then applies an emotion analysis algorithm to determine their emotional state for the day and sends that data to the server. For example, if the user is tired, it will report that data.

[1259] 4. Running the Music Recommendation Engine

[1260] The server integrates user profile, environmental data, and emotional data to generate an optimal playlist using a music recommendation engine. For example, if it's a clear morning and the user is in a good mood, it will recommend up-tempo pop music.

[1261] 5. Providing and Controlling Playlists

[1262] The device displays the playlist received from the server on the user interface and automatically starts playback. The user can then use voice commands or touch controls to play, skip, or add songs to favorites.

[1263] Specific examples

[1264] Case 1: Morning commute (sunny)

[1265] First, the user gets into their car in the morning and connects the in-car system to their smartphone. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends these to the server. At the same time, the server analyzes the user's smile and cheerful voice and reports that they are in a "cheerful mood." The server uses this data to create a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," it performs the additional process according to the instruction.

[1266] Case 2: Returning home (rainy day, tired)

[1267] In the evening, the user gets into their car after work and connects their smartphone. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the server analyzes the user's tired face and calm voice and reports that they are "tired." Based on this data, the server generates a playlist of calming acoustic music and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[1268] As a result, this system individually optimizes the user's driving experience while providing safe and comfortable music entertainment.

[1269] The processing flow will be explained below.

[1270] Step 1:

[1271] When a user gets into the car, they connect their smartphone to the in-car system, which activates the entire system.

[1272] Step 2:

[1273] The device collects real-time location information (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is obtained through the environmental data acquisition module.

[1274] Step 3:

[1275] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion analysis module. The emotion analysis algorithm analyzes the user's mood and emotions, determining their state, for example, "relaxed" or "tired."

[1276] Step 4:

[1277] The device sends the data (environmental data, emotional data) collected in steps 2 and 3 to the server, including the current location, weather, traffic conditions, and the user's mood.

[1278] Step 5:

[1279] The server updates the user profile based on the received data, which includes the user's music preferences, past playback history, and emotional data, all of which are updated based on the latest information.

[1280] Step 6:

[1281] The server runs a music recommendation engine based on the updated user profile, environmental data, and emotional data. For example, a playlist of up-tempo pop music is generated for a sunny, cheerful morning. The generated playlist is then sent to the device.

[1282] Step 7:

[1283] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[1284] Step 8:

[1285] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[1286] Step 9:

[1287] Every time the user makes a change, the device sends that information (new favorite songs, changes to the playlist) to the server, updating the user profile accordingly, which makes future playlists even more accurate.

[1288] Through the above processing steps, the system can provide the optimal music experience according to the user's individual preferences and circumstances.

[1289] Example 1

[1290] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1291] In today's information society, it is becoming increasingly important to individually optimize each user's music experience. Conventional music recommendation systems only recommend music based on a user's preferences and past playback history, but there is a growing demand for music recommendations that appropriately reflect a variety of factors, such as the user's real-time situation and emotional state. Therefore, it is necessary to realize a more accurate music recommendation system that can respond to the user's individual situation and emotions.

[1292] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1293] In this invention, the server includes a means for managing a user's music preferences and past playback history, a means for acquiring real-time data such as weather, location information, and traffic volume, and a means for analyzing the user's voice and facial expression to determine their mood for the day. This provides a means for using a generative model to generate an optimal music playlist based on the user's preferences, mood, weather, and location information, and enables music control via voice commands and touch operations. This enables a more personalized music experience that takes into account the user's real-time situation and emotional state.

[1294] "User's music preferences" refers to information about the user's tastes accumulated based on the music the user has listened to in the past and their ratings of that music.

[1295] "Playback history" is history data of music that the user has played in the past, and includes information such as specific dates and times and the number of times the music has been played.

[1296] "Weather data" is data that indicates the current weather conditions, and includes environmental information such as whether it is sunny, rainy, wind speed, and temperature.

[1297] "Location information" refers to information about the user's current location and travel route obtained by GPS or the like.

[1298] "Traffic volume data" refers to data that indicates real-time traffic conditions, including congestion information and road congestion levels.

[1299] "User's voice and facial expression" refers to the user's voice and facial expression, which are information used to determine the user's emotional state by analyzing them.

[1300] "Mood" refers to the user's emotional or mental state, and includes happiness, sadness, fatigue, and the like.

[1301] A "generative model" is an algorithm or AI model that generates optimal music playlists based on various collected data.

[1302] A "music playlist" is a list of multiple songs that are played in a specified order, and is generated to suit the user's preferences and mood.

[1303] A "voice command" is an input method that allows a user to give instructions to a device by voice.

[1304] "Touch operation" is an input method in which a user issues instructions to a device by operating a touch screen.

[1305] "Electronic devices" are devices with a user interface, including smartphones, tablets, in-vehicle systems, etc.

[1306] "Emotion data" is data relating to the emotional state of the user obtained by analyzing the user's voice and facial expression.

[1307] "Environmental data" refers to real-time environmental information surrounding the user, such as weather, location information, and traffic volume.

[1308] A "user profile" is individual setting information generated based on a user's past behavior and preferences.

[1309] The present invention is a system for individually optimizing a user's music experience, and is implemented using the following major hardware and software:

[1310] System Configuration

[1311] 1. User Profile Management Module

[1312] The server stores the user's past music playback history and rating data, and updates the user profile based on this information. It also records the user's preferences for genres and artists in detail and stores them in a profile database.

[1313] 2. Environmental data acquisition module

[1314] The device connects to the user's smartphone or in-car system to collect real-time information such as current location, weather data, traffic volume, etc. For example, it uses a GPS sensor and weather API to obtain this data and send it to a server.

[1315] 3. Emotion Data Acquisition Module

[1316] The device uses a built-in camera and microphone to capture the user's facial expressions and voice. This data is processed by an emotion analysis algorithm, and the user's emotional state is transmitted to the server. For example, the camera analyzes the user's smile and the microphone analyzes the tone of the voice.

[1317] 4. Music recommendation engine

[1318] The server combines user profiles, environmental data, and emotional data to generate an optimal music playlist using a generative AI model, which includes algorithms for selecting music based on weather and the user's mood.

[1319] 5. User Interface

[1320] The device displays the playlist sent from the server, and the user can control the music by voice or touch operation, such as playing, skipping, adding to favorites, etc. The user interface has an intuitive and easy-to-use design.

[1321] Specific examples

[1322] Case 1: Morning commute (sunny)

[1323] When a user gets into their car in the morning, they connect their smartphone to the in-car system. The device collects weather data (clear), location information (home), and traffic conditions (smooth), and sends this information to the server.

[1324] At the same time, the device captures the user's smile and cheerful voice, and uses an emotion analysis algorithm to determine that the user is in a "cheerful mood," then sends the data to the server.

[1325] The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. When the user issues a voice command such as "Add this song to my favorites," the device adds it to the playlist accordingly.

[1326] Case 2: Returning home (rainy day, tired)

[1327] In the evening, the user gets into the car after work and connects the smartphone. The device collects weather data (rain), location information (workplace), and traffic conditions (traffic jam), and sends them to the server.

[1328] The device also captures tired facial expressions and calm voices, determines whether the user is "tired" using an emotion analysis algorithm, and sends the data to a server.

[1329] The server uses this data to generate a playlist of calming acoustic music and sends it to the device. When the user issues a voice command such as "play the next song," the device plays the next song as requested.

[1330] Prompt Sentence Examples

[1331] By inputting the following prompt sentences into the generative AI model, the optimal music playlist can be generated.

[1332] Create the perfect music playlist for your users, taking into account their preferred music genres, current weather, location, and emotional state. Specific situations include:

[1333] User Preferences: Rock and Pop

[1334] Current weather: Sunny

[1335] Location: On the way from home to work

[1336] Emotional state: cheerful and lively

[1337] Suggest music playlist content to be recommended.

[1338] As described above, the present invention individually optimizes the user's driving experience and provides safe and comfortable music entertainment.

[1339] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1340] Step 1: Manage your user profile

[1341] The server collects the user's past music playback history and rating data, and updates the user profile based on this.

[1342] Input: User play history and rating data

[1343] Data processing: Statistical analysis of play counts and ratings

[1344] Output: Updated user profile

[1345] Specific operation: For example, the server analyzes the playback history for the past month and stores the most played genres and artists in a profile database.

[1346] Step 2: Collect environmental data

[1347] The terminal works in conjunction with smartphones and in-vehicle systems to collect current location information, weather data, and traffic volume data, and transmits it to a server.

[1348] Input: GPS data, weather API information, traffic information API data

[1349] Data processing: location information acquisition, real-time updates of weather and traffic information

[1350] Output: Collected environmental data

[1351] Specific operation: The device obtains location information using the GPS sensor, collects current weather data using the weather API, and obtains traffic volume data using the traffic information API.

[1352] Step 3: Obtaining emotion data

[1353] The device uses a camera and microphone to capture the user's facial expressions and voice, and then uses an analysis algorithm to determine their emotional state and send the results to a server.

[1354] Input: Video data captured by the camera, audio data recorded by the microphone

[1355] Data processing: Emotion analysis using facial recognition and tone of voice analysis

[1356] Output: Parsed emotional state data

[1357] Specific operation: The device captures the user's facial expression with a camera and determines whether they are smiling. It also analyzes the tone of their voice recorded by a microphone to determine whether they are in good spirits.

[1358] Step 4: Running the Music Recommendation Engine

[1359] The server integrates user profiles, environmental data, and emotional data, and generates an optimal music playlist using a generative AI model.

[1360] Input: User profile, real-time environmental data, emotional state data

[1361] Data processing: Integrating various data and recommending music using AI models

[1362] Output: Generated music playlist

[1363] Specific operation: The server runs an algorithm that prioritizes up-tempo pop music based on data indicating that it is a clear morning and the user is in a cheerful mood.

[1364] Step 5: Providing and Controlling Playlists

[1365] The device displays the playlist received from the server on the user interface and automatically plays music. The user can use voice commands or touch operations to play, skip, add to favorites, etc.

[1366] Input: Generated music playlist, user voice commands or touch actions

[1367] Data processing: display in the user interface and processing of user commands

[1368] Output: Music playback control according to user instructions

[1369] Specific behavior: The device will display the playlist and start playing. If the user issues a voice command such as "Play the next song," the device will play the next song. If the user issues a voice command such as "Add this song to my favorites," the device will add the song to the favorites list.

[1370] (Application example 1)

[1371] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1372] In recent years, there has been a demand for improving the in-car user experience. In particular, providing highly personalized entertainment contributes greatly to customer satisfaction in self-driving vehicles. However, conventional music recommendation systems have difficulty comprehensively considering user preferences, emotional state, and environmental data, making it impossible to achieve high-quality personalization. The purpose of this invention is to solve these problems and provide users with the optimal music experience.

[1373] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1374] In this invention, the server includes means for managing a user's music preferences and past playback history, means for acquiring real-time data such as weather, location information, and traffic volume, and means for analyzing the user's voice and facial expression to determine the user's mood for the day. This makes it possible to generate an optimal music playlist based on the user's preferences, mood, weather, location information, and traffic conditions, and a highly personalized music experience can be realized by means for playing the generated music playlist, means for controlling music with voice commands and touch operations, means for inputting a user profile, environmental data, and emotional data into an AI model to generate a music playlist, and means for displaying the generated playlist on the infotainment system and accepting operations with voice commands.

[1375] A "user profile" is a collection of information including a user's music preferences and past playback history.

[1376] "Environmental data" refers to real-time information such as weather, location, and traffic volume.

[1377] "Emotion data" is data that indicates the mood and emotional state of the user on that day, analyzed from the user's voice and facial expression.

[1378] A "music playlist" is a list of selected music tracks in a playback order.

[1379] An "AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate optimal results.

[1380] An "infotainment system" is a system installed in a vehicle that provides information and entertainment.

[1381] A "voice command" is an instruction given by a user to operate the system by voice.

[1382] "Touch operation" refers to an operation method performed by touching a screen or panel with a finger.

[1383] The present invention is a system for recommending music to a user based on a personalized music recommendation system that combines a user profile, environmental data, and emotional data to optimize the user's music experience. Specific embodiments of the present invention are described below.

[1384] System configuration

[1385] The system of the present invention consists of the following major components:

[1386] 1. User profile management module: This module manages users' music preferences and past playback history. It uses information stored in the database server to accurately grasp users' tastes.

[1387] 2. Environmental Data Acquisition Module: This module collects real-time data such as weather, location information, traffic volume, etc. It uses, for example, OpenWeatherMap and Google Maps APIs to collect data.

[1388] 3. Sentiment Analysis Module: This module uses the in-car camera and microphone to capture the user's facial expressions and voice, and analyzes their emotional state. It uses Face++ for facial recognition and Google Speech Recognition for voice recognition.

[1389] 4. Music recommendation engine: Integrates user profiles, environmental data, and emotional data to generate optimal music playlists using AI models. For example, TensorFlow and PyTorch can be used to build AI models.

[1390] 5. User Interface: Users can easily control music with voice commands and touch. This interface is integrated with the vehicle's infotainment system.

[1391] Program processing

[1392] The server collects and manages user profiles, environmental data, and emotional data, and runs the music recommendation engine. For example, the user profile management module processes the music data that users have listened to in the past and their ratings, and stores them in a database.

[1393] The device collects real-time weather, location, and traffic information through the environmental data acquisition module and sends it to the server.The emotion analysis module uses the in-car camera and microphone to capture the user's facial expressions and voice, analyzes the data, and identifies the user's emotional state, which is then sent to the server.

[1394] The server runs a music recommendation engine based on the collected user profile, environmental data, and emotional data, and uses AI models to generate an optimal music playlist, which is then sent to the device and displayed on the infotainment system.

[1395] Users control the playlist with voice commands or touch gestures, such as "play the next song" or "add this song to my favorites," and the system responds.

[1396] Specific examples

[1397] Case 1: Morning commute (sunny)

[1398] A user gets into a self-driving car in the morning and connects to the system. The in-car camera captures the user's smile, and the microphone analyzes their cheerful voice. Environmental data, such as location (home), weather data (sunny), and traffic conditions (smooth), are collected and sent to the server. The server uses this data to create a playlist of up-tempo pop music, sends it to the infotainment system, and begins playback. When the user issues a voice command such as "Add this song to my favorites," the system follows the instructions and performs additional processing.

[1399] Example prompts for generative AI models

[1400] Consider the user's music profile, location, weather, traffic conditions, and emotional state to generate the best music playlist based on the following information:

[1401] Profile: Pop, Rock

[1402] Location: Home

[1403] Weather: Sunny

[1404] Traffic Condition: Smooth

[1405] Emotion: Cheerful mood

[1406] Example of generated playlist: "Song A", "Song B", "Song C"

[1407] In this way, the system can provide the high-quality music experience that users desire.

[1408] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1409] Step 1:

[1410] The device retrieves the user's music preferences and past playback history from a database. The user ID is required as input, and user profile information (music preferences, ratings, playback history) is obtained as output. Specifically, the device queries the database, and the user profile management module returns the information.

[1411] Step 2:

[1412] The device collects real-time data such as location information, weather, and traffic volume. It receives the current location, weather API, and traffic information API URLs as input, and obtains the current location, weather conditions, and traffic conditions as output. Specifically, it obtains location information from the GPS sensor, sends HTTP requests to the weather and traffic information APIs, and analyzes the obtained data.

[1413] Step 3:

[1414] The device uses an in-car camera and microphone to capture the user's facial expressions and voice and perform emotion analysis. It requires camera feed and audio data as input, and obtains the user's emotional state (e.g., cheerful, tired, etc.) as output. Specifically, the device inputs the images and audio data captured by the camera into the emotion analysis module, which then obtains the analysis results.

[1415] Step 4:

[1416] The server runs a music recommendation engine based on user profile, environmental data, and emotional data to generate an optimal music playlist. It integrates user profile, location information, weather, traffic conditions, and emotional state data as input, and obtains a music playlist as output. Specifically, it inputs this data into an AI model and generates an optimal playlist using a recommendation algorithm.

[1417] Step 5:

[1418] The server sends the generated music playlist to the terminal. The server requires the playlist data as input and obtains the playlist sent to the terminal as output. The specific operation is to send the playlist to the terminal via the network.

[1419] Step 6:

[1420] The device displays the received playlist on the infotainment system and starts playback. The input is the received playlist data, and the output is the music displayed on the screen and played through the car speakers. The specific operation is to display the playlist and control playback using the infotainment system's API.

[1421] Step 7:

[1422] Users can use voice commands or touch operations to play music, skip, add to favorites, etc. Voice commands and touch operation data are required as input, and the output is the music control results according to the user's instructions. Specifically, the voice recognition system analyzes the command, and the infotainment system executes the corresponding operation.

[1423] Through this series of processes, users can enjoy a highly personalized music experience in their autonomous vehicles.

[1424] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1425] The present invention is a music recommendation system that combines an emotion engine that recognizes the user's emotions, thereby generating an optimal music playlist based on the driver's emotional state, providing a personalized music experience in real time.

[1426] System configuration

[1427] The system of the present invention consists of the following major components:

[1428] 1. User profile management module: Manages users' music preferences and past playing history.

[1429] 2. Environmental data acquisition module: Collects real-time data such as weather, location information, and traffic volume.

[1430] 3. Emotion engine: Analyzes the user's voice and facial expressions to recognize their mood and emotions for the day.

[1431] 4. Music recommendation engine: Generates optimal music playlists based on collected data.

[1432] 5. User interface: Users can easily control music with voice or touch.

[1433] Program processing

[1434] 1. Managing your user profile

[1435] The server updates the user profile based on the music data the user has listened to in the past and their ratings. For example, if the user has a tendency to like a particular genre, the server stores that information.

[1436] 2. Environmental data collection

[1437] The device works in conjunction with the user's smartphone or in-car system to collect real-time data such as current location, weather, and traffic volume, and transmits it to a server.

[1438] 3. Acquisition and Analysis of Emotion Data

[1439] The device uses the in-car camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine, which analyzes the user's mood and emotions and determines their state, such as "relaxed" or "tired."

[1440] 4. Running the Music Recommendation Engine

[1441] The server integrates the user profile, environmental data, and emotional data and generates an optimal playlist using a music recommendation engine. For example, it recommends an up-tempo pop playlist for a cheerful, sunny morning. The generated playlist is then sent to the device.

[1442] 5. Providing and Controlling Playlists

[1443] The device displays the playlist received from the server on the user interface and automatically starts playback. The playback is performed through the speaker and managed by the device.

[1444] 6. Interactive Operation

[1445] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform actions such as playing or pausing music, skipping to the next song, or adding music to favorites.

[1446] Specific examples

[1447] Case 1: Morning commute (sunny)

[1448] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather data (sunny), location information (current location: home), and traffic conditions (smooth), and sends them to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device follows the instruction and adds the song to the favorites list.

[1449] Case 2: Returning home (rainy day, tired)

[1450] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather data (rainy weather), location information (workplace), and traffic conditions (traffic jams), and sends them to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[1451] These specific examples enable the system to better personalize the user's driving experience and provide safe and comfortable music entertainment.

[1452] The processing flow will be explained below.

[1453] Step 1:

[1454] When the user gets into the car, they connect their smartphone to the in-car system, which activates the system.

[1455] Step 2:

[1456] The device activates the environmental data acquisition module to collect real-time information on the current location (GPS), weather data (sunny, rainy, snowy, etc.), and traffic data (smooth, congested, etc.). This data is then sent to the server as soon as it is collected.

[1457] Step 3:

[1458] The device activates the camera and microphone inside the car to capture the user's facial expressions and voice, and this data is sent to the emotion engine.

[1459] Step 4:

[1460] The emotion engine analyzes the received data and determines the user's mood and emotions. For example, if the user's face looks relaxed, it will recognize the user as "relaxed" and send the result of the determination to the server.

[1461] Step 5:

[1462] The server integrates the received environmental data, emotional data, and user profile information to update the user profile management module, taking into account past playback history and music preferences.

[1463] Step 6:

[1464] The server runs a music recommendation engine based on the updated user profile and collected data. For example, it generates an up-tempo pop playlist for a sunny, cheerful morning. The generated playlist is then sent to the device.

[1465] Step 7:

[1466] The device displays the received playlist on the user interface and starts playing the music, which is then provided through the car's audio system.

[1467] Step 8:

[1468] Users can control music using voice commands or touch gestures. For example, they can say, "Play the next song." The device will then follow the instructions and perform operations such as playing or pausing music, skipping to the next song, or adding music to favorites.

[1469] Step 9:

[1470] Every time the user operates the music, the device sends the operation data to the server and updates the user profile accordingly, which increases the accuracy of playlist generation from the next time onwards.

[1471] As a result, this system can individually optimize the user's driving experience and provide safe and comfortable music entertainment.

[1472] Example 2

[1473] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1474] Conventional music recommendation systems only recommend music based on a user's music preferences and past playback history, and do not take into account the user's current emotional state or environmental conditions (weather, location information, traffic volume, etc.), making it difficult to provide an optimal music experience for the user's mood and situation.In addition, users are only able to control music in a limited way, requiring intuitive operation.

[1475] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1476] In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression using a camera and microphone to determine the user's mood and emotions for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, location information, traffic volume, and emotional state; means for playing the generated music playlist; and means for controlling music with voice commands and touch operations. This enables optimal music recommendations that take into account the user's current emotional state and environmental conditions, providing a more intuitive and interactive music experience.

[1477] "Means for managing users' music preferences and past playback history" refers to a system that records and analyzes the music that users have played in the past and their ratings, thereby understanding the users' music preferences.

[1478] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to sensors and data acquisition systems for collecting current weather, user location information, and traffic conditions in real time.

[1479] "Means for analyzing the user's voice and facial expressions using a camera and microphone to determine the user's mood and emotions for that day" refers to a system that uses a camera and microphone installed in the vehicle to capture the user's facial expressions and voice, and analyzes them to determine the user's emotional state.

[1480] "Means for generating optimal music playlists based on user preferences, mood, weather, location information, traffic volume, and emotional state" refers to algorithms and systems that integrate and analyze multiple collected data to generate music playlists that are most suitable for users.

[1481] The "means for playing the generated music playlist" is a system for automatically transmitting the optimal playlist to a playback device and playing music.

[1482] "A means for controlling music with voice commands and touch operations" refers to an interface and system that allows users to intuitively control music playback, stopping, changing songs, etc. through voice instructions or a touch screen.

[1483] This invention is a music recommendation system that recognizes user emotions and generates and plays optimal music playlists based on the driver's emotional state. The system consists of the following main components: a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface.

[1484] 1. User Profile Management Module

[1485] The server manages the user's music preferences and past playback history. This module understands the user's tastes by accumulating and analyzing data such as the songs the user has listened to in the past, their ratings, and the number of times they have played the songs. For example, if a user likes rock music, this preference is saved in their profile and reflected in the next music recommendation.

[1486] 2. Environmental data acquisition module

[1487] The device connects to the user's smartphone or in-car system to obtain real-time data such as current location, weather, and traffic volume. This is achieved using hardware and software such as a GPS sensor, weather information API, and traffic information API. The collected data is sent to a server in real time.

[1488] 3. Emotion Engine

[1489] The device uses an in-car camera and microphone to capture the user's voice and facial expressions. The emotion engine recognizes the user's mood and emotional state by analyzing the user's facial muscle movements with the camera and the tone of voice with the microphone. For example, if the user is smiling, it will be determined that the user is in a "cheerful mood."

[1490] 4. Music recommendation engine

[1491] The server combines user profile data, location information, weather, traffic conditions, and emotional state, and inputs them into a music recommendation engine. This engine has an algorithm that generates a music playlist that best suits the user's current state. For example, it might recommend up-tempo pop music for a cheerful, sunny morning, or calm acoustic music for a tired evening. The generated playlist is then sent to the device.

[1492] 5. User Interface

[1493] The device displays the received playlist and automatically plays music. The user can control the playlist using voice commands (e.g., "Play next song") or touch interactions (e.g., using the touchscreen to select songs). For example, the user can say, "Add this song to my favorites," and the song will be added to the favorites list.

[1494] Specific examples

[1495] Case 1: Morning commute (sunny)

[1496] A user gets into their car in the morning and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's smile and cheerful voice and reports the user as "in a cheerful mood." The server uses this data to generate a playlist of up-tempo pop songs and sends it to the device. The device starts playing the playlist, and when the user issues a voice command such as "Add this song to my favorites," the device adds the song to the favorites list.

[1497] Case 2: Returning home (rainy day, tired)

[1498] In the evening, the user gets into their car after work and connects their smartphone to the in-car system. The device collects weather, location, and traffic information and sends it to the server. At the same time, the emotion engine analyzes the user's tired face and calm voice and reports that they are "tired." The server uses this data to generate a calming acoustic playlist and sends it to the device. The device plays the playlist, and when the user issues a voice command such as "Play the next song," the device plays the next song as requested.

[1499] Specific prompt examples

[1500] "Describe a music recommendation system that recognizes user emotions and generates music playlists. The system consists of a user profile management module, an environmental data acquisition module, an emotion engine, a music recommendation engine, and a user interface. Explain the function and processing flow of each module in detail, and provide examples of specific cases and prompt sentences."

[1501] The present invention allows the user to enjoy the most suitable music experience according to the mood and situation at the time, making driving even more comfortable.

[1502] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1503] Step 1:

[1504] Managing User Profiles

[1505] The server manages the user's past music playing history and ratings.

[1506] Input: User play history data and rating data

[1507] Specific behavior:

[1508] The server accesses a database to obtain the music that the user has played in the past and its rating data.

[1509] Based on the acquired data, the system analyzes trends such as users' favorite genres, artists, and songs.

[1510] Output: User profile data reflecting the user's music preferences

[1511] Step 2:

[1512] Environmental data collection

[1513] The device collects real-time environmental data from the user's smartphone or in-vehicle system and transmits it to a server.

[1514] Input: GPS data, weather data, traffic data

[1515] Specific behavior:

[1516] The device uses GPS to obtain its current location information.

[1517] To obtain weather information, a weather information API is accessed via the Internet.

[1518] Use the traffic information API to obtain traffic volume data.

[1519] The collected data is sent to the server.

[1520] Output: Environment data sent to the server

[1521] Step 3:

[1522] Acquiring and analyzing emotion data

[1523] The device uses the in-car camera and microphone to analyze the user's voice and facial expressions, and transmits emotional data to the server.

[1524] Input: Camera video data, audio data

[1525] Specific behavior:

[1526] The camera captures the user's facial expressions and analyzes the movement of facial muscles.

[1527] The microphone collects the user's voice and analyzes the tone and intonation of the voice.

[1528] Camera data and audio data are input into an emotion engine to determine the user's emotional state.

[1529] The determined emotion data is transmitted to the server.

[1530] Output: Emotion data sent to the server

[1531] Step 4:

[1532] Running a music recommendation engine

[1533] The server runs a music recommendation engine based on the integrated data to generate an optimal playlist.

[1534] Input: User profile data, environmental data, emotional data

[1535] Specific behavior:

[1536] The server integrates the user profile data, the environmental data, and the emotional data.

[1537] The integrated data is input into a music recommendation engine, which runs an algorithm to generate an optimal music playlist.

[1538] The generated playlist is sent to the terminal.

[1539] Output: Music playlist sent to device

[1540] Step 5:

[1541] Playlist provision and control

[1542] The terminal displays the playlist received from the server on the user interface and starts automatic playback.

[1543] Input: Music playlist data

[1544] Specific behavior:

[1545] The terminal displays the playlist received from the server.

[1546] Music is automatically played through the car's speakers.

[1547] Manage playback progress.

[1548] Output: Music being played

[1549] Step 6:

[1550] Interactive Operation

[1551] Users control the music using voice commands and touch gestures.

[1552] Input: Voice command, touch operation

[1553] Specific behavior:

[1554] The user issues voice commands such as "play next song" or "add this song to my favorites."

[1555] The device recognizes the voice command and performs the corresponding operation.

[1556] The user uses touch to change songs or stop playback.

[1557] Output: Music playback controlled by user instructions

[1558] (Application example 2)

[1559] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1560] There is a need to more individually optimize the music experience for passengers in autonomous vehicles and recommend music that matches their emotional state in real time. However, conventional music recommendation systems are based only on the user's static profile information and past playback history, and have the problem of not being able to reflect the user's real-time emotional state or current environmental data (weather, traffic conditions, etc.). This makes it difficult for passengers in autonomous vehicles to receive the optimal music experience that matches their mood at any given time.

[1561] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for managing a user's music preferences and past playback history; means for acquiring real-time data such as weather, location information, and traffic volume; means for analyzing the user's voice and facial expression to determine the user's mood for the day; means for generating an optimal music playlist based on the user's preferences, mood, weather, and location information; means for playing the generated music playlist; means for controlling music with voice commands and touch operations; means for integrating user emotional data and environmental data in an autonomous vehicle to generate and play an optimal music playlist; and means for recognizing a user's voice command and performing operations such as playing the next song or adding to a favorites list. This enables passengers in autonomous vehicles to enjoy an optimal music experience in real time.

[1562] The "means for managing the user's music preferences and past playback history" refers to a means for recording data on music that the user has listened to in the past and their ratings, and for grasping the user's music preferences.

[1563] "Means for acquiring real-time data such as weather, location information, and traffic volume" refers to means for collecting the latest information such as current weather conditions, the user's current location, and traffic flow.

[1564] The "means for analyzing the user's voice and facial expression to determine the mood of the day" is a means for analyzing the user's voice and facial expression, identifying the user's emotional state, and estimating the mood of the day.

[1565] The "means for generating an optimal music playlist" is a means for comprehensively analyzing data such as the user's preferences, mood, weather, and location information to create the most suitable music list.

[1566] The "means for playing the generated music playlist" refers to a means for starting playback to provide the music in the generated playlist to the user.

[1567] "Means for controlling music with voice commands and touch operations" refers to means for a user to perform operations such as playing, stopping, and skipping songs through voice instructions and touch operations.

[1568] The "means for integrating user emotional data and environmental data to generate and play an optimal music playlist" is a means for integrating driver emotional information and real-time environmental information, and automatically creating and playing an optimal music playlist based on that information.

[1569] "Means for performing actions such as next song or adding to favorites list" means means for a user to perform actions such as playing the next song or adding a specific song to a favorites list through voice commands.

[1570] The present invention is a music recommendation system that recognizes the user's emotions in an autonomous vehicle and provides an optimal music playlist based on the emotions. This system is composed of several main components. The role of each component and a specific embodiment are described below.

[1571] System configuration

[1572] 1. User Profile Management Module:

[1573] The server updates the user profile based on the music data the user has listened to in the past and their ratings. This profile serves as a database to understand the user's musical preferences. Specifically, if the user tends to like a particular genre or artist, that information is saved and analyzed.

[1574] 2. Environmental Data Acquisition Module:

[1575] The device connects with the autonomous vehicle's sensors and smartphone to collect real-time weather data, location information, traffic volume, etc. For example, if the current weather is sunny and traffic is smooth, that information is sent to the server.

[1576] 3. Emotion Engine:

[1577] The device uses the in-car camera and microphone to capture the user's facial expressions and voice to obtain emotional data. The emotion engine analyzes this data to determine the user's emotional state, such as "relaxed" or "tired." This analysis is performed using emotion analysis software such as Microsoft Azure Cognitive Services.

[1578] 4. Music recommendation engine:

[1579] The server integrates user profiles, environmental data, and emotional data to generate optimal music playlists. This recommendation engine runs in a cloud environment such as Amazon AWS Lambda. The generated playlists are optimized for the user's emotions and the current environment.

[1580] 5. User Interface:

[1581] The device displays the playlist as it plays and allows control via voice commands or touch gestures, for example, when the user says "play the next song," it skips to the next song using a voice recognition framework like Google Speech-to-Text.

[1582] Specific use cases

[1583] Case 1: Morning commute (sunny)

[1584] The user gets into the car and it starts driving automatically.

[1585] The device collects weather data (sunny), location information (home), and traffic conditions (smooth) and sends them to the server.

[1586] The emotion engine analyzes the user's smile and cheerful voice and determines that they are in a "cheerful mood."

[1587] The server generates an up-tempo playlist based on this data and sends it to the device.

[1588] When the device plays the playlist and the user issues a voice command such as "Add this song to my favorites," the device follows the instructions and adds the song to the favorites list.

[1589] Case 2: Returning home (rainy day, tired)

[1590] The user finishes work and gets into the car.

[1591] The device collects weather data (rain), location information (company), and traffic conditions (traffic jams) and sends them to the server.

[1592] The emotion engine analyzes the user's tired face and calm voice and determines that they are tired.

[1593] The server uses this data to generate a playlist of calming acoustic music and sends it to the device.

[1594] When the device plays a playlist and the user issues a voice command such as "play the next song," the device will follow the instruction and play the next song.

[1595] Prompt Sentence Examples

[1596] Implement a system that recognizes emotions through a camera and microphone while the user is in the car, and recommends music based on real-time weather and location information. Meet the following requirements:

[1597] 1. User Profile Management

[1598] 2. Real-time collection of environmental data

[1599] 3. Emotion Recognition Engine Integration

[1600] 4. Playlist generation by recommendation engine

[1601] 5. Interactive operation via voice commands

[1602] Through the above embodiments, the system of the present invention can more individually optimize the user's driving experience and provide safe and comfortable music entertainment to passengers in autonomous vehicles.

[1603] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1604] Step 1:

[1605] The user enters the car and begins operating the autonomous vehicle. The device establishes a connection with the user's smartphone and in-vehicle system. This connection transmits the user's past music playback history and ratings to a server, which populates the user profile. The server uses this data to update the user's music preferences.

[1606] Input: User's music playback history, rating data

[1607] Output: Updated user profile

[1608] Step 2:

[1609] The device uses the autonomous vehicle's built-in sensors and internet connection to collect real-time weather data, location information, and traffic volume, which is then sent to a server.

[1610] Input: Weather data, location information, traffic

[1611] Output: Collected environmental data

[1612] Step 3:

[1613] The emotion engine captures the user's facial expressions and voice through the car's camera and microphone. It then uses emotion analysis software, such as Microsoft Azure Cognitive Services, to determine the user's emotional state. For example, if the user is smiling while speaking, the emotional state is recognized as "cheerful."

[1614] Input: User's facial expression data, voice data

[1615] Output: Parsed emotion data

[1616] Step 4:

[1617] The server integrates user profiles, environmental data, and emotional data, and uses a music recommendation engine to generate optimal music playlists. Utilizing cloud environments such as Amazon AWS Lambda, the server comprehensively analyzes information from multiple data sources and selects music that matches the user's mood.

[1618] Input: User profile, environmental data, emotional data

[1619] Output: Optimized music playlist

[1620] Step 5:

[1621] The generated music playlist is sent to the terminal and displayed on the user interface, and the terminal activates a function to play music based on the playlist, allowing the user to listen to the music through the car speakers.

[1622] Input: Generated music playlist

[1623] Output: Music being played

[1624] Step 6:

[1625] Users can control music through the car's voice command system or touch controls. For example, if a user says, "Play the next song," the device will recognize the command using voice recognition software like Google Speech-to-Text and play the next song. Users can also add a song to their "favorites" list, which will be carried out.

[1626] Input: User voice commands, touch actions

[1627] Output: Music playback control and favorites update

[1628] Through these steps, the system can provide optimal music in real time based on the user's emotions and environmental data, making the user's driving experience even more comfortable.

[1629] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1630] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1631] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1632] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1633] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1634] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1635] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1636] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1637] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1638] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1639] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1640] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1641] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1642] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1643] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1644] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1645] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1646] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1647] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1648] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1649] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1650] The following is further disclosed regarding the above embodiment.

[1651] (Claim 1)

[1652] a means for managing the user's music preferences and past playback history;

[1653] A means of obtaining real-time data such as weather, location, and traffic volume;

[1654] A means of analyzing the user's voice and facial expressions to determine their mood that day,

[1655] A means for generating an optimal music playlist based on a user's preferences, mood, weather, and location information;

[1656] means for playing the generated music playlist;

[1657] a means of controlling music through voice commands and touch;

[1658] A system including:

[1659] (Claim 2)

[1660] 10. The system of claim 1, further comprising means for updating a user profile based on the user's emotional data and environmental data.

[1661] (Claim 3)

[1662] 10. The system of claim 1, further comprising means for displaying the generated music playlist on an in-vehicle system.

[1663] "Example 1"

[1664] (Claim 1)

[1665] a means for managing the user's music preferences and past playback history;

[1666] A means of obtaining real-time data such as weather, location, and traffic volume;

[1667] A means of analyzing the user's voice and facial expressions to determine their mood that day,

[1668] a means for using a generative model to generate an optimal music playlist based on a user's preferences, mood, weather, and location;

[1669] means for playing the generated music playlist;

[1670] a means of controlling music through voice commands and touch;

[1671] A system including:

[1672] (Claim 2)

[1673] 10. The system of claim 1, further comprising means for updating a user profile based on the user's emotional data and environmental data.

[1674] (Claim 3)

[1675] 10. The system of claim 1, further comprising: means for displaying the generated music playlist on an electronic device.

[1676] "Application Example 1"

[1677] (Claim 1)

[1678] a means for managing the user's music preferences and past playback history;

[1679] A means of obtaining real-time data such as weather, location, and traffic volume;

[1680] A means of analyzing the user's voice and facial expressions to determine their mood that day,

[1681] A means for generating an optimal music playlist based on a user's preferences, mood, weather, location information, and traffic conditions;

[1682] means for playing the generated music playlist;

[1683] a means of controlling music through voice commands and touch;

[1684] A means for inputting user profile, environmental data, and emotional data into an AI model to generate a music playlist;

[1685] a means for displaying the generated playlist on an infotainment system and accepting operation by voice command;

[1686] A system including:

[1687] (Claim 2)

[1688] 10. The system of claim 1, further comprising means for updating a user profile based on the user's emotional data and environmental data.

[1689] (Claim 3)

[1690] 10. The system of claim 1, further comprising: means for displaying the generated music playlist on an infotainment system.

[1691] "Example 2: Combining Emotion Engines"

[1692] (Claim 1)

[1693] a means for managing the user's music preferences and past playback history;

[1694] A means of obtaining real-time data such as weather, location, and traffic volume;

[1695] A means of analyzing the user's voice and facial expressions using a camera and microphone to determine their mood and emotions for the day;

[1696] means for generating an optimal music playlist based on a user's preferences, mood, weather, location, traffic, and emotional state;

[1697] means for playing the generated music playlist;

[1698] a means of controlling music through voice commands and touch;

[1699] A system including:

[1700] (Claim 2)

[1701] 10. The system of claim 1, further comprising means for updating a user profile based on the user's emotional data and environmental data.

[1702] (Claim 3)

[1703] 10. The system of claim 1, further comprising means for displaying the generated music playlist on an in-vehicle system.

[1704] "Application example 2 when combining emotion engines"

[1705] (Claim 1)

[1706] a means for managing the user's music preferences and past playback history;

[1707] A means of obtaining real-time data such as weather, location, and traffic volume;

[1708] A means of analyzing the user's voice and facial expressions to determine their mood that day,

[1709] A means for generating an optimal music playlist based on a user's preferences, mood, weather, and location information;

[1710] means for playing the generated music playlist;

[1711] a means of controlling music through voice commands and touch;

[1712] A means for integrating user emotion data and environmental data in an autonomous vehicle to generate and play an optimal music playlist;

[1713] a means for recognizing a user's voice commands and performing actions such as next song or adding to a favorites list;

[1714] A system including:

[1715] (Claim 2)

[1716] 10. The system of claim 1, further comprising means for updating a user profile based on the user's emotional data and environmental data.

[1717] (Claim 3)

[1718] 10. The system of claim 1, further comprising means for displaying and playing the generated music playlist on a vehicle system or a smart device. [Explanation of symbols]

[1719] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for managing the user's music preferences and past playback history; A means of obtaining real-time data such as weather, location, and traffic volume; A means of analyzing the user's voice and facial expressions to determine their mood that day, A means for generating an optimal music playlist based on a user's preferences, mood, weather, and location information; means for playing the generated music playlist; a means of controlling music through voice commands and touch; A system including:

2. The system of claim 1 , further comprising means for updating a user profile based on the user's emotional data and environmental data.

3. The system of claim 1 further comprising means for displaying the generated music playlist on an in-vehicle system.

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