System

Abstract

A system is provided.SOLUTION: The system includes a means for generating user's music preference data, a means for generating store's music preference data, and a means for presenting a store having a high matching rate to the user by using the user's music preference data and the store's music preference data.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] Conventional facilities and user matching systems do not properly select facilities such as restaurants and beauty salons based on the user's musical preferences, resulting in problems such as not being able to meet the user's true needs and lowering satisfaction. Additionally, businesses often use generic background music, which creates the challenge of not being able to provide an optimal music environment that reflects the musical preferences of customers. Furthermore, using music with copyright issues carries legal risks, so an appropriate solution was needed. [Means for solving the problem]

[0005] The present invention is a system including a means for generating user music preference data, a means for generating store music preference data, and a means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data. The present invention also is a system including a means for generating new copyright-free background music using copyright-free music data, and a means for playing copyright-free background music provided to a matched store. Furthermore, by including a means for acquiring the user's music preference data when the user visits a store and automatically playing background music at the store, the system provides an optimal music environment based on the user's music preferences.

[0006] "User music preference data" is information generated based on data regarding a user's music genre preferences and mood, as well as their listening history on music distribution services.

[0007] "Store music preference data" is information generated based on the store's music concept, background music history, and the music preference data of customers.

[0008] The "matching rate" is an index showing the degree of match between the user music preference data and the store music preference data.

[0009] "Copyright-free BGM" is BGM that has been generated from music that can be used without copyright restrictions.

[0010] The "playback means" refers to a device or software function for playing back the selected music data via an audio output device. [Brief explanation of the drawings]

[0011] [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

[0012] 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.

[0013] First, the terms used in the following description will be explained.

[0014] 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).

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] The system according to the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and also optimize background music playback in the store.

[0033] The system consists of the following main elements:

[0034] 1. Generating user music preference data

[0035] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[0036] Server: Based on the user's listening history, the server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) to generate "user music preference data."

[0037] 2. Generating store music preference data

[0038] Terminal: The store manager inputs the store's music concept and playlist history into the system.

[0039] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data."

[0040] Server: For example, acquire information such as the music played at store A is classical and customers prefer a quiet atmosphere.

[0041] 3. Perform matching

[0042] User: Uses the system's search function to submit a request to find the best store.

[0043] Server: Compares user music preference data with store music preference data and calculates stores with the highest matching rate.

[0044] Server: Presents matching results to the user. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[0045] 4. Creation and provision of copyright-free background music

[0046] Server: Collects multiple copyright-free music data and generates new music based on this data. Using generative AI, it creates music in a variety of genres tailored to the user's preferences.

[0047] Server: Provides the generated royalty-free music to each store so that it can be used on the store's sound system. For example, if a store needs jazz background music, the server distributes the newly generated jazz track.

[0048] 5. Development of an automatic background music playback system

[0049] User: Visits the store and logs in or checks into the system.

[0050] Terminal: The store's terminal obtains the user's music preference data from the server.

[0051] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[0052] As a concrete example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests establishments that match their preferences (e.g., cafes that play jazz in-store and are relaxing). When the user visits the establishment, the in-store device automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the establishment can increase customer satisfaction.

[0053] This system allows users to easily find the store that best suits their musical tastes, and stores can improve the quality of their service by providing background music that suits customers' musical tastes.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] User: Signs in to the system. Users enter their login information to access the system.

[0057] Step 2:

[0058] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[0059] Step 3:

[0060] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[0061] Step 4:

[0062] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[0063] Step 5:

[0064] Server: Combines music genre and mood parameters to generate "user music preference data" and stores it in the user's profile.

[0065] Step 6:

[0066] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[0067] Step 7:

[0068] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[0069] Step 8:

[0070] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[0071] Step 9:

[0072] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and save it in the store's profile.

[0073] Step 10:

[0074] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[0075] Step 11:

[0076] Server: Compares user music preference data with store music preference data, calculates matching rate, and lists stores that most closely match the user's preferences.

[0077] Step 12:

[0078] Server: Presents the matching results to the user. For example, it displays "There are five cafes that have high music preferences for the user."

[0079] Step 13:

[0080] Server: Collects multiple copyright-free music data sets and trains the AI ​​based on them to generate new copyright-free background music.

[0081] Step 14:

[0082] Server: Provides generated copyright-free background music to each store, distributing music data via a portal site or dedicated app.

[0083] Step 15:

[0084] User: Visits the store and checks in by connecting to the store's terminal or scanning a QR code.

[0085] Step 16:

[0086] Terminal: The store's terminal obtains the user's music preference data from the server.

[0087] Step 17:

[0088] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music that matches the user's preferences. For example, it can play smooth jazz for a user who wants to relax.

[0089] In this way, a system is realized that suggests the most suitable store based on the user's musical preferences and provides a musical environment tailored to individual preferences even after the user visits the store.

[0090] Example 1

[0091] 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."

[0092] Many users today want to control their moods and activities and enjoy themselves through music. However, it is not easy to find a store that matches the user's preferred music genre and atmosphere. Stores are also required to understand customers' musical preferences and provide optimal background music to increase customer satisfaction, but this is time-consuming. An efficient and effective system to solve these problems is needed.

[0093] 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.

[0094] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring the user's music streaming service listening history, means for analyzing the listening history and extracting the user's favorite music genre and mood parameters, means for saving the listening history data in a database, means for a store manager to input the store's music concept and playlist history, and means for analyzing survey results and feedback and generating store music preference data. This allows users to easily find a store that best suits their music preferences, and stores can increase customer satisfaction by providing background music that suits customers' music preferences.

[0095] "User music preference data" is data that analyzes the viewing history of music distribution services and compiles the user's preferred music genres and mood parameters.

[0096] "Store music preference data" is data on the music genres and atmosphere preferred in a store, based on the store's music concept, playlist history, and customer survey results and feedback.

[0097] The "matching rate" is an index showing the degree of agreement between the user's music preference data and the store's music preference data, and serves as a criterion for recommending stores that match the user's preferences.

[0098] "Listening history" refers to a user's past playback history data on a music distribution service, and specifically includes information on the songs and artists the user has listened to.

[0099] "Mood parameters" are numerical or categorical expressions of the emotions and states that music evokes in users, and include classifications such as relaxed, active, and focused.

[0100] A "database" is a system that can organize and store multiple pieces of data and efficiently manage and search them, and in the present invention, it is used to store viewing history data and the like.

[0101] "Survey results" are the results of questionnaire surveys answered by users and customers, compiled and converted into data.

[0102] "Feedback" refers to reactions such as comments and ratings provided by users and customers, and serves as a reference when generating music preference data for a store.

[0103] "Copyright-free music data" refers to music files that are not subject to copyright restrictions and can be freely used and distributed.

[0104] A "generative AI model" is a model for generating new data or content using artificial intelligence technology, and in this invention is used to generate copyright-free music.

[0105] A "prompt" is an instruction entered into a generative AI model to specify the conditions and characteristics of the music to be generated.

[0106] "Cloud storage" is a storage service for saving and managing data on the Internet, and is used to distribute created music to stores.

[0107] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize background music playback in the store. This system functions through three parties: a server, a terminal, and the user.

[0108] Server Features

[0109] 1. Generating user music preference data

[0110] When a user signs in to the system, the server obtains listening history data from music distribution services (e.g., Spotify or Apple Music) via API. This listening history includes information on the songs and artists played by the user. Based on the obtained listening history, a machine learning model (e.g., K-means clustering) is used to classify and extract the user's favorite music genres and mood parameters (e.g., relaxed, active, focused), generating "user music preference data."

[0111] 2. Generating store music preference data

[0112] Store managers use dedicated terminals to input the store's music concept and playlist history. Based on the input data, the server also collects and analyzes survey results and feedback from past customers to generate "store music preference data." Text mining technology is used to analyze the survey and feedback data and extract keywords related to specific music genres and moods.

[0113] 3. Perform matching

[0114] The user uses the system's search function to send a request to the server to find the best store. The server compares the user's music preference data with the store's music preference data and uses algorithms such as cosine similarity calculations to recommend the store with the highest matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest a cafe that plays jazz.

[0115] 4. Creation and provision of copyright-free background music

[0116] The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on the prompt. The generated music is stored in cloud storage and provided to the store. An example of a specific prompt might be, "Please generate a relaxing jazz track using piano and saxophone, with a calm and soothing tempo."

[0117] Device Features

[0118] 1. Data entry and manipulation

[0119] The store manager uses the terminal to use an interface to input the store's music concept and playlist history, as well as to input customer surveys and collect feedback.

[0120] 2. Automatic BGM playback

[0121] When a user visits a store and logs in or checks in to the system, the store's terminal receives the user's music preference data from the server in real time. Based on the data, the system automatically plays royalty-free background music. For example, if the user wants to relax, smooth jazz background music will be played automatically.

[0122] User Actions

[0123] 1. Sign in to the system

[0124] Users sign in to the system and provide their music streaming service listening history, which is an important source of information for generating user music preference data.

[0125] 2. Search and select a store

[0126] Users can use the system's search function to find stores that match their music preferences, and the server responds to this request by recommending the most suitable stores.

[0127] Specific operation example

[0128] Consider a case where a user likes jazz and is looking for a relaxing cafe. The user first signs in to the system and provides their listening history from a music streaming service. The server analyzes the listening history and determines that the user likes jazz. When the user then searches for "a relaxing jazz cafe," the server compares the user's music preference data with the store's music preference data and recommends the most suitable cafe. When the user visits the cafe, the terminal in the store automatically plays smooth jazz background music.

[0129] This allows users to enjoy a more comfortable music experience in a store that best suits their musical tastes, and stores can further increase customer satisfaction by providing background music that matches customers' musical tastes.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1: Collecting user viewing history

[0132] Input: User's music streaming service login information

[0133] How it works: When a user signs in to the system, the server uses the music streaming service's API to obtain the user's listening history data via OAuth authentication, such as the songs, artists, and playback time that the user has listened to.

[0134] Output: Viewing history data

[0135] Step 2: Analyzing viewing history and generating preference data

[0136] Input: Viewing history data

[0137] How it works: The server analyzes the acquired listening history using a machine learning model (e.g., K-means clustering) to extract the user's favorite music genres and mood parameters (relaxed, active, focused, etc.). The analysis uses the frequency and duration of playback for specific artists and genres.

[0138] Output: User music preference data

[0139] Step 3: Enter store data

[0140] Input: Store music concept, playlist history, survey results, feedback

[0141] Operation: The store manager uses a terminal to input the store's music concept and playlist history. The store manager also inputs customer survey results and feedback from the terminal. This data is sent to the server.

[0142] Output: Sending input data to the server

[0143] Step 4: Generate store music preference data

[0144] Input: Store data (music concept, playlist history, survey results, feedback)

[0145] How it works: The server analyzes collected store data and extracts keywords related to specific music genres and moods. It then uses text mining technology to analyze surveys and feedback and generates "store music preference data."

[0146] Output: Store music preference data

[0147] Step 5: Matching users and stores

[0148] Input: User music preference data, store music preference data

[0149] How it works: A user uses the system's search function to send a request to find a store that matches their needs. The server compares the user's music preference data with the store's music preference data using algorithms such as cosine similarity calculations, and recommends the store that best matches.

[0150] Output: Matching results (store recommendations displayed in list format)

[0151] Step 6: Generate royalty-free background music

[0152] Input: User music preference data, store music request (prompt sentence)

[0153] How it works: The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on a prompt, such as "Please generate a relaxing jazz track with piano and saxophone, at a calm and soothing tempo."

[0154] Output: Generated music data

[0155] Step 7: Save to cloud storage and provide to stores

[0156] Input: Generated music data

[0157] How it works: The server stores the generated royalty-free music on a cloud storage service (e.g., Amazon S3), then provides a download link to the store, through which the store can download the music.

[0158] Output: Song data on cloud storage, download link to store

[0159] Step 8: User visits the store and automatic background music plays

[0160] Input: User music preference data, check-in information

[0161] How it works: A user visits a store and logs in or checks in to the system. The store's terminal retrieves the user's music preference data from the server in real time and automatically plays the most appropriate background music over the store's sound system. For example, if the user wants to relax, smooth jazz will be played.

[0162] Output: BGM playback in the store

[0163] This allows users to enjoy a comfortable music experience in a store that best suits their musical tastes, and stores can improve customer satisfaction.

[0164] (Application example 1)

[0165] 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."

[0166] In today's world, it is difficult to choose a store that meets a user's individual music preferences, resulting in a poor user experience. Furthermore, stores are limited to playing generic music, making it difficult to provide services tailored to their customers. Furthermore, there is a need for a system that can provide appropriate background music without worrying about copyright issues.

[0167] 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.

[0168] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, and means for automatically playing background music in the physical store based on the user music preference data. This allows the user to choose a store that suits their music preferences, and the store to provide background music that suits the customer's preferences.

[0169] "User music preference data" is data that indicates a user's music preferences, generated by analyzing the user's listening history, such as the genres, artists, and songs that the user normally listens to.

[0170] "Store music preference data" is data that indicates the characteristics and trends of the music offered by a store, generated based on the store's music concept, playlists, and customer feedback.

[0171] The "matching rate" is a numerical value or index that indicates the degree of match when comparing a user's music preference data with a store's music preference data.

[0172] "Automatic playback means" refers to a system or mechanism that automatically plays appropriate background music when a user visits a physical store, based on the user's music preference data.

[0173] "Copyright-free background music" is music that can be freely used without being restricted by copyright, and is a type of music that is automatically played by this system.

[0174] A "generative AI model" is an algorithm or system that uses machine learning and artificial intelligence techniques to generate new music or data based on user preferences and characteristics.

[0175] A "prompt" is an instruction or question that is input into a generative AI model to generate appropriate background music.

[0176] "Physical store" refers to a commercial facility or service provider that exists in a physical location, and means a specific store visited by customers who use the system.

[0177] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize the background music playback in the store. This system is composed of the following main elements.

[0178] 1. Generating user music preference data

[0179] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history. For example, it analyzes the user's frequently listened-to music genres and artist list. Examples of music streaming service APIs used include the Spotify API and Apple Music API.

[0180] Server: The server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user's music preference data."

[0181] 2. Generating store music preference data

[0182] Terminal: The store manager uses the terminal to input the store's music concept and playlist history into the system, which then aggregates the store's music preference data.

[0183] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data." For example, it may acquire information that the music played at Store A is classical and that customers prefer a quiet atmosphere.

[0184] 3. Perform matching

[0185] User: The user uses the system's search function to submit a request to find the best store.

[0186] Server: The server compares the user's music preference data with that of the stores and calculates stores with a high matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[0187] Server: The server presents the matching results to the user.

[0188] 4. Creation and provision of copyright-free background music

[0189] Server: The server collects multiple copyright-free music data and generates new music based on this data. Using a generative AI model, it creates music in various genres tailored to the user's preferences. For example, to generate a jazz song, the server uses the prompt "Generate a relaxing jazz song."

[0190] Server: The server provides the generated copyright-free music to each store so that it can be used on the store's sound system.

[0191] 5. Development of an automatic background music playback system

[0192] User: A user visits a physical store and logs in or checks into the system.

[0193] Terminal: The store's terminal acquires the user's music preference data from the server.

[0194] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[0195] As a specific example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests stores that match their preferences (e.g., cafes that play jazz music and are relaxing). When the user visits the store, the device inside the store automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the store can also increase customer satisfaction.

[0196] An example of a prompt for generating background music using a generative AI model is "Generate a relaxing jazz song."

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] Acquiring user music preference data

[0200] Input: User login information and permissions to access music streaming services

[0201] Processing: The server uses the music streaming service API (such as Spotify API or Apple Music API) to obtain the user's listening history, which includes information such as song genre and artist.

[0202] Data processing: Analyze viewing history data to extract the user's favorite music genres and mood parameters.

[0203] Output: User's music preference data

[0204] Step 2:

[0205] Generating store music preference data

[0206] Input: Store manager inputs store music concept, playlist history, and customer feedback

[0207] Processing: The terminal inputs this information into the system, and the server aggregates the information stored in the database.

[0208] Data calculation: Based on the input data, the server analyzes the survey results and feedback of customers and generates music preference data for the store.

[0209] Output: Store music preference data

[0210] Step 3:

[0211] Matching users and stores

[0212] Input: User music preference data and music preference data for multiple stores

[0213] Processing: The server compares the user's music preference data with the store's music preference data and calculates the matching rate.

[0214] Data calculation: Using a matching algorithm, it identifies the store that best suits a user's preferences. For example, it suggests cafes that play jazz to a user who likes jazz.

[0215] Output: Matching results (list of stores with high matching rates)

[0216] Step 4:

[0217] Copyright-free background music generation

[0218] Input: Multiple copyright-free music data stored on the server and a prompt for the generative AI model (e.g., "Generate a relaxing jazz song").

[0219] Processing: The server uses a generative AI model to generate new royalty-free background music based on the user's preferences.

[0220] Data computation: A generative AI model analyzes music data and creates new songs based on prompts.

[0221] Output: New royalty-free background music

[0222] Step 5:

[0223] Development of an automatic background music playback system

[0224] Input: Information that users enter when they visit a physical store and log in or check in to the system

[0225] Processing: The store's terminal obtains the user's music preference data from the server and selects the appropriate copyright-free background music.

[0226] Data calculation: Based on the acquired data, the device selects the most suitable song and plays it on the store's sound system.

[0227] Output: Play background music in the store. For example, relaxing smooth jazz is played.

[0228] 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.

[0229] The system of the present invention uses the user's music preference data and the store's music preference data to match the most suitable store to the user, and further combines an emotion engine to take into account the user's real-time emotional state and optimize the store's background music playback.

[0230] The system consists of the following main elements:

[0231] 1. Generating user music preference data

[0232] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[0233] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user music preference data."

[0234] 2. Generating store music preference data

[0235] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[0236] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[0237] Server: Analyzes customers' music preferences based on collected background music history and customer survey results and feedback, and generates "store music preference data."

[0238] 3. Emotion Recognition by Emotion Engine

[0239] On the device: The user uses the emotion engine within the app to input or recognize their real-time emotional state. For example, the user inputs, "I want to relax right now."

[0240] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[0241] 4. Perform matching

[0242] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[0243] Server: Compares the user's music preference data with the store's music preference data, as well as the emotional state data from the emotion engine, and calculates the matching rate.

[0244] Server: Presents matching results to the user, for example, presenting the store that is closest to the user's current emotional state.

[0245] 5. Creation and provision of copyright-free background music

[0246] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new music.

[0247] Server: Provides the generated copyright-free music to each store so that it can be used on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[0248] 6. Development of an automatic background music playback system

[0249] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[0250] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[0251] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[0252] As a specific example, if a user likes relaxing jazz music, the user can input their emotional state of "I want to relax," and the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a device inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preferences.

[0253] This system allows users to easily find the store that best suits their emotions and musical preferences, and stores can improve the quality of their service by providing background music that suits customers' emotions and preferences.

[0254] The processing flow will be explained below.

[0255] Step 1:

[0256] User: Signs in to the system. Users enter their login information to access the system.

[0257] Step 2:

[0258] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[0259] Step 3:

[0260] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[0261] Step 4:

[0262] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[0263] Step 5:

[0264] Server: Combines the extracted music genre and mood parameters to generate "user music preference data" and saves it in the user's profile.

[0265] Step 6:

[0266] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[0267] Step 7:

[0268] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[0269] Step 8:

[0270] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[0271] Step 9:

[0272] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and saves it in the store's profile.

[0273] Step 10:

[0274] User: Input or recognize their real-time emotional state using the emotion engine. For example, inputting an emotional state like "I want to relax right now" within the app.

[0275] Step 11:

[0276] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[0277] Step 12:

[0278] User: Searches for a store within the app, for example, searching for "relaxing jazz cafe."

[0279] Step 13:

[0280] Server: Compares user music preference data, store music preference data, and real-time emotion data from the emotion engine to calculate the matching rate.

[0281] Step 14:

[0282] Server: Presents the matching results to the user, for example, presenting the store that best suits the user's current emotional state.

[0283] Step 15:

[0284] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new copyright-free background music.

[0285] Step 16:

[0286] Server: Provides the generated copyright-free music to each store, allowing it to be played on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[0287] Step 17:

[0288] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[0289] Step 18:

[0290] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[0291] Step 19:

[0292] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[0293] Step 20:

[0294] Server: When there is a change in the user's emotional state, the emotion engine recognizes it and sends it to the server.

[0295] Step 21:

[0296] Server: Based on the new emotional state, reevaluate the store's background music and send instructions to the device to change the background music if necessary.

[0297] Step 22:

[0298] Device: The device that receives the instruction will change the background music and play a new song. For example, if the user inputs "I want to feel refreshed this time," the device will change to a refreshing song.

[0299] In this way, the system of the present invention can optimally match a store with a user based on the user's music preferences and real-time emotional state, and automatically play appropriate background music in the store, thereby improving the user's music experience and increasing customer satisfaction in the store.

[0300] Example 2

[0301] 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."

[0302] With conventional music distribution systems and store background music systems, it is difficult to find a store that matches a user's preferred music genre or emotional state at the time. Stores are also unable to provide background music that responds to customers' real-time emotions, resulting in a decline in user satisfaction. Furthermore, there is no way to generate and provide new music that is not subject to copyright restrictions, which limits the music content available to stores.

[0303] 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.

[0304] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for recognizing the user's real-time emotional state, means for presenting stores with a high matching rate to the user using the user music preference data, store music preference data, and the user's real-time emotional state data, means for generating new copyright-free background music using copyright-free music data, means for providing the generated copyright-free background music to a matched store, means for playing the provided copyright-free background music, and means for acquiring the user's music preference data and emotional state data when the user visits a store and automatically playing optimal background music at the store based on the user's preferences and emotional state. This makes it possible to suggest optimal stores tailored to the user's current emotional state and music preferences, and to generate and play optimal copyright-free background music.

[0305] "User music preference data" refers to data that includes the user's favorite music genres and artists, as well as mood parameters based on listening history.

[0306] "Store music preference data" is data generated based on background music history collected from the store's sound system and playlist management system, as well as customer feedback.

[0307] "Real-time emotional state data" refers to data that indicates a user's current emotional state as analyzed through user input, voice, and facial expression recognition technology.

[0308] The "matching rate" is an indicator of compatibility calculated by comparing user music preference data, store music preference data, and real-time emotional state data.

[0309] "Copyright-free BGM" is background music that can be used without copyright restrictions.

[0310] A "generative AI model" is an artificial intelligence model that generates new music or data based on input data.

[0311] A "prompt" is an instruction given to a generative AI model, a document intended to encourage data generation for a specific purpose.

[0312] A "server" is a computer system that has functions such as data collection, analysis, storage, and matching calculation.

[0313] A "terminal" is a device that allows users or store managers to input information and retrieve data from a server.

[0314] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and further optimize the store's background music taking into account the user's real-time emotional state. This system is composed of the following main elements.

[0315] 1. Generating user music preference data

[0316] Server: When a user signs in to the system, the server retrieves the user's listening history from music streaming services (e.g., Spotify, Apple Music) via API. This data includes the user's recently listened songs and frequently played artists.

[0317] Server: Analyzes the acquired listening history, extracts the user's preferred music genre and mood parameters (relaxed, active, focused, etc.), and generates "user music preference data."

[0318] 2. Generating store music preference data

[0319] Terminal: The store manager uses the terminal to input store information and the music concept. For example, they might register information such as "a cafe that mainly serves classical music."

[0320] Server: The server automatically collects the history of background music played in the past from the store's sound system and playlist management system.

[0321] Server: Based on the collected background music history and customer feedback, the server analyzes the music genres and moods preferred by customers and generates "store music preference data."

[0322] 3. Emotion Recognition by Emotion Engine

[0323] On the device: Users use the emotion engine within the app to input their real-time emotional state, for example, "I feel like relaxing right now."

[0324] Server: The emotion engine uses user input, voice, and facial expression recognition technology to analyze the user's current emotional state and stores the results in the user's emotional profile.

[0325] 4. Perform matching

[0326] User: A user searches for "relaxing jazz cafe" within the app.

[0327] Server: The server compares the user's music preference data, the store's music preference data, and the emotion engine data, and calculates the matching rate using an algorithm.

[0328] Server: Based on the calculation results, presents the user with a list of the best stores.

[0329] 5. Creation and provision of copyright-free background music

[0330] Server: The server collects copyright-free music data on the Internet.

[0331] Server: Based on the collected data, a generative AI model (e.g., music generation AI) is used to generate new music. An example prompt is, "Generate jazz music that matches a relaxing mood. The tempo should be slow and the melody should have a soft tone."

[0332] Server: Provides the generated music to each store so that it can be used on the store's sound system.

[0333] 6. Development of an automatic background music playback system

[0334] User: The user visits the designated store and checks in using the app. The user's emotional state is automatically updated upon check-in.

[0335] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data.

[0336] Device: Based on the acquired data, the device automatically selects royalty-free background music that best suits the user's preferences and emotional state and plays it in the store. For example, it plays smooth jazz for a user who requests relaxation.

[0337] As a concrete example, consider the case where a user likes jazz music that is relaxing. When the user inputs their emotional state of "I want to relax," the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a terminal inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preference.

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Step 1:

[0340] Generating user music preference data

[0341] Server: When a user signs in to the system, the server uses the API of the music distribution service (e.g., Spotify, Apple Music) to obtain the user's listening history. The input is the user's sign-in information, and the output is the listening history data.

[0342] Server: Analyzes the acquired listening history, including frequently played songs, artists, genres, and playback times. The input is listening history data, and the output is the user's music preference data.

[0343] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) from the music preference data and generates "user music preference data." The input is the analyzed music preference data, and the output is the user music preference data.

[0344] Step 2:

[0345] Generating store music preference data

[0346] Terminal: The store manager uses the terminal to register detailed store information (e.g., a cafe that mainly serves classical music) and the music concept. The input is the information provided by the store manager, and the output is store information data.

[0347] Server: The server collects the history of BGM played in the past from the store's sound system and playlist management system. The input is the history data from the sound system and playlist management system, and the output is the store's BGM history data.

[0348] Server: Analyzes the collected BGM history and customer feedback data to analyze the music genres and moods preferred by customers. The input is store BGM history data and feedback data, and the output is store music preference data.

[0349] Step 3:

[0350] Emotion recognition by emotion engine

[0351] Terminal: The user inputs an emotional state, such as "I want to relax now," into the app. The input is the user's emotional state data, and the output is the emotional state input data.

[0352] Server: The emotion engine analyzes the current emotional state using user input, voice, and facial expression recognition technology. The input is the emotional state input data, and the output is the real-time emotional state data.

[0353] Server: Based on the analysis results, store the real-time emotional state data in the user's emotional profile. The input is the real-time emotional state data, and the output is the updated emotional profile.

[0354] Step 4:

[0355] Performing matching

[0356] User: A user searches for "relaxing jazz cafe" within the app. The input is the user's search query, and the output is the search criteria data.

[0357] Server: Compares user music preference data, store music preference data, and real-time emotional state data, and calculates the matching rate using an algorithm. The inputs are user music preference data, store music preference data, and real-time emotional state data, and the output is the matching result data.

[0358] Server: Based on the calculation results, it presents the user with a list of optimal stores. The input is the matching result data, and the output is the store list displayed to the user.

[0359] Step 5:

[0360] Creation and provision of copyright-free background music

[0361] Server: The server collects copyright-free music data on the Internet. The input is free music data on the Internet, and the output is the collected music data.

[0362] Server: Generates new music using a generative AI model based on the collected data. An example prompt is, "Generate jazz music that suits a relaxing mood. The tempo should be slow and the melody should have a soft tone." The input is the collected music data and the prompt, and the output is the generated music data.

[0363] Server: Provides the generated music to each store so that it can be used on the store's sound system. The input is the generated music data, and the output is the provided music data.

[0364] Step 6:

[0365] Development of an automatic background music playback system

[0366] User: The user visits the designated store and checks in using the app. The emotional state is automatically updated at check-in. The input is the user's check-in information, and the output is the updated emotional state data.

[0367] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data. The input is the access information to the server, and the output is the obtained data.

[0368] Terminal: Based on the acquired data, the terminal automatically selects copyright-free background music that best suits the user's preferences and emotional state, and plays it in the store. The input is the acquired data, and the output is the background music that is played.

[0369] (Application example 2)

[0370] 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."

[0371] Conventional matching systems that use music preference data have difficulty in considering the user's real-time emotional state, making it difficult to provide the music environment that the user desires. Furthermore, stores lacked a mechanism for providing optimal background music that matches the diverse musical preferences of their customers. This resulted in an inability to sufficiently improve user satisfaction and limited the quality of store services.

[0372] The identification process by the identification 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 generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring and recording the user's real-time emotional state, and means for automatically playing optimal background music based on the acquired user emotional state in the store's background music playback system. This makes it possible to suggest optimal stores based on the user's real-time emotional state, and enables stores to provide optimal background music tailored to each user's emotions. This increases user satisfaction and dramatically improves the quality of store service.

[0373] "User's music preference data" refers to data including the user's preferred music genres, artists, listening history, and emotional state related to music.

[0374] "Store music preference data" refers to data including the music genres and playlists offered by a particular store, past background music history, and survey results and feedback from customers.

[0375] The "matching rate" is an index that indicates the degree to which the user's music preference data matches the store's music preference data.

[0376] "Emotional state" refers to the user's current mental and sensory state, including, for example, relaxed, focused, active, etc.

[0377] A "BGM playback system" is an audio system for playing background music (BGM) in a store.

[0378] "Copyright-free background music" is music data that can be used freely without being dependent on any specific copyright.

[0379] A "generative AI model" is an artificial intelligence model that generates new music based on copyright-free music data.

[0380] The system for implementing the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store, and also takes into account the user's real-time emotional state to automatically play the most suitable background music in the store. This system is composed of the following main elements.

[0381] The server acquires the user's listening history from the music streaming service, extracts the user's favorite music genre and emotional parameters (relaxed, focused, active, etc.), and generates "user music preference data" based on the listening history, playback frequency, and user feedback.

[0382] At the store, the store manager registers store information and music concept in the system, and the server generates "store music preference data" based on the history of background music played in the past and the results of customer surveys. This clearly defines the music environment for each store.

[0383] Users can use the emotion engine within the application to input or recognize their real-time emotional state. The server uses the emotion engine to analyze the emotional state from the user's input, voice, and facial expression recognition technology to generate real-time emotional data, which then creates an emotional profile for the user.

[0384] When a user searches for a store, the server compares the user's music preference data with that of the store, and also considers the user's real-time emotional state to present stores with a high matching rate, allowing the user to easily find a store that best suits their emotions and musical preferences.

[0385] When a customer visits a store and checks in, the store's terminal obtains the user's music preference data and emotional state data from the server. Based on the obtained data, the store's background music playback system automatically selects and plays royalty-free background music that matches the user's preferences and emotional state. The server uses multiple copyright-free music data sets to train a generative AI model and generate new music. The generated music is optimized based on the customer's emotional state and provided to the store.

[0386] For example, if a user feels like "I want to relax," and their favorite music genre is jazz, they might enter a prompt to search for a cafe where they can relax:

[0387] "I feel like I want to relax. I like jazz music. I'm looking for a cafe where I can relax here."

[0388] Based on this prompt, the system will suggest the most suitable store, and when the user checks in at that store, relaxing jazz music will be played automatically. In this way, the system can provide the optimal musical environment according to the user's real-time emotional state and musical preferences.

[0389] The hardware used is a server with Django (web framework), MySQL (database), smartphones (iOS / Android), and API communication (HTTP requests). The emotion engine uses Emotion API (a cloud-based emotion recognition API), and the background music playback system is the in-store sound system.

[0390] This invention can improve user satisfaction and dramatically improve the quality of service in stores.

[0391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0392] Step 1:

[0393] The server obtains the user's listening history from the music streaming service. The user ID is required as input, and the user's listening history data is obtained as output. Specifically, the listening history is obtained through an API request, and data such as the number of plays and listening time of the user is collected.

[0394] Step 2:

[0395] The server generates user music preference data based on the listening history data. The listening history data is required as input, and a list of the user's favorite music genres and artists is obtained as output. The data is processed by performing statistical analysis of the listening history to extract the main genres and artists.

[0396] Step 3:

[0397] The store manager inputs store information and music concept into the system. The store's music genre and concept are required as input, and the information is recorded on the server as output. Specifically, data is entered using a web form and sent to the server.

[0398] Step 4:

[0399] The server collects the history of background music played in the past from the store's sound system. The store ID is required as input, and background music history data is obtained as output. Specifically, the server analyzes the sound system's log data and collects the songs that have been played and the number of times they have been played.

[0400] Step 5:

[0401] The server generates music preference data for the store based on the background music history data and the results of customer surveys. The store's background music history data and the survey results are required as input, and the store's music preference data is obtained as output. The data is then processed by statistically analyzing the evaluations of the survey results to extract the main music genres and songs.

[0402] Step 6:

[0403] Users use the emotion engine within the application to input their real-time emotional state or have it recognized by the device's camera. Emotional state and voice input are required as input, and analyzed emotional data is obtained as output. Specifically, emotions are analyzed from facial expressions and voice through the emotion recognition API and sent to the server.

[0404] Step 7:

[0405] The server updates the user's emotional profile based on the user's music preference data and real-time emotional state data. The server requires real-time emotional data and music preference data as input, and obtains the updated user's emotional profile as output. Specifically, the server updates the profile database based on the emotional data.

[0406] Step 8:

[0407] The user enters a prompt to search for a store in the app. For example, the prompt might be "I'm looking for a relaxing jazz cafe." The prompt is required as input, and a list of suitable stores is obtained as output. The server analyzes the prompt and uses a matching algorithm to present the most suitable stores.

[0408] Step 9:

[0409] The server compares the user's music preference data, the store's music preference data, and real-time emotional state data to match the optimal store. Each piece of data for comparison is required as input, and stores with a high matching rate are obtained as output. Specifically, each piece of data is integrated and analyzed to create a list of the most suitable stores.

[0410] Step 10:

[0411] A user visits a store and checks in at a terminal in the store. The user ID is required as input, and the user's check-in information is obtained as output. The terminal in the store obtains the user's latest music preference data and emotional state data from the server.

[0412] Step 11:

[0413] The store's terminal retrieves copyright-free background music from the server that matches the user's preferences and current emotional state, and automatically plays it. The user's preference data and emotional state data are required as input, and the optimal background music is obtained as output. The terminal controls the background music playback system based on the retrieved data, providing the user with the optimal musical environment.

[0414] Through the above processing steps, users can find the most suitable store according to their real-time emotional state and music preferences, and stores can provide the best service.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Second embodiment]

[0419] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] In the smart glasses 214, 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.

[0430] 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."

[0431] The system according to the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and also optimize background music playback in the store.

[0432] The system consists of the following main elements:

[0433] 1. Generating user music preference data

[0434] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[0435] Server: Based on the user's listening history, the server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) to generate "user music preference data."

[0436] 2. Generating store music preference data

[0437] Terminal: The store manager inputs the store's music concept and playlist history into the system.

[0438] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data."

[0439] Server: For example, acquire information such as the music played at store A is classical and customers prefer a quiet atmosphere.

[0440] 3. Perform matching

[0441] User: Uses the system's search function to submit a request to find the best store.

[0442] Server: Compares user music preference data with store music preference data and calculates stores with the highest matching rate.

[0443] Server: Presents matching results to the user. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[0444] 4. Creation and provision of copyright-free background music

[0445] Server: Collects multiple copyright-free music data and generates new music based on this data. Using generative AI, it creates music in a variety of genres tailored to the user's preferences.

[0446] Server: Provides the generated royalty-free music to each store so that it can be used on the store's sound system. For example, if a store needs jazz background music, the server distributes the newly generated jazz track.

[0447] 5. Development of an automatic background music playback system

[0448] User: Visits the store and logs in or checks into the system.

[0449] Terminal: The store's terminal obtains the user's music preference data from the server.

[0450] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[0451] As a concrete example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests establishments that match their preferences (e.g., cafes that play jazz in-store and are relaxing). When the user visits the establishment, the in-store device automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the establishment can increase customer satisfaction.

[0452] This system allows users to easily find the store that best suits their musical tastes, and stores can improve the quality of their service by providing background music that suits customers' musical tastes.

[0453] The processing flow will be explained below.

[0454] Step 1:

[0455] User: Signs in to the system. Users enter their login information to access the system.

[0456] Step 2:

[0457] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[0458] Step 3:

[0459] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[0460] Step 4:

[0461] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[0462] Step 5:

[0463] Server: Combines music genre and mood parameters to generate "user music preference data" and stores it in the user's profile.

[0464] Step 6:

[0465] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[0466] Step 7:

[0467] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[0468] Step 8:

[0469] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[0470] Step 9:

[0471] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and save it in the store's profile.

[0472] Step 10:

[0473] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[0474] Step 11:

[0475] Server: Compares user music preference data with store music preference data, calculates matching rate, and lists stores that most closely match the user's preferences.

[0476] Step 12:

[0477] Server: Presents the matching results to the user. For example, it displays "There are five cafes that have high music preferences for the user."

[0478] Step 13:

[0479] Server: Collects multiple copyright-free music data sets and trains the AI ​​based on them to generate new copyright-free background music.

[0480] Step 14:

[0481] Server: Provides generated copyright-free background music to each store, distributing music data via a portal site or dedicated app.

[0482] Step 15:

[0483] User: Visits the store and checks in by connecting to the store's terminal or scanning a QR code.

[0484] Step 16:

[0485] Terminal: The store's terminal obtains the user's music preference data from the server.

[0486] Step 17:

[0487] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music that matches the user's preferences. For example, it can play smooth jazz for a user who wants to relax.

[0488] In this way, a system is realized that suggests the most suitable store based on the user's musical preferences and provides a musical environment tailored to individual preferences even after the user visits the store.

[0489] Example 1

[0490] 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."

[0491] Many users today want to control their moods and activities and enjoy themselves through music. However, it is not easy to find a store that matches the user's preferred music genre and atmosphere. Stores are also required to understand customers' musical preferences and provide optimal background music to increase customer satisfaction, but this is time-consuming. An efficient and effective system to solve these problems is needed.

[0492] 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.

[0493] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring the user's music streaming service listening history, means for analyzing the listening history and extracting the user's favorite music genre and mood parameters, means for saving the listening history data in a database, means for a store manager to input the store's music concept and playlist history, and means for analyzing survey results and feedback and generating store music preference data. This allows users to easily find a store that best suits their music preferences, and stores can increase customer satisfaction by providing background music that suits customers' music preferences.

[0494] "User music preference data" is data that analyzes the viewing history of music distribution services and compiles the user's preferred music genres and mood parameters.

[0495] "Store music preference data" is data on the music genres and atmosphere preferred in a store, based on the store's music concept, playlist history, and customer survey results and feedback.

[0496] The "matching rate" is an index showing the degree of agreement between the user's music preference data and the store's music preference data, and serves as a criterion for recommending stores that match the user's preferences.

[0497] "Listening history" refers to a user's past playback history data on a music distribution service, and specifically includes information on the songs and artists the user has listened to.

[0498] "Mood parameters" are numerical or categorical expressions of the emotions and states that music evokes in users, and include classifications such as relaxed, active, and focused.

[0499] A "database" is a system that can organize and store multiple pieces of data and efficiently manage and search them, and in the present invention, it is used to store viewing history data and the like.

[0500] "Survey results" are the results of questionnaire surveys answered by users and customers, compiled and converted into data.

[0501] "Feedback" refers to reactions such as comments and ratings provided by users and customers, and serves as a reference when generating music preference data for a store.

[0502] "Copyright-free music data" refers to music files that are not subject to copyright restrictions and can be freely used and distributed.

[0503] A "generative AI model" is a model for generating new data or content using artificial intelligence technology, and in this invention is used to generate copyright-free music.

[0504] A "prompt" is an instruction entered into a generative AI model to specify the conditions and characteristics of the music to be generated.

[0505] "Cloud storage" is a storage service for saving and managing data on the Internet, and is used to distribute created music to stores.

[0506] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize background music playback in the store. This system functions through three parties: a server, a terminal, and the user.

[0507] Server Features

[0508] 1. Generating user music preference data

[0509] When a user signs in to the system, the server obtains listening history data from music distribution services (e.g., Spotify or Apple Music) via API. This listening history includes information on the songs and artists played by the user. Based on the obtained listening history, a machine learning model (e.g., K-means clustering) is used to classify and extract the user's favorite music genres and mood parameters (e.g., relaxed, active, focused), generating "user music preference data."

[0510] 2. Generating store music preference data

[0511] Store managers use dedicated terminals to input the store's music concept and playlist history. Based on the input data, the server also collects and analyzes survey results and feedback from past customers to generate "store music preference data." Text mining technology is used to analyze the survey and feedback data and extract keywords related to specific music genres and moods.

[0512] 3. Perform matching

[0513] The user uses the system's search function to send a request to the server to find the best store. The server compares the user's music preference data with the store's music preference data and uses algorithms such as cosine similarity calculations to recommend the store with the highest matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest a cafe that plays jazz.

[0514] 4. Creation and provision of copyright-free background music

[0515] The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on the prompt. The generated music is stored in cloud storage and provided to the store. An example of a specific prompt might be, "Please generate a relaxing jazz track using piano and saxophone, with a calm and soothing tempo."

[0516] Device Features

[0517] 1. Data entry and manipulation

[0518] The store manager uses the terminal to use an interface to input the store's music concept and playlist history, as well as to input customer surveys and collect feedback.

[0519] 2. Automatic BGM playback

[0520] When a user visits a store and logs in or checks in to the system, the store's terminal receives the user's music preference data from the server in real time. Based on the data, the system automatically plays royalty-free background music. For example, if the user wants to relax, smooth jazz background music will be played automatically.

[0521] User Actions

[0522] 1. Sign in to the system

[0523] Users sign in to the system and provide their music streaming service listening history, which is an important source of information for generating user music preference data.

[0524] 2. Search and select a store

[0525] Users can use the system's search function to find stores that match their music preferences, and the server responds to this request by recommending the most suitable stores.

[0526] Specific operation example

[0527] Consider a case where a user likes jazz and is looking for a relaxing cafe. The user first signs in to the system and provides their listening history from a music streaming service. The server analyzes the listening history and determines that the user likes jazz. When the user then searches for "a relaxing jazz cafe," the server compares the user's music preference data with the store's music preference data and recommends the most suitable cafe. When the user visits the cafe, the terminal in the store automatically plays smooth jazz background music.

[0528] This allows users to enjoy a more comfortable music experience in a store that best suits their musical tastes, and stores can further increase customer satisfaction by providing background music that matches customers' musical tastes.

[0529] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0530] Step 1: Collecting user viewing history

[0531] Input: User's music streaming service login information

[0532] How it works: When a user signs in to the system, the server uses the music streaming service's API to obtain the user's listening history data via OAuth authentication, such as the songs, artists, and playback time that the user has listened to.

[0533] Output: Viewing history data

[0534] Step 2: Analyzing viewing history and generating preference data

[0535] Input: Viewing history data

[0536] How it works: The server analyzes the acquired listening history using a machine learning model (e.g., K-means clustering) to extract the user's favorite music genres and mood parameters (relaxed, active, focused, etc.). The analysis uses the frequency and duration of playback for specific artists and genres.

[0537] Output: User music preference data

[0538] Step 3: Enter store data

[0539] Input: Store music concept, playlist history, survey results, feedback

[0540] Operation: The store manager uses a terminal to input the store's music concept and playlist history. The store manager also inputs customer survey results and feedback from the terminal. This data is sent to the server.

[0541] Output: Sending input data to the server

[0542] Step 4: Generate store music preference data

[0543] Input: Store data (music concept, playlist history, survey results, feedback)

[0544] How it works: The server analyzes collected store data and extracts keywords related to specific music genres and moods. It then uses text mining technology to analyze surveys and feedback and generates "store music preference data."

[0545] Output: Store music preference data

[0546] Step 5: Matching users and stores

[0547] Input: User music preference data, store music preference data

[0548] How it works: A user uses the system's search function to send a request to find a store that matches their needs. The server compares the user's music preference data with the store's music preference data using algorithms such as cosine similarity calculations, and recommends the store that best matches.

[0549] Output: Matching results (store recommendations displayed in list format)

[0550] Step 6: Generate royalty-free background music

[0551] Input: User music preference data, store music request (prompt sentence)

[0552] How it works: The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on a prompt, such as "Please generate a relaxing jazz track with piano and saxophone, at a calm and soothing tempo."

[0553] Output: Generated music data

[0554] Step 7: Save to cloud storage and provide to stores

[0555] Input: Generated music data

[0556] How it works: The server stores the generated royalty-free music on a cloud storage service (e.g., Amazon S3), then provides a download link to the store, through which the store can download the music.

[0557] Output: Song data on cloud storage, download link to store

[0558] Step 8: User visits the store and automatic background music plays

[0559] Input: User music preference data, check-in information

[0560] How it works: A user visits a store and logs in or checks in to the system. The store's terminal retrieves the user's music preference data from the server in real time and automatically plays the most appropriate background music over the store's sound system. For example, if the user wants to relax, smooth jazz will be played.

[0561] Output: BGM playback in the store

[0562] This allows users to enjoy a comfortable music experience in a store that best suits their musical tastes, and stores can improve customer satisfaction.

[0563] (Application example 1)

[0564] 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."

[0565] In today's world, it is difficult to choose a store that meets a user's individual music preferences, resulting in a poor user experience. Furthermore, stores are limited to playing generic music, making it difficult to provide services tailored to their customers. Furthermore, there is a need for a system that can provide appropriate background music without worrying about copyright issues.

[0566] 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.

[0567] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, and means for automatically playing background music in the physical store based on the user music preference data. This allows the user to choose a store that suits their music preferences, and the store to provide background music that suits the customer's preferences.

[0568] "User music preference data" is data that indicates a user's music preferences, generated by analyzing the user's listening history, such as the genres, artists, and songs that the user normally listens to.

[0569] "Store music preference data" is data that indicates the characteristics and trends of the music offered by a store, generated based on the store's music concept, playlists, and customer feedback.

[0570] The "matching rate" is a numerical value or index that indicates the degree of match when comparing a user's music preference data with a store's music preference data.

[0571] "Automatic playback means" refers to a system or mechanism that automatically plays appropriate background music when a user visits a physical store, based on the user's music preference data.

[0572] "Copyright-free background music" is music that can be freely used without being restricted by copyright, and is a type of music that is automatically played by this system.

[0573] A "generative AI model" is an algorithm or system that uses machine learning and artificial intelligence techniques to generate new music or data based on user preferences and characteristics.

[0574] A "prompt" is an instruction or question that is input into a generative AI model to generate appropriate background music.

[0575] "Physical store" refers to a commercial facility or service provider that exists in a physical location, and means a specific store visited by customers who use the system.

[0576] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize the background music playback in the store. This system is composed of the following main elements.

[0577] 1. Generating user music preference data

[0578] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history. For example, it analyzes the user's frequently listened-to music genres and artist list. Examples of music streaming service APIs used include the Spotify API and Apple Music API.

[0579] Server: The server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user's music preference data."

[0580] 2. Generating store music preference data

[0581] Terminal: The store manager uses the terminal to input the store's music concept and playlist history into the system, which then aggregates the store's music preference data.

[0582] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data." For example, it may acquire information that the music played at Store A is classical and that customers prefer a quiet atmosphere.

[0583] 3. Perform matching

[0584] User: The user uses the system's search function to submit a request to find the best store.

[0585] Server: The server compares the user's music preference data with that of the stores and calculates stores with a high matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[0586] Server: The server presents the matching results to the user.

[0587] 4. Creation and provision of copyright-free background music

[0588] Server: The server collects multiple copyright-free music data and generates new music based on this data. Using a generative AI model, it creates music in various genres tailored to the user's preferences. For example, to generate a jazz song, the server uses the prompt "Generate a relaxing jazz song."

[0589] Server: The server provides the generated copyright-free music to each store so that it can be used on the store's sound system.

[0590] 5. Development of an automatic background music playback system

[0591] User: A user visits a physical store and logs in or checks into the system.

[0592] Terminal: The store's terminal acquires the user's music preference data from the server.

[0593] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[0594] As a specific example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests stores that match their preferences (e.g., cafes that play jazz music and are relaxing). When the user visits the store, the device inside the store automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the store can also increase customer satisfaction.

[0595] An example of a prompt for generating background music using a generative AI model is "Generate a relaxing jazz song."

[0596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0597] Step 1:

[0598] Acquiring user music preference data

[0599] Input: User login information and permissions to access music streaming services

[0600] Processing: The server uses the music streaming service API (such as Spotify API or Apple Music API) to obtain the user's listening history, which includes information such as song genre and artist.

[0601] Data processing: Analyze viewing history data to extract the user's favorite music genres and mood parameters.

[0602] Output: User's music preference data

[0603] Step 2:

[0604] Generating store music preference data

[0605] Input: Store manager inputs store music concept, playlist history, and customer feedback

[0606] Processing: The terminal inputs this information into the system, and the server aggregates the information stored in the database.

[0607] Data calculation: Based on the input data, the server analyzes the survey results and feedback of customers and generates music preference data for the store.

[0608] Output: Store music preference data

[0609] Step 3:

[0610] Matching users and stores

[0611] Input: User music preference data and music preference data for multiple stores

[0612] Processing: The server compares the user's music preference data with the store's music preference data and calculates the matching rate.

[0613] Data calculation: Using a matching algorithm, it identifies the store that best suits a user's preferences. For example, it suggests cafes that play jazz to a user who likes jazz.

[0614] Output: Matching results (list of stores with high matching rates)

[0615] Step 4:

[0616] Copyright-free background music generation

[0617] Input: Multiple copyright-free music data stored on the server and a prompt for the generative AI model (e.g., "Generate a relaxing jazz song").

[0618] Processing: The server uses a generative AI model to generate new royalty-free background music based on the user's preferences.

[0619] Data computation: A generative AI model analyzes music data and creates new songs based on prompts.

[0620] Output: New royalty-free background music

[0621] Step 5:

[0622] Development of an automatic background music playback system

[0623] Input: Information that users enter when they visit a physical store and log in or check in to the system

[0624] Processing: The store's terminal obtains the user's music preference data from the server and selects the appropriate copyright-free background music.

[0625] Data calculation: Based on the acquired data, the device selects the most suitable song and plays it on the store's sound system.

[0626] Output: Play background music in the store. For example, relaxing smooth jazz is played.

[0627] 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.

[0628] The system of the present invention uses the user's music preference data and the store's music preference data to match the most suitable store to the user, and further combines an emotion engine to take into account the user's real-time emotional state and optimize the store's background music playback.

[0629] The system consists of the following main elements:

[0630] 1. Generating user music preference data

[0631] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[0632] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user music preference data."

[0633] 2. Generating store music preference data

[0634] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[0635] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[0636] Server: Analyzes customers' music preferences based on collected background music history and customer survey results and feedback, and generates "store music preference data."

[0637] 3. Emotion Recognition by Emotion Engine

[0638] On the device: The user uses the emotion engine within the app to input or recognize their real-time emotional state. For example, the user inputs, "I want to relax right now."

[0639] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[0640] 4. Perform matching

[0641] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[0642] Server: Compares the user's music preference data with the store's music preference data, as well as the emotional state data from the emotion engine, and calculates the matching rate.

[0643] Server: Presents matching results to the user, for example, presenting the store that is closest to the user's current emotional state.

[0644] 5. Creation and provision of copyright-free background music

[0645] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new music.

[0646] Server: Provides the generated copyright-free music to each store so that it can be used on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[0647] 6. Development of an automatic background music playback system

[0648] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[0649] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[0650] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[0651] As a specific example, if a user likes relaxing jazz music, the user can input their emotional state of "I want to relax," and the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a device inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preferences.

[0652] This system allows users to easily find the store that best suits their emotions and musical preferences, and stores can improve the quality of their service by providing background music that suits customers' emotions and preferences.

[0653] The processing flow will be explained below.

[0654] Step 1:

[0655] User: Signs in to the system. Users enter their login information to access the system.

[0656] Step 2:

[0657] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[0658] Step 3:

[0659] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[0660] Step 4:

[0661] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[0662] Step 5:

[0663] Server: Combines the extracted music genre and mood parameters to generate "user music preference data" and saves it in the user's profile.

[0664] Step 6:

[0665] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[0666] Step 7:

[0667] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[0668] Step 8:

[0669] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[0670] Step 9:

[0671] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and saves it in the store's profile.

[0672] Step 10:

[0673] User: Input or recognize their real-time emotional state using the emotion engine. For example, inputting an emotional state like "I want to relax right now" within the app.

[0674] Step 11:

[0675] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[0676] Step 12:

[0677] User: Searches for a store within the app, for example, searching for "relaxing jazz cafe."

[0678] Step 13:

[0679] Server: Compares user music preference data, store music preference data, and real-time emotion data from the emotion engine to calculate the matching rate.

[0680] Step 14:

[0681] Server: Presents the matching results to the user, for example, presenting the store that best suits the user's current emotional state.

[0682] Step 15:

[0683] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new copyright-free background music.

[0684] Step 16:

[0685] Server: Provides the generated copyright-free music to each store, allowing it to be played on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[0686] Step 17:

[0687] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[0688] Step 18:

[0689] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[0690] Step 19:

[0691] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[0692] Step 20:

[0693] Server: When there is a change in the user's emotional state, the emotion engine recognizes it and sends it to the server.

[0694] Step 21:

[0695] Server: Based on the new emotional state, reevaluate the store's background music and send instructions to the device to change the background music if necessary.

[0696] Step 22:

[0697] Device: The device that receives the instruction will change the background music and play a new song. For example, if the user inputs "I want to feel refreshed this time," the device will change to a refreshing song.

[0698] In this way, the system of the present invention can optimally match a store with a user based on the user's music preferences and real-time emotional state, and automatically play appropriate background music in the store, thereby improving the user's music experience and increasing customer satisfaction in the store.

[0699] Example 2

[0700] 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."

[0701] With conventional music distribution systems and store background music systems, it is difficult to find a store that matches a user's preferred music genre or emotional state at the time. Stores are also unable to provide background music that responds to customers' real-time emotions, resulting in a decline in user satisfaction. Furthermore, there is no way to generate and provide new music that is not subject to copyright restrictions, which limits the music content available to stores.

[0702] 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.

[0703] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for recognizing the user's real-time emotional state, means for presenting stores with a high matching rate to the user using the user music preference data, store music preference data, and the user's real-time emotional state data, means for generating new copyright-free background music using copyright-free music data, means for providing the generated copyright-free background music to a matched store, means for playing the provided copyright-free background music, and means for acquiring the user's music preference data and emotional state data when the user visits a store and automatically playing optimal background music at the store based on the user's preferences and emotional state. This makes it possible to suggest optimal stores tailored to the user's current emotional state and music preferences, and to generate and play optimal copyright-free background music.

[0704] "User music preference data" refers to data that includes the user's favorite music genres and artists, as well as mood parameters based on listening history.

[0705] "Store music preference data" is data generated based on background music history collected from the store's sound system and playlist management system, as well as customer feedback.

[0706] "Real-time emotional state data" refers to data that indicates a user's current emotional state as analyzed through user input, voice, and facial expression recognition technology.

[0707] The "matching rate" is an indicator of compatibility calculated by comparing user music preference data, store music preference data, and real-time emotional state data.

[0708] "Copyright-free BGM" is background music that can be used without copyright restrictions.

[0709] A "generative AI model" is an artificial intelligence model that generates new music or data based on input data.

[0710] A "prompt" is an instruction given to a generative AI model, a document intended to encourage data generation for a specific purpose.

[0711] A "server" is a computer system that has functions such as data collection, analysis, storage, and matching calculation.

[0712] A "terminal" is a device that allows users or store managers to input information and retrieve data from a server.

[0713] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and further optimize the store's background music taking into account the user's real-time emotional state. This system is composed of the following main elements.

[0714] 1. Generating user music preference data

[0715] Server: When a user signs in to the system, the server retrieves the user's listening history from music streaming services (e.g., Spotify, Apple Music) via API. This data includes the user's recently listened songs and frequently played artists.

[0716] Server: Analyzes the acquired listening history, extracts the user's preferred music genre and mood parameters (relaxed, active, focused, etc.), and generates "user music preference data."

[0717] 2. Generating store music preference data

[0718] Terminal: The store manager uses the terminal to input store information and the music concept. For example, they might register information such as "a cafe that mainly serves classical music."

[0719] Server: The server automatically collects the history of background music played in the past from the store's sound system and playlist management system.

[0720] Server: Based on the collected background music history and customer feedback, the server analyzes the music genres and moods preferred by customers and generates "store music preference data."

[0721] 3. Emotion Recognition by Emotion Engine

[0722] On the device: Users use the emotion engine within the app to input their real-time emotional state, for example, "I feel like relaxing right now."

[0723] Server: The emotion engine uses user input, voice, and facial expression recognition technology to analyze the user's current emotional state and stores the results in the user's emotional profile.

[0724] 4. Perform matching

[0725] User: A user searches for "relaxing jazz cafe" within the app.

[0726] Server: The server compares the user's music preference data, the store's music preference data, and the emotion engine data, and calculates the matching rate using an algorithm.

[0727] Server: Based on the calculation results, presents the user with a list of the best stores.

[0728] 5. Creation and provision of copyright-free background music

[0729] Server: The server collects copyright-free music data on the Internet.

[0730] Server: Based on the collected data, a generative AI model (e.g., music generation AI) is used to generate new music. An example prompt is, "Generate jazz music that matches a relaxing mood. The tempo should be slow and the melody should have a soft tone."

[0731] Server: Provides the generated music to each store so that it can be used on the store's sound system.

[0732] 6. Development of an automatic background music playback system

[0733] User: The user visits the designated store and checks in using the app. The user's emotional state is automatically updated upon check-in.

[0734] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data.

[0735] Device: Based on the acquired data, the device automatically selects royalty-free background music that best suits the user's preferences and emotional state and plays it in the store. For example, it plays smooth jazz for a user who requests relaxation.

[0736] As a concrete example, consider the case where a user likes jazz music that is relaxing. When the user inputs their emotional state of "I want to relax," the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a terminal inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preference.

[0737] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0738] Step 1:

[0739] Generating user music preference data

[0740] Server: When a user signs in to the system, the server uses the API of the music distribution service (e.g., Spotify, Apple Music) to obtain the user's listening history. The input is the user's sign-in information, and the output is the listening history data.

[0741] Server: Analyzes the acquired listening history, including frequently played songs, artists, genres, and playback times. The input is listening history data, and the output is the user's music preference data.

[0742] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) from the music preference data and generates "user music preference data." The input is the analyzed music preference data, and the output is the user music preference data.

[0743] Step 2:

[0744] Generating store music preference data

[0745] Terminal: The store manager uses the terminal to register detailed store information (e.g., a cafe that mainly serves classical music) and the music concept. The input is the information provided by the store manager, and the output is store information data.

[0746] Server: The server collects the history of BGM played in the past from the store's sound system and playlist management system. The input is the history data from the sound system and playlist management system, and the output is the store's BGM history data.

[0747] Server: Analyzes the collected BGM history and customer feedback data to analyze the music genres and moods preferred by customers. The input is store BGM history data and feedback data, and the output is store music preference data.

[0748] Step 3:

[0749] Emotion recognition by emotion engine

[0750] Terminal: The user inputs an emotional state, such as "I want to relax now," into the app. The input is the user's emotional state data, and the output is the emotional state input data.

[0751] Server: The emotion engine analyzes the current emotional state using user input, voice, and facial expression recognition technology. The input is the emotional state input data, and the output is the real-time emotional state data.

[0752] Server: Based on the analysis results, store the real-time emotional state data in the user's emotional profile. The input is the real-time emotional state data, and the output is the updated emotional profile.

[0753] Step 4:

[0754] Performing matching

[0755] User: A user searches for "relaxing jazz cafe" within the app. The input is the user's search query, and the output is the search criteria data.

[0756] Server: Compares user music preference data, store music preference data, and real-time emotional state data, and calculates the matching rate using an algorithm. The inputs are user music preference data, store music preference data, and real-time emotional state data, and the output is the matching result data.

[0757] Server: Based on the calculation results, it presents the user with a list of optimal stores. The input is the matching result data, and the output is the store list displayed to the user.

[0758] Step 5:

[0759] Creation and provision of copyright-free background music

[0760] Server: The server collects copyright-free music data on the Internet. The input is free music data on the Internet, and the output is the collected music data.

[0761] Server: Generates new music using a generative AI model based on the collected data. An example prompt is, "Generate jazz music that suits a relaxing mood. The tempo should be slow and the melody should have a soft tone." The input is the collected music data and the prompt, and the output is the generated music data.

[0762] Server: Provides the generated music to each store so that it can be used on the store's sound system. The input is the generated music data, and the output is the provided music data.

[0763] Step 6:

[0764] Development of an automatic background music playback system

[0765] User: The user visits the designated store and checks in using the app. The emotional state is automatically updated at check-in. The input is the user's check-in information, and the output is the updated emotional state data.

[0766] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data. The input is the access information to the server, and the output is the obtained data.

[0767] Terminal: Based on the acquired data, the terminal automatically selects copyright-free background music that best suits the user's preferences and emotional state, and plays it in the store. The input is the acquired data, and the output is the background music that is played.

[0768] (Application example 2)

[0769] 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."

[0770] Conventional matching systems that use music preference data have difficulty in considering the user's real-time emotional state, making it difficult to provide the music environment that the user desires. Furthermore, stores lacked a mechanism for providing optimal background music that matches the diverse musical preferences of their customers. This resulted in an inability to sufficiently improve user satisfaction and limited the quality of store services.

[0771] The identification process by the identification 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 generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring and recording the user's real-time emotional state, and means for automatically playing optimal background music based on the acquired user emotional state in the store's background music playback system. This makes it possible to suggest optimal stores based on the user's real-time emotional state, and enables stores to provide optimal background music tailored to each user's emotions. This increases user satisfaction and dramatically improves the quality of store service.

[0772] "User's music preference data" refers to data including the user's preferred music genres, artists, listening history, and emotional state related to music.

[0773] "Store music preference data" refers to data including the music genres and playlists offered by a particular store, past background music history, and survey results and feedback from customers.

[0774] The "matching rate" is an index that indicates the degree to which the user's music preference data matches the store's music preference data.

[0775] "Emotional state" refers to the user's current mental and sensory state, including, for example, relaxed, focused, active, etc.

[0776] A "BGM playback system" is an audio system for playing background music (BGM) in a store.

[0777] "Copyright-free background music" is music data that can be used freely without being dependent on any specific copyright.

[0778] A "generative AI model" is an artificial intelligence model that generates new music based on copyright-free music data.

[0779] The system for implementing the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store, and also takes into account the user's real-time emotional state to automatically play the most suitable background music in the store. This system is composed of the following main elements.

[0780] The server acquires the user's listening history from the music streaming service, extracts the user's favorite music genre and emotional parameters (relaxed, focused, active, etc.), and generates "user music preference data" based on the listening history, playback frequency, and user feedback.

[0781] At the store, the store manager registers store information and music concept in the system, and the server generates "store music preference data" based on the history of background music played in the past and the results of customer surveys. This clearly defines the music environment for each store.

[0782] Users can use the emotion engine within the application to input or recognize their real-time emotional state. The server uses the emotion engine to analyze the emotional state from the user's input, voice, and facial expression recognition technology to generate real-time emotional data, which then creates an emotional profile for the user.

[0783] When a user searches for a store, the server compares the user's music preference data with that of the store, and also considers the user's real-time emotional state to present stores with a high matching rate, allowing the user to easily find a store that best suits their emotions and musical preferences.

[0784] When a customer visits a store and checks in, the store's terminal obtains the user's music preference data and emotional state data from the server. Based on the obtained data, the store's background music playback system automatically selects and plays royalty-free background music that matches the user's preferences and emotional state. The server uses multiple copyright-free music data sets to train a generative AI model and generate new music. The generated music is optimized based on the customer's emotional state and provided to the store.

[0785] For example, if a user feels like "I want to relax," and their favorite music genre is jazz, they might enter a prompt to search for a cafe where they can relax:

[0786] "I feel like I want to relax. I like jazz music. I'm looking for a cafe where I can relax here."

[0787] Based on this prompt, the system will suggest the most suitable store, and when the user checks in at that store, relaxing jazz music will be played automatically. In this way, the system can provide the optimal musical environment according to the user's real-time emotional state and musical preferences.

[0788] The hardware used is a server with Django (web framework), MySQL (database), smartphones (iOS / Android), and API communication (HTTP requests). The emotion engine uses Emotion API (a cloud-based emotion recognition API), and the background music playback system is the in-store sound system.

[0789] This invention can improve user satisfaction and dramatically improve the quality of service in stores.

[0790] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0791] Step 1:

[0792] The server obtains the user's listening history from the music streaming service. The user ID is required as input, and the user's listening history data is obtained as output. Specifically, the listening history is obtained through an API request, and data such as the number of plays and listening time of the user is collected.

[0793] Step 2:

[0794] The server generates user music preference data based on the listening history data. The listening history data is required as input, and a list of the user's favorite music genres and artists is obtained as output. The data is processed by performing statistical analysis of the listening history to extract the main genres and artists.

[0795] Step 3:

[0796] The store manager inputs store information and music concept into the system. The store's music genre and concept are required as input, and the information is recorded on the server as output. Specifically, data is entered using a web form and sent to the server.

[0797] Step 4:

[0798] The server collects the history of background music played in the past from the store's sound system. The store ID is required as input, and background music history data is obtained as output. Specifically, the server analyzes the sound system's log data and collects the songs that have been played and the number of times they have been played.

[0799] Step 5:

[0800] The server generates music preference data for the store based on the background music history data and the results of customer surveys. The store's background music history data and the survey results are required as input, and the store's music preference data is obtained as output. The data is then processed by statistically analyzing the evaluations of the survey results to extract the main music genres and songs.

[0801] Step 6:

[0802] Users use the emotion engine within the application to input their real-time emotional state or have it recognized by the device's camera. Emotional state and voice input are required as input, and analyzed emotional data is obtained as output. Specifically, emotions are analyzed from facial expressions and voice through the emotion recognition API and sent to the server.

[0803] Step 7:

[0804] The server updates the user's emotional profile based on the user's music preference data and real-time emotional state data. The server requires real-time emotional data and music preference data as input, and obtains the updated user's emotional profile as output. Specifically, the server updates the profile database based on the emotional data.

[0805] Step 8:

[0806] The user enters a prompt to search for a store in the app. For example, the prompt might be "I'm looking for a relaxing jazz cafe." The prompt is required as input, and a list of suitable stores is obtained as output. The server analyzes the prompt and uses a matching algorithm to present the most suitable stores.

[0807] Step 9:

[0808] The server compares the user's music preference data, the store's music preference data, and real-time emotional state data to match the optimal store. Each piece of data for comparison is required as input, and stores with a high matching rate are obtained as output. Specifically, each piece of data is integrated and analyzed to create a list of the most suitable stores.

[0809] Step 10:

[0810] A user visits a store and checks in at a terminal in the store. The user ID is required as input, and the user's check-in information is obtained as output. The terminal in the store obtains the user's latest music preference data and emotional state data from the server.

[0811] Step 11:

[0812] The store's terminal retrieves copyright-free background music from the server that matches the user's preferences and current emotional state, and automatically plays it. The user's preference data and emotional state data are required as input, and the optimal background music is obtained as output. The terminal controls the background music playback system based on the retrieved data, providing the user with the optimal musical environment.

[0813] Through the above processing steps, users can find the most suitable store according to their real-time emotional state and music preferences, and stores can provide the best service.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] [Third embodiment]

[0818] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0819] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0820] 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).

[0821] 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.

[0822] 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.

[0823] 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).

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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."

[0830] The system according to the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and also optimize background music playback in the store.

[0831] The system consists of the following main elements:

[0832] 1. Generating user music preference data

[0833] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[0834] Server: Based on the user's listening history, the server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) to generate "user music preference data."

[0835] 2. Generating store music preference data

[0836] Terminal: The store manager inputs the store's music concept and playlist history into the system.

[0837] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data."

[0838] Server: For example, acquire information such as the music played at store A is classical and customers prefer a quiet atmosphere.

[0839] 3. Perform matching

[0840] User: Uses the system's search function to submit a request to find the best store.

[0841] Server: Compares user music preference data with store music preference data and calculates stores with the highest matching rate.

[0842] Server: Presents matching results to the user. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[0843] 4. Creation and provision of copyright-free background music

[0844] Server: Collects multiple copyright-free music data and generates new music based on this data. Using generative AI, it creates music in a variety of genres tailored to the user's preferences.

[0845] Server: Provides the generated royalty-free music to each store so that it can be used on the store's sound system. For example, if a store needs jazz background music, the server distributes the newly generated jazz track.

[0846] 5. Development of an automatic background music playback system

[0847] User: Visits the store and logs in or checks into the system.

[0848] Terminal: The store's terminal obtains the user's music preference data from the server.

[0849] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[0850] As a concrete example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests establishments that match their preferences (e.g., cafes that play jazz in-store and are relaxing). When the user visits the establishment, the in-store device automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the establishment can increase customer satisfaction.

[0851] This system allows users to easily find the store that best suits their musical tastes, and stores can improve the quality of their service by providing background music that suits customers' musical tastes.

[0852] The processing flow will be explained below.

[0853] Step 1:

[0854] User: Signs in to the system. Users enter their login information to access the system.

[0855] Step 2:

[0856] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[0857] Step 3:

[0858] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[0859] Step 4:

[0860] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[0861] Step 5:

[0862] Server: Combines music genre and mood parameters to generate "user music preference data" and stores it in the user's profile.

[0863] Step 6:

[0864] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[0865] Step 7:

[0866] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[0867] Step 8:

[0868] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[0869] Step 9:

[0870] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and save it in the store's profile.

[0871] Step 10:

[0872] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[0873] Step 11:

[0874] Server: Compares user music preference data with store music preference data, calculates matching rate, and lists stores that most closely match the user's preferences.

[0875] Step 12:

[0876] Server: Presents the matching results to the user. For example, it displays "There are five cafes that have high music preferences for the user."

[0877] Step 13:

[0878] Server: Collects multiple copyright-free music data sets and trains the AI ​​based on them to generate new copyright-free background music.

[0879] Step 14:

[0880] Server: Provides generated copyright-free background music to each store, distributing music data via a portal site or dedicated app.

[0881] Step 15:

[0882] User: Visits the store and checks in by connecting to the store's terminal or scanning a QR code.

[0883] Step 16:

[0884] Terminal: The store's terminal obtains the user's music preference data from the server.

[0885] Step 17:

[0886] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music that matches the user's preferences. For example, it can play smooth jazz for a user who wants to relax.

[0887] In this way, a system is realized that suggests the most suitable store based on the user's musical preferences and provides a musical environment tailored to individual preferences even after the user visits the store.

[0888] Example 1

[0889] 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."

[0890] Many users today want to control their moods and activities and enjoy themselves through music. However, it is not easy to find a store that matches the user's preferred music genre and atmosphere. Stores are also required to understand customers' musical preferences and provide optimal background music to increase customer satisfaction, but this is time-consuming. An efficient and effective system to solve these problems is needed.

[0891] 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.

[0892] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring the user's music streaming service listening history, means for analyzing the listening history and extracting the user's favorite music genre and mood parameters, means for saving the listening history data in a database, means for a store manager to input the store's music concept and playlist history, and means for analyzing survey results and feedback and generating store music preference data. This allows users to easily find a store that best suits their music preferences, and stores can increase customer satisfaction by providing background music that suits customers' music preferences.

[0893] "User music preference data" is data that analyzes the viewing history of music distribution services and compiles the user's preferred music genres and mood parameters.

[0894] "Store music preference data" is data on the music genres and atmosphere preferred in a store, based on the store's music concept, playlist history, and customer survey results and feedback.

[0895] The "matching rate" is an index showing the degree of agreement between the user's music preference data and the store's music preference data, and serves as a criterion for recommending stores that match the user's preferences.

[0896] "Listening history" refers to a user's past playback history data on a music distribution service, and specifically includes information on the songs and artists the user has listened to.

[0897] "Mood parameters" are numerical or categorical expressions of the emotions and states that music evokes in users, and include classifications such as relaxed, active, and focused.

[0898] A "database" is a system that can organize and store multiple pieces of data and efficiently manage and search them, and in the present invention, it is used to store viewing history data and the like.

[0899] "Survey results" are the results of questionnaire surveys answered by users and customers, compiled and converted into data.

[0900] "Feedback" refers to reactions such as comments and ratings provided by users and customers, and serves as a reference when generating music preference data for a store.

[0901] "Copyright-free music data" refers to music files that are not subject to copyright restrictions and can be freely used and distributed.

[0902] A "generative AI model" is a model for generating new data or content using artificial intelligence technology, and in this invention is used to generate copyright-free music.

[0903] A "prompt" is an instruction entered into a generative AI model to specify the conditions and characteristics of the music to be generated.

[0904] "Cloud storage" is a storage service for saving and managing data on the Internet, and is used to distribute created music to stores.

[0905] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize background music playback in the store. This system functions through three parties: a server, a terminal, and the user.

[0906] Server Features

[0907] 1. Generating user music preference data

[0908] When a user signs in to the system, the server obtains listening history data from music distribution services (e.g., Spotify or Apple Music) via API. This listening history includes information on the songs and artists played by the user. Based on the obtained listening history, a machine learning model (e.g., K-means clustering) is used to classify and extract the user's favorite music genres and mood parameters (e.g., relaxed, active, focused), generating "user music preference data."

[0909] 2. Generating store music preference data

[0910] Store managers use dedicated terminals to input the store's music concept and playlist history. Based on the input data, the server also collects and analyzes survey results and feedback from past customers to generate "store music preference data." Text mining technology is used to analyze the survey and feedback data and extract keywords related to specific music genres and moods.

[0911] 3. Perform matching

[0912] The user uses the system's search function to send a request to the server to find the best store. The server compares the user's music preference data with the store's music preference data and uses algorithms such as cosine similarity calculations to recommend the store with the highest matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest a cafe that plays jazz.

[0913] 4. Creation and provision of copyright-free background music

[0914] The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on the prompt. The generated music is stored in cloud storage and provided to the store. An example of a specific prompt might be, "Please generate a relaxing jazz track using piano and saxophone, with a calm and soothing tempo."

[0915] Device Features

[0916] 1. Data entry and manipulation

[0917] The store manager uses the terminal to use an interface to input the store's music concept and playlist history, as well as to input customer surveys and collect feedback.

[0918] 2. Automatic BGM playback

[0919] When a user visits a store and logs in or checks in to the system, the store's terminal receives the user's music preference data from the server in real time. Based on the data, the system automatically plays royalty-free background music. For example, if the user wants to relax, smooth jazz background music will be played automatically.

[0920] User Actions

[0921] 1. Sign in to the system

[0922] Users sign in to the system and provide their music streaming service listening history, which is an important source of information for generating user music preference data.

[0923] 2. Search and select a store

[0924] Users can use the system's search function to find stores that match their music preferences, and the server responds to this request by recommending the most suitable stores.

[0925] Specific operation example

[0926] Consider a case where a user likes jazz and is looking for a relaxing cafe. The user first signs in to the system and provides their listening history from a music streaming service. The server analyzes the listening history and determines that the user likes jazz. When the user then searches for "a relaxing jazz cafe," the server compares the user's music preference data with the store's music preference data and recommends the most suitable cafe. When the user visits the cafe, the terminal in the store automatically plays smooth jazz background music.

[0927] This allows users to enjoy a more comfortable music experience in a store that best suits their musical tastes, and stores can further increase customer satisfaction by providing background music that matches customers' musical tastes.

[0928] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0929] Step 1: Collecting user viewing history

[0930] Input: User's music streaming service login information

[0931] How it works: When a user signs in to the system, the server uses the music streaming service's API to obtain the user's listening history data via OAuth authentication, such as the songs, artists, and playback time that the user has listened to.

[0932] Output: Viewing history data

[0933] Step 2: Analyzing viewing history and generating preference data

[0934] Input: Viewing history data

[0935] How it works: The server analyzes the acquired listening history using a machine learning model (e.g., K-means clustering) to extract the user's favorite music genres and mood parameters (relaxed, active, focused, etc.). The analysis uses the frequency and duration of playback for specific artists and genres.

[0936] Output: User music preference data

[0937] Step 3: Enter store data

[0938] Input: Store music concept, playlist history, survey results, feedback

[0939] Operation: The store manager uses a terminal to input the store's music concept and playlist history. The store manager also inputs customer survey results and feedback from the terminal. This data is sent to the server.

[0940] Output: Sending input data to the server

[0941] Step 4: Generate store music preference data

[0942] Input: Store data (music concept, playlist history, survey results, feedback)

[0943] How it works: The server analyzes collected store data and extracts keywords related to specific music genres and moods. It then uses text mining technology to analyze surveys and feedback and generates "store music preference data."

[0944] Output: Store music preference data

[0945] Step 5: Matching users and stores

[0946] Input: User music preference data, store music preference data

[0947] How it works: A user uses the system's search function to send a request to find a store that matches their needs. The server compares the user's music preference data with the store's music preference data using algorithms such as cosine similarity calculations, and recommends the store that best matches.

[0948] Output: Matching results (store recommendations displayed in list format)

[0949] Step 6: Generate royalty-free background music

[0950] Input: User music preference data, store music request (prompt sentence)

[0951] How it works: The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on a prompt, such as "Please generate a relaxing jazz track with piano and saxophone, at a calm and soothing tempo."

[0952] Output: Generated music data

[0953] Step 7: Save to cloud storage and provide to stores

[0954] Input: Generated music data

[0955] How it works: The server stores the generated royalty-free music on a cloud storage service (e.g., Amazon S3), then provides a download link to the store, through which the store can download the music.

[0956] Output: Song data on cloud storage, download link to store

[0957] Step 8: User visits the store and automatic background music plays

[0958] Input: User music preference data, check-in information

[0959] How it works: A user visits a store and logs in or checks in to the system. The store's terminal retrieves the user's music preference data from the server in real time and automatically plays the most appropriate background music over the store's sound system. For example, if the user wants to relax, smooth jazz will be played.

[0960] Output: BGM playback in the store

[0961] This allows users to enjoy a comfortable music experience in a store that best suits their musical tastes, and stores can improve customer satisfaction.

[0962] (Application example 1)

[0963] 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."

[0964] In today's world, it is difficult to choose a store that meets a user's individual music preferences, resulting in a poor user experience. Furthermore, stores are limited to playing generic music, making it difficult to provide services tailored to their customers. Furthermore, there is a need for a system that can provide appropriate background music without worrying about copyright issues.

[0965] 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.

[0966] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, and means for automatically playing background music in the physical store based on the user music preference data. This allows the user to choose a store that suits their music preferences, and the store to provide background music that suits the customer's preferences.

[0967] "User music preference data" is data that indicates a user's music preferences, generated by analyzing the user's listening history, such as the genres, artists, and songs that the user normally listens to.

[0968] "Store music preference data" is data that indicates the characteristics and trends of the music offered by a store, generated based on the store's music concept, playlists, and customer feedback.

[0969] The "matching rate" is a numerical value or index that indicates the degree of match when comparing a user's music preference data with a store's music preference data.

[0970] "Automatic playback means" refers to a system or mechanism that automatically plays appropriate background music when a user visits a physical store, based on the user's music preference data.

[0971] "Copyright-free background music" is music that can be freely used without being restricted by copyright, and is a type of music that is automatically played by this system.

[0972] A "generative AI model" is an algorithm or system that uses machine learning and artificial intelligence techniques to generate new music or data based on user preferences and characteristics.

[0973] A "prompt" is an instruction or question that is input into a generative AI model to generate appropriate background music.

[0974] "Physical store" refers to a commercial facility or service provider that exists in a physical location, and means a specific store visited by customers who use the system.

[0975] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize the background music playback in the store. This system is composed of the following main elements.

[0976] 1. Generating user music preference data

[0977] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history. For example, it analyzes the user's frequently listened-to music genres and artist list. Examples of music streaming service APIs used include the Spotify API and Apple Music API.

[0978] Server: The server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user's music preference data."

[0979] 2. Generating store music preference data

[0980] Terminal: The store manager uses the terminal to input the store's music concept and playlist history into the system, which then aggregates the store's music preference data.

[0981] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data." For example, it may acquire information that the music played at Store A is classical and that customers prefer a quiet atmosphere.

[0982] 3. Perform matching

[0983] User: The user uses the system's search function to submit a request to find the best store.

[0984] Server: The server compares the user's music preference data with that of the stores and calculates stores with a high matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[0985] Server: The server presents the matching results to the user.

[0986] 4. Creation and provision of copyright-free background music

[0987] Server: The server collects multiple copyright-free music data and generates new music based on this data. Using a generative AI model, it creates music in various genres tailored to the user's preferences. For example, to generate a jazz song, the server uses the prompt "Generate a relaxing jazz song."

[0988] Server: The server provides the generated copyright-free music to each store so that it can be used on the store's sound system.

[0989] 5. Development of an automatic background music playback system

[0990] User: A user visits a physical store and logs in or checks into the system.

[0991] Terminal: The store's terminal acquires the user's music preference data from the server.

[0992] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[0993] As a specific example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests stores that match their preferences (e.g., cafes that play jazz music and are relaxing). When the user visits the store, the device inside the store automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the store can also increase customer satisfaction.

[0994] An example of a prompt for generating background music using a generative AI model is "Generate a relaxing jazz song."

[0995] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0996] Step 1:

[0997] Acquiring user music preference data

[0998] Input: User login information and permissions to access music streaming services

[0999] Processing: The server uses the music streaming service API (such as Spotify API or Apple Music API) to obtain the user's listening history, which includes information such as song genre and artist.

[1000] Data processing: Analyze viewing history data to extract the user's favorite music genres and mood parameters.

[1001] Output: User's music preference data

[1002] Step 2:

[1003] Generating store music preference data

[1004] Input: Store manager inputs store music concept, playlist history, and customer feedback

[1005] Processing: The terminal inputs this information into the system, and the server aggregates the information stored in the database.

[1006] Data calculation: Based on the input data, the server analyzes the survey results and feedback of customers and generates music preference data for the store.

[1007] Output: Store music preference data

[1008] Step 3:

[1009] Matching users and stores

[1010] Input: User music preference data and music preference data for multiple stores

[1011] Processing: The server compares the user's music preference data with the store's music preference data and calculates the matching rate.

[1012] Data calculation: Using a matching algorithm, it identifies the store that best suits a user's preferences. For example, it suggests cafes that play jazz to a user who likes jazz.

[1013] Output: Matching results (list of stores with high matching rates)

[1014] Step 4:

[1015] Copyright-free background music generation

[1016] Input: Multiple copyright-free music data stored on the server and a prompt for the generative AI model (e.g., "Generate a relaxing jazz song").

[1017] Processing: The server uses a generative AI model to generate new royalty-free background music based on the user's preferences.

[1018] Data computation: A generative AI model analyzes music data and creates new songs based on prompts.

[1019] Output: New royalty-free background music

[1020] Step 5:

[1021] Development of an automatic background music playback system

[1022] Input: Information that users enter when they visit a physical store and log in or check in to the system

[1023] Processing: The store's terminal obtains the user's music preference data from the server and selects the appropriate copyright-free background music.

[1024] Data calculation: Based on the acquired data, the device selects the most suitable song and plays it on the store's sound system.

[1025] Output: Play background music in the store. For example, relaxing smooth jazz is played.

[1026] 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.

[1027] The system of the present invention uses the user's music preference data and the store's music preference data to match the most suitable store to the user, and further combines an emotion engine to take into account the user's real-time emotional state and optimize the store's background music playback.

[1028] The system consists of the following main elements:

[1029] 1. Generating user music preference data

[1030] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[1031] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user music preference data."

[1032] 2. Generating store music preference data

[1033] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[1034] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[1035] Server: Analyzes customers' music preferences based on collected background music history and customer survey results and feedback, and generates "store music preference data."

[1036] 3. Emotion Recognition by Emotion Engine

[1037] On the device: The user uses the emotion engine within the app to input or recognize their real-time emotional state. For example, the user inputs, "I want to relax right now."

[1038] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[1039] 4. Perform matching

[1040] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[1041] Server: Compares the user's music preference data with the store's music preference data, as well as the emotional state data from the emotion engine, and calculates the matching rate.

[1042] Server: Presents matching results to the user, for example, presenting the store that is closest to the user's current emotional state.

[1043] 5. Creation and provision of copyright-free background music

[1044] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new music.

[1045] Server: Provides the generated copyright-free music to each store so that it can be used on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[1046] 6. Development of an automatic background music playback system

[1047] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[1048] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[1049] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[1050] As a specific example, if a user likes relaxing jazz music, the user can input their emotional state of "I want to relax," and the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a device inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preferences.

[1051] This system allows users to easily find the store that best suits their emotions and musical preferences, and stores can improve the quality of their service by providing background music that suits customers' emotions and preferences.

[1052] The processing flow will be explained below.

[1053] Step 1:

[1054] User: Signs in to the system. Users enter their login information to access the system.

[1055] Step 2:

[1056] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[1057] Step 3:

[1058] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[1059] Step 4:

[1060] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[1061] Step 5:

[1062] Server: Combines the extracted music genre and mood parameters to generate "user music preference data" and saves it in the user's profile.

[1063] Step 6:

[1064] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[1065] Step 7:

[1066] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[1067] Step 8:

[1068] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[1069] Step 9:

[1070] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and saves it in the store's profile.

[1071] Step 10:

[1072] User: Input or recognize their real-time emotional state using the emotion engine. For example, inputting an emotional state like "I want to relax right now" within the app.

[1073] Step 11:

[1074] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[1075] Step 12:

[1076] User: Searches for a store within the app, for example, searching for "relaxing jazz cafe."

[1077] Step 13:

[1078] Server: Compares user music preference data, store music preference data, and real-time emotion data from the emotion engine to calculate the matching rate.

[1079] Step 14:

[1080] Server: Presents the matching results to the user, for example, presenting the store that best suits the user's current emotional state.

[1081] Step 15:

[1082] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new copyright-free background music.

[1083] Step 16:

[1084] Server: Provides the generated copyright-free music to each store, allowing it to be played on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[1085] Step 17:

[1086] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[1087] Step 18:

[1088] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[1089] Step 19:

[1090] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[1091] Step 20:

[1092] Server: When there is a change in the user's emotional state, the emotion engine recognizes it and sends it to the server.

[1093] Step 21:

[1094] Server: Based on the new emotional state, reevaluate the store's background music and send instructions to the device to change the background music if necessary.

[1095] Step 22:

[1096] Device: The device that receives the instruction will change the background music and play a new song. For example, if the user inputs "I want to feel refreshed this time," the device will change to a refreshing song.

[1097] In this way, the system of the present invention can optimally match a store with a user based on the user's music preferences and real-time emotional state, and automatically play appropriate background music in the store, thereby improving the user's music experience and increasing customer satisfaction in the store.

[1098] Example 2

[1099] 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."

[1100] With conventional music distribution systems and store background music systems, it is difficult to find a store that matches a user's preferred music genre or emotional state at the time. Stores are also unable to provide background music that responds to customers' real-time emotions, resulting in a decline in user satisfaction. Furthermore, there is no way to generate and provide new music that is not subject to copyright restrictions, which limits the music content available to stores.

[1101] 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.

[1102] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for recognizing the user's real-time emotional state, means for presenting stores with a high matching rate to the user using the user music preference data, store music preference data, and the user's real-time emotional state data, means for generating new copyright-free background music using copyright-free music data, means for providing the generated copyright-free background music to a matched store, means for playing the provided copyright-free background music, and means for acquiring the user's music preference data and emotional state data when the user visits a store and automatically playing optimal background music at the store based on the user's preferences and emotional state. This makes it possible to suggest optimal stores tailored to the user's current emotional state and music preferences, and to generate and play optimal copyright-free background music.

[1103] "User music preference data" refers to data that includes the user's favorite music genres and artists, as well as mood parameters based on listening history.

[1104] "Store music preference data" is data generated based on background music history collected from the store's sound system and playlist management system, as well as customer feedback.

[1105] "Real-time emotional state data" refers to data that indicates a user's current emotional state as analyzed through user input, voice, and facial expression recognition technology.

[1106] The "matching rate" is an indicator of compatibility calculated by comparing user music preference data, store music preference data, and real-time emotional state data.

[1107] "Copyright-free BGM" is background music that can be used without copyright restrictions.

[1108] A "generative AI model" is an artificial intelligence model that generates new music or data based on input data.

[1109] A "prompt" is an instruction given to a generative AI model, a document intended to encourage data generation for a specific purpose.

[1110] A "server" is a computer system that has functions such as data collection, analysis, storage, and matching calculation.

[1111] A "terminal" is a device that allows users or store managers to input information and retrieve data from a server.

[1112] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and further optimize the store's background music taking into account the user's real-time emotional state. This system is composed of the following main elements.

[1113] 1. Generating user music preference data

[1114] Server: When a user signs in to the system, the server retrieves the user's listening history from music streaming services (e.g., Spotify, Apple Music) via API. This data includes the user's recently listened songs and frequently played artists.

[1115] Server: Analyzes the acquired listening history, extracts the user's preferred music genre and mood parameters (relaxed, active, focused, etc.), and generates "user music preference data."

[1116] 2. Generating store music preference data

[1117] Terminal: The store manager uses the terminal to input store information and the music concept. For example, they might register information such as "a cafe that mainly serves classical music."

[1118] Server: The server automatically collects the history of background music played in the past from the store's sound system and playlist management system.

[1119] Server: Based on the collected background music history and customer feedback, the server analyzes the music genres and moods preferred by customers and generates "store music preference data."

[1120] 3. Emotion Recognition by Emotion Engine

[1121] On the device: Users use the emotion engine within the app to input their real-time emotional state, for example, "I feel like relaxing right now."

[1122] Server: The emotion engine uses user input, voice, and facial expression recognition technology to analyze the user's current emotional state and stores the results in the user's emotional profile.

[1123] 4. Perform matching

[1124] User: A user searches for "relaxing jazz cafe" within the app.

[1125] Server: The server compares the user's music preference data, the store's music preference data, and the emotion engine data, and calculates the matching rate using an algorithm.

[1126] Server: Based on the calculation results, presents the user with a list of the best stores.

[1127] 5. Creation and provision of copyright-free background music

[1128] Server: The server collects copyright-free music data on the Internet.

[1129] Server: Based on the collected data, a generative AI model (e.g., music generation AI) is used to generate new music. An example prompt is, "Generate jazz music that matches a relaxing mood. The tempo should be slow and the melody should have a soft tone."

[1130] Server: Provides the generated music to each store so that it can be used on the store's sound system.

[1131] 6. Development of an automatic background music playback system

[1132] User: The user visits the designated store and checks in using the app. The user's emotional state is automatically updated upon check-in.

[1133] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data.

[1134] Device: Based on the acquired data, the device automatically selects royalty-free background music that best suits the user's preferences and emotional state and plays it in the store. For example, it plays smooth jazz for a user who requests relaxation.

[1135] As a concrete example, consider the case where a user likes jazz music that is relaxing. When the user inputs their emotional state of "I want to relax," the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a terminal inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preference.

[1136] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1137] Step 1:

[1138] Generating user music preference data

[1139] Server: When a user signs in to the system, the server uses the API of the music distribution service (e.g., Spotify, Apple Music) to obtain the user's listening history. The input is the user's sign-in information, and the output is the listening history data.

[1140] Server: Analyzes the acquired listening history, including frequently played songs, artists, genres, and playback times. The input is listening history data, and the output is the user's music preference data.

[1141] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) from the music preference data and generates "user music preference data." The input is the analyzed music preference data, and the output is the user music preference data.

[1142] Step 2:

[1143] Generating store music preference data

[1144] Terminal: The store manager uses the terminal to register detailed store information (e.g., a cafe that mainly serves classical music) and the music concept. The input is the information provided by the store manager, and the output is store information data.

[1145] Server: The server collects the history of BGM played in the past from the store's sound system and playlist management system. The input is the history data from the sound system and playlist management system, and the output is the store's BGM history data.

[1146] Server: Analyzes the collected BGM history and customer feedback data to analyze the music genres and moods preferred by customers. The input is store BGM history data and feedback data, and the output is store music preference data.

[1147] Step 3:

[1148] Emotion recognition by emotion engine

[1149] Terminal: The user inputs an emotional state, such as "I want to relax now," into the app. The input is the user's emotional state data, and the output is the emotional state input data.

[1150] Server: The emotion engine analyzes the current emotional state using user input, voice, and facial expression recognition technology. The input is the emotional state input data, and the output is the real-time emotional state data.

[1151] Server: Based on the analysis results, store the real-time emotional state data in the user's emotional profile. The input is the real-time emotional state data, and the output is the updated emotional profile.

[1152] Step 4:

[1153] Performing matching

[1154] User: A user searches for "relaxing jazz cafe" within the app. The input is the user's search query, and the output is the search criteria data.

[1155] Server: Compares user music preference data, store music preference data, and real-time emotional state data, and calculates the matching rate using an algorithm. The inputs are user music preference data, store music preference data, and real-time emotional state data, and the output is the matching result data.

[1156] Server: Based on the calculation results, it presents the user with a list of optimal stores. The input is the matching result data, and the output is the store list displayed to the user.

[1157] Step 5:

[1158] Creation and provision of copyright-free background music

[1159] Server: The server collects copyright-free music data on the Internet. The input is free music data on the Internet, and the output is the collected music data.

[1160] Server: Generates new music using a generative AI model based on the collected data. An example prompt is, "Generate jazz music that suits a relaxing mood. The tempo should be slow and the melody should have a soft tone." The input is the collected music data and the prompt, and the output is the generated music data.

[1161] Server: Provides the generated music to each store so that it can be used on the store's sound system. The input is the generated music data, and the output is the provided music data.

[1162] Step 6:

[1163] Development of an automatic background music playback system

[1164] User: The user visits the designated store and checks in using the app. The emotional state is automatically updated at check-in. The input is the user's check-in information, and the output is the updated emotional state data.

[1165] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data. The input is the access information to the server, and the output is the obtained data.

[1166] Terminal: Based on the acquired data, the terminal automatically selects copyright-free background music that best suits the user's preferences and emotional state, and plays it in the store. The input is the acquired data, and the output is the background music that is played.

[1167] (Application example 2)

[1168] 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."

[1169] Conventional matching systems that use music preference data have difficulty in considering the user's real-time emotional state, making it difficult to provide the music environment that the user desires. Furthermore, stores lacked a mechanism for providing optimal background music that matches the diverse musical preferences of their customers. This resulted in an inability to sufficiently improve user satisfaction and limited the quality of store services.

[1170] The identification process by the identification 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 generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring and recording the user's real-time emotional state, and means for automatically playing optimal background music based on the acquired user emotional state in the store's background music playback system. This makes it possible to suggest optimal stores based on the user's real-time emotional state, and enables stores to provide optimal background music tailored to each user's emotions. This increases user satisfaction and dramatically improves the quality of store service.

[1171] "User's music preference data" refers to data including the user's preferred music genres, artists, listening history, and emotional state related to music.

[1172] "Store music preference data" refers to data including the music genres and playlists offered by a particular store, past background music history, and survey results and feedback from customers.

[1173] The "matching rate" is an index that indicates the degree to which the user's music preference data matches the store's music preference data.

[1174] "Emotional state" refers to the user's current mental and sensory state, including, for example, relaxed, focused, active, etc.

[1175] A "BGM playback system" is an audio system for playing background music (BGM) in a store.

[1176] "Copyright-free background music" is music data that can be used freely without being dependent on any specific copyright.

[1177] A "generative AI model" is an artificial intelligence model that generates new music based on copyright-free music data.

[1178] The system for implementing the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store, and also takes into account the user's real-time emotional state to automatically play the most suitable background music in the store. This system is composed of the following main elements.

[1179] The server acquires the user's listening history from the music streaming service, extracts the user's favorite music genre and emotional parameters (relaxed, focused, active, etc.), and generates "user music preference data" based on the listening history, playback frequency, and user feedback.

[1180] At the store, the store manager registers store information and music concept in the system, and the server generates "store music preference data" based on the history of background music played in the past and the results of customer surveys. This clearly defines the music environment for each store.

[1181] Users can use the emotion engine within the application to input or recognize their real-time emotional state. The server uses the emotion engine to analyze the emotional state from the user's input, voice, and facial expression recognition technology to generate real-time emotional data, which then creates an emotional profile for the user.

[1182] When a user searches for a store, the server compares the user's music preference data with that of the store, and also considers the user's real-time emotional state to present stores with a high matching rate, allowing the user to easily find a store that best suits their emotions and musical preferences.

[1183] When a customer visits a store and checks in, the store's terminal obtains the user's music preference data and emotional state data from the server. Based on the obtained data, the store's background music playback system automatically selects and plays royalty-free background music that matches the user's preferences and emotional state. The server uses multiple copyright-free music data sets to train a generative AI model and generate new music. The generated music is optimized based on the customer's emotional state and provided to the store.

[1184] For example, if a user feels like "I want to relax," and their favorite music genre is jazz, they might enter a prompt to search for a cafe where they can relax:

[1185] "I feel like I want to relax. I like jazz music. I'm looking for a cafe where I can relax here."

[1186] Based on this prompt, the system will suggest the most suitable store, and when the user checks in at that store, relaxing jazz music will be played automatically. In this way, the system can provide the optimal musical environment according to the user's real-time emotional state and musical preferences.

[1187] The hardware used is a server with Django (web framework), MySQL (database), smartphones (iOS / Android), and API communication (HTTP requests). The emotion engine uses Emotion API (a cloud-based emotion recognition API), and the background music playback system is the in-store sound system.

[1188] This invention can improve user satisfaction and dramatically improve the quality of service in stores.

[1189] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1190] Step 1:

[1191] The server obtains the user's listening history from the music streaming service. The user ID is required as input, and the user's listening history data is obtained as output. Specifically, the listening history is obtained through an API request, and data such as the number of plays and listening time of the user is collected.

[1192] Step 2:

[1193] The server generates user music preference data based on the listening history data. The listening history data is required as input, and a list of the user's favorite music genres and artists is obtained as output. The data is processed by performing statistical analysis of the listening history to extract the main genres and artists.

[1194] Step 3:

[1195] The store manager inputs store information and music concept into the system. The store's music genre and concept are required as input, and the information is recorded on the server as output. Specifically, data is entered using a web form and sent to the server.

[1196] Step 4:

[1197] The server collects the history of background music played in the past from the store's sound system. The store ID is required as input, and background music history data is obtained as output. Specifically, the server analyzes the sound system's log data and collects the songs that have been played and the number of times they have been played.

[1198] Step 5:

[1199] The server generates music preference data for the store based on the background music history data and the results of customer surveys. The store's background music history data and the survey results are required as input, and the store's music preference data is obtained as output. The data is then processed by statistically analyzing the evaluations of the survey results to extract the main music genres and songs.

[1200] Step 6:

[1201] Users use the emotion engine within the application to input their real-time emotional state or have it recognized by the device's camera. Emotional state and voice input are required as input, and analyzed emotional data is obtained as output. Specifically, emotions are analyzed from facial expressions and voice through the emotion recognition API and sent to the server.

[1202] Step 7:

[1203] The server updates the user's emotional profile based on the user's music preference data and real-time emotional state data. The server requires real-time emotional data and music preference data as input, and obtains the updated user's emotional profile as output. Specifically, the server updates the profile database based on the emotional data.

[1204] Step 8:

[1205] The user enters a prompt to search for a store in the app. For example, the prompt might be "I'm looking for a relaxing jazz cafe." The prompt is required as input, and a list of suitable stores is obtained as output. The server analyzes the prompt and uses a matching algorithm to present the most suitable stores.

[1206] Step 9:

[1207] The server compares the user's music preference data, the store's music preference data, and real-time emotional state data to match the optimal store. Each piece of data for comparison is required as input, and stores with a high matching rate are obtained as output. Specifically, each piece of data is integrated and analyzed to create a list of the most suitable stores.

[1208] Step 10:

[1209] A user visits a store and checks in at a terminal in the store. The user ID is required as input, and the user's check-in information is obtained as output. The terminal in the store obtains the user's latest music preference data and emotional state data from the server.

[1210] Step 11:

[1211] The store's terminal retrieves copyright-free background music from the server that matches the user's preferences and current emotional state, and automatically plays it. The user's preference data and emotional state data are required as input, and the optimal background music is obtained as output. The terminal controls the background music playback system based on the retrieved data, providing the user with the optimal musical environment.

[1212] Through the above processing steps, users can find the most suitable store according to their real-time emotional state and music preferences, and stores can provide the best service.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] [Fourth embodiment]

[1217] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1218] 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.

[1219] 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).

[1220] 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.

[1221] 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.

[1222] 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).

[1223] 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.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] 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."

[1230] The system according to the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and also optimize background music playback in the store.

[1231] The system consists of the following main elements:

[1232] 1. Generating user music preference data

[1233] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[1234] Server: Based on the user's listening history, the server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) to generate "user music preference data."

[1235] 2. Generating store music preference data

[1236] Terminal: The store manager inputs the store's music concept and playlist history into the system.

[1237] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data."

[1238] Server: For example, acquire information such as the music played at store A is classical and customers prefer a quiet atmosphere.

[1239] 3. Perform matching

[1240] User: Uses the system's search function to submit a request to find the best store.

[1241] Server: Compares user music preference data with store music preference data and calculates stores with the highest matching rate.

[1242] Server: Presents matching results to the user. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[1243] 4. Creation and provision of copyright-free background music

[1244] Server: Collects multiple copyright-free music data and generates new music based on this data. Using generative AI, it creates music in a variety of genres tailored to the user's preferences.

[1245] Server: Provides the generated royalty-free music to each store so that it can be used on the store's sound system. For example, if a store needs jazz background music, the server distributes the newly generated jazz track.

[1246] 5. Development of an automatic background music playback system

[1247] User: Visits the store and logs in or checks into the system.

[1248] Terminal: The store's terminal obtains the user's music preference data from the server.

[1249] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[1250] As a concrete example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests establishments that match their preferences (e.g., cafes that play jazz in-store and are relaxing). When the user visits the establishment, the in-store device automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the establishment can increase customer satisfaction.

[1251] This system allows users to easily find the store that best suits their musical tastes, and stores can improve the quality of their service by providing background music that suits customers' musical tastes.

[1252] The processing flow will be explained below.

[1253] Step 1:

[1254] User: Signs in to the system. Users enter their login information to access the system.

[1255] Step 2:

[1256] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[1257] Step 3:

[1258] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[1259] Step 4:

[1260] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[1261] Step 5:

[1262] Server: Combines music genre and mood parameters to generate "user music preference data" and stores it in the user's profile.

[1263] Step 6:

[1264] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[1265] Step 7:

[1266] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[1267] Step 8:

[1268] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[1269] Step 9:

[1270] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and save it in the store's profile.

[1271] Step 10:

[1272] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[1273] Step 11:

[1274] Server: Compares user music preference data with store music preference data, calculates matching rate, and lists stores that most closely match the user's preferences.

[1275] Step 12:

[1276] Server: Presents the matching results to the user. For example, it displays "There are five cafes that have high music preferences for the user."

[1277] Step 13:

[1278] Server: Collects multiple copyright-free music data sets and trains the AI ​​based on them to generate new copyright-free background music.

[1279] Step 14:

[1280] Server: Provides generated copyright-free background music to each store, distributing music data via a portal site or dedicated app.

[1281] Step 15:

[1282] User: Visits the store and checks in by connecting to the store's terminal or scanning a QR code.

[1283] Step 16:

[1284] Terminal: The store's terminal obtains the user's music preference data from the server.

[1285] Step 17:

[1286] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music that matches the user's preferences. For example, it can play smooth jazz for a user who wants to relax.

[1287] In this way, a system is realized that suggests the most suitable store based on the user's musical preferences and provides a musical environment tailored to individual preferences even after the user visits the store.

[1288] Example 1

[1289] 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."

[1290] Many users today want to control their moods and activities and enjoy themselves through music. However, it is not easy to find a store that matches the user's preferred music genre and atmosphere. Stores are also required to understand customers' musical preferences and provide optimal background music to increase customer satisfaction, but this is time-consuming. An efficient and effective system to solve these problems is needed.

[1291] 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.

[1292] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring the user's music streaming service listening history, means for analyzing the listening history and extracting the user's favorite music genre and mood parameters, means for saving the listening history data in a database, means for a store manager to input the store's music concept and playlist history, and means for analyzing survey results and feedback and generating store music preference data. This allows users to easily find a store that best suits their music preferences, and stores can increase customer satisfaction by providing background music that suits customers' music preferences.

[1293] "User music preference data" is data that analyzes the viewing history of music distribution services and compiles the user's preferred music genres and mood parameters.

[1294] "Store music preference data" is data on the music genres and atmosphere preferred in a store, based on the store's music concept, playlist history, and customer survey results and feedback.

[1295] The "matching rate" is an index showing the degree of agreement between the user's music preference data and the store's music preference data, and serves as a criterion for recommending stores that match the user's preferences.

[1296] "Listening history" refers to a user's past playback history data on a music distribution service, and specifically includes information on the songs and artists the user has listened to.

[1297] "Mood parameters" are numerical or categorical expressions of the emotions and states that music evokes in users, and include classifications such as relaxed, active, and focused.

[1298] A "database" is a system that can organize and store multiple pieces of data and efficiently manage and search them, and in the present invention, it is used to store viewing history data and the like.

[1299] "Survey results" are the results of questionnaire surveys answered by users and customers, compiled and converted into data.

[1300] "Feedback" refers to reactions such as comments and ratings provided by users and customers, and serves as a reference when generating music preference data for a store.

[1301] "Copyright-free music data" refers to music files that are not subject to copyright restrictions and can be freely used and distributed.

[1302] A "generative AI model" is a model for generating new data or content using artificial intelligence technology, and in this invention is used to generate copyright-free music.

[1303] A "prompt" is an instruction entered into a generative AI model to specify the conditions and characteristics of the music to be generated.

[1304] "Cloud storage" is a storage service for saving and managing data on the Internet, and is used to distribute created music to stores.

[1305] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize background music playback in the store. This system functions through three parties: a server, a terminal, and the user.

[1306] Server Features

[1307] 1. Generating user music preference data

[1308] When a user signs in to the system, the server obtains listening history data from music distribution services (e.g., Spotify or Apple Music) via API. This listening history includes information on the songs and artists played by the user. Based on the obtained listening history, a machine learning model (e.g., K-means clustering) is used to classify and extract the user's favorite music genres and mood parameters (e.g., relaxed, active, focused), generating "user music preference data."

[1309] 2. Generating store music preference data

[1310] Store managers use dedicated terminals to input the store's music concept and playlist history. Based on the input data, the server also collects and analyzes survey results and feedback from past customers to generate "store music preference data." Text mining technology is used to analyze the survey and feedback data and extract keywords related to specific music genres and moods.

[1311] 3. Perform matching

[1312] The user uses the system's search function to send a request to the server to find the best store. The server compares the user's music preference data with the store's music preference data and uses algorithms such as cosine similarity calculations to recommend the store with the highest matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest a cafe that plays jazz.

[1313] 4. Creation and provision of copyright-free background music

[1314] The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on the prompt. The generated music is stored in cloud storage and provided to the store. An example of a specific prompt might be, "Please generate a relaxing jazz track using piano and saxophone, with a calm and soothing tempo."

[1315] Device Features

[1316] 1. Data entry and manipulation

[1317] The store manager uses the terminal to use an interface to input the store's music concept and playlist history, as well as to input customer surveys and collect feedback.

[1318] 2. Automatic BGM playback

[1319] When a user visits a store and logs in or checks in to the system, the store's terminal receives the user's music preference data from the server in real time. Based on the data, the system automatically plays royalty-free background music. For example, if the user wants to relax, smooth jazz background music will be played automatically.

[1320] User Actions

[1321] 1. Sign in to the system

[1322] Users sign in to the system and provide their music streaming service listening history, which is an important source of information for generating user music preference data.

[1323] 2. Search and select a store

[1324] Users can use the system's search function to find stores that match their music preferences, and the server responds to this request by recommending the most suitable stores.

[1325] Specific operation example

[1326] Consider a case where a user likes jazz and is looking for a relaxing cafe. The user first signs in to the system and provides their listening history from a music streaming service. The server analyzes the listening history and determines that the user likes jazz. When the user then searches for "a relaxing jazz cafe," the server compares the user's music preference data with the store's music preference data and recommends the most suitable cafe. When the user visits the cafe, the terminal in the store automatically plays smooth jazz background music.

[1327] This allows users to enjoy a more comfortable music experience in a store that best suits their musical tastes, and stores can further increase customer satisfaction by providing background music that matches customers' musical tastes.

[1328] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1329] Step 1: Collecting user viewing history

[1330] Input: User's music streaming service login information

[1331] How it works: When a user signs in to the system, the server uses the music streaming service's API to obtain the user's listening history data via OAuth authentication, such as the songs, artists, and playback time that the user has listened to.

[1332] Output: Viewing history data

[1333] Step 2: Analyzing viewing history and generating preference data

[1334] Input: Viewing history data

[1335] How it works: The server analyzes the acquired listening history using a machine learning model (e.g., K-means clustering) to extract the user's favorite music genres and mood parameters (relaxed, active, focused, etc.). The analysis uses the frequency and duration of playback for specific artists and genres.

[1336] Output: User music preference data

[1337] Step 3: Enter store data

[1338] Input: Store music concept, playlist history, survey results, feedback

[1339] Operation: The store manager uses a terminal to input the store's music concept and playlist history. The store manager also inputs customer survey results and feedback from the terminal. This data is sent to the server.

[1340] Output: Sending input data to the server

[1341] Step 4: Generate store music preference data

[1342] Input: Store data (music concept, playlist history, survey results, feedback)

[1343] How it works: The server analyzes collected store data and extracts keywords related to specific music genres and moods. It then uses text mining technology to analyze surveys and feedback and generates "store music preference data."

[1344] Output: Store music preference data

[1345] Step 5: Matching users and stores

[1346] Input: User music preference data, store music preference data

[1347] How it works: A user uses the system's search function to send a request to find a store that matches their needs. The server compares the user's music preference data with the store's music preference data using algorithms such as cosine similarity calculations, and recommends the store that best matches.

[1348] Output: Matching results (store recommendations displayed in list format)

[1349] Step 6: Generate royalty-free background music

[1350] Input: User music preference data, store music request (prompt sentence)

[1351] How it works: The server uses a generative AI model (e.g., OpenAI's GPT-4, DALL-E) to generate a new royalty-free piece of music based on a prompt, such as "Please generate a relaxing jazz track with piano and saxophone, at a calm and soothing tempo."

[1352] Output: Generated music data

[1353] Step 7: Save to cloud storage and provide to stores

[1354] Input: Generated music data

[1355] How it works: The server stores the generated royalty-free music on a cloud storage service (e.g., Amazon S3), then provides a download link to the store, through which the store can download the music.

[1356] Output: Song data on cloud storage, download link to store

[1357] Step 8: User visits the store and automatic background music plays

[1358] Input: User music preference data, check-in information

[1359] How it works: A user visits a store and logs in or checks in to the system. The store's terminal retrieves the user's music preference data from the server in real time and automatically plays the most appropriate background music over the store's sound system. For example, if the user wants to relax, smooth jazz will be played.

[1360] Output: BGM playback in the store

[1361] This allows users to enjoy a comfortable music experience in a store that best suits their musical tastes, and stores can improve customer satisfaction.

[1362] (Application example 1)

[1363] 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."

[1364] In today's world, it is difficult to choose a store that meets a user's individual music preferences, resulting in a poor user experience. Furthermore, stores are limited to playing generic music, making it difficult to provide services tailored to their customers. Furthermore, there is a need for a system that can provide appropriate background music without worrying about copyright issues.

[1365] 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.

[1366] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, and means for automatically playing background music in the physical store based on the user music preference data. This allows the user to choose a store that suits their music preferences, and the store to provide background music that suits the customer's preferences.

[1367] "User music preference data" is data that indicates a user's music preferences, generated by analyzing the user's listening history, such as the genres, artists, and songs that the user normally listens to.

[1368] "Store music preference data" is data that indicates the characteristics and trends of the music offered by a store, generated based on the store's music concept, playlists, and customer feedback.

[1369] The "matching rate" is a numerical value or index that indicates the degree of match when comparing a user's music preference data with a store's music preference data.

[1370] "Automatic playback means" refers to a system or mechanism that automatically plays appropriate background music when a user visits a physical store, based on the user's music preference data.

[1371] "Copyright-free background music" is music that can be freely used without being restricted by copyright, and is a type of music that is automatically played by this system.

[1372] A "generative AI model" is an algorithm or system that uses machine learning and artificial intelligence techniques to generate new music or data based on user preferences and characteristics.

[1373] A "prompt" is an instruction or question that is input into a generative AI model to generate appropriate background music.

[1374] "Physical store" refers to a commercial facility or service provider that exists in a physical location, and means a specific store visited by customers who use the system.

[1375] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and optimize the background music playback in the store. This system is composed of the following main elements.

[1376] 1. Generating user music preference data

[1377] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history. For example, it analyzes the user's frequently listened-to music genres and artist list. Examples of music streaming service APIs used include the Spotify API and Apple Music API.

[1378] Server: The server extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user's music preference data."

[1379] 2. Generating store music preference data

[1380] Terminal: The store manager uses the terminal to input the store's music concept and playlist history into the system, which then aggregates the store's music preference data.

[1381] Server: Based on the input data, the server analyzes the survey results and feedback of past customers and generates "store music preference data." For example, it may acquire information that the music played at Store A is classical and that customers prefer a quiet atmosphere.

[1382] 3. Perform matching

[1383] User: The user uses the system's search function to submit a request to find the best store.

[1384] Server: The server compares the user's music preference data with that of the stores and calculates stores with a high matching rate. For example, if the user's preference is for a relaxing environment with jazz music, the server will suggest cafes that play jazz.

[1385] Server: The server presents the matching results to the user.

[1386] 4. Creation and provision of copyright-free background music

[1387] Server: The server collects multiple copyright-free music data and generates new music based on this data. Using a generative AI model, it creates music in various genres tailored to the user's preferences. For example, to generate a jazz song, the server uses the prompt "Generate a relaxing jazz song."

[1388] Server: The server provides the generated copyright-free music to each store so that it can be used on the store's sound system.

[1389] 5. Development of an automatic background music playback system

[1390] User: A user visits a physical store and logs in or checks into the system.

[1391] Terminal: The store's terminal acquires the user's music preference data from the server.

[1392] Device: Based on the acquired data, the device automatically selects and plays royalty-free background music. For example, if the user wants to relax, the device will play relaxing smooth jazz.

[1393] As a specific example, consider the case where a user likes jazz and searches for a cafe where they can relax. The user provides their listening history, and the system suggests stores that match their preferences (e.g., cafes that play jazz music and are relaxing). When the user visits the store, the device inside the store automatically plays jazz background music that matches the user's preferences. In this way, the user's music experience is improved, and the store can also increase customer satisfaction.

[1394] An example of a prompt for generating background music using a generative AI model is "Generate a relaxing jazz song."

[1395] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1396] Step 1:

[1397] Acquiring user music preference data

[1398] Input: User login information and permissions to access music streaming services

[1399] Processing: The server uses the music streaming service API (such as Spotify API or Apple Music API) to obtain the user's listening history, which includes information such as song genre and artist.

[1400] Data processing: Analyze viewing history data to extract the user's favorite music genres and mood parameters.

[1401] Output: User's music preference data

[1402] Step 2:

[1403] Generating store music preference data

[1404] Input: Store manager inputs store music concept, playlist history, and customer feedback

[1405] Processing: The terminal inputs this information into the system, and the server aggregates the information stored in the database.

[1406] Data calculation: Based on the input data, the server analyzes the survey results and feedback of customers and generates music preference data for the store.

[1407] Output: Store music preference data

[1408] Step 3:

[1409] Matching users and stores

[1410] Input: User music preference data and music preference data for multiple stores

[1411] Processing: The server compares the user's music preference data with the store's music preference data and calculates the matching rate.

[1412] Data calculation: Using a matching algorithm, it identifies the store that best suits a user's preferences. For example, it suggests cafes that play jazz to a user who likes jazz.

[1413] Output: Matching results (list of stores with high matching rates)

[1414] Step 4:

[1415] Copyright-free background music generation

[1416] Input: Multiple copyright-free music data stored on the server and a prompt for the generative AI model (e.g., "Generate a relaxing jazz song").

[1417] Processing: The server uses a generative AI model to generate new royalty-free background music based on the user's preferences.

[1418] Data computation: A generative AI model analyzes music data and creates new songs based on prompts.

[1419] Output: New royalty-free background music

[1420] Step 5:

[1421] Development of an automatic background music playback system

[1422] Input: Information that users enter when they visit a physical store and log in or check in to the system

[1423] Processing: The store's terminal obtains the user's music preference data from the server and selects the appropriate copyright-free background music.

[1424] Data calculation: Based on the acquired data, the device selects the most suitable song and plays it on the store's sound system.

[1425] Output: Play background music in the store. For example, relaxing smooth jazz is played.

[1426] 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.

[1427] The system of the present invention uses the user's music preference data and the store's music preference data to match the most suitable store to the user, and further combines an emotion engine to take into account the user's real-time emotional state and optimize the store's background music playback.

[1428] The system consists of the following main elements:

[1429] 1. Generating user music preference data

[1430] Server: When a user signs in to the system, the server obtains the user's music streaming service listening history, for example, analyzing the user's frequently listened-to music genres and artist list.

[1431] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) based on their listening history, and generates "user music preference data."

[1432] 2. Generating store music preference data

[1433] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[1434] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[1435] Server: Analyzes customers' music preferences based on collected background music history and customer survey results and feedback, and generates "store music preference data."

[1436] 3. Emotion Recognition by Emotion Engine

[1437] On the device: The user uses the emotion engine within the app to input or recognize their real-time emotional state. For example, the user inputs, "I want to relax right now."

[1438] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[1439] 4. Perform matching

[1440] User: A user searches for a business within the app, for example, "relaxing jazz cafe."

[1441] Server: Compares the user's music preference data with the store's music preference data, as well as the emotional state data from the emotion engine, and calculates the matching rate.

[1442] Server: Presents matching results to the user, for example, presenting the store that is closest to the user's current emotional state.

[1443] 5. Creation and provision of copyright-free background music

[1444] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new music.

[1445] Server: Provides the generated copyright-free music to each store so that it can be used on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[1446] 6. Development of an automatic background music playback system

[1447] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[1448] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[1449] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[1450] As a specific example, if a user likes relaxing jazz music, the user can input their emotional state of "I want to relax," and the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a device inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preferences.

[1451] This system allows users to easily find the store that best suits their emotions and musical preferences, and stores can improve the quality of their service by providing background music that suits customers' emotions and preferences.

[1452] The processing flow will be explained below.

[1453] Step 1:

[1454] User: Signs in to the system. Users enter their login information to access the system.

[1455] Step 2:

[1456] Server: Obtain the signed-in user's music streaming service listening history. With permission, collect the listening history data via API.

[1457] Step 3:

[1458] Server: Analyzes the acquired listening history and identifies the user's favorite music genres and artists. Analysis is performed based on the number of views and playback time.

[1459] Step 4:

[1460] Server: Extracts mood parameters (e.g., relaxed, active, focused) from the user's listening data. For example, if a user often listens to relaxing music at night, set "relaxed" as the mood parameter.

[1461] Step 5:

[1462] Server: Combines the extracted music genre and mood parameters to generate "user music preference data" and saves it in the user's profile.

[1463] Step 6:

[1464] Terminal: The store manager registers store information and music concept in the system. For example, they enter information about a cafe that mainly serves classical music.

[1465] Step 7:

[1466] Server: Collects the history of background music played in the past from the store's sound system and playlist management system.

[1467] Step 8:

[1468] Server: Analyzes customers' musical preferences based on collected background music history and customer survey results and feedback. For example, it can extract trends such as "many customers prefer classical music."

[1469] Step 9:

[1470] Server: Integrates the store's music concept, background music history, and customer preference data to generate "store music preference data" and saves it in the store's profile.

[1471] Step 10:

[1472] User: Input or recognize their real-time emotional state using the emotion engine. For example, inputting an emotional state like "I want to relax right now" within the app.

[1473] Step 11:

[1474] Server: The emotion engine analyzes the user's current emotional state using user input, voice, and facial expression recognition technology. Based on the analysis results, it generates real-time emotion data and stores it in the user's emotion profile.

[1475] Step 12:

[1476] User: Searches for a store within the app, for example, searching for "relaxing jazz cafe."

[1477] Step 13:

[1478] Server: Compares user music preference data, store music preference data, and real-time emotion data from the emotion engine to calculate the matching rate.

[1479] Step 14:

[1480] Server: Presents the matching results to the user, for example, presenting the store that best suits the user's current emotional state.

[1481] Step 15:

[1482] Server: Collects multiple copyright-free music data sets and uses them to train a generative AI to generate new copyright-free background music.

[1483] Step 16:

[1484] Server: Provides the generated copyright-free music to each store, allowing it to be played on the store's sound system. For example, it provides relaxing music based on emotional data indicating a desire to relax.

[1485] Step 17:

[1486] User: Visits the store and checks in. The user's emotional state is also updated upon check-in.

[1487] Step 18:

[1488] Terminal: The store terminal acquires the user's music preference data and emotional state data from the server.

[1489] Step 19:

[1490] Device: Based on the acquired data, royalty-free background music that matches the user's preferences and current emotional state is selected and automatically played. For example, smooth jazz can be played for a user who wants to "relax."

[1491] Step 20:

[1492] Server: When there is a change in the user's emotional state, the emotion engine recognizes it and sends it to the server.

[1493] Step 21:

[1494] Server: Based on the new emotional state, reevaluate the store's background music and send instructions to the device to change the background music if necessary.

[1495] Step 22:

[1496] Device: The device that receives the instruction will change the background music and play a new song. For example, if the user inputs "I want to feel refreshed this time," the device will change to a refreshing song.

[1497] In this way, the system of the present invention can optimally match a store with a user based on the user's music preferences and real-time emotional state, and automatically play appropriate background music in the store, thereby improving the user's music experience and increasing customer satisfaction in the store.

[1498] Example 2

[1499] 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."

[1500] With conventional music distribution systems and store background music systems, it is difficult to find a store that matches a user's preferred music genre or emotional state at the time. Stores are also unable to provide background music that responds to customers' real-time emotions, resulting in a decline in user satisfaction. Furthermore, there is no way to generate and provide new music that is not subject to copyright restrictions, which limits the music content available to stores.

[1501] 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.

[1502] In this invention, the server includes means for generating user music preference data, means for generating store music preference data, means for recognizing the user's real-time emotional state, means for presenting stores with a high matching rate to the user using the user music preference data, store music preference data, and the user's real-time emotional state data, means for generating new copyright-free background music using copyright-free music data, means for providing the generated copyright-free background music to a matched store, means for playing the provided copyright-free background music, and means for acquiring the user's music preference data and emotional state data when the user visits a store and automatically playing optimal background music at the store based on the user's preferences and emotional state. This makes it possible to suggest optimal stores tailored to the user's current emotional state and music preferences, and to generate and play optimal copyright-free background music.

[1503] "User music preference data" refers to data that includes the user's favorite music genres and artists, as well as mood parameters based on listening history.

[1504] "Store music preference data" is data generated based on background music history collected from the store's sound system and playlist management system, as well as customer feedback.

[1505] "Real-time emotional state data" refers to data that indicates a user's current emotional state as analyzed through user input, voice, and facial expression recognition technology.

[1506] The "matching rate" is an indicator of compatibility calculated by comparing user music preference data, store music preference data, and real-time emotional state data.

[1507] "Copyright-free BGM" is background music that can be used without copyright restrictions.

[1508] A "generative AI model" is an artificial intelligence model that generates new music or data based on input data.

[1509] A "prompt" is an instruction given to a generative AI model, a document intended to encourage data generation for a specific purpose.

[1510] A "server" is a computer system that has functions such as data collection, analysis, storage, and matching calculation.

[1511] A "terminal" is a device that allows users or store managers to input information and retrieve data from a server.

[1512] The system of the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store and further optimize the store's background music taking into account the user's real-time emotional state. This system is composed of the following main elements.

[1513] 1. Generating user music preference data

[1514] Server: When a user signs in to the system, the server retrieves the user's listening history from music streaming services (e.g., Spotify, Apple Music) via API. This data includes the user's recently listened songs and frequently played artists.

[1515] Server: Analyzes the acquired listening history, extracts the user's preferred music genre and mood parameters (relaxed, active, focused, etc.), and generates "user music preference data."

[1516] 2. Generating store music preference data

[1517] Terminal: The store manager uses the terminal to input store information and the music concept. For example, they might register information such as "a cafe that mainly serves classical music."

[1518] Server: The server automatically collects the history of background music played in the past from the store's sound system and playlist management system.

[1519] Server: Based on the collected background music history and customer feedback, the server analyzes the music genres and moods preferred by customers and generates "store music preference data."

[1520] 3. Emotion Recognition by Emotion Engine

[1521] On the device: Users use the emotion engine within the app to input their real-time emotional state, for example, "I feel like relaxing right now."

[1522] Server: The emotion engine uses user input, voice, and facial expression recognition technology to analyze the user's current emotional state and stores the results in the user's emotional profile.

[1523] 4. Perform matching

[1524] User: A user searches for "relaxing jazz cafe" within the app.

[1525] Server: The server compares the user's music preference data, the store's music preference data, and the emotion engine data, and calculates the matching rate using an algorithm.

[1526] Server: Based on the calculation results, presents the user with a list of the best stores.

[1527] 5. Creation and provision of copyright-free background music

[1528] Server: The server collects copyright-free music data on the Internet.

[1529] Server: Based on the collected data, a generative AI model (e.g., music generation AI) is used to generate new music. An example prompt is, "Generate jazz music that matches a relaxing mood. The tempo should be slow and the melody should have a soft tone."

[1530] Server: Provides the generated music to each store so that it can be used on the store's sound system.

[1531] 6. Development of an automatic background music playback system

[1532] User: The user visits the designated store and checks in using the app. The user's emotional state is automatically updated upon check-in.

[1533] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data.

[1534] Device: Based on the acquired data, the device automatically selects royalty-free background music that best suits the user's preferences and emotional state and plays it in the store. For example, it plays smooth jazz for a user who requests relaxation.

[1535] As a concrete example, consider the case where a user likes jazz music that is relaxing. When the user inputs their emotional state of "I want to relax," the system will suggest a store that matches that emotion and preference (for example, a relaxing cafe that plays jazz). When the user visits that store, a terminal inside the store will play the optimal background music (relaxing jazz) taking into account the user's emotional state. In this way, the system can provide the optimal musical environment according to the user's current emotional state and musical preference.

[1536] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1537] Step 1:

[1538] Generating user music preference data

[1539] Server: When a user signs in to the system, the server uses the API of the music distribution service (e.g., Spotify, Apple Music) to obtain the user's listening history. The input is the user's sign-in information, and the output is the listening history data.

[1540] Server: Analyzes the acquired listening history, including frequently played songs, artists, genres, and playback times. The input is listening history data, and the output is the user's music preference data.

[1541] Server: Extracts the user's favorite music genre and mood parameters (relaxed, active, focused, etc.) from the music preference data and generates "user music preference data." The input is the analyzed music preference data, and the output is the user music preference data.

[1542] Step 2:

[1543] Generating store music preference data

[1544] Terminal: The store manager uses the terminal to register detailed store information (e.g., a cafe that mainly serves classical music) and the music concept. The input is the information provided by the store manager, and the output is store information data.

[1545] Server: The server collects the history of BGM played in the past from the store's sound system and playlist management system. The input is the history data from the sound system and playlist management system, and the output is the store's BGM history data.

[1546] Server: Analyzes the collected BGM history and customer feedback data to analyze the music genres and moods preferred by customers. The input is store BGM history data and feedback data, and the output is store music preference data.

[1547] Step 3:

[1548] Emotion recognition by emotion engine

[1549] Terminal: The user inputs an emotional state, such as "I want to relax now," into the app. The input is the user's emotional state data, and the output is the emotional state input data.

[1550] Server: The emotion engine analyzes the current emotional state using user input, voice, and facial expression recognition technology. The input is the emotional state input data, and the output is the real-time emotional state data.

[1551] Server: Based on the analysis results, store the real-time emotional state data in the user's emotional profile. The input is the real-time emotional state data, and the output is the updated emotional profile.

[1552] Step 4:

[1553] Performing matching

[1554] User: A user searches for "relaxing jazz cafe" within the app. The input is the user's search query, and the output is the search criteria data.

[1555] Server: Compares user music preference data, store music preference data, and real-time emotional state data, and calculates the matching rate using an algorithm. The inputs are user music preference data, store music preference data, and real-time emotional state data, and the output is the matching result data.

[1556] Server: Based on the calculation results, it presents the user with a list of optimal stores. The input is the matching result data, and the output is the store list displayed to the user.

[1557] Step 5:

[1558] Creation and provision of copyright-free background music

[1559] Server: The server collects copyright-free music data on the Internet. The input is free music data on the Internet, and the output is the collected music data.

[1560] Server: Generates new music using a generative AI model based on the collected data. An example prompt is, "Generate jazz music that suits a relaxing mood. The tempo should be slow and the melody should have a soft tone." The input is the collected music data and the prompt, and the output is the generated music data.

[1561] Server: Provides the generated music to each store so that it can be used on the store's sound system. The input is the generated music data, and the output is the provided music data.

[1562] Step 6:

[1563] Development of an automatic background music playback system

[1564] User: The user visits the designated store and checks in using the app. The emotional state is automatically updated at check-in. The input is the user's check-in information, and the output is the updated emotional state data.

[1565] Terminal: The terminal in the store accesses the server and obtains the user's latest music preference data and emotional state data. The input is the access information to the server, and the output is the obtained data.

[1566] Terminal: Based on the acquired data, the terminal automatically selects copyright-free background music that best suits the user's preferences and emotional state, and plays it in the store. The input is the acquired data, and the output is the background music that is played.

[1567] (Application example 2)

[1568] 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."

[1569] Conventional matching systems that use music preference data have difficulty in considering the user's real-time emotional state, making it difficult to provide the music environment that the user desires. Furthermore, stores lacked a mechanism for providing optimal background music that matches the diverse musical preferences of their customers. This resulted in an inability to sufficiently improve user satisfaction and limited the quality of store services.

[1570] The identification process by the identification 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 generating user music preference data, means for generating store music preference data, means for presenting stores with a high matching rate to the user using the user music preference data and store music preference data, means for acquiring and recording the user's real-time emotional state, and means for automatically playing optimal background music based on the acquired user emotional state in the store's background music playback system. This makes it possible to suggest optimal stores based on the user's real-time emotional state, and enables stores to provide optimal background music tailored to each user's emotions. This increases user satisfaction and dramatically improves the quality of store service.

[1571] "User's music preference data" refers to data including the user's preferred music genres, artists, listening history, and emotional state related to music.

[1572] "Store music preference data" refers to data including the music genres and playlists offered by a particular store, past background music history, and survey results and feedback from customers.

[1573] The "matching rate" is an index that indicates the degree to which the user's music preference data matches the store's music preference data.

[1574] "Emotional state" refers to the user's current mental and sensory state, including, for example, relaxed, focused, active, etc.

[1575] A "BGM playback system" is an audio system for playing background music (BGM) in a store.

[1576] "Copyright-free background music" is music data that can be used freely without being dependent on any specific copyright.

[1577] A "generative AI model" is an artificial intelligence model that generates new music based on copyright-free music data.

[1578] The system for implementing the present invention uses the user's music preference data and the store's music preference data to match the user with the most suitable store, and also takes into account the user's real-time emotional state to automatically play the most suitable background music in the store. This system is composed of the following main elements.

[1579] The server acquires the user's listening history from the music streaming service, extracts the user's favorite music genre and emotional parameters (relaxed, focused, active, etc.), and generates "user music preference data" based on the listening history, playback frequency, and user feedback.

[1580] At the store, the store manager registers store information and music concept in the system, and the server generates "store music preference data" based on the history of background music played in the past and the results of customer surveys. This clearly defines the music environment for each store.

[1581] Users can use the emotion engine within the application to input or recognize their real-time emotional state. The server uses the emotion engine to analyze the emotional state from the user's input, voice, and facial expression recognition technology to generate real-time emotional data, which then creates an emotional profile for the user.

[1582] When a user searches for a store, the server compares the user's music preference data with that of the store, and also considers the user's real-time emotional state to present stores with a high matching rate, allowing the user to easily find a store that best suits their emotions and musical preferences.

[1583] When a customer visits a store and checks in, the store's terminal obtains the user's music preference data and emotional state data from the server. Based on the obtained data, the store's background music playback system automatically selects and plays royalty-free background music that matches the user's preferences and emotional state. The server uses multiple copyright-free music data sets to train a generative AI model and generate new music. The generated music is optimized based on the customer's emotional state and provided to the store.

[1584] For example, if a user feels like "I want to relax," and their favorite music genre is jazz, they might enter a prompt to search for a cafe where they can relax:

[1585] "I feel like I want to relax. I like jazz music. I'm looking for a cafe where I can relax here."

[1586] Based on this prompt, the system will suggest the most suitable store, and when the user checks in at that store, relaxing jazz music will be played automatically. In this way, the system can provide the optimal musical environment according to the user's real-time emotional state and musical preferences.

[1587] The hardware used is a server with Django (web framework), MySQL (database), smartphones (iOS / Android), and API communication (HTTP requests). The emotion engine uses Emotion API (a cloud-based emotion recognition API), and the background music playback system is the in-store sound system.

[1588] This invention can improve user satisfaction and dramatically improve the quality of service in stores.

[1589] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1590] Step 1:

[1591] The server obtains the user's listening history from the music streaming service. The user ID is required as input, and the user's listening history data is obtained as output. Specifically, the listening history is obtained through an API request, and data such as the number of plays and listening time of the user is collected.

[1592] Step 2:

[1593] The server generates user music preference data based on the listening history data. The listening history data is required as input, and a list of the user's favorite music genres and artists is obtained as output. The data is processed by performing statistical analysis of the listening history to extract the main genres and artists.

[1594] Step 3:

[1595] The store manager inputs store information and music concept into the system. The store's music genre and concept are required as input, and the information is recorded on the server as output. Specifically, data is entered using a web form and sent to the server.

[1596] Step 4:

[1597] The server collects the history of background music played in the past from the store's sound system. The store ID is required as input, and background music history data is obtained as output. Specifically, the server analyzes the sound system's log data and collects the songs that have been played and the number of times they have been played.

[1598] Step 5:

[1599] The server generates music preference data for the store based on the background music history data and the results of customer surveys. The store's background music history data and the survey results are required as input, and the store's music preference data is obtained as output. The data is then processed by statistically analyzing the evaluations of the survey results to extract the main music genres and songs.

[1600] Step 6:

[1601] Users use the emotion engine within the application to input their real-time emotional state or have it recognized by the device's camera. Emotional state and voice input are required as input, and analyzed emotional data is obtained as output. Specifically, emotions are analyzed from facial expressions and voice through the emotion recognition API and sent to the server.

[1602] Step 7:

[1603] The server updates the user's emotional profile based on the user's music preference data and real-time emotional state data. The server requires real-time emotional data and music preference data as input, and obtains the updated user's emotional profile as output. Specifically, the server updates the profile database based on the emotional data.

[1604] Step 8:

[1605] The user enters a prompt to search for a store in the app. For example, the prompt might be "I'm looking for a relaxing jazz cafe." The prompt is required as input, and a list of suitable stores is obtained as output. The server analyzes the prompt and uses a matching algorithm to present the most suitable stores.

[1606] Step 9:

[1607] The server compares the user's music preference data, the store's music preference data, and real-time emotional state data to match the optimal store. Each piece of data for comparison is required as input, and stores with a high matching rate are obtained as output. Specifically, each piece of data is integrated and analyzed to create a list of the most suitable stores.

[1608] Step 10:

[1609] A user visits a store and checks in at a terminal in the store. The user ID is required as input, and the user's check-in information is obtained as output. The terminal in the store obtains the user's latest music preference data and emotional state data from the server.

[1610] Step 11:

[1611] The store's terminal retrieves copyright-free background music from the server that matches the user's preferences and current emotional state, and automatically plays it. The user's preference data and emotional state data are required as input, and the optimal background music is obtained as output. The terminal controls the background music playback system based on the retrieved data, providing the user with the optimal musical environment.

[1612] Through the above processing steps, users can find the most suitable store according to their real-time emotional state and music preferences, and stores can provide the best service.

[1613] 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.

[1614] 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.

[1615] 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.

[1616] 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.

[1617] 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.

[1618] 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.

[1619] 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).

[1620] 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.

[1621] 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."

[1622] 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.

[1623] 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).

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] The following is further disclosed regarding the above embodiment.

[1635] (Claim 1)

[1636] means for generating user music preference data;

[1637] A means for generating music preference data for a store;

[1638] A means for presenting stores with a high matching rate to users using user music preference data and store music preference data;

[1639] A system including:

[1640] (Claim 2)

[1641] A means for generating new copyright-free background music using copyright-free music data;

[1642] A means for playing copyright-free background music provided to the matched store;

[1643] The system of claim 1 further comprising:

[1644] (Claim 3)

[1645] 2. The system according to claim 1, further comprising means for acquiring the user's music preference data when the user visits the store and automatically playing background music in the store.

[1646] "Example 1"

[1647] (Claim 1)

[1648] means for generating user music preference data;

[1649] A means for generating music preference data for a store;

[1650] A means for presenting stores with a high matching rate to users using user music preference data and store music preference data;

[1651] A means for obtaining the user's music streaming service viewing history;

[1652] A means of analyzing the viewing history and extracting the user's favorite music genre and mood parameters,

[1653] a means for storing viewing history data in a database;

[1654] A way for store managers to input the store's music concept and playlist history,

[1655] A means for analyzing survey results and feedback to generate store music preference data;

[1656] A system including:

[1657] (Claim 2)

[1658] A means for generating new copyright-free background music using copyright-free music data;

[1659] A means for playing copyright-free background music provided to the matched store;

[1660] a means for generating a piece of music based on a prompt using a generative AI model;

[1661] A means to store copyright-free music in cloud storage and provide it to stores,

[1662] The system of claim 1 further comprising:

[1663] (Claim 3)

[1664] 2. The system according to claim 1, further comprising means for acquiring the user's music preference data when the user visits the store and automatically playing background music in the store.

[1665] "Application Example 1"

[1666] (Claim 1)

[1667] means for generating user music preference data;

[1668] A means for generating music preference data for a store;

[1669] A means for presenting stores with a high matching rate to users using user music preference data and store music preference data;

[1670] A means to automatically play background music in physical stores based on user music preference data,

[1671] A system including:

[1672] (Claim 2)

[1673] A means for generating new copyright-free background music using copyright-free music data;

[1674] A means to play copyright-free background music provided to matched physical stores,

[1675] The system of claim 1 further comprising:

[1676] (Claim 3)

[1677] When a user visits a physical store, a means of acquiring the user's music preference data and automatically playing background music in the physical store;

[1678] A means to generate background music that matches the user's musical tastes using a generative AI model;

[1679] a means for generating a prompt sentence for playing the generated background music;

[1680] The system of claim 1 further comprising:

[1681] "Example 2: Combining Emotion Engines"

[1682] (Claim 1)

[1683] means for generating user music preference data;

[1684] A means for generating music preference data for a store;

[1685] a means for recognizing a user's real-time emotional state;

[1686] a means for presenting stores with a high matching rate to the user using the user's music preference data, the store's music preference data, and the user's real-time emotional state data;

[1687] A system including:

[1688] (Claim 2)

[1689] A means for generating new copyright-free background music using copyright-free music data;

[1690] A means for providing the generated copyright-free background music to the matched store;

[1691] A means to play the provided copyright-free background music,

[1692] The system of claim 1 further comprising:

[1693] (Claim 3)

[1694] When a user visits a store, the user's music preference data and emotional state data are acquired,

[1695] 10. The system according to claim 1, further comprising means for automatically playing optimal background music in the store based on the user's preferences and emotional state.

[1696] "Application example 2 when combining emotion engines"

[1697] (Claim 1)

[1698] means for generating user music preference data;

[1699] A means for generating music preference data for a store;

[1700] A means for presenting stores with a high matching rate to users using user music preference data and store music preference data;

[1701] a means for capturing and recording a user's real-time emotional state;

[1702] A means for automatically playing optimal background music based on the acquired emotional state of the user in a background music playback system in the store;

[1703] A system including:

[1704] (Claim 2)

[1705] A means for generating new copyright-free background music using copyright-free music data;

[1706] A means for playing copyright-free background music provided to the matched store;

[1707] A means for generating new background music using a generative AI model;

[1708] The system of claim 1 further comprising:

[1709] (Claim 3)

[1710] A means for acquiring the user's music preference data when the user visits a store and automatically playing background music in the store;

[1711] a means for updating the user's emotional state and adjusting the background music accordingly;

[1712] The system of claim 1 further comprising: [Explanation of symbols]

[1713] 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. means for generating user music preference data; A means for generating music preference data for a store; A means for presenting stores with a high matching rate to users using user music preference data and store music preference data; A system including:

2. A means for generating new copyright-free background music using copyright-free music data; A means for playing copyright-free background music provided to the matched store; The system of claim 1 further comprising:

3. 2. The system according to claim 1, further comprising means for acquiring the user's music preference data when the user visits the store and automatically playing background music in the store.

Images