system

Abstract

The system according to the embodiment aims to analyze scent data and generate and release the optimal scent. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an adjustment unit, a generation unit, and an emission unit. The collection unit collects scent data. The analysis unit analyzes the data collected by the collection unit. The adjustment unit adjusts the scent based on the data analyzed by the analysis unit. The generation unit generates the scent adjusted by the adjustment unit. The emission unit emits the scent generated by the generation unit into a space.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not been able to effectively collect and analyze scent data and generate and release optimal scents, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze scent data and generate and release the optimal scent. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an adjustment unit, a generation unit, and an emission unit. The collection unit collects scent data. The analysis unit analyzes the data collected by the collection unit. The adjustment unit adjusts the scent based on the data analyzed by the analysis unit. The generation unit generates the scent adjusted by the adjustment unit. The emission unit emits the scent generated by the generation unit into a space. [Effects of the Invention]

[0007] The system according to the embodiment can analyze scent data and generate and emit the optimal scent. [Brief explanation of the drawings]

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

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The flavor AI system of an embodiment of the present invention controls the appropriate scent and makes scents a valuable ally in business. This flavor AI system collects, analyzes, adjusts, generates, and releases scent data to provide the optimal scent for business purposes. First, a gas sensor is installed to collect scent data. The gas sensor detects surrounding scents in real time and collects the data. Next, the collected scent data is analyzed using an analytical algorithm based on deep learning. AI analyzes the scent components, intensity, duration, etc. to identify the optimal scent combination. The AI ​​then adjusts the scent based on the business objective. For example, a scent tailored to the purpose, such as a scent to enhance relaxation or a scent to improve concentration, is generated. An actuator is used to mix the scent components. The actuator precisely mixes the scent components based on the scent combination identified by the AI. Finally, the generated scent is released into the space through a diffuser using ultrasonic technology. The diffuser converts the scent into fine particles and distributes them evenly throughout the space. This system allows for the optimal control of scents according to business objectives, making scents an ally in business. For example, this system can be applied to specific situations, such as scents that enhance relaxation in the office or scents that increase purchasing motivation in stores. This system allows for specific situations, such as scents that enhance relaxation in the office or scents that increase purchasing motivation in stores. In this way, the flavor AI system can provide the optimal scent according to business objectives.

[0029] A flavor AI system according to an embodiment includes a collection unit, an analysis unit, an adjustment unit, a generation unit, and an emission unit. The collection unit collects scent data. The scent data includes, but is not limited to, scent components, intensity, and duration. The collection unit collects the scent data using, for example, a gas sensor. The gas sensor detects surrounding scents in real time and collects the data. The analysis unit analyzes the scent data collected by the collection unit using deep learning. The analysis unit analyzes, for example, scent components, intensity, and duration. Deep learning is a machine learning algorithm using a neural network, and can learn from large amounts of data and perform highly accurate analysis. The adjustment unit adjusts the scent according to business objectives. The adjustment unit generates a scent tailored to a purpose, such as a scent to enhance relaxation or a scent to improve concentration. The generation unit uses an actuator to blend the scent components adjusted by the adjustment unit. The actuator accurately blends the scent components based on the scent combination identified by the AI. The emission unit emits the aroma generated by the generation unit into a space. The emission unit converts the aroma into fine particles using ultrasonic technology and diffuses them evenly throughout the space. This allows the flavor AI system according to the embodiment to consistently perform a process from aroma data collection to analysis, adjustment, generation, and emission. Some or all of the above-described processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can control the emission of the aroma using an AI model that inputs the aroma generated by the generation unit and outputs the emission of the aroma.

[0030] The collection unit can collect scent data using a gas sensor. The gas sensor, for example, detects surrounding scents in real time and collects the data. Specific types and usage of the gas sensor include, but are not limited to, the sensor's sensitivity and measurement range. For example, the gas sensor is highly sensitive to specific chemical components and can detect trace amounts of scent components. The gas sensor can also simultaneously detect a wide range of scent components. Furthermore, the gas sensor can exhibit stable performance regardless of environmental conditions such as temperature and humidity. This allows accurate collection of scent data using the gas sensor. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the gas sensor into a generation AI and have the generation AI analyze the scent data.

[0031] The analysis unit can analyze the components, intensity, and duration of a scent using deep learning. Deep learning is a machine learning algorithm that uses neural networks and can learn large amounts of data to perform highly accurate analysis. For example, the analysis unit can identify scent components using deep learning to analyze the components. The analysis unit can also evaluate scent intensity using deep learning to analyze scent intensity. Furthermore, the analysis unit can predict scent duration using deep learning to analyze scent duration. For example, in scent component analysis, the analysis unit evaluates the concentration of a specific component and analyzes the effect of that component on the overall scent. In scent intensity analysis, the analysis unit evaluates the scent concentration and diffusion range and quantifies the scent intensity. In scent duration analysis, the analysis unit predicts scent duration taking into account the volatility of the scent components and environmental conditions. Thus, deep learning enables highly accurate analysis of scent components, intensity, and duration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input scent data into the generation AI and have the generation AI analyze the scent's components, intensity, and duration.

[0032] The adjustment unit can adjust the scent according to the business objective. The adjustment unit generates a scent according to the objective, such as a scent to enhance relaxation or a scent to improve concentration. Business objectives include, but are not limited to, marketing purposes and improving customer satisfaction. For example, the adjustment unit can adjust ingredients such as lavender or chamomile to enhance relaxation. The adjustment unit can also adjust ingredients such as rosemary or lemon to improve concentration. The adjustment unit can also adjust ingredients such as peppermint or eucalyptus to enhance stress reduction. For example, the adjustment unit can adjust ingredients such as lavender or chamomile to enhance relaxation. The adjustment unit can also adjust ingredients such as rosemary or lemon to improve concentration. The adjustment unit can also adjust ingredients such as peppermint or eucalyptus to enhance stress reduction. This makes it possible to adjust the scent according to the business objective. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can have the generation AI adjust the scent according to business purposes.

[0033] The generation unit can blend fragrance components using an actuator. The actuator accurately blends fragrance components based on the fragrance combination identified by the AI. Specific types and usage methods of the actuator include, but are not limited to, the actuator's operating principle and control method. For example, the actuator can accurately blend fragrance components through electrical control. Alternatively, the actuator can blend fragrance components through mechanical operation. Furthermore, the actuator can blend fragrance components using chemical reactions. For example, the actuator can accurately blend fragrance components through electrical control. Alternatively, the actuator can blend fragrance components through mechanical operation. Furthermore, the actuator can blend fragrance components using chemical reactions. As a result, the actuator can accurately blend fragrance components. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause the generation AI to execute instructions to control the actuator.

[0034] The emission unit can use ultrasonic technology to convert fragrance components into fine particles and diffuse them evenly throughout a space. Ultrasonic technology is a technology for converting fragrance components into fine particles and diffusing them evenly throughout a space. Specific types and usage methods of ultrasonic technology include, but are not limited to, ultrasonic frequencies and particle generation methods. For example, ultrasonic technology converts fragrance components into fine particles using high-frequency sound waves. Furthermore, ultrasonic technology can also use a device for uniformly diffusing fine particles throughout a space. Furthermore, ultrasonic technology can also include a control method for efficiently diffusing the fragrance components. For example, ultrasonic technology converts fragrance components into fine particles using high-frequency sound waves. Furthermore, ultrasonic technology can also use a device for uniformly diffusing fine particles throughout a space. Furthermore, ultrasonic technology can also include a control method for efficiently diffusing the fragrance components. Thus, ultrasonic technology can be used to uniformly diffuse the fragrance. Some or all of the above-described processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emitter can have the generating AI diffuse the scent using ultrasonic technology.

[0035] The collection unit can collect scent data based on the surrounding environmental conditions. For example, when the temperature is high, the collection unit increases the frequency of data collection because scent volatility increases. Furthermore, when the humidity is low, the collection unit can also adjust the timing of data collection because scent diffusion slows. Furthermore, when the environment changes, the collection unit can monitor the temperature and humidity in real time and dynamically change the data collection method. For example, when the temperature is high, the collection unit increases the frequency of data collection because scent volatility increases. Furthermore, when the humidity is low, the collection unit can also adjust the timing of data collection because scent diffusion slows. Furthermore, when the environment changes, the collection unit can monitor the temperature and humidity in real time and dynamically change the data collection method. This enables more accurate scent data collection by taking environmental conditions into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data acquired by an environmental sensor into the generation AI and cause the generation AI to adjust the scent data collection method.

[0036] The collection unit can change the collection method when collecting scent data based on a specific event or activity. For example, the collection unit may collect scent data less during a meeting and resume collection after the meeting. The collection unit can also collect scent data more frequently during relaxation time to collect detailed data. The collection unit can also suspend scent data collection during exercise and resume it after the exercise. For example, the collection unit may collect scent data less during a meeting and resume collection after the meeting. The collection unit can also collect scent data more frequently during relaxation time to collect detailed data. The collection unit can also suspend scent data collection during exercise and resume it after the exercise. This allows for more appropriate data collection by changing the collection method based on a specific event or activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can cause the generation AI to change the data collection method based on a specific event or activity.

[0037] When collecting scent data, the collection unit can prioritize collecting highly relevant data by taking into account the user's location information. For example, when the user is in the office, the collection unit prioritizes collecting data on scents that have a concentration-improving effect. Furthermore, when the user is at home, the collection unit can prioritize collecting data on scents that have a relaxing effect. Furthermore, when the user is out, the collection unit can prioritize collecting data on scents that have a stress-reducing effect. For example, when the user is in the office, the collection unit prioritizes collecting data on scents that have a concentration-improving effect. Furthermore, when the user is at home, the collection unit can prioritize collecting data on scents that have a relaxing effect. Furthermore, when the user is out, the collection unit can prioritize collecting data on scents that have a stress-reducing effect. This allows highly relevant data to be collected preferentially by taking the user's location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information to the generation AI and cause the generation AI to determine the priority of scent data collection.

[0038] When collecting scent data, the collection unit can analyze the user's social media activity and collect related data. For example, if the user posts about relaxation on social media, the collection unit can collect data on scents with a relaxing effect. Furthermore, if the user posts about stress, the collection unit can collect data on scents with a stress-reducing effect. Furthermore, if the user posts about concentration, the collection unit can collect data on scents with a concentration-improving effect. For example, if the user posts about relaxation on social media, the collection unit can collect data on scents with a relaxing effect. Furthermore, if the user posts about stress, the collection unit can collect data on scents with a stress-reducing effect. Furthermore, if the user posts about concentration, the collection unit can collect data on scents with a concentration-improving effect. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to determine the priority of scent data collection.

[0039] When analyzing the scent components, the analysis unit can identify different patterns by comparing with past data. For example, the analysis unit can identify a new scent pattern with a relaxing effect by comparing with past data of scents with a relaxing effect. The analysis unit can also identify a new scent pattern with a stress-reducing effect by comparing with past data of scents with a stress-reducing effect. Furthermore, the analysis unit can identify a new scent pattern with a concentration-improving effect by comparing with past data of scents with a concentration-improving effect. For example, the analysis unit can identify a new scent pattern with a relaxing effect by comparing with past data of scents with a stress-reducing effect. The analysis unit can also identify a new scent pattern with a stress-reducing effect by comparing with past data of scents with a concentration-improving effect. Furthermore, the analysis unit can identify a new scent pattern with a concentration-improving effect by comparing with past data of scents with a concentration-improving effect. In this way, a new scent pattern can be identified by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to identify a new scent pattern.

[0040] The analysis unit can improve accuracy by combining different analysis methods when analyzing scent intensity and duration. For example, the analysis unit combines gas sensor data and user feedback in scent intensity analysis. The analysis unit can also combine environmental sensor data and past data in scent duration analysis. Furthermore, the analysis unit can improve accuracy by combining deep learning and conventional analysis methods in scent component analysis. For example, the analysis unit combines gas sensor data and user feedback in scent intensity analysis. The analysis unit can also combine environmental sensor data and past data in scent duration analysis. Furthermore, the analysis unit can improve accuracy by combining deep learning and conventional analysis methods in scent component analysis. Thus, combining different analysis methods improves analysis accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input different analysis methods into the generation AI and cause the generation AI to analyze scent intensity and duration.

[0041] When analyzing scent components, the analysis unit can combine data from other sensors to perform the analysis. For example, the analysis unit combines data from a temperature sensor to perform scent component analysis. The analysis unit can also combine data from a humidity sensor to perform scent component analysis. Furthermore, the analysis unit can combine data from an environmental sensor to perform scent component analysis. For example, the analysis unit combines data from a temperature sensor to perform scent component analysis. The analysis unit can also combine data from a humidity sensor to perform scent component analysis. Furthermore, the analysis unit can combine data from an environmental sensor to perform scent component analysis. This enables more accurate analysis by combining data from other sensors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other sensors into the generation AI and have the generation AI perform scent component analysis.

[0042] The analysis unit can perform analysis by combining different data sources when analyzing the intensity and duration of a scent. For example, the analysis unit can perform a scent intensity analysis by combining user feedback. The analysis unit can also perform a scent duration analysis by combining user feedback. Furthermore, the analysis unit can perform a scent component analysis by combining user feedback. For example, the analysis unit can perform a scent intensity analysis by combining user feedback. The analysis unit can also perform a scent duration analysis by combining user feedback. Furthermore, the analysis unit can perform a scent component analysis by combining user feedback. In this way, by combining different data sources, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user feedback to a generation AI and cause the generation AI to analyze the intensity and duration of a scent.

[0043] The adjustment unit can dynamically adjust the ratio of scent components according to the business objective. For example, the adjustment unit can increase the ratio of scent components with a relaxing effect to enhance the relaxation effect. The adjustment unit can also increase the ratio of scent components with a concentration-enhancing effect to improve concentration. The adjustment unit can also increase the ratio of scent components with a stress-reducing effect to enhance the stress-reducing effect. For example, the adjustment unit can increase the ratio of scent components such as lavender and chamomile to enhance the relaxation effect. The adjustment unit can also increase the ratio of scent components such as rosemary and lemon to improve concentration. The adjustment unit can also increase the ratio of scent components such as peppermint and eucalyptus to enhance the stress-reducing effect. This enables more effective scent adjustment by dynamically adjusting the ratio of scent components according to the business objective. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can cause the generation AI to adjust the ratio of scent components according to the business objective.

[0044] The adjustment unit can adjust the scent based on real-time feedback when adjusting the scent. For example, the adjustment unit adjusts the ratio of scent components based on the user's real-time feedback. The adjustment unit can also adjust the scent intensity based on the user's real-time feedback. The adjustment unit can also adjust the scent duration based on the user's real-time feedback. For example, the adjustment unit adjusts the ratio of scent components based on the user's real-time feedback. The adjustment unit can also adjust the scent intensity based on the user's real-time feedback. The adjustment unit can also adjust the scent duration based on the user's real-time feedback. This makes it possible to adjust the scent more appropriately by making adjustments based on real-time feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's real-time feedback to the generation AI and cause the generation AI to adjust the scent.

[0045] The adjustment unit can apply different scent profiles when adjusting the scent depending on the business purpose. For example, the adjustment unit applies a scent profile with a relaxing effect to enhance the relaxation effect. The adjustment unit can also apply a scent profile with a concentration-improving effect to improve concentration. The adjustment unit can also apply a scent profile with a stress-reducing effect to enhance the stress-reducing effect. For example, the adjustment unit applies a scent profile with a relaxing effect to enhance the relaxation effect. The adjustment unit can also apply a scent profile with a concentration-improving effect to improve concentration. The adjustment unit can also apply a scent profile with a stress-reducing effect to enhance the stress-reducing effect. This enables more effective scent adjustment by applying different scent profiles depending on the business purpose. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can cause the generation AI to apply a scent profile according to the business purpose.

[0046] The adjustment unit can adjust the scent taking into account the user's past scent preferences. For example, the adjustment unit adjusts the scent components having a relaxing effect based on the user's past scent preferences. The adjustment unit can also adjust the scent components having a stress-reducing effect based on the user's past scent preferences. Furthermore, the adjustment unit can adjust the scent components having a concentration-enhancing effect based on the user's past scent preferences. For example, the adjustment unit adjusts the scent components having a relaxing effect based on the user's past scent preferences. The adjustment unit can also adjust the scent components having a stress-reducing effect based on the user's past scent preferences. Furthermore, the adjustment unit can adjust the scent components having a concentration-enhancing effect based on the user's past scent preferences. This enables more appropriate scent adjustment by taking into account the user's past scent preferences. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past scent preferences into the generation AI and cause the generation AI to adjust the scent.

[0047] When generating a fragrance, the generation unit can try different combinations of ingredients to generate a fragrance. For example, the generation unit can combine lavender and chamomile to generate a fragrance with a relaxing effect. The generation unit can also combine peppermint and eucalyptus to generate a fragrance with a stress-reducing effect. The generation unit can also combine rosemary and lemon to generate a fragrance with a concentration-enhancing effect. For example, the generation unit can combine lavender and chamomile to generate a fragrance with a relaxing effect. The generation unit can also combine peppermint and eucalyptus to generate a fragrance with a stress-reducing effect. The generation unit can also combine rosemary and lemon to generate a fragrance with a concentration-enhancing effect. In this way, by trying different combinations of ingredients, the optimal fragrance can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input different combinations of ingredients into the generation AI and cause the generation AI to generate a fragrance.

[0048] The generation unit can dynamically adjust parameters of the generation process when generating a fragrance. For example, the generation unit dynamically changes the concentration of components to adjust the intensity of the fragrance. The generation unit can also dynamically change the volatility of components to adjust the duration of the fragrance. Furthermore, the generation unit can dynamically change the ratio of components to adjust the balance of the fragrance. For example, the generation unit dynamically changes the concentration of components to adjust the intensity of the fragrance. The generation unit can also dynamically change the volatility of components to adjust the duration of the fragrance. Furthermore, the generation unit can dynamically change the ratio of components to adjust the balance of the fragrance. In this way, by dynamically adjusting the parameters of the generation process, a more appropriate fragrance can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input parameters of the generation process to the generation AI and cause the generation AI to generate the fragrance.

[0049] The generation unit can combine different generation technologies when generating a fragrance. For example, the generation unit can use nanotechnology to convert fragrance components into fine particles and diffuse them evenly. The generation unit can also use ultrasound technology to convert fragrance components into a fine mist and diffuse it evenly throughout a space. The generation unit can also use microencapsulation technology to encapsulate fragrance components and release them continuously. For example, the generation unit can use nanotechnology to convert fragrance components into fine particles and diffuse them evenly. The generation unit can also use ultrasound technology to convert fragrance components into a fine mist and diffuse it evenly throughout a space. The generation unit can also use microencapsulation technology to encapsulate fragrance components and release them continuously. This allows for more effective fragrance generation by combining different generation technologies. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input different generation technologies into the generation AI and have the generation AI generate the fragrance.

[0050] When generating a fragrance, the generation unit can optimize the generation process based on the user's past feedback. For example, the generation unit can optimize fragrance components with a relaxing effect based on the user's past feedback. The generation unit can also optimize fragrance components with a stress-reducing effect based on the user's past feedback. Furthermore, the generation unit can optimize fragrance components with a concentration-improving effect based on the user's past feedback. For example, the generation unit can optimize fragrance components with a relaxing effect based on the user's past feedback. The generation unit can also optimize fragrance components with a stress-reducing effect based on the user's past feedback. Furthermore, the generation unit can optimize fragrance components with a concentration-improving effect based on the user's past feedback. In this way, by optimizing the generation process based on the user's past feedback, a more appropriate fragrance can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to optimize the fragrance generation process.

[0051] When emitting a scent, the emission unit can change the emission method taking into account the layout of the space and people's movements. The emission unit, for example, adjusts the direction of scent emission according to the layout of the space. The emission unit can also adjust the timing of scent emission according to people's movements. Furthermore, the emission unit can adjust the amount of scent emission according to the size of the space. For example, the emission unit adjusts the direction of scent emission according to the layout of the space. The emission unit can also adjust the timing of scent emission according to people's movements. Furthermore, the emission unit can adjust the amount of scent emission according to the size of the space. This enables more effective scent emission by taking into account the layout of the space and people's movements. Some or all of the above-mentioned processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input data on the layout of the space and people's movements into the generation AI and cause the generation AI to change the scent emission method.

[0052] The emission unit can combine different emission technologies when emitting a fragrance. For example, the emission unit can use mist technology to convert fragrance components into fine particles and diffuse them evenly throughout the space. The emission unit can also use spray technology to diffuse the fragrance components over a wide area. Furthermore, the emission unit can use ultrasonic technology to convert fragrance components into fine mist and diffuse them evenly throughout the space. For example, the emission unit can use mist technology to convert fragrance components into fine particles and diffuse them evenly throughout the space. Furthermore, the emission unit can use spray technology to diffuse the fragrance components over a wide area. Furthermore, the emission unit can use ultrasonic technology to convert fragrance components into fine mist and diffuse them evenly throughout the space. Thus, by combining different emission technologies, more effective fragrance emission is possible. Some or all of the above-described processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input different emission technologies into the generation AI and cause the generation AI to emit the fragrance.

[0053] When emitting a fragrance, the emission unit can optimize the emission method by taking into account the temperature and humidity of the space. For example, when the temperature is high, the emission unit increases the amount of fragrance emitted to promote diffusion. Also, when the humidity is low, the emission unit can increase the amount of fragrance emitted to promote diffusion. Furthermore, the emission unit can dynamically adjust the fragrance emission method in response to changes in temperature and humidity. For example, when the temperature is high, the emission unit increases the amount of fragrance emitted to promote diffusion. Also, when the humidity is low, the emission unit can increase the amount of fragrance emitted to promote diffusion. Furthermore, the emission unit can dynamically adjust the fragrance emission method in response to changes in temperature and humidity. This enables more effective fragrance emission by taking into account the temperature and humidity of the space. Some or all of the above-mentioned processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input temperature and humidity data into the generation AI and cause the generation AI to optimize the fragrance emission method.

[0054] The emission unit can emit a scent while taking into account the user's past scent preferences. For example, the emission unit emits a scent with a relaxing effect based on the user's past scent preferences. The emission unit can also emit a scent with a stress-reducing effect based on the user's past scent preferences. Furthermore, the emission unit can emit a scent with a concentration-enhancing effect based on the user's past scent preferences. For example, the emission unit emits a scent with a relaxing effect based on the user's past scent preferences. The emission unit can also emit a scent with a stress-reducing effect based on the user's past scent preferences. Furthermore, the emission unit can emit a scent with a concentration-enhancing effect based on the user's past scent preferences. This enables the emission of a more appropriate scent by taking into account the user's past scent preferences. Some or all of the above-described processing in the emission unit can be performed using, for example, AI, or without AI. For example, the emission unit can input the user's past scent preferences into the generation AI and cause the generation AI to emit a scent.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The flavor AI system may further include a behavior history recording unit that records the user's behavior history. The behavior history recording unit records, for example, what scents the user has preferred in the past and which scents were effective in what situations. This makes it possible to provide the optimal scent based on the user's behavioral patterns. For example, if the user has previously preferred the scent of lavender when wanting to relax, the scent of lavender can be provided in similar situations. Also, if the user has preferred the scent of rosemary when wanting to concentrate, the scent of rosemary can be provided in similar situations. Furthermore, if the user has preferred the scent of peppermint when feeling stressed, the scent of peppermint can be provided in similar situations. This makes it possible to provide more effective scents based on the user's behavior history.

[0057] The flavor AI system can further include a preference learning unit that learns the user's preferences. The preference learning unit, for example, learns what kind of scents the user likes and provides scents based on those preferences. This makes it possible to provide scents that match the user's individual preferences. For example, if the user likes floral scents, a floral scent can be provided. Also, if the user likes citrus scents, a citrus scent can be provided. Furthermore, if the user likes woody scents, a woody scent can be provided. This makes it possible to provide more satisfying scents based on the user's preferences.

[0058] The flavor AI system can further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit acquires the user's location information using, for example, GPS or Wi-Fi. This makes it possible to provide the optimal fragrance based on the user's location information. For example, if the user is in an office, a fragrance that improves concentration can be provided. Also, if the user is at home, a fragrance that has a relaxing effect can be provided. Furthermore, if the user is out, a fragrance that has a stress-reducing effect can be provided. This makes it possible to provide a more appropriate fragrance based on the user's location information.

[0059] The flavor AI system can further include a social media analysis unit that analyzes the user's social media activity. For example, if the user posts about relaxation on social media, the social media analysis unit can provide a scent with a relaxing effect. Also, if the user posts about stress, the social media analysis unit can provide a scent with a stress-reducing effect. Furthermore, if the user posts about concentration, the social media analysis unit can provide a scent with a concentration-improving effect. This makes it possible to provide relevant scents by analyzing the user's social media activity.

[0060] The flavor AI system can further optimize the fragrance generation process based on the user's past feedback. For example, it can optimize fragrance ingredients that have a relaxing effect based on the user's past feedback. It can also optimize fragrance ingredients that have a stress-reducing effect based on the user's past feedback. It can also optimize fragrance ingredients that have a concentration-improving effect based on the user's past feedback. In this way, by optimizing the generation process based on the user's past feedback, it becomes possible to provide a more appropriate fragrance.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects scent data. The scent data includes scent components, intensity, duration, etc. The collection unit collects scent data using a gas sensor and detects surrounding scents in real time. Step 2: The analysis unit uses deep learning to analyze the scent data collected by the collection unit. The analysis unit analyzes the scent components, intensity, duration, etc., and performs high-precision analysis using a machine learning algorithm that uses a neural network. Step 3: The adjustment unit adjusts the scent according to the business's purpose. For example, it generates a scent that enhances relaxation or improves concentration. Step 4: The generator uses the actuator to blend the fragrance components adjusted by the adjuster. The actuator accurately blends the fragrance components based on the scent combination identified by the AI. Step 5: The emitter emits the scent generated by the generator into the space. The emitter uses ultrasonic technology to convert the scent into fine particles and diffuse them evenly throughout the space. The processing in the emitter may be performed using AI.

[0063] (Example 2) The flavor AI system of an embodiment of the present invention controls the appropriate scent and makes scents a valuable ally in business. This flavor AI system collects, analyzes, adjusts, generates, and releases scent data to provide the optimal scent for business purposes. First, a gas sensor is installed to collect scent data. The gas sensor detects surrounding scents in real time and collects the data. Next, the collected scent data is analyzed using an analytical algorithm based on deep learning. AI analyzes the scent components, intensity, duration, etc. to identify the optimal scent combination. The AI ​​then adjusts the scent based on the business objective. For example, a scent tailored to the purpose, such as a scent to enhance relaxation or a scent to improve concentration, is generated. An actuator is used to mix the scent components. The actuator precisely mixes the scent components based on the scent combination identified by the AI. Finally, the generated scent is released into the space through a diffuser using ultrasonic technology. The diffuser converts the scent into fine particles and distributes them evenly throughout the space. This system allows for the optimal control of scents according to business objectives, making scents an ally in business. For example, this system can be applied to specific situations, such as scents that enhance relaxation in the office or scents that increase purchasing motivation in stores. This system allows for specific situations, such as scents that enhance relaxation in the office or scents that increase purchasing motivation in stores. In this way, the flavor AI system can provide the optimal scent according to business objectives.

[0064] A flavor AI system according to an embodiment includes a collection unit, an analysis unit, an adjustment unit, a generation unit, and an emission unit. The collection unit collects scent data. The scent data includes, but is not limited to, scent components, intensity, and duration. The collection unit collects the scent data using, for example, a gas sensor. The gas sensor detects surrounding scents in real time and collects the data. The analysis unit analyzes the scent data collected by the collection unit using deep learning. The analysis unit analyzes, for example, scent components, intensity, and duration. Deep learning is a machine learning algorithm using a neural network, and can learn from large amounts of data and perform highly accurate analysis. The adjustment unit adjusts the scent according to business objectives. The adjustment unit generates a scent tailored to a purpose, such as a scent to enhance relaxation or a scent to improve concentration. The generation unit uses an actuator to blend the scent components adjusted by the adjustment unit. The actuator accurately blends the scent components based on the scent combination identified by the AI. The emission unit emits the aroma generated by the generation unit into a space. The emission unit converts the aroma into fine particles using ultrasonic technology and diffuses them evenly throughout the space. This allows the flavor AI system according to the embodiment to consistently perform a process from aroma data collection to analysis, adjustment, generation, and emission. Some or all of the above-described processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can control the emission of the aroma using an AI model that inputs the aroma generated by the generation unit and outputs the emission of the aroma.

[0065] The collection unit can collect scent data using a gas sensor. The gas sensor, for example, detects surrounding scents in real time and collects the data. Specific types and usage of the gas sensor include, but are not limited to, the sensor's sensitivity and measurement range. For example, the gas sensor is highly sensitive to specific chemical components and can detect trace amounts of scent components. The gas sensor can also simultaneously detect a wide range of scent components. Furthermore, the gas sensor can exhibit stable performance regardless of environmental conditions such as temperature and humidity. This allows accurate collection of scent data using the gas sensor. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the gas sensor into a generation AI and have the generation AI analyze the scent data.

[0066] The analysis unit can analyze the components, intensity, and duration of a scent using deep learning. Deep learning is a machine learning algorithm that uses neural networks and can learn large amounts of data to perform highly accurate analysis. For example, the analysis unit can identify scent components using deep learning to analyze the components. The analysis unit can also evaluate scent intensity using deep learning to analyze scent intensity. Furthermore, the analysis unit can predict scent duration using deep learning to analyze scent duration. For example, in scent component analysis, the analysis unit evaluates the concentration of a specific component and analyzes the effect of that component on the overall scent. In scent intensity analysis, the analysis unit evaluates the scent concentration and diffusion range and quantifies the scent intensity. In scent duration analysis, the analysis unit predicts scent duration taking into account the volatility of the scent components and environmental conditions. Thus, deep learning enables highly accurate analysis of scent components, intensity, and duration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input scent data into the generation AI and have the generation AI analyze the scent's components, intensity, and duration.

[0067] The adjustment unit can adjust the scent according to the business objective. The adjustment unit generates a scent according to the objective, such as a scent to enhance relaxation or a scent to improve concentration. Business objectives include, but are not limited to, marketing purposes and improving customer satisfaction. For example, the adjustment unit can adjust ingredients such as lavender or chamomile to enhance relaxation. The adjustment unit can also adjust ingredients such as rosemary or lemon to improve concentration. The adjustment unit can also adjust ingredients such as peppermint or eucalyptus to enhance stress reduction. For example, the adjustment unit can adjust ingredients such as lavender or chamomile to enhance relaxation. The adjustment unit can also adjust ingredients such as rosemary or lemon to improve concentration. The adjustment unit can also adjust ingredients such as peppermint or eucalyptus to enhance stress reduction. This makes it possible to adjust the scent according to the business objective. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can have the generation AI adjust the scent according to business purposes.

[0068] The generation unit can blend fragrance components using an actuator. The actuator accurately blends fragrance components based on the fragrance combination identified by the AI. Specific types and usage methods of the actuator include, but are not limited to, the actuator's operating principle and control method. For example, the actuator can accurately blend fragrance components through electrical control. Alternatively, the actuator can blend fragrance components through mechanical operation. Furthermore, the actuator can blend fragrance components using chemical reactions. For example, the actuator can accurately blend fragrance components through electrical control. Alternatively, the actuator can blend fragrance components through mechanical operation. Furthermore, the actuator can blend fragrance components using chemical reactions. As a result, the actuator can accurately blend fragrance components. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause the generation AI to execute instructions to control the actuator.

[0069] The emission unit can use ultrasonic technology to convert fragrance components into fine particles and diffuse them evenly throughout a space. Ultrasonic technology is a technology for converting fragrance components into fine particles and diffusing them evenly throughout a space. Specific types and usage methods of ultrasonic technology include, but are not limited to, ultrasonic frequencies and particle generation methods. For example, ultrasonic technology converts fragrance components into fine particles using high-frequency sound waves. Furthermore, ultrasonic technology can also use a device for uniformly diffusing fine particles throughout a space. Furthermore, ultrasonic technology can also include a control method for efficiently diffusing the fragrance components. For example, ultrasonic technology converts fragrance components into fine particles using high-frequency sound waves. Furthermore, ultrasonic technology can also use a device for uniformly diffusing fine particles throughout a space. Furthermore, ultrasonic technology can also include a control method for efficiently diffusing the fragrance components. Thus, ultrasonic technology can be used to uniformly diffuse the fragrance. Some or all of the above-described processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emitter can have the generating AI diffuse the scent using ultrasonic technology.

[0070] The collection unit can estimate the user's emotions and adjust the timing of scent data collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects scent data frequently to collect detailed data. Furthermore, when the user is stressed, the collection unit can also reduce the amount of scent data collection to reduce the burden on the user. Furthermore, when the user is concentrating, the collection unit can suspend scent data collection to avoid disruption to the user's concentration. For example, when the user is relaxed, the collection unit collects scent data frequently to collect detailed data. Furthermore, when the user is stressed, the collection unit can also reduce the amount of scent data collection to reduce the burden on the user. Furthermore, when the user is concentrating, the collection unit can also suspend scent data collection to avoid disruption to the user's concentration. This allows for more appropriate data collection by adjusting the timing of scent data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the timing of scent data collection.

[0071] The collection unit can collect scent data based on the surrounding environmental conditions. For example, when the temperature is high, the collection unit increases the frequency of data collection because scent volatility increases. Furthermore, when the humidity is low, the collection unit can also adjust the timing of data collection because scent diffusion slows. Furthermore, when the environment changes, the collection unit can monitor the temperature and humidity in real time and dynamically change the data collection method. For example, when the temperature is high, the collection unit increases the frequency of data collection because scent volatility increases. Furthermore, when the humidity is low, the collection unit can also adjust the timing of data collection because scent diffusion slows. Furthermore, when the environment changes, the collection unit can monitor the temperature and humidity in real time and dynamically change the data collection method. This enables more accurate scent data collection by taking environmental conditions into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data acquired by an environmental sensor into the generation AI and cause the generation AI to adjust the scent data collection method.

[0072] The collection unit can change the collection method when collecting scent data based on a specific event or activity. For example, the collection unit may collect scent data less during a meeting and resume collection after the meeting. The collection unit can also collect scent data more frequently during relaxation time to collect detailed data. The collection unit can also suspend scent data collection during exercise and resume it after the exercise. For example, the collection unit may collect scent data less during a meeting and resume collection after the meeting. The collection unit can also collect scent data more frequently during relaxation time to collect detailed data. The collection unit can also suspend scent data collection during exercise and resume it after the exercise. This allows for more appropriate data collection by changing the collection method based on a specific event or activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can cause the generation AI to change the data collection method based on a specific event or activity.

[0073] The collection unit can estimate the user's emotions and determine the priority of scent data to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit prioritizes collecting scent data with a relaxing effect. Furthermore, when the user is stressed, the collection unit can prioritize collecting scent data with a stress-reducing effect. Furthermore, when the user is concentrating, the collection unit can prioritize collecting scent data with a concentration-enhancing effect. For example, when the user is relaxed, the collection unit prioritizes collecting scent data with a relaxing effect. Furthermore, when the user is stressed, the collection unit can prioritize collecting scent data with a stress-reducing effect. Furthermore, when the user is concentrating, the collection unit can prioritize collecting scent data with a concentration-enhancing effect. This enables more effective data collection by prioritizing data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and have the generation AI determine the priority of the scent data.

[0074] When collecting scent data, the collection unit can prioritize collecting highly relevant data by taking into account the user's location information. For example, when the user is in the office, the collection unit prioritizes collecting data on scents that have a concentration-improving effect. Furthermore, when the user is at home, the collection unit can prioritize collecting data on scents that have a relaxing effect. Furthermore, when the user is out, the collection unit can prioritize collecting data on scents that have a stress-reducing effect. For example, when the user is in the office, the collection unit prioritizes collecting data on scents that have a concentration-improving effect. Furthermore, when the user is at home, the collection unit can prioritize collecting data on scents that have a relaxing effect. Furthermore, when the user is out, the collection unit can prioritize collecting data on scents that have a stress-reducing effect. This allows highly relevant data to be collected preferentially by taking the user's location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information to the generation AI and cause the generation AI to determine the priority of scent data collection.

[0075] When collecting scent data, the collection unit can analyze the user's social media activity and collect related data. For example, if the user posts about relaxation on social media, the collection unit can collect data on scents with a relaxing effect. Furthermore, if the user posts about stress, the collection unit can collect data on scents with a stress-reducing effect. Furthermore, if the user posts about concentration, the collection unit can collect data on scents with a concentration-improving effect. For example, if the user posts about relaxation on social media, the collection unit can collect data on scents with a relaxing effect. Furthermore, if the user posts about stress, the collection unit can collect data on scents with a stress-reducing effect. Furthermore, if the user posts about concentration, the collection unit can collect data on scents with a concentration-improving effect. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to determine the priority of scent data collection.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can emphasize fragrance components with a relaxing effect in the analysis. Furthermore, if the user is stressed, the analysis unit can emphasize fragrance components with a stress-reducing effect in the analysis. Furthermore, if the user is concentrating, the analysis unit can emphasize fragrance components with a concentration-improving effect in the analysis. For example, if the user is relaxed, the analysis unit can emphasize fragrance components with a relaxing effect in the analysis. Furthermore, if the user is stressed, the analysis unit can emphasize fragrance components with a stress-reducing effect in the analysis. Furthermore, if the user is concentrating, the analysis unit can emphasize fragrance components with a concentration-improving effect in the analysis. This allows for more appropriate analysis by adjusting the analysis algorithm based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can 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 processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0077] When analyzing the scent components, the analysis unit can identify different patterns by comparing with past data. For example, the analysis unit can identify a new scent pattern with a relaxing effect by comparing with past data of scents with a relaxing effect. The analysis unit can also identify a new scent pattern with a stress-reducing effect by comparing with past data of scents with a stress-reducing effect. Furthermore, the analysis unit can identify a new scent pattern with a concentration-improving effect by comparing with past data of scents with a concentration-improving effect. For example, the analysis unit can identify a new scent pattern with a relaxing effect by comparing with past data of scents with a stress-reducing effect. The analysis unit can also identify a new scent pattern with a stress-reducing effect by comparing with past data of scents with a concentration-improving effect. Furthermore, the analysis unit can identify a new scent pattern with a concentration-improving effect by comparing with past data of scents with a concentration-improving effect. In this way, a new scent pattern can be identified by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to identify a new scent pattern.

[0078] The analysis unit can improve accuracy by combining different analysis methods when analyzing scent intensity and duration. For example, the analysis unit combines gas sensor data and user feedback in scent intensity analysis. The analysis unit can also combine environmental sensor data and past data in scent duration analysis. Furthermore, the analysis unit can improve accuracy by combining deep learning and conventional analysis methods in scent component analysis. For example, the analysis unit combines gas sensor data and user feedback in scent intensity analysis. The analysis unit can also combine environmental sensor data and past data in scent duration analysis. Furthermore, the analysis unit can improve accuracy by combining deep learning and conventional analysis methods in scent component analysis. Thus, combining different analysis methods improves analysis accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input different analysis methods into the generation AI and cause the generation AI to analyze scent intensity and duration.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit can emphasize and display fragrance components with a relaxing effect. Furthermore, when the user is stressed, the analysis unit can emphasize and display fragrance components with a stress-reducing effect. Furthermore, when the user is concentrating, the analysis unit can emphasize and display fragrance components with a concentration-improving effect. For example, when the user is relaxed, the analysis unit can emphasize and display fragrance components with a relaxing effect. Furthermore, when the user is stressed, the analysis unit can emphasize and display fragrance components with a stress-reducing effect. Furthermore, when the user is concentrating, the analysis unit can emphasize and display fragrance components with a concentration-improving effect. This allows the display method of the analysis results to be adjusted based on the user's emotions, making it easier to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI adjust the display method of the analysis results.

[0080] When analyzing scent components, the analysis unit can combine data from other sensors to perform the analysis. For example, the analysis unit combines data from a temperature sensor to perform scent component analysis. The analysis unit can also combine data from a humidity sensor to perform scent component analysis. Furthermore, the analysis unit can combine data from an environmental sensor to perform scent component analysis. For example, the analysis unit combines data from a temperature sensor to perform scent component analysis. The analysis unit can also combine data from a humidity sensor to perform scent component analysis. Furthermore, the analysis unit can combine data from an environmental sensor to perform scent component analysis. This enables more accurate analysis by combining data from other sensors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other sensors into the generation AI and have the generation AI perform scent component analysis.

[0081] The analysis unit can perform analysis by combining different data sources when analyzing the intensity and duration of a scent. For example, the analysis unit can perform a scent intensity analysis by combining user feedback. The analysis unit can also perform a scent duration analysis by combining user feedback. Furthermore, the analysis unit can perform a scent component analysis by combining user feedback. For example, the analysis unit can perform a scent intensity analysis by combining user feedback. The analysis unit can also perform a scent duration analysis by combining user feedback. Furthermore, the analysis unit can perform a scent component analysis by combining user feedback. In this way, by combining different data sources, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user feedback to a generation AI and cause the generation AI to analyze the intensity and duration of a scent.

[0082] The adjustment unit can estimate the user's emotions and change the fragrance adjustment method based on the estimated user's emotions. For example, if the user is relaxed, the adjustment unit can increase the amount of fragrance components with a relaxing effect. Furthermore, if the user is stressed, the adjustment unit can also increase the amount of fragrance components with a stress-reducing effect. Furthermore, if the user is concentrating, the adjustment unit can also increase the amount of fragrance components with a concentration-improving effect. For example, if the user is relaxed, the adjustment unit can increase the amount of fragrance components with a relaxing effect. Furthermore, if the user is stressed, the adjustment unit can also increase the amount of fragrance components with a stress-reducing effect. Furthermore, if the user is concentrating, the adjustment unit can also increase the amount of fragrance components with a concentration-improving effect. This allows for more appropriate fragrance adjustment by changing the fragrance adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, an AI, or without an AI. For example, the adjustment unit can input the user's emotional data into the generation AI and cause the generation AI to change the fragrance adjustment method.

[0083] The adjustment unit can dynamically adjust the ratio of scent components according to the business objective. For example, the adjustment unit can increase the ratio of scent components with a relaxing effect to enhance the relaxation effect. The adjustment unit can also increase the ratio of scent components with a concentration-enhancing effect to improve concentration. The adjustment unit can also increase the ratio of scent components with a stress-reducing effect to enhance the stress-reducing effect. For example, the adjustment unit can increase the ratio of scent components such as lavender and chamomile to enhance the relaxation effect. The adjustment unit can also increase the ratio of scent components such as rosemary and lemon to improve concentration. The adjustment unit can also increase the ratio of scent components such as peppermint and eucalyptus to enhance the stress-reducing effect. This enables more effective scent adjustment by dynamically adjusting the ratio of scent components according to the business objective. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can cause the generation AI to adjust the ratio of scent components according to the business objective.

[0084] The adjustment unit can adjust the scent based on real-time feedback when adjusting the scent. For example, the adjustment unit adjusts the ratio of scent components based on the user's real-time feedback. The adjustment unit can also adjust the scent intensity based on the user's real-time feedback. The adjustment unit can also adjust the scent duration based on the user's real-time feedback. For example, the adjustment unit adjusts the ratio of scent components based on the user's real-time feedback. The adjustment unit can also adjust the scent intensity based on the user's real-time feedback. The adjustment unit can also adjust the scent duration based on the user's real-time feedback. This makes it possible to adjust the scent more appropriately by making adjustments based on real-time feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's real-time feedback to the generation AI and cause the generation AI to adjust the scent.

[0085] The adjustment unit can estimate the user's emotions and determine the frequency of scent adjustment based on the estimated user's emotions. For example, the adjustment unit can increase the frequency of scent adjustment when the user is relaxed. Furthermore, the adjustment unit can decrease the frequency of scent adjustment when the user is stressed. Furthermore, the adjustment unit can increase the frequency of scent adjustment when the user is concentrating. For example, the adjustment unit can increase the frequency of scent adjustment when the user is relaxed. Furthermore, the adjustment unit can decrease the frequency of scent adjustment when the user is stressed. Furthermore, the adjustment unit can increase the frequency of scent adjustment when the user is concentrating. This enables more appropriate scent adjustment by determining the frequency of scent adjustment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the user's emotional data into the generation AI and have the generation AI determine the frequency of fragrance adjustment.

[0086] The adjustment unit can apply different scent profiles when adjusting the scent depending on the business purpose. For example, the adjustment unit applies a scent profile with a relaxing effect to enhance the relaxation effect. The adjustment unit can also apply a scent profile with a concentration-improving effect to improve concentration. The adjustment unit can also apply a scent profile with a stress-reducing effect to enhance the stress-reducing effect. For example, the adjustment unit applies a scent profile with a relaxing effect to enhance the relaxation effect. The adjustment unit can also apply a scent profile with a concentration-improving effect to improve concentration. The adjustment unit can also apply a scent profile with a stress-reducing effect to enhance the stress-reducing effect. This enables more effective scent adjustment by applying different scent profiles depending on the business purpose. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can cause the generation AI to apply a scent profile according to the business purpose.

[0087] The adjustment unit can adjust the scent taking into account the user's past scent preferences. For example, the adjustment unit adjusts the scent components having a relaxing effect based on the user's past scent preferences. The adjustment unit can also adjust the scent components having a stress-reducing effect based on the user's past scent preferences. Furthermore, the adjustment unit can adjust the scent components having a concentration-enhancing effect based on the user's past scent preferences. For example, the adjustment unit adjusts the scent components having a relaxing effect based on the user's past scent preferences. The adjustment unit can also adjust the scent components having a stress-reducing effect based on the user's past scent preferences. Furthermore, the adjustment unit can adjust the scent components having a concentration-enhancing effect based on the user's past scent preferences. This enables more appropriate scent adjustment by taking into account the user's past scent preferences. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past scent preferences into the generation AI and cause the generation AI to adjust the scent.

[0088] The generation unit can estimate the user's emotions and select fragrance components to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can select relaxing ingredients such as lavender or chamomile. Furthermore, if the user is stressed, the generation unit can select stress-reducing ingredients such as peppermint or eucalyptus. Furthermore, if the user is concentrating, the generation unit can select concentration-enhancing ingredients such as rosemary or lemon. For example, if the user is relaxed, the generation unit can select relaxing ingredients such as lavender or chamomile. Furthermore, if the user is stressed, the generation unit can select stress-reducing ingredients such as peppermint or eucalyptus. Furthermore, if the user is concentrating, the generation unit can select concentration-enhancing ingredients such as rosemary or lemon. This allows for the generation of more appropriate fragrances by selecting fragrance components 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 processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to select fragrance components.

[0089] When generating a fragrance, the generation unit can try different combinations of ingredients to generate a fragrance. For example, the generation unit can combine lavender and chamomile to generate a fragrance with a relaxing effect. The generation unit can also combine peppermint and eucalyptus to generate a fragrance with a stress-reducing effect. The generation unit can also combine rosemary and lemon to generate a fragrance with a concentration-enhancing effect. For example, the generation unit can combine lavender and chamomile to generate a fragrance with a relaxing effect. The generation unit can also combine peppermint and eucalyptus to generate a fragrance with a stress-reducing effect. The generation unit can also combine rosemary and lemon to generate a fragrance with a concentration-enhancing effect. In this way, by trying different combinations of ingredients, the optimal fragrance can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input different combinations of ingredients into the generation AI and cause the generation AI to generate a fragrance.

[0090] The generation unit can dynamically adjust parameters of the generation process when generating a fragrance. For example, the generation unit dynamically changes the concentration of components to adjust the intensity of the fragrance. The generation unit can also dynamically change the volatility of components to adjust the duration of the fragrance. Furthermore, the generation unit can dynamically change the ratio of components to adjust the balance of the fragrance. For example, the generation unit dynamically changes the concentration of components to adjust the intensity of the fragrance. The generation unit can also dynamically change the volatility of components to adjust the duration of the fragrance. Furthermore, the generation unit can dynamically change the ratio of components to adjust the balance of the fragrance. In this way, by dynamically adjusting the parameters of the generation process, a more appropriate fragrance can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input parameters of the generation process to the generation AI and cause the generation AI to generate the fragrance.

[0091] The generation unit can estimate the user's emotion and adjust the intensity of the generated scent based on the estimated user's emotion. For example, when the user is relaxed, the generation unit adjusts the intensity of the scent to a moderate level. Furthermore, when the user is stressed, the generation unit can also adjust the intensity of the scent to a strong level. Furthermore, when the user is concentrating, the generation unit can also adjust the intensity of the scent to a moderate level. For example, when the user is relaxed, the generation unit adjusts the intensity of the scent to a moderate level. Furthermore, when the user is stressed, the generation unit can also adjust the intensity of the scent to a strong level. Furthermore, when the user is concentrating, the generation unit can also adjust the intensity of the scent to a moderate level. This allows for the generation of a more appropriate scent by adjusting the intensity of the scent based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the intensity of the fragrance.

[0092] The generation unit can combine different generation technologies when generating a fragrance. For example, the generation unit can use nanotechnology to convert fragrance components into fine particles and diffuse them evenly. The generation unit can also use ultrasound technology to convert fragrance components into a fine mist and diffuse it evenly throughout a space. The generation unit can also use microencapsulation technology to encapsulate fragrance components and release them continuously. For example, the generation unit can use nanotechnology to convert fragrance components into fine particles and diffuse them evenly. The generation unit can also use ultrasound technology to convert fragrance components into a fine mist and diffuse it evenly throughout a space. The generation unit can also use microencapsulation technology to encapsulate fragrance components and release them continuously. This allows for more effective fragrance generation by combining different generation technologies. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input different generation technologies into the generation AI and have the generation AI generate the fragrance.

[0093] When generating a fragrance, the generation unit can optimize the generation process based on the user's past feedback. For example, the generation unit can optimize fragrance components with a relaxing effect based on the user's past feedback. The generation unit can also optimize fragrance components with a stress-reducing effect based on the user's past feedback. Furthermore, the generation unit can optimize fragrance components with a concentration-improving effect based on the user's past feedback. For example, the generation unit can optimize fragrance components with a relaxing effect based on the user's past feedback. The generation unit can also optimize fragrance components with a stress-reducing effect based on the user's past feedback. Furthermore, the generation unit can optimize fragrance components with a concentration-improving effect based on the user's past feedback. In this way, by optimizing the generation process based on the user's past feedback, a more appropriate fragrance can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to optimize the fragrance generation process.

[0094] The emission unit can estimate the user's emotions and adjust the timing of scent emission based on the estimated user's emotions. For example, when the user is relaxed, the emission unit adjusts the timing of scent emission frequently. Furthermore, when the user is stressed, the emission unit can adjust the timing of scent emission moderately. Furthermore, when the user is concentrating, the emission unit can adjust the timing of scent emission moderately. For example, when the user is relaxed, the emission unit adjusts the timing of scent emission frequently. Furthermore, when the user is stressed, the emission unit can adjust the timing of scent emission moderately. Furthermore, when the user is concentrating, the emission unit can adjust the timing of scent emission moderately. This allows for more appropriate scent emission by adjusting the timing of scent emission based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the emission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emission unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of scent emission.

[0095] When emitting a scent, the emission unit can change the emission method taking into account the layout of the space and people's movements. The emission unit, for example, adjusts the direction of scent emission according to the layout of the space. The emission unit can also adjust the timing of scent emission according to people's movements. Furthermore, the emission unit can adjust the amount of scent emission according to the size of the space. For example, the emission unit adjusts the direction of scent emission according to the layout of the space. The emission unit can also adjust the timing of scent emission according to people's movements. Furthermore, the emission unit can adjust the amount of scent emission according to the size of the space. This enables more effective scent emission by taking into account the layout of the space and people's movements. Some or all of the above-mentioned processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input data on the layout of the space and people's movements into the generation AI and cause the generation AI to change the scent emission method.

[0096] The emission unit can combine different emission technologies when emitting a fragrance. For example, the emission unit can use mist technology to convert fragrance components into fine particles and diffuse them evenly throughout the space. The emission unit can also use spray technology to diffuse the fragrance components over a wide area. Furthermore, the emission unit can use ultrasonic technology to convert fragrance components into fine mist and diffuse them evenly throughout the space. For example, the emission unit can use mist technology to convert fragrance components into fine particles and diffuse them evenly throughout the space. Furthermore, the emission unit can use spray technology to diffuse the fragrance components over a wide area. Furthermore, the emission unit can use ultrasonic technology to convert fragrance components into fine mist and diffuse them evenly throughout the space. Thus, by combining different emission technologies, more effective fragrance emission is possible. Some or all of the above-described processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input different emission technologies into the generation AI and cause the generation AI to emit the fragrance.

[0097] The emission unit can estimate the user's emotions and adjust the amount of scent emitted based on the estimated user's emotions. For example, when the user is relaxed, the emission unit can moderately adjust the amount of scent emitted. Furthermore, when the user is stressed, the emission unit can also strongly adjust the amount of scent emitted. Furthermore, when the user is concentrating, the emission unit can moderately adjust the amount of scent emitted. For example, when the user is relaxed, the emission unit can moderately adjust the amount of scent emitted. Furthermore, when the user is stressed, the emission unit can also strongly adjust the amount of scent emitted. Furthermore, when the user is concentrating, the emission unit can moderately adjust the amount of scent emitted. This allows for more appropriate scent emission by adjusting the amount of scent emitted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the emission unit may be performed using, for example, AI, or without AI. For example, the emission unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the amount of fragrance emitted.

[0098] When emitting a fragrance, the emission unit can optimize the emission method by taking into account the temperature and humidity of the space. For example, when the temperature is high, the emission unit increases the amount of fragrance emitted to promote diffusion. Also, when the humidity is low, the emission unit can increase the amount of fragrance emitted to promote diffusion. Furthermore, the emission unit can dynamically adjust the fragrance emission method in response to changes in temperature and humidity. For example, when the temperature is high, the emission unit increases the amount of fragrance emitted to promote diffusion. Also, when the humidity is low, the emission unit can increase the amount of fragrance emitted to promote diffusion. Furthermore, the emission unit can dynamically adjust the fragrance emission method in response to changes in temperature and humidity. This enables more effective fragrance emission by taking into account the temperature and humidity of the space. Some or all of the above-mentioned processing in the emission unit may be performed using, for example, AI, or may be performed without using AI. For example, the emission unit can input temperature and humidity data into the generation AI and cause the generation AI to optimize the fragrance emission method.

[0099] The emission unit can emit a scent while taking into account the user's past scent preferences. For example, the emission unit emits a scent with a relaxing effect based on the user's past scent preferences. The emission unit can also emit a scent with a stress-reducing effect based on the user's past scent preferences. Furthermore, the emission unit can emit a scent with a concentration-enhancing effect based on the user's past scent preferences. For example, the emission unit emits a scent with a relaxing effect based on the user's past scent preferences. The emission unit can also emit a scent with a stress-reducing effect based on the user's past scent preferences. Furthermore, the emission unit can emit a scent with a concentration-enhancing effect based on the user's past scent preferences. This enables the emission of a more appropriate scent by taking into account the user's past scent preferences. Some or all of the above-described processing in the emission unit can be performed using, for example, AI, or without AI. For example, the emission unit can input the user's past scent preferences into the generation AI and cause the generation AI to emit a scent. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, adjustment unit, generation unit, and emission unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects scent data using a gas sensor in the smart device 14. The analysis unit analyzes the scent data using deep learning with the specific processing unit 290 in the data processing device 12. The adjustment unit adjusts the scent according to the business purpose with the specific processing unit 290 in the data processing device 12. The generation unit blends scent components using an actuator in the smart device 14. The emission unit emits the generated scent into the space using a diffuser in the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, adjustment unit, generation unit, and emission unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects scent data using a gas sensor in the smart glasses 214. The analysis unit analyzes the scent data using deep learning by the specific processing unit 290 in the data processing device 12. The adjustment unit adjusts the scent according to the business purpose by the specific processing unit 290 in the data processing device 12. The generation unit mixes scent components using an actuator in the smart glasses 214. The emission unit emits the generated scent into space using a diffuser in the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, adjustment unit, generation unit, and emission unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects scent data using a gas sensor in the headset type terminal 314. The analysis unit analyzes the scent data using deep learning by the specific processing unit 290 in the data processing device 12. The adjustment unit adjusts the scent according to the business purpose by the specific processing unit 290 in the data processing device 12. The generation unit blends scent components using an actuator in the headset type terminal 314. The emission unit emits the generated scent into the space using a diffuser in the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, adjustment unit, generation unit, and emission unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects scent data using a gas sensor in the robot 414. The analysis unit analyzes the scent data using deep learning by the specific processing unit 290 in the data processing device 12. The adjustment unit adjusts the scent according to the business purpose by the specific processing unit 290 in the data processing device 12. The generation unit blends scent components using an actuator in the robot 414. The emission unit emits the generated scent into space using a diffuser in the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The flavor AI system can further include a biometric information acquisition unit that acquires the user's biometric information. The biometric information acquisition unit monitors biometric information such as heart rate, skin temperature, and sweat rate in real time. This allows for a more accurate understanding of the user's stress level and relaxation level. For example, if the heart rate is increasing, it can be assumed that the user is feeling stressed, and a fragrance with a stress-reducing effect can be provided. Alternatively, if the skin temperature is decreasing, it can be assumed that the user is relaxed, and a fragrance with a relaxing effect can be provided. Furthermore, if the amount of sweat is increasing, it can be assumed that the user is tense, and a fragrance with a tension-reducing effect can be provided. This makes it possible to provide a more appropriate fragrance based on the user's biometric information.

[0102] The flavor AI system may further include a behavior history recording unit that records the user's behavior history. The behavior history recording unit records, for example, what scents the user has preferred in the past and which scents were effective in what situations. This makes it possible to provide the optimal scent based on the user's behavioral patterns. For example, if the user has previously preferred the scent of lavender when wanting to relax, the scent of lavender can be provided in similar situations. Also, if the user has preferred the scent of rosemary when wanting to concentrate, the scent of rosemary can be provided in similar situations. Furthermore, if the user has preferred the scent of peppermint when feeling stressed, the scent of peppermint can be provided in similar situations. This makes it possible to provide more effective scents based on the user's behavior history.

[0103] The flavor AI system can further include a preference learning unit that learns the user's preferences. The preference learning unit, for example, learns what kind of scents the user likes and provides scents based on those preferences. This makes it possible to provide scents that match the user's individual preferences. For example, if the user likes floral scents, a floral scent can be provided. Also, if the user likes citrus scents, a citrus scent can be provided. Furthermore, if the user likes woody scents, a woody scent can be provided. This makes it possible to provide more satisfying scents based on the user's preferences.

[0104] The flavor AI system can further estimate the user's emotions and adjust the amount of scent emitted based on the estimated user emotions. For example, if the user is relaxed, the amount of scent emitted can be adjusted to a moderate level. If the user is stressed, the amount of scent emitted can be adjusted to a stronger level. Furthermore, if the user is concentrating, the amount of scent emitted can be adjusted to a moderate level. This allows for the provision of a more appropriate scent by adjusting the amount of scent emitted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The flavor AI system can further estimate the user's emotions and select fragrance components based on the estimated user emotions. For example, if the user is relaxed, it can select relaxing ingredients such as lavender or chamomile. If the user is stressed, it can select stress-reducing ingredients such as peppermint or eucalyptus. If the user is concentrating, it can select concentration-improving ingredients such as rosemary or lemon. By selecting fragrance components based on the user's emotions, it is possible to provide a more appropriate fragrance. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The flavor AI system can further estimate the user's emotions and optimize the fragrance generation process based on the estimated user emotions. For example, if the user is relaxed, fragrance components with a relaxing effect can be optimized. Also, if the user is stressed, fragrance components with a stress-reducing effect can be optimized. Furthermore, if the user is concentrating, fragrance components with a concentration-improving effect can be optimized. By optimizing the fragrance generation process based on the user's emotions, it becomes possible to provide a more appropriate fragrance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0107] The flavor AI system can further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit acquires the user's location information using, for example, GPS or Wi-Fi. This makes it possible to provide the optimal fragrance based on the user's location information. For example, if the user is in an office, a fragrance that improves concentration can be provided. Also, if the user is at home, a fragrance that has a relaxing effect can be provided. Furthermore, if the user is out, a fragrance that has a stress-reducing effect can be provided. This makes it possible to provide a more appropriate fragrance based on the user's location information.

[0108] The flavor AI system can further include a social media analysis unit that analyzes the user's social media activity. For example, if the user posts about relaxation on social media, the social media analysis unit can provide a scent with a relaxing effect. Also, if the user posts about stress, the social media analysis unit can provide a scent with a stress-reducing effect. Furthermore, if the user posts about concentration, the social media analysis unit can provide a scent with a concentration-improving effect. This makes it possible to provide relevant scents by analyzing the user's social media activity.

[0109] The flavor AI system can further optimize the fragrance generation process based on the user's past feedback. For example, it can optimize fragrance ingredients that have a relaxing effect based on the user's past feedback. It can also optimize fragrance ingredients that have a stress-reducing effect based on the user's past feedback. It can also optimize fragrance ingredients that have a concentration-improving effect based on the user's past feedback. In this way, by optimizing the generation process based on the user's past feedback, it becomes possible to provide a more appropriate fragrance.

[0110] The flavor AI system can further estimate the user's emotions and adjust the timing of scent emission based on the estimated user emotions. For example, if the user is relaxed, the timing of scent emission can be adjusted frequently. Also, if the user is feeling stressed, the timing of scent emission can be adjusted more moderately. Furthermore, if the user is concentrating, the timing of scent emission can be adjusted moderately. This allows for the provision of a more appropriate scent by adjusting the timing of scent emission based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects scent data. The scent data includes scent components, intensity, duration, etc. The collection unit collects scent data using a gas sensor and detects surrounding scents in real time. Step 2: The analysis unit uses deep learning to analyze the scent data collected by the collection unit. The analysis unit analyzes the scent components, intensity, duration, etc., and performs high-precision analysis using a machine learning algorithm that uses a neural network. Step 3: The adjustment unit adjusts the scent according to the business's purpose. For example, it generates a scent that enhances relaxation or improves concentration. Step 4: The generator uses the actuator to blend the fragrance components adjusted by the adjuster. The actuator accurately blends the fragrance components based on the scent combination identified by the AI. Step 5: The emitter emits the scent generated by the generator into the space. The emitter uses ultrasonic technology to convert the scent into fine particles and diffuse them evenly throughout the space. The processing in the emitter may be performed using AI.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects scent data; an analysis unit that analyzes the data collected by the collection unit; an adjusting unit that adjusts the fragrance based on the data analyzed by the analyzing unit; a generating unit that generates the fragrance adjusted by the adjusting unit; a release unit that releases the scent generated by the generation unit into a space; Equipped with A system characterized by:

2. The collecting unit Collecting scent data using gas sensors The system of claim 1 .

3. The analysis unit Analyzing fragrance components, intensity, and duration using deep learning The system of claim 1 .

4. The adjustment unit Tailor your scent to your business goals The system of claim 1 .

5. The generation unit Blending fragrance ingredients using actuators The system of claim 1 .

6. The release section is Uses ultrasonic technology to convert scent into fine particles that are dispersed evenly throughout the space The system of claim 1 .

7. The collecting unit Estimate the user's emotions and adjust the timing of scent data collection based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Collecting scent data based on surrounding environmental conditions The system of claim 1 .

Images