system

Abstract

We provide the system. [Solution] A means of entering and saving the user's basic information, A means of inputting and analyzing user status and needs information, A means of providing multiple basic scents and collecting evaluation data for them, A method for generating the optimal scent using an AI algorithm based on collected evaluation data, A means of providing the generated fragrance data 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, it is difficult to provide an optimal scent according to the situation in which individual users are placed and the needs to be improved. In particular, proposals for scents according to individual needs such as stress relief and improved concentration need to finely reflect user preferences and feedback. Since such a system does not exist, users have difficulty finding an effective scent, and as a result, there is a problem that the expected effect cannot be sufficiently obtained.

Means for Solving the Problems

[0005] This invention solves the above problem with a system comprising means for inputting and saving basic user information, means for inputting and analyzing user situation and needs information, means for providing multiple basic scents and collecting evaluation data thereof, means for generating an optimal scent using an AI algorithm based on the collected evaluation data, and means for providing the generated scent data to the user. Specifically, it analyzes the user's scent evaluation data, uses AI to generate the optimal scent combination for that user, and provides it appropriately, thereby realizing an effective scent tailored to individual needs.

[0006] "User basic information" refers to information used to identify an individual user, including name, age, gender, and preferences.

[0007] "Situation and needs information" refers to information that includes the user's current situation, the problems they are facing, and their desire for solutions.

[0008] "Multiple basic scents" refers to several representative scents that the system offers to the user, such as lavender, mint, and citrus.

[0009] "Evaluation data" refers to feedback information provided by users when they try several basic fragrances, including, for example, their likes and dislikes of each fragrance and their evaluation scores.

[0010] An "AI algorithm" is an artificial intelligence computation method that processes user evaluation data as input information to generate the optimal fragrance combination.

[0011] The "optimal scent" is the combination of fragrances that best suits that user, generated by an AI algorithm based on user evaluation data and needs information.

[0012] "Fragrance data" refers to information such as the specific components of the optimal fragrance generated and their respective proportions.

[0013] "Means of providing" include interfaces and devices for communicating generated fragrance data to users and suggesting ways to use it. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, and means for providing the generated fragrance data to the user.

[0036] Program Processing Description

[0037] 1. User registration and initial setup:

[0038] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[0039] The terminal sends the entered user information to the server.

[0040] The server stores the received user information in a database.

[0041] 2. Inputting the situation and understanding the needs:

[0042] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[0043] The terminal sends the entered status and needs data to the server.

[0044] The server stores and analyzes the data for analysis.

[0045] 3. Scent selection test:

[0046] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0047] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[0048] The device collects evaluation data and converts it into a standardized format.

[0049] 4. Data collection and transmission:

[0050] The device sends all evaluation data collected from the user to the server.

[0051] 5. Fragrance optimization:

[0052] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[0053] The server sends the generated fragrance data (such as ingredients and proportions) to the terminal.

[0054] 6. Providing fragrance:

[0055] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[0056] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[0057] Specific examples

[0058] 1. User registration and initial setup:

[0059] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[0060] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[0061] The server saves this information to the database.

[0062] 2. Inputting the situation and understanding the needs:

[0063] The user types, "I'm nervous because of the upcoming test. I want to relax."

[0064] The terminal sends this input data to the server.

[0065] The server analyzes the data and recognizes that "tension relief" is the need.

[0066] 3. Scent selection test:

[0067] The device displays the following message: "Please try the following scents: lavender, mint, citrus. Please rate them."

[0068] Users try out the scents and enter their ratings, such as "Lavender 8, Mint 9, Citrus 5".

[0069] The device sends evaluation data to the server.

[0070] 4. Data collection and transmission:

[0071] The device converts the evaluation data "Lavender 8, Mint 9, Citrus 5" into a unified format and sends it to the server.

[0072] 5. Fragrance optimization:

[0073] The server uses an AI algorithm to generate a new scent: "70% mint, 30% lavender."

[0074] The server sends that scent data to the terminal.

[0075] 6. Providing fragrance:

[0076] The device displays the message, "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[0077] The user sets the fragrance in the diffuser according to the suggestion and enjoys relaxation.

[0078] Through the above process, users can easily access the most effective fragrance that best suits their needs.

[0079] The following describes the processing flow.

[0080] Step 1:

[0081] The user launches the application and enters basic information (name, age, gender, preferences) on the user registration screen.

[0082] Step 2:

[0083] The terminal sends the entered user information to the server.

[0084] Step 3:

[0085] The server saves the received user information to the database.

[0086] Step 4:

[0087] The user inputs their current situation (e.g., nervousness before a test) and their needs in that situation (e.g., wanting to relax) through the device's interface.

[0088] Step 5:

[0089] The terminal sends the entered status and needs data to the server.

[0090] Step 6:

[0091] The server saves the status and needs data to the database and prepares it for analysis.

[0092] Step 7:

[0093] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0094] Step 8:

[0095] Users try each scent and enter their rating for each (e.g., on a 10-point scale).

[0096] Step 9:

[0097] The device collects user evaluation data and converts it into a unified format.

[0098] Step 10:

[0099] The device sends the collected evaluation data to the server.

[0100] Step 11:

[0101] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs.

[0102] Step 12:

[0103] The server sends the generated optimal fragrance data (ingredients and their proportions) to the terminal.

[0104] Step 13:

[0105] The device displays a suggested fragrance that it has generated for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[0106] Step 14:

[0107] Users can enjoy the relaxing effects by setting the suggested fragrance in a diffuser or similar device and using it.

[0108] These steps allow users to easily obtain and use fragrances optimized for their own situation and needs.

[0109] (Example 1)

[0110] 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."

[0111] Conventional systems struggled to provide personalized fragrances tailored to each user's situation and needs, and were unable to suggest optimal fragrances based on user data. Furthermore, the inability to efficiently collect and analyze fragrance evaluation data and quickly reflect the results made it difficult to improve user satisfaction. There were also challenges in the efficiency of converting user-entered data into a unified format and transmitting it to the server.

[0112] 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.

[0113] In this invention, the server includes means for inputting and storing user attribute information, means for inputting and analyzing user status information and demand information, means for providing multiple basic scents and collecting evaluation information thereof, means for generating an optimal scent using a machine learning algorithm based on the collected evaluation information, and means for providing the generated scent information to the user. This makes it possible to suggest the optimal scent according to the user's situation and needs, and enables efficient analysis of collected data and rapid reflection of the results. Furthermore, input data can be converted into a standard format and efficiently transmitted to the server, improving overall processing efficiency.

[0114] "User" refers to an individual who uses the system.

[0115] "Attribute information" refers to basic information such as the user's name, age, gender, and preferences.

[0116] "Status information" refers to information about the user's current situation and psychological state.

[0117] "Demand information" refers to what a user needs or requests at that particular moment.

[0118] "Basic scent" refers to a scent that is individually prepared from among the multiple scents that the system provides to the user.

[0119] "Evaluation information" refers to feedback and evaluations that users have given to basic fragrances.

[0120] A "machine learning algorithm" refers to artificial intelligence technology that analyzes collected data and derives the optimal result.

[0121] "Fragrance information" refers to data on the optimal fragrance generated by a machine learning algorithm based on user demand.

[0122] A "standard format" refers to a format used to convert different types of information into a unified format.

[0123] A "server" refers to a computer system that stores data, performs analysis, and controls the entire system.

[0124] This invention relates to a system that provides the optimal fragrance according to the user's situation and needs. This system provides the user with a personalized fragrance through the following process.

[0125] First, the user installs and launches a dedicated application on their device. As part of the initial setup, the user enters their basic information (name, age, gender, preferences, etc.). The device sends this information to the server, which then stores the received information in a database. For example, if the user enters "Taro, 25 years old, male, likes to relax," this information is converted to JSON format and sent to the server.

[0126] Next, the user enters their current situation and the needs they have in that situation. For example, they might enter, "I'm nervous before the test. I want to relax." The device sends this situation and needs data to the server, which uses natural language processing to analyze the data and recognize the need for "stress relief." This data is also sent in JSON format.

[0127] The device then provides the user with an interface to try several basic scents (e.g., lavender, mint, citrus). The user tries each scent and enters their rating. For example, they might rate "Lavender 8, Mint 9, Citrus 5". The device sends this rating data to a server, which stores the received rating data in a database.

[0128] The server uses a machine learning algorithm based on the collected evaluation data to generate the fragrance combination best suited to the user's needs. In this example, the AI ​​model calculates the optimal blend as "70% mint, 30% lavender." The generated fragrance data is sent to the device, which then displays a suggestion for the best fragrance to the user. Specifically, it might display, "A Mint-Lavender Blend fragrance has been generated. Let's use the diffuser."

[0129] The user follows this suggestion and enjoys relaxation by using the generated fragrance. For example, the user might set up a diffuser according to the suggestion and enjoy the new scent.

[0130] Examples of prompt messages include, "Please try the following scents and rate them: lavender, mint, citrus."

[0131] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[0132] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0133] Step 1:

[0134] User registration and basic information entry

[0135] 1.1 The user installs and launches the dedicated application on their device.

[0136] 1.2 The user enters basic information such as name, age, gender, and preferences on the user registration screen. For example, they might enter "Taro, 25 years old, male, likes to relax."

[0137] 1.3 The terminal converts the entered basic information into JSON format and sends it to the server. Input: "Taro, 25 years old, male, likes to relax" Output: {"name":"Taro","age":25,"gender":"male","preference":"relax"}

[0138] 1.4 The server saves the received user information to the database. Specific example: Execute "INSERT INTO users (name, age, gender, preference) VALUES ('Taro', 25, 'Male', 'Relax')".

[0139] Step 2:

[0140] Input of situation and needs

[0141] 2.1 The user inputs their current situation and needs through the terminal interface. For example, they might input, "I'm nervous because of the upcoming test. I want to relax."

[0142] 2.2 The terminal converts this situation and needs data into JSON format and sends it to the server. Input: "I'm nervous before the test. I want to relax." Output: {"situation":"I'm nervous before the test", "need":"I want to relax"}

[0143] 2.3 The server receives and analyzes the data. Using natural language processing, it extracts the keywords "tension" and "relaxation" and internally records "Needs: Tension relief". Input: {"situation": "I'm nervous before the test","need": "I want to relax"} Output: "Needs: Tension relief"

[0144] Step 3:

[0145] Scent selection test

[0146] 3.1 The device provides an interface that allows the user to try several basic scents (lavender, mint, citrus). For example, it displays: "Please try the following scents. Please rate them: lavender, mint, citrus."

[0147] 3.2 The user tries each scent and enters a rating for each. For example, they might enter "Lavender 8, Mint 9, Citrus 5".

[0148] 3.3 The terminal converts this evaluation data into JSON format and sends it to the server. Input: "Lavender 8, Mint 9, Citrus 5" Output: {"lavender":8,"mint":9,"citrus":5}

[0149] 3.4 The server saves the received evaluation data to the database. Specific example: Execute "INSERT INTO feedback (user_id, lavender, mint, citrus) VALUES (1, 8, 9, 5)".

[0150] Step 4:

[0151] Data collection and optimization

[0152] 4.1 The server inputs the collected evaluation data into a machine learning algorithm to generate the most effective scent combination for the user's needs. Input: {"lavender":8,"mint":9,"citrus":5} Output: {"mint":70,"lavender":30}

[0153] 4.2 The server sends the generated scent data to the terminal. Output: {"mint":70,"lavender":30}

[0154] Step 5:

[0155] Providing fragrances on the device

[0156] 5.1 The terminal suggests fragrance information received from the server to the user. Specific example: It displays "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[0157] 5.2 The user enjoys the generated fragrance using a diffuser as suggested. Input: "Mint-Lavender Blend fragrance has been generated." Action: Set the fragrance in the diffuser and relax.

[0158] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[0159] (Application Example 1)

[0160] 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."

[0161] Traditional food delivery services lacked a mechanism to provide optimal aromas tailored to the user's situation and needs, making it difficult to enhance the dining experience. In particular, there is a need for technology that provides aromas that alleviate the anxiety and expectations users feel when ordering food.

[0162] 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.

[0163] In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, and means for providing multiple basic scents and collecting evaluation data thereof. This makes it possible to generate the optimal scent using an AI algorithm based on the collected evaluation data and provide it to the user in a food delivery service.

[0164] "User basic information" refers to basic personal information about individual users, such as name, age, gender, and preferences.

[0165] "Fragrance" refers to volatile substances produced from specific plants or chemicals, which are perceived through the sense of smell.

[0166] "Situation and needs information" refers to information about the environment and psychological state in which the user is placed, as well as the specific desires and requests that they feel are desirable under those circumstances.

[0167] "Evaluation data" refers to data on feedback and impressions that users give to the provided fragrances, specifically including ratings and comments.

[0168] An "AI algorithm" is a series of computational procedures that use artificial intelligence technology to analyze data and generate optimal answers or suggestions.

[0169] A "food delivery service" is a service that allows users to order meals and food items online or by phone, and have them delivered to a specified location.

[0170] The "dining experience" refers to the overall satisfaction and enjoyment that users feel when eating, and is influenced by factors such as the taste, aroma, appearance, and atmosphere of the food.

[0171] A "server" is a computer system that stores and processes data on a network, and is hardware or software that provides services in response to requests from clients.

[0172] This invention is a system that provides the optimal fragrance according to the user's situation and needs. The following hardware and software are used to implement this invention.

[0173] Hardware and software to use

[0174] Server: A computer system used for storing and analyzing data. Includes databases.

[0175] Terminal: A mobile device such as a smartphone or tablet. It collects user input and communicates with the server.

[0176] Generative AI Model: Software for implementing AI algorithms. It analyzes evaluation data and generates optimal fragrances.

[0177] System Processing Description

[0178] 1. User registration and saving of basic information:

[0179] Users launch the application using their mobile device and enter basic information such as their name, age, gender, and preferences on the registration screen. This entered data is then sent from the device to the server.

[0180] The server stores the received user information in a database.

[0181] 2. Inputting the situation and understanding the needs:

[0182] The user inputs their current mood or expectations through the device (e.g., tired, want to relax).

[0183] The terminal sends the input data to the server, which then analyzes the data.

[0184] 3. Collection of fragrance evaluation data:

[0185] The device displays several basic scents (e.g., lavender, mint, citrus) in sequence and asks the user for their evaluation.

[0186] Users try the provided fragrances and provide a rating (e.g., on a 10-point scale) for each fragrance.

[0187] The device collects this evaluation data, converts it to a unified format, and then sends it to the server.

[0188] 4. Scent optimization and generation:

[0189] Based on the received evaluation data, the server uses a generative AI model to generate the most effective fragrance combination to meet the user's needs.

[0190] For example, it's possible to create a new scent like "70% mint, 30% lavender."

[0191] This generated fragrance data is then sent back to the terminal.

[0192] 5. Providing and suggesting fragrances:

[0193] The device suggests the most suitable scent to the user based on the received scent data (e.g., "A relaxing Mint-Lavender Blend scent has been generated. Using a diffuser would be recommended.").

[0194] Users can enjoy the fragrance according to the suggestions.

[0195] Specific examples and prompt statements

[0196] Specific example:

[0197] User registration: The user installs the app and enters basic information. For example, "Taro, 25 years old, male, likes to relax."

[0198] Situation input: The user enters "I'm nervous because of the upcoming test. I want to relax."

[0199] Inputting evaluation data: Users test the scents and input their evaluations, such as "Lavender 8, Mint 9, Citrus 5".

[0200] Results of the generation AI model: The server uses AI to generate and suggest a scent of "70% mint, 30% lavender".

[0201] Prompts for the generative AI model:

[0202] Please generate ideas for the perfect aroma for the delivered food. Consider the following information:

[0203] User name: Taro

[0204] Age: 25

[0205] Gender: Male

[0206] Preferences: Italian

[0207] Current mood: Tired

[0208] Needs: I want to relax

[0209] Suitable scent for ordered meal is:

[0210] This invention makes it possible to provide users with the optimal aroma when ordering food in a food delivery service, thereby improving the dining experience.

[0211] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0212] Step 1:

[0213] User registration and saving of basic information

[0214] Users launch the application using a mobile device such as a smartphone or tablet and enter basic information such as their name, age, gender, and preferences.

[0215] The terminal sends the entered basic information data to the server.

[0216] The server stores the received user information in a database. At this time, a unique identifier such as a user ID is generated.

[0217] Input: User's basic information (name, age, gender, preferences)

[0218] Output: Basic information data stored on the server, user ID

[0219] Step 2:

[0220] Situation input and understanding of needs

[0221] The user inputs information about their current mood and expectations (e.g., tired, want to relax) through their device.

[0222] The terminal sends the entered status and needs data to the server.

[0223] The server analyzes the received data and identifies the corresponding needs. For example, in response to a situation of "being tired," the need for "relaxation" might be identified.

[0224] Input: User status and needs information

[0225] Output: Needs data analyzed by the server

[0226] Step 3:

[0227] Collection of fragrance evaluation data

[0228] The device displays an interface that sequentially presents the user with several basic scents (e.g., lavender, mint, citrus) and asks the user to rate each scent.

[0229] The user tries the provided fragrances and enters a rating (e.g., on a 10-point scale) for each fragrance into the device.

[0230] The terminal collects this evaluation data, converts it to a unified format, and then sends it to the server. At this time, each evaluation data is linked to a user ID.

[0231] Input: User evaluation data for each fragrance

[0232] Output: Evaluation data in a unified format sent to the server

[0233] Step 4:

[0234] Scent optimization and generation

[0235] The server uses a generative AI model based on the received evaluation data to generate the most effective fragrance combination for the user's needs.

[0236] For example, a new fragrance might be created that is "70% mint, 30% lavender."

[0237] The generated fragrance data is sent back to the terminal and is ready to be provided to the user.

[0238] Input: Evaluation data in a unified format, user needs data

[0239] Output: Generated fragrance data

[0240] Step 5:

[0241] Providing and suggesting fragrances

[0242] The device suggests the most suitable scent to the user based on the received scent data. For example, it might display, "A Mint-Lavender Blend scent with high relaxation effects has been generated. Using a diffuser would be beneficial."

[0243] Users can enjoy fragrances according to the suggestions. When using a diffuser, they can set the suggested fragrance to achieve a relaxing effect.

[0244] Input: Generated fragrance data

[0245] Output: Fragrance suggestion information provided to the user

[0246] 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.

[0247] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, means for providing the generated fragrance data to the user, and an emotion engine that recognizes the user's emotions.

[0248] Program Processing Description

[0249] 1. User registration and initial setup:

[0250] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[0251] The terminal sends the entered user information to the server.

[0252] The server stores the received user information in a database.

[0253] 2. Inputting the situation and understanding the needs:

[0254] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[0255] The terminal sends the entered status and needs data to the server.

[0256] The server stores and analyzes the data for analysis.

[0257] 3. Emotion recognition:

[0258] The device uses its built-in emotion engine to collect and analyze biometric information such as the user's facial expressions, voice, and heart rate, and recognizes the user's emotions.

[0259] The device sends the recognized emotion data to the server.

[0260] 4. Scent selection test:

[0261] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0262] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[0263] The device collects evaluation data and converts it into a standardized format.

[0264] 5. Data collection and transmission:

[0265] The device transmits all collected evaluation and sentiment data to the server.

[0266] 6. Fragrance optimization:

[0267] The server inputs the received evaluation and emotional data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[0268] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[0269] 7. Providing fragrance:

[0270] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[0271] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[0272] Specific examples

[0273] 1. User registration and initial setup:

[0274] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[0275] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[0276] The server saves this information to the database.

[0277] 2. Inputting the situation and understanding the needs:

[0278] The user types, "I'm nervous because of the upcoming test. I want to relax."

[0279] The terminal sends this input data to the server.

[0280] The server analyzes the data and recognizes that "stress relief" is a need.

[0281] 3. Emotion Recognition:

[0282] The terminal monitors the user's expression, voice, and heartbeat, and recognizes the emotion of "being tense" by analyzing the data with an emotion engine.

[0283] The terminal sends this emotion data to the server.

[0284] 4. Scent Selection Test:

[0285] The terminal displays "Please try the following scents: lavender, mint, citrus. Please provide an evaluation."

[0286] The user tries the scents and enters an evaluation of "lavender 8, mint 9, citrus 5".

[0287] The terminal sends the evaluation data to the server.

[0288] 5. Data Collection and Transmission:

[0289] The terminal converts the evaluation data "lavender 8, mint 9, citrus 5" and the emotion data into a unified format and sends them to the server.

[0290] 6. Scent Optimization:

[0291] The server uses an AI algorithm to generate a new scent of "70% mint, 30% lavender".

[0292] The server sends the scent data to the terminal.

[0293] 7. Scent Provision:

[0294] The terminal displays "The scent of Mint-Lavender Blend has been generated. Let's use the diffuser."

[0295] The user sets the scent on the diffuser according to the suggestion and enjoys relaxation.

[0296] Through these steps, the user can easily access the scent optimized for their emotions and needs and experience the expected effects.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[0300] Step 2:

[0301] The terminal sends the input user information to the server.

[0302] Step 3:

[0303] The server saves the received user information in the database.

[0304] Step 4:

[0305] The user enters the current situation (e.g., nervousness before a test) and the needs in that situation (e.g., wanting to relax) through the terminal interface.

[0306] Step 5:

[0307] The terminal sends the input situation and needs data to the server.

[0308] Step 6:

[0309] The server saves the received status and needs data to the database and prepares it for analysis.

[0310] Step 7:

[0311] The device activates an emotion engine and collects biometric information such as the user's facial expressions, voice, and heart rate.

[0312] Step 8:

[0313] The device analyzes collected biometric information to recognize the user's emotions. For example, it might analyze the user's emotion as "nervous."

[0314] Step 9:

[0315] The device sends the recognized emotion data to the server.

[0316] Step 10:

[0317] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0318] Step 11:

[0319] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[0320] Step 12:

[0321] The device collects user evaluation data and converts it into a unified format.

[0322] Step 13:

[0323] The device sends the collected evaluation data and sentiment data to the server.

[0324] Step 14:

[0325] The server inputs evaluation and emotional data into an AI algorithm to generate the most effective scent combination for the user's needs. For example, it might generate a blend of 70% mint and 30% lavender.

[0326] Step 15:

[0327] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[0328] Step 16:

[0329] The device displays a suggested optimal fragrance for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[0330] Step 17:

[0331] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[0332] This processing flow allows users to utilize the optimal scent based on emotion recognition and evaluation data.

[0333] (Example 2)

[0334] 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".

[0335] There is a problem with manually adjusting and selecting fragrances to find the one that best suits the user's emotions and needs: users face the challenge of manual adjustments and selections. Furthermore, there is a lack of methods to provide fragrances that respond to users' emotions and situations in real time, making it difficult to accurately suggest fragrances to individual users.

[0336] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting evaluation data thereof, means for generating an optimal scent using an AI algorithm based on the collected evaluation data and emotional data, and means for providing the generated scent data to the user. This makes it possible to propose an optimal scent in real time based on the user's emotional state and needs.

[0337] "User" refers to anyone who uses this system.

[0338] "Basic information" refers to information such as the user's name, age, gender, and preferences.

[0339] "Situation" refers to the specific environment or state in which the user is currently in.

[0340] "Needs" refer to the desires and demands that a user has in a given situation.

[0341] "Fragrance" refers to the multiple basic scent components provided to the user.

[0342] "Evaluation data" refers to the evaluation results that users have given to the provided fragrances.

[0343] An "AI algorithm" refers to a computational method that uses artificial intelligence to analyze data and derive the optimal result.

[0344] "Fragrance data" refers to data generated by an AI algorithm that shows the optimal fragrance combination for the user.

[0345] "Emotions" refer to the psychological state perceived from the user's facial expressions, voice, heart rate, etc.

[0346] "Emotional data" refers to data about a user's emotional state obtained using an emotion engine.

[0347] An "emotion engine" refers to a device or software that analyzes a user's facial expressions, voice, heart rate, and other biometric information to recognize their emotional state.

[0348] A "database" refers to a system on a server used to store and manage basic user information, evaluation data, and sentiment data.

[0349] "Terminal" refers to a device used by a user to input information (e.g., smartphone, tablet).

[0350] A "server" refers to a central computer that stores and analyzes information sent from user terminals and sends the results back to the terminals.

[0351] This invention relates to a system that provides the optimal fragrance according to the user's needs. The system collects the user's basic information, current situation, and emotional data, and uses an AI algorithm to generate and provide the optimal fragrance to the user.

[0352] Hardware and software configuration

[0353] The main components of this system include the following:

[0354] Device: A device used by a user to input information, such as a smartphone or tablet. These devices typically include a camera, microphone, heart rate sensor, and other electronic components.

[0355] Server: A central computer that stores and analyzes data, and includes databases and execution environments for AI algorithms (e.g., TENSORFLOW®).

[0356] Emotion engine: This is software that acquires user facial expressions, voice, and heart rate data and analyzes their emotions. A specific example is the Emotion API of Microsoft Azure®.

[0357] Specific data processing and calculations

[0358] 1. Enter and save user information:

[0359] The user launches the app and enters basic information, such as their name, age, gender, and preferences. This information is sent from the device to the server and stored in a database.

[0360] 2. Input and analysis of the situation and needs:

[0361] The user enters their current situation (e.g., feeling nervous before a test) and needs (e.g., wanting to relax). The device sends this data to the server, which analyzes the situation and needs.

[0362] 3. Emotion recognition:

[0363] The device uses a camera, microphone, and heart rate sensor to collect biometric information such as the user's facial expressions, voice, and heart rate. This data is analyzed by an emotion engine, which recognizes emotions such as "feeling nervous."

[0364] 4. Fragrance selection and evaluation:

[0365] The device presents the user with several basic scents (lavender, mint, citrus, etc.) and asks for their evaluation. The user enters their evaluation for each scent, and the device collects the evaluation data and sends it to the server.

[0366] 5. Data analysis and fragrance generation:

[0367] The server inputs collected evaluation and emotional data into an AI algorithm to generate the optimal scent combination. For example, it might blend mint 70% with lavender 30%.

[0368] 6. Providing fragrance:

[0369] The device displays fragrance suggestions to the user based on fragrance data received from the server. The user then sets the suggested fragrance in the diffuser and enjoys relaxation.

[0370] Examples of specific cases and prompt statements

[0371] 1. Example of user registration:

[0372] User: Enters "Taro, 25 years old, male, likes to relax."

[0373] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[0374] Server: Stored in the database.

[0375] 2. Examples of situation input and needs assessment:

[0376] User: "I'm nervous because of the upcoming test. I want to relax."

[0377] Terminal: Send to server.

[0378] Server: Recognizes that "tension relief" is the need.

[0379] 3. Examples of emotion recognition:

[0380] Device: Monitors facial expressions, voice, and heart rate.

[0381] Terminal: Recognizes the emotion "feeling nervous" and sends it to the server.

[0382] 4. Example of a fragrance selection test:

[0383] Terminal: Enter the rating as "Lavender 8, Mint 9, Citrus 5".

[0384] Terminal: Sends evaluation data to the server.

[0385] Examples of prompts for generative AI models

[0386] "How can we provide users with the perfect scent to help them relax? We need a system that uses an AI algorithm to determine the optimal scent based on the user's basic information, current situation, and emotional state."

[0387] This allows users to easily access fragrances optimized for their emotions and needs, and experience the desired effects.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Program processing steps

[0390] Step 1:

[0391] The user launches the application and enters basic information (name, age, gender, preferences).

[0392] Input: Name, age, gender, preferences

[0393] Specific action: The user enters the information "Taro, 25 years old, male, likes to relax" into the input field.

[0394] Output: Structured user information

[0395] Step 2:

[0396] The terminal converts the entered user information into a structured data format (such as JSON) and sends it to the server.

[0397] Input: Basic information entered by the user

[0398] Specific action: The device generates JSON data with the format "Name: Taro, Age: 25, Gender: Male, Preferences: Relaxation" and sends it to the server.

[0399] Output: JSON data of basic information sent to the server

[0400] Step 3:

[0401] The server saves the received user information to the database.

[0402] Input: JSON data of user information sent from the device.

[0403] Specific operation: The server executes the following query on the database: "INSERT INTO users (name, age, gender, preferences) VALUES ('Taro', 25, 'Male', 'Relax')".

[0404] Output: User information stored in the database

[0405] Step 4:

[0406] The user uses the app's interface to input their current situation (e.g., feeling nervous before a test) and their needs (e.g., wanting to relax).

[0407] Input: Current situation, needs

[0408] Specific action: The user enters "Situation: Before test" and "Needs: Relax" into the interface.

[0409] Output: Structured situation and needs information

[0410] Step 5:

[0411] The terminal sends the entered status and needs data to the server.

[0412] Input: User-entered information about the situation and needs.

[0413] Specific action: The device generates JSON data containing "Status: Before test" and "Needs: Relax" and sends it to the server.

[0414] Output: JSON data containing status and needs information sent to the server.

[0415] Step 6:

[0416] The server stores and analyzes situation and needs data in a database.

[0417] Input: JSON data of status and needs information sent from the device.

[0418] Specific operation: The server executes the query "SELECT FROM relaxation_methods WHERE situations LIKE '%beforetest%'" to identify appropriate relaxation methods.

[0419] Output: Appropriate relaxation methods as a result of the analysis

[0420] Step 7:

[0421] The device uses a camera, microphone, and heart rate sensor to collect data on the user's facial expressions, voice, and heart rate.

[0422] Input: User's facial expressions, voice, and heart rate data

[0423] Specific operation: The device's camera scans the user's face, and facial expressions are analyzed through dedicated software (e.g., OpenCV).

[0424] Output: Collected biometric data

[0425] Step 8:

[0426] The device analyzes the collected biometric information using an emotion engine to recognize the user's emotional state.

[0427] Input: Collected biometric data

[0428] Specific operation: Send image data to the Emotion API and obtain an emotion score for "tension".

[0429] Output: Recognized emotion data

[0430] Step 9:

[0431] The device sends the recognized emotion data to the server.

[0432] Input: Recognized emotion data

[0433] Specific action: The device generates JSON data containing "Emotion: Stress, Score: 0.85" and sends it to the server.

[0434] Output: JSON data of emotion sent to the server

[0435] Step 10:

[0436] The device presents the user with several basic scents to try (e.g., lavender, mint, citrus) and asks for their evaluation.

[0437] Input: User ratings for scents

[0438] Specific actions: The user tries out different scents and rates each scent on a 10-point scale.

[0439] Output: Structured evaluation data

[0440] Step 11:

[0441] The device sends the collected evaluation data to the server.

[0442] Input: Fragrance evaluation data obtained from users

[0443] Specific operation: The device sends evaluation data of "Lavender 8, Mint 9, Citrus 5" to the server in JSON format.

[0444] Output: JSON data of the evaluation data sent to the server

[0445] Step 12:

[0446] The server uses an AI algorithm based on evaluation data and emotional data to generate the optimal scent combination.

[0447] Input: Evaluation data and sentiment data

[0448] Specific operation: The server uses an AI algorithm (e.g., TensorFlow) to calculate the optimal scent ratio (e.g., 70% mint, 30% lavender) for the user's emotions and needs.

[0449] Output: Generated fragrance data

[0450] Step 13:

[0451] The server sends the generated fragrance data to the terminal.

[0452] Input: Generated fragrance data

[0453] Specific operation: The server generates data including "Fragrance: Mint-Lavender Blend, Ratio: Mint 70%-Lavender 30%" and sends it to the terminal.

[0454] Output: Scent data sent to the terminal

[0455] Step 14:

[0456] The device displays fragrance suggestions to the user based on fragrance data received from the server.

[0457] Input: Scent data sent from the server

[0458] Specific action: The device displays the message, "Mint-Lavender Blend scent is recommended. Please use a diffuser."

[0459] Output: Scent suggestions displayed to the user

[0460] Step 15:

[0461] The user sets a new fragrance in the diffuser according to the suggestion and uses it.

[0462] Input: Scent suggestion from the device

[0463] Specific operation: The user inserts a new fragrance cartridge into the diffuser and diffuses the fragrance according to the instruction manual.

[0464] Output: The environment in which the fragrance is provided using a diffuser.

[0465] In this way, this system can generate and provide users with individually optimized fragrances based on their situation and emotions in real time.

[0466] (Application Example 2)

[0467] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0468] In today's world, personalized services based on user emotions and needs are crucial for improving the user experience in stores. However, with existing technology, it has been difficult to recognize user emotions in real time and provide the optimal fragrance based on them. Furthermore, there have been insufficient means to convert input information and collected data into a unified format and efficiently transmit it to servers. As a result, the effective provision of fragrances that meet user expectations has not been achieved.

[0469] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting their evaluation data, means for generating an optimal scent using an AI algorithm based on the collected evaluation data, means for providing the generated scent data to the user, means for collecting the user's emotional data using emotion recognition technology, means for converting the emotional data and evaluation data into a unified format and transmitting it to the server, and means for providing an optimal scent to enhance the user's personal experience in a physical store. This makes it possible to generate and provide an optimal scent based on the user's emotions and needs in real time.

[0470] "User basic information" refers to information used to identify an individual, such as the user's name, age, gender, and preferences.

[0471] "Situation" refers to the environment or state in which the user is currently in, and specifically includes situations such as fatigue, tension, or a desire to relax.

[0472] "Needs information" refers to information that indicates the effects or desired results that users seek in specific situations.

[0473] "Basic scents" refer to common types of fragrances such as lavender, mint, and citrus.

[0474] "Evaluation data" refers to data showing the evaluation results of fragrances that users have tried.

[0475] An "AI algorithm" is a computational method of artificial intelligence that analyzes patterns based on large amounts of data and derives the optimal solution.

[0476] "Fragrance data" refers to information that includes the components and proportions of a particular fragrance.

[0477] "Emotion recognition technology" is a technology that analyzes a user's emotions from biometric information such as facial expressions, voice, and heart rate.

[0478] A "unified format" is a method of converting different data formats into a single standard format, and is important for maintaining data consistency.

[0479] A "server" is a computer system used for storing, processing, and managing data.

[0480] A "physical store" refers to a store that exists physically, rather than a virtual store on the internet.

[0481] "Personal experience" refers to the experiences and feelings that each user individually acquires.

[0482] The "optimal scent" is the most effective combination of fragrances, generated based on the user's situation, needs, and emotional data.

[0483] This invention describes a system that provides optimal fragrances based on user emotions and needs in physical stores. The main components of the system are the following hardware and software.

[0484] 1. User registration and initial setup

[0485] The server provides an interface for users to input basic information such as their name, age, gender, and preferences. This information is then stored in a database.

[0486] The hardware used includes smartphones, smart glasses, and head-mounted displays.

[0487] 2. Inputting the situation and understanding the needs

[0488] Users input their current situation and needs through an input interface. For example, information such as "I want to relieve fatigue and relax."

[0489] The terminal sends the input data to the server, which then analyzes the data.

[0490] 3. Emotion recognition

[0491] The server uses the camera and heart rate sensor built into the smart device to analyze the user's facial expressions and heart rate. This allows it to collect user emotion data in real time and send it to the server.

[0492] One example of software that can be used is an AI emotion recognition API (e.g., Microsoft Azure Cognitive Services).

[0493] 4. Scent selection test

[0494] The terminal displays a list of available scents to the user and asks for their evaluation of each scent. The user tries the scents and enters their evaluation data.

[0495] The server receives the evaluation data and stores it in the database.

[0496] 5. Data collection and transmission

[0497] The device converts the collected evaluation and sentiment data into a unified format and sends it to the server. This ensures data consistency.

[0498] 6. Scent optimization

[0499] The server uses evaluation and sentiment data to run AI algorithms and generate the scent combination best suited to the user's needs. TensorFlow is one example of a machine learning platform that can be used.

[0500] 7. Providing fragrance

[0501] The terminal displays the optimal fragrance combination for the user based on the best fragrance data received from the server. The user then uses the fragrance in a diffuser or similar device at a physical store according to that information.

[0502] Specific examples and prompt statements

[0503] As a concrete example, when a user visits a store, the system operates in the following steps:

[0504] 1. User Registration

[0505] Prompt message: "Please enter your name and basic information (e.g., Taro, 25 years old, male, likes to relax)."

[0506] 2. Inputting the situation and needs

[0507] Prompt: "Please tell me your current situation and the effect you are looking for (e.g., I'm nervous before a test. I want to relax)."

[0508] 3. Emotion recognition

[0509] Prompt message: "We are monitoring emotions in real time. Thank you for your cooperation."

[0510] 4. Selecting a fragrance

[0511] Prompt: "Please try the following scents. Please enter your rating for each scent (e.g., Lavender 8, Peppermint 9, Citrus 5)."

[0512] 5. Provision of optimization results and fragrance

[0513] Prompt message: "The optimal fragrance has been generated based on your needs. Please set the fragrance in the diffuser and enjoy your relaxation."

[0514] Through the above steps, this system enables the creation and delivery of optimal fragrances based on the user's emotions and needs. This allows users to enjoy a personalized fragrance experience in physical stores.

[0515] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0516] Step 1:

[0517] User registration and initial setup

[0518] The terminal provides an interface for the user to launch an application and enter basic information such as name, age, gender, and preferences. For example, if the user enters "Taro, 25 years old, male, likes to relax."

[0519] The terminal sends the entered user information to the server.

[0520] The server stores the received user information in a database.

[0521] Step 2:

[0522] Situation input and needs assessment

[0523] The user inputs their current situation and needs through the device's interface. For example, information such as, "I'm nervous because of the upcoming test. I want to relax."

[0524] The terminal sends this input data to the server.

[0525] The server analyzes the received data to understand the user's needs.

[0526] Step 3:

[0527] emotion recognition

[0528] The device uses its built-in camera and heart rate sensor to collect the user's facial expressions and heart rate in real time.

[0529] The device inputs this data into the emotion engine, which then analyzes the user's emotional state. For example, the emotion engine might recognize that the user is "stressed."

[0530] The device sends emotional data to the server.

[0531] Step 4:

[0532] Scent selection test

[0533] The device, through its interface, sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each.

[0534] Users try out different scents and enter a rating for each. For example, they might rate them as "Lavender 8, Mint 9, Citrus 5".

[0535] The device sends the collected evaluation data to the server.

[0536] Step 5:

[0537] Data collection and transmission

[0538] The device converts all evaluation and sentiment data into a unified format.

[0539] The terminal sends the data, converted to a unified format, to the server.

[0540] Step 6:

[0541] Scent optimization

[0542] The server inputs the received evaluation data and sentiment data into the AI ​​algorithm.

[0543] The AI ​​algorithm analyzes data and generates the most suitable scent combination for the user's needs. For example, it might generate a scent of "70% lavender and 30% mint."

[0544] The server sends the generated fragrance data to the terminal.

[0545] Step 7:

[0546] Providing fragrance

[0547] The device uses fragrance data received from the server to suggest the optimal way to deliver the fragrance to the user. For example, it might display, "Mint-Lavender Blend fragrance has been generated. Please use a diffuser."

[0548] Users enjoy the generated fragrance using devices such as diffusers.

[0549] These steps make it possible to provide the optimal fragrance in physical stores based on the user's emotions and needs.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] [Second Embodiment]

[0554] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0555] 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.

[0556] 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).

[0557] 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.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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".

[0566] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, and means for providing the generated fragrance data to the user.

[0567] Program Processing Description

[0568] 1. User registration and initial setup:

[0569] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[0570] The terminal sends the entered user information to the server.

[0571] The server stores the received user information in a database.

[0572] 2. Inputting the situation and understanding the needs:

[0573] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[0574] The terminal sends the entered status and needs data to the server.

[0575] The server stores and analyzes the data for analysis.

[0576] 3. Scent selection test:

[0577] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0578] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[0579] The device collects evaluation data and converts it into a standardized format.

[0580] 4. Data collection and transmission:

[0581] The device sends all evaluation data collected from the user to the server.

[0582] 5. Fragrance optimization:

[0583] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[0584] The server sends the generated fragrance data (such as ingredients and proportions) to the terminal.

[0585] 6. Providing fragrance:

[0586] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[0587] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[0588] Specific examples

[0589] 1. User registration and initial setup:

[0590] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[0591] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[0592] The server saves this information to the database.

[0593] 2. Inputting the situation and understanding the needs:

[0594] The user types, "I'm nervous because of the upcoming test. I want to relax."

[0595] The terminal sends this input data to the server.

[0596] The server analyzes the data and recognizes that "tension relief" is the need.

[0597] 3. Scent selection test:

[0598] The device displays the following message: "Please try the following scents: lavender, mint, citrus. Please rate them."

[0599] Users try out the scents and enter their ratings, such as "Lavender 8, Mint 9, Citrus 5".

[0600] The device sends evaluation data to the server.

[0601] 4. Data collection and transmission:

[0602] The device converts the evaluation data "Lavender 8, Mint 9, Citrus 5" into a unified format and sends it to the server.

[0603] 5. Fragrance optimization:

[0604] The server uses an AI algorithm to generate a new scent: "70% mint, 30% lavender."

[0605] The server sends that scent data to the terminal.

[0606] 6. Providing fragrance:

[0607] The device displays the message, "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[0608] The user sets the fragrance in the diffuser according to the suggestion and enjoys relaxation.

[0609] Through the above process, users can easily access the most effective fragrance that best suits their needs.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The user launches the application and enters basic information (name, age, gender, preferences) on the user registration screen.

[0613] Step 2:

[0614] The terminal sends the entered user information to the server.

[0615] Step 3:

[0616] The server saves the received user information to the database.

[0617] Step 4:

[0618] The user inputs their current situation (e.g., nervousness before a test) and their needs in that situation (e.g., wanting to relax) through the device's interface.

[0619] Step 5:

[0620] The terminal sends the entered status and needs data to the server.

[0621] Step 6:

[0622] The server saves the status and needs data to the database and prepares it for analysis.

[0623] Step 7:

[0624] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0625] Step 8:

[0626] Users try each scent and enter their rating for each (e.g., on a 10-point scale).

[0627] Step 9:

[0628] The device collects user evaluation data and converts it into a unified format.

[0629] Step 10:

[0630] The device sends the collected evaluation data to the server.

[0631] Step 11:

[0632] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs.

[0633] Step 12:

[0634] The server sends the generated optimal fragrance data (ingredients and their proportions) to the terminal.

[0635] Step 13:

[0636] The device displays a suggested fragrance that it has generated for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[0637] Step 14:

[0638] Users can enjoy the relaxing effects by setting the suggested fragrance in a diffuser or similar device and using it.

[0639] These steps allow users to easily obtain and use fragrances optimized for their own situation and needs.

[0640] (Example 1)

[0641] 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."

[0642] Conventional systems struggled to provide personalized fragrances tailored to each user's situation and needs, and were unable to suggest optimal fragrances based on user data. Furthermore, the inability to efficiently collect and analyze fragrance evaluation data and quickly reflect the results made it difficult to improve user satisfaction. There were also challenges in the efficiency of converting user-entered data into a unified format and transmitting it to the server.

[0643] 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.

[0644] In this invention, the server includes means for inputting and storing user attribute information, means for inputting and analyzing user status information and demand information, means for providing multiple basic scents and collecting evaluation information thereof, means for generating an optimal scent using a machine learning algorithm based on the collected evaluation information, and means for providing the generated scent information to the user. This makes it possible to suggest the optimal scent according to the user's situation and needs, and enables efficient analysis of collected data and rapid reflection of the results. Furthermore, input data can be converted into a standard format and efficiently transmitted to the server, improving overall processing efficiency.

[0645] "User" refers to an individual who uses the system.

[0646] "Attribute information" refers to basic information such as the user's name, age, gender, and preferences.

[0647] "Status information" refers to information about the user's current situation and psychological state.

[0648] "Demand information" refers to what a user needs or requests at that particular moment.

[0649] "Basic scent" refers to a scent that is individually prepared from among the multiple scents that the system provides to the user.

[0650] "Evaluation information" refers to feedback and evaluations that users have given to basic fragrances.

[0651] A "machine learning algorithm" refers to artificial intelligence technology that analyzes collected data and derives the optimal result.

[0652] "Fragrance information" refers to data on the optimal fragrance generated by a machine learning algorithm based on user demand.

[0653] A "standard format" refers to a format used to convert different types of information into a unified format.

[0654] A "server" refers to a computer system that stores data, performs analysis, and controls the entire system.

[0655] This invention relates to a system that provides the optimal fragrance according to the user's situation and needs. This system provides the user with a personalized fragrance through the following process.

[0656] First, the user installs and launches a dedicated application on their device. As part of the initial setup, the user enters their basic information (name, age, gender, preferences, etc.). The device sends this information to the server, which then stores the received information in a database. For example, if the user enters "Taro, 25 years old, male, likes to relax," this information is converted to JSON format and sent to the server.

[0657] Next, the user enters their current situation and the needs they have in that situation. For example, they might enter, "I'm nervous before the test. I want to relax." The device sends this situation and needs data to the server, which uses natural language processing to analyze the data and recognize the need for "stress relief." This data is also sent in JSON format.

[0658] The device then provides the user with an interface to try several basic scents (e.g., lavender, mint, citrus). The user tries each scent and enters their rating. For example, they might rate "Lavender 8, Mint 9, Citrus 5". The device sends this rating data to a server, which stores the received rating data in a database.

[0659] The server uses a machine learning algorithm based on the collected evaluation data to generate the fragrance combination best suited to the user's needs. In this example, the AI ​​model calculates the optimal blend as "70% mint, 30% lavender." The generated fragrance data is sent to the device, which then displays a suggestion for the best fragrance to the user. Specifically, it might display, "A Mint-Lavender Blend fragrance has been generated. Let's use the diffuser."

[0660] The user follows this suggestion and enjoys relaxation by using the generated fragrance. For example, the user might set up a diffuser according to the suggestion and enjoy the new scent.

[0661] Examples of prompt messages include, "Please try the following scents and rate them: lavender, mint, citrus."

[0662] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] User registration and basic information entry

[0666] 1.1 The user installs and launches the dedicated application on their device.

[0667] 1.2 The user enters basic information such as name, age, gender, and preferences on the user registration screen. For example, they might enter "Taro, 25 years old, male, likes to relax."

[0668] 1.3 The terminal converts the entered basic information into JSON format and sends it to the server. Input: "Taro, 25 years old, male, likes to relax" Output: {"name":"Taro","age":25,"gender":"male","preference":"relax"}

[0669] 1.4 The server saves the received user information to the database. Specific example: Execute "INSERT INTO users (name, age, gender, preference) VALUES ('Taro', 25, 'Male', 'Relax')".

[0670] Step 2:

[0671] Input of situation and needs

[0672] 2.1 The user inputs their current situation and needs through the terminal interface. For example, they might input, "I'm nervous because of the upcoming test. I want to relax."

[0673] 2.2 The terminal converts this situation and needs data into JSON format and sends it to the server. Input: "I'm nervous before the test. I want to relax." Output: {"situation":"I'm nervous before the test", "need":"I want to relax"}

[0674] 2.3 The server receives and analyzes the data. Using natural language processing, it extracts the keywords "tension" and "relaxation" and internally records "Needs: Tension relief". Input: {"situation": "I'm nervous before the test","need": "I want to relax"} Output: "Needs: Tension relief"

[0675] Step 3:

[0676] Scent selection test

[0677] 3.1 The device provides an interface that allows the user to try several basic scents (lavender, mint, citrus). For example, it displays: "Please try the following scents. Please rate them: lavender, mint, citrus."

[0678] 3.2 The user tries each scent and enters a rating for each. For example, they might enter "Lavender 8, Mint 9, Citrus 5".

[0679] 3.3 The terminal converts this evaluation data into JSON format and sends it to the server. Input: "Lavender 8, Mint 9, Citrus 5" Output: {"lavender":8,"mint":9,"citrus":5}

[0680] 3.4 The server saves the received evaluation data to the database. Specific example: Execute "INSERT INTO feedback (user_id, lavender, mint, citrus) VALUES (1, 8, 9, 5)".

[0681] Step 4:

[0682] Data collection and optimization

[0683] 4.1 The server inputs the collected evaluation data into a machine learning algorithm to generate the most effective scent combination for the user's needs. Input: {"lavender":8,"mint":9,"citrus":5} Output: {"mint":70,"lavender":30}

[0684] 4.2 The server sends the generated scent data to the terminal. Output: {"mint":70,"lavender":30}

[0685] Step 5:

[0686] Providing fragrances on the device

[0687] 5.1 The terminal suggests fragrance information received from the server to the user. Specific example: It displays "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[0688] 5.2 The user enjoys the generated fragrance using a diffuser as suggested. Input: "Mint-Lavender Blend fragrance has been generated." Action: Set the fragrance in the diffuser and relax.

[0689] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[0690] (Application Example 1)

[0691] 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."

[0692] Traditional food delivery services lacked a mechanism to provide optimal aromas tailored to the user's situation and needs, making it difficult to enhance the dining experience. In particular, there is a need for technology that provides aromas that alleviate the anxiety and expectations users feel when ordering food.

[0693] 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.

[0694] In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, and means for providing multiple basic scents and collecting evaluation data thereof. This makes it possible to generate the optimal scent using an AI algorithm based on the collected evaluation data and provide it to the user in a food delivery service.

[0695] "User basic information" refers to basic personal information about individual users, such as name, age, gender, and preferences.

[0696] "Fragrance" refers to volatile substances produced from specific plants or chemicals, which are perceived through the sense of smell.

[0697] "Situation and needs information" refers to information about the environment and psychological state in which the user is placed, as well as the specific desires and requests that they feel are desirable under those circumstances.

[0698] "Evaluation data" refers to data on feedback and impressions that users give to the provided fragrances, specifically including ratings and comments.

[0699] An "AI algorithm" is a series of computational procedures that use artificial intelligence technology to analyze data and generate optimal answers or suggestions.

[0700] A "food delivery service" is a service that allows users to order meals and food items online or by phone, and have them delivered to a specified location.

[0701] The "dining experience" refers to the overall satisfaction and enjoyment that users feel when eating, and is influenced by factors such as the taste, aroma, appearance, and atmosphere of the food.

[0702] A "server" is a computer system that stores and processes data on a network, and is hardware or software that provides services in response to requests from clients.

[0703] This invention is a system that provides the optimal fragrance according to the user's situation and needs. The following hardware and software are used to implement this invention.

[0704] Hardware and software to use

[0705] Server: A computer system used for storing and analyzing data. Includes databases.

[0706] Terminal: A mobile device such as a smartphone or tablet. It collects user input and communicates with the server.

[0707] Generative AI Model: Software for implementing AI algorithms. It analyzes evaluation data and generates optimal fragrances.

[0708] System Processing Description

[0709] 1. User registration and saving of basic information:

[0710] Users launch the application using their mobile device and enter basic information such as their name, age, gender, and preferences on the registration screen. This entered data is then sent from the device to the server.

[0711] The server stores the received user information in a database.

[0712] 2. Inputting the situation and understanding the needs:

[0713] The user inputs their current mood or expectations through the device (e.g., tired, want to relax).

[0714] The terminal sends the input data to the server, which then analyzes the data.

[0715] 3. Collection of fragrance evaluation data:

[0716] The device displays several basic scents (e.g., lavender, mint, citrus) in sequence and asks the user for their evaluation.

[0717] Users try the provided fragrances and provide a rating (e.g., on a 10-point scale) for each fragrance.

[0718] The device collects this evaluation data, converts it to a unified format, and then sends it to the server.

[0719] 4. Scent optimization and generation:

[0720] Based on the received evaluation data, the server uses a generative AI model to generate the most effective fragrance combination to meet the user's needs.

[0721] For example, it's possible to create a new scent like "70% mint, 30% lavender."

[0722] This generated fragrance data is then sent back to the terminal.

[0723] 5. Providing and suggesting fragrances:

[0724] The device suggests the most suitable scent to the user based on the received scent data (e.g., "A relaxing Mint-Lavender Blend scent has been generated. Using a diffuser would be recommended.").

[0725] Users can enjoy the fragrance according to the suggestions.

[0726] Specific examples and prompt statements

[0727] Specific example:

[0728] User registration: The user installs the app and enters basic information. For example, "Taro, 25 years old, male, likes to relax."

[0729] Situation input: The user enters "I'm nervous because of the upcoming test. I want to relax."

[0730] Inputting evaluation data: Users test the scents and input their evaluations, such as "Lavender 8, Mint 9, Citrus 5".

[0731] Results of the generation AI model: The server uses AI to generate and suggest a scent of "70% mint, 30% lavender".

[0732] Prompts for the generative AI model:

[0733] Please generate ideas for the perfect aroma for the delivered food. Consider the following information:

[0734] User name: Taro

[0735] Age: 25

[0736] Gender: Male

[0737] Preferences: Italian

[0738] Current mood: Tired

[0739] Needs: I want to relax

[0740] Suitable scent for ordered meal is:

[0741] This invention makes it possible to provide users with the optimal aroma when ordering food in a food delivery service, thereby improving the dining experience.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] User registration and saving of basic information

[0745] Users launch the application using a mobile device such as a smartphone or tablet and enter basic information such as their name, age, gender, and preferences.

[0746] The terminal sends the entered basic information data to the server.

[0747] The server stores the received user information in a database. At this time, a unique identifier such as a user ID is generated.

[0748] Input: User's basic information (name, age, gender, preferences)

[0749] Output: Basic information data stored on the server, user ID

[0750] Step 2:

[0751] Situation input and understanding of needs

[0752] The user inputs information about their current mood and expectations (e.g., tired, want to relax) through their device.

[0753] The terminal sends the entered status and needs data to the server.

[0754] The server analyzes the received data and identifies the corresponding needs. For example, in response to a situation of "being tired," the need for "relaxation" might be identified.

[0755] Input: User status and needs information

[0756] Output: Needs data analyzed by the server

[0757] Step 3:

[0758] Collection of fragrance evaluation data

[0759] The device displays an interface that sequentially presents the user with several basic scents (e.g., lavender, mint, citrus) and asks the user to rate each scent.

[0760] The user tries the provided fragrances and enters a rating (e.g., on a 10-point scale) for each fragrance into the device.

[0761] The terminal collects this evaluation data, converts it to a unified format, and then sends it to the server. At this time, each evaluation data is linked to a user ID.

[0762] Input: User evaluation data for each fragrance

[0763] Output: Evaluation data in a unified format sent to the server

[0764] Step 4:

[0765] Scent optimization and generation

[0766] The server uses a generative AI model based on the received evaluation data to generate the most effective fragrance combination for the user's needs.

[0767] For example, a new fragrance might be created that is "70% mint, 30% lavender."

[0768] The generated fragrance data is sent back to the terminal and is ready to be provided to the user.

[0769] Input: Evaluation data in a unified format, user needs data

[0770] Output: Generated fragrance data

[0771] Step 5:

[0772] Providing and suggesting fragrances

[0773] The device suggests the most suitable scent to the user based on the received scent data. For example, it might display, "A Mint-Lavender Blend scent with high relaxation effects has been generated. Using a diffuser would be beneficial."

[0774] Users can enjoy fragrances according to the suggestions. When using a diffuser, they can set the suggested fragrance to achieve a relaxing effect.

[0775] Input: Generated fragrance data

[0776] Output: Fragrance suggestion information provided to the user

[0777] 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.

[0778] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, means for providing the generated fragrance data to the user, and an emotion engine that recognizes the user's emotions.

[0779] Program Processing Description

[0780] 1. User registration and initial setup:

[0781] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[0782] The terminal sends the entered user information to the server.

[0783] The server stores the received user information in a database.

[0784] 2. Inputting the situation and understanding the needs:

[0785] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[0786] The terminal sends the entered status and needs data to the server.

[0787] The server stores and analyzes the data for analysis.

[0788] 3. Emotion recognition:

[0789] The device uses its built-in emotion engine to collect and analyze biometric information such as the user's facial expressions, voice, and heart rate, and recognizes the user's emotions.

[0790] The device sends the recognized emotion data to the server.

[0791] 4. Scent selection test:

[0792] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0793] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[0794] The device collects evaluation data and converts it into a standardized format.

[0795] 5. Data collection and transmission:

[0796] The device transmits all collected evaluation and sentiment data to the server.

[0797] 6. Fragrance optimization:

[0798] The server inputs the received evaluation and emotional data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[0799] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[0800] 7. Providing fragrance:

[0801] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[0802] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[0803] Specific examples

[0804] 1. User registration and initial setup:

[0805] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[0806] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[0807] The server saves this information to the database.

[0808] 2. Inputting the situation and understanding the needs:

[0809] The user types, "I'm nervous because of the upcoming test. I want to relax."

[0810] The terminal sends this input data to the server.

[0811] The server analyzes the data and recognizes that "tension relief" is the need.

[0812] 3. Emotion recognition:

[0813] The device monitors the user's facial expressions, voice, and heart rate, and analyzes the data with an emotion engine to recognize the emotion of "being nervous."

[0814] The device sends this emotion data to the server.

[0815] 4. Scent selection test:

[0816] The device displays the following message: "Please try the following scents: lavender, mint, citrus. Please rate them."

[0817] Users try out the scents and enter their ratings, such as "Lavender 8, Mint 9, Citrus 5".

[0818] The device sends evaluation data to the server.

[0819] 5. Data collection and transmission:

[0820] The device converts the evaluation data ("Lavender 8, Mint 9, Citrus 5") and emotion data into a unified format and sends it to the server.

[0821] 6. Fragrance optimization:

[0822] The server uses an AI algorithm to generate a new scent: "70% mint, 30% lavender."

[0823] The server sends that scent data to the terminal.

[0824] 7. Providing fragrance:

[0825] The device displays the message, "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[0826] The user sets the fragrance in the diffuser according to the suggestion and enjoys relaxation.

[0827] These steps allow users to easily access fragrances optimized for their emotions and needs, and experience the desired effects.

[0828] The following describes the processing flow.

[0829] Step 1:

[0830] The user launches the application and enters basic information such as their name, age, gender, and preferences on the user registration screen.

[0831] Step 2:

[0832] The terminal sends the entered user information to the server.

[0833] Step 3:

[0834] The server saves the received user information to the database.

[0835] Step 4:

[0836] The user inputs their current situation (e.g., nervousness before a test) and their needs in that situation (e.g., wanting to relax) through the device's interface.

[0837] Step 5:

[0838] The terminal sends the entered status and needs data to the server.

[0839] Step 6:

[0840] The server saves the received status and needs data to the database and prepares it for analysis.

[0841] Step 7:

[0842] The device activates an emotion engine and collects biometric information such as the user's facial expressions, voice, and heart rate.

[0843] Step 8:

[0844] The device analyzes collected biometric information to recognize the user's emotions. For example, it might analyze the user's emotion as "nervous."

[0845] Step 9:

[0846] The device sends the recognized emotion data to the server.

[0847] Step 10:

[0848] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[0849] Step 11:

[0850] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[0851] Step 12:

[0852] The device collects user evaluation data and converts it into a unified format.

[0853] Step 13:

[0854] The device sends the collected evaluation data and sentiment data to the server.

[0855] Step 14:

[0856] The server inputs evaluation and emotional data into an AI algorithm to generate the most effective scent combination for the user's needs. For example, it might generate a blend of 70% mint and 30% lavender.

[0857] Step 15:

[0858] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[0859] Step 16:

[0860] The device displays a suggested optimal fragrance for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[0861] Step 17:

[0862] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[0863] This processing flow allows users to utilize the optimal scent based on emotion recognition and evaluation data.

[0864] (Example 2)

[0865] 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".

[0866] There is a problem with manually adjusting and selecting fragrances to find the one that best suits the user's emotions and needs: users face the challenge of manual adjustments and selections. Furthermore, there is a lack of methods to provide fragrances that respond to users' emotions and situations in real time, making it difficult to accurately suggest fragrances to individual users.

[0867] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting evaluation data thereof, means for generating an optimal scent using an AI algorithm based on the collected evaluation data and emotional data, and means for providing the generated scent data to the user. This makes it possible to propose an optimal scent in real time based on the user's emotional state and needs.

[0868] "User" refers to anyone who uses this system.

[0869] "Basic information" refers to information such as the user's name, age, gender, and preferences.

[0870] "Situation" refers to the specific environment or state in which the user is currently in.

[0871] "Needs" refer to the desires and demands that a user has in a given situation.

[0872] "Fragrance" refers to the multiple basic scent components provided to the user.

[0873] "Evaluation data" refers to the evaluation results that users have given to the provided fragrances.

[0874] An "AI algorithm" refers to a computational method that uses artificial intelligence to analyze data and derive the optimal result.

[0875] "Fragrance data" refers to data generated by an AI algorithm that shows the optimal fragrance combination for the user.

[0876] "Emotions" refer to the psychological state perceived from the user's facial expressions, voice, heart rate, etc.

[0877] "Emotional data" refers to data about a user's emotional state obtained using an emotion engine.

[0878] An "emotion engine" refers to a device or software that analyzes a user's facial expressions, voice, heart rate, and other biometric information to recognize their emotional state.

[0879] A "database" refers to a system on a server used to store and manage basic user information, evaluation data, and sentiment data.

[0880] "Terminal" refers to a device used by a user to input information (e.g., smartphone, tablet).

[0881] A "server" refers to a central computer that stores and analyzes information sent from user terminals and sends the results back to the terminals.

[0882] This invention relates to a system that provides the optimal fragrance according to the user's needs. The system collects the user's basic information, current situation, and emotional data, and uses an AI algorithm to generate and provide the optimal fragrance to the user.

[0883] Hardware and software configuration

[0884] The main components of this system include the following:

[0885] Device: A device used by a user to input information, such as a smartphone or tablet. These devices typically include a camera, microphone, heart rate sensor, and other electronic components.

[0886] Server: A central computer that stores and analyzes data, and includes databases and execution environments for AI algorithms (e.g., TensorFlow).

[0887] Emotion engine: This is software that acquires user facial expressions, voice, and heart rate data and analyzes their emotions. A specific example is Microsoft Azure's Emotion API.

[0888] Specific data processing and calculations

[0889] 1. Enter and save user information:

[0890] The user launches the app and enters basic information, such as their name, age, gender, and preferences. This information is sent from the device to the server and stored in a database.

[0891] 2. Input and analysis of the situation and needs:

[0892] The user enters their current situation (e.g., feeling nervous before a test) and needs (e.g., wanting to relax). The device sends this data to the server, which analyzes the situation and needs.

[0893] 3. Emotion recognition:

[0894] The device uses a camera, microphone, and heart rate sensor to collect biometric information such as the user's facial expressions, voice, and heart rate. This data is analyzed by an emotion engine, which recognizes emotions such as "feeling nervous."

[0895] 4. Fragrance selection and evaluation:

[0896] The device presents the user with several basic scents (lavender, mint, citrus, etc.) and asks for their evaluation. The user enters their evaluation for each scent, and the device collects the evaluation data and sends it to the server.

[0897] 5. Data analysis and fragrance generation:

[0898] The server inputs collected evaluation and emotional data into an AI algorithm to generate the optimal scent combination. For example, it might blend mint 70% with lavender 30%.

[0899] 6. Providing fragrance:

[0900] The device displays fragrance suggestions to the user based on fragrance data received from the server. The user then sets the suggested fragrance in the diffuser and enjoys relaxation.

[0901] Examples of specific cases and prompt statements

[0902] 1. Example of user registration:

[0903] User: Enters "Taro, 25 years old, male, likes to relax."

[0904] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[0905] Server: Stored in the database.

[0906] 2. Examples of situation input and needs assessment:

[0907] User: "I'm nervous because of the upcoming test. I want to relax."

[0908] Terminal: Send to server.

[0909] Server: Recognizes that "tension relief" is the need.

[0910] 3. Examples of emotion recognition:

[0911] Device: Monitors facial expressions, voice, and heart rate.

[0912] Terminal: Recognizes the emotion "feeling nervous" and sends it to the server.

[0913] 4. Example of a fragrance selection test:

[0914] Terminal: Enter the rating as "Lavender 8, Mint 9, Citrus 5".

[0915] Terminal: Sends evaluation data to the server.

[0916] Examples of prompts for generative AI models

[0917] "How can we provide users with the perfect scent to help them relax? We need a system that uses an AI algorithm to determine the optimal scent based on the user's basic information, current situation, and emotional state."

[0918] This allows users to easily access fragrances optimized for their emotions and needs, and experience the desired effects.

[0919] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0920] Program processing steps

[0921] Step 1:

[0922] The user launches the application and enters basic information (name, age, gender, preferences).

[0923] Input: Name, age, gender, preferences

[0924] Specific action: The user enters the information "Taro, 25 years old, male, likes to relax" into the input field.

[0925] Output: Structured user information

[0926] Step 2:

[0927] The terminal converts the entered user information into a structured data format (such as JSON) and sends it to the server.

[0928] Input: Basic information entered by the user

[0929] Specific action: The device generates JSON data with the format "Name: Taro, Age: 25, Gender: Male, Preferences: Relaxation" and sends it to the server.

[0930] Output: JSON data of basic information sent to the server

[0931] Step 3:

[0932] The server saves the received user information to the database.

[0933] Input: JSON data of user information sent from the device.

[0934] Specific operation: The server executes the following query on the database: "INSERT INTO users (name, age, gender, preferences) VALUES ('Taro', 25, 'Male', 'Relax')".

[0935] Output: User information stored in the database

[0936] Step 4:

[0937] The user uses the app's interface to input their current situation (e.g., feeling nervous before a test) and their needs (e.g., wanting to relax).

[0938] Input: Current situation, needs

[0939] Specific action: The user enters "Situation: Before test" and "Needs: Relax" into the interface.

[0940] Output: Structured situation and needs information

[0941] Step 5:

[0942] The terminal sends the entered status and needs data to the server.

[0943] Input: User-entered information about the situation and needs.

[0944] Specific action: The device generates JSON data containing "Status: Before test" and "Needs: Relax" and sends it to the server.

[0945] Output: JSON data containing status and needs information sent to the server.

[0946] Step 6:

[0947] The server stores and analyzes situation and needs data in a database.

[0948] Input: JSON data of status and needs information sent from the device.

[0949] Specific operation: The server executes the query "SELECT FROM relaxation_methods WHERE situations LIKE '%beforetest%'" to identify appropriate relaxation methods.

[0950] Output: Appropriate relaxation methods as a result of the analysis

[0951] Step 7:

[0952] The device uses a camera, microphone, and heart rate sensor to collect data on the user's facial expressions, voice, and heart rate.

[0953] Input: User's facial expressions, voice, and heart rate data

[0954] Specific operation: The device's camera scans the user's face, and facial expressions are analyzed through dedicated software (e.g., OpenCV).

[0955] Output: Collected biometric data

[0956] Step 8:

[0957] The device analyzes the collected biometric information using an emotion engine to recognize the user's emotional state.

[0958] Input: Collected biometric data

[0959] Specific operation: Send image data to the Emotion API and obtain an emotion score for "tension".

[0960] Output: Recognized emotion data

[0961] Step 9:

[0962] The device sends the recognized emotion data to the server.

[0963] Input: Recognized emotion data

[0964] Specific action: The device generates JSON data containing "Emotion: Stress, Score: 0.85" and sends it to the server.

[0965] Output: JSON data of emotion sent to the server

[0966] Step 10:

[0967] The device presents the user with several basic scents to try (e.g., lavender, mint, citrus) and asks for their evaluation.

[0968] Input: User ratings for scents

[0969] Specific actions: The user tries out different scents and rates each scent on a 10-point scale.

[0970] Output: Structured evaluation data

[0971] Step 11:

[0972] The device sends the collected evaluation data to the server.

[0973] Input: Fragrance evaluation data obtained from users

[0974] Specific operation: The device sends evaluation data of "Lavender 8, Mint 9, Citrus 5" to the server in JSON format.

[0975] Output: JSON data of the evaluation data sent to the server

[0976] Step 12:

[0977] The server uses an AI algorithm based on evaluation data and emotional data to generate the optimal scent combination.

[0978] Input: Evaluation data and sentiment data

[0979] Specific operation: The server uses an AI algorithm (e.g., TensorFlow) to calculate the optimal scent ratio (e.g., 70% mint, 30% lavender) for the user's emotions and needs.

[0980] Output: Generated fragrance data

[0981] Step 13:

[0982] The server sends the generated fragrance data to the terminal.

[0983] Input: Generated fragrance data

[0984] Specific operation: The server generates data including "Fragrance: Mint-Lavender Blend, Ratio: Mint 70%-Lavender 30%" and sends it to the terminal.

[0985] Output: Scent data sent to the terminal

[0986] Step 14:

[0987] The device displays fragrance suggestions to the user based on fragrance data received from the server.

[0988] Input: Scent data sent from the server

[0989] Specific action: The device displays the message, "Mint-Lavender Blend scent is recommended. Please use a diffuser."

[0990] Output: Scent suggestions displayed to the user

[0991] Step 15:

[0992] The user sets a new fragrance in the diffuser according to the suggestion and uses it.

[0993] Input: Scent suggestion from the device

[0994] Specific operation: The user inserts a new fragrance cartridge into the diffuser and diffuses the fragrance according to the instruction manual.

[0995] Output: The environment in which the fragrance is provided using a diffuser.

[0996] In this way, this system can generate and provide users with individually optimized fragrances based on their situation and emotions in real time.

[0997] (Application Example 2)

[0998] 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."

[0999] In today's world, personalized services based on user emotions and needs are crucial for improving the user experience in stores. However, with existing technology, it has been difficult to recognize user emotions in real time and provide the optimal fragrance based on them. Furthermore, there have been insufficient means to convert input information and collected data into a unified format and efficiently transmit it to servers. As a result, the effective provision of fragrances that meet user expectations has not been achieved.

[1000] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting their evaluation data, means for generating an optimal scent using an AI algorithm based on the collected evaluation data, means for providing the generated scent data to the user, means for collecting the user's emotional data using emotion recognition technology, means for converting the emotional data and evaluation data into a unified format and transmitting it to the server, and means for providing an optimal scent to enhance the user's personal experience in a physical store. This makes it possible to generate and provide an optimal scent based on the user's emotions and needs in real time.

[1001] "User basic information" refers to information used to identify an individual, such as the user's name, age, gender, and preferences.

[1002] "Situation" refers to the environment or state in which the user is currently in, and specifically includes situations such as fatigue, tension, or a desire to relax.

[1003] "Needs information" refers to information that indicates the effects or desired results that users seek in specific situations.

[1004] "Basic scents" refer to common types of fragrances such as lavender, mint, and citrus.

[1005] "Evaluation data" refers to data showing the evaluation results of fragrances that users have tried.

[1006] An "AI algorithm" is a computational method of artificial intelligence that analyzes patterns based on large amounts of data and derives the optimal solution.

[1007] "Fragrance data" refers to information that includes the components and proportions of a particular fragrance.

[1008] "Emotion recognition technology" is a technology that analyzes a user's emotions from biometric information such as facial expressions, voice, and heart rate.

[1009] A "unified format" is a method of converting different data formats into a single standard format, and is important for maintaining data consistency.

[1010] A "server" is a computer system used for storing, processing, and managing data.

[1011] A "physical store" refers to a store that exists physically, rather than a virtual store on the internet.

[1012] "Personal experience" refers to the experiences and feelings that each user individually acquires.

[1013] The "optimal scent" is the most effective combination of fragrances, generated based on the user's situation, needs, and emotional data.

[1014] This invention describes a system that provides optimal fragrances based on user emotions and needs in physical stores. The main components of the system are the following hardware and software.

[1015] 1. User registration and initial setup

[1016] The server provides an interface for users to input basic information such as their name, age, gender, and preferences. This information is then stored in a database.

[1017] The hardware used includes smartphones, smart glasses, and head-mounted displays.

[1018] 2. Inputting the situation and understanding the needs

[1019] Users input their current situation and needs through an input interface. For example, information such as "I want to relieve fatigue and relax."

[1020] The terminal sends the input data to the server, which then analyzes the data.

[1021] 3. Emotion recognition

[1022] The server uses the camera and heart rate sensor built into the smart device to analyze the user's facial expressions and heart rate. This allows it to collect user emotion data in real time and send it to the server.

[1023] One example of software that can be used is an AI emotion recognition API (e.g., Microsoft Azure Cognitive Services).

[1024] 4. Scent selection test

[1025] The terminal displays a list of available scents to the user and asks for their evaluation of each scent. The user tries the scents and enters their evaluation data.

[1026] The server receives the evaluation data and stores it in the database.

[1027] 5. Data collection and transmission

[1028] The device converts the collected evaluation and sentiment data into a unified format and sends it to the server. This ensures data consistency.

[1029] 6. Scent optimization

[1030] The server uses evaluation and sentiment data to run AI algorithms and generate the scent combination best suited to the user's needs. TensorFlow is one example of a machine learning platform that can be used.

[1031] 7. Providing fragrance

[1032] The terminal displays the optimal fragrance combination for the user based on the best fragrance data received from the server. The user then uses the fragrance in a diffuser or similar device at a physical store according to that information.

[1033] Specific examples and prompt statements

[1034] As a concrete example, when a user visits a store, the system operates in the following steps:

[1035] 1. User Registration

[1036] Prompt message: "Please enter your name and basic information (e.g., Taro, 25 years old, male, likes to relax)."

[1037] 2. Inputting the situation and needs

[1038] Prompt: "Please tell me your current situation and the effect you are looking for (e.g., I'm nervous before a test. I want to relax)."

[1039] 3. Emotion recognition

[1040] Prompt message: "We are monitoring emotions in real time. Thank you for your cooperation."

[1041] 4. Selecting a fragrance

[1042] Prompt: "Please try the following scents. Please enter your rating for each scent (e.g., Lavender 8, Peppermint 9, Citrus 5)."

[1043] 5. Provision of optimization results and fragrance

[1044] Prompt message: "The optimal fragrance has been generated based on your needs. Please set the fragrance in the diffuser and enjoy your relaxation."

[1045] Through the above steps, this system enables the creation and delivery of optimal fragrances based on the user's emotions and needs. This allows users to enjoy a personalized fragrance experience in physical stores.

[1046] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1047] Step 1:

[1048] User registration and initial setup

[1049] The terminal provides an interface for the user to launch an application and enter basic information such as name, age, gender, and preferences. For example, if the user enters "Taro, 25 years old, male, likes to relax."

[1050] The terminal sends the entered user information to the server.

[1051] The server stores the received user information in a database.

[1052] Step 2:

[1053] Situation input and needs assessment

[1054] The user inputs their current situation and needs through the device's interface. For example, information such as, "I'm nervous because of the upcoming test. I want to relax."

[1055] The terminal sends this input data to the server.

[1056] The server analyzes the received data to understand the user's needs.

[1057] Step 3:

[1058] emotion recognition

[1059] The device uses its built-in camera and heart rate sensor to collect the user's facial expressions and heart rate in real time.

[1060] The device inputs this data into the emotion engine, which then analyzes the user's emotional state. For example, the emotion engine might recognize that the user is "stressed."

[1061] The device sends emotional data to the server.

[1062] Step 4:

[1063] Scent selection test

[1064] The device, through its interface, sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each.

[1065] Users try out different scents and enter a rating for each. For example, they might rate them as "Lavender 8, Mint 9, Citrus 5".

[1066] The device sends the collected evaluation data to the server.

[1067] Step 5:

[1068] Data collection and transmission

[1069] The device converts all evaluation and sentiment data into a unified format.

[1070] The terminal sends the data, converted to a unified format, to the server.

[1071] Step 6:

[1072] Scent optimization

[1073] The server inputs the received evaluation data and sentiment data into the AI ​​algorithm.

[1074] The AI ​​algorithm analyzes data and generates the most suitable scent combination for the user's needs. For example, it might generate a scent of "70% lavender and 30% mint."

[1075] The server sends the generated fragrance data to the terminal.

[1076] Step 7:

[1077] Providing fragrance

[1078] The device uses fragrance data received from the server to suggest the optimal way to deliver the fragrance to the user. For example, it might display, "Mint-Lavender Blend fragrance has been generated. Please use a diffuser."

[1079] Users enjoy the generated fragrance using devices such as diffusers.

[1080] These steps make it possible to provide the optimal fragrance in physical stores based on the user's emotions and needs.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] [Third Embodiment]

[1085] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1086] 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.

[1087] 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).

[1088] 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.

[1089] 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.

[1090] 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).

[1091] 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.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] 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.

[1096] 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".

[1097] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, and means for providing the generated fragrance data to the user.

[1098] Program Processing Description

[1099] 1. User registration and initial setup:

[1100] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[1101] The terminal sends the entered user information to the server.

[1102] The server stores the received user information in a database.

[1103] 2. Inputting the situation and understanding the needs:

[1104] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[1105] The terminal sends the entered status and needs data to the server.

[1106] The server stores and analyzes the data for analysis.

[1107] 3. Scent selection test:

[1108] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1109] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[1110] The device collects evaluation data and converts it into a standardized format.

[1111] 4. Data collection and transmission:

[1112] The device sends all evaluation data collected from the user to the server.

[1113] 5. Fragrance optimization:

[1114] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[1115] The server sends the generated fragrance data (such as ingredients and proportions) to the terminal.

[1116] 6. Providing fragrance:

[1117] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[1118] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[1119] Specific examples

[1120] 1. User registration and initial setup:

[1121] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[1122] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[1123] The server saves this information to the database.

[1124] 2. Inputting the situation and understanding the needs:

[1125] The user types, "I'm nervous because of the upcoming test. I want to relax."

[1126] The terminal sends this input data to the server.

[1127] The server analyzes the data and recognizes that "tension relief" is the need.

[1128] 3. Scent selection test:

[1129] The device displays the following message: "Please try the following scents: lavender, mint, citrus. Please rate them."

[1130] Users try out the scents and enter their ratings, such as "Lavender 8, Mint 9, Citrus 5".

[1131] The device sends evaluation data to the server.

[1132] 4. Data collection and transmission:

[1133] The device converts the evaluation data "Lavender 8, Mint 9, Citrus 5" into a unified format and sends it to the server.

[1134] 5. Fragrance optimization:

[1135] The server uses an AI algorithm to generate a new scent: "70% mint, 30% lavender."

[1136] The server sends that scent data to the terminal.

[1137] 6. Providing fragrance:

[1138] The device displays the message, "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[1139] The user sets the fragrance in the diffuser according to the suggestion and enjoys relaxation.

[1140] Through the above process, users can easily access the most effective fragrance that best suits their needs.

[1141] The following describes the processing flow.

[1142] Step 1:

[1143] The user launches the application and enters basic information (name, age, gender, preferences) on the user registration screen.

[1144] Step 2:

[1145] The terminal sends the entered user information to the server.

[1146] Step 3:

[1147] The server saves the received user information to the database.

[1148] Step 4:

[1149] The user inputs their current situation (e.g., nervousness before a test) and their needs in that situation (e.g., wanting to relax) through the device's interface.

[1150] Step 5:

[1151] The terminal sends the entered status and needs data to the server.

[1152] Step 6:

[1153] The server saves the status and needs data to the database and prepares it for analysis.

[1154] Step 7:

[1155] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1156] Step 8:

[1157] Users try each scent and enter their rating for each (e.g., on a 10-point scale).

[1158] Step 9:

[1159] The device collects user evaluation data and converts it into a unified format.

[1160] Step 10:

[1161] The device sends the collected evaluation data to the server.

[1162] Step 11:

[1163] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs.

[1164] Step 12:

[1165] The server sends the generated optimal fragrance data (ingredients and their proportions) to the terminal.

[1166] Step 13:

[1167] The device displays a suggested fragrance that it has generated for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[1168] Step 14:

[1169] Users can enjoy the relaxing effects by setting the suggested fragrance in a diffuser or similar device and using it.

[1170] These steps allow users to easily obtain and use fragrances optimized for their own situation and needs.

[1171] (Example 1)

[1172] 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."

[1173] Conventional systems struggled to provide personalized fragrances tailored to each user's situation and needs, and were unable to suggest optimal fragrances based on user data. Furthermore, the inability to efficiently collect and analyze fragrance evaluation data and quickly reflect the results made it difficult to improve user satisfaction. There were also challenges in the efficiency of converting user-entered data into a unified format and transmitting it to the server.

[1174] 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.

[1175] In this invention, the server includes means for inputting and storing user attribute information, means for inputting and analyzing user status information and demand information, means for providing multiple basic scents and collecting evaluation information thereof, means for generating an optimal scent using a machine learning algorithm based on the collected evaluation information, and means for providing the generated scent information to the user. This makes it possible to suggest the optimal scent according to the user's situation and needs, and enables efficient analysis of collected data and rapid reflection of the results. Furthermore, input data can be converted into a standard format and efficiently transmitted to the server, improving overall processing efficiency.

[1176] "User" refers to an individual who uses the system.

[1177] "Attribute information" refers to basic information such as the user's name, age, gender, and preferences.

[1178] "Status information" refers to information about the user's current situation and psychological state.

[1179] "Demand information" refers to what a user needs or requests at that particular moment.

[1180] "Basic scent" refers to a scent that is individually prepared from among the multiple scents that the system provides to the user.

[1181] "Evaluation information" refers to feedback and evaluations that users have given to basic fragrances.

[1182] A "machine learning algorithm" refers to artificial intelligence technology that analyzes collected data and derives the optimal result.

[1183] "Fragrance information" refers to data on the optimal fragrance generated by a machine learning algorithm based on user demand.

[1184] A "standard format" refers to a format used to convert different types of information into a unified format.

[1185] A "server" refers to a computer system that stores data, performs analysis, and controls the entire system.

[1186] This invention relates to a system that provides the optimal fragrance according to the user's situation and needs. This system provides the user with a personalized fragrance through the following process.

[1187] First, the user installs and launches a dedicated application on their device. As part of the initial setup, the user enters their basic information (name, age, gender, preferences, etc.). The device sends this information to the server, which then stores the received information in a database. For example, if the user enters "Taro, 25 years old, male, likes to relax," this information is converted to JSON format and sent to the server.

[1188] Next, the user enters their current situation and the needs they have in that situation. For example, they might enter, "I'm nervous before the test. I want to relax." The device sends this situation and needs data to the server, which uses natural language processing to analyze the data and recognize the need for "stress relief." This data is also sent in JSON format.

[1189] The device then provides the user with an interface to try several basic scents (e.g., lavender, mint, citrus). The user tries each scent and enters their rating. For example, they might rate "Lavender 8, Mint 9, Citrus 5". The device sends this rating data to a server, which stores the received rating data in a database.

[1190] The server uses a machine learning algorithm based on the collected evaluation data to generate the fragrance combination best suited to the user's needs. In this example, the AI ​​model calculates the optimal blend as "70% mint, 30% lavender." The generated fragrance data is sent to the device, which then displays a suggestion for the best fragrance to the user. Specifically, it might display, "A Mint-Lavender Blend fragrance has been generated. Let's use the diffuser."

[1191] The user follows this suggestion and enjoys relaxation by using the generated fragrance. For example, the user might set up a diffuser according to the suggestion and enjoy the new scent.

[1192] Examples of prompt messages include, "Please try the following scents and rate them: lavender, mint, citrus."

[1193] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[1194] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1195] Step 1:

[1196] User registration and basic information entry

[1197] 1.1 The user installs and launches the dedicated application on their device.

[1198] 1.2 The user enters basic information such as name, age, gender, and preferences on the user registration screen. For example, they might enter "Taro, 25 years old, male, likes to relax."

[1199] 1.3 The terminal converts the entered basic information into JSON format and sends it to the server. Input: "Taro, 25 years old, male, likes to relax" Output: {"name":"Taro","age":25,"gender":"male","preference":"relax"}

[1200] 1.4 The server saves the received user information to the database. Specific example: Execute "INSERT INTO users (name, age, gender, preference) VALUES ('Taro', 25, 'Male', 'Relax')".

[1201] Step 2:

[1202] Input of situation and needs

[1203] 2.1 The user inputs their current situation and needs through the terminal interface. For example, they might input, "I'm nervous because of the upcoming test. I want to relax."

[1204] 2.2 The terminal converts this situation and needs data into JSON format and sends it to the server. Input: "I'm nervous before the test. I want to relax." Output: {"situation":"I'm nervous before the test", "need":"I want to relax"}

[1205] 2.3 The server receives and analyzes the data. Using natural language processing, it extracts the keywords "tension" and "relaxation" and internally records "Needs: Tension relief". Input: {"situation": "I'm nervous before the test","need": "I want to relax"} Output: "Needs: Tension relief"

[1206] Step 3:

[1207] Scent selection test

[1208] 3.1 The device provides an interface that allows the user to try several basic scents (lavender, mint, citrus). For example, it displays: "Please try the following scents. Please rate them: lavender, mint, citrus."

[1209] 3.2 The user tries each scent and enters a rating for each. For example, they might enter "Lavender 8, Mint 9, Citrus 5".

[1210] 3.3 The terminal converts this evaluation data into JSON format and sends it to the server. Input: "Lavender 8, Mint 9, Citrus 5" Output: {"lavender":8,"mint":9,"citrus":5}

[1211] 3.4 The server saves the received evaluation data to the database. Specific example: Execute "INSERT INTO feedback (user_id, lavender, mint, citrus) VALUES (1, 8, 9, 5)".

[1212] Step 4:

[1213] Data collection and optimization

[1214] 4.1 The server inputs the collected evaluation data into a machine learning algorithm to generate the most effective scent combination for the user's needs. Input: {"lavender":8,"mint":9,"citrus":5} Output: {"mint":70,"lavender":30}

[1215] 4.2 The server sends the generated scent data to the terminal. Output: {"mint":70,"lavender":30}

[1216] Step 5:

[1217] Providing fragrances on the device

[1218] 5.1 The terminal suggests fragrance information received from the server to the user. Specific example: It displays "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[1219] 5.2 The user enjoys the generated fragrance using a diffuser as suggested. Input: "Mint-Lavender Blend fragrance has been generated." Action: Set the fragrance in the diffuser and relax.

[1220] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[1221] (Application Example 1)

[1222] 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."

[1223] Traditional food delivery services lacked a mechanism to provide optimal aromas tailored to the user's situation and needs, making it difficult to enhance the dining experience. In particular, there is a need for technology that provides aromas that alleviate the anxiety and expectations users feel when ordering food.

[1224] 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.

[1225] In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, and means for providing multiple basic scents and collecting evaluation data thereof. This makes it possible to generate the optimal scent using an AI algorithm based on the collected evaluation data and provide it to the user in a food delivery service.

[1226] "User basic information" refers to basic personal information about individual users, such as name, age, gender, and preferences.

[1227] "Fragrance" refers to volatile substances produced from specific plants or chemicals, which are perceived through the sense of smell.

[1228] "Situation and needs information" refers to information about the environment and psychological state in which the user is placed, as well as the specific desires and requests that they feel are desirable under those circumstances.

[1229] "Evaluation data" refers to data on feedback and impressions that users give to the provided fragrances, specifically including ratings and comments.

[1230] An "AI algorithm" is a series of computational procedures that use artificial intelligence technology to analyze data and generate optimal answers or suggestions.

[1231] A "food delivery service" is a service that allows users to order meals and food items online or by phone, and have them delivered to a specified location.

[1232] The "dining experience" refers to the overall satisfaction and enjoyment that users feel when eating, and is influenced by factors such as the taste, aroma, appearance, and atmosphere of the food.

[1233] A "server" is a computer system that stores and processes data on a network, and is hardware or software that provides services in response to requests from clients.

[1234] This invention is a system that provides the optimal fragrance according to the user's situation and needs. The following hardware and software are used to implement this invention.

[1235] Hardware and software to use

[1236] Server: A computer system used for storing and analyzing data. Includes databases.

[1237] Terminal: A mobile device such as a smartphone or tablet. It collects user input and communicates with the server.

[1238] Generative AI Model: Software for implementing AI algorithms. It analyzes evaluation data and generates optimal fragrances.

[1239] System Processing Description

[1240] 1. User registration and saving of basic information:

[1241] Users launch the application using their mobile device and enter basic information such as their name, age, gender, and preferences on the registration screen. This entered data is then sent from the device to the server.

[1242] The server stores the received user information in a database.

[1243] 2. Inputting the situation and understanding the needs:

[1244] The user inputs their current mood or expectations through the device (e.g., tired, want to relax).

[1245] The terminal sends the input data to the server, which then analyzes the data.

[1246] 3. Collection of fragrance evaluation data:

[1247] The device displays several basic scents (e.g., lavender, mint, citrus) in sequence and asks the user for their evaluation.

[1248] Users try the provided fragrances and provide a rating (e.g., on a 10-point scale) for each fragrance.

[1249] The device collects this evaluation data, converts it to a unified format, and then sends it to the server.

[1250] 4. Scent optimization and generation:

[1251] Based on the received evaluation data, the server uses a generative AI model to generate the most effective fragrance combination to meet the user's needs.

[1252] For example, it's possible to create a new scent like "70% mint, 30% lavender."

[1253] This generated fragrance data is then sent back to the terminal.

[1254] 5. Providing and suggesting fragrances:

[1255] The device suggests the most suitable scent to the user based on the received scent data (e.g., "A relaxing Mint-Lavender Blend scent has been generated. Using a diffuser would be recommended.").

[1256] Users can enjoy the fragrance according to the suggestions.

[1257] Specific examples and prompt statements

[1258] Specific example:

[1259] User registration: The user installs the app and enters basic information. For example, "Taro, 25 years old, male, likes to relax."

[1260] Situation input: The user enters "I'm nervous because of the upcoming test. I want to relax."

[1261] Inputting evaluation data: Users test the scents and input their evaluations, such as "Lavender 8, Mint 9, Citrus 5".

[1262] Results of the generation AI model: The server uses AI to generate and suggest a scent of "70% mint, 30% lavender".

[1263] Prompts for the generative AI model:

[1264] Please generate ideas for the perfect aroma for the delivered food. Consider the following information:

[1265] User name: Taro

[1266] Age: 25

[1267] Gender: Male

[1268] Preferences: Italian

[1269] Current mood: Tired

[1270] Needs: I want to relax

[1271] Suitable scent for ordered meal is:

[1272] This invention makes it possible to provide users with the optimal aroma when ordering food in a food delivery service, thereby improving the dining experience.

[1273] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1274] Step 1:

[1275] User registration and saving of basic information

[1276] Users launch the application using a mobile device such as a smartphone or tablet and enter basic information such as their name, age, gender, and preferences.

[1277] The terminal sends the entered basic information data to the server.

[1278] The server stores the received user information in a database. At this time, a unique identifier such as a user ID is generated.

[1279] Input: User's basic information (name, age, gender, preferences)

[1280] Output: Basic information data stored on the server, user ID

[1281] Step 2:

[1282] Situation input and understanding of needs

[1283] The user inputs information about their current mood and expectations (e.g., tired, want to relax) through their device.

[1284] The terminal sends the entered status and needs data to the server.

[1285] The server analyzes the received data and identifies the corresponding needs. For example, in response to a situation of "being tired," the need for "relaxation" might be identified.

[1286] Input: User status and needs information

[1287] Output: Needs data analyzed by the server

[1288] Step 3:

[1289] Collection of fragrance evaluation data

[1290] The device displays an interface that sequentially presents the user with several basic scents (e.g., lavender, mint, citrus) and asks the user to rate each scent.

[1291] The user tries the provided fragrances and enters a rating (e.g., on a 10-point scale) for each fragrance into the device.

[1292] The terminal collects this evaluation data, converts it to a unified format, and then sends it to the server. At this time, each evaluation data is linked to a user ID.

[1293] Input: User evaluation data for each fragrance

[1294] Output: Evaluation data in a unified format sent to the server

[1295] Step 4:

[1296] Scent optimization and generation

[1297] The server uses a generative AI model based on the received evaluation data to generate the most effective fragrance combination for the user's needs.

[1298] For example, a new fragrance might be created that is "70% mint, 30% lavender."

[1299] The generated fragrance data is sent back to the terminal and is ready to be provided to the user.

[1300] Input: Evaluation data in a unified format, user needs data

[1301] Output: Generated fragrance data

[1302] Step 5:

[1303] Providing and suggesting fragrances

[1304] The device suggests the most suitable scent to the user based on the received scent data. For example, it might display, "A Mint-Lavender Blend scent with high relaxation effects has been generated. Using a diffuser would be beneficial."

[1305] Users can enjoy fragrances according to the suggestions. When using a diffuser, they can set the suggested fragrance to achieve a relaxing effect.

[1306] Input: Generated fragrance data

[1307] Output: Fragrance suggestion information provided to the user

[1308] 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.

[1309] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, means for providing the generated fragrance data to the user, and an emotion engine that recognizes the user's emotions.

[1310] Program Processing Description

[1311] 1. User registration and initial setup:

[1312] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[1313] The terminal sends the entered user information to the server.

[1314] The server stores the received user information in a database.

[1315] 2. Inputting the situation and understanding the needs:

[1316] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[1317] The terminal sends the entered status and needs data to the server.

[1318] The server stores and analyzes the data for analysis.

[1319] 3. Emotion recognition:

[1320] The device uses its built-in emotion engine to collect and analyze biometric information such as the user's facial expressions, voice, and heart rate, and recognizes the user's emotions.

[1321] The device sends the recognized emotion data to the server.

[1322] 4. Scent selection test:

[1323] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1324] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[1325] The device collects evaluation data and converts it into a standardized format.

[1326] 5. Data collection and transmission:

[1327] The device transmits all collected evaluation and sentiment data to the server.

[1328] 6. Fragrance optimization:

[1329] The server inputs the received evaluation and emotional data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[1330] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[1331] 7. Providing fragrance:

[1332] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[1333] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[1334] Specific examples

[1335] 1. User registration and initial setup:

[1336] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[1337] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[1338] The server saves this information to the database.

[1339] 2. Inputting the situation and understanding the needs:

[1340] The user types, "I'm nervous because of the upcoming test. I want to relax."

[1341] The terminal sends this input data to the server.

[1342] The server analyzes the data and recognizes that "tension relief" is the need.

[1343] 3. Emotion recognition:

[1344] The device monitors the user's facial expressions, voice, and heart rate, and analyzes the data with an emotion engine to recognize the emotion of "being nervous."

[1345] The device sends this emotion data to the server.

[1346] 4. Scent selection test:

[1347] The device displays the following message: "Please try the following scents: lavender, mint, citrus. Please rate them."

[1348] Users try out the scents and enter their ratings, such as "Lavender 8, Mint 9, Citrus 5".

[1349] The device sends evaluation data to the server.

[1350] 5. Data collection and transmission:

[1351] The device converts the evaluation data ("Lavender 8, Mint 9, Citrus 5") and emotion data into a unified format and sends it to the server.

[1352] 6. Fragrance optimization:

[1353] The server uses an AI algorithm to generate a new scent: "70% mint, 30% lavender."

[1354] The server sends that scent data to the terminal.

[1355] 7. Providing fragrance:

[1356] The device displays the message, "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[1357] The user sets the fragrance in the diffuser according to the suggestion and enjoys relaxation.

[1358] These steps allow users to easily access fragrances optimized for their emotions and needs, and experience the desired effects.

[1359] The following describes the processing flow.

[1360] Step 1:

[1361] The user launches the application and enters basic information such as their name, age, gender, and preferences on the user registration screen.

[1362] Step 2:

[1363] The terminal sends the entered user information to the server.

[1364] Step 3:

[1365] The server saves the received user information to the database.

[1366] Step 4:

[1367] The user inputs their current situation (e.g., nervousness before a test) and their needs in that situation (e.g., wanting to relax) through the device's interface.

[1368] Step 5:

[1369] The terminal sends the entered status and needs data to the server.

[1370] Step 6:

[1371] The server saves the received status and needs data to the database and prepares it for analysis.

[1372] Step 7:

[1373] The device activates an emotion engine and collects biometric information such as the user's facial expressions, voice, and heart rate.

[1374] Step 8:

[1375] The device analyzes collected biometric information to recognize the user's emotions. For example, it might analyze the user's emotion as "nervous."

[1376] Step 9:

[1377] The device sends the recognized emotion data to the server.

[1378] Step 10:

[1379] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1380] Step 11:

[1381] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[1382] Step 12:

[1383] The device collects user evaluation data and converts it into a unified format.

[1384] Step 13:

[1385] The device sends the collected evaluation data and sentiment data to the server.

[1386] Step 14:

[1387] The server inputs evaluation and emotional data into an AI algorithm to generate the most effective scent combination for the user's needs. For example, it might generate a blend of 70% mint and 30% lavender.

[1388] Step 15:

[1389] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[1390] Step 16:

[1391] The device displays a suggested optimal fragrance for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[1392] Step 17:

[1393] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[1394] This processing flow allows users to utilize the optimal scent based on emotion recognition and evaluation data.

[1395] (Example 2)

[1396] 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."

[1397] There is a problem with manually adjusting and selecting fragrances to find the one that best suits the user's emotions and needs: users face the challenge of manual adjustments and selections. Furthermore, there is a lack of methods to provide fragrances that respond to users' emotions and situations in real time, making it difficult to accurately suggest fragrances to individual users.

[1398] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting evaluation data thereof, means for generating an optimal scent using an AI algorithm based on the collected evaluation data and emotional data, and means for providing the generated scent data to the user. This makes it possible to propose an optimal scent in real time based on the user's emotional state and needs.

[1399] "User" refers to anyone who uses this system.

[1400] "Basic information" refers to information such as the user's name, age, gender, and preferences.

[1401] "Situation" refers to the specific environment or state in which the user is currently in.

[1402] "Needs" refer to the desires and demands that a user has in a given situation.

[1403] "Fragrance" refers to the multiple basic scent components provided to the user.

[1404] "Evaluation data" refers to the evaluation results that users have given to the provided fragrances.

[1405] An "AI algorithm" refers to a computational method that uses artificial intelligence to analyze data and derive the optimal result.

[1406] "Fragrance data" refers to data generated by an AI algorithm that shows the optimal fragrance combination for the user.

[1407] "Emotions" refer to the psychological state perceived from the user's facial expressions, voice, heart rate, etc.

[1408] "Emotional data" refers to data about a user's emotional state obtained using an emotion engine.

[1409] An "emotion engine" refers to a device or software that analyzes a user's facial expressions, voice, heart rate, and other biometric information to recognize their emotional state.

[1410] A "database" refers to a system on a server used to store and manage basic user information, evaluation data, and sentiment data.

[1411] "Terminal" refers to a device used by a user to input information (e.g., smartphone, tablet).

[1412] A "server" refers to a central computer that stores and analyzes information sent from user terminals and sends the results back to the terminals.

[1413] This invention relates to a system that provides the optimal fragrance according to the user's needs. The system collects the user's basic information, current situation, and emotional data, and uses an AI algorithm to generate and provide the optimal fragrance to the user.

[1414] Hardware and software configuration

[1415] The main components of this system include the following:

[1416] Device: A device used by a user to input information, such as a smartphone or tablet. These devices typically include a camera, microphone, heart rate sensor, and other electronic components.

[1417] Server: A central computer that stores and analyzes data, and includes databases and execution environments for AI algorithms (e.g., TensorFlow).

[1418] Emotion engine: This is software that acquires user facial expressions, voice, and heart rate data and analyzes their emotions. A specific example is Microsoft Azure's Emotion API.

[1419] Specific data processing and calculations

[1420] 1. Enter and save user information:

[1421] The user launches the app and enters basic information, such as their name, age, gender, and preferences. This information is sent from the device to the server and stored in a database.

[1422] 2. Input and analysis of the situation and needs:

[1423] The user enters their current situation (e.g., feeling nervous before a test) and needs (e.g., wanting to relax). The device sends this data to the server, which analyzes the situation and needs.

[1424] 3. Emotion recognition:

[1425] The device uses a camera, microphone, and heart rate sensor to collect biometric information such as the user's facial expressions, voice, and heart rate. This data is analyzed by an emotion engine, which recognizes emotions such as "feeling nervous."

[1426] 4. Fragrance selection and evaluation:

[1427] The device presents the user with several basic scents (lavender, mint, citrus, etc.) and asks for their evaluation. The user enters their evaluation for each scent, and the device collects the evaluation data and sends it to the server.

[1428] 5. Data analysis and fragrance generation:

[1429] The server inputs collected evaluation and emotional data into an AI algorithm to generate the optimal scent combination. For example, it might blend mint 70% with lavender 30%.

[1430] 6. Providing fragrance:

[1431] The device displays fragrance suggestions to the user based on fragrance data received from the server. The user then sets the suggested fragrance in the diffuser and enjoys relaxation.

[1432] Examples of specific cases and prompt statements

[1433] 1. Example of user registration:

[1434] User: Enters "Taro, 25 years old, male, likes to relax."

[1435] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[1436] Server: Stored in the database.

[1437] 2. Examples of situation input and needs assessment:

[1438] User: "I'm nervous because of the upcoming test. I want to relax."

[1439] Terminal: Send to server.

[1440] Server: Recognizes that "tension relief" is the need.

[1441] 3. Examples of emotion recognition:

[1442] Device: Monitors facial expressions, voice, and heart rate.

[1443] Terminal: Recognizes the emotion "feeling nervous" and sends it to the server.

[1444] 4. Example of a fragrance selection test:

[1445] Terminal: Enter the rating as "Lavender 8, Mint 9, Citrus 5".

[1446] Terminal: Sends evaluation data to the server.

[1447] Examples of prompts for generative AI models

[1448] "How can we provide users with the perfect scent to help them relax? We need a system that uses an AI algorithm to determine the optimal scent based on the user's basic information, current situation, and emotional state."

[1449] This allows users to easily access fragrances optimized for their emotions and needs, and experience the desired effects.

[1450] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1451] Program processing steps

[1452] Step 1:

[1453] The user launches the application and enters basic information (name, age, gender, preferences).

[1454] Input: Name, age, gender, preferences

[1455] Specific action: The user enters the information "Taro, 25 years old, male, likes to relax" into the input field.

[1456] Output: Structured user information

[1457] Step 2:

[1458] The terminal converts the entered user information into a structured data format (such as JSON) and sends it to the server.

[1459] Input: Basic information entered by the user

[1460] Specific action: The device generates JSON data with the format "Name: Taro, Age: 25, Gender: Male, Preferences: Relaxation" and sends it to the server.

[1461] Output: JSON data of basic information sent to the server

[1462] Step 3:

[1463] The server saves the received user information to the database.

[1464] Input: JSON data of user information sent from the device.

[1465] Specific operation: The server executes the following query on the database: "INSERT INTO users (name, age, gender, preferences) VALUES ('Taro', 25, 'Male', 'Relax')".

[1466] Output: User information stored in the database

[1467] Step 4:

[1468] The user uses the app's interface to input their current situation (e.g., feeling nervous before a test) and their needs (e.g., wanting to relax).

[1469] Input: Current situation, needs

[1470] Specific action: The user enters "Situation: Before test" and "Needs: Relax" into the interface.

[1471] Output: Structured situation and needs information

[1472] Step 5:

[1473] The terminal sends the entered status and needs data to the server.

[1474] Input: User-entered information about the situation and needs.

[1475] Specific action: The device generates JSON data containing "Status: Before test" and "Needs: Relax" and sends it to the server.

[1476] Output: JSON data containing status and needs information sent to the server.

[1477] Step 6:

[1478] The server stores and analyzes situation and needs data in a database.

[1479] Input: JSON data of status and needs information sent from the device.

[1480] Specific operation: The server executes the query "SELECT FROM relaxation_methods WHERE situations LIKE '%beforetest%'" to identify appropriate relaxation methods.

[1481] Output: Appropriate relaxation methods as a result of the analysis

[1482] Step 7:

[1483] The device uses a camera, microphone, and heart rate sensor to collect data on the user's facial expressions, voice, and heart rate.

[1484] Input: User's facial expressions, voice, and heart rate data

[1485] Specific operation: The device's camera scans the user's face, and facial expressions are analyzed through dedicated software (e.g., OpenCV).

[1486] Output: Collected biometric data

[1487] Step 8:

[1488] The device analyzes the collected biometric information using an emotion engine to recognize the user's emotional state.

[1489] Input: Collected biometric data

[1490] Specific operation: Send image data to the Emotion API and obtain an emotion score for "tension".

[1491] Output: Recognized emotion data

[1492] Step 9:

[1493] The device sends the recognized emotion data to the server.

[1494] Input: Recognized emotion data

[1495] Specific action: The device generates JSON data containing "Emotion: Stress, Score: 0.85" and sends it to the server.

[1496] Output: JSON data of emotion sent to the server

[1497] Step 10:

[1498] The device presents the user with several basic scents to try (e.g., lavender, mint, citrus) and asks for their evaluation.

[1499] Input: User ratings for scents

[1500] Specific actions: The user tries out different scents and rates each scent on a 10-point scale.

[1501] Output: Structured evaluation data

[1502] Step 11:

[1503] The device sends the collected evaluation data to the server.

[1504] Input: Fragrance evaluation data obtained from users

[1505] Specific operation: The device sends evaluation data of "Lavender 8, Mint 9, Citrus 5" to the server in JSON format.

[1506] Output: JSON data of the evaluation data sent to the server

[1507] Step 12:

[1508] The server uses an AI algorithm based on evaluation data and emotional data to generate the optimal scent combination.

[1509] Input: Evaluation data and sentiment data

[1510] Specific operation: The server uses an AI algorithm (e.g., TensorFlow) to calculate the optimal scent ratio (e.g., 70% mint, 30% lavender) for the user's emotions and needs.

[1511] Output: Generated fragrance data

[1512] Step 13:

[1513] The server sends the generated fragrance data to the terminal.

[1514] Input: Generated fragrance data

[1515] Specific operation: The server generates data including "Fragrance: Mint-Lavender Blend, Ratio: Mint 70%-Lavender 30%" and sends it to the terminal.

[1516] Output: Scent data sent to the terminal

[1517] Step 14:

[1518] The device displays fragrance suggestions to the user based on fragrance data received from the server.

[1519] Input: Scent data sent from the server

[1520] Specific action: The device displays the message, "Mint-Lavender Blend scent is recommended. Please use a diffuser."

[1521] Output: Scent suggestions displayed to the user

[1522] Step 15:

[1523] The user sets a new fragrance in the diffuser according to the suggestion and uses it.

[1524] Input: Scent suggestion from the device

[1525] Specific operation: The user inserts a new fragrance cartridge into the diffuser and diffuses the fragrance according to the instruction manual.

[1526] Output: The environment in which the fragrance is provided using a diffuser.

[1527] In this way, this system can generate and provide users with individually optimized fragrances based on their situation and emotions in real time.

[1528] (Application Example 2)

[1529] 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."

[1530] In today's world, personalized services based on user emotions and needs are crucial for improving the user experience in stores. However, with existing technology, it has been difficult to recognize user emotions in real time and provide the optimal fragrance based on them. Furthermore, there have been insufficient means to convert input information and collected data into a unified format and efficiently transmit it to servers. As a result, the effective provision of fragrances that meet user expectations has not been achieved.

[1531] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting their evaluation data, means for generating an optimal scent using an AI algorithm based on the collected evaluation data, means for providing the generated scent data to the user, means for collecting the user's emotional data using emotion recognition technology, means for converting the emotional data and evaluation data into a unified format and transmitting it to the server, and means for providing an optimal scent to enhance the user's personal experience in a physical store. This makes it possible to generate and provide an optimal scent based on the user's emotions and needs in real time.

[1532] "User basic information" refers to information used to identify an individual, such as the user's name, age, gender, and preferences.

[1533] "Situation" refers to the environment or state in which the user is currently in, and specifically includes situations such as fatigue, tension, or a desire to relax.

[1534] "Needs information" refers to information that indicates the effects or desired results that users seek in specific situations.

[1535] "Basic scents" refer to common types of fragrances such as lavender, mint, and citrus.

[1536] "Evaluation data" refers to data showing the evaluation results of fragrances that users have tried.

[1537] An "AI algorithm" is a computational method of artificial intelligence that analyzes patterns based on large amounts of data and derives the optimal solution.

[1538] "Fragrance data" refers to information that includes the components and proportions of a particular fragrance.

[1539] "Emotion recognition technology" is a technology that analyzes a user's emotions from biometric information such as facial expressions, voice, and heart rate.

[1540] A "unified format" is a method of converting different data formats into a single standard format, and is important for maintaining data consistency.

[1541] A "server" is a computer system used for storing, processing, and managing data.

[1542] A "physical store" refers to a store that exists physically, rather than a virtual store on the internet.

[1543] "Personal experience" refers to the experiences and feelings that each user individually acquires.

[1544] The "optimal scent" is the most effective combination of fragrances, generated based on the user's situation, needs, and emotional data.

[1545] This invention describes a system that provides optimal fragrances based on user emotions and needs in physical stores. The main components of the system are the following hardware and software.

[1546] 1. User registration and initial setup

[1547] The server provides an interface for users to input basic information such as their name, age, gender, and preferences. This information is then stored in a database.

[1548] The hardware used includes smartphones, smart glasses, and head-mounted displays.

[1549] 2. Inputting the situation and understanding the needs

[1550] Users input their current situation and needs through an input interface. For example, information such as "I want to relieve fatigue and relax."

[1551] The terminal sends the input data to the server, which then analyzes the data.

[1552] 3. Emotion recognition

[1553] The server uses the camera and heart rate sensor built into the smart device to analyze the user's facial expressions and heart rate. This allows it to collect user emotion data in real time and send it to the server.

[1554] One example of software that can be used is an AI emotion recognition API (e.g., Microsoft Azure Cognitive Services).

[1555] 4. Scent selection test

[1556] The terminal displays a list of available scents to the user and asks for their evaluation of each scent. The user tries the scents and enters their evaluation data.

[1557] The server receives the evaluation data and stores it in the database.

[1558] 5. Data collection and transmission

[1559] The device converts the collected evaluation and sentiment data into a unified format and sends it to the server. This ensures data consistency.

[1560] 6. Scent optimization

[1561] The server uses evaluation and sentiment data to run AI algorithms and generate the scent combination best suited to the user's needs. TensorFlow is one example of a machine learning platform that can be used.

[1562] 7. Providing fragrance

[1563] The terminal displays the optimal fragrance combination for the user based on the best fragrance data received from the server. The user then uses the fragrance in a diffuser or similar device at a physical store according to that information.

[1564] Specific examples and prompt statements

[1565] As a concrete example, when a user visits a store, the system operates in the following steps:

[1566] 1. User Registration

[1567] Prompt message: "Please enter your name and basic information (e.g., Taro, 25 years old, male, likes to relax)."

[1568] 2. Inputting the situation and needs

[1569] Prompt: "Please tell me your current situation and the effect you are looking for (e.g., I'm nervous before a test. I want to relax)."

[1570] 3. Emotion recognition

[1571] Prompt message: "We are monitoring emotions in real time. Thank you for your cooperation."

[1572] 4. Selecting a fragrance

[1573] Prompt: "Please try the following scents. Please enter your rating for each scent (e.g., Lavender 8, Peppermint 9, Citrus 5)."

[1574] 5. Provision of optimization results and fragrance

[1575] Prompt message: "The optimal fragrance has been generated based on your needs. Please set the fragrance in the diffuser and enjoy your relaxation."

[1576] Through the above steps, this system enables the creation and delivery of optimal fragrances based on the user's emotions and needs. This allows users to enjoy a personalized fragrance experience in physical stores.

[1577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1578] Step 1:

[1579] User registration and initial setup

[1580] The terminal provides an interface for the user to launch an application and enter basic information such as name, age, gender, and preferences. For example, if the user enters "Taro, 25 years old, male, likes to relax."

[1581] The terminal sends the entered user information to the server.

[1582] The server stores the received user information in a database.

[1583] Step 2:

[1584] Situation input and needs assessment

[1585] The user inputs their current situation and needs through the device's interface. For example, information such as, "I'm nervous because of the upcoming test. I want to relax."

[1586] The terminal sends this input data to the server.

[1587] The server analyzes the received data to understand the user's needs.

[1588] Step 3:

[1589] emotion recognition

[1590] The device uses its built-in camera and heart rate sensor to collect the user's facial expressions and heart rate in real time.

[1591] The device inputs this data into the emotion engine, which then analyzes the user's emotional state. For example, the emotion engine might recognize that the user is "stressed."

[1592] The device sends emotional data to the server.

[1593] Step 4:

[1594] Scent selection test

[1595] The device, through its interface, sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each.

[1596] Users try out different scents and enter a rating for each. For example, they might rate them as "Lavender 8, Mint 9, Citrus 5".

[1597] The device sends the collected evaluation data to the server.

[1598] Step 5:

[1599] Data collection and transmission

[1600] The device converts all evaluation and sentiment data into a unified format.

[1601] The terminal sends the data, converted to a unified format, to the server.

[1602] Step 6:

[1603] Scent optimization

[1604] The server inputs the received evaluation data and sentiment data into the AI ​​algorithm.

[1605] The AI ​​algorithm analyzes data and generates the most suitable scent combination for the user's needs. For example, it might generate a scent of "70% lavender and 30% mint."

[1606] The server sends the generated fragrance data to the terminal.

[1607] Step 7:

[1608] Providing fragrance

[1609] The device uses fragrance data received from the server to suggest the optimal way to deliver the fragrance to the user. For example, it might display, "Mint-Lavender Blend fragrance has been generated. Please use a diffuser."

[1610] Users enjoy the generated fragrance using devices such as diffusers.

[1611] These steps make it possible to provide the optimal fragrance in physical stores based on the user's emotions and needs.

[1612] 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.

[1613] 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.

[1614] 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.

[1615] [Fourth Embodiment]

[1616] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1617] 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.

[1618] 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).

[1619] 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.

[1620] 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.

[1621] 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).

[1622] 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.

[1623] 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.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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".

[1629] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, and means for providing the generated fragrance data to the user.

[1630] Program Processing Description

[1631] 1. User registration and initial setup:

[1632] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[1633] The terminal sends the entered user information to the server.

[1634] The server stores the received user information in a database.

[1635] 2. Inputting the situation and understanding the needs:

[1636] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[1637] The terminal sends the entered status and needs data to the server.

[1638] The server stores and analyzes the data for analysis.

[1639] 3. Scent selection test:

[1640] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1641] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[1642] The device collects evaluation data and converts it into a standardized format.

[1643] 4. Data collection and transmission:

[1644] The device sends all evaluation data collected from the user to the server.

[1645] 5. Fragrance optimization:

[1646] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[1647] The server sends the generated fragrance data (such as ingredients and proportions) to the terminal.

[1648] 6. Providing fragrance:

[1649] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[1650] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[1651] Specific examples

[1652] 1. User registration and initial setup:

[1653] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[1654] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[1655] The server saves this information to the database.

[1656] 2. Inputting the situation and understanding the needs:

[1657] The user types, "I'm nervous because of the upcoming test. I want to relax."

[1658] The terminal sends this input data to the server.

[1659] The server analyzes the data and recognizes that "tension relief" is the need.

[1660] 3. Scent selection test:

[1661] The device displays the following message: "Please try the following scents: lavender, mint, citrus. Please rate them."

[1662] Users try out the scents and enter their ratings, such as "Lavender 8, Mint 9, Citrus 5".

[1663] The device sends evaluation data to the server.

[1664] 4. Data collection and transmission:

[1665] The device converts the evaluation data "Lavender 8, Mint 9, Citrus 5" into a unified format and sends it to the server.

[1666] 5. Fragrance optimization:

[1667] The server uses an AI algorithm to generate a new scent: "70% mint, 30% lavender."

[1668] The server sends that scent data to the terminal.

[1669] 6. Providing fragrance:

[1670] The device displays the message, "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[1671] The user sets the fragrance in the diffuser according to the suggestion and enjoys relaxation.

[1672] Through the above process, users can easily access the most effective fragrance that best suits their needs.

[1673] The following describes the processing flow.

[1674] Step 1:

[1675] The user launches the application and enters basic information (name, age, gender, preferences) on the user registration screen.

[1676] Step 2:

[1677] The terminal sends the entered user information to the server.

[1678] Step 3:

[1679] The server saves the received user information to the database.

[1680] Step 4:

[1681] The user inputs their current situation (e.g., nervousness before a test) and their needs in that situation (e.g., wanting to relax) through the device's interface.

[1682] Step 5:

[1683] The terminal sends the entered status and needs data to the server.

[1684] Step 6:

[1685] The server saves the status and needs data to the database and prepares it for analysis.

[1686] Step 7:

[1687] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1688] Step 8:

[1689] Users try each scent and enter their rating for each (e.g., on a 10-point scale).

[1690] Step 9:

[1691] The device collects user evaluation data and converts it into a unified format.

[1692] Step 10:

[1693] The device sends the collected evaluation data to the server.

[1694] Step 11:

[1695] The server inputs the received evaluation data into an AI algorithm to generate the most effective fragrance combination for the user's needs.

[1696] Step 12:

[1697] The server sends the generated optimal fragrance data (ingredients and their proportions) to the terminal.

[1698] Step 13:

[1699] The device displays a suggested fragrance that it has generated for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[1700] Step 14:

[1701] Users can enjoy the relaxing effects by setting the suggested fragrance in a diffuser or similar device and using it.

[1702] These steps allow users to easily obtain and use fragrances optimized for their own situation and needs.

[1703] (Example 1)

[1704] 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".

[1705] Conventional systems struggled to provide personalized fragrances tailored to each user's situation and needs, and were unable to suggest optimal fragrances based on user data. Furthermore, the inability to efficiently collect and analyze fragrance evaluation data and quickly reflect the results made it difficult to improve user satisfaction. There were also challenges in the efficiency of converting user-entered data into a unified format and transmitting it to the server.

[1706] 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.

[1707] In this invention, the server includes means for inputting and storing user attribute information, means for inputting and analyzing user status information and demand information, means for providing multiple basic scents and collecting evaluation information thereof, means for generating an optimal scent using a machine learning algorithm based on the collected evaluation information, and means for providing the generated scent information to the user. This makes it possible to suggest the optimal scent according to the user's situation and needs, and enables efficient analysis of collected data and rapid reflection of the results. Furthermore, input data can be converted into a standard format and efficiently transmitted to the server, improving overall processing efficiency.

[1708] "User" refers to an individual who uses the system.

[1709] "Attribute information" refers to basic information such as the user's name, age, gender, and preferences.

[1710] "Status information" refers to information about the user's current situation and psychological state.

[1711] "Demand information" refers to what a user needs or requests at that particular moment.

[1712] "Basic scent" refers to a scent that is individually prepared from among the multiple scents that the system provides to the user.

[1713] "Evaluation information" refers to feedback and evaluations that users have given to basic fragrances.

[1714] A "machine learning algorithm" refers to artificial intelligence technology that analyzes collected data and derives the optimal result.

[1715] "Fragrance information" refers to data on the optimal fragrance generated by a machine learning algorithm based on user demand.

[1716] A "standard format" refers to a format used to convert different types of information into a unified format.

[1717] A "server" refers to a computer system that stores data, performs analysis, and controls the entire system.

[1718] This invention relates to a system that provides the optimal fragrance according to the user's situation and needs. This system provides the user with a personalized fragrance through the following process.

[1719] First, the user installs and launches a dedicated application on their device. As part of the initial setup, the user enters their basic information (name, age, gender, preferences, etc.). The device sends this information to the server, which then stores the received information in a database. For example, if the user enters "Taro, 25 years old, male, likes to relax," this information is converted to JSON format and sent to the server.

[1720] Next, the user enters their current situation and the needs they have in that situation. For example, they might enter, "I'm nervous before the test. I want to relax." The device sends this situation and needs data to the server, which uses natural language processing to analyze the data and recognize the need for "stress relief." This data is also sent in JSON format.

[1721] The device then provides the user with an interface to try several basic scents (e.g., lavender, mint, citrus). The user tries each scent and enters their rating. For example, they might rate "Lavender 8, Mint 9, Citrus 5". The device sends this rating data to a server, which stores the received rating data in a database.

[1722] The server uses a machine learning algorithm based on the collected evaluation data to generate the fragrance combination best suited to the user's needs. In this example, the AI ​​model calculates the optimal blend as "70% mint, 30% lavender." The generated fragrance data is sent to the device, which then displays a suggestion for the best fragrance to the user. Specifically, it might display, "A Mint-Lavender Blend fragrance has been generated. Let's use the diffuser."

[1723] The user follows this suggestion and enjoys relaxation by using the generated fragrance. For example, the user might set up a diffuser according to the suggestion and enjoy the new scent.

[1724] Examples of prompt messages include, "Please try the following scents and rate them: lavender, mint, citrus."

[1725] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[1726] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1727] Step 1:

[1728] User registration and basic information entry

[1729] 1.1 The user installs and launches the dedicated application on their device.

[1730] 1.2 The user enters basic information such as name, age, gender, and preferences on the user registration screen. For example, they might enter "Taro, 25 years old, male, likes to relax."

[1731] 1.3 The terminal converts the entered basic information into JSON format and sends it to the server. Input: "Taro, 25 years old, male, likes to relax" Output: {"name":"Taro","age":25,"gender":"male","preference":"relax"}

[1732] 1.4 The server saves the received user information to the database. Specific example: Execute "INSERT INTO users (name, age, gender, preference) VALUES ('Taro', 25, 'Male', 'Relax')".

[1733] Step 2:

[1734] Input of situation and needs

[1735] 2.1 The user inputs their current situation and needs through the terminal interface. For example, they might input, "I'm nervous because of the upcoming test. I want to relax."

[1736] 2.2 The terminal converts this situation and needs data into JSON format and sends it to the server. Input: "I'm nervous before the test. I want to relax." Output: {"situation":"I'm nervous before the test", "need":"I want to relax"}

[1737] 2.3 The server receives and analyzes the data. Using natural language processing, it extracts the keywords "tension" and "relaxation" and internally records "Needs: Tension relief". Input: {"situation": "I'm nervous before the test","need": "I want to relax"} Output: "Needs: Tension relief"

[1738] Step 3:

[1739] Scent selection test

[1740] 3.1 The device provides an interface that allows the user to try several basic scents (lavender, mint, citrus). For example, it displays: "Please try the following scents. Please rate them: lavender, mint, citrus."

[1741] 3.2 The user tries each scent and enters a rating for each. For example, they might enter "Lavender 8, Mint 9, Citrus 5".

[1742] 3.3 The terminal converts this evaluation data into JSON format and sends it to the server. Input: "Lavender 8, Mint 9, Citrus 5" Output: {"lavender":8,"mint":9,"citrus":5}

[1743] 3.4 The server saves the received evaluation data to the database. Specific example: Execute "INSERT INTO feedback (user_id, lavender, mint, citrus) VALUES (1, 8, 9, 5)".

[1744] Step 4:

[1745] Data collection and optimization

[1746] 4.1 The server inputs the collected evaluation data into a machine learning algorithm to generate the most effective scent combination for the user's needs. Input: {"lavender":8,"mint":9,"citrus":5} Output: {"mint":70,"lavender":30}

[1747] 4.2 The server sends the generated scent data to the terminal. Output: {"mint":70,"lavender":30}

[1748] Step 5:

[1749] Providing fragrances on the device

[1750] 5.1 The terminal suggests fragrance information received from the server to the user. Specific example: It displays "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[1751] 5.2 The user enjoys the generated fragrance using a diffuser as suggested. Input: "Mint-Lavender Blend fragrance has been generated." Action: Set the fragrance in the diffuser and relax.

[1752] Through these steps, users can easily find the fragrance that best suits their needs. This system efficiently collects user evaluation data and analyzes it using AI algorithms, enabling it to provide personalized experiences to individual users.

[1753] (Application Example 1)

[1754] 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".

[1755] Traditional food delivery services lacked a mechanism to provide optimal aromas tailored to the user's situation and needs, making it difficult to enhance the dining experience. In particular, there is a need for technology that provides aromas that alleviate the anxiety and expectations users feel when ordering food.

[1756] 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.

[1757] In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, and means for providing multiple basic scents and collecting evaluation data thereof. This makes it possible to generate the optimal scent using an AI algorithm based on the collected evaluation data and provide it to the user in a food delivery service.

[1758] "User basic information" refers to basic personal information about individual users, such as name, age, gender, and preferences.

[1759] "Fragrance" refers to volatile substances produced from specific plants or chemicals, which are perceived through the sense of smell.

[1760] "Situation and needs information" refers to information about the environment and psychological state in which the user is placed, as well as the specific desires and requests that they feel are desirable under those circumstances.

[1761] "Evaluation data" refers to data on feedback and impressions that users give to the provided fragrances, specifically including ratings and comments.

[1762] An "AI algorithm" is a series of computational procedures that use artificial intelligence technology to analyze data and generate optimal answers or suggestions.

[1763] A "food delivery service" is a service that allows users to order meals and food items online or by phone, and have them delivered to a specified location.

[1764] The "dining experience" refers to the overall satisfaction and enjoyment that users feel when eating, and is influenced by factors such as the taste, aroma, appearance, and atmosphere of the food.

[1765] A "server" is a computer system that stores and processes data on a network, and is hardware or software that provides services in response to requests from clients.

[1766] This invention is a system that provides the optimal fragrance according to the user's situation and needs. The following hardware and software are used to implement this invention.

[1767] Hardware and software to use

[1768] Server: A computer system used for storing and analyzing data. Includes databases.

[1769] Terminal: A mobile device such as a smartphone or tablet. It collects user input and communicates with the server.

[1770] Generative AI Model: Software for implementing AI algorithms. It analyzes evaluation data and generates optimal fragrances.

[1771] System Processing Description

[1772] 1. User registration and saving of basic information:

[1773] Users launch the application using their mobile device and enter basic information such as their name, age, gender, and preferences on the registration screen. This entered data is then sent from the device to the server.

[1774] The server stores the received user information in a database.

[1775] 2. Inputting the situation and understanding the needs:

[1776] The user inputs their current mood or expectations through the device (e.g., tired, want to relax).

[1777] The terminal sends the input data to the server, which then analyzes the data.

[1778] 3. Collection of fragrance evaluation data:

[1779] The device displays several basic scents (e.g., lavender, mint, citrus) in sequence and asks the user for their evaluation.

[1780] Users try the provided fragrances and provide a rating (e.g., on a 10-point scale) for each fragrance.

[1781] The device collects this evaluation data, converts it to a unified format, and then sends it to the server.

[1782] 4. Scent optimization and generation:

[1783] Based on the received evaluation data, the server uses a generative AI model to generate the most effective fragrance combination to meet the user's needs.

[1784] For example, it's possible to create a new scent like "70% mint, 30% lavender."

[1785] This generated fragrance data is then sent back to the terminal.

[1786] 5. Providing and suggesting fragrances:

[1787] The device suggests the most suitable scent to the user based on the received scent data (e.g., "A relaxing Mint-Lavender Blend scent has been generated. Using a diffuser would be recommended.").

[1788] Users can enjoy the fragrance according to the suggestions.

[1789] Specific examples and prompt statements

[1790] Specific example:

[1791] User registration: The user installs the app and enters basic information. For example, "Taro, 25 years old, male, likes to relax."

[1792] Situation input: The user enters "I'm nervous because of the upcoming test. I want to relax."

[1793] Inputting evaluation data: Users test the scents and input their evaluations, such as "Lavender 8, Mint 9, Citrus 5".

[1794] Results of the generation AI model: The server uses AI to generate and suggest a scent of "70% mint, 30% lavender".

[1795] Prompts for the generative AI model:

[1796] Please generate ideas for the perfect aroma for the delivered food. Consider the following information:

[1797] User name: Taro

[1798] Age: 25

[1799] Gender: Male

[1800] Preferences: Italian

[1801] Current mood: Tired

[1802] Needs: I want to relax

[1803] Suitable scent for ordered meal is:

[1804] This invention makes it possible to provide users with the optimal aroma when ordering food in a food delivery service, thereby improving the dining experience.

[1805] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1806] Step 1:

[1807] User registration and saving of basic information

[1808] Users launch the application using a mobile device such as a smartphone or tablet and enter basic information such as their name, age, gender, and preferences.

[1809] The terminal sends the entered basic information data to the server.

[1810] The server stores the received user information in a database. At this time, a unique identifier such as a user ID is generated.

[1811] Input: User's basic information (name, age, gender, preferences)

[1812] Output: Basic information data stored on the server, user ID

[1813] Step 2:

[1814] Situation input and understanding of needs

[1815] The user inputs information about their current mood and expectations (e.g., tired, want to relax) through their device.

[1816] The terminal sends the entered status and needs data to the server.

[1817] The server analyzes the received data and identifies the corresponding needs. For example, in response to a situation of "being tired," the need for "relaxation" might be identified.

[1818] Input: User status and needs information

[1819] Output: Needs data analyzed by the server

[1820] Step 3:

[1821] Collection of fragrance evaluation data

[1822] The device displays an interface that sequentially presents the user with several basic scents (e.g., lavender, mint, citrus) and asks the user to rate each scent.

[1823] The user tries the provided fragrances and enters a rating (e.g., on a 10-point scale) for each fragrance into the device.

[1824] The terminal collects this evaluation data, converts it to a unified format, and then sends it to the server. At this time, each evaluation data is linked to a user ID.

[1825] Input: User evaluation data for each fragrance

[1826] Output: Evaluation data in a unified format sent to the server

[1827] Step 4:

[1828] Scent optimization and generation

[1829] The server uses a generative AI model based on the received evaluation data to generate the most effective fragrance combination for the user's needs.

[1830] For example, a new fragrance might be created that is "70% mint, 30% lavender."

[1831] The generated fragrance data is sent back to the terminal and is ready to be provided to the user.

[1832] Input: Evaluation data in a unified format, user needs data

[1833] Output: Generated fragrance data

[1834] Step 5:

[1835] Providing and suggesting fragrances

[1836] The device suggests the most suitable scent to the user based on the received scent data. For example, it might display, "A Mint-Lavender Blend scent with high relaxation effects has been generated. Using a diffuser would be beneficial."

[1837] Users can enjoy fragrances according to the suggestions. When using a diffuser, they can set the suggested fragrance to achieve a relaxing effect.

[1838] Input: Generated fragrance data

[1839] Output: Fragrance suggestion information provided to the user

[1840] 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.

[1841] This invention relates to a system that provides an optimal fragrance according to the user's situation and needs, and its main components include means for inputting and storing the user's basic information, means for inputting and analyzing situation and needs information, means for providing multiple basic fragrances and collecting evaluation data, means for generating an optimal fragrance using an AI algorithm based on the evaluation data, means for providing the generated fragrance data to the user, and an emotion engine that recognizes the user's emotions.

[1842] Program Processing Description

[1843] 1. User registration and initial setup:

[1844] The user launches the application and enters basic information such as name, age, gender, and preferences on the user registration screen.

[1845] The terminal sends the entered user information to the server.

[1846] The server stores the received user information in a database.

[1847] 2. Inputting the situation and understanding the needs:

[1848] The user inputs their current situation (e.g., anxiety before a test) and their needs in that situation (e.g., wanting to relax) through the terminal's interface.

[1849] The terminal sends the entered status and needs data to the server.

[1850] The server stores and analyzes the data for analysis.

[1851] 3. Emotion recognition:

[1852] The device uses its built-in emotion engine to collect and analyze biometric information such as the user's facial expressions, voice, and heart rate, and recognizes the user's emotions.

[1853] The device sends the recognized emotion data to the server.

[1854] 4. Scent selection test:

[1855] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1856] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[1857] The device collects evaluation data and converts it into a standardized format.

[1858] 5. Data collection and transmission:

[1859] The device transmits all collected evaluation and sentiment data to the server.

[1860] 6. Fragrance optimization:

[1861] The server inputs the received evaluation and emotional data into an AI algorithm to generate the most effective fragrance combination for the user's needs. For example, it might blend lavender and mint in specific proportions.

[1862] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[1863] 7. Providing fragrance:

[1864] The device displays a fragrance suggestion to the user based on fragrance data received from the server. For example, it might display, "A Mint-Lavender Blend fragrance has been generated. Using a diffuser would be recommended."

[1865] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[1866] Specific examples

[1867] 1. User registration and initial setup:

[1868] The user installs and launches the app. Next, they enter "Taro, 25 years old, male, likes to relax."

[1869] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[1870] The server saves this information to the database.

[1871] 2. Inputting the situation and understanding the needs:

[1872] The user types, "I'm nervous because of the upcoming test. I want to relax."

[1873] The terminal sends this input data to the server.

[1874] The server analyzes the data and recognizes that "tension relief" is the need.

[1875] 3. Emotion recognition:

[1876] The device monitors the user's facial expressions, voice, and heart rate, and analyzes the data with an emotion engine to recognize the emotion of "being nervous."

[1877] The device sends this emotion data to the server.

[1878] 4. Scent selection test:

[1879] The device displays the following message: "Please try the following scents: lavender, mint, citrus. Please rate them."

[1880] Users try out the scents and enter their ratings, such as "Lavender 8, Mint 9, Citrus 5".

[1881] The device sends evaluation data to the server.

[1882] 5. Data collection and transmission:

[1883] The device converts the evaluation data ("Lavender 8, Mint 9, Citrus 5") and emotion data into a unified format and sends it to the server.

[1884] 6. Fragrance optimization:

[1885] The server uses an AI algorithm to generate a new scent: "70% mint, 30% lavender."

[1886] The server sends that scent data to the terminal.

[1887] 7. Providing fragrance:

[1888] The device displays the message, "Mint-Lavender Blend fragrance has been generated. Please use the diffuser."

[1889] The user sets the fragrance in the diffuser according to the suggestion and enjoys relaxation.

[1890] These steps allow users to easily access fragrances optimized for their emotions and needs, and experience the desired effects.

[1891] The following describes the processing flow.

[1892] Step 1:

[1893] The user launches the application and enters basic information such as their name, age, gender, and preferences on the user registration screen.

[1894] Step 2:

[1895] The terminal sends the entered user information to the server.

[1896] Step 3:

[1897] The server saves the received user information to the database.

[1898] Step 4:

[1899] The user inputs their current situation (e.g., nervousness before a test) and their needs in that situation (e.g., wanting to relax) through the device's interface.

[1900] Step 5:

[1901] The terminal sends the entered status and needs data to the server.

[1902] Step 6:

[1903] The server saves the received status and needs data to the database and prepares it for analysis.

[1904] Step 7:

[1905] The device activates an emotion engine and collects biometric information such as the user's facial expressions, voice, and heart rate.

[1906] Step 8:

[1907] The device analyzes collected biometric information to recognize the user's emotions. For example, it might analyze the user's emotion as "nervous."

[1908] Step 9:

[1909] The device sends the recognized emotion data to the server.

[1910] Step 10:

[1911] The device displays an interface that sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each scent.

[1912] Step 11:

[1913] Users try out different scents and enter their ratings for each (e.g., on a 10-point scale).

[1914] Step 12:

[1915] The device collects user evaluation data and converts it into a unified format.

[1916] Step 13:

[1917] The device sends the collected evaluation data and sentiment data to the server.

[1918] Step 14:

[1919] The server inputs evaluation and emotional data into an AI algorithm to generate the most effective scent combination for the user's needs. For example, it might generate a blend of 70% mint and 30% lavender.

[1920] Step 15:

[1921] The server sends the generated fragrance data (ingredients and their proportions) to the terminal.

[1922] Step 16:

[1923] The device displays a suggested optimal fragrance for the user and provides instructions on how to use it. For example, it might display: "Mint-Lavender Blend fragrance has been generated. Using a diffuser is recommended."

[1924] Step 17:

[1925] The user uses the fragrance according to the suggestion. For example, they might enjoy a new fragrance set in a diffuser.

[1926] This processing flow allows users to utilize the optimal scent based on emotion recognition and evaluation data.

[1927] (Example 2)

[1928] 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".

[1929] There is a problem with manually adjusting and selecting fragrances to find the one that best suits the user's emotions and needs: users face the challenge of manual adjustments and selections. Furthermore, there is a lack of methods to provide fragrances that respond to users' emotions and situations in real time, making it difficult to accurately suggest fragrances to individual users.

[1930] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting evaluation data thereof, means for generating an optimal scent using an AI algorithm based on the collected evaluation data and emotional data, and means for providing the generated scent data to the user. This makes it possible to propose an optimal scent in real time based on the user's emotional state and needs.

[1931] "User" refers to anyone who uses this system.

[1932] "Basic information" refers to information such as the user's name, age, gender, and preferences.

[1933] "Situation" refers to the specific environment or state in which the user is currently in.

[1934] "Needs" refer to the desires and demands that a user has in a given situation.

[1935] "Fragrance" refers to the multiple basic scent components provided to the user.

[1936] "Evaluation data" refers to the evaluation results that users have given to the provided fragrances.

[1937] An "AI algorithm" refers to a computational method that uses artificial intelligence to analyze data and derive the optimal result.

[1938] "Fragrance data" refers to data generated by an AI algorithm that shows the optimal fragrance combination for the user.

[1939] "Emotions" refer to the psychological state perceived from the user's facial expressions, voice, heart rate, etc.

[1940] "Emotional data" refers to data about a user's emotional state obtained using an emotion engine.

[1941] An "emotion engine" refers to a device or software that analyzes a user's facial expressions, voice, heart rate, and other biometric information to recognize their emotional state.

[1942] A "database" refers to a system on a server used to store and manage basic user information, evaluation data, and sentiment data.

[1943] "Terminal" refers to a device used by a user to input information (e.g., smartphone, tablet).

[1944] A "server" refers to a central computer that stores and analyzes information sent from user terminals and sends the results back to the terminals.

[1945] This invention relates to a system that provides the optimal fragrance according to the user's needs. The system collects the user's basic information, current situation, and emotional data, and uses an AI algorithm to generate and provide the optimal fragrance to the user.

[1946] Hardware and software configuration

[1947] The main components of this system include the following:

[1948] Device: A device used by a user to input information, such as a smartphone or tablet. These devices typically include a camera, microphone, heart rate sensor, and other electronic components.

[1949] Server: A central computer that stores and analyzes data, and includes databases and execution environments for AI algorithms (e.g., TensorFlow).

[1950] Emotion engine: This is software that acquires user facial expressions, voice, and heart rate data and analyzes their emotions. A specific example is Microsoft Azure's Emotion API.

[1951] Specific data processing and calculations

[1952] 1. Enter and save user information:

[1953] The user launches the app and enters basic information, such as their name, age, gender, and preferences. This information is sent from the device to the server and stored in a database.

[1954] 2. Input and analysis of the situation and needs:

[1955] The user enters their current situation (e.g., feeling nervous before a test) and needs (e.g., wanting to relax). The device sends this data to the server, which analyzes the situation and needs.

[1956] 3. Emotion recognition:

[1957] The device uses a camera, microphone, and heart rate sensor to collect biometric information such as the user's facial expressions, voice, and heart rate. This data is analyzed by an emotion engine, which recognizes emotions such as "feeling nervous."

[1958] 4. Fragrance selection and evaluation:

[1959] The device presents the user with several basic scents (lavender, mint, citrus, etc.) and asks for their evaluation. The user enters their evaluation for each scent, and the device collects the evaluation data and sends it to the server.

[1960] 5. Data analysis and fragrance generation:

[1961] The server inputs collected evaluation and emotional data into an AI algorithm to generate the optimal scent combination. For example, it might blend mint 70% with lavender 30%.

[1962] 6. Providing fragrance:

[1963] The device displays fragrance suggestions to the user based on fragrance data received from the server. The user then sets the suggested fragrance in the diffuser and enjoys relaxation.

[1964] Examples of specific cases and prompt statements

[1965] 1. Example of user registration:

[1966] User: Enters "Taro, 25 years old, male, likes to relax."

[1967] The device sends the data "Taro, 25 years old, male, likes to relax" to the server.

[1968] Server: Stored in the database.

[1969] 2. Examples of situation input and needs assessment:

[1970] User: "I'm nervous because of the upcoming test. I want to relax."

[1971] Terminal: Send to server.

[1972] Server: Recognizes that "tension relief" is the need.

[1973] 3. Examples of emotion recognition:

[1974] Device: Monitors facial expressions, voice, and heart rate.

[1975] Terminal: Recognizes the emotion "feeling nervous" and sends it to the server.

[1976] 4. Example of a fragrance selection test:

[1977] Terminal: Enter the rating as "Lavender 8, Mint 9, Citrus 5".

[1978] Terminal: Sends evaluation data to the server.

[1979] Examples of prompts for generative AI models

[1980] "How can we provide users with the perfect scent to help them relax? We need a system that uses an AI algorithm to determine the optimal scent based on the user's basic information, current situation, and emotional state."

[1981] This allows users to easily access fragrances optimized for their emotions and needs, and experience the desired effects.

[1982] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1983] Program processing steps

[1984] Step 1:

[1985] The user launches the application and enters basic information (name, age, gender, preferences).

[1986] Input: Name, age, gender, preferences

[1987] Specific action: The user enters the information "Taro, 25 years old, male, likes to relax" into the input field.

[1988] Output: Structured user information

[1989] Step 2:

[1990] The terminal converts the entered user information into a structured data format (such as JSON) and sends it to the server.

[1991] Input: Basic information entered by the user

[1992] Specific action: The device generates JSON data with the format "Name: Taro, Age: 25, Gender: Male, Preferences: Relaxation" and sends it to the server.

[1993] Output: JSON data of basic information sent to the server

[1994] Step 3:

[1995] The server saves the received user information to the database.

[1996] Input: JSON data of user information sent from the device.

[1997] Specific operation: The server executes the following query on the database: "INSERT INTO users (name, age, gender, preferences) VALUES ('Taro', 25, 'Male', 'Relax')".

[1998] Output: User information stored in the database

[1999] Step 4:

[2000] The user uses the app's interface to input their current situation (e.g., feeling nervous before a test) and their needs (e.g., wanting to relax).

[2001] Input: Current situation, needs

[2002] Specific action: The user enters "Situation: Before test" and "Needs: Relax" into the interface.

[2003] Output: Structured situation and needs information

[2004] Step 5:

[2005] The terminal sends the entered status and needs data to the server.

[2006] Input: User-entered information about the situation and needs.

[2007] Specific action: The device generates JSON data containing "Status: Before test" and "Needs: Relax" and sends it to the server.

[2008] Output: JSON data containing status and needs information sent to the server.

[2009] Step 6:

[2010] The server stores and analyzes situation and needs data in a database.

[2011] Input: JSON data of status and needs information sent from the device.

[2012] Specific operation: The server executes the query "SELECT FROM relaxation_methods WHERE situations LIKE '%beforetest%'" to identify appropriate relaxation methods.

[2013] Output: Appropriate relaxation methods as a result of the analysis

[2014] Step 7:

[2015] The device uses a camera, microphone, and heart rate sensor to collect data on the user's facial expressions, voice, and heart rate.

[2016] Input: User's facial expressions, voice, and heart rate data

[2017] Specific operation: The device's camera scans the user's face, and facial expressions are analyzed through dedicated software (e.g., OpenCV).

[2018] Output: Collected biometric data

[2019] Step 8:

[2020] The device analyzes the collected biometric information using an emotion engine to recognize the user's emotional state.

[2021] Input: Collected biometric data

[2022] Specific operation: Send image data to the Emotion API and obtain an emotion score for "tension".

[2023] Output: Recognized emotion data

[2024] Step 9:

[2025] The device sends the recognized emotion data to the server.

[2026] Input: Recognized emotion data

[2027] Specific action: The device generates JSON data containing "Emotion: Stress, Score: 0.85" and sends it to the server.

[2028] Output: JSON data of emotion sent to the server

[2029] Step 10:

[2030] The device presents the user with several basic scents to try (e.g., lavender, mint, citrus) and asks for their evaluation.

[2031] Input: User ratings for scents

[2032] Specific actions: The user tries out different scents and rates each scent on a 10-point scale.

[2033] Output: Structured evaluation data

[2034] Step 11:

[2035] The device sends the collected evaluation data to the server.

[2036] Input: Fragrance evaluation data obtained from users

[2037] Specific operation: The device sends evaluation data of "Lavender 8, Mint 9, Citrus 5" to the server in JSON format.

[2038] Output: JSON data of the evaluation data sent to the server

[2039] Step 12:

[2040] The server uses an AI algorithm based on evaluation data and emotional data to generate the optimal scent combination.

[2041] Input: Evaluation data and sentiment data

[2042] Specific operation: The server uses an AI algorithm (e.g., TensorFlow) to calculate the optimal scent ratio (e.g., 70% mint, 30% lavender) for the user's emotions and needs.

[2043] Output: Generated fragrance data

[2044] Step 13:

[2045] The server sends the generated fragrance data to the terminal.

[2046] Input: Generated fragrance data

[2047] Specific operation: The server generates data including "Fragrance: Mint-Lavender Blend, Ratio: Mint 70%-Lavender 30%" and sends it to the terminal.

[2048] Output: Scent data sent to the terminal

[2049] Step 14:

[2050] The device displays fragrance suggestions to the user based on fragrance data received from the server.

[2051] Input: Scent data sent from the server

[2052] Specific action: The device displays the message, "Mint-Lavender Blend scent is recommended. Please use a diffuser."

[2053] Output: Scent suggestions displayed to the user

[2054] Step 15:

[2055] The user sets a new fragrance in the diffuser according to the suggestion and uses it.

[2056] Input: Scent suggestion from the device

[2057] Specific operation: The user inserts a new fragrance cartridge into the diffuser and diffuses the fragrance according to the instruction manual.

[2058] Output: The environment in which the fragrance is provided using a diffuser.

[2059] In this way, this system can generate and provide users with individually optimized fragrances based on their situation and emotions in real time.

[2060] (Application Example 2)

[2061] 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".

[2062] In today's world, personalized services based on user emotions and needs are crucial for improving the user experience in stores. However, with existing technology, it has been difficult to recognize user emotions in real time and provide the optimal fragrance based on them. Furthermore, there have been insufficient means to convert input information and collected data into a unified format and efficiently transmit it to servers. As a result, the effective provision of fragrances that meet user expectations has not been achieved.

[2063] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting and storing the user's basic information, means for inputting and analyzing the user's situation and needs information, means for providing a plurality of basic scents and collecting their evaluation data, means for generating an optimal scent using an AI algorithm based on the collected evaluation data, means for providing the generated scent data to the user, means for collecting the user's emotional data using emotion recognition technology, means for converting the emotional data and evaluation data into a unified format and transmitting it to the server, and means for providing an optimal scent to enhance the user's personal experience in a physical store. This makes it possible to generate and provide an optimal scent based on the user's emotions and needs in real time.

[2064] "User basic information" refers to information used to identify an individual, such as the user's name, age, gender, and preferences.

[2065] "Situation" refers to the environment or state in which the user is currently in, and specifically includes situations such as fatigue, tension, or a desire to relax.

[2066] "Needs information" refers to information that indicates the effects or desired results that users seek in specific situations.

[2067] "Basic scents" refer to common types of fragrances such as lavender, mint, and citrus.

[2068] "Evaluation data" refers to data showing the evaluation results of fragrances that users have tried.

[2069] An "AI algorithm" is a computational method of artificial intelligence that analyzes patterns based on large amounts of data and derives the optimal solution.

[2070] "Fragrance data" refers to information that includes the components and proportions of a particular fragrance.

[2071] "Emotion recognition technology" is a technology that analyzes a user's emotions from biometric information such as facial expressions, voice, and heart rate.

[2072] A "unified format" is a method of converting different data formats into a single standard format, and is important for maintaining data consistency.

[2073] A "server" is a computer system used for storing, processing, and managing data.

[2074] A "physical store" refers to a store that exists physically, rather than a virtual store on the internet.

[2075] "Personal experience" refers to the experiences and feelings that each user individually acquires.

[2076] The "optimal scent" is the most effective combination of fragrances, generated based on the user's situation, needs, and emotional data.

[2077] This invention describes a system that provides optimal fragrances based on user emotions and needs in physical stores. The main components of the system are the following hardware and software.

[2078] 1. User registration and initial setup

[2079] The server provides an interface for users to input basic information such as their name, age, gender, and preferences. This information is then stored in a database.

[2080] The hardware used includes smartphones, smart glasses, and head-mounted displays.

[2081] 2. Inputting the situation and understanding the needs

[2082] Users input their current situation and needs through an input interface. For example, information such as "I want to relieve fatigue and relax."

[2083] The terminal sends the input data to the server, which then analyzes the data.

[2084] 3. Emotion recognition

[2085] The server uses the camera and heart rate sensor built into the smart device to analyze the user's facial expressions and heart rate. This allows it to collect user emotion data in real time and send it to the server.

[2086] One example of software that can be used is an AI emotion recognition API (e.g., Microsoft Azure Cognitive Services).

[2087] 4. Scent selection test

[2088] The terminal displays a list of available scents to the user and asks for their evaluation of each scent. The user tries the scents and enters their evaluation data.

[2089] The server receives the evaluation data and stores it in the database.

[2090] 5. Data collection and transmission

[2091] The device converts the collected evaluation and sentiment data into a unified format and sends it to the server. This ensures data consistency.

[2092] 6. Scent optimization

[2093] The server uses evaluation and sentiment data to run AI algorithms and generate the scent combination best suited to the user's needs. TensorFlow is one example of a machine learning platform that can be used.

[2094] 7. Providing fragrance

[2095] The terminal displays the optimal fragrance combination for the user based on the best fragrance data received from the server. The user then uses the fragrance in a diffuser or similar device at a physical store according to that information.

[2096] Specific examples and prompt statements

[2097] As a concrete example, when a user visits a store, the system operates in the following steps:

[2098] 1. User Registration

[2099] Prompt message: "Please enter your name and basic information (e.g., Taro, 25 years old, male, likes to relax)."

[2100] 2. Inputting the situation and needs

[2101] Prompt: "Please tell me your current situation and the effect you are looking for (e.g., I'm nervous before a test. I want to relax)."

[2102] 3. Emotion recognition

[2103] Prompt message: "We are monitoring emotions in real time. Thank you for your cooperation."

[2104] 4. Selecting a fragrance

[2105] Prompt: "Please try the following scents. Please enter your rating for each scent (e.g., Lavender 8, Peppermint 9, Citrus 5)."

[2106] 5. Provision of optimization results and fragrance

[2107] Prompt message: "The optimal fragrance has been generated based on your needs. Please set the fragrance in the diffuser and enjoy your relaxation."

[2108] Through the above steps, this system enables the creation and delivery of optimal fragrances based on the user's emotions and needs. This allows users to enjoy a personalized fragrance experience in physical stores.

[2109] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2110] Step 1:

[2111] User registration and initial setup

[2112] The terminal provides an interface for the user to launch an application and enter basic information such as name, age, gender, and preferences. For example, if the user enters "Taro, 25 years old, male, likes to relax."

[2113] The terminal sends the entered user information to the server.

[2114] The server stores the received user information in a database.

[2115] Step 2:

[2116] Situation input and needs assessment

[2117] The user inputs their current situation and needs through the device's interface. For example, information such as, "I'm nervous because of the upcoming test. I want to relax."

[2118] The terminal sends this input data to the server.

[2119] The server analyzes the received data to understand the user's needs.

[2120] Step 3:

[2121] emotion recognition

[2122] The device uses its built-in camera and heart rate sensor to collect the user's facial expressions and heart rate in real time.

[2123] The device inputs this data into the emotion engine, which then analyzes the user's emotional state. For example, the emotion engine might recognize that the user is "stressed."

[2124] The device sends emotional data to the server.

[2125] Step 4:

[2126] Scent selection test

[2127] The device, through its interface, sequentially offers the user several basic scents (e.g., lavender, mint, citrus) and requests feedback on each.

[2128] Users try out different scents and enter a rating for each. For example, they might rate them as "Lavender 8, Mint 9, Citrus 5".

[2129] The device sends the collected evaluation data to the server.

[2130] Step 5:

[2131] Data collection and transmission

[2132] The device converts all evaluation and sentiment data into a unified format.

[2133] The terminal sends the data, converted to a unified format, to the server.

[2134] Step 6:

[2135] Scent optimization

[2136] The server inputs the received evaluation data and sentiment data into the AI ​​algorithm.

[2137] The AI ​​algorithm analyzes data and generates the most suitable scent combination for the user's needs. For example, it might generate a scent of "70% lavender and 30% mint."

[2138] The server sends the generated fragrance data to the terminal.

[2139] Step 7:

[2140] Providing fragrance

[2141] The device uses fragrance data received from the server to suggest the optimal way to deliver the fragrance to the user. For example, it might display, "Mint-Lavender Blend fragrance has been generated. Please use a diffuser."

[2142] Users enjoy the generated fragrance using devices such as diffusers.

[2143] These steps make it possible to provide the optimal fragrance in physical stores based on the user's emotions and needs.

[2144] 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.

[2145] 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.

[2146] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2147] 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.

[2148] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[2149] 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.

[2150] 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.

[2151] 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.

[2152] 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."

[2153] 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.

[2154] 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.

[2155] 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.

[2156] 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.

[2157] 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.

[2158] 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.

[2159] 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.

[2160] 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.

[2161] 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.

[2162] 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.

[2163] 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.

[2164] 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.

[2165] The following is further disclosed regarding the embodiments described above.

[2166] (Claim 1)

[2167] A means of entering and saving the user's basic information,

[2168] A means of inputting and analyzing user status and needs information,

[2169] A means of providing multiple basic scents and collecting evaluation data for them,

[2170] A method for generating the optimal scent using an AI algorithm based on collected evaluation data,

[2171] A means of providing the generated fragrance data to the user,

[2172] A system that includes this.

[2173] (Claim 2)

[2174] The system according to claim 1, further comprising means for proposing a method for providing a fragrance to a user based on generated fragrance data.

[2175] (Claim 3)

[2176] The system according to claim 1, further comprising means for converting user-entered information and collected evaluation data into a unified format and transmitting it to a server.

[2177] "Example 1"

[2178] (Claim 1)

[2179] A means of inputting and saving user attribute information,

[2180] A means of inputting and analyzing user status information and demand information,

[2181] A means of providing multiple basic scents and collecting evaluation information on them,

[2182] A method for generating the optimal scent using a machine learning algorithm based on collected evaluation information,

[2183] A means of providing the generated fragrance information to the user,

[2184] A system that includes this.

[2185] (Claim 2)

[2186] The system according to claim 1, further comprising means for proposing a method for providing a fragrance to a user based on generated fragrance information.

[2187] (Claim 3)

[2188] The system according to claim 1, further comprising means for converting user-entered information and collected evaluation information into a standard format and transmitting it to a server.

[2189] "Application Example 1"

[2190] (Claim 1)

[2191] A means of entering and saving the user's basic information,

[2192] A means of inputting and analyzing user status and needs information,

[2193] A means of providing multiple basic scents and collecting evaluation data for them,

[2194] A method for generating the optimal scent using an AI algorithm based on collected evaluation data,

[2195] A means of providing the generated fragrance data to the user,

[2196] In food delivery services, providing the optimal aroma in response to the anxieties and expectations users have when ordering food is a means of improving the dining experience.

[2197] A system that includes this.

[2198] (Claim 2)

[2199] The system according to claim 1, further comprising means for proposing a method for providing a fragrance to a user based on generated fragrance data.

[2200] (Claim 3)

[2201] The system according to claim 1, further comprising means for converting user-entered information and collected evaluation data into a unified format and transmitting it to a server.

[2202] "Example 2 of combining an emotion engine"

[2203] (Claim 1)

[2204] A means of entering and saving the user's basic information,

[2205] A means of inputting and analyzing user status and needs information,

[2206] A means of providing multiple basic scents and collecting evaluation data for them,

[2207] A method for generating the optimal scent using an AI algorithm based on collected evaluation data,

[2208] A means of providing the generated fragrance data to the user,

[2209] Means for recognizing user emotions and collecting that data,

[2210] A method for analyzing collected emotional data to determine the optimal scent,

[2211] A system that includes this.

[2212] (Claim 2)

[2213] The system according to claim 1, which proposes a method for providing a fragrance to a user based on generated fragrance data.

[2214] (Claim 3)

[2215] The system according to claim 1, which converts user-inputted information, collected evaluation data, and sentiment data into a unified format and transmits them to a server.

[2216] "Application example 2 when combining with an emotional engine"

[2217] (Claim 1)

[2218] A means of entering and saving the user's basic information,

[2219] A means of inputting and analyzing user status and needs information,

[2220] A means of providing multiple basic scents and collecting evaluation data for them,

[2221] A method for generating the optimal scent using an AI algorithm based on collected evaluation data,

[2222] A means of providing the generated fragrance data to the user,

[2223] A means of collecting user emotional data using emotion recognition technology,

[2224] A means for converting emotional data and evaluation data into a unified format and sending it to a server,

[2225] In order to enhance the user's personal experience in physical stores, we need a means to provide the optimal fragrance,

[2226] A system that includes this.

[2227] (Claim 2)

[2228] The system according to claim 1, further comprising means for proposing a method for providing a fragrance to a user based on generated fragrance data.

[2229] (Claim 3)

[2230] The system according to claim 1, further comprising means for converting emotion data and evaluation data into a unified format and transmitting it to a server. [Explanation of symbols]

[2231] 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 of entering and saving the user's basic information, A means of inputting and analyzing user status and needs information, A means of providing multiple basic scents and collecting evaluation data for them, A method for generating the optimal scent using an AI algorithm based on collected evaluation data, A means of providing the generated fragrance data to the user, A system that includes this.

2. The system according to claim 1, further comprising means for proposing a method for providing a fragrance to a user based on generated fragrance data.

3. The system according to claim 1, further comprising means for converting user-entered information and collected evaluation data into a unified format and transmitting it to a server.

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