system

Abstract

The system according to the embodiment aims to automatically select and release an appropriate aroma according to the user's psychological state and activity. [Solution] A system according to an embodiment includes a monitoring unit, an analysis unit, a selection unit, and an emission unit. The monitoring unit monitors the user's psychological state and activity. The analysis unit analyzes the data collected by the monitoring unit. The selection unit selects an appropriate aroma based on the results obtained by the analysis unit. The emission unit emits the aroma selected by the selection unit.

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 automatically select and release appropriate aromas according to the user's psychological state or activity, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically select and release an appropriate aroma according to the user's psychological state and activity. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, a selection unit, and an emission unit. The monitoring unit monitors the user's psychological state and activity. The analysis unit analyzes the data collected by the monitoring unit. The selection unit selects an appropriate aroma based on the results obtained by the analysis unit. The emission unit emits the aroma selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically select and release an appropriate aroma depending on the user's psychological state and activity. [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 aroma selection system according to an embodiment of the present invention uses AI to analyze a user's psychological state and activity and automatically select an aroma suitable for concentration and relaxation. This aroma selection system aims to improve the user's work efficiency and well-being. Specifically, the AI ​​first monitors the user's psychological state and activity in real time. The AI ​​then analyzes the collected data and selects the optimal aroma for the user. The selected aroma is automatically released from a diffuser, optimizing the user's environment. This system employs a subscription model, ensuring a constantly optimal environment. This allows for differentiation using aroma in the generative AI market. For example, a "monitoring unit" is required to monitor the user's psychological state and activity. This unit uses sensors to measure heart rate and stress level. Next, an "analysis unit" is required to analyze the data obtained from the monitoring unit. This analysis unit uses AI to analyze the data and determine the user's psychological state. A "selection unit" is required to select the optimal aroma based on the results of the analysis unit. This selection unit uses AI to select the optimal aroma for the user. Finally, a "release unit" is required to automatically release the selected aroma from the diffuser. This release unit releases the aroma based on instructions from the selection unit. These units are interrelated. For example, the monitoring unit collects data, the analysis unit analyzes the data, the selection unit selects the optimal aroma, and the release unit releases the aroma. This allows the aroma selection system to provide the optimal aroma based on the user's psychological state and activity, improving work efficiency and well-being.

[0029] An aroma selection system according to an embodiment includes a monitoring unit, an analysis unit, a selection unit, and an emission unit. The monitoring unit monitors a user's psychological state and activity details. The monitoring unit measures, for example, a heart rate and a stress level using a sensor. For example, the monitoring unit measures the user's heart rate in real time using a heart rate sensor. The monitoring unit can also measure the user's stress level using an electrodermal activity sensor. The monitoring unit can also measure the user's brain waves using an electrodermal activity sensor. The analysis unit analyzes data collected by the monitoring unit. The analysis unit analyzes the data using, for example, AI to determine the user's psychological state. For example, the analysis unit can analyze heart rate data using an AI model to determine the user's stress level. The analysis unit can also analyze electrodermal activity data using AI to determine the user's concentration level. The analysis unit can also analyze electrodermal activity data using AI to determine the user's relaxation level. The selection unit selects an optimal aroma based on the results obtained by the analysis unit. For example, the selection unit selects an optimal aroma for the user using AI. For example, the selection unit uses an AI model to select an aroma with a relaxing effect based on the user's psychological state. The selection unit can also use AI to select an aroma that enhances concentration based on the user's activity. Furthermore, the selection unit can also use AI to select an aroma with a stress-reducing effect based on the user's stress level. The emission unit emits the aroma based on instructions from the selection unit. The emission unit automatically emits the selected aroma, for example, using a diffuser. For example, the emission unit emits an aroma with a relaxing effect using a diffuser. The emission unit can also emit an aroma that enhances concentration using a diffuser. Furthermore, the emission unit can also emit an aroma with a stress-reducing effect using a diffuser. Thus, the aroma selection system according to the embodiment can provide an optimal aroma based on the user's psychological state and activity, thereby improving work efficiency and well-being.

[0030] The monitoring unit can measure the heart rate or stress level using a sensor. The monitoring unit, for example, measures the user's heart rate in real time using a heart rate sensor. For example, the monitoring unit may attach a heart rate sensor to the user's wrist and measure the heart rate. The monitoring unit can also measure the user's stress level using an electrodermal activity sensor. For example, the monitoring unit may attach an electrodermal activity sensor to the user's fingertip and measure the stress level. The monitoring unit can also measure the user's brain waves using an electroencephalogram (EEG) sensor. For example, the monitoring unit may attach an EEG sensor to the user's head and measure the brain waves. This allows for accurate measurement of the user's heart rate and stress level, thereby enabling the selection of a more appropriate aroma. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input data obtained from the heart rate sensor into the generation AI and cause the generation AI to analyze the heart rate.

[0031] The analysis unit can analyze the data collected by the monitoring unit and determine the user's psychological state. The analysis unit can, for example, use AI to analyze the data and determine the user's psychological state. For example, the analysis unit can analyze heart rate data using an AI model and determine the user's stress level. The analysis unit can also analyze brain wave data using AI and determine the user's concentration level. Furthermore, the analysis unit can analyze electrodermal activity data using AI and determine the user's level of relaxation. This allows the user's psychological state to be accurately determined, thereby enabling the selection of the optimal aroma. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input heart rate data to the generation AI and have the generation AI determine the stress level.

[0032] The selection unit can select an aroma appropriate for the user based on the results obtained by the analysis unit. The selection unit can, for example, use AI to select the aroma optimal for the user. For example, the selection unit can use an AI model to select an aroma with a relaxing effect based on the user's psychological state. The selection unit can also use AI to select an aroma that improves concentration based on the user's activity. Furthermore, the selection unit can use AI to select an aroma with a stress-reducing effect based on the user's stress level. This improves work efficiency and well-being by selecting the aroma optimal for the user. Some or all of the above-mentioned processing in the selection unit can be performed using AI, for example, or without AI. For example, the selection unit can input data obtained from the analysis unit into a generation AI and have the generation AI select the optimal aroma.

[0033] The emission unit can emit an aroma based on instructions from the selection unit. The emission unit, for example, automatically emits the selected aroma using a diffuser. For example, the emission unit can emit an aroma with a relaxing effect using a diffuser. The emission unit can also emit an aroma that enhances concentration using a diffuser. Furthermore, the emission unit can also emit an aroma that has a stress-reducing effect using a diffuser. In this way, the user's environment is optimized by automatically emitting the selected aroma. 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 instructions from the selection unit to a generation AI and cause the generation AI to emit the aroma.

[0034] The selection unit employs a subscription model to always provide an appropriate aroma. The selection unit employs, for example, a subscription model to provide the user with the optimal aroma. For example, the selection unit provides the user with the aroma for a monthly fee. The selection unit can also periodically update the aroma based on the user's psychological state and activity. Furthermore, the selection unit can adjust the type and amount of aroma based on user feedback. This makes it possible to always continue to provide the optimal aroma using the subscription model. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input user feedback data into the generation AI and cause the generation AI to update the aroma.

[0035] The monitoring unit can analyze the user's past psychological state data and select an appropriate monitoring method. The monitoring unit, for example, analyzes the user's past psychological state data and selects the optimal monitoring method. For example, if the monitoring unit determines from the user's past data that stress tends to increase during a specific time period, it can intensify monitoring during that time period. The monitoring unit can also identify times when the user is relaxed from the user's past data and reduce monitoring during those times. Furthermore, the monitoring unit can intensify monitoring during specific activities based on the user's past data. This enables more effective data collection by selecting the optimal monitoring method based on the past data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input past psychological state data into a generation AI and have the generation AI select the optimal monitoring method.

[0036] The monitoring unit can use different sensors during monitoring depending on the user's activity. For example, the monitoring unit can use different sensors during monitoring depending on the user's activity. For example, when the user is exercising, the monitoring unit can collect data using a heart rate sensor and an acceleration sensor. When the user is doing desk work, the monitoring unit can also collect data using a stress level sensor and an electroencephalogram sensor. Furthermore, when the user is relaxing, the monitoring unit can also collect data using a respiration sensor and an electrodermal activity sensor. This enables more accurate data collection by using appropriate sensors depending on the user's activity. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can cause a generation AI to select sensors depending on the activity.

[0037] The monitoring unit can prioritize collecting highly relevant data based on the user's geographical location information during monitoring. For example, the monitoring unit prioritizes collecting highly relevant data based on the user's geographical location information during monitoring. For example, when the user is at home, the monitoring unit prioritizes collecting data for monitoring a relaxation state. Furthermore, when the user is at work, the monitoring unit can prioritize collecting data for monitoring a concentration state. Furthermore, when the user is out, the monitoring unit can prioritize collecting data for monitoring a stress level. This allows more relevant data to be collected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input geographical location information to a generation AI and cause the generation AI to collect highly relevant data.

[0038] The monitoring unit can analyze the user's social media activity during monitoring and collect related data. For example, the monitoring unit can analyze the user's social media activity during monitoring and collect related data. For example, if the user feels stressed on social media, the monitoring unit can collect data related to that activity. In addition, if the user is relaxing on social media, the monitoring unit can also collect data related to that activity. Furthermore, if the user is concentrating on social media, the monitoring unit can also collect data related to that activity. This allows for more relevant data to be collected by analyzing the user's social media activity. Some or all of the above-described processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input social media activity data into a generation AI and cause the generation AI to collect related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected data during analysis. For example, the analysis unit analyzes data of high importance (heart rate, stress level, etc.) in detail. The analysis unit can also moderately analyze data of medium importance (breathing pattern, brain waves, etc.). Furthermore, the analysis unit can also simply analyze data of low importance (electrodermal activity, etc.). This enables more effective analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit applies different analysis methods depending on the data category during analysis. For example, the analysis unit applies time domain analysis to heart rate data. The analysis unit can also apply frequency domain analysis to electroencephalogram data. Furthermore, the analysis unit can apply statistical analysis to stress level data. This enables more accurate analysis by applying an appropriate analysis method depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis method.

[0041] The analysis unit can adjust the order of analysis based on the time of data collection during analysis. For example, the analysis unit can adjust the order of analysis based on the time of data collection during analysis. For example, the analysis unit can prioritize analyzing the most recent data and provide real-time feedback. The analysis unit can also refer to past data to analyze long-term trends. Furthermore, the analysis unit can prioritize analyzing data from a specific event period. This enables real-time feedback by adjusting the order of analysis based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI adjust the order of analysis.

[0042] The analysis unit can improve the accuracy of the analysis based on related external data during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to related external data during analysis. For example, the analysis unit can refer to weather data and analyze the impact on the user's psychological state. The analysis unit can also refer to social media data to analyze changes in the user's emotions. Furthermore, the analysis unit can refer to health data and analyze the user's overall health condition. By referring to related external data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input external data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The selection unit can analyze the user's past aroma use history to select the optimal aroma at the time of selection. For example, the selection unit analyzes the user's past aroma use history to select the optimal aroma at the time of selection. For example, the selection unit preferentially selects aromas that the user has used favorably in the past. The selection unit can also select aromas that the user has felt effective in the past. Furthermore, the selection unit can analyze the effects of aromas the user has used in the past and select the optimal aroma. In this way, the optimal aroma for the user can be selected by analyzing the past aroma use history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the past aroma use history into the generation AI and cause the generation AI to select the optimal aroma.

[0044] The selection unit can customize the aroma selection based on the user's current activity at the time of selection. For example, the selection unit customizes the aroma selection based on the user's current activity at the time of selection. For example, if the user is exercising, the selection unit can select an aroma that increases energy. Also, if the user is doing desk work, the selection unit can select an aroma that increases concentration. Furthermore, if the user is relaxing, the selection unit can select an aroma that has a relaxation effect. In this way, by customizing the aroma based on the user's current activity, a more effective aroma can be provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input current activity content data into the generation AI and cause the generation AI to select and customize the aroma.

[0045] The selection unit can select an appropriate aroma based on the user's geographical location information at the time of selection. The selection unit, for example, selects an appropriate aroma based on the user's geographical location information at the time of selection. For example, when the user is at home, the selection unit selects an aroma with a relaxing effect. Furthermore, when the user is at work, the selection unit can select an aroma that improves concentration. Furthermore, when the user is out, the selection unit can select an aroma that has a stress-reducing effect. In this way, by taking the user's geographical location information into consideration, a more appropriate aroma can be selected. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographical location information to a generation AI and cause the generation AI to select an aroma.

[0046] The selection unit can select an aroma by analyzing the user's social media activity at the time of selection. For example, the selection unit can select an aroma by analyzing the user's social media activity at the time of selection. For example, if the user is feeling stressed because of social media, the selection unit can select an aroma that has a stress-reducing effect. Furthermore, if the user is relaxing because of social media, the selection unit can select an aroma that has a relaxation effect. Furthermore, if the user is concentrating on social media, the selection unit can select an aroma that improves concentration. In this way, by analyzing the user's social media activity, a more appropriate aroma can be selected. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can input social media activity data into a generation AI and cause the generation AI to select an aroma.

[0047] The release unit can select the optimal release method by referring to the user's past aroma use history when releasing aroma. For example, the release unit selects the optimal release method by referring to the user's past aroma use history when releasing aroma. For example, the release unit preferentially selects an aroma release method that the user has used in the past as preferred. The release unit can also select an aroma release method that the user has previously felt effective. Furthermore, the release unit can analyze the effects of aromas the user has used in the past and select the optimal release method. In this way, the optimal release method for the user can be selected by referring to the past aroma use history. Some or all of the above-described processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input the past aroma use history into the generation AI and have the generation AI select the optimal release method.

[0048] The emission unit can adjust the timing of emission based on the user's current environmental conditions when emitting. For example, the emission unit adjusts the timing of emission based on the user's current environmental conditions when emitting. For example, when the user is at home, the emission unit emits an aroma with a relaxing effect at an appropriate timing. Also, when the user is at work, the emission unit can emit an aroma that improves concentration at an appropriate timing. Furthermore, when the user is out, the emission unit can emit an aroma with a stress-reducing effect at an appropriate timing. In this way, by adjusting the timing of emission based on the user's current environmental conditions, a more effective aroma can be provided. 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 environmental condition data to the generation AI and cause the generation AI to adjust the timing of emission.

[0049] The emission unit can select an appropriate emission method based on the user's geographical location information at the time of emission. For example, the emission unit selects an appropriate emission method based on the user's geographical location information at the time of emission. For example, when the user is at home, the emission unit emits an aroma with a relaxing effect in an appropriate manner. Furthermore, when the user is at work, the emission unit can emit an aroma that improves concentration in an appropriate manner. Furthermore, when the user is out, the emission unit can emit an aroma that has a stress-reducing effect in an appropriate manner. In this way, a more appropriate emission method can be selected by taking the user's geographical location information into consideration. 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 geographical location information to the generation AI and cause the generation AI to select the emission method.

[0050] The emitting unit can analyze the user's social media activity and determine the timing of the release at the time of release. For example, the emitting unit can analyze the user's social media activity and determine the timing of the release at the time of release. For example, if the user is feeling stressed because of social media, the emitting unit can emit an aroma with a stress-reducing effect at an appropriate time. Also, if the user is relaxing because of social media, the emitting unit can emit an aroma with a relaxation effect at an appropriate time. Furthermore, if the user is concentrating on social media, the emitting unit can emit an aroma that improves concentration at an appropriate time. In this way, by analyzing the user's social media activity, the aroma can be released at a more appropriate time. Some or all of the above-described processing in the emitting unit can be performed, for example, using AI or without AI. For example, the emitting unit can input social media activity data into the generation AI and have the generation AI determine the timing of the release.

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

[0052] The monitoring unit can analyze the user's past psychological state data and select an appropriate monitoring method. For example, if the monitoring unit determines from the user's past data that stress tends to increase during certain time periods, it can intensify monitoring during those time periods. The monitoring unit can also identify times when the user is relaxed from the user's past data and reduce monitoring during those time periods. Furthermore, the monitoring unit can intensify monitoring during specific activities based on the user's past data. This enables more effective data collection by selecting the optimal monitoring method based on past data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past psychological state data into a generation AI and have the generation AI select the optimal monitoring method.

[0053] The monitoring unit can use different sensors during monitoring depending on the user's activity. For example, when the user is exercising, the monitoring unit can collect data using a heart rate sensor and an acceleration sensor. When the user is doing desk work, the monitoring unit can also collect data using a stress level sensor and an electroencephalogram sensor. When the user is relaxing, the monitoring unit can also collect data using a respiration sensor and an electrodermal activity sensor. This enables more accurate data collection by using appropriate sensors depending on the user's activity. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can cause a generation AI to select sensors depending on the activity.

[0054] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data of high importance (heart rate, stress level, etc.) in detail. The analysis unit can also analyze data of medium importance (breathing pattern, brain waves, etc.) moderately. Furthermore, the analysis unit can also analyze data of low importance (electrodermal activity, etc.) simply. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0055] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, the analysis unit can apply time domain analysis to heart rate data. The analysis unit can also apply frequency domain analysis to electroencephalogram data. Furthermore, the analysis unit can apply statistical analysis to stress level data. This allows for more accurate analysis by applying an appropriate analysis method depending on the data category. Some or all of the above-mentioned 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 the data category to the generation AI and have the generation AI apply the analysis method.

[0056] When making a selection, the selection unit can analyze the user's past aroma use history to select the optimal aroma. For example, the selection unit can prioritize the selection of aromas that the user has used favorably in the past. The selection unit can also select aromas that the user has previously felt effective. Furthermore, the selection unit can analyze the effects of aromas the user has used in the past and select the optimal aroma. In this way, the optimal aroma for the user can be selected by analyzing the past aroma use history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the past aroma use history into the generation AI and cause the generation AI to select the optimal aroma.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The monitoring unit monitors the user's psychological state and activity. For example, it can measure the user's heart rate in real time using a heart rate sensor, measure the user's stress level using an electrodermal activity sensor, and measure the user's brain waves using an electroencephalogram (EEG) sensor. Step 2: The analysis unit analyzes the data collected by the monitoring unit. For example, it can use AI to analyze heart rate data to determine the user's stress level. It can also analyze brain wave data to determine the user's level of concentration, and electrodermal activity data to determine the user's level of relaxation. Step 3: The selection unit selects the optimal aroma based on the results obtained by the analysis unit. For example, it can use AI to select an aroma with a relaxing effect based on the user's psychological state, or an aroma that improves concentration based on the user's activity. It can also select an aroma with a stress-reducing effect. Step 4: The emitting unit emits the aroma based on the instruction from the selecting unit. For example, a diffuser is used to automatically emit an aroma that has a relaxing effect, an aroma that improves concentration, or an aroma that has a stress-reducing effect.

[0059] (Example 2) The aroma selection system according to an embodiment of the present invention uses AI to analyze a user's psychological state and activity and automatically select an aroma suitable for concentration and relaxation. This aroma selection system aims to improve the user's work efficiency and well-being. Specifically, the AI ​​first monitors the user's psychological state and activity in real time. The AI ​​then analyzes the collected data and selects the optimal aroma for the user. The selected aroma is automatically released from a diffuser, optimizing the user's environment. This system employs a subscription model, ensuring a constantly optimal environment. This allows for differentiation using aroma in the generative AI market. For example, a "monitoring unit" is required to monitor the user's psychological state and activity. This unit uses sensors to measure heart rate and stress level. Next, an "analysis unit" is required to analyze the data obtained from the monitoring unit. This analysis unit uses AI to analyze the data and determine the user's psychological state. A "selection unit" is required to select the optimal aroma based on the results of the analysis unit. This selection unit uses AI to select the optimal aroma for the user. Finally, a "release unit" is required to automatically release the selected aroma from the diffuser. This release unit releases the aroma based on instructions from the selection unit. These units are interrelated. For example, the monitoring unit collects data, the analysis unit analyzes the data, the selection unit selects the optimal aroma, and the release unit releases the aroma. This allows the aroma selection system to provide the optimal aroma based on the user's psychological state and activity, improving work efficiency and well-being.

[0060] An aroma selection system according to an embodiment includes a monitoring unit, an analysis unit, a selection unit, and an emission unit. The monitoring unit monitors a user's psychological state and activity details. The monitoring unit measures, for example, a heart rate and a stress level using a sensor. For example, the monitoring unit measures the user's heart rate in real time using a heart rate sensor. The monitoring unit can also measure the user's stress level using an electrodermal activity sensor. The monitoring unit can also measure the user's brain waves using an electrodermal activity sensor. The analysis unit analyzes data collected by the monitoring unit. The analysis unit analyzes the data using, for example, AI to determine the user's psychological state. For example, the analysis unit can analyze heart rate data using an AI model to determine the user's stress level. The analysis unit can also analyze electrodermal activity data using AI to determine the user's concentration level. The analysis unit can also analyze electrodermal activity data using AI to determine the user's relaxation level. The selection unit selects an optimal aroma based on the results obtained by the analysis unit. For example, the selection unit selects an optimal aroma for the user using AI. For example, the selection unit uses an AI model to select an aroma with a relaxing effect based on the user's psychological state. The selection unit can also use AI to select an aroma that enhances concentration based on the user's activity. Furthermore, the selection unit can also use AI to select an aroma with a stress-reducing effect based on the user's stress level. The emission unit emits the aroma based on instructions from the selection unit. The emission unit automatically emits the selected aroma, for example, using a diffuser. For example, the emission unit emits an aroma with a relaxing effect using a diffuser. The emission unit can also emit an aroma that enhances concentration using a diffuser. Furthermore, the emission unit can also emit an aroma with a stress-reducing effect using a diffuser. Thus, the aroma selection system according to the embodiment can provide an optimal aroma based on the user's psychological state and activity, thereby improving work efficiency and well-being.

[0061] The monitoring unit can measure the heart rate or stress level using a sensor. The monitoring unit, for example, measures the user's heart rate in real time using a heart rate sensor. For example, the monitoring unit may attach a heart rate sensor to the user's wrist and measure the heart rate. The monitoring unit can also measure the user's stress level using an electrodermal activity sensor. For example, the monitoring unit may attach an electrodermal activity sensor to the user's fingertip and measure the stress level. The monitoring unit can also measure the user's brain waves using an electroencephalogram (EEG) sensor. For example, the monitoring unit may attach an EEG sensor to the user's head and measure the brain waves. This allows for accurate measurement of the user's heart rate and stress level, thereby enabling the selection of a more appropriate aroma. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input data obtained from the heart rate sensor into the generation AI and cause the generation AI to analyze the heart rate.

[0062] The analysis unit can analyze the data collected by the monitoring unit and determine the user's psychological state. The analysis unit can, for example, use AI to analyze the data and determine the user's psychological state. For example, the analysis unit can analyze heart rate data using an AI model and determine the user's stress level. The analysis unit can also analyze brain wave data using AI and determine the user's concentration level. Furthermore, the analysis unit can analyze electrodermal activity data using AI and determine the user's level of relaxation. This allows the user's psychological state to be accurately determined, thereby enabling the selection of the optimal aroma. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input heart rate data to the generation AI and have the generation AI determine the stress level.

[0063] The selection unit can select an aroma appropriate for the user based on the results obtained by the analysis unit. The selection unit can, for example, use AI to select the aroma optimal for the user. For example, the selection unit can use an AI model to select an aroma with a relaxing effect based on the user's psychological state. The selection unit can also use AI to select an aroma that improves concentration based on the user's activity. Furthermore, the selection unit can use AI to select an aroma with a stress-reducing effect based on the user's stress level. This improves work efficiency and well-being by selecting the aroma optimal for the user. Some or all of the above-mentioned processing in the selection unit can be performed using AI, for example, or without AI. For example, the selection unit can input data obtained from the analysis unit into a generation AI and have the generation AI select the optimal aroma.

[0064] The emission unit can emit an aroma based on instructions from the selection unit. The emission unit, for example, automatically emits the selected aroma using a diffuser. For example, the emission unit can emit an aroma with a relaxing effect using a diffuser. The emission unit can also emit an aroma that enhances concentration using a diffuser. Furthermore, the emission unit can also emit an aroma that has a stress-reducing effect using a diffuser. In this way, the user's environment is optimized by automatically emitting the selected aroma. 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 instructions from the selection unit to a generation AI and cause the generation AI to emit the aroma.

[0065] The selection unit employs a subscription model to always provide an appropriate aroma. The selection unit employs, for example, a subscription model to provide the user with the optimal aroma. For example, the selection unit provides the user with the aroma for a monthly fee. The selection unit can also periodically update the aroma based on the user's psychological state and activity. Furthermore, the selection unit can adjust the type and amount of aroma based on user feedback. This makes it possible to always continue to provide the optimal aroma using the subscription model. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input user feedback data into the generation AI and cause the generation AI to update the aroma.

[0066] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, the monitoring unit estimates the user's emotions and adjusts the monitoring frequency based on the estimated user emotions. For example, when the user is feeling stressed, the monitoring unit increases the monitoring frequency to collect data in real time. Furthermore, when the user is relaxed, the monitoring unit can reduce the monitoring frequency to reduce the burden of data collection. Furthermore, when the user is concentrating, the monitoring unit can set the monitoring frequency to a medium level and collect appropriate data. This enables more appropriate data collection by adjusting the monitoring frequency according to the user's emotions. The emotion estimation is realized 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring frequency.

[0067] The monitoring unit can analyze the user's past psychological state data and select an appropriate monitoring method. The monitoring unit, for example, analyzes the user's past psychological state data and selects the optimal monitoring method. For example, if the monitoring unit determines from the user's past data that stress tends to increase during a specific time period, it can intensify monitoring during that time period. The monitoring unit can also identify times when the user is relaxed from the user's past data and reduce monitoring during those times. Furthermore, the monitoring unit can intensify monitoring during specific activities based on the user's past data. This enables more effective data collection by selecting the optimal monitoring method based on the past data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input past psychological state data into a generation AI and have the generation AI select the optimal monitoring method.

[0068] The monitoring unit can use different sensors during monitoring depending on the user's activity. For example, the monitoring unit can use different sensors during monitoring depending on the user's activity. For example, when the user is exercising, the monitoring unit can collect data using a heart rate sensor and an acceleration sensor. When the user is doing desk work, the monitoring unit can also collect data using a stress level sensor and an electroencephalogram sensor. Furthermore, when the user is relaxing, the monitoring unit can also collect data using a respiration sensor and an electrodermal activity sensor. This enables more accurate data collection by using appropriate sensors depending on the user's activity. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can cause a generation AI to select sensors depending on the activity.

[0069] The monitoring unit can estimate the user's emotions and determine the priority of the data to be monitored based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions and determine the priority of the data to be monitored based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring heart rate and stress level data. Also, if the user is relaxed, the monitoring unit can prioritize monitoring breathing pattern and brain wave data. Furthermore, if the user is concentrating, the monitoring unit can prioritize monitoring brain wave and heart rate variability data. This allows for prioritized data collection based on the user's emotions, thereby prioritizing more important data. 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 monitoring unit can be performed using, for example, an AI. For example, the monitoring unit can input emotion data to a generation AI and have the generation AI determine the priority of the data.

[0070] The monitoring unit can prioritize collecting highly relevant data based on the user's geographical location information during monitoring. For example, the monitoring unit prioritizes collecting highly relevant data based on the user's geographical location information during monitoring. For example, when the user is at home, the monitoring unit prioritizes collecting data for monitoring a relaxation state. Furthermore, when the user is at work, the monitoring unit can prioritize collecting data for monitoring a concentration state. Furthermore, when the user is out, the monitoring unit can prioritize collecting data for monitoring a stress level. This allows more relevant data to be collected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input geographical location information to a generation AI and cause the generation AI to collect highly relevant data.

[0071] The monitoring unit can analyze the user's social media activity during monitoring and collect related data. For example, the monitoring unit can analyze the user's social media activity during monitoring and collect related data. For example, if the user feels stressed on social media, the monitoring unit can collect data related to that activity. In addition, if the user is relaxing on social media, the monitoring unit can also collect data related to that activity. Furthermore, if the user is concentrating on social media, the monitoring unit can also collect data related to that activity. This allows for more relevant data to be collected by analyzing the user's social media activity. Some or all of the above-described processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input social media activity data into a generation AI and cause the generation AI to collect related data.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can apply an algorithm specialized for stress reduction. Furthermore, if the user is relaxed, the analysis unit can apply an algorithm that enhances relaxation effects. Furthermore, if the user is concentrating, the analysis unit can apply an algorithm for maintaining concentration. This allows for more appropriate analysis by adjusting the analysis algorithm 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 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the algorithm.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected data during analysis. For example, the analysis unit analyzes data of high importance (heart rate, stress level, etc.) in detail. The analysis unit can also moderately analyze data of medium importance (breathing pattern, brain waves, etc.). Furthermore, the analysis unit can also simply analyze data of low importance (electrodermal activity, etc.). This enables more effective analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit applies different analysis methods depending on the data category during analysis. For example, the analysis unit applies time domain analysis to heart rate data. The analysis unit can also apply frequency domain analysis to electroencephalogram data. Furthermore, the analysis unit can apply statistical analysis to stress level data. This enables more accurate analysis by applying an appropriate analysis method depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis method.

[0075] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize the analysis of stress-related data. Furthermore, if the user is relaxed, the analysis unit can prioritize the analysis of relaxation-related data. Furthermore, if the user is concentrating, the analysis unit can prioritize the analysis of concentration-related data. By determining the analysis priorities based on the user's emotions, more important data can be prioritized for analysis. The emotion estimation is realized 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input emotion data to the generation AI and have the generation AI determine the analysis priorities.

[0076] The analysis unit can adjust the order of analysis based on the time of data collection during analysis. For example, the analysis unit can adjust the order of analysis based on the time of data collection during analysis. For example, the analysis unit can prioritize analyzing the most recent data and provide real-time feedback. The analysis unit can also refer to past data to analyze long-term trends. Furthermore, the analysis unit can prioritize analyzing data from a specific event period. This enables real-time feedback by adjusting the order of analysis based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI adjust the order of analysis.

[0077] The analysis unit can improve the accuracy of the analysis based on related external data during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to related external data during analysis. For example, the analysis unit can refer to weather data and analyze the impact on the user's psychological state. The analysis unit can also refer to social media data to analyze changes in the user's emotions. Furthermore, the analysis unit can refer to health data and analyze the user's overall health condition. By referring to related external data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input external data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0078] The selection unit can estimate the user's emotions and adjust aroma selection criteria based on the estimated user emotions. The selection unit, for example, estimates the user's emotions and adjusts the aroma selection criteria based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can select an aroma with a stress-reducing effect. Furthermore, if the user is relaxed, the selection unit can select an aroma with a relaxation effect. Furthermore, if the user is concentrating, the selection unit can select an aroma that enhances concentration. By adjusting the aroma selection criteria according to the user's emotions, more appropriate aromas can be selected. The emotion estimation is realized 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 selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input emotion data to the generation AI and cause the generation AI to adjust the aroma selection criteria.

[0079] The selection unit can analyze the user's past aroma use history to select the optimal aroma at the time of selection. For example, the selection unit analyzes the user's past aroma use history to select the optimal aroma at the time of selection. For example, the selection unit preferentially selects aromas that the user has used favorably in the past. The selection unit can also select aromas that the user has felt effective in the past. Furthermore, the selection unit can analyze the effects of aromas the user has used in the past and select the optimal aroma. In this way, the optimal aroma for the user can be selected by analyzing the past aroma use history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the past aroma use history into the generation AI and cause the generation AI to select the optimal aroma.

[0080] The selection unit can customize the aroma selection based on the user's current activity at the time of selection. For example, the selection unit customizes the aroma selection based on the user's current activity at the time of selection. For example, if the user is exercising, the selection unit can select an aroma that increases energy. Also, if the user is doing desk work, the selection unit can select an aroma that increases concentration. Furthermore, if the user is relaxing, the selection unit can select an aroma that has a relaxation effect. In this way, by customizing the aroma based on the user's current activity, a more effective aroma can be provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input current activity content data into the generation AI and cause the generation AI to select and customize the aroma.

[0081] The selection unit can estimate the user's emotion and adjust the selection order of aromas based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the selection order of aromas based on the estimated user's emotion. For example, if the user is feeling stressed, the selection unit can first select an aroma with a stress-reducing effect. Furthermore, if the user is relaxed, the selection unit can first select an aroma with a relaxation effect. Furthermore, if the user is concentrating, the selection unit can first select an aroma that improves concentration. This allows for adjusting the selection order of aromas according to the user's emotion, thereby providing more appropriate aromas. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The 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. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input emotion data to the generation AI and cause the generation AI to adjust the selection order of aromas.

[0082] The selection unit can select an appropriate aroma based on the user's geographical location information at the time of selection. The selection unit, for example, selects an appropriate aroma based on the user's geographical location information at the time of selection. For example, when the user is at home, the selection unit selects an aroma with a relaxing effect. Furthermore, when the user is at work, the selection unit can select an aroma that improves concentration. Furthermore, when the user is out, the selection unit can select an aroma that has a stress-reducing effect. In this way, by taking the user's geographical location information into consideration, a more appropriate aroma can be selected. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographical location information to a generation AI and cause the generation AI to select an aroma.

[0083] The selection unit can select an aroma by analyzing the user's social media activity at the time of selection. For example, the selection unit can select an aroma by analyzing the user's social media activity at the time of selection. For example, if the user is feeling stressed because of social media, the selection unit can select an aroma that has a stress-reducing effect. Furthermore, if the user is relaxing because of social media, the selection unit can select an aroma that has a relaxation effect. Furthermore, if the user is concentrating on social media, the selection unit can select an aroma that improves concentration. In this way, by analyzing the user's social media activity, a more appropriate aroma can be selected. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can input social media activity data into a generation AI and cause the generation AI to select an aroma.

[0084] The emission unit can estimate the user's emotions and adjust the amount of aroma emitted based on the estimated user's emotions. The emission unit, for example, estimates the user's emotions and adjusts the amount of aroma emitted based on the estimated user's emotions. For example, if the user is feeling stressed, the emission unit increases the amount of aroma that has a stress-reducing effect. Furthermore, if the user is relaxed, the emission unit can also appropriately adjust the amount of aroma that has a relaxation effect. Furthermore, if the user is concentrating, the emission unit can also appropriately adjust the amount of aroma that enhances concentration. This allows for the provision of a more effective aroma by adjusting the amount of aroma emitted according to the user's emotions. The emotion estimation is realized 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 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 emotion data to the generation AI and cause the generation AI to adjust the amount of aroma emitted.

[0085] The release unit can select the optimal release method by referring to the user's past aroma use history when releasing aroma. For example, the release unit selects the optimal release method by referring to the user's past aroma use history when releasing aroma. For example, the release unit preferentially selects an aroma release method that the user has used in the past as preferred. The release unit can also select an aroma release method that the user has previously felt effective. Furthermore, the release unit can analyze the effects of aromas the user has used in the past and select the optimal release method. In this way, the optimal release method for the user can be selected by referring to the past aroma use history. Some or all of the above-described processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input the past aroma use history into the generation AI and have the generation AI select the optimal release method.

[0086] The emission unit can adjust the timing of emission based on the user's current environmental conditions when emitting. For example, the emission unit adjusts the timing of emission based on the user's current environmental conditions when emitting. For example, when the user is at home, the emission unit emits an aroma with a relaxing effect at an appropriate timing. Also, when the user is at work, the emission unit can emit an aroma that improves concentration at an appropriate timing. Furthermore, when the user is out, the emission unit can emit an aroma with a stress-reducing effect at an appropriate timing. In this way, by adjusting the timing of emission based on the user's current environmental conditions, a more effective aroma can be provided. 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 environmental condition data to the generation AI and cause the generation AI to adjust the timing of emission.

[0087] The emission unit can estimate the user's emotions and adjust the aroma emission order based on the estimated user emotions. The emission unit, for example, estimates the user's emotions and adjusts the aroma emission order based on the estimated user emotions. For example, if the user is feeling stressed, the emission unit may first emit an aroma with a stress-reducing effect. Furthermore, if the user is relaxed, the emission unit may first emit an aroma with a relaxation effect. Furthermore, if the user is concentrating, the emission unit may first emit an aroma that enhances concentration. This allows for adjusting the aroma emission order according to the user's emotions, thereby providing more appropriate aromas. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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-described 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 emotion data to the generation AI and cause the generation AI to adjust the aroma emission order.

[0088] The emission unit can select an appropriate emission method based on the user's geographical location information at the time of emission. For example, the emission unit selects an appropriate emission method based on the user's geographical location information at the time of emission. For example, when the user is at home, the emission unit emits an aroma with a relaxing effect in an appropriate manner. Furthermore, when the user is at work, the emission unit can emit an aroma that improves concentration in an appropriate manner. Furthermore, when the user is out, the emission unit can emit an aroma that has a stress-reducing effect in an appropriate manner. In this way, a more appropriate emission method can be selected by taking the user's geographical location information into consideration. 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 geographical location information to the generation AI and cause the generation AI to select the emission method.

[0089] The emitting unit can analyze the user's social media activity and determine the timing of the release at the time of release. For example, the emitting unit can analyze the user's social media activity and determine the timing of the release at the time of release. For example, if the user is feeling stressed because of social media, the emitting unit can emit an aroma with a stress-reducing effect at an appropriate time. Also, if the user is relaxing because of social media, the emitting unit can emit an aroma with a relaxation effect at an appropriate time. Furthermore, if the user is concentrating on social media, the emitting unit can emit an aroma that improves concentration at an appropriate time. In this way, by analyzing the user's social media activity, the aroma can be released at a more appropriate time. Some or all of the above-described processing in the emitting unit can be performed, for example, using AI or without AI. For example, the emitting unit can input social media activity data into the generation AI and have the generation AI determine the timing of the release. === Hard Collateral 1-1 === Each of the multiple elements including the monitoring unit, analysis unit, selection 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 monitoring unit measures the heart rate and stress level using a sensor in the smart device 14, and the measured values ​​are analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes data using AI to determine the user's psychological state. The selection unit is realized by the specific processing unit 290 in the data processing device 12 and selects an aroma optimal for the user using AI. The emission unit automatically emits the selected aroma using a diffuser in the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the monitoring unit, analysis unit, selection 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 monitoring unit measures a heart rate and a stress level using a sensor in the smart glasses 214, and the measured values ​​are analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes data using AI to determine the user's psychological state. The selection unit is realized by the specific processing unit 290 in the data processing device 12 and selects an aroma optimal for the user using AI. The emission unit automatically emits the selected aroma using a diffuser in the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, analysis unit, selection 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 monitoring unit measures the heart rate and stress level using sensors in the headset type terminal 314, and the results are analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes data using AI to determine the user's psychological state. The selection unit is realized by the specific processing unit 290 in the data processing device 12 and selects an aroma optimal for the user using AI. The emission unit automatically emits the selected aroma using a diffuser in the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, analysis unit, selection 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 monitoring unit measures the heart rate and stress level using sensors in the robot 414, and the results are analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes data using AI to determine the user's psychological state. The selection unit is realized by the specific processing unit 290 in the data processing device 12 and selects an aroma optimal for the user using AI. The emission unit automatically emits the selected aroma using a diffuser in the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, when the user is feeling stressed, the monitoring unit can increase the monitoring frequency and collect data in real time. When the user is relaxed, the monitoring unit can also reduce the monitoring frequency to reduce the burden of data collection. Furthermore, when the user is concentrating, the monitoring unit can set the monitoring frequency to a medium level and collect appropriate data. This allows for more appropriate data collection by adjusting the monitoring frequency according to the user's emotions. The 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 monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring frequency.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can apply an algorithm specialized for stress reduction. Furthermore, if the user is relaxed, the analysis unit can apply an algorithm that enhances relaxation. Furthermore, if the user is concentrating, the analysis unit can apply an algorithm for maintaining concentration. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. The 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the algorithm.

[0093] The selection unit can estimate the user's emotions and adjust aroma selection criteria based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can select an aroma with a stress-reducing effect. Furthermore, if the user is relaxed, the selection unit can select an aroma that enhances concentration. By adjusting the aroma selection criteria according to the user's emotions, more appropriate aromas can be selected. The emotion estimation is realized 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 selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input emotion data to the generation AI and cause the generation AI to adjust the aroma selection criteria.

[0094] The emission unit can estimate the user's emotions and adjust the amount of aroma emitted based on the estimated user's emotions. For example, if the user is feeling stressed, the emission unit can increase the amount of aroma with a stress-reducing effect. Furthermore, if the user is relaxed, the emission unit can also appropriately adjust the amount of aroma with a relaxation effect. Furthermore, if the user is concentrating, the emission unit can also appropriately adjust the amount of aroma that enhances concentration. This allows for adjusting the amount of aroma emitted according to the user's emotions, thereby providing a more effective aroma. The emotion estimation is realized using an emotion estimation function, for example, 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 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 emotion data into the generation AI and cause the generation AI to adjust the amount of aroma emitted.

[0095] The emission unit can estimate the user's emotions and adjust the aroma emission order based on the estimated user emotions. For example, if the user is feeling stressed, the emission unit can first emit an aroma with a stress-reducing effect. Furthermore, if the user is relaxed, the emission unit can first emit an aroma with a relaxation effect. Furthermore, if the user is concentrating, the emission unit can first emit an aroma that enhances concentration. This allows for adjusting the aroma emission order according to the user's emotions, thereby providing more appropriate aromas. The emotion estimation is realized 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 emission unit can be performed using, for example, an AI, or without an AI. For example, the emission unit can input emotion data into the generation AI and cause the generation AI to adjust the aroma emission order.

[0096] The monitoring unit can analyze the user's past psychological state data and select an appropriate monitoring method. For example, if the monitoring unit determines from the user's past data that stress tends to increase during certain time periods, it can intensify monitoring during those time periods. The monitoring unit can also identify times when the user is relaxed from the user's past data and reduce monitoring during those time periods. Furthermore, the monitoring unit can intensify monitoring during specific activities based on the user's past data. This enables more effective data collection by selecting the optimal monitoring method based on past data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past psychological state data into a generation AI and have the generation AI select the optimal monitoring method.

[0097] The monitoring unit can use different sensors during monitoring depending on the user's activity. For example, when the user is exercising, the monitoring unit can collect data using a heart rate sensor and an acceleration sensor. When the user is doing desk work, the monitoring unit can also collect data using a stress level sensor and an electroencephalogram sensor. When the user is relaxing, the monitoring unit can also collect data using a respiration sensor and an electrodermal activity sensor. This enables more accurate data collection by using appropriate sensors depending on the user's activity. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can cause a generation AI to select sensors depending on the activity.

[0098] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data of high importance (heart rate, stress level, etc.) in detail. The analysis unit can also analyze data of medium importance (breathing pattern, brain waves, etc.) moderately. Furthermore, the analysis unit can also analyze data of low importance (electrodermal activity, etc.) simply. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0099] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, the analysis unit can apply time domain analysis to heart rate data. The analysis unit can also apply frequency domain analysis to electroencephalogram data. Furthermore, the analysis unit can apply statistical analysis to stress level data. This allows for more accurate analysis by applying an appropriate analysis method depending on the data category. Some or all of the above-mentioned 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 the data category to the generation AI and have the generation AI apply the analysis method.

[0100] When making a selection, the selection unit can analyze the user's past aroma use history to select the optimal aroma. For example, the selection unit can prioritize the selection of aromas that the user has used favorably in the past. The selection unit can also select aromas that the user has previously felt effective. Furthermore, the selection unit can analyze the effects of aromas the user has used in the past and select the optimal aroma. In this way, the optimal aroma for the user can be selected by analyzing the past aroma use history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the past aroma use history into the generation AI and cause the generation AI to select the optimal aroma.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The monitoring unit monitors the user's psychological state and activity. For example, it can measure the user's heart rate in real time using a heart rate sensor, measure the user's stress level using an electrodermal activity sensor, and measure the user's brain waves using an electroencephalogram (EEG) sensor. Step 2: The analysis unit analyzes the data collected by the monitoring unit. For example, it can use AI to analyze heart rate data to determine the user's stress level. It can also analyze brain wave data to determine the user's level of concentration, and electrodermal activity data to determine the user's level of relaxation. Step 3: The selection unit selects the optimal aroma based on the results obtained by the analysis unit. For example, it can use AI to select an aroma with a relaxing effect based on the user's psychological state, or an aroma that improves concentration based on the user's activity. It can also select an aroma with a stress-reducing effect. Step 4: The emitting unit emits the aroma based on the instruction from the selecting unit. For example, a diffuser is used to automatically emit an aroma that has a relaxing effect, an aroma that improves concentration, or an aroma that has a stress-reducing effect.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 monitoring unit that monitors the user's psychological state and activity; an analysis unit that analyzes the data collected by the monitoring unit; a selection unit that selects an appropriate aroma based on the results obtained by the analysis unit; and a release unit that releases the aroma selected by the selection unit. A system characterized by:

2. The monitoring unit Use sensors to measure heart rate or stress levels 2. The system of claim 1.

3. The analysis unit The data collected by the monitoring unit is analyzed to determine the psychological state of the user.

2. The system of claim 1.

4. The selection unit Based on the results obtained by the analysis unit, an aroma appropriate for the user is selected.

2. The system of claim 1.

5. The release section is The aroma is released based on the instruction from the selection unit.

2. The system of claim 1.

6. The selection unit Adopting a subscription model to always provide the right aroma 2. The system of claim 1.

7. The monitoring unit Estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions.

2. The system of claim 1.

8. The monitoring unit Analyze the user's past psychological state data and select the appropriate monitoring method 2. The system of claim 1.

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