system

Abstract

The system according to this embodiment aims to reproduce the sense of smell in a digital space and improve the user experience. [Solution] The system according to the embodiment comprises a generation unit, a supply unit, and a collection unit. The generation unit generates olfactory sensations. The supply unit provides the olfactory sensations generated by the generation unit to the user. The collection unit collects the user's response to the olfactory sensations provided by the supply unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to reproduce smell in the digital space, and there are limitations in improving the user experience.

[0005] The system according to the embodiment aims to reproduce smell in the digital space and improve the user experience.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a provision unit, and a collection unit. The generation unit generates smell. The provision unit provides the smell generated by the generation unit to the user. The collection unit collects the user's reaction to the smell provided by the provision unit.

Effects of the Invention

[0007] The system according to this embodiment can reproduce the sense of smell in a digital space and improve the user experience. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The olfactory generation system according to an embodiment of the present invention is a system that generates and provides olfactory sensations and collects user responses. This olfactory generation system adopts a strategy to gain market share ahead of others in the field of olfaction, which is considered to have room for digital transformation among the five senses. Currently, services related to olfaction are not yet widespread in either 2D or 3D spaces, so the aim is to enter this field. Potential markets for entry include the realization of olfaction in metaverse space, the realization of olfaction in the gaming industry, and fields where appealing to the sense of smell adds value to the user experience, such as cooking shows. Using generative AI, the company will develop an olfactory generation AI for generating olfactory sensations, or combine generative AI with existing technologies to develop services. For example, let's explain the realization of olfaction in metaverse space. In metaverse space, users can have various experiences in a virtual space, but the reproduction of olfaction has not yet been realized. By using generative AI to generate scents corresponding to specific scenes in the virtual space and providing them to the user, a more realistic experience can be provided. For example, when walking through a virtual flower field, the user can smell the scent of flowers. Next, let's explain the realization of olfaction in the gaming industry. In the gaming industry, users are seeking more immersive experiences. By using generative AI to generate scents corresponding to in-game scenes and providing them to users, a more realistic gaming experience can be offered. For example, in a cooking scene in a game, users could actually smell the food. Furthermore, we will discuss cooking shows and other fields where appealing to the sense of smell can add value to the user experience. In cooking shows, allowing viewers to smell the food can provide a more realistic experience. By using generative AI to generate scents corresponding to cooking scenes and providing them to viewers, it is possible to attract viewers' interest and enhance the appeal of the show. In this way, by recreating the sense of smell using generative AI, value can be added to the user experience in various fields. This will promote the digital transformation of the sense of smell and allow for market share acquisition. As a result, the olfactory generation system can improve the user experience.

[0029] The olfactory generation system according to the embodiment comprises a generation unit, a supply unit, and a collection unit. The generation unit generates olfactory sensations. The generation unit generates scents corresponding to specific scenes in a virtual space, for example, using a generation AI. The generation unit can generate, for example, floral scents, food scents, natural scents, etc. The generation unit generates a scent when, for example, the generation AI receives a prompt such as "Generate a scent suitable for this scene." The generation unit can also generate a specific scent by combining scent components, for example, using the generation AI. The generation unit can also generate an optimal scent based on the user's past reaction data, for example, using the generation AI. The supply unit provides the generated scent to the user. The supply unit provides the scent using, for example, a diffuser. The supply unit can also provide the scent using, for example, a spray. The supply unit can also provide the scent in real time, for example. The supply unit can also select the optimal supply point based on the user's location information, for example. The collection unit collects user reactions. The collection unit collects user reactions using, for example, a questionnaire. The collection unit can, for example, collect the user's biometric information using sensors. The collection unit can also, for example, estimate the user's emotions and collect reaction data based on the estimated emotions. The collection unit can also, for example, analyze the collected reaction data in real time and reflect it in the next fragrance generation. As a result, the olfactory generation system according to the embodiment can improve the user experience.

[0030] The generation unit generates scents. For example, it uses a generation AI to generate scents appropriate to specific scenes in a virtual space. The generation AI utilizes natural language processing technology to generate appropriate scents based on prompts. For example, upon receiving the prompt "Generate a scent suitable for this scene," the generation AI analyzes the characteristics of the scene and selects appropriate scent components. The generation AI can generate a variety of scents, such as floral scents, food scents, and natural scents. Specifically, when generating floral scents, the generation AI combines scent components such as rose, lavender, and jasmine to create a pleasant scent for the user. When generating food scents, it combines components such as vanilla, cinnamon, and chocolate to reproduce realistic food scents. The generation AI can also generate optimal scents based on the user's past reaction data. For example, it analyzes data on scents the user has liked in the past and generates new scents based on that data. This allows the generation unit to provide personalized scents tailored to the user's preferences. Furthermore, the generation AI can adjust the intensity and duration of the scent, allowing for customization according to the user's needs. This allows the fragrance generator to provide users with a high-quality and diverse fragrance experience.

[0031] The service provider delivers the generated fragrance to the user. The service provider can deliver the fragrance using, for example, a diffuser. A diffuser is a device that converts liquid fragrance components into a fine mist and diffuses it into the space. This allows the user to enjoy the fragrance in a natural way. The service provider can also deliver the fragrance using, for example, a spray. A spray can be applied directly to specific places or objects, allowing the user to enjoy the fragrance in specific scenes or situations. The service provider can also deliver the fragrance in real time. Real-time delivery allows the user to enjoy the fragrance in accordance with specific scenes or events. For example, during a virtual reality experience, the fragrance can be instantly switched in accordance with changes in the scene. The service provider can also select the optimal delivery point based on the user's location information. By utilizing location information, the service provider can deliver the optimal fragrance according to the user's location and situation. For example, when the user wants to relax, a lavender fragrance can be provided, and when they want to concentrate, a mint fragrance can be provided. This allows the service provider to achieve flexible fragrance delivery according to the user's needs and situation, improving the user experience.

[0032] The data collection unit collects user responses. For example, the data collection unit collects user responses using questionnaires. These questionnaires collect information such as how users felt about the scents and which scents they preferred. The data collection unit can also collect user biometric information using sensors. Sensors measure biometric information such as heart rate, skin electrical activity, and respiratory rate, and are used to estimate the user's emotions and responses. The data collection unit can also estimate the user's emotions and collect response data based on the estimated emotions. Facial recognition and voice analysis technologies can be used for emotion estimation. For example, the user's facial expressions and tone of voice can be analyzed to estimate emotions such as joy, surprise, and relaxation. The data collection unit can also analyze the collected response data in real time and reflect it in the next scent generation. Real-time analysis allows for immediate understanding of user responses and provides feedback to the generation unit. This enables the generation unit to adjust the scent based on user responses and produce a more appropriate scent. Furthermore, the data collection unit can accumulate long-term data and analyze user preferences and response trends. This allows the data collection unit to provide crucial data for delivering personalized fragrance experiences tailored to the individual needs of each user.

[0033] The generation unit can generate scents corresponding to specific scenes in a virtual space. For example, in a virtual flower field scene, the generation AI can generate the scent of flowers. For example, in a virtual beach scene, the generation AI can generate the scent of the sea. For example, in a virtual cafe scene, the generation AI can generate the scent of coffee. This allows for a more realistic experience by generating scents corresponding to specific scenes in a virtual space. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input scene information from the virtual space into the generation AI and cause the generation AI to generate a scent appropriate for the scene.

[0034] The dispensing unit can provide the generated fragrance to the user. The dispensing unit can provide the fragrance, for example, using a diffuser. The dispensing unit can also provide the fragrance, for example, using a spray. The dispensing unit can also provide the fragrance in real time. This improves the user experience by providing the generated fragrance to the user. Some or all of the above processing in the dispensing unit may be performed using AI or not. For example, the dispensing unit inputs the generated fragrance into a diffuser, and the diffuser releases the fragrance.

[0035] The data collection unit can collect user responses and reflect them in subsequent generation. The data collection unit can collect user responses, for example, using questionnaires. The data collection unit can also collect user biometric information, for example, using sensors. The data collection unit can also estimate the user's emotions and collect response data based on the estimated emotions. This improves the accuracy of fragrance generation by collecting user responses and reflecting them in subsequent generation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can acquire user biometric information using sensors, and AI can analyze that data to collect responses.

[0036] The generation unit can generate scents that correspond to scenes in the game. For example, in a combat scene, the generation AI can generate the scent of gunpowder. In an exploration scene, the generation AI can also generate the scent of grass. In a cooking scene, the generation AI can also generate the scent of cooking. By generating scents that correspond to scenes in the game, a more immersive gaming experience can be provided. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input scene information from the game into the generation AI and cause the generation AI to generate a scent appropriate for the scene.

[0037] The supply unit can provide viewers with scents appropriate to the scenes in a cooking show. For example, during a cooking scene, the supply unit can provide the generated scent using a diffuser. For example, during a tasting scene, the supply unit can also provide the generated scent using a spray. By providing viewers with scents appropriate to the scenes in the cooking show, the supply unit can attract viewers' interest and enhance the appeal of the program. Some or all of the above processing in the supply unit may be performed using AI or not. For example, the supply unit inputs the generated scent into a diffuser, and the diffuser releases the scent.

[0038] The generation unit can combine multiple scents to create new scents depending on a specific scene in the virtual space. For example, in a virtual forest scene, the generation AI can combine the scents of trees and grass. In a virtual beach scene, for example, the generation AI can combine the scents of the sea and sand. In a virtual cafe scene, for example, the generation AI can combine the scents of coffee and bread. By combining multiple scents to create new scents, a more realistic experience can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can input scene information from the virtual space into the generation AI and have the generation AI generate a scent appropriate for the scene.

[0039] The generation unit can analyze the user's past reaction data and apply an optimal fragrance generation algorithm. For example, the generation unit can use a generating AI to generate an optimal fragrance based on fragrances the user has liked in the past. The generation unit can also use an algorithm to avoid fragrances the user has avoided in the past. For example, the generation unit can analyze the user's past reaction data and the generating AI can adjust the strength and type of fragrance based on that data. In this way, an optimal fragrance can be generated by analyzing the user's past reaction data. Some or all of the above processing in the generation unit may be performed using a generating AI or not. For example, the generation unit inputs the user's past reaction data into the generating AI, and the generating AI generates an optimal fragrance.

[0040] The generation unit can adjust the duration of the scent according to the scene in the game. For example, in a combat scene, the generation AI can release a strong scent for a short period of time. In an exploration scene, for example, the generation AI can release a gentle scent for a longer period of time. In a rest scene, for example, the generation AI can release a scent for a moderate period of time. By adjusting the duration of the scent according to the scene in the game, a more realistic gaming experience can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit can input scene information from the game into the generation AI and have the generation AI adjust the duration of the scent to be appropriate for the scene.

[0041] The generation unit can generate changes in aroma in real time according to the scenes in a cooking show. For example, the generation unit's AI can gradually change the aroma according to the cooking process. For example, the generation unit can release the strongest aroma when the dish is finished. For example, the generation unit can release the aroma gently during a scene where the dish is tasted. By generating changes in aroma in real time according to the scenes in the cooking show, it can attract viewers' interest and enhance the appeal of the program. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input cooking show scene information into the generation AI and cause the generation AI to generate changes in aroma appropriate to the scene.

[0042] The fragrance dispenser can select the optimal distribution point based on the user's location information when distributing fragrance. For example, if the user is in the center of the room, the dispenser will evenly distribute the fragrance throughout the entire room. If the user is in a corner of the room, the dispenser can concentrate the fragrance release in that direction. If the user is moving, the dispenser can adjust and release the fragrance in real time. This maximizes the effect of the fragrance by selecting the optimal distribution point based on the user's location information. Some or all of the above processing in the dispenser may be performed using AI or not. For example, the dispenser inputs the user's location information into the AI, and the AI ​​selects the optimal distribution point.

[0043] The dispensing unit can optimize its dispensing method by referring to the user's past reaction data when dispensing fragrance. For example, the dispensing unit may release fragrance based on dispensing methods the user has preferred in the past. The dispensing unit may also avoid dispensing methods the user has avoided in the past. For example, the dispensing unit may analyze the user's past reaction data and adjust the fragrance dispensing method based on that data. This allows for the provision of more appropriate fragrances by optimizing the dispensing method by referring to the user's past reaction data. Some or all of the above processing in the dispensing unit may be performed using AI or not. For example, the dispensing unit may input the user's past reaction data into AI, and the AI ​​may select the optimal dispensing method.

[0044] The dispensing unit can select the optimal dispensing method based on the user's device information when dispensing fragrance. For example, if the user is using a smartphone, the dispensing unit will release the fragrance near the smartphone. For example, if the user is using a tablet, the dispensing unit can release the fragrance near the tablet. For example, if the user is using a smartwatch, the dispensing unit can release the fragrance near the smartwatch. This maximizes the effect of the fragrance by selecting the optimal dispensing method based on the user's device information. Some or all of the above processing in the dispensing unit may be performed using AI or not. For example, the dispensing unit inputs the user's device information into the AI, and the AI ​​selects the optimal dispensing method.

[0045] The fragrance dispenser can customize its distribution method based on the user's environmental information. For example, if the user is indoors, the dispenser will release the fragrance throughout the entire room. If the user is outdoors, the dispenser can release the fragrance in accordance with the wind direction. If the user is in a car, the dispenser can release the fragrance throughout the entire car. By customizing the distribution method based on the user's environmental information, the effect of the fragrance can be maximized. Some or all of the above processing in the dispenser may be performed using AI or not. For example, the dispenser can input the user's environmental information into the AI, and the AI ​​will select the optimal distribution method.

[0046] The collection unit can analyze the collected reaction data in real time and reflect it in the next fragrance generation. For example, the collection unit can analyze the user's real-time reaction data and feed it back to the generation unit. For example, the collection unit can allow the generation unit to adjust the fragrance intensity based on the user's real-time reaction data. For example, the collection unit can allow the generation unit to change the type of fragrance based on the user's real-time reaction data. This allows for rapid reflection in the next fragrance generation by analyzing the reaction data in real time. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs real-time reaction data into the AI, the AI ​​analyzes the data and feeds it back to the generation unit.

[0047] The data collection unit can compare the collected reaction data with past data to improve the accuracy of the fragrance generation algorithm. For example, the data collection unit can compare the user's past reaction data with the current reaction data and provide feedback to the generation unit. The data collection unit can also, for example, allow the generation unit to adjust the fragrance generation algorithm based on the user's past reaction data. The data collection unit can also, for example, allow the generation unit to optimize the strength and type of fragrance based on the user's past and current reaction data. This improves the accuracy of the fragrance generation algorithm by comparing it with past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past and current reaction data into the AI, which can then compare the data and provide feedback to the generation unit.

[0048] The data collection unit can analyze the collected response data in association with user attribute information. For example, the data collection unit can analyze response data in association with attribute information such as the user's age and gender. The data collection unit can also analyze response data in association with attribute information such as the user's hobbies and preferences. The data collection unit can also analyze response data in association with attribute information such as the user's health status. This allows for more detailed data analysis by analyzing the data in association with user attribute information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's attribute information and response data into the AI, and the AI ​​analyzes that data.

[0049] The data collection unit can analyze the collected response data in combination with other sensor data. For example, the data collection unit can analyze the user's heart rate data in combination with the response data. For example, the data collection unit can also analyze the user's body temperature data in combination with the response data. For example, the data collection unit can also analyze the user's activity level data in combination with the response data. By combining and analyzing the data with other sensor data, more accurate data analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit inputs other sensor data and response data into the AI, and the AI ​​analyzes that data.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The fragrance generator can learn the user's olfactory preferences and generate individually optimized fragrances. For example, it can collect data on fragrances the user has liked in the past and generate new fragrances based on that data. It can also learn the fragrances the user prefers in specific situations and generate fragrances appropriate for those situations. Furthermore, it can track changes in the user's sense of smell in real time and adjust the fragrance accordingly. This allows for a more personalized experience by generating fragrances based on the user's olfactory preferences.

[0052] The dispenser can measure the user's olfactory sensitivity and adjust the fragrance intensity based on that sensitivity. For example, the dispenser measures the user's olfactory sensitivity with a sensor and adjusts the fragrance intensity based on that data. If the user perceives the fragrance as too strong, the dispenser can reduce the fragrance intensity. Furthermore, if the user's olfactory sensitivity is low, the dispenser can increase the fragrance intensity. In this way, by adjusting the fragrance intensity based on the user's olfactory sensitivity, a more appropriate fragrance can be provided.

[0053] The data collection unit can track the user's olfactory responses over the long term and use that data to improve the fragrance generation algorithm. For example, the data collection unit periodically collects the user's olfactory responses and provides feedback to the generation unit based on that data. If the user's olfactory responses change over time, the data collection unit can track those changes, allowing the generation unit to adjust the fragrance generation algorithm. Furthermore, if the user's olfactory responses change under specific conditions, the data collection unit can identify those conditions, allowing the generation unit to optimize the fragrance generation algorithm. This allows for continuous improvement of the fragrance generation algorithm by tracking the user's olfactory responses over the long term.

[0054] The fragrance generator can detect the user's olfactory fatigue and adjust the fragrance generation based on that fatigue. For example, the generator can detect the user's olfactory fatigue using a sensor and adjust the fragrance generation based on that data. If the user's olfactory fatigue is present, the generator can reduce the fragrance intensity. Furthermore, if the user's olfactory fatigue is not present, the generator can maintain the normal fragrance intensity. In this way, by adjusting the fragrance generation based on the user's olfactory fatigue, a more comfortable fragrance can be provided.

[0055] The fragrance dispenser can learn the user's olfactory preferences and adjust the way fragrances are dispensed based on those preferences. For example, the dispenser can learn the fragrance dispenser's preferred methods in the past and dispense fragrances based on those methods. The dispenser can also learn the fragrance dispenser's preferred methods in specific situations and dispense fragrances in a way that is appropriate for those situations. Furthermore, if the user's olfactory preferences change, the dispenser can track those changes and adjust the dispenser accordingly. This allows for a more personalized experience by adjusting the fragrance dispenser based on the user's olfactory preferences.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The generation unit generates scents. The generation unit generates scents appropriate to a specific scene in a virtual space, for example, using a generation AI. The generation unit can generate scents of flowers, food, nature, etc. The generation unit generates scents when the generation AI receives a prompt such as "Please generate a scent appropriate for this scene." The generation unit can also generate specific scents by having the generation AI combine scent components. The generation unit can also generate the optimal scent based on the user's past reaction data. Step 2: The dispenser provides the generated fragrance to the user. The dispenser provides the fragrance using, for example, a diffuser. The dispenser can also provide the fragrance using a spray. The dispenser can also provide the fragrance in real time. The dispenser can also select the optimal distribution point based on the user's location information. Step 3: The collection unit collects user responses. The collection unit can collect user responses, for example, using questionnaires. The collection unit can also collect user biometric information using sensors. The collection unit can also estimate the user's emotions and collect response data based on the estimated emotions. The collection unit can also analyze the collected response data in real time and reflect it in the next fragrance generation.

[0058] (Example of form 2) The olfactory generation system according to an embodiment of the present invention is a system that generates and provides olfactory sensations and collects user responses. This olfactory generation system adopts a strategy to gain market share ahead of others in the field of olfaction, which is considered to have room for digital transformation among the five senses. Currently, services related to olfaction are not yet widespread in either 2D or 3D spaces, so the aim is to enter this field. Potential markets for entry include the realization of olfaction in metaverse space, the realization of olfaction in the gaming industry, and fields where appealing to the sense of smell adds value to the user experience, such as cooking shows. Using generative AI, the company will develop an olfactory generation AI for generating olfactory sensations, or combine generative AI with existing technologies to develop services. For example, let's explain the realization of olfaction in metaverse space. In metaverse space, users can have various experiences in a virtual space, but the reproduction of olfaction has not yet been realized. By using generative AI to generate scents corresponding to specific scenes in the virtual space and providing them to the user, a more realistic experience can be provided. For example, when walking through a virtual flower field, the user can smell the scent of flowers. Next, let's explain the realization of olfaction in the gaming industry. In the gaming industry, users are seeking more immersive experiences. By using generative AI to generate scents corresponding to in-game scenes and providing them to users, a more realistic gaming experience can be offered. For example, in a cooking scene in a game, users could actually smell the food. Furthermore, we will discuss cooking shows and other fields where appealing to the sense of smell can add value to the user experience. In cooking shows, allowing viewers to smell the food can provide a more realistic experience. By using generative AI to generate scents corresponding to cooking scenes and providing them to viewers, it is possible to attract viewers' interest and enhance the appeal of the show. In this way, by recreating the sense of smell using generative AI, value can be added to the user experience in various fields. This will promote the digital transformation of the sense of smell and allow for market share acquisition. As a result, the olfactory generation system can improve the user experience.

[0059] The olfactory generation system according to the embodiment comprises a generation unit, a supply unit, and a collection unit. The generation unit generates olfactory sensations. The generation unit generates scents corresponding to specific scenes in a virtual space, for example, using a generation AI. The generation unit can generate, for example, floral scents, food scents, natural scents, etc. The generation unit generates a scent when, for example, the generation AI receives a prompt such as "Generate a scent suitable for this scene." The generation unit can also generate a specific scent by combining scent components, for example, using the generation AI. The generation unit can also generate an optimal scent based on the user's past reaction data, for example, using the generation AI. The supply unit provides the generated scent to the user. The supply unit provides the scent using, for example, a diffuser. The supply unit can also provide the scent using, for example, a spray. The supply unit can also provide the scent in real time, for example. The supply unit can also select the optimal supply point based on the user's location information, for example. The collection unit collects user reactions. The collection unit collects user reactions using, for example, a questionnaire. The collection unit can, for example, collect the user's biometric information using sensors. The collection unit can also, for example, estimate the user's emotions and collect reaction data based on the estimated emotions. The collection unit can also, for example, analyze the collected reaction data in real time and reflect it in the next fragrance generation. As a result, the olfactory generation system according to the embodiment can improve the user experience.

[0060] The generation unit generates scents. For example, it uses a generation AI to generate scents appropriate to specific scenes in a virtual space. The generation AI utilizes natural language processing technology to generate appropriate scents based on prompts. For example, upon receiving the prompt "Generate a scent suitable for this scene," the generation AI analyzes the characteristics of the scene and selects appropriate scent components. The generation AI can generate a variety of scents, such as floral scents, food scents, and natural scents. Specifically, when generating floral scents, the generation AI combines scent components such as rose, lavender, and jasmine to create a pleasant scent for the user. When generating food scents, it combines components such as vanilla, cinnamon, and chocolate to reproduce realistic food scents. The generation AI can also generate optimal scents based on the user's past reaction data. For example, it analyzes data on scents the user has liked in the past and generates new scents based on that data. This allows the generation unit to provide personalized scents tailored to the user's preferences. Furthermore, the generation AI can adjust the intensity and duration of the scent, allowing for customization according to the user's needs. This allows the fragrance generator to provide users with a high-quality and diverse fragrance experience.

[0061] The service provider delivers the generated fragrance to the user. The service provider can deliver the fragrance using, for example, a diffuser. A diffuser is a device that converts liquid fragrance components into a fine mist and diffuses it into the space. This allows the user to enjoy the fragrance in a natural way. The service provider can also deliver the fragrance using, for example, a spray. A spray can be applied directly to specific places or objects, allowing the user to enjoy the fragrance in specific scenes or situations. The service provider can also deliver the fragrance in real time. Real-time delivery allows the user to enjoy the fragrance in accordance with specific scenes or events. For example, during a virtual reality experience, the fragrance can be instantly switched in accordance with changes in the scene. The service provider can also select the optimal delivery point based on the user's location information. By utilizing location information, the service provider can deliver the optimal fragrance according to the user's location and situation. For example, when the user wants to relax, a lavender fragrance can be provided, and when they want to concentrate, a mint fragrance can be provided. This allows the service provider to achieve flexible fragrance delivery according to the user's needs and situation, improving the user experience.

[0062] The data collection unit collects user responses. For example, the data collection unit collects user responses using questionnaires. These questionnaires collect information such as how users felt about the scents and which scents they preferred. The data collection unit can also collect user biometric information using sensors. Sensors measure biometric information such as heart rate, skin electrical activity, and respiratory rate, and are used to estimate the user's emotions and responses. The data collection unit can also estimate the user's emotions and collect response data based on the estimated emotions. Facial recognition and voice analysis technologies can be used for emotion estimation. For example, the user's facial expressions and tone of voice can be analyzed to estimate emotions such as joy, surprise, and relaxation. The data collection unit can also analyze the collected response data in real time and reflect it in the next scent generation. Real-time analysis allows for immediate understanding of user responses and provides feedback to the generation unit. This enables the generation unit to adjust the scent based on user responses and produce a more appropriate scent. Furthermore, the data collection unit can accumulate long-term data and analyze user preferences and response trends. This allows the data collection unit to provide crucial data for delivering personalized fragrance experiences tailored to the individual needs of each user.

[0063] The generation unit can generate scents corresponding to specific scenes in a virtual space. For example, in a virtual flower field scene, the generation AI can generate the scent of flowers. For example, in a virtual beach scene, the generation AI can generate the scent of the sea. For example, in a virtual cafe scene, the generation AI can generate the scent of coffee. This allows for a more realistic experience by generating scents corresponding to specific scenes in a virtual space. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input scene information from the virtual space into the generation AI and cause the generation AI to generate a scent appropriate for the scene.

[0064] The dispensing unit can provide the generated fragrance to the user. The dispensing unit can provide the fragrance, for example, using a diffuser. The dispensing unit can also provide the fragrance, for example, using a spray. The dispensing unit can also provide the fragrance in real time. This improves the user experience by providing the generated fragrance to the user. Some or all of the above processing in the dispensing unit may be performed using AI or not. For example, the dispensing unit inputs the generated fragrance into a diffuser, and the diffuser releases the fragrance.

[0065] The data collection unit can collect user responses and reflect them in subsequent generation. The data collection unit can collect user responses, for example, using questionnaires. The data collection unit can also collect user biometric information, for example, using sensors. The data collection unit can also estimate the user's emotions and collect response data based on the estimated emotions. This improves the accuracy of fragrance generation by collecting user responses and reflecting them in subsequent generation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can acquire user biometric information using sensors, and AI can analyze that data to collect responses.

[0066] The generation unit can generate scents that correspond to scenes in the game. For example, in a combat scene, the generation AI can generate the scent of gunpowder. In an exploration scene, the generation AI can also generate the scent of grass. In a cooking scene, the generation AI can also generate the scent of cooking. By generating scents that correspond to scenes in the game, a more immersive gaming experience can be provided. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input scene information from the game into the generation AI and cause the generation AI to generate a scent appropriate for the scene.

[0067] The supply unit can provide viewers with scents appropriate to the scenes in a cooking show. For example, during a cooking scene, the supply unit can provide the generated scent using a diffuser. For example, during a tasting scene, the supply unit can also provide the generated scent using a spray. By providing viewers with scents appropriate to the scenes in the cooking show, the supply unit can attract viewers' interest and enhance the appeal of the program. Some or all of the above processing in the supply unit may be performed using AI or not. For example, the supply unit inputs the generated scent into a diffuser, and the diffuser releases the scent.

[0068] The generation unit can estimate the user's emotions and adjust the type and intensity of the fragrance it generates based on the estimated emotions. For example, if the user is relaxed, the generation AI may generate a mild fragrance with a moderate intensity. If the user is excited, the generation AI may generate a stimulating fragrance with a stronger intensity. If the user is stressed, the generation AI may generate a relaxing fragrance with a moderate intensity. This allows for the provision of a more appropriate fragrance by adjusting the type and intensity of the fragrance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the type and intensity of the fragrance.

[0069] The generation unit can combine multiple scents to create new scents depending on a specific scene in the virtual space. For example, in a virtual forest scene, the generation AI can combine the scents of trees and grass. In a virtual beach scene, for example, the generation AI can combine the scents of the sea and sand. In a virtual cafe scene, for example, the generation AI can combine the scents of coffee and bread. By combining multiple scents to create new scents, a more realistic experience can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can input scene information from the virtual space into the generation AI and have the generation AI generate a scent appropriate for the scene.

[0070] The generation unit can analyze the user's past reaction data and apply an optimal fragrance generation algorithm. For example, the generation unit can use a generating AI to generate an optimal fragrance based on fragrances the user has liked in the past. The generation unit can also use an algorithm to avoid fragrances the user has avoided in the past. For example, the generation unit can analyze the user's past reaction data and the generating AI can adjust the strength and type of fragrance based on that data. In this way, an optimal fragrance can be generated by analyzing the user's past reaction data. Some or all of the above processing in the generation unit may be performed using a generating AI or not. For example, the generation unit inputs the user's past reaction data into the generating AI, and the generating AI generates an optimal fragrance.

[0071] The generation unit can estimate the user's emotions and adjust the timing of the fragrance it generates based on the estimated emotions. For example, if the user is relaxed, the generation AI can slowly release the fragrance. If the user is excited, the generation AI can release the fragrance quickly. If the user is stressed, the generation AI can release the fragrance intermittently. This allows for the fragrance to be delivered at a more appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the timing of the fragrance.

[0072] The generation unit can adjust the duration of the scent according to the scene in the game. For example, in a combat scene, the generation AI can release a strong scent for a short period of time. In an exploration scene, for example, the generation AI can release a gentle scent for a longer period of time. In a rest scene, for example, the generation AI can release a scent for a moderate period of time. By adjusting the duration of the scent according to the scene in the game, a more realistic gaming experience can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit can input scene information from the game into the generation AI and have the generation AI adjust the duration of the scent to be appropriate for the scene.

[0073] The generation unit can generate changes in aroma in real time according to the scenes in a cooking show. For example, the generation unit's AI can gradually change the aroma according to the cooking process. For example, the generation unit can release the strongest aroma when the dish is finished. For example, the generation unit can release the aroma gently during a scene where the dish is tasted. By generating changes in aroma in real time according to the scenes in the cooking show, it can attract viewers' interest and enhance the appeal of the program. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input cooking show scene information into the generation AI and cause the generation AI to generate changes in aroma appropriate to the scene.

[0074] The dispenser can estimate the user's emotions and adjust the way it dispenses the fragrance based on those emotions. For example, if the user is relaxed, the dispenser will release the fragrance slowly. If the user is excited, the dispenser may release the fragrance quickly. If the user is stressed, the dispenser may release the fragrance intermittently. This allows for the provision of a more appropriate fragrance by adjusting the way it dispenses the fragrance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the dispenser may be performed using AI or not. For example, the dispenser inputs user emotion data into the generative AI, and the generative AI adjusts the way it dispenses the fragrance.

[0075] The fragrance dispenser can select the optimal distribution point based on the user's location information when distributing fragrance. For example, if the user is in the center of the room, the dispenser will evenly distribute the fragrance throughout the entire room. If the user is in a corner of the room, the dispenser can concentrate the fragrance release in that direction. If the user is moving, the dispenser can adjust and release the fragrance in real time. This maximizes the effect of the fragrance by selecting the optimal distribution point based on the user's location information. Some or all of the above processing in the dispenser may be performed using AI or not. For example, the dispenser inputs the user's location information into the AI, and the AI ​​selects the optimal distribution point.

[0076] The dispensing unit can optimize its dispensing method by referring to the user's past reaction data when dispensing fragrance. For example, the dispensing unit may release fragrance based on dispensing methods the user has preferred in the past. The dispensing unit may also avoid dispensing methods the user has avoided in the past. For example, the dispensing unit may analyze the user's past reaction data and adjust the fragrance dispensing method based on that data. This allows for the provision of more appropriate fragrances by optimizing the dispensing method by referring to the user's past reaction data. Some or all of the above processing in the dispensing unit may be performed using AI or not. For example, the dispensing unit may input the user's past reaction data into AI, and the AI ​​may select the optimal dispensing method.

[0077] The dispenser can estimate the user's emotions and adjust the frequency of scent delivery based on the estimated emotions. For example, if the user is relaxed, the dispenser may release the scent at a low frequency. If the user is excited, the dispenser may release the scent at a high frequency. If the user is stressed, the dispenser may release the scent at a medium frequency. By adjusting the frequency of scent delivery based on the user's emotions, a more appropriate scent can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the dispenser may be performed using AI or not. For example, the dispenser inputs user emotion data into the generative AI, and the generative AI adjusts the frequency of scent delivery.

[0078] The dispensing unit can select the optimal dispensing method based on the user's device information when dispensing fragrance. For example, if the user is using a smartphone, the dispensing unit will release the fragrance near the smartphone. For example, if the user is using a tablet, the dispensing unit can release the fragrance near the tablet. For example, if the user is using a smartwatch, the dispensing unit can release the fragrance near the smartwatch. This maximizes the effect of the fragrance by selecting the optimal dispensing method based on the user's device information. Some or all of the above processing in the dispensing unit may be performed using AI or not. For example, the dispensing unit inputs the user's device information into the AI, and the AI ​​selects the optimal dispensing method.

[0079] The fragrance dispenser can customize its distribution method based on the user's environmental information. For example, if the user is indoors, the dispenser will release the fragrance throughout the entire room. If the user is outdoors, the dispenser can release the fragrance in accordance with the wind direction. If the user is in a car, the dispenser can release the fragrance throughout the entire car. By customizing the distribution method based on the user's environmental information, the effect of the fragrance can be maximized. Some or all of the above processing in the dispenser may be performed using AI or not. For example, the dispenser can input the user's environmental information into the AI, and the AI ​​will select the optimal distribution method.

[0080] The data collection unit can estimate the user's emotions and adjust the method of collecting response data based on the estimated user emotions. For example, if the user is relaxed, the data collection unit can collect response data slowly. If the user is excited, for example, the data collection unit can collect response data quickly. If the user is stressed, for example, the data collection unit can collect response data intermittently. This allows for the collection of more appropriate data by adjusting the method of collecting response data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the method of collecting response data.

[0081] The collection unit can analyze the collected reaction data in real time and reflect it in the next fragrance generation. For example, the collection unit can analyze the user's real-time reaction data and feed it back to the generation unit. For example, the collection unit can allow the generation unit to adjust the fragrance intensity based on the user's real-time reaction data. For example, the collection unit can allow the generation unit to change the type of fragrance based on the user's real-time reaction data. This allows for rapid reflection in the next fragrance generation by analyzing the reaction data in real time. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs real-time reaction data into the AI, the AI ​​analyzes the data and feeds it back to the generation unit.

[0082] The data collection unit can compare the collected reaction data with past data to improve the accuracy of the fragrance generation algorithm. For example, the data collection unit can compare the user's past reaction data with the current reaction data and provide feedback to the generation unit. The data collection unit can also, for example, allow the generation unit to adjust the fragrance generation algorithm based on the user's past reaction data. The data collection unit can also, for example, allow the generation unit to optimize the strength and type of fragrance based on the user's past and current reaction data. This improves the accuracy of the fragrance generation algorithm by comparing it with past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past and current reaction data into the AI, which can then compare the data and provide feedback to the generation unit.

[0083] The data collection unit can estimate the user's emotions and adjust the frequency of response data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit may collect response data at a low frequency. If the user is excited, for example, the data collection unit may collect response data at a high frequency. If the user is stressed, for example, the data collection unit may collect response data at a medium frequency. By adjusting the frequency of response data collection based on the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the frequency of response data collection.

[0084] The data collection unit can analyze the collected response data in association with user attribute information. For example, the data collection unit can analyze response data in association with attribute information such as the user's age and gender. The data collection unit can also analyze response data in association with attribute information such as the user's hobbies and preferences. The data collection unit can also analyze response data in association with attribute information such as the user's health status. This allows for more detailed data analysis by analyzing the data in association with user attribute information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's attribute information and response data into the AI, and the AI ​​analyzes that data.

[0085] The data collection unit can analyze the collected response data in combination with other sensor data. For example, the data collection unit can analyze the user's heart rate data in combination with the response data. For example, the data collection unit can also analyze the user's body temperature data in combination with the response data. For example, the data collection unit can also analyze the user's activity level data in combination with the response data. By combining and analyzing the data with other sensor data, more accurate data analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit inputs other sensor data and response data into the AI, and the AI ​​analyzes that data.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The fragrance generator can learn the user's olfactory preferences and generate individually optimized fragrances. For example, it can collect data on fragrances the user has liked in the past and generate new fragrances based on that data. It can also learn the fragrances the user prefers in specific situations and generate fragrances appropriate for those situations. Furthermore, it can track changes in the user's sense of smell in real time and adjust the fragrance accordingly. This allows for a more personalized experience by generating fragrances based on the user's olfactory preferences.

[0088] The dispenser can measure the user's olfactory sensitivity and adjust the fragrance intensity based on that sensitivity. For example, the dispenser measures the user's olfactory sensitivity with a sensor and adjusts the fragrance intensity based on that data. If the user perceives the fragrance as too strong, the dispenser can reduce the fragrance intensity. Furthermore, if the user's olfactory sensitivity is low, the dispenser can increase the fragrance intensity. In this way, by adjusting the fragrance intensity based on the user's olfactory sensitivity, a more appropriate fragrance can be provided.

[0089] The data collection unit can track the user's olfactory responses over the long term and use that data to improve the fragrance generation algorithm. For example, the data collection unit periodically collects the user's olfactory responses and provides feedback to the generation unit based on that data. If the user's olfactory responses change over time, the data collection unit can track those changes, allowing the generation unit to adjust the fragrance generation algorithm. Furthermore, if the user's olfactory responses change under specific conditions, the data collection unit can identify those conditions, allowing the generation unit to optimize the fragrance generation algorithm. This allows for continuous improvement of the fragrance generation algorithm by tracking the user's olfactory responses over the long term.

[0090] The fragrance generator can detect the user's olfactory fatigue and adjust the fragrance generation based on that fatigue. For example, the generator can detect the user's olfactory fatigue using a sensor and adjust the fragrance generation based on that data. If the user's olfactory fatigue is present, the generator can reduce the fragrance intensity. Furthermore, if the user's olfactory fatigue is not present, the generator can maintain the normal fragrance intensity. In this way, by adjusting the fragrance generation based on the user's olfactory fatigue, a more comfortable fragrance can be provided.

[0091] The fragrance dispenser can learn the user's olfactory preferences and adjust the way fragrances are dispensed based on those preferences. For example, the dispenser can learn the fragrance dispenser's preferred methods in the past and dispense fragrances based on those methods. The dispenser can also learn the fragrance dispenser's preferred methods in specific situations and dispense fragrances in a way that is appropriate for those situations. Furthermore, if the user's olfactory preferences change, the dispenser can track those changes and adjust the dispenser accordingly. This allows for a more personalized experience by adjusting the fragrance dispenser based on the user's olfactory preferences.

[0092] The fragrance generator can estimate the user's emotions and select a fragrance type based on those emotions. For example, if the user is relaxed, the AI ​​can generate a lavender fragrance. If the user is excited, the AI ​​can also generate a mint fragrance. Furthermore, if the user is stressed, the AI ​​can generate a chamomile fragrance. This allows for the provision of a more appropriate fragrance by selecting a fragrance type based on the user's emotions.

[0093] The dispenser can estimate the user's emotions and adjust the timing of fragrance release based on those emotions. For example, if the user is relaxed, the dispenser will release the fragrance slowly. If the user is excited, the dispenser can release the fragrance quickly. Furthermore, if the user is stressed, the dispenser can release the fragrance intermittently. By adjusting the timing of fragrance release based on the user's emotions, the fragrance can be delivered at a more appropriate time.

[0094] The data collection unit can estimate the user's emotions and adjust the method of collecting response data based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect response data slowly. If the user is excited, the data collection unit can also collect response data quickly. Furthermore, if the user is stressed, the data collection unit can collect response data intermittently. This allows for the collection of more relevant data by adjusting the method of collecting response data based on the user's emotions.

[0095] The fragrance generator can estimate the user's emotions and adjust the duration of the fragrance based on those emotions. For example, if the user is relaxed, the generator will make the fragrance last longer. If the user is excited, the generator can also release a strong fragrance for a short period of time. Furthermore, if the user is stressed, the generator can release the fragrance for a moderate amount of time. This allows for the provision of a more appropriate fragrance by adjusting the duration of the fragrance based on the user's emotions.

[0096] The dispenser can estimate the user's emotions and adjust the frequency of scent release based on those emotions. For example, if the user is relaxed, the dispenser will release the scent at a low frequency. If the user is excited, the dispenser can release the scent at a high frequency. Furthermore, if the user is stressed, the dispenser can release the scent at a moderate frequency. By adjusting the scent release frequency based on the user's emotions, a more appropriate scent can be provided.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The generation unit generates scents. The generation unit generates scents appropriate to a specific scene in a virtual space, for example, using a generation AI. The generation unit can generate scents of flowers, food, nature, etc. The generation unit generates scents when the generation AI receives a prompt such as "Please generate a scent appropriate for this scene." The generation unit can also generate specific scents by having the generation AI combine scent components. The generation unit can also generate the optimal scent based on the user's past reaction data. Step 2: The dispenser provides the generated fragrance to the user. The dispenser provides the fragrance using, for example, a diffuser. The dispenser can also provide the fragrance using a spray. The dispenser can also provide the fragrance in real time. The dispenser can also select the optimal distribution point based on the user's location information. Step 3: The collection unit collects user responses. The collection unit can collect user responses, for example, using questionnaires. The collection unit can also collect user biometric information using sensors. The collection unit can also estimate the user's emotions and collect response data based on the estimated emotions. The collection unit can also analyze the collected response data in real time and reflect it in the next fragrance generation.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the generation unit, supply unit, and collection unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates a fragrance in a virtual space that corresponds to a specific scene using a generation AI. The supply unit provides the fragrance using, for example, the diffuser or spray of the smart device 14. The collection unit collects user responses using, for example, the sensors or survey functions of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the generation unit, supply unit, and collection unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates a fragrance in a virtual space that corresponds to a specific scene using a generation AI. The supply unit provides the fragrance using, for example, the diffuser or spray of the smart glasses 214. The collection unit collects user responses using, for example, the sensors or questionnaire functions of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the generation unit, supply unit, and collection unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates a fragrance in a virtual space that corresponds to a specific scene using a generation AI. The supply unit provides the fragrance using, for example, the diffuser or spray of the headset terminal 314. The collection unit collects user responses using, for example, the sensors or questionnaire functions of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the generation unit, supply unit, and collection unit, is implemented in at least one of the robot 414 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates a fragrance in a virtual space that corresponds to a specific scene using a generation AI. The supply unit provides the fragrance using, for example, the robot 414's diffuser or spray. The collection unit collects user responses using, for example, the robot 414's sensors or questionnaire function. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A generating unit that generates the sense of smell, A providing unit that provides the olfactory sensation generated by the generating unit to the user, The system includes a collection unit that collects the user's response to the olfactory sensation provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The generating unit is Generate scents that correspond to specific scenes within a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The generated fragrance is provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect user feedback and use it to improve future generation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generates scents corresponding to scenes in the game. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing viewers with aromas that match the scenes in the cooking show. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the type and intensity of the fragrance generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It generates new scents by combining multiple fragrances according to a specific scene in a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is We analyze past user response data and apply the optimal fragrance generation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the timing of scent generation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The duration of the scent is adjusted according to the scene in the game. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system generates real-time changes in aroma according to the scenes in a cooking show. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way fragrances are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing fragrance, the optimal delivery point is selected based on the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing fragrances, we optimize the delivery method by referring to past user response data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the frequency of fragrance delivery based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing fragrance, the optimal delivery method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing fragrances, the delivery method is customized based on the user's environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting response data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is The collected reaction data is analyzed in real time and used to inform the next fragrance generation process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is By comparing collected reaction data with past data, we improve the accuracy of the fragrance generation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting response data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is The collected response data is analyzed in relation to the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is The collected reaction data is analyzed in combination with data from other sensors. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generating unit that generates the sense of smell, A providing unit that provides the olfactory sensation generated by the generating unit to the user, The system includes a collection unit that collects the user's response to the olfactory sensation provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The generating unit is Generate scents that correspond to specific scenes within a virtual space. The system according to feature 1.

3. The aforementioned supply unit is, The generated fragrance is provided to the user. The system according to feature 1.

4. The aforementioned collection unit is Collect user feedback and use it to improve future generation. The system according to feature 1.

5. The generating unit is Generates scents corresponding to scenes in the game. The system according to feature 1.

6. The aforementioned supply unit is, Providing viewers with aromas that match the scenes in the cooking show. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the type and intensity of the fragrance generated based on those estimated emotions. The system according to feature 1.

8. The generating unit is It generates new scents by combining multiple fragrances according to a specific scene in a virtual space. The system according to feature 1.

9. The generating unit is We analyze past user response data and apply the optimal fragrance generation algorithm. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the timing of scent generation based on the estimated user emotions. The system according to feature 1.

Images