system

Abstract

We provide the system. [Solution] A means for receiving image information entered by the user, A means for analyzing the features of an image based on the aforementioned image information, A means for selecting suitable music information based on the analyzed characteristics, A means for providing the selected music information to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, in a music service, a user has to select a music genre or a specific piece of music by themselves, which is difficult or troublesome for a user who is not very familiar with music. Also, since it is very subjective for a user to select music according to their mood or state, it may be difficult to find appropriate music.

Means for Solving the Problems

[0005] This invention provides a system that automatically suggests music that matches the atmosphere of a photo simply by the user uploading it. To achieve this, it analyzes the characteristics of the input image information and selects music information according to the analysis results. It also takes into account arbitrary mood information entered by the user to provide music that is more suitable for the user's state of mind. With this mechanism, even users who are not knowledgeable about music can easily enjoy music that matches their mood and environment at that time.

[0006] "Image information" refers to visual data entered by users into the system, and includes photographs or image files.

[0007] "Image features" are elements extracted from image information, including the shape of an object, color distribution, overall tone, and special visual patterns.

[0008] "Music information" refers to data about music analyzed and selected by a generating AI, and includes the song itself, its sound style, and atmosphere.

[0009] "Generative AI" refers to a model that uses artificial intelligence technology to generate new information and data, and is particularly used to select the most suitable music based on input data. [Brief explanation of the drawing]

[0010] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0011] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0014] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0016] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0017] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] As shown in Figure 1, the 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.

[0021] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0022] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0023] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0024] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0025] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0028] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0029] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0030] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0031] This invention is a system that automatically selects and provides music based on photographs taken by the user. The following is a detailed description of an embodiment of this system.

[0032] First, the user selects the photos they have taken using their device and uploads them to the system. These uploaded photos are then sent to the server and temporarily stored.

[0033] Subsequently, the server uses an image analysis module to extract features from the received image information. These features include visual elements and colors within the photograph, as well as the overall atmosphere of the photograph. The results of this analysis are used as basic data for the music selection process.

[0034] Furthermore, the server receives mood information from the user upon input. This information specifically reflects the user's current mood and desired musical style. This allows the music selection to reflect not only images but also the user's emotional state.

[0035] Based on this information, the server uses a generative AI to select music. The generative AI combines image features and mood information to output optimal music information. Here, music information refers to specific songs and sound styles.

[0036] The selected music information is sent to the user's device. The user's device then plays music based on the received information and provides it to the user. For example, if a user uploads a landscape photo and enters the feeling of "wanting to relax," the server may suggest music with a calm tempo.

[0037] This system eliminates the need for manual music selection and allows for an experience tailored to the individual user's emotions. The music selection process is completed quickly and appropriately in the background, unseen by the user, thus enhancing customer satisfaction.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user operates the device, selects the photos they have taken, and presses the upload button to send them to the system. The user's device then transfers these photos to the server.

[0041] Step 2:

[0042] The server verifies the received photo data and saves it to temporary storage. This saving process includes a data integrity check.

[0043] Step 3:

[0044] The server activates an image analysis module and analyzes the stored photographic data. Specifically, it recognizes the colors, composition, and main objects in the images, and quantifies or categorizes the atmosphere of the photographs.

[0045] Step 4:

[0046] The server temporarily stores the analysis results and displays a mood input interface to the user. Here, the user selects and inputs a mood such as "cheerful" or "calm" from their terminal.

[0047] Step 5:

[0048] The user's device sends the entered mood information to the server. After this transmission, the server integrates the image analysis results with the mood information and prepares to pass the data to the generating AI.

[0049] Step 6:

[0050] The server runs a generative AI that selects the most suitable music for an image and mood based on integrated data. This process references a historical database associated with similar images and moods.

[0051] Step 7:

[0052] The server sends the selected music information to the user's device. The device receives this music information and begins streaming or downloading the music.

[0053] Step 8:

[0054] The user's device plays the received music, providing the user with an integrated music experience. After playback, the server displays an interface to request feedback from the user, if possible.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] Manually selecting music that matches the user's emotions and situation based on visual data captured by the user is difficult and burdensome. Therefore, there is a need to provide a system that allows users to easily and appropriately acquire music.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for receiving visual data selected by the user, means for temporarily storing the visual data, means for analyzing the features of the visual data, means for receiving emotional information provided by the user, means for using a generative model that generates music information based on the features and emotional information, and means for providing the generated music information to the user. This enables the user to automatically and quickly obtain music that matches their emotions and situation.

[0060] "Visual data" refers to visual information, such as images and photographs, that users acquire and submit through their devices.

[0061] "Means of temporary storage" refers to a function in a server or storage device for temporarily storing received data.

[0062] "Means of analyzing features" refers to the process or function of extracting visual elements from received visual data and identifying their structure and content.

[0063] "Emotional information" refers to information that users provide to express their emotional state or desired musical style.

[0064] A "generative model" refers to an artificial intelligence or machine learning program that takes visual data features and emotional information as input and outputs appropriate musical information.

[0065] "Music information" refers to information necessary for selecting or playing music, such as the song title, artist, song characteristics, and playback links.

[0066] "Means of delivery" refers to the methods or means of delivering generated music information to users, including, for example, streaming and downloading.

[0067] This invention is a system that automatically selects appropriate music based on visual data captured by the user. The user first selects the captured visual data via their device and uploads it to the system. The device then transmits this visual data to the server using a standard data communication protocol.

[0068] The server temporarily stores the received visual data. This storage is expected to be performed using cloud storage services or temporary storage areas within the server. The stored data is processed by an image analysis module. High-performance data analysis libraries such as TENSORFLOW® and OpenCV are used for this analysis. The purpose of the analysis is to extract the visual features, colors, and atmosphere of the image.

[0069] If the user wishes, they can input their current emotional information using their device and send it to the server. This emotional information plays a significant role in music selection, and the server treats it as important data.

[0070] The server uses this data to select music using a generative AI model. This generative AI model, for example, is built using deep learning technology and is designed to derive the most suitable music under specific conditions. A concrete example of a prompt would be "Bright sunny day." This is expected to select bright and cheerful music.

[0071] Finally, the selected music information is sent to the user's device and played through the device's music player function or an external streaming service. Through this process, users can quickly enjoy a music experience based on visual data and emotions.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user selects visual data captured using their device and uploads it to the system. In this process, the user specifies the image file using a smartphone or computer interface and clicks the upload button. The device transmits the image data, which is then sent to the server. The input is visual data, and the output is the data until it is saved on the server.

[0075] Step 2:

[0076] The server temporarily stores the received visual data. Database systems or cloud storage are commonly used for this purpose. Specifically, data storage is performed using database commands or APIs. Storing the input visual data in storage ensures data persistence. The input is the visual data, and the output is confirmation of successful storage.

[0077] Step 3:

[0078] The server launches an image analysis module to analyze the stored visual data. Here, features from the image are extracted using libraries such as TensorFlow or OpenCV. Color and shape patterns within the image are analyzed and output as data. The input is visual data, and the output is extracted feature data.

[0079] Step 4:

[0080] The user inputs emotional information from their device and sends it to the server. By inputting tags that represent emotions, the user can reflect their emotional state in music selection. The device sends this emotional information data, and the server receives it. The input is emotional information, and the output is confirmation of the successful data transfer to the server.

[0081] Step 5:

[0082] The server uses visual data features and emotional information to generate music information using a generative AI model. Here, specific prompts and data are combined and input into the generative AI model; for example, a prompt like "Bright sunny day" is used. The generative AI analyzes the input data and selects the most suitable music style and song. The output is the selected music information.

[0083] Step 6:

[0084] The server sends selected music information to the user's device, which then receives it. At this stage, the information is sent in a format that allows for music playback, so the device plays the received data using a music player. The input is music information, and the output is music playback. This allows the user to experience music based on visual data and emotions.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] There is a growing need for a system that automatically and personally provides the most suitable music based on the atmosphere of a photograph and the user's mood. Furthermore, there is a growing need for a way to eliminate the need for users to manually select music, providing a faster and more appropriate musical experience. However, current systems have limitations in their ability to accurately provide music based on user emotions and the atmosphere of a photograph.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for receiving image information input by a user, means for analyzing the features of the image based on the image information, means for selecting appropriate music information using a generating AI based on the analyzed features and mood information from the user, and means for providing the selected music information to the user via a music playback system. This enables the automation of music provision according to the atmosphere of the photograph and the user's mood, significantly reducing the effort required for the user to select music and realizing a richer musical experience.

[0090] A "user" is an individual or group that operates this system and receives music information.

[0091] "Image information" refers to photographs or graphical data entered into the system by the user.

[0092] "Analysis" is the process of extracting features based on input image information and analyzing the data.

[0093] "Features" refer to data such as visual elements, composition, color, and atmosphere extracted from image information.

[0094] "Mood information" refers to information that users input into the system to communicate their current emotional state and desired musical style.

[0095] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate music information based on input data.

[0096] "Music information" refers to songs, sound styles, or music distribution media selected based on analysis results and mood information.

[0097] A "music playback system" is a device or application that enables users to instantly listen to selected music information.

[0098] A "prompt message" is a text command used to instruct the AI ​​on criteria and requirements for music selection.

[0099] This application example provides a system that automatically selects and plays the most suitable music based on images taken by the user. When a user uploads an image taken with a smartphone or other device to the application, the image information is sent to a server in the cloud. Upon receiving the image, the server analyzes it and extracts its visual features. Image analysis modules such as TensorFlow are used for this analysis.

[0100] The server also receives mood information entered by users through the application. This mood information is used to further personalize the selection of music for images and becomes an element input into the generative AI. The generative AI utilizes OpenAI's GPT-4® and other technologies to generate optimal music information based on image features and mood information.

[0101] The generated music information is sent to the user's device and played through a music playback application. At this time, it can integrate with music streaming services such as the Spotify API to directly stream selected songs.

[0102] As a concrete example, if a user uploads a photo of a spring landscape and selects the mood "I want to relax," the server analyzes the soft colors of the image and suggests a playlist containing music with a calming tempo. The following prompt is used for the generating AI model: "Based on the above image analysis results and mood information, please suggest the most suitable music."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user selects and uploads captured images via a smartphone application. The input is the user's image data, and the output is the transfer of image data to the server. Specifically, the application selects the image files, compresses them (if necessary), and securely sends them to the server.

[0106] Step 2:

[0107] The server receives uploaded image data and stores it temporarily. The input is image data sent by the user, and the output is storage of the data in the server's internal storage. Specifically, the server stores the images in a database and prepares them for image analysis.

[0108] Step 3:

[0109] The server uses an image analysis module (such as TensorFlow) to extract visual features from received image information. The input is stored image data, and the output is the analysis results, such as color, composition, and atmosphere. Specifically, the server runs an image analysis model and converts its output into a structured data format.

[0110] Step 4:

[0111] The user inputs mood information through the application. The input is the user's mood data, and the output is the transfer of mood data to the server. Specifically, the user selects a mood via the interface and sends the result to the server.

[0112] Step 5:

[0113] The server uses a generative AI (such as OpenAI GPT-4) to select music based on pre-processed image features and mood data. The input is the image analysis results and mood information, and the output is the selected music information. Specifically, the server generates a prompt message saying, "Please suggest the most suitable music based on the above image analysis results and mood information," and queries the generative AI.

[0114] Step 6:

[0115] The server sends the selected music information to the user's device. The input is music information, and the output is preparation for music playback on the user's device. Specifically, the server interacts with a music distribution API (such as the Spotify API) and provides the music streaming URL to the user's device.

[0116] Step 7:

[0117] The user's device plays music based on the received music information. The input is a streaming URL, and the output is music playback. Specifically, the device's music player immediately plays the selected music, providing the user with an experience.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention is a system that recognizes the user's emotional state and automatically selects and provides music based on that state. The following describes embodiments of this system.

[0120] Users first input facial images or voice data into the system using their own devices. It's also possible to acquire information in real time using the device's camera or microphone. The acquired data is sent to a server and analyzed by an emotion engine.

[0121] The emotion engine on the server analyzes received facial images and audio data to recognize the user's emotional state. By using multiple emotion recognition models, such as facial expression recognition and voice tone analysis, more accurate emotional information can be obtained.

[0122] This emotional information is integrated with image information and arbitrary mood information entered by the user. The server uses the integrated data to run a generative AI and select music that is best suited to the user's state. The selected music is in line with the user's mood and emotional state.

[0123] The selection results are sent to the user's device, which then prepares to play music. For example, if the user shows a cheerful expression and uploads a photo of a sunset, the server might provide upbeat pop music.

[0124] This system aims to provide a more personalized and satisfying service by not only suggesting music, but also enabling a music experience that takes into account the user's current emotions. Music selection is seamless, allowing users to enjoy music that fits their emotions and situation without having to go through complicated steps.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user uses their device's camera or microphone to capture facial images or audio data and selects which to input into the system. The selected data is then sent from the device to the server.

[0128] Step 2:

[0129] The server checks the received facial images and audio data and stores them temporarily. Then, it activates the emotion engine and analyzes this data to recognize the user's emotional state.

[0130] Step 3:

[0131] The emotion engine uses facial recognition algorithms and voice analysis technology to extract the user's emotional information. This emotional information is recorded in a database and used in subsequent processes.

[0132] Step 4:

[0133] The server retrieves photo data sent by the user and uses an image analysis module to analyze the characteristics of the photo. This analyzes the color, composition, subject matter, etc., and determines the overall atmosphere of the photo.

[0134] Step 5:

[0135] The server displays a mood input interface on the user's device as needed. The user selects mood information such as "happy" or "relaxed" and sends it from the device to the server.

[0136] Step 6:

[0137] The server integrates image features, sentiment information, and mood information, preparing the data for the generative AI. This process also references relevant historical data to optimize the integration.

[0138] Step 7:

[0139] The server runs a generation AI and selects the most suitable music based on integrated data. The AI ​​processes the dataset and numerous parameters to determine the music best suited to the user.

[0140] Step 8:

[0141] The server sends the selected music information to the user's device. The device then streams or downloads the received music and prepares it for playback.

[0142] Step 9:

[0143] The user's device begins playing music, providing the user with music in real time. Furthermore, it is possible to measure user satisfaction using a feedback function.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] In modern society, providing a music experience that matches users' emotions and moods is crucial. However, existing music streaming systems have struggled to accurately grasp users' instantaneous emotional states and provide music that fits them. In particular, systems that analyze emotional states in real time and seamlessly select and provide personalized music are not yet commonplace. Therefore, there is a need for a system that allows users to easily enjoy music that matches their emotions at any given moment.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for receiving information, means for integrating emotional states and arbitrary mood information, and means for executing a generative model to select suitable music. This enables the automatic provision of a music experience tailored to the user's emotions.

[0149] "Means for inputting information" refers to functions that allow users to acquire facial images and voice data using their own devices.

[0150] "Means for receiving the aforementioned information" refers to the method by which the server receives data for sentiment analysis transmitted from the terminal.

[0151] "Means for analyzing emotional states" refers to a function where an emotion engine residing within the server evaluates the user's emotions using facial recognition and voice tone analysis.

[0152] "Means for integrating arbitrary mood information" refers to the process of integrating additional mood information entered by the user into data by combining it with emotional states.

[0153] "Means for executing a generative model" refers to a function that uses generative AI based on integrated emotional information to operate a model in order to select suitable music.

[0154] "The means of providing the selected music information" refers to the process of sending the selected music data from the server to the user's terminal and preparing it for playback.

[0155] To implement this invention, the user must first acquire facial images and audio data using their own device. The device is equipped with a camera and microphone, and data can be collected using this hardware. The acquired data is transmitted to the server via an encryption protocol.

[0156] The server analyzes the received facial images and audio data using an emotion engine. This analysis utilizes facial recognition and voice tone analysis software tools, enabling highly accurate recognition of emotional states. The analyzed emotional information is integrated with additional mood information entered by the user. For example, if the user enters "I want to relax," this is also taken into consideration.

[0157] The integrated data is input as prompts into the generative AI model. Based on this, the generative AI selects music that is best suited to the user's emotional state. For example, a prompt such as "You have entered an image of the user smiling. Please perform an emotion analysis and select relaxing music." might be used.

[0158] The selected music is sent from the server to the user's terminal, which then prepares to play it. The user can then seamlessly enjoy music that matches their emotions. This invention enables users to obtain a personalized music experience that fits their feelings.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users input facial images and audio data using their own devices. They can take facial images using their device's camera or record audio data using the microphone. This input data serves as foundational information for analyzing the user's emotional state.

[0162] Step 2:

[0163] The device transmits facial images and voice data acquired from the user to the server. This transmission is performed using an encrypted communication protocol to maintain data confidentiality. Upon receiving this data, the server begins preparing for sentiment analysis.

[0164] Step 3:

[0165] The server analyzes received facial images and audio data using an emotion engine. Facial recognition algorithms are applied to facial images, and voice tone analysis is applied to audio data. This allows the server to identify the user's emotional state, such as happiness or sadness. The output is the user's estimated emotional state.

[0166] Step 4:

[0167] The server integrates the results of the emotion analysis with mood information optionally entered by the user. For example, if mood information such as "I want to relax" is entered, that will also be taken into consideration. This integration is done to make the emotional information more comprehensive.

[0168] Step 5:

[0169] The server runs a generative AI model based on integrated emotional information. The generative AI model considers the input emotional state and mood information to generate prompts for selecting the most suitable music. This selects music that matches the user's preferences. The output is the information of the selected music.

[0170] Step 6:

[0171] The server sends the selected music information to the user's device. The music information is often sent in a streaming or downloadable format. Upon receiving this information, the device prepares to play the music.

[0172] Step 7:

[0173] The user's device buffers the music data received from the server and then begins playback. This allows the user to seamlessly enjoy music that matches their mood.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0176] Traditional shopping experiences have had the problem that the in-store audio environment is not adjusted to take into account the user's emotional state, and therefore does not always provide a comfortable shopping experience. Furthermore, there is a need for a system that automatically provides an audio environment tailored to each user's emotional state.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes means for receiving visual information input by the user, means for analyzing visual features based on the visual information, and means for selecting appropriate audio information based on the analyzed features. This makes it possible to automatically control the audio environment based on the user's emotional state and provide a comfortable shopping experience.

[0179] "Visual information" refers to video data of the user's face and surrounding scenery acquired by cameras and devices.

[0180] "Visual characteristics" refer to data about facial expressions and environmental attributes, analyzed based on visual information.

[0181] "Audio information" refers to music and audio data selected based on the user's emotional state.

[0182] An "emotion analysis engine" is a program or system that analyzes a user's visual information and recognizes their emotional state.

[0183] "Means for controlling the audio environment" refers to methods or devices for playing audio information suitable for the user based on the analyzed emotional state.

[0184] To implement this invention, the system utilizes hardware such as a server, a smart device (e.g., smart glasses), and an audio output device. The server receives visual information input by the user through the camera of the smart device, and this information is used by an emotion analysis engine to analyze the user's emotional state. The analysis uses computer vision technology to analyze facial expressions and identify emotions.

[0185] After analysis, the server uses a generative AI model to select appropriate audio information based on the identified emotions. This audio information consists of music and sounds that enhance the user's experience, and the selection criteria are determined by the emotion analysis engine. The selected audio information is sent from the server to the user's device and played back through the audio output device.

[0186] For example, if a user is wearing smart glasses while shopping, the glasses' camera analyzes the user's face and reads their emotions from their facial expressions. If a cheerful emotion is detected, the system can select upbeat pop music and play it through the store's sound system, making the user's shopping experience even more enjoyable.

[0187] An example of a prompt for a generative AI model is: "Design an algorithm that detects emotions from a user's facial image and selects audio information that matches the emotional state. If the user is happy, select cheerful audio information." This prompt is used to enhance the generative AI model's process for selecting appropriate audio.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The user wears smart glasses and walks around the store. During this time, the glasses' camera acquires real-time visual information of the user's face. The input is video data from the camera, which is then sent to the server.

[0191] Step 2:

[0192] The server inputs the received visual information into the emotion analysis engine. The emotion analysis engine analyzes the user's facial expressions based on the visual information and identifies the user's emotional state. In this process, data processing is performed to extract facial feature points and match them with facial expression patterns. The output is data related to the user's emotional state.

[0193] Step 3:

[0194] The server uses a generative AI model with the identified emotional state as input. The generative AI model uses prompts to select audio information that matches the user's emotion. As a data calculation, it matches the emotional state with musical characteristics to make the optimal selection. The output is the selected audio information.

[0195] Step 4:

[0196] The server transmits the selected audio information to the audio output device. This audio information is then played back through the store's sound system. Specifically, the process involves converting the audio file to an appropriate format and transferring the data to the output device via the network.

[0197] Step 5:

[0198] The device provides the user with an optimal sound environment based on the played audio information. This allows users to enjoy a music experience that matches their emotions and continue shopping comfortably. As an output, an improvement in the overall sound environment of the store is achieved.

[0199] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0215] This invention is a system that automatically selects and provides music based on photographs taken by the user. The following is a detailed description of an embodiment of this system.

[0216] First, the user selects the photos they have taken using their device and uploads them to the system. These uploaded photos are then sent to the server and temporarily stored.

[0217] Subsequently, the server uses an image analysis module to extract features from the received image information. These features include visual elements and colors within the photograph, as well as the overall atmosphere of the photograph. The results of this analysis are used as basic data for the music selection process.

[0218] Furthermore, the server receives mood information from the user upon input. This information specifically reflects the user's current mood and desired musical style. This allows the music selection to reflect not only images but also the user's emotional state.

[0219] Based on this information, the server uses a generative AI to select music. The generative AI combines image features and mood information to output optimal music information. Here, music information refers to specific songs and sound styles.

[0220] The selected music information is sent to the user's device. The user's device then plays music based on the received information and provides it to the user. For example, if a user uploads a landscape photo and enters the feeling of "wanting to relax," the server may suggest music with a calm tempo.

[0221] This system eliminates the need for manual music selection and allows for an experience tailored to the individual user's emotions. The music selection process is completed quickly and appropriately in the background, unseen by the user, thus enhancing customer satisfaction.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user operates the device, selects the photos they have taken, and presses the upload button to send them to the system. The user's device then transfers these photos to the server.

[0225] Step 2:

[0226] The server verifies the received photo data and saves it to temporary storage. This saving process includes a data integrity check.

[0227] Step 3:

[0228] The server activates an image analysis module and analyzes the stored photographic data. Specifically, it recognizes the colors, composition, and main objects in the images, and quantifies or categorizes the atmosphere of the photographs.

[0229] Step 4:

[0230] The server temporarily stores the analysis results and displays a mood input interface to the user. Here, the user selects and inputs a mood such as "cheerful" or "calm" from their terminal.

[0231] Step 5:

[0232] The user's device sends the entered mood information to the server. After this transmission, the server integrates the image analysis results with the mood information and prepares to pass the data to the generating AI.

[0233] Step 6:

[0234] The server runs a generative AI that selects the most suitable music for an image and mood based on integrated data. This process references a historical database associated with similar images and moods.

[0235] Step 7:

[0236] The server sends the selected music information to the user's device. The device receives this music information and begins streaming or downloading the music.

[0237] Step 8:

[0238] The user's device plays the received music, providing the user with an integrated music experience. After playback, the server displays an interface to request feedback from the user, if possible.

[0239] (Example 1)

[0240] Next, we will describe Example 1. 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."

[0241] Manually selecting music that matches the user's emotions and situation based on visual data captured by the user is difficult and burdensome. Therefore, there is a need to provide a system that allows users to easily and appropriately acquire music.

[0242] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0243] In this invention, the server includes means for receiving visual data selected by the user, means for temporarily storing the visual data, means for analyzing the features of the visual data, means for receiving emotional information provided by the user, means for using a generative model that generates music information based on the features and emotional information, and means for providing the generated music information to the user. This enables the user to automatically and quickly obtain music that matches their emotions and situation.

[0244] "Visual data" refers to visual information, such as images and photographs, that users acquire and submit through their devices.

[0245] "Means of temporary storage" refers to a function in a server or storage device for temporarily storing received data.

[0246] "Means of analyzing features" refers to the process or function of extracting visual elements from received visual data and identifying their structure and content.

[0247] "Emotional information" refers to information that users provide to express their emotional state or desired musical style.

[0248] A "generative model" refers to an artificial intelligence or machine learning program that takes visual data features and emotional information as input and outputs appropriate musical information.

[0249] "Music information" refers to information necessary for selecting or playing music, such as the song title, artist, song characteristics, and playback links.

[0250] "Means of delivery" refers to the methods or means of delivering generated music information to users, including, for example, streaming and downloading.

[0251] This invention is a system that automatically selects appropriate music based on visual data captured by the user. The user first selects the captured visual data via their device and uploads it to the system. The device then transmits this visual data to the server using a standard data communication protocol.

[0252] The server temporarily stores the received visual data. This storage is expected to be done using cloud storage services or temporary storage areas within the server. The stored data is processed by an image analysis module. High-performance data analysis libraries such as TensorFlow and OpenCV are used for this analysis. The purpose of the analysis is to extract the visual features, colors, and atmosphere of the image.

[0253] If the user wishes, they can input their current emotional information using their device and send it to the server. This emotional information plays a significant role in music selection, and the server treats it as important data.

[0254] The server uses this data to select music using a generative AI model. This generative AI model, for example, is built using deep learning technology and is designed to derive the most suitable music under specific conditions. A concrete example of a prompt would be "Bright sunny day." This is expected to select bright and cheerful music.

[0255] Finally, the selected music information is sent to the user's device and played through the device's music player function or an external streaming service. Through this process, users can quickly enjoy a music experience based on visual data and emotions.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The user selects visual data captured using their device and uploads it to the system. In this process, the user specifies the image file using a smartphone or computer interface and clicks the upload button. The device transmits the image data, which is then sent to the server. The input is visual data, and the output is the data until it is saved on the server.

[0259] Step 2:

[0260] The server temporarily stores the received visual data. Database systems or cloud storage are commonly used for this purpose. Specifically, data storage is performed using database commands or APIs. Storing the input visual data in storage ensures data persistence. The input is the visual data, and the output is confirmation of successful storage.

[0261] Step 3:

[0262] The server launches an image analysis module to analyze the stored visual data. Here, features from the image are extracted using libraries such as TensorFlow or OpenCV. Color and shape patterns within the image are analyzed and output as data. The input is visual data, and the output is extracted feature data.

[0263] Step 4:

[0264] The user inputs emotional information from their device and sends it to the server. By inputting tags that represent emotions, the user can reflect their emotional state in music selection. The device sends this emotional information data, and the server receives it. The input is emotional information, and the output is confirmation of the successful data transfer to the server.

[0265] Step 5:

[0266] The server uses visual data features and emotional information to generate music information using a generative AI model. Here, specific prompts and data are combined and input into the generative AI model; for example, a prompt like "Bright sunny day" is used. The generative AI analyzes the input data and selects the most suitable music style and song. The output is the selected music information.

[0267] Step 6:

[0268] The server sends selected music information to the user's device, which then receives it. At this stage, the information is sent in a format that allows for music playback, so the device plays the received data using a music player. The input is music information, and the output is music playback. This allows the user to experience music based on visual data and emotions.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] There is a growing need for a system that automatically and personally provides the most suitable music based on the atmosphere of a photograph and the user's mood. Furthermore, there is a growing need for a way to eliminate the need for users to manually select music, providing a faster and more appropriate musical experience. However, current systems have limitations in their ability to accurately provide music based on user emotions and the atmosphere of a photograph.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes means for receiving image information input by a user, means for analyzing the features of the image based on the image information, means for selecting appropriate music information using a generating AI based on the analyzed features and mood information from the user, and means for providing the selected music information to the user via a music playback system. This enables the automation of music provision according to the atmosphere of the photograph and the user's mood, significantly reducing the effort required for the user to select music and realizing a richer musical experience.

[0274] A "user" is an individual or group that operates this system and receives music information.

[0275] "Image information" refers to photographs or graphical data entered into the system by the user.

[0276] "Analysis" is the process of extracting features based on input image information and analyzing the data.

[0277] "Features" refer to data such as visual elements, composition, color, and atmosphere extracted from image information.

[0278] "Mood information" refers to information that users input into the system to communicate their current emotional state and desired musical style.

[0279] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate music information based on input data.

[0280] "Music information" refers to songs, sound styles, or music distribution media selected based on analysis results and mood information.

[0281] A "music playback system" is a device or application that enables users to instantly listen to selected music information.

[0282] A "prompt message" is a text command used to instruct the AI ​​on criteria and requirements for music selection.

[0283] This application example provides a system that automatically selects and plays the most suitable music based on images taken by the user. When a user uploads an image taken with a smartphone or other device to the application, the image information is sent to a server in the cloud. Upon receiving the image, the server analyzes it and extracts its visual features. Image analysis modules such as TensorFlow are used for this analysis.

[0284] The server also receives the mood information input by the user through the application. The mood information is used to further personalize the selection of music for the image and serves as an element input to the generative AI. For the generative AI, such as OpenAI's GPT-4, is utilized to generate optimal music information based on the image features and mood information.

[0285] The generated music information is sent to the user's terminal and played through the music playback application. At this time, it can be linked with a music distribution service such as the Spotify API to directly stream and play the selected music. [[ID=XXX]]

[0286] As a specific example, when the user uploads a spring landscape photo and selects the mood of "wanting to relax", the server analyzes the soft colors of the image and proposes a playlist containing music with a calm tempo. The following prompt sentence is used for the generative AI model: "Please propose the optimal music based on the above image analysis results and mood information." [[ID=XX]]

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The user selects and uploads the captured image via the smartphone application. The input is the user's image data, and the output is the transfer of the image data to the server. As a specific operation, the application selects the image file, compresses it (if necessary), and securely sends it to the server.

[0290] Step 2:

[0291] The server receives the uploaded image data and temporarily stores it. The input is the image data sent from the user, and the output is the storage in the data storage within the server. As a specific operation, the server saves the image in the database and prepares for image analysis.

[0292] Step 3:

[0293] The server uses an image analysis module (such as TensorFlow) to extract visual features from received image information. The input is stored image data, and the output is the analysis results, such as color, composition, and atmosphere. Specifically, the server runs an image analysis model and converts its output into a structured data format.

[0294] Step 4:

[0295] The user inputs mood information through the application. The input is the user's mood data, and the output is the transfer of mood data to the server. Specifically, the user selects a mood via the interface and sends the result to the server.

[0296] Step 5:

[0297] The server uses a generative AI (such as OpenAI GPT-4) to select music based on pre-processed image features and mood data. The input is the image analysis results and mood information, and the output is the selected music information. Specifically, the server generates a prompt message saying, "Please suggest the most suitable music based on the above image analysis results and mood information," and queries the generative AI.

[0298] Step 6:

[0299] The server sends the selected music information to the user's device. The input is music information, and the output is preparation for music playback on the user's device. Specifically, the server interacts with a music distribution API (such as the Spotify API) and provides the music streaming URL to the user's device.

[0300] Step 7:

[0301] The user's terminal plays music based on the received music information. The input is a streaming URL, and the output is the playback of music. As a specific operation, the music selected by the music player on the terminal is immediately played to provide an experience for the user.

[0302] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0303] This invention is a system that recognizes the emotional state of a user, automatically selects music based on it, and provides it. Hereinafter, embodiments of this system will be described.

[0304] The user uses their own terminal to first input face images or voice data into the system. It is also possible to obtain information in real time using the camera or microphone of the terminal. The acquired data is sent to the server and analyzed by the emotion engine.

[0305] The emotion engine in the server analyzes the received face images and voice data to recognize the emotional state of the user. For the analysis, multiple emotion recognition models such as expression recognition and voice tone analysis are used in combination to obtain more accurate emotion information.

[0306] This emotion information is integrated with the image information and any mood information input by the user. The server uses the integrated data to execute a generation AI to select the music optimal for the user's state. The selected music is in line with the user's mood and emotional state.

[0307] The selection result is sent to the user's terminal, and the terminal prepares to play the music. For example, when the user shows a happy expression and uploads a photo of the sunset, the server may provide bright pop music.

[0308] This system aims to provide a more personalized and satisfying service by not only suggesting music, but also enabling a music experience that takes into account the user's current emotions. Music selection is seamless, allowing users to enjoy music that fits their emotions and situation without having to go through complicated steps.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The user uses their device's camera or microphone to capture facial images or audio data and selects which to input into the system. The selected data is then sent from the device to the server.

[0312] Step 2:

[0313] The server checks the received facial images and audio data and stores them temporarily. Then, it activates the emotion engine and analyzes this data to recognize the user's emotional state.

[0314] Step 3:

[0315] The emotion engine uses facial recognition algorithms and voice analysis technology to extract the user's emotional information. This emotional information is recorded in a database and used in subsequent processes.

[0316] Step 4:

[0317] The server retrieves photo data sent by the user and uses an image analysis module to analyze the characteristics of the photo. This analyzes the color, composition, subject matter, etc., and determines the overall atmosphere of the photo.

[0318] Step 5:

[0319] The server displays a mood input interface on the user's device as needed. The user selects mood information such as "happy" or "relaxed" and sends it from the device to the server.

[0320] Step 6:

[0321] The server integrates image features, sentiment information, and mood information, preparing the data for the generative AI. This process also references relevant historical data to optimize the integration.

[0322] Step 7:

[0323] The server runs a generation AI and selects the most suitable music based on integrated data. The AI ​​processes the dataset and numerous parameters to determine the music best suited to the user.

[0324] Step 8:

[0325] The server sends the selected music information to the user's device. The device then streams or downloads the received music and prepares it for playback.

[0326] Step 9:

[0327] The user's device begins playing music, providing the user with music in real time. Furthermore, it is possible to measure user satisfaction using a feedback function.

[0328] (Example 2)

[0329] Next, we will describe Example 2. 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".

[0330] In modern society, providing a music experience that matches users' emotions and moods is crucial. However, existing music streaming systems have struggled to accurately grasp users' instantaneous emotional states and provide music that fits them. In particular, systems that analyze emotional states in real time and seamlessly select and provide personalized music are not yet commonplace. Therefore, there is a need for a system that allows users to easily enjoy music that matches their emotions at any given moment.

[0331] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0332] In this invention, the server includes means for receiving information, means for integrating emotional states and arbitrary mood information, and means for executing a generative model to select suitable music. This enables the automatic provision of a music experience tailored to the user's emotions.

[0333] "Means for inputting information" refers to functions that allow users to acquire facial images and voice data using their own devices.

[0334] "Means for receiving the aforementioned information" refers to the method by which the server receives data for sentiment analysis transmitted from the terminal.

[0335] "Means for analyzing emotional states" refers to a function where an emotion engine residing within the server evaluates the user's emotions using facial recognition and voice tone analysis.

[0336] "Means for integrating arbitrary mood information" refers to the process of integrating additional mood information entered by the user into data by combining it with emotional states.

[0337] "Means for executing a generative model" refers to a function that uses generative AI based on integrated emotional information to operate a model in order to select suitable music.

[0338] "The means of providing the selected music information" refers to the process of sending the selected music data from the server to the user's terminal and preparing it for playback.

[0339] To implement this invention, the user must first acquire facial images and audio data using their own device. The device is equipped with a camera and microphone, and data can be collected using this hardware. The acquired data is transmitted to the server via an encryption protocol.

[0340] The server analyzes the received facial images and audio data using an emotion engine. This analysis utilizes facial recognition and voice tone analysis software tools, enabling highly accurate recognition of emotional states. The analyzed emotional information is integrated with additional mood information entered by the user. For example, if the user enters "I want to relax," this is also taken into consideration.

[0341] The integrated data is input as prompts into the generative AI model. Based on this, the generative AI selects music that is best suited to the user's emotional state. For example, a prompt such as "You have entered an image of the user smiling. Please perform an emotion analysis and select relaxing music." might be used.

[0342] The selected music is sent from the server to the user's terminal, which then prepares to play it. The user can then seamlessly enjoy music that matches their emotions. This invention enables users to obtain a personalized music experience that fits their feelings.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] Users input facial images and audio data using their own devices. They can take facial images using their device's camera or record audio data using the microphone. This input data serves as foundational information for analyzing the user's emotional state.

[0346] Step 2:

[0347] The device transmits facial images and voice data acquired from the user to the server. This transmission is performed using an encrypted communication protocol to maintain data confidentiality. Upon receiving this data, the server begins preparing for sentiment analysis.

[0348] Step 3:

[0349] The server analyzes received facial images and audio data using an emotion engine. Facial recognition algorithms are applied to facial images, and voice tone analysis is applied to audio data. This allows the server to identify the user's emotional state, such as happiness or sadness. The output is the user's estimated emotional state.

[0350] Step 4:

[0351] The server integrates the results of the emotion analysis with mood information optionally entered by the user. For example, if mood information such as "I want to relax" is entered, that will also be taken into consideration. This integration is done to make the emotional information more comprehensive.

[0352] Step 5:

[0353] The server runs a generative AI model based on integrated emotional information. The generative AI model considers the input emotional state and mood information to generate prompts for selecting the most suitable music. This selects music that matches the user's preferences. The output is the information of the selected music.

[0354] Step 6:

[0355] The server sends the selected music information to the user's device. The music information is often sent in a streaming or downloadable format. Upon receiving this information, the device prepares to play the music.

[0356] Step 7:

[0357] The user's device buffers the music data received from the server and then begins playback. This allows the user to seamlessly enjoy music that matches their mood.

[0358] (Application Example 2)

[0359] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0360] Traditional shopping experiences have had the problem that the in-store audio environment is not adjusted to take into account the user's emotional state, and therefore does not always provide a comfortable shopping experience. Furthermore, there is a need for a system that automatically provides an audio environment tailored to each user's emotional state.

[0361] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0362] In this invention, the server includes means for receiving visual information input by the user, means for analyzing visual features based on the visual information, and means for selecting appropriate audio information based on the analyzed features. This makes it possible to automatically control the audio environment based on the user's emotional state and provide a comfortable shopping experience.

[0363] "Visual information" refers to video data of the user's face and surrounding scenery acquired by cameras and devices.

[0364] "Visual characteristics" refer to data about facial expressions and environmental attributes, analyzed based on visual information.

[0365] "Audio information" refers to music and audio data selected based on the user's emotional state.

[0366] An "emotion analysis engine" is a program or system that analyzes a user's visual information and recognizes their emotional state.

[0367] "Means for controlling the audio environment" refers to methods or devices for playing audio information suitable for the user based on the analyzed emotional state.

[0368] To implement this invention, the system utilizes hardware such as a server, a smart device (e.g., smart glasses), and an audio output device. The server receives visual information input by the user through the camera of the smart device, and this information is used by an emotion analysis engine to analyze the user's emotional state. The analysis uses computer vision technology to analyze facial expressions and identify emotions.

[0369] After analysis, the server uses a generative AI model to select appropriate audio information based on the identified emotions. This audio information consists of music and sounds that enhance the user's experience, and the selection criteria are determined by the emotion analysis engine. The selected audio information is sent from the server to the user's device and played back through the audio output device.

[0370] For example, if a user is wearing smart glasses while shopping, the glasses' camera analyzes the user's face and reads their emotions from their facial expressions. If a cheerful emotion is detected, the system can select upbeat pop music and play it through the store's sound system, making the user's shopping experience even more enjoyable.

[0371] An example of a prompt for a generative AI model is: "Design an algorithm that detects emotions from a user's facial image and selects audio information that matches the emotional state. If the user is happy, select cheerful audio information." This prompt is used to enhance the generative AI model's process for selecting appropriate audio.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The user wears smart glasses and walks around the store. During this time, the glasses' camera acquires real-time visual information of the user's face. The input is video data from the camera, which is then sent to the server.

[0375] Step 2:

[0376] The server inputs the received visual information into the emotion analysis engine. The emotion analysis engine analyzes the user's facial expressions based on the visual information and identifies the user's emotional state. In this process, data processing is performed to extract facial feature points and match them with facial expression patterns. The output is data related to the user's emotional state.

[0377] Step 3:

[0378] The server uses a generative AI model with the identified emotional state as input. The generative AI model uses prompts to select audio information that matches the user's emotion. As a data calculation, it matches the emotional state with musical characteristics to make the optimal selection. The output is the selected audio information.

[0379] Step 4:

[0380] The server transmits the selected audio information to the audio output device. This audio information is then played back through the store's sound system. Specifically, the process involves converting the audio file to an appropriate format and transferring the data to the output device via the network.

[0381] Step 5:

[0382] The device provides the user with an optimal sound environment based on the played audio information. This allows users to enjoy a music experience that matches their emotions and continue shopping comfortably. As an output, an improvement in the overall sound environment of the store is achieved.

[0383] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0384] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0389] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0391] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0393] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0394] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0395] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0396] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0398] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0399] This invention is a system that automatically selects and provides music based on photographs taken by the user. The following is a detailed description of an embodiment of this system.

[0400] First, the user selects the photos they have taken using their device and uploads them to the system. These uploaded photos are then sent to the server and temporarily stored.

[0401] Subsequently, the server uses an image analysis module to extract features from the received image information. These features include visual elements and colors within the photograph, as well as the overall atmosphere of the photograph. The results of this analysis are used as basic data for the music selection process.

[0402] Furthermore, the server receives mood information from the user upon input. This information specifically reflects the user's current mood and desired musical style. This allows the music selection to reflect not only images but also the user's emotional state.

[0403] Based on this information, the server uses a generative AI to select music. The generative AI combines image features and mood information to output optimal music information. Here, music information refers to specific songs and sound styles.

[0404] The selected music information is sent to the user's device. The user's device then plays music based on the received information and provides it to the user. For example, if a user uploads a landscape photo and enters the feeling of "wanting to relax," the server may suggest music with a calm tempo.

[0405] This system eliminates the need for manual music selection and allows for an experience tailored to the individual user's emotions. The music selection process is completed quickly and appropriately in the background, unseen by the user, thus enhancing customer satisfaction.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The user operates the device, selects the photos they have taken, and presses the upload button to send them to the system. The user's device then transfers these photos to the server.

[0409] Step 2:

[0410] The server verifies the received photo data and saves it to temporary storage. This saving process includes a data integrity check.

[0411] Step 3:

[0412] The server activates an image analysis module and analyzes the stored photographic data. Specifically, it recognizes the colors, composition, and main objects in the images, and quantifies or categorizes the atmosphere of the photographs.

[0413] Step 4:

[0414] The server temporarily stores the analysis results and displays a mood input interface to the user. Here, the user selects and inputs a mood such as "cheerful" or "calm" from their terminal.

[0415] Step 5:

[0416] The user's device sends the entered mood information to the server. After this transmission, the server integrates the image analysis results with the mood information and prepares to pass the data to the generating AI.

[0417] Step 6:

[0418] The server runs a generative AI that selects the most suitable music for an image and mood based on integrated data. This process references a historical database associated with similar images and moods.

[0419] Step 7:

[0420] The server sends the selected music information to the user's device. The device receives this music information and begins streaming or downloading the music.

[0421] Step 8:

[0422] The user's device plays the received music, providing the user with an integrated music experience. After playback, the server displays an interface to request feedback from the user, if possible.

[0423] (Example 1)

[0424] Next, we will describe Example 1. 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."

[0425] Manually selecting music that matches the user's emotions and situation based on visual data captured by the user is difficult and burdensome. Therefore, there is a need to provide a system that allows users to easily and appropriately acquire music.

[0426] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0427] In this invention, the server includes means for receiving visual data selected by the user, means for temporarily storing the visual data, means for analyzing the features of the visual data, means for receiving emotional information provided by the user, means for using a generative model that generates music information based on the features and emotional information, and means for providing the generated music information to the user. This enables the user to automatically and quickly obtain music that matches their emotions and situation.

[0428] "Visual data" refers to visual information, such as images and photographs, that users acquire and submit through their devices.

[0429] "Means of temporary storage" refers to a function in a server or storage device for temporarily storing received data.

[0430] "Means of analyzing features" refers to the process or function of extracting visual elements from received visual data and identifying their structure and content.

[0431] "Emotional information" refers to information that users provide to express their emotional state or desired musical style.

[0432] A "generative model" refers to an artificial intelligence or machine learning program that takes visual data features and emotional information as input and outputs appropriate musical information.

[0433] "Music information" refers to information necessary for selecting or playing music, such as the song title, artist, song characteristics, and playback links.

[0434] "Means of delivery" refers to the methods or means of delivering generated music information to users, including, for example, streaming and downloading.

[0435] This invention is a system that automatically selects appropriate music based on visual data captured by the user. The user first selects the captured visual data via their device and uploads it to the system. The device then transmits this visual data to the server using a standard data communication protocol.

[0436] The server temporarily stores the received visual data. This storage is expected to be done using cloud storage services or temporary storage areas within the server. The stored data is processed by an image analysis module. High-performance data analysis libraries such as TensorFlow and OpenCV are used for this analysis. The purpose of the analysis is to extract the visual features, colors, and atmosphere of the image.

[0437] If the user wishes, they can input their current emotional information using their device and send it to the server. This emotional information plays a significant role in music selection, and the server treats it as important data.

[0438] The server uses this data to select music using a generative AI model. This generative AI model, for example, is built using deep learning technology and is designed to derive the most suitable music under specific conditions. A concrete example of a prompt would be "Bright sunny day." This is expected to select bright and cheerful music.

[0439] Finally, the selected music information is sent to the user's device and played through the device's music player function or an external streaming service. Through this process, users can quickly enjoy a music experience based on visual data and emotions.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The user selects visual data captured using their device and uploads it to the system. In this process, the user specifies the image file using a smartphone or computer interface and clicks the upload button. The device transmits the image data, which is then sent to the server. The input is visual data, and the output is the data until it is saved on the server.

[0443] Step 2:

[0444] The server temporarily stores the received visual data. Database systems or cloud storage are commonly used for this purpose. Specifically, data storage is performed using database commands or APIs. Storing the input visual data in storage ensures data persistence. The input is the visual data, and the output is confirmation of successful storage.

[0445] Step 3:

[0446] The server launches an image analysis module to analyze the stored visual data. Here, features from the image are extracted using libraries such as TensorFlow or OpenCV. Color and shape patterns within the image are analyzed and output as data. The input is visual data, and the output is extracted feature data.

[0447] Step 4:

[0448] The user inputs emotional information from their device and sends it to the server. By inputting tags that represent emotions, the user can reflect their emotional state in music selection. The device sends this emotional information data, and the server receives it. The input is emotional information, and the output is confirmation of the successful data transfer to the server.

[0449] Step 5:

[0450] The server uses visual data features and emotional information to generate music information using a generative AI model. Here, specific prompts and data are combined and input into the generative AI model; for example, a prompt like "Bright sunny day" is used. The generative AI analyzes the input data and selects the most suitable music style and song. The output is the selected music information.

[0451] Step 6:

[0452] The server sends selected music information to the user's device, which then receives it. At this stage, the information is sent in a format that allows for music playback, so the device plays the received data using a music player. The input is music information, and the output is music playback. This allows the user to experience music based on visual data and emotions.

[0453] (Application Example 1)

[0454] Next, we will explain Application Example 1. In the following explanation, 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."

[0455] There is a growing need for a system that automatically and personally provides the most suitable music based on the atmosphere of a photograph and the user's mood. Furthermore, there is a growing need for a way to eliminate the need for users to manually select music, providing a faster and more appropriate musical experience. However, current systems have limitations in their ability to accurately provide music based on user emotions and the atmosphere of a photograph.

[0456] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0457] In this invention, the server includes means for receiving image information input by a user, means for analyzing the features of the image based on the image information, means for selecting appropriate music information using a generating AI based on the analyzed features and mood information from the user, and means for providing the selected music information to the user via a music playback system. This enables the automation of music provision according to the atmosphere of the photograph and the user's mood, significantly reducing the effort required for the user to select music and realizing a richer musical experience.

[0458] A "user" is an individual or group that operates this system and receives music information.

[0459] "Image information" refers to photographs or graphical data entered into the system by the user.

[0460] "Analysis" is the process of extracting features based on input image information and analyzing the data.

[0461] "Features" refer to data such as visual elements, composition, color, and atmosphere extracted from image information.

[0462] "Mood information" refers to information that users input into the system to communicate their current emotional state and desired musical style.

[0463] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate music information based on input data.

[0464] "Music information" refers to songs, sound styles, or music distribution media selected based on analysis results and mood information.

[0465] A "music playback system" is a device or application that enables users to instantly listen to selected music information.

[0466] A "prompt message" is a text command used to instruct the AI ​​on criteria and requirements for music selection.

[0467] This application example provides a system that automatically selects and plays the most suitable music based on images taken by the user. When a user uploads an image taken with a smartphone or other device to the application, the image information is sent to a server in the cloud. Upon receiving the image, the server analyzes it and extracts its visual features. Image analysis modules such as TensorFlow are used for this analysis.

[0468] The server also receives mood information entered by users through the application. This mood information is used to further personalize the selection of music for images and becomes an element input to the generative AI. The generative AI utilizes OpenAI's GPT-4 and other technologies to generate optimal music information based on image features and mood information.

[0469] The generated music information is sent to the user's device and played through a music playback application. At this time, it can integrate with music streaming services such as the Spotify API to directly stream selected songs.

[0470] As a concrete example, if a user uploads a photo of a spring landscape and selects the mood "I want to relax," the server analyzes the soft colors of the image and suggests a playlist containing music with a calming tempo. The following prompt is used for the generating AI model: "Based on the above image analysis results and mood information, please suggest the most suitable music."

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The user selects and uploads captured images via a smartphone application. The input is the user's image data, and the output is the transfer of image data to the server. Specifically, the application selects the image files, compresses them (if necessary), and securely sends them to the server.

[0474] Step 2:

[0475] The server receives uploaded image data and stores it temporarily. The input is image data sent by the user, and the output is storage of the data in the server's internal storage. Specifically, the server stores the images in a database and prepares them for image analysis.

[0476] Step 3:

[0477] The server uses an image analysis module (such as TensorFlow) to extract visual features from received image information. The input is stored image data, and the output is the analysis results, such as color, composition, and atmosphere. Specifically, the server runs an image analysis model and converts its output into a structured data format.

[0478] Step 4:

[0479] The user inputs mood information through the application. The input is the user's mood data, and the output is the transfer of mood data to the server. Specifically, the user selects a mood via the interface and sends the result to the server.

[0480] Step 5:

[0481] The server uses a generative AI (such as OpenAI GPT-4) to select music based on pre-processed image features and mood data. The input is the image analysis results and mood information, and the output is the selected music information. Specifically, the server generates a prompt message saying, "Please suggest the most suitable music based on the above image analysis results and mood information," and queries the generative AI.

[0482] Step 6:

[0483] The server sends the selected music information to the user's device. The input is music information, and the output is preparation for music playback on the user's device. Specifically, the server interacts with a music distribution API (such as the Spotify API) and provides the music streaming URL to the user's device.

[0484] Step 7:

[0485] The user's device plays music based on the received music information. The input is a streaming URL, and the output is music playback. Specifically, the device's music player immediately plays the selected music, providing the user with an experience.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] This invention is a system that recognizes the user's emotional state and automatically selects and provides music based on that state. The following describes embodiments of this system.

[0488] Users first input facial images or voice data into the system using their own devices. It's also possible to acquire information in real time using the device's camera or microphone. The acquired data is sent to a server and analyzed by an emotion engine.

[0489] The emotion engine on the server analyzes received facial images and audio data to recognize the user's emotional state. By using multiple emotion recognition models, such as facial expression recognition and voice tone analysis, more accurate emotional information can be obtained.

[0490] This emotional information is integrated with image information and arbitrary mood information entered by the user. The server uses the integrated data to run a generative AI and select music that is best suited to the user's state. The selected music is in line with the user's mood and emotional state.

[0491] The selection results are sent to the user's device, which then prepares to play music. For example, if the user shows a cheerful expression and uploads a photo of a sunset, the server might provide upbeat pop music.

[0492] This system aims to provide a more personalized and satisfying service by not only suggesting music, but also enabling a music experience that takes into account the user's current emotions. Music selection is seamless, allowing users to enjoy music that fits their emotions and situation without having to go through complicated steps.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The user uses their device's camera or microphone to capture facial images or audio data and selects which to input into the system. The selected data is then sent from the device to the server.

[0496] Step 2:

[0497] The server checks the received facial images and audio data and stores them temporarily. Then, it activates the emotion engine and analyzes this data to recognize the user's emotional state.

[0498] Step 3:

[0499] The emotion engine uses facial recognition algorithms and voice analysis technology to extract the user's emotional information. This emotional information is recorded in a database and used in subsequent processes.

[0500] Step 4:

[0501] The server retrieves photo data sent by the user and uses an image analysis module to analyze the characteristics of the photo. This analyzes the color, composition, subject matter, etc., and determines the overall atmosphere of the photo.

[0502] Step 5:

[0503] The server displays a mood input interface on the user's device as needed. The user selects mood information such as "happy" or "relaxed" and sends it from the device to the server.

[0504] Step 6:

[0505] The server integrates image features, sentiment information, and mood information, preparing the data for the generative AI. This process also references relevant historical data to optimize the integration.

[0506] Step 7:

[0507] The server runs a generation AI and selects the most suitable music based on integrated data. The AI ​​processes the dataset and numerous parameters to determine the music best suited to the user.

[0508] Step 8:

[0509] The server sends the selected music information to the user's device. The device then streams or downloads the received music and prepares it for playback.

[0510] Step 9:

[0511] The user's device begins playing music, providing the user with music in real time. Furthermore, it is possible to measure user satisfaction using a feedback function.

[0512] (Example 2)

[0513] Next, we will describe Example 2. 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."

[0514] In modern society, providing a music experience that matches users' emotions and moods is crucial. However, existing music streaming systems have struggled to accurately grasp users' instantaneous emotional states and provide music that fits them. In particular, systems that analyze emotional states in real time and seamlessly select and provide personalized music are not yet commonplace. Therefore, there is a need for a system that allows users to easily enjoy music that matches their emotions at any given moment.

[0515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0516] In this invention, the server includes means for receiving information, means for integrating emotional states and arbitrary mood information, and means for executing a generative model to select suitable music. This enables the automatic provision of a music experience tailored to the user's emotions.

[0517] "Means for inputting information" refers to functions that allow users to acquire facial images and voice data using their own devices.

[0518] "Means for receiving the aforementioned information" refers to the method by which the server receives data for sentiment analysis transmitted from the terminal.

[0519] "Means for analyzing emotional states" refers to a function where an emotion engine residing within the server evaluates the user's emotions using facial recognition and voice tone analysis.

[0520] "Means for integrating arbitrary mood information" refers to the process of integrating additional mood information entered by the user into data by combining it with emotional states.

[0521] "Means for executing a generative model" refers to a function that uses generative AI based on integrated emotional information to operate a model in order to select suitable music.

[0522] "The means of providing the selected music information" refers to the process of sending the selected music data from the server to the user's terminal and preparing it for playback.

[0523] To implement this invention, the user must first acquire facial images and audio data using their own device. The device is equipped with a camera and microphone, and data can be collected using this hardware. The acquired data is transmitted to the server via an encryption protocol.

[0524] The server analyzes the received facial images and audio data using an emotion engine. This analysis utilizes facial recognition and voice tone analysis software tools, enabling highly accurate recognition of emotional states. The analyzed emotional information is integrated with additional mood information entered by the user. For example, if the user enters "I want to relax," this is also taken into consideration.

[0525] The integrated data is input as prompts into the generative AI model. Based on this, the generative AI selects music that is best suited to the user's emotional state. For example, a prompt such as "You have entered an image of the user smiling. Please perform an emotion analysis and select relaxing music." might be used.

[0526] The selected music is sent from the server to the user's terminal, which then prepares to play it. The user can then seamlessly enjoy music that matches their emotions. This invention enables users to obtain a personalized music experience that fits their feelings.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] Users input facial images and audio data using their own devices. They can take facial images using their device's camera or record audio data using the microphone. This input data serves as foundational information for analyzing the user's emotional state.

[0530] Step 2:

[0531] The device transmits facial images and voice data acquired from the user to the server. This transmission is performed using an encrypted communication protocol to maintain data confidentiality. Upon receiving this data, the server begins preparing for sentiment analysis.

[0532] Step 3:

[0533] The server analyzes received facial images and audio data using an emotion engine. Facial recognition algorithms are applied to facial images, and voice tone analysis is applied to audio data. This allows the server to identify the user's emotional state, such as happiness or sadness. The output is the user's estimated emotional state.

[0534] Step 4:

[0535] The server integrates the results of the emotion analysis with mood information optionally entered by the user. For example, if mood information such as "I want to relax" is entered, that will also be taken into consideration. This integration is done to make the emotional information more comprehensive.

[0536] Step 5:

[0537] The server runs a generative AI model based on integrated emotional information. The generative AI model considers the input emotional state and mood information to generate prompts for selecting the most suitable music. This selects music that matches the user's preferences. The output is the information of the selected music.

[0538] Step 6:

[0539] The server sends the selected music information to the user's device. The music information is often sent in a streaming or downloadable format. Upon receiving this information, the device prepares to play the music.

[0540] Step 7:

[0541] The user's device buffers the music data received from the server and then begins playback. This allows the user to seamlessly enjoy music that matches their mood.

[0542] (Application Example 2)

[0543] Next, we will explain application example 2. In the following explanation, 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."

[0544] Traditional shopping experiences have had the problem that the in-store audio environment is not adjusted to take into account the user's emotional state, and therefore does not always provide a comfortable shopping experience. Furthermore, there is a need for a system that automatically provides an audio environment tailored to each user's emotional state.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0546] In this invention, the server includes means for receiving visual information input by the user, means for analyzing visual features based on the visual information, and means for selecting appropriate audio information based on the analyzed features. This makes it possible to automatically control the audio environment based on the user's emotional state and provide a comfortable shopping experience.

[0547] "Visual information" refers to video data of the user's face and surrounding scenery acquired by cameras and devices.

[0548] "Visual characteristics" refer to data about facial expressions and environmental attributes, analyzed based on visual information.

[0549] "Audio information" refers to music and audio data selected based on the user's emotional state.

[0550] An "emotion analysis engine" is a program or system that analyzes a user's visual information and recognizes their emotional state.

[0551] "Means for controlling the audio environment" refers to methods or devices for playing audio information suitable for the user based on the analyzed emotional state.

[0552] To implement this invention, the system utilizes hardware such as a server, a smart device (e.g., smart glasses), and an audio output device. The server receives visual information input by the user through the camera of the smart device, and this information is used by an emotion analysis engine to analyze the user's emotional state. The analysis uses computer vision technology to analyze facial expressions and identify emotions.

[0553] After analysis, the server uses a generative AI model to select appropriate audio information based on the identified emotions. This audio information consists of music and sounds that enhance the user's experience, and the selection criteria are determined by the emotion analysis engine. The selected audio information is sent from the server to the user's device and played back through the audio output device.

[0554] For example, if a user is wearing smart glasses while shopping, the glasses' camera analyzes the user's face and reads their emotions from their facial expressions. If a cheerful emotion is detected, the system can select upbeat pop music and play it through the store's sound system, making the user's shopping experience even more enjoyable.

[0555] An example of a prompt for a generative AI model is: "Design an algorithm that detects emotions from a user's facial image and selects audio information that matches the emotional state. If the user is happy, select cheerful audio information." This prompt is used to enhance the generative AI model's process for selecting appropriate audio.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The user wears smart glasses and walks around the store. During this time, the glasses' camera acquires real-time visual information of the user's face. The input is video data from the camera, which is then sent to the server.

[0559] Step 2:

[0560] The server inputs the received visual information into the emotion analysis engine. The emotion analysis engine analyzes the user's facial expressions based on the visual information and identifies the user's emotional state. In this process, data processing is performed to extract facial feature points and match them with facial expression patterns. The output is data related to the user's emotional state.

[0561] Step 3:

[0562] The server uses a generative AI model with the identified emotional state as input. The generative AI model uses prompts to select audio information that matches the user's emotion. As a data calculation, it matches the emotional state with musical characteristics to make the optimal selection. The output is the selected audio information.

[0563] Step 4:

[0564] The server transmits the selected audio information to the audio output device. This audio information is then played back through the store's sound system. Specifically, the process involves converting the audio file to an appropriate format and transferring the data to the output device via the network.

[0565] Step 5:

[0566] The device provides the user with an optimal sound environment based on the played audio information. This allows users to enjoy a music experience that matches their emotions and continue shopping comfortably. As an output, an improvement in the overall sound environment of the store is achieved.

[0567] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0569] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] As shown in Figure 7, the 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.

[0573] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0574] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0575] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0576] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0577] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0578] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0579] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0580] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0581] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0583] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] This invention is a system that automatically selects and provides music based on photographs taken by the user. The following is a detailed description of an embodiment of this system.

[0585] First, the user selects the photos they have taken using their device and uploads them to the system. These uploaded photos are then sent to the server and temporarily stored.

[0586] Subsequently, the server uses an image analysis module to extract features from the received image information. These features include visual elements and colors within the photograph, as well as the overall atmosphere of the photograph. The results of this analysis are used as basic data for the music selection process.

[0587] Furthermore, the server receives mood information from the user upon input. This information specifically reflects the user's current mood and desired musical style. This allows the music selection to reflect not only images but also the user's emotional state.

[0588] Based on this information, the server uses a generative AI to select music. The generative AI combines image features and mood information to output optimal music information. Here, music information refers to specific songs and sound styles.

[0589] The selected music information is sent to the user's device. The user's device then plays music based on the received information and provides it to the user. For example, if a user uploads a landscape photo and enters the feeling of "wanting to relax," the server may suggest music with a calm tempo.

[0590] This system eliminates the need for manual music selection and allows for an experience tailored to the individual user's emotions. The music selection process is completed quickly and appropriately in the background, unseen by the user, thus enhancing customer satisfaction.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The user operates the device, selects the photos they have taken, and presses the upload button to send them to the system. The user's device then transfers these photos to the server.

[0594] Step 2:

[0595] The server verifies the received photo data and saves it to temporary storage. This saving process includes a data integrity check.

[0596] Step 3:

[0597] The server activates an image analysis module and analyzes the stored photographic data. Specifically, it recognizes the colors, composition, and main objects in the images, and quantifies or categorizes the atmosphere of the photographs.

[0598] Step 4:

[0599] The server temporarily stores the analysis results and displays a mood input interface to the user. Here, the user selects and inputs a mood such as "cheerful" or "calm" from their terminal.

[0600] Step 5:

[0601] The user's device sends the entered mood information to the server. After this transmission, the server integrates the image analysis results with the mood information and prepares to pass the data to the generating AI.

[0602] Step 6:

[0603] The server runs a generative AI that selects the most suitable music for an image and mood based on integrated data. This process references a historical database associated with similar images and moods.

[0604] Step 7:

[0605] The server sends the selected music information to the user's device. The device receives this music information and begins streaming or downloading the music.

[0606] Step 8:

[0607] The user's device plays the received music, providing the user with an integrated music experience. After playback, the server displays an interface to request feedback from the user, if possible.

[0608] (Example 1)

[0609] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0610] Manually selecting music that matches the user's emotions and situation based on visual data captured by the user is difficult and burdensome. Therefore, there is a need to provide a system that allows users to easily and appropriately acquire music.

[0611] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0612] In this invention, the server includes means for receiving visual data selected by the user, means for temporarily storing the visual data, means for analyzing the features of the visual data, means for receiving emotional information provided by the user, means for using a generative model that generates music information based on the features and emotional information, and means for providing the generated music information to the user. This enables the user to automatically and quickly obtain music that matches their emotions and situation.

[0613] "Visual data" refers to visual information, such as images and photographs, that users acquire and submit through their devices.

[0614] "Means of temporary storage" refers to a function in a server or storage device for temporarily storing received data.

[0615] "Means of analyzing features" refers to the process or function of extracting visual elements from received visual data and identifying their structure and content.

[0616] "Emotional information" refers to information that users provide to express their emotional state or desired musical style.

[0617] A "generative model" refers to an artificial intelligence or machine learning program that takes visual data features and emotional information as input and outputs appropriate musical information.

[0618] "Music information" refers to information necessary for selecting or playing music, such as the song title, artist, song characteristics, and playback links.

[0619] "Means of delivery" refers to the methods or means of delivering generated music information to users, including, for example, streaming and downloading.

[0620] This invention is a system that automatically selects appropriate music based on visual data captured by the user. The user first selects the captured visual data via their device and uploads it to the system. The device then transmits this visual data to the server using a standard data communication protocol.

[0621] The server temporarily stores the received visual data. This storage is expected to be done using cloud storage services or temporary storage areas within the server. The stored data is processed by an image analysis module. High-performance data analysis libraries such as TensorFlow and OpenCV are used for this analysis. The purpose of the analysis is to extract the visual features, colors, and atmosphere of the image.

[0622] If the user wishes, they can input their current emotional information using their device and send it to the server. This emotional information plays a significant role in music selection, and the server treats it as important data.

[0623] The server uses this data to select music using a generative AI model. This generative AI model, for example, is built using deep learning technology and is designed to derive the most suitable music under specific conditions. A concrete example of a prompt would be "Bright sunny day." This is expected to select bright and cheerful music.

[0624] Finally, the selected music information is sent to the user's device and played through the device's music player function or an external streaming service. Through this process, users can quickly enjoy a music experience based on visual data and emotions.

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The user selects visual data captured using their device and uploads it to the system. In this process, the user specifies the image file using a smartphone or computer interface and clicks the upload button. The device transmits the image data, which is then sent to the server. The input is visual data, and the output is the data until it is saved on the server.

[0628] Step 2:

[0629] The server temporarily stores the received visual data. Database systems or cloud storage are commonly used for this purpose. Specifically, data storage is performed using database commands or APIs. Storing the input visual data in storage ensures data persistence. The input is the visual data, and the output is confirmation of successful storage.

[0630] Step 3:

[0631] The server launches an image analysis module to analyze the stored visual data. Here, features from the image are extracted using libraries such as TensorFlow or OpenCV. Color and shape patterns within the image are analyzed and output as data. The input is visual data, and the output is extracted feature data.

[0632] Step 4:

[0633] The user inputs emotional information from their device and sends it to the server. By inputting tags that represent emotions, the user can reflect their emotional state in music selection. The device sends this emotional information data, and the server receives it. The input is emotional information, and the output is confirmation of the successful data transfer to the server.

[0634] Step 5:

[0635] The server uses visual data features and emotional information to generate music information using a generative AI model. Here, specific prompts and data are combined and input into the generative AI model; for example, a prompt like "Bright sunny day" is used. The generative AI analyzes the input data and selects the most suitable music style and song. The output is the selected music information.

[0636] Step 6:

[0637] The server sends selected music information to the user's device, which then receives it. At this stage, the information is sent in a format that allows for music playback, so the device plays the received data using a music player. The input is music information, and the output is music playback. This allows the user to experience music based on visual data and emotions.

[0638] (Application Example 1)

[0639] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0640] There is a growing need for a system that automatically and personally provides the most suitable music based on the atmosphere of a photograph and the user's mood. Furthermore, there is a growing need for a way to eliminate the need for users to manually select music, providing a faster and more appropriate musical experience. However, current systems have limitations in their ability to accurately provide music based on user emotions and the atmosphere of a photograph.

[0641] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0642] In this invention, the server includes means for receiving image information input by a user, means for analyzing the features of the image based on the image information, means for selecting appropriate music information using a generating AI based on the analyzed features and mood information from the user, and means for providing the selected music information to the user via a music playback system. This enables the automation of music provision according to the atmosphere of the photograph and the user's mood, significantly reducing the effort required for the user to select music and realizing a richer musical experience.

[0643] A "user" is an individual or group that operates this system and receives music information.

[0644] "Image information" refers to photographs or graphical data entered into the system by the user.

[0645] "Analysis" is the process of extracting features based on input image information and analyzing the data.

[0646] "Features" refer to data such as visual elements, composition, color, and atmosphere extracted from image information.

[0647] "Mood information" refers to information that users input into the system to communicate their current emotional state and desired musical style.

[0648] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate music information based on input data.

[0649] "Music information" refers to songs, sound styles, or music distribution media selected based on analysis results and mood information.

[0650] A "music playback system" is a device or application that enables users to instantly listen to selected music information.

[0651] A "prompt message" is a text command used to instruct the AI ​​on criteria and requirements for music selection.

[0652] This application example provides a system that automatically selects and plays the most suitable music based on images taken by the user. When a user uploads an image taken with a smartphone or other device to the application, the image information is sent to a server in the cloud. Upon receiving the image, the server analyzes it and extracts its visual features. Image analysis modules such as TensorFlow are used for this analysis.

[0653] The server also receives mood information entered by users through the application. This mood information is used to further personalize the selection of music for images and becomes an element input to the generative AI. The generative AI utilizes OpenAI's GPT-4 and other technologies to generate optimal music information based on image features and mood information.

[0654] The generated music information is sent to the user's device and played through a music playback application. At this time, it can integrate with music streaming services such as the Spotify API to directly stream selected songs.

[0655] As a concrete example, if a user uploads a photo of a spring landscape and selects the mood "I want to relax," the server analyzes the soft colors of the image and suggests a playlist containing music with a calming tempo. The following prompt is used for the generating AI model: "Based on the above image analysis results and mood information, please suggest the most suitable music."

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The user selects and uploads captured images via a smartphone application. The input is the user's image data, and the output is the transfer of image data to the server. Specifically, the application selects the image files, compresses them (if necessary), and securely sends them to the server.

[0659] Step 2:

[0660] The server receives uploaded image data and stores it temporarily. The input is image data sent by the user, and the output is storage of the data in the server's internal storage. Specifically, the server stores the images in a database and prepares them for image analysis.

[0661] Step 3:

[0662] The server uses an image analysis module (such as TensorFlow) to extract visual features from received image information. The input is stored image data, and the output is the analysis results, such as color, composition, and atmosphere. Specifically, the server runs an image analysis model and converts its output into a structured data format.

[0663] Step 4:

[0664] The user inputs mood information through the application. The input is the user's mood data, and the output is the transfer of mood data to the server. Specifically, the user selects a mood via the interface and sends the result to the server.

[0665] Step 5:

[0666] The server uses a generative AI (such as OpenAI GPT-4) to select music based on pre-processed image features and mood data. The input is the image analysis results and mood information, and the output is the selected music information. Specifically, the server generates a prompt message saying, "Please suggest the most suitable music based on the above image analysis results and mood information," and queries the generative AI.

[0667] Step 6:

[0668] The server sends the selected music information to the user's device. The input is music information, and the output is preparation for music playback on the user's device. Specifically, the server interacts with a music distribution API (such as the Spotify API) and provides the music streaming URL to the user's device.

[0669] Step 7:

[0670] The user's device plays music based on the received music information. The input is a streaming URL, and the output is music playback. Specifically, the device's music player immediately plays the selected music, providing the user with an experience.

[0671] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0672] This invention is a system that recognizes the user's emotional state and automatically selects and provides music based on that state. The following describes embodiments of this system.

[0673] Users first input facial images or voice data into the system using their own devices. It's also possible to acquire information in real time using the device's camera or microphone. The acquired data is sent to a server and analyzed by an emotion engine.

[0674] The emotion engine on the server analyzes received facial images and audio data to recognize the user's emotional state. By using multiple emotion recognition models, such as facial expression recognition and voice tone analysis, more accurate emotional information can be obtained.

[0675] This emotional information is integrated with image information and arbitrary mood information entered by the user. The server uses the integrated data to run a generative AI and select music that is best suited to the user's state. The selected music is in line with the user's mood and emotional state.

[0676] The selection results are sent to the user's device, which then prepares to play music. For example, if the user shows a cheerful expression and uploads a photo of a sunset, the server might provide upbeat pop music.

[0677] This system aims to provide a more personalized and satisfying service by not only suggesting music, but also enabling a music experience that takes into account the user's current emotions. Music selection is seamless, allowing users to enjoy music that fits their emotions and situation without having to go through complicated steps.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The user uses their device's camera or microphone to capture facial images or audio data and selects which to input into the system. The selected data is then sent from the device to the server.

[0681] Step 2:

[0682] The server checks the received facial images and audio data and stores them temporarily. Then, it activates the emotion engine and analyzes this data to recognize the user's emotional state.

[0683] Step 3:

[0684] The emotion engine uses facial recognition algorithms and voice analysis technology to extract the user's emotional information. This emotional information is recorded in a database and used in subsequent processes.

[0685] Step 4:

[0686] The server retrieves photo data sent by the user and uses an image analysis module to analyze the characteristics of the photo. This analyzes the color, composition, subject matter, etc., and determines the overall atmosphere of the photo.

[0687] Step 5:

[0688] The server displays a mood input interface on the user's device as needed. The user selects mood information such as "happy" or "relaxed" and sends it from the device to the server.

[0689] Step 6:

[0690] The server integrates image features, sentiment information, and mood information, preparing the data for the generative AI. This process also references relevant historical data to optimize the integration.

[0691] Step 7:

[0692] The server runs a generation AI and selects the most suitable music based on integrated data. The AI ​​processes the dataset and numerous parameters to determine the music best suited to the user.

[0693] Step 8:

[0694] The server sends the selected music information to the user's device. The device then streams or downloads the received music and prepares it for playback.

[0695] Step 9:

[0696] The user's device begins playing music, providing the user with music in real time. Furthermore, it is possible to measure user satisfaction using a feedback function.

[0697] (Example 2)

[0698] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] In modern society, providing a music experience that matches users' emotions and moods is crucial. However, existing music streaming systems have struggled to accurately grasp users' instantaneous emotional states and provide music that fits them. In particular, systems that analyze emotional states in real time and seamlessly select and provide personalized music are not yet commonplace. Therefore, there is a need for a system that allows users to easily enjoy music that matches their emotions at any given moment.

[0700] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0701] In this invention, the server includes means for receiving information, means for integrating emotional states and arbitrary mood information, and means for executing a generative model to select suitable music. This enables the automatic provision of a music experience tailored to the user's emotions.

[0702] "Means for inputting information" refers to functions that allow users to acquire facial images and voice data using their own devices.

[0703] "Means for receiving the aforementioned information" refers to the method by which the server receives data for sentiment analysis transmitted from the terminal.

[0704] "Means for analyzing emotional states" refers to a function where an emotion engine residing within the server evaluates the user's emotions using facial recognition and voice tone analysis.

[0705] "Means for integrating arbitrary mood information" refers to the process of integrating additional mood information entered by the user into data by combining it with emotional states.

[0706] "Means for executing a generative model" refers to a function that uses generative AI based on integrated emotional information to operate a model in order to select suitable music.

[0707] "The means of providing the selected music information" refers to the process of sending the selected music data from the server to the user's terminal and preparing it for playback.

[0708] To implement this invention, the user must first acquire facial images and audio data using their own device. The device is equipped with a camera and microphone, and data can be collected using this hardware. The acquired data is transmitted to the server via an encryption protocol.

[0709] The server analyzes the received facial images and audio data using an emotion engine. This analysis utilizes facial recognition and voice tone analysis software tools, enabling highly accurate recognition of emotional states. The analyzed emotional information is integrated with additional mood information entered by the user. For example, if the user enters "I want to relax," this is also taken into consideration.

[0710] The integrated data is input as prompts into the generative AI model. Based on this, the generative AI selects music that is best suited to the user's emotional state. For example, a prompt such as "You have entered an image of the user smiling. Please perform an emotion analysis and select relaxing music." might be used.

[0711] The selected music is sent from the server to the user's terminal, which then prepares to play it. The user can then seamlessly enjoy music that matches their emotions. This invention enables users to obtain a personalized music experience that fits their feelings.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] Users input facial images and audio data using their own devices. They can take facial images using their device's camera or record audio data using the microphone. This input data serves as foundational information for analyzing the user's emotional state.

[0715] Step 2:

[0716] The device transmits facial images and voice data acquired from the user to the server. This transmission is performed using an encrypted communication protocol to maintain data confidentiality. Upon receiving this data, the server begins preparing for sentiment analysis.

[0717] Step 3:

[0718] The server analyzes received facial images and audio data using an emotion engine. Facial recognition algorithms are applied to facial images, and voice tone analysis is applied to audio data. This allows the server to identify the user's emotional state, such as happiness or sadness. The output is the user's estimated emotional state.

[0719] Step 4:

[0720] The server integrates the results of the emotion analysis with mood information optionally entered by the user. For example, if mood information such as "I want to relax" is entered, that will also be taken into consideration. This integration is done to make the emotional information more comprehensive.

[0721] Step 5:

[0722] The server runs a generative AI model based on integrated emotional information. The generative AI model considers the input emotional state and mood information to generate prompts for selecting the most suitable music. This selects music that matches the user's preferences. The output is the information of the selected music.

[0723] Step 6:

[0724] The server sends the selected music information to the user's device. The music information is often sent in a streaming or downloadable format. Upon receiving this information, the device prepares to play the music.

[0725] Step 7:

[0726] The user's device buffers the music data received from the server and then begins playback. This allows the user to seamlessly enjoy music that matches their mood.

[0727] (Application Example 2)

[0728] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] Traditional shopping experiences have had the problem that the in-store audio environment is not adjusted to take into account the user's emotional state, and therefore does not always provide a comfortable shopping experience. Furthermore, there is a need for a system that automatically provides an audio environment tailored to each user's emotional state.

[0730] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0731] In this invention, the server includes means for receiving visual information input by the user, means for analyzing visual features based on the visual information, and means for selecting appropriate audio information based on the analyzed features. This makes it possible to automatically control the audio environment based on the user's emotional state and provide a comfortable shopping experience.

[0732] "Visual information" refers to video data of the user's face and surrounding scenery acquired by cameras and devices.

[0733] "Visual characteristics" refer to data about facial expressions and environmental attributes, analyzed based on visual information.

[0734] "Audio information" refers to music and audio data selected based on the user's emotional state.

[0735] An "emotion analysis engine" is a program or system that analyzes a user's visual information and recognizes their emotional state.

[0736] "Means for controlling the audio environment" refers to methods or devices for playing audio information suitable for the user based on the analyzed emotional state.

[0737] To implement this invention, the system utilizes hardware such as a server, a smart device (e.g., smart glasses), and an audio output device. The server receives visual information input by the user through the camera of the smart device, and this information is used by an emotion analysis engine to analyze the user's emotional state. The analysis uses computer vision technology to analyze facial expressions and identify emotions.

[0738] After analysis, the server uses a generative AI model to select appropriate audio information based on the identified emotions. This audio information consists of music and sounds that enhance the user's experience, and the selection criteria are determined by the emotion analysis engine. The selected audio information is sent from the server to the user's device and played back through the audio output device.

[0739] For example, if a user is wearing smart glasses while shopping, the glasses' camera analyzes the user's face and reads their emotions from their facial expressions. If a cheerful emotion is detected, the system can select upbeat pop music and play it through the store's sound system, making the user's shopping experience even more enjoyable.

[0740] An example of a prompt for a generative AI model is: "Design an algorithm that detects emotions from a user's facial image and selects audio information that matches the emotional state. If the user is happy, select cheerful audio information." This prompt is used to enhance the generative AI model's process for selecting appropriate audio.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The user wears smart glasses and walks around the store. During this time, the glasses' camera acquires real-time visual information of the user's face. The input is video data from the camera, which is then sent to the server.

[0744] Step 2:

[0745] The server inputs the received visual information into the emotion analysis engine. The emotion analysis engine analyzes the user's facial expressions based on the visual information and identifies the user's emotional state. In this process, data processing is performed to extract facial feature points and match them with facial expression patterns. The output is data related to the user's emotional state.

[0746] Step 3:

[0747] The server uses a generative AI model with the identified emotional state as input. The generative AI model uses prompts to select audio information that matches the user's emotion. As a data calculation, it matches the emotional state with musical characteristics to make the optimal selection. The output is the selected audio information.

[0748] Step 4:

[0749] The server transmits the selected audio information to the audio output device. This audio information is then played back through the store's sound system. Specifically, the process involves converting the audio file to an appropriate format and transferring the data to the output device via the network.

[0750] Step 5:

[0751] The device provides the user with an optimal sound environment based on the played audio information. This allows users to enjoy a music experience that matches their emotions and continue shopping comfortably. As an output, an improvement in the overall sound environment of the store is achieved.

[0752] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0753] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0754] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0755] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0756] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0757] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0758] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0759] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0760] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0761] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0762] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0763] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0764] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0765] 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.

[0766] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0767] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0768] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0769] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0770] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0771] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0772] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0773] The following is further disclosed regarding the embodiments described above.

[0774] (Claim 1)

[0775] A means for receiving image information entered by the user,

[0776] A means for analyzing the features of an image based on the aforementioned image information,

[0777] A means for selecting suitable music information based on the analyzed characteristics,

[0778] A means for providing the selected music information to the user,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising means for receiving mood information entered by a user and selecting music information taking the mood information into consideration.

[0782] (Claim 3)

[0783] The system according to claim 1, characterized in that it uses a generation AI to select the aforementioned music information.

[0784] "Example 1"

[0785] (Claim 1)

[0786] A means of receiving the visual data selected by the user,

[0787] Means for temporarily storing the aforementioned visual data,

[0788] A means for analyzing the characteristics of the aforementioned visual data,

[0789] A means of receiving emotional information provided by the user,

[0790] A means of using a generative model that generates musical information based on the aforementioned features and emotional information,

[0791] The means for providing the generated music information to the user,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising means for selecting music information while taking into account the user's emotional information.

[0795] (Claim 3)

[0796] The system according to claim 1, characterized in that a generative model is used to generate the aforementioned music information.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] A means for receiving image information entered by the user,

[0800] A means for analyzing the features of an image based on the aforementioned image information,

[0801] A means for selecting suitable music information using a generating AI based on the analyzed features and mood information from the user,

[0802] A means for providing the selected music information to the user via a music playback system,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for providing music information by linking a music distribution service based on user input.

[0806] (Claim 3)

[0807] The system according to claim 1, characterized in that it generates prompt sentences for suggesting music using an AI model based on image analysis results and mood information.

[0808] "Example 2 of combining an emotion engine"

[0809] (Claim 1)

[0810] Means for inputting information,

[0811] means for receiving the aforementioned information,

[0812] A means for analyzing emotional states based on the aforementioned information,

[0813] Means for integrating the aforementioned emotional state and arbitrary mood information,

[0814] A means for executing a generative model to select suitable music based on the aforementioned integrated information,

[0815] The means for providing the selected music information,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for acquiring data in real time from information entered by a user.

[0819] (Claim 3)

[0820] The system according to claim 1, characterized in that it uses emotional states as prompts for selecting musical information.

[0821] "Application example 2 of combining emotional engines"

[0822] (Claim 1)

[0823] A means of receiving visual information entered by the user,

[0824] A means for analyzing visual characteristics based on the aforementioned visual information,

[0825] A means for selecting suitable audio information based on the analyzed features,

[0826] A means for supplying the selected audio information to the user,

[0827] A means including an emotion analysis engine for recognizing the emotional state of the user,

[0828] Means for controlling the audio environment based on the aforementioned emotional state,

[0829] ...

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising means for the user to control the provided audio environment and improve the experience.

[0833] (Claim 3)

[0834] The system according to claim 1, characterized in that it uses a generative AI in its emotion analysis engine. [Explanation of Symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving image information entered by the user, A means for analyzing the features of an image based on the aforementioned image information, A means for selecting suitable music information based on the analyzed characteristics, A means for providing the selected music information to the user, A system that includes this.

2. The system according to claim 1, further comprising means for receiving mood information entered by a user and selecting music information taking the mood information into consideration.

3. The system according to claim 1, characterized in that it uses a generating AI for selecting the aforementioned music information.

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