System

Abstract

To provide a system that easily makes up the fragrance users desire to smell.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a blending unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit and identifies components of a fragrance. The blending unit blends the components of the fragrance identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques make it difficult to specifically mix the scent a user desires to smell, and there is room for improvement.

[0005] The system according to the embodiment aims to allow a user to easily mix the scent he or she desires to smell. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a blending unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit and identifies components of a fragrance. The blending unit blends the components of the fragrance identified by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment allows the user to easily mix the scent he or she desires to smell. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded 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), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The fragrance blending system according to the embodiment of the present invention is a system in which a user inputs an image of a fragrance they want to smell, and blends a fragrance that matches that image on the spot. This fragrance blending system blends a fragrance that matches that image on the spot by inputting an image of a fragrance they want to smell into a generation AI. For example, the user inputs a specific image such as "the scent of a fresh morning forest" or "the scent of sweet vanilla". This information is input into the generation AI. Next, the generation AI analyzes the input image and identifies the fragrance components that match that image. The generation AI selects the optimal fragrance components based on past data and learned models. For example, in the case of "the scent of a fresh morning forest", the fragrance components of pine trees and wet earth are selected. After that, the fragrance blending device blends the fragrance components identified by the generation AI and provides it to the user. The fragrance blending device combines multiple fragrance components to blend a fragrance that matches the image input by the user on the spot. This allows the user to enjoy the fragrance they want to smell in real time. It also includes a system that creates a sense of immersion by blending a scent imagined from a video and releasing the scent in real time. For example, when a video is played, scent information related to the video is input to the generation AI. For example, if the video shows a forest scene, information such as "forest scent" is input. Next, the generation AI analyzes the input video information and identifies the scent components that match the video. The generation AI selects the optimal scent components based on past data and learned models. For example, in the case of a forest scene, the scent components of pine trees and wet soil are selected. After that, the scent blending device blends the scent components identified by the generation AI and releases the scent in real time. This allows the user to enjoy the scent along with the video, providing an even greater sense of immersion. This allows the scent blending system to blend and provide the scent the user wants to smell in real time. In addition, blending scents in conjunction with the video can enhance the user's sense of immersion.

[0029] The fragrance blending system according to the embodiment includes a reception unit, an analysis unit, and a blending unit. The reception unit receives a user's input. The user's input includes, but is not limited to, text input, voice input, video input, and the like. The reception unit receives, for example, a keyboard or handwritten input as text input. In addition, a voice recognition technology using a microphone can be used as the voice input. Furthermore, a video file or live video can be received as the video input. The analysis unit uses a generation AI to analyze the information received by the reception unit and identify the fragrance components. The analysis is performed, for example, based on past data or a learned model, but is not limited to the example. For example, the generation AI performs analysis based on past fragrance component data and user history data. In addition, the generation AI can also perform analysis using a machine learning model or a neural network. The blending unit blends the fragrance components identified by the generation AI. Blending is performed, for example, by combining multiple fragrance components, but is not limited to the example. For example, the blending unit blends fragrance components such as floral and citrus fragrance components. The blending unit can also blend based on the ratio of ingredients and blending procedures. This allows the fragrance blending system according to the embodiment to blend fragrances based on user input. Some or all of the above-mentioned processing in the blending unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the blending unit can blend using an AI model that takes the ingredients of the fragrance identified by the generation AI as input and outputs a blend. This allows the fragrance blending system to blend and provide the fragrance that the user wants to smell in real time.

[0030] The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and video input. The reception unit receives, for example, keyboard or handwritten input as text input. Specifically, the user can input the scent desires and preferences using a keyboard. In addition, in the case of handwritten input, the user can freely draw letters and pictures using a tablet or stylus pen. For voice input, voice recognition technology using a microphone can be used. When the user speaks into the microphone, the system converts the voice into text and can use it for analysis. Voice recognition technology can perform noise cancellation and voice filtering to obtain accurate voice data. Furthermore, video files and live video can be received as video input. For example, a user can upload a video of a specific flower or fruit, and the video can provide hints about the scent. In the case of live video, the user can transmit the video in real time through a camera, and the system can analyze the video to identify the scent components. This allows the reception unit to provide a variety of input methods and flexibly respond to the needs of users.

[0031] The analysis unit uses the generation AI to analyze the information received by the reception unit and identify the fragrance components. The analysis is performed, for example, based on past data and a learned model, but is not limited to such examples. Specifically, the generation AI performs the analysis based on past fragrance component data and user history data. For example, it refers to data on fragrances that the user liked in the past and identifies the optimal fragrance components by comparing them with the current input. The generation AI can also perform the analysis using machine learning models and neural networks. This allows the system to quickly process large amounts of data and provide highly accurate analysis results. Furthermore, the generation AI can analyze the user's text input using natural language processing technology and extract keywords and phrases related to the fragrance. For example, from the input "I like floral fragrances," the keyword "floral" is extracted and the related fragrance components are identified. In addition, in the case of voice input, the voice data can be converted into text in cooperation with voice recognition technology, and the text can be analyzed. In the case of video input, it is possible to use image recognition technology to identify objects in the video and analyze the related fragrance components. This enables the analysis unit to efficiently analyze a variety of input data and identify the fragrance components that meet the user's preferences.

[0032] The blending unit blends the fragrance components identified by the generation AI. Blending is performed, for example, by combining multiple fragrance components, but is not limited to such an example. Specifically, the blending unit blends fragrance components such as floral and citrus fragrances. For example, if a user desires a "relaxing fragrance," it is possible to combine fragrance components with a relaxing effect, such as lavender and chamomile. The blending unit can also blend based on the ratio of ingredients and the blending procedure. For example, blending is performed in a specific ratio, such as 30% lavender, 20% chamomile, and 50% other ingredients. The blending procedure includes the mixing order of ingredients, temperature control, time management, and the like. This allows the blending unit to realize blending of fragrances with high accuracy. Furthermore, some or all of the above-mentioned processing in the blending unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the blending unit can blend using an AI model that inputs the fragrance components identified by the generation AI and outputs a blend. This allows the fragrance blending system to blend and provide the fragrance the user desires in real time. The blending unit can also adjust the blending results based on the user's feedback and reflect them in future blends. This allows customization to suit the user's preferences, providing a fragrance that will provide greater satisfaction.

[0033] The reception unit can receive text input or voice input as a user's input method. The reception unit, for example, receives keyboard or handwritten input as text input. The reception unit can also use voice recognition technology using a microphone as voice input. For example, the reception unit receives text input by the user using a keyboard. The reception unit can also scan text input by the user using handwriting and convert it into digital data. Furthermore, the reception unit can convert content input by the user using a microphone by voice into text data using voice recognition technology. This allows the reception unit to receive a variety of user input methods. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can convert voice data into text data by a generation AI.

[0034] The analysis unit can identify the fragrance components based on past data or a learned model. The analysis unit performs analysis based on, for example, past fragrance component data or user history data. For example, the analysis unit identifies the optimal fragrance components for the current input based on fragrance component data that the user liked in the past. The analysis unit can also perform analysis using a learned model. For example, the analysis unit identifies the fragrance components using a machine learning model or a neural network. This allows the analysis unit to identify the fragrance components by utilizing past data or a learned model. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs past data into the generation AI to identify the fragrance components.

[0035] The blending unit can blend a fragrance by combining multiple fragrance components. The blending unit blends, for example, floral and citrus fragrance components. For example, the blending unit selects rose and jasmine as floral fragrance components, and lemon and orange as citrus fragrance components. The blending unit can also blend based on the ratio of ingredients and the blending procedure. For example, the blending unit sets the ratio of rose and jasmine to 1:1, and the ratio of lemon and orange to 1:2. This allows the blending unit to blend a fragrance by combining multiple fragrance components. Some or all of the above-mentioned processing in the blending unit is performed using the generation AI. For example, the blending unit inputs data on the fragrance components into the generation AI and identifies the optimal blending method.

[0036] The reception unit can receive video information. The reception unit receives, for example, video files and live video. For example, the reception unit receives video files uploaded by a user. The reception unit can also receive live video in real time. This allows the reception unit to receive video information. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs video data to the generation AI and analyzes the video information.

[0037] The analysis unit can identify different scent components for each scene of the video. The analysis unit, for example, identifies different scent components for each scene of the video. For example, the analysis unit identifies scent components of pine trees and wet soil in a forest scene, and identifies scent components of the ocean and seaweed in an ocean scene. The analysis unit can also identify scent components based on the timing of scene switching and the content of the scene. For example, the analysis unit detects the timing of scene switching and identifies different scent components for each scene. This allows the analysis unit to identify different scent components for each scene of the video. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs video data to the generation AI and identifies scent components for each scene.

[0038] The blending unit can blend fragrances in real time. The blending unit, for example, blends fragrances in real time. For example, the blending unit blends fragrances in real time based on an image of a fragrance input by the user. The blending unit can also blend different fragrance components in real time for each scene of a video. For example, the blending unit blends different fragrance components in real time every time a scene of a video changes. This allows the blending unit to blend fragrances in real time. A part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs data of fragrance components to the generation AI and performs blending in real time.

[0039] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display scent images that the user has input in the past as candidates. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past input history. This allows the reception unit to suggest an optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs past input history data to the generation AI and suggests an optimal input method.

[0040] The reception unit can provide an input assistance function according to the user's input. For example, if the user inputs "fresh morning forest scent", the reception unit automatically suggests related scent components. In addition, if the user inputs "sweet vanilla scent", the reception unit can also suggest optimal vanilla scent components from past data. Furthermore, if the user makes the input content unclear, the reception unit can also provide an option to select a specific scent image. This allows the reception unit to provide an input assistance function according to the user's input content. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the input content to the generation AI and provides an optimal input assistance function.

[0041] The reception unit can accept input of a region-specific scent based on the user's geographical location information. For example, when the user is in a specific region, the reception unit provides an option to input an image of a scent specific to that region. In addition, when the user is traveling, the reception unit can also suggest a scent specific to the region visited. Furthermore, when the user inputs a local scent, the reception unit can automatically suggest a scent component specific to that region. This allows the reception unit to accept input of a region-specific scent based on the user's geographical location information. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs geographical location information to the generation AI and accepts input of a region-specific scent.

[0042] The reception unit can analyze the user's social media activity and suggest input of a related scent. For example, the reception unit can suggest an image of a related scent based on photos and post content shared by the user on social media. The reception unit can also analyze post content of accounts the user follows on social media and suggest related scents. Furthermore, the reception unit can also suggest related scents based on event information in which the user is participating on social media. This allows the reception unit to suggest input of a related scent based on the user's social media activity. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs social media activity data to the generation AI and suggests input of a related scent.

[0043] The analysis unit can identify fragrance components by combining past data and current trends. For example, the analysis unit identifies popular fragrance components from past data and combines them with current trends. The analysis unit can also identify new fragrance components based on current trends. Furthermore, the analysis unit can analyze past data and current trends and select optimal fragrance components. This allows the analysis unit to identify fragrance components by combining past data and current trends. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs past data and current trend data into the generation AI to identify fragrance components.

[0044] The analysis unit can generate a detailed profile of the fragrance components based on the user's input. For example, if the user inputs "fresh morning forest scent", the analysis unit generates a detailed profile of the fragrance components of pine trees and wet earth. In addition, if the user inputs "sweet vanilla scent", the analysis unit can also generate a detailed profile of vanilla fragrance components. Furthermore, the analysis unit can generate detailed profiles of multiple fragrance components based on the image of the fragrance input by the user. This allows the analysis unit to generate a detailed profile of the fragrance components based on the user's input. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's input to the generation AI and generates a detailed profile of the fragrance components.

[0045] When analyzing the video information, the analysis unit can identify the fragrance components based on the audio information of the video. For example, the analysis unit can identify the relevant fragrance components based on the genre of music playing in the video. The analysis unit can also identify the fragrance components that match a specific scene from the audio information of the video. Furthermore, the analysis unit can combine the audio information of the video with the video information to identify the optimal fragrance components. This allows the analysis unit to identify the fragrance components taking into account the audio information of the video. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the audio information of the video to the generation AI to identify the fragrance components.

[0046] The analysis unit can analyze the user's past fragrance preferences and suggest optimal fragrance components. The analysis unit can, for example, suggest optimal fragrance components based on fragrance components that the user has liked in the past. The analysis unit can also analyze the user's past fragrance preferences and suggest related fragrance components. Furthermore, the analysis unit can also suggest new fragrance components based on the user's past fragrance preferences. This allows the analysis unit to suggest optimal fragrance components based on the user's past fragrance preferences. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's past fragrance preference data into the generation AI and suggests optimal fragrance components.

[0047] The blending unit can select an appropriate blending method in consideration of the interactions between the fragrance components when blending. For example, the blending unit selects an optimal blending ratio in consideration of the interactions between fragrance components A and B. The blending unit can also analyze the interactions between multiple fragrance components and select an optimal blending method. Furthermore, the blending unit can optimize the blending order based on the interactions between the fragrance components. This allows the blending unit to select an optimal blending method in consideration of the interactions between the fragrance components. Some or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs data on fragrance components to the generation AI, analyzes the interactions, and selects an optimal blending method.

[0048] The blending unit can add ingredients for adjusting the duration of the fragrance during blending. For example, the blending unit adds ingredients for extending the duration of the fragrance. The blending unit can also add ingredients for shortening the duration of the fragrance. Furthermore, the blending unit can combine ingredients for adjusting the duration of the fragrance. This allows the blending unit to add ingredients for adjusting the duration of the fragrance. A part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs data of fragrance ingredients to the generation AI and identifies ingredients for adjusting the duration.

[0049] The blending unit can add a region-specific fragrance component in consideration of the user's geographical location information when blending. For example, when the user is in a specific region, the blending unit adds a region-specific fragrance component. In addition, when the user is traveling, the blending unit can also add a region-specific fragrance component to the region visited. Furthermore, when the user desires a local fragrance, the blending unit can also add a region-specific fragrance component. This allows the blending unit to add a region-specific fragrance component based on the user's geographical location information. Part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs geographical location information to the generation AI and identifies a region-specific fragrance component.

[0050] The blending unit can analyze the user's social media activities and add related fragrance components when blending. The blending unit can add related fragrance components based on, for example, photos and post contents shared by the user on social media. The blending unit can also analyze post contents of accounts the user follows on social media and add related fragrance components. Furthermore, the blending unit can add related fragrance components based on event information in which the user participates on social media. This allows the blending unit to add related fragrance components based on the user's social media activities. A part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs social media activity data into the generation AI and identifies related fragrance components.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The fragrance blending system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires biometric information such as the user's heart rate, blood pressure, and stress level, and provides it to the analysis unit. The analysis unit can identify fragrance components suitable for the user's health condition based on this biometric information. For example, if the user's heart rate is high, fragrance components such as lavender or chamomile, which have a relaxing effect, may be selected. Also, if the user's stress level is high, fragrance components such as bergamot or sandalwood, which have a stress-reducing effect, may be selected. This allows the fragrance blending system to provide a fragrance that corresponds to the user's health condition.

[0053] The fragrance blending system may further include an olfactory measurement unit that measures the user's olfactory sensitivity. The olfactory measurement unit measures how sensitive the user is to a particular fragrance component, and provides the result to the analysis unit. The analysis unit may adjust the concentration of the fragrance component based on the user's olfactory sensitivity. For example, if the user is sensitive to vanilla fragrance, the concentration of the vanilla fragrance component may be set low. Also, if the user is insensitive to citrus fragrances, the concentration of the citrus fragrance component may be set high. This allows the fragrance blending system to provide a fragrance according to the user's olfactory sensitivity.

[0054] The fragrance blending system may further include a learning unit that learns the user's preferences. The learning unit records the history of fragrances previously selected by the user and provides it to the analysis unit. The analysis unit can identify fragrance components that match the user's preferences based on this history data. For example, if the user has previously preferred floral fragrances, floral fragrance components can be preferentially selected. Also, if the user has previously avoided citrus fragrances, citrus fragrance components can be excluded. This allows the fragrance blending system to provide fragrances that match the user's preferences.

[0055] The fragrance blending system may further include an activity monitoring unit that monitors the activity level of the user. The activity monitoring unit acquires activity data such as the number of steps taken by the user and the amount of exercise, and provides the data to the analysis unit. Based on the activity data, the analysis unit may identify fragrance components suitable for the activity level of the user. For example, if the user has just exercised, a mint or eucalyptus fragrance component that has a refreshing effect may be selected. Also, if the user has been sitting for a long period of time, a citrus fragrance component that has a refreshing effect may be selected. This allows the fragrance blending system to provide a fragrance that corresponds to the activity level of the user.

[0056] The fragrance blending system may further include a sleep monitoring unit that monitors the user's sleep state. The sleep monitoring unit acquires the user's sleep patterns and sleep quality and provides them to the analysis unit. The analysis unit can identify fragrance components suitable for the user's sleep state based on this sleep data. For example, if the user is not getting a deep sleep, fragrance components such as lavender or chamomile, which have a relaxing effect, may be selected. Also, if the user has difficulty falling asleep, fragrance components such as bergamot or sandalwood, which have a relaxing effect, may be selected. This allows the fragrance blending system to provide a fragrance that corresponds to the user's sleep state.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The reception unit receives user input. User input includes text input, voice input, video input, etc. For example, it is possible to receive keyboard or handwritten input as text input, voice recognition technology using a microphone as voice input, and video files or live video as video input. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and identify the fragrance components. The analysis is performed based on past data and learned models, and the generation AI performs the analysis based on past fragrance component data and user history data. Analysis can also be performed using machine learning models and neural networks. Step 3: The blending unit blends the fragrance components identified by the generation AI. Blending is performed by combining multiple fragrance components, for example, by combining floral and citrus fragrance components. Blending can also be performed based on the ratio of ingredients and blending procedures. Processing in the blending unit may be performed using the generation AI or without the generation AI.

[0059] (Example 2) The fragrance blending system according to the embodiment of the present invention is a system in which a user inputs an image of a fragrance they want to smell, and blends a fragrance that matches that image on the spot. This fragrance blending system blends a fragrance that matches that image on the spot by inputting an image of a fragrance they want to smell into a generation AI. For example, the user inputs a specific image such as "the scent of a fresh morning forest" or "the scent of sweet vanilla". This information is input into the generation AI. Next, the generation AI analyzes the input image and identifies the fragrance components that match that image. The generation AI selects the optimal fragrance components based on past data and learned models. For example, in the case of "the scent of a fresh morning forest", the fragrance components of pine trees and wet earth are selected. After that, the fragrance blending device blends the fragrance components identified by the generation AI and provides it to the user. The fragrance blending device combines multiple fragrance components to blend a fragrance that matches the image input by the user on the spot. This allows the user to enjoy the fragrance they want to smell in real time. It also includes a system that creates a sense of immersion by blending a scent imagined from a video and releasing the scent in real time. For example, when a video is played, scent information related to the video is input to the generation AI. For example, if the video shows a forest scene, information such as "forest scent" is input. Next, the generation AI analyzes the input video information and identifies the scent components that match the video. The generation AI selects the optimal scent components based on past data and learned models. For example, in the case of a forest scene, the scent components of pine trees and wet soil are selected. After that, the scent blending device blends the scent components identified by the generation AI and releases the scent in real time. This allows the user to enjoy the scent along with the video, providing an even greater sense of immersion. This allows the scent blending system to blend and provide the scent the user wants to smell in real time. In addition, blending scents in conjunction with the video can enhance the user's sense of immersion.

[0060] The fragrance blending system according to the embodiment includes a reception unit, an analysis unit, and a blending unit. The reception unit receives a user's input. The user's input includes, but is not limited to, text input, voice input, video input, and the like. The reception unit receives, for example, a keyboard or handwritten input as text input. In addition, a voice recognition technology using a microphone can be used as the voice input. Furthermore, a video file or live video can be received as the video input. The analysis unit uses a generation AI to analyze the information received by the reception unit and identify the fragrance components. The analysis is performed, for example, based on past data or a learned model, but is not limited to the example. For example, the generation AI performs analysis based on past fragrance component data and user history data. In addition, the generation AI can also perform analysis using a machine learning model or a neural network. The blending unit blends the fragrance components identified by the generation AI. Blending is performed, for example, by combining multiple fragrance components, but is not limited to the example. For example, the blending unit blends fragrance components such as floral and citrus fragrance components. The blending unit can also blend based on the ratio of ingredients and blending procedures. This allows the fragrance blending system according to the embodiment to blend fragrances based on user input. Some or all of the above-mentioned processing in the blending unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the blending unit can blend using an AI model that takes the ingredients of the fragrance identified by the generation AI as input and outputs a blend. This allows the fragrance blending system to blend and provide the fragrance that the user wants to smell in real time.

[0061] The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and video input. The reception unit receives, for example, keyboard or handwritten input as text input. Specifically, the user can input the scent desires and preferences using a keyboard. In addition, in the case of handwritten input, the user can freely draw letters and pictures using a tablet or stylus pen. For voice input, voice recognition technology using a microphone can be used. When the user speaks into the microphone, the system converts the voice into text and can use it for analysis. Voice recognition technology can perform noise cancellation and voice filtering to obtain accurate voice data. Furthermore, video files and live video can be received as video input. For example, a user can upload a video of a specific flower or fruit, and the video can provide hints about the scent. In the case of live video, the user can transmit the video in real time through a camera, and the system can analyze the video to identify the scent components. This allows the reception unit to provide a variety of input methods and flexibly respond to the needs of users.

[0062] The analysis unit uses the generation AI to analyze the information received by the reception unit and identify the fragrance components. The analysis is performed, for example, based on past data and a learned model, but is not limited to such examples. Specifically, the generation AI performs the analysis based on past fragrance component data and user history data. For example, it refers to data on fragrances that the user liked in the past and identifies the optimal fragrance components by comparing them with the current input. The generation AI can also perform the analysis using machine learning models and neural networks. This allows the system to quickly process large amounts of data and provide highly accurate analysis results. Furthermore, the generation AI can analyze the user's text input using natural language processing technology and extract keywords and phrases related to the fragrance. For example, from the input "I like floral fragrances," the keyword "floral" is extracted and the related fragrance components are identified. In addition, in the case of voice input, the voice data can be converted into text in cooperation with voice recognition technology, and the text can be analyzed. In the case of video input, it is possible to use image recognition technology to identify objects in the video and analyze the related fragrance components. This enables the analysis unit to efficiently analyze a variety of input data and identify the fragrance components that meet the user's preferences.

[0063] The blending unit blends the fragrance components identified by the generation AI. Blending is performed, for example, by combining multiple fragrance components, but is not limited to such an example. Specifically, the blending unit blends fragrance components such as floral and citrus fragrances. For example, if a user desires a "relaxing fragrance," it is possible to combine fragrance components with a relaxing effect, such as lavender and chamomile. The blending unit can also blend based on the ratio of ingredients and the blending procedure. For example, blending is performed in a specific ratio, such as 30% lavender, 20% chamomile, and 50% other ingredients. The blending procedure includes the mixing order of ingredients, temperature control, time management, and the like. This allows the blending unit to realize blending of fragrances with high accuracy. Furthermore, some or all of the above-mentioned processing in the blending unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the blending unit can blend using an AI model that inputs the fragrance components identified by the generation AI and outputs a blend. This allows the fragrance blending system to blend and provide the fragrance the user desires in real time. The blending unit can also adjust the blending results based on the user's feedback and reflect them in future blends. This allows customization to suit the user's preferences, providing a fragrance that will provide greater satisfaction.

[0064] The reception unit can receive text input or voice input as a user's input method. The reception unit, for example, receives keyboard or handwritten input as text input. The reception unit can also use voice recognition technology using a microphone as voice input. For example, the reception unit receives text input by the user using a keyboard. The reception unit can also scan text input by the user using handwriting and convert it into digital data. Furthermore, the reception unit can convert content input by the user using a microphone by voice into text data using voice recognition technology. This allows the reception unit to receive a variety of user input methods. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can convert voice data into text data by a generation AI.

[0065] The analysis unit can identify the fragrance components based on past data or a learned model. The analysis unit performs analysis based on, for example, past fragrance component data or user history data. For example, the analysis unit identifies the optimal fragrance components for the current input based on fragrance component data that the user liked in the past. The analysis unit can also perform analysis using a learned model. For example, the analysis unit identifies the fragrance components using a machine learning model or a neural network. This allows the analysis unit to identify the fragrance components by utilizing past data or a learned model. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs past data into the generation AI to identify the fragrance components.

[0066] The blending unit can blend a fragrance by combining multiple fragrance components. The blending unit blends, for example, floral and citrus fragrance components. For example, the blending unit selects rose and jasmine as floral fragrance components, and lemon and orange as citrus fragrance components. The blending unit can also blend based on the ratio of ingredients and the blending procedure. For example, the blending unit sets the ratio of rose and jasmine to 1:1, and the ratio of lemon and orange to 1:2. This allows the blending unit to blend a fragrance by combining multiple fragrance components. Some or all of the above-mentioned processing in the blending unit is performed using the generation AI. For example, the blending unit inputs data on the fragrance components into the generation AI and identifies the optimal blending method.

[0067] The reception unit can receive video information. The reception unit receives, for example, video files and live video. For example, the reception unit receives video files uploaded by a user. The reception unit can also receive live video in real time. This allows the reception unit to receive video information. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs video data to the generation AI and analyzes the video information.

[0068] The analysis unit can identify different scent components for each scene of the video. The analysis unit, for example, identifies different scent components for each scene of the video. For example, the analysis unit identifies scent components of pine trees and wet soil in a forest scene, and identifies scent components of the ocean and seaweed in an ocean scene. The analysis unit can also identify scent components based on the timing of scene switching and the content of the scene. For example, the analysis unit detects the timing of scene switching and identifies different scent components for each scene. This allows the analysis unit to identify different scent components for each scene of the video. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs video data to the generation AI and identifies scent components for each scene.

[0069] The blending unit can blend fragrances in real time. The blending unit, for example, blends fragrances in real time. For example, the blending unit blends fragrances in real time based on an image of a fragrance input by the user. The blending unit can also blend different fragrance components in real time for each scene of a video. For example, the blending unit blends different fragrance components in real time every time a scene of a video changes. This allows the blending unit to blend fragrances in real time. A part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs data of fragrance components to the generation AI and performs blending in real time.

[0070] The reception unit can estimate the user's emotion and automatically select an appropriate input method based on the estimated user's emotion. For example, when the user is relaxed, the reception unit prioritizes voice input and allows the user to input an image of a scent in a natural conversational format. In addition, when the user is stressed, the reception unit can provide a simple text input interface to minimize the input procedure. Furthermore, when the user is excited, the reception unit provides an interactive input method and enables input with visual feedback. This allows the reception unit to select the optimal input method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotion data to the generation AI and selects the optimal input method.

[0071] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display scent images that the user has input in the past as candidates. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past input history. This allows the reception unit to suggest an optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs past input history data to the generation AI and suggests an optimal input method.

[0072] The reception unit can provide an input assistance function according to the user's input. For example, if the user inputs "fresh morning forest scent", the reception unit automatically suggests related scent components. In addition, if the user inputs "sweet vanilla scent", the reception unit can also suggest optimal vanilla scent components from past data. Furthermore, if the user makes the input content unclear, the reception unit can also provide an option to select a specific scent image. This allows the reception unit to provide an input assistance function according to the user's input content. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the input content to the generation AI and provides an optimal input assistance function.

[0073] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. For example, when the user is relaxed, the reception unit preferentially receives detailed input contents. In addition, when the user is in a hurry, the reception unit can also preferentially receive brief input contents. Furthermore, when the user is excited, the reception unit can also preferentially receive interactive input contents. This allows the reception unit to determine the priority of the input contents based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotion data to the generation AI and determines the priority of the input contents.

[0074] The reception unit can accept input of a region-specific scent based on the user's geographical location information. For example, when the user is in a specific region, the reception unit provides an option to input an image of a scent specific to that region. In addition, when the user is traveling, the reception unit can also suggest a scent specific to the region visited. Furthermore, when the user inputs a local scent, the reception unit can automatically suggest a scent component specific to that region. This allows the reception unit to accept input of a region-specific scent based on the user's geographical location information. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs geographical location information to the generation AI and accepts input of a region-specific scent.

[0075] The reception unit can analyze the user's social media activity and suggest input of a related scent. For example, the reception unit can suggest an image of a related scent based on photos and post content shared by the user on social media. The reception unit can also analyze post content of accounts the user follows on social media and suggest related scents. Furthermore, the reception unit can also suggest related scents based on event information in which the user is participating on social media. This allows the reception unit to suggest input of a related scent based on the user's social media activity. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs social media activity data to the generation AI and suggests input of a related scent.

[0076] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis and combines multiple scent components. Also, when the user is in a hurry, the analysis unit can perform a concise analysis and identify the main scent components. Furthermore, when the user is excited, the analysis unit can preferentially analyze visually stimulating scent components. This allows the analysis unit to adjust the analysis algorithm based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and adjusts the analysis algorithm.

[0077] The analysis unit can identify fragrance components by combining past data and current trends. For example, the analysis unit identifies popular fragrance components from past data and combines them with current trends. The analysis unit can also identify new fragrance components based on current trends. Furthermore, the analysis unit can analyze past data and current trends and select optimal fragrance components. This allows the analysis unit to identify fragrance components by combining past data and current trends. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs past data and current trend data into the generation AI to identify fragrance components.

[0078] The analysis unit can generate a detailed profile of the fragrance components based on the user's input. For example, if the user inputs "fresh morning forest scent", the analysis unit generates a detailed profile of the fragrance components of pine trees and wet earth. In addition, if the user inputs "sweet vanilla scent", the analysis unit can also generate a detailed profile of vanilla fragrance components. Furthermore, the analysis unit can generate detailed profiles of multiple fragrance components based on the image of the fragrance input by the user. This allows the analysis unit to generate a detailed profile of the fragrance components based on the user's input. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's input to the generation AI and generates a detailed profile of the fragrance components.

[0079] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit displays a detailed analysis result. In addition, when the user is in a hurry, the analysis unit can also display a concise analysis result. Furthermore, when the user is excited, the analysis unit can display a visually stimulating analysis result. This allows the analysis unit to adjust the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and adjusts the display method of the analysis result.

[0080] When analyzing the video information, the analysis unit can identify the fragrance components based on the audio information of the video. For example, the analysis unit can identify the relevant fragrance components based on the genre of music playing in the video. The analysis unit can also identify the fragrance components that match a specific scene from the audio information of the video. Furthermore, the analysis unit can combine the audio information of the video with the video information to identify the optimal fragrance components. This allows the analysis unit to identify the fragrance components taking into account the audio information of the video. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the audio information of the video to the generation AI to identify the fragrance components.

[0081] The analysis unit can analyze the user's past fragrance preferences and suggest optimal fragrance components. The analysis unit can, for example, suggest optimal fragrance components based on fragrance components that the user has liked in the past. The analysis unit can also analyze the user's past fragrance preferences and suggest related fragrance components. Furthermore, the analysis unit can also suggest new fragrance components based on the user's past fragrance preferences. This allows the analysis unit to suggest optimal fragrance components based on the user's past fragrance preferences. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's past fragrance preference data into the generation AI and suggests optimal fragrance components.

[0082] The blending unit can estimate the user's emotion and adjust the intensity of the blended fragrance based on the estimated user's emotion. For example, when the user is relaxed, the blending unit blends a gentle fragrance. Also, when the user is excited, the blending unit can blend a stronger fragrance. Furthermore, when the user is stressed, the blending unit can blend a fragrance with a relaxing effect. This allows the blending unit to adjust the fragrance intensity based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the blending unit is performed using the generation AI. For example, the blending unit inputs the user's emotion data into the generation AI and adjusts the fragrance intensity.

[0083] The blending unit can select an appropriate blending method in consideration of the interactions between the fragrance components when blending. For example, the blending unit selects an optimal blending ratio in consideration of the interactions between fragrance components A and B. The blending unit can also analyze the interactions between multiple fragrance components and select an optimal blending method. Furthermore, the blending unit can optimize the blending order based on the interactions between the fragrance components. This allows the blending unit to select an optimal blending method in consideration of the interactions between the fragrance components. Some or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs data on fragrance components to the generation AI, analyzes the interactions, and selects an optimal blending method.

[0084] The blending unit can add ingredients for adjusting the duration of the fragrance during blending. For example, the blending unit adds ingredients for extending the duration of the fragrance. The blending unit can also add ingredients for shortening the duration of the fragrance. Furthermore, the blending unit can combine ingredients for adjusting the duration of the fragrance. This allows the blending unit to add ingredients for adjusting the duration of the fragrance. A part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs data of fragrance ingredients to the generation AI and identifies ingredients for adjusting the duration.

[0085] The blending unit can estimate the user's emotion and adjust the order of the scents to be blended based on the estimated user's emotion. For example, when the user is relaxed, the blending unit blends a gentle scent component first. Also, when the user is excited, the blending unit can blend a stimulating scent component first. Furthermore, when the user is stressed, the blending unit can blend a scent component with a relaxing effect first. This allows the blending unit to adjust the order of the scents based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the blending unit is performed using the generation AI. For example, the blending unit inputs the user's emotion data into the generation AI and adjusts the order of the scents.

[0086] The blending unit can add a region-specific fragrance component in consideration of the user's geographical location information when blending. For example, when the user is in a specific region, the blending unit adds a region-specific fragrance component. In addition, when the user is traveling, the blending unit can also add a region-specific fragrance component to the region visited. Furthermore, when the user desires a local fragrance, the blending unit can also add a region-specific fragrance component. This allows the blending unit to add a region-specific fragrance component based on the user's geographical location information. Part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs geographical location information to the generation AI and identifies a region-specific fragrance component.

[0087] The blending unit can analyze the user's social media activities and add related fragrance components when blending. The blending unit can add related fragrance components based on, for example, photos and post contents shared by the user on social media. The blending unit can also analyze post contents of accounts the user follows on social media and add related fragrance components. Furthermore, the blending unit can add related fragrance components based on event information in which the user participates on social media. This allows the blending unit to add related fragrance components based on the user's social media activities. A part or all of the above-mentioned processing in the blending unit is performed using a generation AI. For example, the blending unit inputs social media activity data into the generation AI and identifies related fragrance components.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The fragrance blending system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires biometric information such as the user's heart rate, blood pressure, and stress level, and provides it to the analysis unit. The analysis unit can identify fragrance components suitable for the user's health condition based on this biometric information. For example, if the user's heart rate is high, fragrance components such as lavender or chamomile, which have a relaxing effect, may be selected. Also, if the user's stress level is high, fragrance components such as bergamot or sandalwood, which have a stress-reducing effect, may be selected. This allows the fragrance blending system to provide a fragrance that corresponds to the user's health condition.

[0090] The fragrance blending system may further include an olfactory measurement unit that measures the user's olfactory sensitivity. The olfactory measurement unit measures how sensitive the user is to a particular fragrance component, and provides the result to the analysis unit. The analysis unit may adjust the concentration of the fragrance component based on the user's olfactory sensitivity. For example, if the user is sensitive to vanilla fragrance, the concentration of the vanilla fragrance component may be set low. Also, if the user is insensitive to citrus fragrances, the concentration of the citrus fragrance component may be set high. This allows the fragrance blending system to provide a fragrance according to the user's olfactory sensitivity.

[0091] The fragrance blending system may further include a learning unit that learns the user's preferences. The learning unit records the history of fragrances previously selected by the user and provides it to the analysis unit. The analysis unit can identify fragrance components that match the user's preferences based on this history data. For example, if the user has previously preferred floral fragrances, floral fragrance components can be preferentially selected. Also, if the user has previously avoided citrus fragrances, citrus fragrance components can be excluded. This allows the fragrance blending system to provide fragrances that match the user's preferences.

[0092] The fragrance blending system may further include an activity monitoring unit that monitors the activity level of the user. The activity monitoring unit acquires activity data such as the number of steps taken by the user and the amount of exercise, and provides the data to the analysis unit. Based on the activity data, the analysis unit may identify fragrance components suitable for the activity level of the user. For example, if the user has just exercised, a mint or eucalyptus fragrance component that has a refreshing effect may be selected. Also, if the user has been sitting for a long period of time, a citrus fragrance component that has a refreshing effect may be selected. This allows the fragrance blending system to provide a fragrance that corresponds to the activity level of the user.

[0093] The fragrance blending system may further include a sleep monitoring unit that monitors the user's sleep state. The sleep monitoring unit acquires the user's sleep patterns and sleep quality and provides them to the analysis unit. The analysis unit can identify fragrance components suitable for the user's sleep state based on this sleep data. For example, if the user is not getting a deep sleep, fragrance components such as lavender or chamomile, which have a relaxing effect, may be selected. Also, if the user has difficulty falling asleep, fragrance components such as bergamot or sandalwood, which have a relaxing effect, may be selected. This allows the fragrance blending system to provide a fragrance that corresponds to the user's sleep state.

[0094] The fragrance blending system can further estimate the user's emotions and suggest a fragrance theme based on the estimated user's emotions. For example, if the user is relaxed, a fragrance theme with a relaxing effect can be suggested. Also, if the user is stressed, a fragrance theme with a stress-reducing effect can be suggested. Furthermore, if the user is excited, a fragrance theme that increases energy can be suggested. In this way, the fragrance blending system can suggest the optimal fragrance theme based on the user's emotions.

[0095] The fragrance blending system can further estimate the user's emotions and adjust the duration of the fragrance based on the estimated user's emotions. For example, if the user is relaxed, a fragrance that lasts for a long time can be provided. Also, if the user is in a hurry, a fragrance that takes effect in a short time can be provided. Furthermore, if the user is excited, a fragrance whose duration can be adjusted so that the user can enjoy the change in fragrance can be provided. This allows the fragrance blending system to adjust the duration of the fragrance based on the user's emotions.

[0096] The fragrance blending system can further estimate the user's emotions and adjust the intensity of the fragrance based on the estimated user's emotions. For example, if the user is relaxed, a gentle fragrance can be provided. If the user is excited, a stronger fragrance can be provided. Furthermore, if the user is stressed, a stronger fragrance with a relaxing effect can be provided. In this way, the fragrance blending system can adjust the intensity of the fragrance based on the user's emotions.

[0097] The fragrance blending system can further estimate the user's emotions and select fragrance components based on the estimated user's emotions. For example, if the user is relaxed, fragrance components with a relaxing effect such as lavender or chamomile can be selected. If the user is stressed, fragrance components with a stress-reducing effect such as bergamot or sandalwood can be selected. Furthermore, if the user is excited, fragrance components that increase energy such as mint or eucalyptus can be selected. This allows the fragrance blending system to select optimal fragrance components based on the user's emotions.

[0098] The fragrance blending system can further estimate the user's emotions and adjust the fragrance blending order based on the estimated user's emotions. For example, if the user is relaxed, a gentle fragrance component can be blended first. Also, if the user is excited, a stimulating fragrance component can be blended first. Furthermore, if the user is stressed, a fragrance component with a relaxing effect can be blended first. This allows the fragrance blending system to adjust the fragrance blending order based on the user's emotions.

[0099] The process flow of the second embodiment will be briefly described below.

[0100] Step 1: The reception unit receives user input. User input includes text input, voice input, video input, etc. For example, it is possible to receive keyboard or handwritten input as text input, voice recognition technology using a microphone as voice input, and video files or live video as video input. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and identify the fragrance components. The analysis is performed based on past data and learned models, and the generation AI performs the analysis based on past fragrance component data and user history data. Analysis can also be performed using machine learning models and neural networks. Step 3: The blending unit blends the fragrance components identified by the generation AI. Blending is performed by combining multiple fragrance components, for example, by combining floral and citrus fragrance components. Blending can also be performed based on the ratio of ingredients and blending procedures. Processing in the blending unit may be performed using the generation AI or without the generation AI.

[0101] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0103] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and blending unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives text input and voice input from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the components of the fragrance using a generation AI. The blending unit is realized, for example, by the control unit 46A of the smart device 14, and blends the identified components of the fragrance. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0112] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0117] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and blending unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a voice input from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the components of the fragrance using a generation AI. The blending unit is realized, for example, by the control unit 46A of the smart glasses 214 and blends the identified components of the fragrance. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and blending unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives voice input from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the components of the fragrance using a generation AI. The blending unit is realized, for example, by the control unit 46A of the headset type terminal 314 and blends the identified components of the fragrance. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and blending unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the components of the fragrance using a generation AI. The blending unit is realized, for example, by the control unit 46A of the robot 414 and blends the identified components of the fragrance. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0155] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0159] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] In addition, 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 upon request from the data processing device 12.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0166] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0170] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0172] (Appendix 1) A reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit and identifies components of the fragrance; A blending unit that blends the aroma components identified by the analysis unit. A system characterized by: (Appendix 2) The reception unit is Accepts text or voice input as a user input method 2. The system of claim 1, (Appendix 3) The analysis unit is Identify scent components based on past data or trained models 2. The system of claim 1, (Appendix 4) The blending unit includes: Combining multiple fragrance ingredients to create a fragrance 2. The system of claim 1, (Appendix 5) The reception unit is Accepts video information 2. The system of claim 1, (Appendix 6) The analysis unit is Identifying different scent components in each video scene 2. The system of claim 1, (Appendix 7) The blending unit includes: Blending scents in real time 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and automatically select an appropriate input method based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 10) The reception unit is Provides input assistance functions according to the user's input 2. The system of claim 1, (Appendix 11) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The reception unit is Accepts input of region-specific scents based on the user's geographic location 2. The system of claim 1, (Appendix 13) The reception unit is Analyzes users' social media activity and suggests related scent inputs 2. The system of claim 1, (Appendix 14) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is Combining historical data with current trends to identify scent ingredients 2. The system of claim 1, (Appendix 16) The analysis unit is Generates detailed profiles of scent components based on user input 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing video information, scent components are identified based on the audio information of the video. 2. The system of claim 1, (Appendix 19) The analysis unit is Analyzes the user's past fragrance preferences and suggests the most suitable fragrance ingredients 2. The system of claim 1, (Appendix 20) The blending unit includes: The user's emotions are estimated, and the intensity of the fragrance to be blended is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 21) The blending unit includes: When mixing, choose the appropriate mixing method by considering the interactions between the fragrance ingredients. 2. The system of claim 1, (Appendix 22) The blending unit includes: When mixing, add ingredients to adjust the duration of the fragrance. 2. The system of claim 1, (Appendix 23) The blending unit includes: The system estimates the user's emotions and adjusts the order of scents to be mixed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 24) The blending unit includes: When mixing, local fragrance ingredients are added based on the user's geographic location. 2. The system of claim 1, (Appendix 25) The blending unit includes: When mixing, it analyzes users' social media activity and adds relevant scent ingredients. 2. The system of claim 1, [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit and identifies components of the fragrance; A blending unit that blends the aroma components identified by the analysis unit. A system characterized by:

2. The reception unit is Accepts text or voice input as a user input method 2. The system of claim 1 .

3. The analysis unit is Identify scent components based on past data or trained models 2. The system of claim 1 .

4. The blending unit includes: Combining multiple fragrance ingredients to create a fragrance 2. The system of claim 1 .

5. The reception unit is Accepts video information 2. The system of claim 1 .

6. The analysis unit is Identifying different scent components in each video scene 2. The system of claim 1 .

7. The blending unit includes: Blending scents in real time 2. The system of claim 1 .

8. The reception unit is Estimate the user's emotions and automatically select an appropriate input method based on the estimated user emotions.

2. The system of claim 1 .

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