System

Abstract

An object of the system according to the embodiment is to analyze the potential originality of an individual and provide an original idea.SOLUTION: A system includes a monitoring unit, an analysis unit, and a provision unit. The monitoring unit collects biological data of a user. The analysis unit analyzes the potential originality of the user based on the data collected by the monitoring unit. The providing unit provides the idea generated by the analyzing unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 techniques have had the problem of making it difficult to effectively draw out the latent creativity of individuals and generate original ideas.

[0005] The system according to the embodiment aims to analyze an individual's latent creativity and provide them with original ideas. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit collects biometric data of a user. The analysis unit analyzes the user's potential creativity based on the data collected by the monitoring unit. The providing unit provides the user with ideas generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze an individual's potential creativity and provide original ideas. [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) An idea creation support system according to an embodiment of the present invention generates and provides original ideas based on a user's biometric data. When a user enters hypnagogia in a relaxed state, a generation AI monitors the user's biometric data, such as brain waves and heart rate, in real time to detect that the user has entered hypnagogia. The generation AI collects and analyzes data, including past creative activities and interests, to analyze the user's latent creativity. The generation AI analyzes the user's latent creativity based on the collected data and generates original ideas. The generated ideas are provided after the user awakens from hypnagogia. For example, when a user enters hypnagogia in a relaxed state, the generation AI monitors the user's biometric data, such as brain waves and heart rate, in real time to detect that the user has entered hypnagogia. Next, the generation AI collects and analyzes data, including past creative activities and interests, to analyze the user's latent creativity. The generation AI analyzes the user's latent creativity based on the collected data and generates original ideas. The generated ideas are provided to the user after they awaken from hypnagogia. This allows the idea creation support system to maximize the user's latent creativity and generate disruptive ideas. This allows the idea creation support system to efficiently generate original ideas that could not be achieved with conventional generative AI. For example, this system is expected to be applied in a variety of fields, such as new product development for companies and creative activities for artists.

[0029] An idea creation support system according to an embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit collects biometric data of a user. The biometric data includes, but is not limited to, brain waves, heart rate, and electrodermal activity. For example, the monitoring unit monitors the user's brain waves in real time using an EEG sensor. The monitoring unit can also monitor the user's heart rate in real time using a heart rate sensor. The monitoring unit can also monitor the user's electrodermal activity in real time using an electrodermal activity sensor. For example, the monitoring unit monitors the user's brain waves in real time using an EEG sensor to detect a state of hypnagogia. The heart rate sensor monitors the user's heart rate in real time to detect a state of relaxation. The electrodermal activity sensor monitors the user's electrodermal activity in real time to detect a state of stress. The analysis unit analyzes the user's potential creativity based on the data collected by the monitoring unit. Potential creativity includes, but is not limited to, creativity test scores and past creative achievements. For example, the analysis unit may analyze a user's potential creativity by taking into account their past creative activities and interests. The analysis unit may also collect and analyze data related to the user's past creative activities and interests. For example, the analysis unit may analyze a user's potential creativity by taking into account their past creative activities and interests, and generate original ideas. The providing unit may provide the user with the ideas generated by the analysis unit. Examples of ideas include, but are not limited to, technical ideas, business ideas, and artistic ideas. For example, the providing unit may provide the user with ideas generated after awakening from hypnagogia. The providing unit may also provide the generated ideas to the user, allowing the user to confirm, modify, or think of additional ideas. The providing unit may also provide the generated ideas to the user, allowing the user to confirm, modify, or think of additional ideas.For example, the providing unit provides the generated idea to the user, allowing the user to confirm, correct, or think up additional ideas. This enables the idea creation support system according to the embodiment to generate and provide original ideas based on the biometric data of the user.

[0030] The analysis unit can analyze a user's potential creativity based on their past creative activities and interests. The analysis unit, for example, collects and analyzes data related to the user's past creative activities and interests. For example, the analysis unit analyzes potential creativity by taking into account the user's past creative activities, such as inventions, designs, and artwork creation. The analysis unit can also collect and analyze data related to interests, such as the user's past survey results and behavioral history. For example, the analysis unit can identify the user's interests based on their past survey results and analyze their potential creativity. The analysis unit can also identify the user's interests based on their past behavioral history and analyze their potential creativity. This allows for the generation of more appropriate ideas by taking into account the user's past creative activities and interests.

[0031] The providing unit can provide the generated idea after the user awakens from the hypnagogia. For example, the providing unit provides the generated idea after the user awakens from the hypnagogia. For example, the providing unit provides the idea after the user awakens from the hypnagogia so that the user can review the idea in a relaxed state. The providing unit can also provide the generated idea to the user, allowing the user to confirm, correct, or think of additional ideas. For example, the providing unit provides the generated idea to the user, allowing the user to confirm, correct, or think of additional ideas. In this way, by providing the idea after the user awakens from the hypnagogia, the user can review the idea in a relaxed state.

[0032] The monitoring unit can monitor biometric data such as the user's brain waves or heart rate in real time. The monitoring unit, for example, monitors the user's brain waves in real time using an EEG sensor. For example, the monitoring unit uses the EEG sensor to monitor the user's brain waves in real time and detect the state of hypnagogia. The monitoring unit can also monitor the user's heart rate in real time using a heart rate sensor. For example, the monitoring unit uses the heart rate sensor to monitor the user's heart rate in real time and detect the state of relaxation. The monitoring unit can also monitor the user's electrodermal activity in real time using an electrodermal activity sensor. For example, the monitoring unit uses the electrodermal activity sensor to monitor the user's electrodermal activity in real time and detect the state of stress. In this way, the state of hypnagogia can be accurately detected by monitoring the user's biometric data in real time.

[0033] The analysis unit can collect data related to the user's past creative activities and interests. The analysis unit, for example, collects data related to the user's past creative activities and interests. For example, the analysis unit collects data related to the user's past creative activities, such as inventions, designs, and art production. The analysis unit can also collect data related to the user's interests, such as past survey results and behavioral history. For example, the analysis unit can identify and collect data related to the user's interests based on the user's past survey results. The analysis unit can also identify and collect data related to the user's interests based on the user's past behavioral history. This enables more accurate analysis by collecting data including the user's past creative activities and interests.

[0034] The providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. For example, the providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. For example, the providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. The providing unit can also provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. For example, the providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. This allows the user to confirm the generated idea and think of an idea for correction or addition.

[0035] The monitoring unit can analyze the user's past biometric data and select the optimal monitoring method. For example, the monitoring unit analyzes the user's past biometric data and selects the optimal monitoring method. For example, the monitoring unit analyzes the user's past electroencephalogram data and selects the most effective monitoring method. The monitoring unit can also analyze the user's past heart rate data and determine the optimal monitoring timing. The monitoring unit can also analyze the user's past sleep patterns and predict the timing to enter hypnagogia. For example, the monitoring unit analyzes the user's past electroencephalogram data and selects the most effective monitoring method. The monitoring unit analyzes the user's past heart rate data and determines the optimal monitoring timing. The monitoring unit analyzes the user's past sleep patterns and predicts the timing to enter hypnagogia. In this way, the optimal monitoring method can be selected by analyzing the user's past biometric 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 the user's past biometric data into a generation AI and cause the generation AI to select the optimal monitoring method.

[0036] The monitoring unit can perform filtering based on the user's current activity status and environment during monitoring. The monitoring unit performs filtering based on the user's current activity status and environment during monitoring, for example. For example, when the user is in a quiet environment, the monitoring unit filters external noise and collects biometric data. Furthermore, when the user is exercising, the monitoring unit can filter data taking into account the effects of the exercise. Furthermore, when the user is working, the monitoring unit can adjust the frequency of monitoring so as not to interfere with the user's concentration. For example, when the user is in a quiet environment, the monitoring unit filters external noise and collects biometric data. When the user is exercising, the monitoring unit filters data taking into account the effects of the exercise. When the user is working, the monitoring unit adjusts the frequency of monitoring so as not to interfere with the user's concentration. In this way, more accurate data can be collected by filtering data based on the user's current activity status and environment. 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 the user's current activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0037] The monitoring unit can select an appropriate monitoring means according to the user's input method during monitoring. For example, the monitoring unit selects an appropriate monitoring means according to the user's input method (voice, text, image, etc.) during monitoring. For example, if the user is using voice input, the monitoring unit analyzes voice data in real time. Also, if the user is using text input, the monitoring unit can analyze the input content to infer emotions. Also, if the user is using image input, the monitoring unit can analyze image data to grasp the user's state. For example, if the user is using voice input, the monitoring unit analyzes voice data in real time. If the user is using text input, the monitoring unit analyzes the input content to infer emotions. If the user is using image input, the monitoring unit analyzes image data to grasp the user's state. This allows more appropriate data to be collected by selecting the optimal monitoring means according to the user's input method. 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 the user's input data to a generation AI and cause the generation AI to select the optimal monitoring means.

[0038] During monitoring, the monitoring unit can prioritize monitoring highly relevant data based on the user's geographical location information. During monitoring, the monitoring unit, for example, prioritizes monitoring highly relevant data based on the user's geographical location information. For example, when the user is at home, the monitoring unit may monitor with an emphasis on a relaxed state. Furthermore, when the user is at work, the monitoring unit may monitor with an emphasis on a stressed state. Furthermore, when the user is traveling, the monitoring unit may adjust the monitoring content in accordance with changes in the environment. For example, when the user is at home, the monitoring unit may monitor with an emphasis on a relaxed state. When the user is at work, the monitoring unit may monitor with an emphasis on a stressed state. When the user is traveling, the monitoring unit may adjust the monitoring content in accordance with changes in the environment. In this way, highly relevant data can be prioritized 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, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's geographical location information to the generation AI and cause the generation AI to monitor highly relevant data.

[0039] The monitoring unit can analyze the user's social media activity and monitor related data during monitoring. The monitoring unit, for example, analyzes the user's social media activity and monitors related data during monitoring. For example, if the user is feeling stressed because of social media, the monitoring unit can prioritize monitoring heart rate data. Also, if the user is feeling relaxed because of social media, the monitoring unit can prioritize monitoring brain wave data. Also, if the user is feeling excited because of social media, the monitoring unit can prioritize monitoring respiratory data. For example, if the user is feeling stressed because of social media, the monitoring unit can prioritize monitoring heart rate data. If the user is feeling relaxed because of social media, the monitoring unit can prioritize monitoring brain wave data. If the user is feeling excited because of social media, the monitoring unit can prioritize monitoring respiratory data. In this way, by analyzing the user's social media activity, related data can be prioritized for monitoring. 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 the user's social media activity data to the generation AI and cause the generation AI to monitor related data.

[0040] The monitoring unit can customize the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit customizes the monitoring method by reflecting the user's past feedback during monitoring, for example. For example, if the user previously came up with a good idea while in a relaxed state, the monitoring unit selects a monitoring method that recreates that state. Furthermore, if the user previously came up with a good idea while in a stressful state, the monitoring unit can select a monitoring method that recreates that state. Furthermore, if the user previously came up with a good idea while in an excited state, the monitoring unit can select a monitoring method that recreates that state. For example, if the user previously came up with a good idea while in a relaxed state, the monitoring unit selects a monitoring method that recreates that state. If the user previously came up with a good idea while in a stressful state, the monitoring unit selects a monitoring method that recreates that state. If the user previously came up with a good idea while in an excited state, the monitoring unit selects a monitoring method that recreates that state. In this way, the monitoring method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without using AI. For example, the monitoring unit can input the user's past feedback data into the generation AI and have the generation AI customize the monitoring method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's creative activities during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's creative activities during analysis. For example, the analysis unit can perform a detailed analysis when the user is working on an important project. The analysis unit can also perform a concise analysis when the user is engaged in everyday creative activities. The analysis unit can also perform a flexible analysis when the user is trying out a new idea. For example, the analysis unit can perform a detailed analysis when the user is working on an important project. The analysis unit can perform a concise analysis when the user is engaged in everyday creative activities. The analysis unit can also perform a flexible analysis when the user is trying out a new idea. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the user's creative activities. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's creative activity data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the user's interest category during analysis. For example, if the user is interested in art, the analysis unit can apply an art-related analysis algorithm. If the user is interested in science, the analysis unit can apply a science-related analysis algorithm. If the user is interested in business, the analysis unit can apply a business-related analysis algorithm. For example, if the user is interested in art, the analysis unit can apply an art-related analysis algorithm. If the user is interested in science, the analysis unit can apply a science-related analysis algorithm. If the user is interested in business, the analysis unit can apply a business-related analysis algorithm. By applying different analysis algorithms depending on the user's interest category, more appropriate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's interest data into a generation AI and cause the generation AI to apply different analysis algorithms.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the success rate of ideas submitted by the user in the past. The analysis unit can also improve the accuracy of the analysis based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis by analyzing the user's past creative activity patterns. For example, the analysis unit can adjust the analysis algorithm based on the success rate of ideas submitted by the user in the past. The analysis unit can improve the accuracy of the analysis based on the user's past feedback. The analysis unit can improve the accuracy of the analysis by analyzing the user's past creative activity patterns. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. 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 user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the submission time of the user's creative activities during analysis. For example, the analysis unit determines the analysis priority based on the submission time of the user's creative activities during analysis. For example, if the user is working on a project with a deadline approaching, the analysis unit prioritizes that analysis. Furthermore, if the user is working on a long-term project, the analysis unit can prioritize other analyses. Furthermore, if the user is working on multiple projects, the analysis unit can determine the analysis priority based on the submission time. For example, if the user is working on a project with a deadline approaching, the analysis unit prioritizes that analysis. If the user is working on a long-term project, the analysis unit prioritizes other analyses. If the user is working on multiple projects, the analysis priority is determined based on the submission time. Thus, by determining the analysis priority based on the submission time of the user's creative activities, more important analyses can be prioritized. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's submission time data into a generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the user's creative activities during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's creative activities during analysis. For example, if the user is working on a highly relevant project, the analysis unit prioritizes that analysis. Furthermore, if the user is working on a less relevant project, the analysis unit can prioritize other analyses. Furthermore, if the user is working on multiple projects, the analysis unit can adjust the order of analysis based on the relevance. For example, if the user is working on a highly relevant project, the analysis unit prioritizes that analysis. If the user is working on a less relevant project, the analysis unit prioritizes other analyses. If the user is working on multiple projects, the analysis unit adjusts the order of analysis based on the relevance. Thus, by adjusting the order of analysis based on the relevance of the user's creative activities, more relevant analyses can be prioritized. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input relevance data of the user's creative activities into a generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit performs an analysis that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can perform a concise and easy-to-understand analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit performs an analysis that uses a lot of technical terms. If the user does not have technical expertise, the analysis unit performs a concise and easy-to-understand analysis. The level of detail of the analysis can be adjusted according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0047] The providing unit can adjust the level of detail of the provided idea based on the importance of the generated idea when providing the idea. For example, the providing unit adjusts the level of detail of the provided idea based on the importance of the generated idea when providing the idea. For example, the providing unit provides an important idea with a detailed description. Furthermore, the providing unit can provide a general idea with a concise description. Furthermore, the providing unit can provide a visually appealing idea for an idea that is likely to interest the user. For example, the providing unit provides an important idea with a detailed description. For a general idea, the providing unit provides a concise description. For an idea that is likely to interest the user, the providing unit provides a visually appealing idea. In this way, by adjusting the level of detail of the provided idea based on the importance of the generated idea, more important ideas can be provided in detail. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input importance data of the generated idea to the generation AI and cause the generation AI to adjust the level of detail of the provided idea.

[0048] The providing unit can apply different providing algorithms depending on the category of the generated idea when providing the idea. For example, the providing unit can apply different providing algorithms depending on the category of the generated idea when providing the idea. For example, the providing unit can provide visually appealing ideas for art-related ideas. Furthermore, the providing unit can provide ideas including logical explanations for science-related ideas. Furthermore, the providing unit can provide ideas including practical explanations for business-related ideas. For example, the providing unit can provide visually appealing ideas for art-related ideas. For science-related ideas, the providing unit can provide ideas including logical explanations. For business-related ideas, the providing unit can provide ideas including practical explanations. In this way, by applying different providing algorithms depending on the category of the generated idea, more appropriate ideas can be provided. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input category data of the generated idea into the generation AI and cause the generation AI to apply different providing algorithms.

[0049] The providing unit can improve the accuracy of the ideas provided by referring to the user's past submission results when providing the ideas. For example, the providing unit can improve the accuracy of the ideas provided by referring to the user's past submission results when providing the ideas. For example, the providing unit can analyze the characteristics of ideas that the user has previously given a high rating and provide similar ideas. The providing unit can also analyze the characteristics of ideas that the user has previously given a low rating and provide ideas that reflect improvements. The providing unit can also improve the accuracy of the ideas provided by referring to the user's past feedback. For example, the providing unit can analyze the characteristics of ideas that the user has previously given a high rating and provide similar ideas. The providing unit can analyze the characteristics of ideas that the user has previously given a low rating and provide ideas that reflect improvements. The accuracy of the ideas provided is improved based on the user's past feedback. In this way, the accuracy of the ideas provided can be improved by referring to the user's past submission results. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past submission result data into the generation AI and cause the generation AI to improve the accuracy of the ideas provided.

[0050] The providing unit can determine the priority of the provided ideas based on the submission time of the generated ideas at the time of providing. For example, the providing unit determines the priority of the provided ideas based on the submission time of the generated ideas at the time of providing. For example, the providing unit prioritizes the provision of ideas with a deadline approaching. The providing unit can also postpone ideas for long-term projects. The providing unit can also adjust the priority of the provided ideas based on the submission time. For example, the providing unit prioritizes the provision of ideas with a deadline approaching. Ideas for long-term projects are postponed. The providing unit adjusts the priority of the provided ideas based on the submission time. In this way, by determining the priority of the provided ideas based on the submission time of the generated ideas, more important ideas can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input submission time data of the generated ideas to the generation AI and cause the generation AI to determine the priority of the provided ideas.

[0051] The providing unit can adjust the order of providing the generated ideas based on the relevance of the ideas at the time of providing. The providing unit, for example, adjusts the order of providing the generated ideas based on the relevance of the ideas at the time of providing. For example, the providing unit provides highly relevant ideas preferentially. The providing unit can also postpone less relevant ideas. The providing unit can also adjust the order of providing the ideas based on the relevance of multiple ideas. For example, the providing unit provides highly relevant ideas preferentially. The providing unit postpones less relevant ideas. The providing unit adjusts the order of providing the ideas based on the relevance of multiple ideas. In this way, by adjusting the order of providing the generated ideas based on the relevance of the generated ideas, more relevant ideas can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the generated ideas to the generation AI and cause the generation AI to adjust the order of providing the ideas.

[0052] The providing unit can adjust the use of technical terminology provided in accordance with the user's level of expertise when providing the ideas. For example, the providing unit adjusts the use of technical terminology provided in accordance with the user's level of expertise when providing the ideas. For example, if the user has technical expertise, the providing unit provides ideas that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide concise and easy-to-understand ideas. Furthermore, the providing unit can adjust the level of detail provided in accordance with the user's level of expertise. For example, if the user has technical expertise, the providing unit provides ideas that use a lot of technical terminology. If the user does not have technical expertise, the providing unit provides concise and easy-to-understand ideas. The level of detail provided is adjusted in accordance with the user's level of expertise. In this way, by adjusting the use of technical terminology provided in accordance with the user's level of expertise, ideas that are easier to understand can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology provided.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The monitoring unit can also predict the optimal monitoring timing based on the user's past biological data. For example, it can analyze the user's past brain wave data to identify the time periods when the user is most relaxed. It can also predict the time periods when the user is least stressed based on the user's past heart rate data. This makes it possible to select the optimal monitoring timing by utilizing the user's past data.

[0055] The analysis unit can also improve the accuracy of the analysis based on the user's past creative activity data. For example, it can analyze the success rate of the user's past inventions and designs and adjust the analysis algorithm. It can also improve the accuracy of the analysis based on the user's past feedback. This makes it possible to utilize the user's past data to perform more accurate analysis.

[0056] The providing unit can also select different providing methods depending on the category of the generated idea. For example, for art-related ideas, a visually appealing presentation can be performed, and for science-related ideas, a presentation including a logical explanation can be performed. Also, for business-related ideas, a presentation including a practical explanation can be performed. This makes it possible to select the optimal providing method depending on the category of the generated idea.

[0057] The monitoring unit can also adjust the monitoring frequency based on the user's current activity status. For example, if the user is concentrating, the monitoring frequency can be reduced, and if the user is relaxing, the monitoring frequency can be increased. Also, if the user is exercising, the monitoring frequency can be adjusted. This makes it possible to adjust the monitoring frequency according to the user's current activity status.

[0058] The analysis unit can also adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has expertise, a detailed analysis is performed, and if the user does not have expertise, a concise analysis is performed. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide the most appropriate analysis according to the user's level of expertise.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The monitoring unit collects the user's biometric data. The biometric data includes brain waves, heart rate, and electrodermal activity. For example, the monitoring unit uses an brain wave sensor to monitor the user's brain waves in real time and detect the state of hypnagogia. The heart rate sensor monitors the user's heart rate in real time and detects the state of relaxation. The electrodermal activity sensor monitors the user's electrodermal activity in real time and detects the state of stress. Step 2: The analysis unit analyzes the user's potential creativity based on the data collected by the monitoring unit. Potential creativity includes creativity test scores and past creative achievements. For example, the analysis unit analyzes the user's potential creativity by taking into account their past creative activities and interests, and generates original ideas. Step 3: The providing unit provides the user with the ideas generated by the analysis unit. Ideas can be technical, business, or artistic. For example, the providing unit provides the user with the ideas generated after waking up from hypnagogia, allowing the user to confirm, modify, or think of additional ideas.

[0061] (Example 2) An idea creation support system according to an embodiment of the present invention generates and provides original ideas based on a user's biometric data. When a user enters hypnagogia in a relaxed state, a generation AI monitors the user's biometric data, such as brain waves and heart rate, in real time to detect that the user has entered hypnagogia. The generation AI collects and analyzes data, including past creative activities and interests, to analyze the user's latent creativity. The generation AI analyzes the user's latent creativity based on the collected data and generates original ideas. The generated ideas are provided after the user awakens from hypnagogia. For example, when a user enters hypnagogia in a relaxed state, the generation AI monitors the user's biometric data, such as brain waves and heart rate, in real time to detect that the user has entered hypnagogia. Next, the generation AI collects and analyzes data, including past creative activities and interests, to analyze the user's latent creativity. The generation AI analyzes the user's latent creativity based on the collected data and generates original ideas. The generated ideas are provided to the user after they awaken from hypnagogia. This allows the idea creation support system to maximize the user's latent creativity and generate disruptive ideas. This allows the idea creation support system to efficiently generate original ideas that could not be achieved with conventional generative AI. For example, this system is expected to be applied in a variety of fields, such as new product development for companies and creative activities for artists.

[0062] An idea creation support system according to an embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit collects biometric data of a user. The biometric data includes, but is not limited to, brain waves, heart rate, and electrodermal activity. For example, the monitoring unit monitors the user's brain waves in real time using an EEG sensor. The monitoring unit can also monitor the user's heart rate in real time using a heart rate sensor. The monitoring unit can also monitor the user's electrodermal activity in real time using an electrodermal activity sensor. For example, the monitoring unit monitors the user's brain waves in real time using an EEG sensor to detect a state of hypnagogia. The heart rate sensor monitors the user's heart rate in real time to detect a state of relaxation. The electrodermal activity sensor monitors the user's electrodermal activity in real time to detect a state of stress. The analysis unit analyzes the user's potential creativity based on the data collected by the monitoring unit. Potential creativity includes, but is not limited to, creativity test scores and past creative achievements. For example, the analysis unit may analyze a user's potential creativity by taking into account their past creative activities and interests. The analysis unit may also collect and analyze data related to the user's past creative activities and interests. For example, the analysis unit may analyze a user's potential creativity by taking into account their past creative activities and interests, and generate original ideas. The providing unit may provide the user with the ideas generated by the analysis unit. Examples of ideas include, but are not limited to, technical ideas, business ideas, and artistic ideas. For example, the providing unit may provide the user with ideas generated after awakening from hypnagogia. The providing unit may also provide the generated ideas to the user, allowing the user to confirm, modify, or think of additional ideas. The providing unit may also provide the generated ideas to the user, allowing the user to confirm, modify, or think of additional ideas.For example, the providing unit provides the generated idea to the user, allowing the user to confirm, correct, or think up additional ideas. This enables the idea creation support system according to the embodiment to generate and provide original ideas based on the biometric data of the user.

[0063] The analysis unit can analyze a user's potential creativity based on their past creative activities and interests. The analysis unit, for example, collects and analyzes data related to the user's past creative activities and interests. For example, the analysis unit analyzes potential creativity by taking into account the user's past creative activities, such as inventions, designs, and artwork creation. The analysis unit can also collect and analyze data related to interests, such as the user's past survey results and behavioral history. For example, the analysis unit can identify the user's interests based on their past survey results and analyze their potential creativity. The analysis unit can also identify the user's interests based on their past behavioral history and analyze their potential creativity. This allows for the generation of more appropriate ideas by taking into account the user's past creative activities and interests.

[0064] The providing unit can provide the generated idea after the user awakens from the hypnagogia. For example, the providing unit provides the generated idea after the user awakens from the hypnagogia. For example, the providing unit provides the idea after the user awakens from the hypnagogia so that the user can review the idea in a relaxed state. The providing unit can also provide the generated idea to the user, allowing the user to confirm, correct, or think of additional ideas. For example, the providing unit provides the generated idea to the user, allowing the user to confirm, correct, or think of additional ideas. In this way, by providing the idea after the user awakens from the hypnagogia, the user can review the idea in a relaxed state.

[0065] The monitoring unit can monitor biometric data such as the user's brain waves or heart rate in real time. The monitoring unit, for example, monitors the user's brain waves in real time using an EEG sensor. For example, the monitoring unit uses the EEG sensor to monitor the user's brain waves in real time and detect the state of hypnagogia. The monitoring unit can also monitor the user's heart rate in real time using a heart rate sensor. For example, the monitoring unit uses the heart rate sensor to monitor the user's heart rate in real time and detect the state of relaxation. The monitoring unit can also monitor the user's electrodermal activity in real time using an electrodermal activity sensor. For example, the monitoring unit uses the electrodermal activity sensor to monitor the user's electrodermal activity in real time and detect the state of stress. In this way, the state of hypnagogia can be accurately detected by monitoring the user's biometric data in real time.

[0066] The analysis unit can collect data related to the user's past creative activities and interests. The analysis unit, for example, collects data related to the user's past creative activities and interests. For example, the analysis unit collects data related to the user's past creative activities, such as inventions, designs, and art production. The analysis unit can also collect data related to the user's interests, such as past survey results and behavioral history. For example, the analysis unit can identify and collect data related to the user's interests based on the user's past survey results. The analysis unit can also identify and collect data related to the user's interests based on the user's past behavioral history. This enables more accurate analysis by collecting data including the user's past creative activities and interests.

[0067] The providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. For example, the providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. For example, the providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. The providing unit can also provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. For example, the providing unit can provide the generated idea to the user and enable the user to think of an idea for confirmation, correction, or addition. This allows the user to confirm the generated idea and think of an idea for correction or addition.

[0068] The monitoring unit can estimate the user's emotions and adjust the timing of monitoring based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the timing of monitoring based on the estimated user emotions. For example, when the user is relaxed, the monitoring unit reduces the monitoring frequency so as not to disrupt the user's concentration. Furthermore, when the user is stressed, the monitoring unit can increase the monitoring frequency and collect detailed data. Furthermore, when the user is excited, the monitoring unit can adjust the timing of monitoring and collect appropriate data. For example, when the user is relaxed, the monitoring unit reduces the monitoring frequency so as not to disrupt the user's concentration. When the user is stressed, the monitoring unit increases the monitoring frequency and collects detailed data. When the user is excited, the monitoring unit adjusts the timing of monitoring and collects appropriate data. In this way, by adjusting the timing of monitoring based on the user's emotions, more appropriate data can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0069] The monitoring unit can analyze the user's past biometric data and select the optimal monitoring method. For example, the monitoring unit analyzes the user's past biometric data and selects the optimal monitoring method. For example, the monitoring unit analyzes the user's past electroencephalogram data and selects the most effective monitoring method. The monitoring unit can also analyze the user's past heart rate data and determine the optimal monitoring timing. The monitoring unit can also analyze the user's past sleep patterns and predict the timing to enter hypnagogia. For example, the monitoring unit analyzes the user's past electroencephalogram data and selects the most effective monitoring method. The monitoring unit analyzes the user's past heart rate data and determines the optimal monitoring timing. The monitoring unit analyzes the user's past sleep patterns and predicts the timing to enter hypnagogia. In this way, the optimal monitoring method can be selected by analyzing the user's past biometric 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 the user's past biometric data into a generation AI and cause the generation AI to select the optimal monitoring method.

[0070] The monitoring unit can perform filtering based on the user's current activity status and environment during monitoring. The monitoring unit performs filtering based on the user's current activity status and environment during monitoring, for example. For example, when the user is in a quiet environment, the monitoring unit filters external noise and collects biometric data. Furthermore, when the user is exercising, the monitoring unit can filter data taking into account the effects of the exercise. Furthermore, when the user is working, the monitoring unit can adjust the frequency of monitoring so as not to interfere with the user's concentration. For example, when the user is in a quiet environment, the monitoring unit filters external noise and collects biometric data. When the user is exercising, the monitoring unit filters data taking into account the effects of the exercise. When the user is working, the monitoring unit adjusts the frequency of monitoring so as not to interfere with the user's concentration. In this way, more accurate data can be collected by filtering data based on the user's current activity status and environment. 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 the user's current activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0071] The monitoring unit can select an appropriate monitoring means according to the user's input method during monitoring. For example, the monitoring unit selects an appropriate monitoring means according to the user's input method (voice, text, image, etc.) during monitoring. For example, if the user is using voice input, the monitoring unit analyzes voice data in real time. Also, if the user is using text input, the monitoring unit can analyze the input content to infer emotions. Also, if the user is using image input, the monitoring unit can analyze image data to grasp the user's state. For example, if the user is using voice input, the monitoring unit analyzes voice data in real time. If the user is using text input, the monitoring unit analyzes the input content to infer emotions. If the user is using image input, the monitoring unit analyzes image data to grasp the user's state. This allows more appropriate data to be collected by selecting the optimal monitoring means according to the user's input method. 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 the user's input data to a generation AI and cause the generation AI to select the optimal monitoring means.

[0072] The monitoring unit can estimate the user's emotions and determine the priority of the biometric data to be monitored based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and determines the priority of the biometric data to be monitored based on the estimated user emotions. For example, when the user is relaxed, the monitoring unit may prioritize monitoring electroencephalogram data. Furthermore, when the user is stressed, the monitoring unit may prioritize monitoring heart rate data. Furthermore, when the user is excited, the monitoring unit may prioritize monitoring respiratory data. For example, when the user is relaxed, the monitoring unit prioritizes monitoring electroencephalogram data. When the user is stressed, the monitoring unit prioritizes monitoring heart rate data. When the user is excited, the monitoring unit prioritizes monitoring respiratory data. In this way, by determining the priority of the biometric data to be monitored based on the user's emotions, more important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-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 the user's emotional data into the generation AI and have the generation AI determine the priority of the biometric data to be monitored.

[0073] During monitoring, the monitoring unit can prioritize monitoring highly relevant data based on the user's geographical location information. During monitoring, the monitoring unit, for example, prioritizes monitoring highly relevant data based on the user's geographical location information. For example, when the user is at home, the monitoring unit may monitor with an emphasis on a relaxed state. Furthermore, when the user is at work, the monitoring unit may monitor with an emphasis on a stressed state. Furthermore, when the user is traveling, the monitoring unit may adjust the monitoring content in accordance with changes in the environment. For example, when the user is at home, the monitoring unit may monitor with an emphasis on a relaxed state. When the user is at work, the monitoring unit may monitor with an emphasis on a stressed state. When the user is traveling, the monitoring unit may adjust the monitoring content in accordance with changes in the environment. In this way, highly relevant data can be prioritized 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, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's geographical location information to the generation AI and cause the generation AI to monitor highly relevant data.

[0074] The monitoring unit can analyze the user's social media activity and monitor related data during monitoring. The monitoring unit, for example, analyzes the user's social media activity and monitors related data during monitoring. For example, if the user is feeling stressed because of social media, the monitoring unit can prioritize monitoring heart rate data. Also, if the user is feeling relaxed because of social media, the monitoring unit can prioritize monitoring brain wave data. Also, if the user is feeling excited because of social media, the monitoring unit can prioritize monitoring respiratory data. For example, if the user is feeling stressed because of social media, the monitoring unit can prioritize monitoring heart rate data. If the user is feeling relaxed because of social media, the monitoring unit can prioritize monitoring brain wave data. If the user is feeling excited because of social media, the monitoring unit can prioritize monitoring respiratory data. In this way, by analyzing the user's social media activity, related data can be prioritized for monitoring. 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 the user's social media activity data to the generation AI and cause the generation AI to monitor related data.

[0075] The monitoring unit can customize the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit customizes the monitoring method by reflecting the user's past feedback during monitoring, for example. For example, if the user previously came up with a good idea while in a relaxed state, the monitoring unit selects a monitoring method that recreates that state. Furthermore, if the user previously came up with a good idea while in a stressful state, the monitoring unit can select a monitoring method that recreates that state. Furthermore, if the user previously came up with a good idea while in an excited state, the monitoring unit can select a monitoring method that recreates that state. For example, if the user previously came up with a good idea while in a relaxed state, the monitoring unit selects a monitoring method that recreates that state. If the user previously came up with a good idea while in a stressful state, the monitoring unit selects a monitoring method that recreates that state. If the user previously came up with a good idea while in an excited state, the monitoring unit selects a monitoring method that recreates that state. In this way, the monitoring method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without using AI. For example, the monitoring unit can input the user's past feedback data into the generation AI and have the generation AI customize the monitoring method.

[0076] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. For example, if the user is relaxed, the analysis unit presents the analysis results in a soft expression. Furthermore, if the user is stressed, the analysis unit can present the analysis results in a concise and clear expression. Furthermore, if the user is excited, the analysis unit can present the analysis results in a visually stimulating expression. For example, if the user is relaxed, the analysis unit presents the analysis results in a soft expression. If the user is stressed, the analysis results can be presented in a concise and clear expression. If the user is excited, the analysis results can be presented in a visually stimulating expression. In this way, by adjusting the presentation method of the analysis based on the user's emotion, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expression of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's creative activities during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's creative activities during analysis. For example, the analysis unit can perform a detailed analysis when the user is working on an important project. The analysis unit can also perform a concise analysis when the user is engaged in everyday creative activities. The analysis unit can also perform a flexible analysis when the user is trying out a new idea. For example, the analysis unit can perform a detailed analysis when the user is working on an important project. The analysis unit can perform a concise analysis when the user is engaged in everyday creative activities. The analysis unit can also perform a flexible analysis when the user is trying out a new idea. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the user's creative activities. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's creative activity data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the user's interest category during analysis. For example, if the user is interested in art, the analysis unit can apply an art-related analysis algorithm. If the user is interested in science, the analysis unit can apply a science-related analysis algorithm. If the user is interested in business, the analysis unit can apply a business-related analysis algorithm. For example, if the user is interested in art, the analysis unit can apply an art-related analysis algorithm. If the user is interested in science, the analysis unit can apply a science-related analysis algorithm. If the user is interested in business, the analysis unit can apply a business-related analysis algorithm. By applying different analysis algorithms depending on the user's interest category, more appropriate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's interest data into a generation AI and cause the generation AI to apply different analysis algorithms.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the success rate of ideas submitted by the user in the past. The analysis unit can also improve the accuracy of the analysis based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis by analyzing the user's past creative activity patterns. For example, the analysis unit can adjust the analysis algorithm based on the success rate of ideas submitted by the user in the past. The analysis unit can improve the accuracy of the analysis based on the user's past feedback. The analysis unit can improve the accuracy of the analysis by analyzing the user's past creative activity patterns. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. 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 user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is stressed. The analysis unit can also perform a visually stimulating analysis when the user is excited. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit performs a concise analysis when the user is stressed. The analysis unit performs a visually stimulating analysis when the user is excited. This allows for adjusting the length of the analysis based on the user's emotion, thereby providing more appropriate analysis results. 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-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0081] The analysis unit can determine the analysis priority based on the submission time of the user's creative activities during analysis. For example, the analysis unit determines the analysis priority based on the submission time of the user's creative activities during analysis. For example, if the user is working on a project with a deadline approaching, the analysis unit prioritizes that analysis. Furthermore, if the user is working on a long-term project, the analysis unit can prioritize other analyses. Furthermore, if the user is working on multiple projects, the analysis unit can determine the analysis priority based on the submission time. For example, if the user is working on a project with a deadline approaching, the analysis unit prioritizes that analysis. If the user is working on a long-term project, the analysis unit prioritizes other analyses. If the user is working on multiple projects, the analysis priority is determined based on the submission time. Thus, by determining the analysis priority based on the submission time of the user's creative activities, more important analyses can be prioritized. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's submission time data into a generation AI and have the generation AI determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the user's creative activities during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's creative activities during analysis. For example, if the user is working on a highly relevant project, the analysis unit prioritizes that analysis. Furthermore, if the user is working on a less relevant project, the analysis unit can prioritize other analyses. Furthermore, if the user is working on multiple projects, the analysis unit can adjust the order of analysis based on the relevance. For example, if the user is working on a highly relevant project, the analysis unit prioritizes that analysis. If the user is working on a less relevant project, the analysis unit prioritizes other analyses. If the user is working on multiple projects, the analysis unit adjusts the order of analysis based on the relevance. Thus, by adjusting the order of analysis based on the relevance of the user's creative activities, more relevant analyses can be prioritized. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input relevance data of the user's creative activities into a generation AI and cause the generation AI to adjust the order of analysis.

[0083] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit performs an analysis that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can perform a concise and easy-to-understand analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit performs an analysis that uses a lot of technical terms. If the user does not have technical expertise, the analysis unit performs a concise and easy-to-understand analysis. The level of detail of the analysis can be adjusted according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0084] The providing unit can estimate the user's emotions and adjust the way in which ideas are presented based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the way in which ideas are presented based on the estimated user emotions. For example, when the user is relaxed, the providing unit provides ideas using soft expressions. Furthermore, when the user is stressed, the providing unit can provide ideas using concise and clear expressions. Furthermore, when the user is excited, the providing unit can provide ideas using visually stimulating expressions. For example, when the user is relaxed, the providing unit provides ideas using soft expressions. When the user is stressed, the providing unit provides ideas using concise and clear expressions. When the user is excited, the providing unit provides ideas using visually stimulating expressions. In this way, by adjusting the way in which ideas are presented based on the user's emotions, more appropriate ideas can be presented. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the way the idea is expressed.

[0085] The providing unit can adjust the level of detail of the provided idea based on the importance of the generated idea when providing the idea. For example, the providing unit adjusts the level of detail of the provided idea based on the importance of the generated idea when providing the idea. For example, the providing unit provides an important idea with a detailed description. Furthermore, the providing unit can provide a general idea with a concise description. Furthermore, the providing unit can provide a visually appealing idea for an idea that is likely to interest the user. For example, the providing unit provides an important idea with a detailed description. For a general idea, the providing unit provides a concise description. For an idea that is likely to interest the user, the providing unit provides a visually appealing idea. In this way, by adjusting the level of detail of the provided idea based on the importance of the generated idea, more important ideas can be provided in detail. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input importance data of the generated idea to the generation AI and cause the generation AI to adjust the level of detail of the provided idea.

[0086] The providing unit can apply different providing algorithms depending on the category of the generated idea when providing the idea. For example, the providing unit can apply different providing algorithms depending on the category of the generated idea when providing the idea. For example, the providing unit can provide visually appealing ideas for art-related ideas. Furthermore, the providing unit can provide ideas including logical explanations for science-related ideas. Furthermore, the providing unit can provide ideas including practical explanations for business-related ideas. For example, the providing unit can provide visually appealing ideas for art-related ideas. For science-related ideas, the providing unit can provide ideas including logical explanations. For business-related ideas, the providing unit can provide ideas including practical explanations. In this way, by applying different providing algorithms depending on the category of the generated idea, more appropriate ideas can be provided. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input category data of the generated idea into the generation AI and cause the generation AI to apply different providing algorithms.

[0087] The providing unit can improve the accuracy of the ideas provided by referring to the user's past submission results when providing the ideas. For example, the providing unit can improve the accuracy of the ideas provided by referring to the user's past submission results when providing the ideas. For example, the providing unit can analyze the characteristics of ideas that the user has previously given a high rating and provide similar ideas. The providing unit can also analyze the characteristics of ideas that the user has previously given a low rating and provide ideas that reflect improvements. The providing unit can also improve the accuracy of the ideas provided by referring to the user's past feedback. For example, the providing unit can analyze the characteristics of ideas that the user has previously given a high rating and provide similar ideas. The providing unit can analyze the characteristics of ideas that the user has previously given a low rating and provide ideas that reflect improvements. The accuracy of the ideas provided is improved based on the user's past feedback. In this way, the accuracy of the ideas provided can be improved by referring to the user's past submission results. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past submission result data into the generation AI and cause the generation AI to improve the accuracy of the ideas provided.

[0088] The providing unit can estimate the user's emotions and adjust the length of the ideas to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the length of the ideas to be provided based on the estimated user emotions. For example, the providing unit can provide detailed ideas when the user is relaxed. The providing unit can also provide concise ideas when the user is stressed. The providing unit can also provide visually stimulating ideas when the user is excited. For example, the providing unit can provide detailed ideas when the user is relaxed. The providing unit can provide concise ideas when the user is stressed. The providing unit can provide visually stimulating ideas when the user is excited. This allows more appropriate ideas to be provided by adjusting the length of the ideas to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the length of the idea.

[0089] The providing unit can determine the priority of the provided ideas based on the submission time of the generated ideas at the time of providing. For example, the providing unit determines the priority of the provided ideas based on the submission time of the generated ideas at the time of providing. For example, the providing unit prioritizes the provision of ideas with a deadline approaching. The providing unit can also postpone ideas for long-term projects. The providing unit can also adjust the priority of the provided ideas based on the submission time. For example, the providing unit prioritizes the provision of ideas with a deadline approaching. Ideas for long-term projects are postponed. The providing unit adjusts the priority of the provided ideas based on the submission time. In this way, by determining the priority of the provided ideas based on the submission time of the generated ideas, more important ideas can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input submission time data of the generated ideas to the generation AI and cause the generation AI to determine the priority of the provided ideas.

[0090] The providing unit can adjust the order of providing the generated ideas based on the relevance of the ideas at the time of providing. The providing unit, for example, adjusts the order of providing the generated ideas based on the relevance of the ideas at the time of providing. For example, the providing unit provides highly relevant ideas preferentially. The providing unit can also postpone less relevant ideas. The providing unit can also adjust the order of providing the ideas based on the relevance of multiple ideas. For example, the providing unit provides highly relevant ideas preferentially. The providing unit postpones less relevant ideas. The providing unit adjusts the order of providing the ideas based on the relevance of multiple ideas. In this way, by adjusting the order of providing the generated ideas based on the relevance of the generated ideas, more relevant ideas can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the generated ideas to the generation AI and cause the generation AI to adjust the order of providing the ideas.

[0091] The providing unit can adjust the use of technical terminology provided in accordance with the user's level of expertise when providing the ideas. For example, the providing unit adjusts the use of technical terminology provided in accordance with the user's level of expertise when providing the ideas. For example, if the user has technical expertise, the providing unit provides ideas that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide concise and easy-to-understand ideas. Furthermore, the providing unit can adjust the level of detail provided in accordance with the user's level of expertise. For example, if the user has technical expertise, the providing unit provides ideas that use a lot of technical terminology. If the user does not have technical expertise, the providing unit provides concise and easy-to-understand ideas. The level of detail provided is adjusted in accordance with the user's level of expertise. In this way, by adjusting the use of technical terminology provided in accordance with the user's level of expertise, ideas that are easier to understand can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology provided. === Hard Collateral 1-1 === Each of the multiple elements including the monitoring unit, analysis unit, and provision 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 can collect the user's biological data in real time using an electroencephalogram sensor or a heart rate sensor of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's potential creativity based on the collected data. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated ideas to the user. === Hard Collateral 1-2 === Each of the multiple elements including the monitoring unit, analysis unit, and provision 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 can collect the user's biological data in real time using an electroencephalogram sensor or a heart rate sensor of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's potential creativity based on the collected data. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated ideas to the user. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, analysis unit, and provision 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 can collect the user's biological data in real time using an electroencephalogram sensor or a heartbeat sensor of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's latent creativity based on the collected data. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated ideas to the user. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, analysis unit, and provision 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 can collect biometric data of the user in real time using an electroencephalogram sensor or a heartbeat sensor of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's latent creativity based on the collected data. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated ideas to the user.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis of creative ideas can be prioritized, and if the user is stressed, the analysis of ideas related to stress reduction can be prioritized. Also, if the user is excited, the analysis of ideas related to new challenges can be prioritized. This makes it possible to provide more appropriate ideas by adjusting the analysis priority according to the user's emotional state.

[0094] The providing unit can also estimate the user's emotions and adjust the timing of providing ideas based on the estimated emotions. For example, if the user is relaxed, the unit can provide ideas immediately, and if the user is stressed, the unit can provide ideas after a short delay. Also, if the user is excited, the unit can provide ideas in stages. This allows for more effective idea provision by adjusting the timing of idea provision according to the user's emotional state.

[0095] The monitoring unit can also estimate the user's emotions and adjust the accuracy of monitoring based on the estimated emotions. For example, if the user is relaxed, the monitoring accuracy can be increased, and if the user is stressed, the monitoring accuracy can be decreased. Also, if the user is excited, the monitoring can focus on specific biometric data. This allows for more appropriate data collection by adjusting the monitoring accuracy according to the user's emotional state.

[0096] The analysis unit can also estimate the user's emotions and customize the analysis method based on the estimated emotions. For example, if the user is relaxed, a detailed analysis is performed, and if the user is stressed, a brief analysis is performed. Also, if the user is excited, a visually stimulating analysis can be performed. This makes it possible to provide more appropriate analysis results by customizing the analysis method according to the user's emotional state.

[0097] The providing unit can also estimate the user's emotions and adjust the format of the ideas to be provided based on the estimated emotions. For example, if the user is relaxed, the ideas are provided in a sentence format, and if the user is stressed, the ideas are provided in a bulleted format. Also, if the user is excited, the ideas can be provided in a visual format. This allows for more effective idea provision by adjusting the idea provision format according to the user's emotional state.

[0098] The monitoring unit can also predict the optimal monitoring timing based on the user's past biological data. For example, it can analyze the user's past brain wave data to identify the time periods when the user is most relaxed. It can also predict the time periods when the user is least stressed based on the user's past heart rate data. This makes it possible to select the optimal monitoring timing by utilizing the user's past data.

[0099] The analysis unit can also improve the accuracy of the analysis based on the user's past creative activity data. For example, it can analyze the success rate of the user's past inventions and designs and adjust the analysis algorithm. It can also improve the accuracy of the analysis based on the user's past feedback. This makes it possible to utilize the user's past data to perform more accurate analysis.

[0100] The providing unit can also select different providing methods depending on the category of the generated idea. For example, for art-related ideas, a visually appealing presentation can be performed, and for science-related ideas, a presentation including a logical explanation can be performed. Also, for business-related ideas, a presentation including a practical explanation can be performed. This makes it possible to select the optimal providing method depending on the category of the generated idea.

[0101] The monitoring unit can also adjust the monitoring frequency based on the user's current activity status. For example, if the user is concentrating, the monitoring frequency can be reduced, and if the user is relaxing, the monitoring frequency can be increased. Also, if the user is exercising, the monitoring frequency can be adjusted. This makes it possible to adjust the monitoring frequency according to the user's current activity status.

[0102] The analysis unit can also adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has expertise, a detailed analysis is performed, and if the user does not have expertise, a concise analysis is performed. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide the most appropriate analysis according to the user's level of expertise.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The monitoring unit collects the user's biometric data. The biometric data includes brain waves, heart rate, and electrodermal activity. For example, the monitoring unit uses an brain wave sensor to monitor the user's brain waves in real time and detect the state of hypnagogia. The heart rate sensor monitors the user's heart rate in real time and detects the state of relaxation. The electrodermal activity sensor monitors the user's electrodermal activity in real time and detects the state of stress. Step 2: The analysis unit analyzes the user's potential creativity based on the data collected by the monitoring unit. Potential creativity includes creativity test scores and past creative achievements. For example, the analysis unit analyzes the user's potential creativity by taking into account their past creative activities and interests, and generates original ideas. Step 3: The providing unit provides the user with the ideas generated by the analysis unit. Ideas can be technical, business, or artistic. For example, the providing unit provides the user with the ideas generated after waking up from hypnagogia, allowing the user to confirm, modify, or think of additional ideas.

[0105] 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.

[0106] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 collects biometric data of a user; an analysis unit that analyzes the user's potential creativity based on the data collected by the monitoring unit; a providing unit that provides the idea generated by the analysis unit to a user. A system characterized by:

2. The analysis unit Analyzing users' potential creativity based on their past creative activities and interests 2. The system of claim 1.

3. The providing unit Provide ideas generated after the user awakens from hypnagogia 2. The system of claim 1.

4. The monitoring unit Real-time monitoring of the user's biometric data, such as brain waves or heart rate 2. The system of claim 1.

5. The analysis unit Collect data about your past creative activities and interests 2. The system of claim 1.

6. The providing unit Provide generated ideas to users, allowing them to confirm, modify, and come up with additional ideas 2. The system of claim 1.

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

2. The system of claim 1.

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

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