system

Abstract

The system according to the embodiment aims to support mental health care by making personalized suggestions using personal information. [Solution] The system according to the embodiment includes a reception unit, a proposal unit, a conversation unit, and a linking unit. The reception unit accepts the selection of how personal information is to be used. The proposal unit makes personalized proposals based on the personal information accepted by the reception unit. The conversation unit starts a conversation when the employee reports that they are starting work. The linking unit detects stress trends and links with industrial health staff.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately utilize personal information to make personalized suggestions or to provide mental health care, so there is room for improvement.

[0005] The system according to the embodiment aims to support mental health care by making personalized suggestions using personal information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a conversation unit, and a linking unit. The reception unit accepts the selection of how to use personal information. The proposal unit makes personalized proposals based on the personal information accepted by the reception unit. The conversation unit starts a conversation when the employee reports that they have started work. The linking unit detects stress trends and links with industrial health staff. [Effects of the Invention]

[0007] The system according to the embodiment can utilize personal information to make personalized suggestions and support mental health care. [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 AI mentor system according to an embodiment of the present invention provides mental health support that users can consult with anytime, anywhere. This AI mentor system utilizes data such as health checkups, medical history, and work history, allowing users to select the use of their personal information and providing personalized suggestions and dialogue. The AI ​​proactively engages in conversations, triggering daily conversations (small talk) when the user reports to work, for example, to make conversations a part of daily life and lower the psychological barrier to interaction. Furthermore, if trends such as stress or overwork are detected, the system also connects with industrial health staff, allowing users to select the appropriate contact point. For example, when a user selects the use of their personal information, data such as health checkups, medical history, and work history is input into the AI. The AI ​​then analyzes this data and provides personalized suggestions and dialogue to the user. For example, it provides appropriate advice based on the user's health condition and work situation. Furthermore, the AI ​​proactively engages in conversations. For example, by triggering daily conversations (small talk) when the user reports to work, the system creates an environment in which the user is psychologically comfortable talking. This allows users to converse with the AI ​​on a daily basis, lowering the psychological barrier to interaction. The AI ​​also has the ability to detect trends such as stress and overwork. For example, it analyzes the user's conversation content and behavioral patterns to detect signs of stress or overwork. If signs are detected, the AI ​​will connect with industrial health staff. The user can choose who to connect with and receive appropriate support. This allows the user's mental health to be effectively managed and their health condition to be maintained. This allows the AI ​​mentor system to make personalized suggestions and dialogue based on the user's personal information, detect tendencies toward stress and overwork, and connect appropriately.

[0029] The AI ​​mentor system according to the embodiment includes a reception unit, a proposal unit, a conversation unit, and a collaboration unit. The reception unit allows a user to select how their personal information is to be used. Examples of personal information include, but are not limited to, a name, address, telephone number, and email address. For example, if the user is relaxed, the reception unit prompts the user to select how their personal information is to be used immediately. If the user is stressed, the reception unit may prompt the user to select how their personal information is to be used after a short delay. If the user is in a hurry, the reception unit may prompt the user to select how their personal information is to be used quickly with a concise explanation. The proposal unit makes personalized proposals and dialogues using data from health checkups, medical history, and work history. The proposal unit provides appropriate advice based on, for example, the user's health condition and work situation. The proposal unit may also estimate the user's emotions and adjust the way the proposal is presented based on the estimated user's emotions. For example, if the user is relaxed, the proposal unit may make detailed proposals. If the user is stressed, the proposal unit may make concise proposals. If the user is in a hurry, the proposal unit may make proposals that focus on the main points. The conversation unit starts a conversation for the day when the user reports that they have started work. For example, if the user is relaxed, the conversation unit starts the conversation immediately. If the user is feeling stressed, the conversation unit can also start the conversation after a short delay. If the user is in a hurry, the conversation unit can also start the conversation with a short greeting. The collaboration unit detects a tendency toward stress or overwork and collaborates with industrial health staff. For example, if the user is relaxed, the collaboration unit provides a detailed collaboration method. If the user is feeling stressed, the collaboration unit can also provide a simple collaboration method. If the user is in a hurry, the collaboration unit can also provide a quick collaboration method. As a result, the AI ​​mentor system according to the embodiment can make personalized suggestions and dialogue based on the user's personal information, detect a tendency toward stress or overwork, and collaborate appropriately.

[0030] The suggestion unit can make personalized suggestions and dialogues using data on health checkups, medical history, and work history. The suggestion unit provides appropriate advice based on, for example, the user's health condition and work situation. For example, the suggestion unit provides health management advice based on the results of the user's health checkup. The suggestion unit can also refer the user to an appropriate medical institution based on the user's medical history. The suggestion unit can also provide career advice based on the user's work history. This enables more personalized suggestions and dialogues by using data on health checkups, medical history, and work history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the results of the user's health checkup into the generation AI and cause the generation AI to provide health management advice.

[0031] The conversation unit can start a conversation for the day using the start of work report as a trigger. For example, if the user is relaxed, the conversation unit can start a conversation immediately. For example, the conversation unit can start small talk immediately after the user reports the start of work. Also, if the user is feeling stressed, the conversation unit can start a conversation after a short delay. For example, the conversation unit can start a conversation a few minutes after the user reports the start of work. Also, if the user is in a hurry, the conversation unit can start a conversation with a short greeting. For example, the conversation unit can make a simple greeting immediately after the user reports the start of work, followed by a detailed conversation. In this way, starting a conversation using the start of work report as a trigger can create an environment in which the user feels psychologically more comfortable talking. Some or all of the above-mentioned processing in the conversation unit can be performed using, for example, AI, or can be performed without AI. For example, the conversation unit can input the user's start of work report into a generation AI and cause the generation AI to start a conversation.

[0032] The linking unit can link to industrial health staff when it detects a tendency toward stress or overwork. The linking unit, for example, analyzes the user's conversation content and behavioral patterns to detect signs of stress or overwork. For example, the linking unit can detect signs of stress from the user's conversation content and link to industrial health staff. The linking unit can also detect signs of overwork from the user's behavioral patterns and link to industrial health staff. For example, the linking unit can analyze the user's behavioral patterns to detect signs of overwork. The linking unit can also estimate the user's emotions and adjust the linking method based on the estimated user emotions. For example, the linking unit can provide a detailed linking method when the user is relaxed. The linking unit can also provide a simple linking method when the user is stressed. The linking unit can also provide a quick linking method when the user is in a hurry. This makes it possible to effectively manage the user's mental health by performing appropriate linking when a tendency toward stress or overwork is detected. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or without AI. For example, the collaboration unit can input the content of a user's conversation into the generation AI and have the generation AI detect signs of stress.

[0033] The linking unit can provide the user with a choice of linking destinations. For example, when the user is relaxed, the linking unit provides the user with detailed options for linking destinations. For example, the linking unit presents the user with options for multiple medical institutions or counseling services. The linking unit can also provide the user with concise options for linking destinations when the user is feeling stressed. For example, the linking unit presents the user with only one most appropriate linking destination. The linking unit can also enable the user to quickly select a linking destination when the user is in a hurry. For example, the linking unit presents the user with concise options for linking destinations, allowing the user to quickly select one. This allows the user to receive appropriate support by selecting a linking destination. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's emotions into the generation AI and cause the generation AI to present options for linking destinations.

[0034] The reception unit can analyze the user's past selection history and present the optimal selection method. The reception unit, for example, presents similar options based on the method of use of personal information selected by the user in the past. For example, the reception unit presents similar options based on the method of use of personal information selected by the user in the past. The reception unit can also prompt the user to make a selection at the optimal timing based on the timing of the user's past selection. For example, the reception unit prompts the user to make a selection at the optimal timing based on the timing of the user's past selection. The reception unit can also prioritize presenting the most frequently selected method from the user's past selection history. For example, the reception unit prioritizes presenting the most frequently selected method from the user's past selection history. In this way, the optimal selection method can be presented to the user by analyzing the past selection history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past selection history to a generation AI and cause the generation AI to present the optimal selection method.

[0035] When selecting the use of personal information, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, if the user is interested in their current health condition, the reception unit can prioritize presenting health-related information. For example, if the user is interested in their current health condition, the reception unit can prioritize presenting health-related information. Furthermore, if the user is interested in their work history, the reception unit can prioritize presenting work-related information. For example, if the user is interested in their work history, the reception unit can prioritize presenting work-related information. Furthermore, if the user is interested in a specific medical history, the reception unit can prioritize presenting information related to that medical history. For example, if the user is interested in a specific medical history, the reception unit can prioritize presenting information related to that medical history. In this way, by filtering based on the user's current situation and areas of interest, more relevant information can be provided. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and areas of interest into the generation AI and cause the generation AI to perform filtering.

[0036] When selecting the use of personal information, the reception unit can prioritize presenting highly relevant options taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes presenting information related to that area. For example, when the user is in a specific area, the reception unit prioritizes presenting information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize presenting information related to the travel destination. For example, when the user is traveling, the reception unit prioritizes presenting information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize presenting information around the user's home. For example, when the user is at home, the reception unit prioritizes presenting information around the user's home. This makes it possible to provide more relevant options by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant options.

[0037] When selecting the use of personal information, the reception unit can analyze the user's social media activity and present relevant options. For example, if the user posts about health on social media, the reception unit can prioritize presenting health-related information. For example, if the user posts about health on social media, the reception unit can prioritize presenting health-related information. Also, if the user posts about work, the reception unit can prioritize presenting work-related information. For example, if the user posts about work, the reception unit can prioritize presenting work-related information. Also, if the user posts about a specific event, the reception unit can prioritize presenting information related to the event. For example, if the user posts about a specific event, the reception unit can prioritize presenting information related to the event. In this way, by analyzing the user's social media activity, more relevant options can be provided. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generation AI and cause the generation AI to present relevant options.

[0038] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the user's health condition and work situation. For example, if the user's health condition is good, the suggestion unit provides detailed health advice. For example, if the user's health condition is good, the suggestion unit provides detailed health advice. The suggestion unit can also provide concise work advice if the user's work situation is busy. For example, if the user's health condition is busy, the suggestion unit can also provide specific improvement measures if the user's health condition is poor. For example, if the user's health condition is poor, the suggestion unit provides specific improvement measures. This allows for more appropriate proposals to be made by adjusting the level of detail of the proposal based on the user's health condition and work situation. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition and work situation into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0039] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the user's category. For example, if the user belongs to a health category, the suggestion unit applies a health-related proposal algorithm. For example, if the user belongs to a health category, the suggestion unit applies a health-related proposal algorithm. Furthermore, the suggestion unit can also apply a job-related proposal algorithm if the user belongs to a job category. For example, if the user belongs to a job category, the suggestion unit applies a job-related proposal algorithm. Furthermore, the suggestion unit can also apply a mental health-related proposal algorithm if the user belongs to a mental health category. For example, if the user belongs to a mental health category, the suggestion unit applies a mental health-related proposal algorithm. In this way, by applying different proposal algorithms depending on the user's category, more appropriate proposals can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's category to a generation AI and cause the generation AI to apply the proposal algorithm.

[0040] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of the user's submission. For example, if the user submits early in the morning, the suggestion unit prioritizes the proposal. For example, if the user submits early in the morning, the suggestion unit can prioritize the proposal for the next day. Furthermore, if the user submits late at night, the suggestion unit can prioritize the proposal for the next day. For example, if the user submits late at night, the suggestion unit can prioritize the proposal for the next day. Furthermore, if the user submits within a specific deadline, the suggestion unit can determine the priority of the proposal based on the deadline. For example, if the user submits within a specific deadline, the suggestion unit determines the priority of the proposal based on the deadline. In this way, by determining the priority of the proposal based on the time of the user's submission, more appropriate proposals can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's submission time into the generation AI and cause the generation AI to determine the priority of the proposals.

[0041] When making a suggestion, the suggestion unit can adjust the order of the suggestions based on the user's relevance. For example, if the user frequently uses health-related information, the suggestion unit prioritizes health-related suggestions. For example, if the user frequently uses health-related information, the suggestion unit prioritizes health-related suggestions. Furthermore, if the user frequently uses work-related information, the suggestion unit can also prioritize work-related suggestions. For example, if the user frequently uses work-related information, the suggestion unit prioritizes work-related suggestions. Furthermore, if the user frequently uses mental health-related information, the suggestion unit can also prioritize mental health-related suggestions. For example, if the user frequently uses mental health-related information, the suggestion unit prioritizes mental health-related suggestions. This allows for more appropriate suggestions to be made by adjusting the order of suggestions based on the user's relevance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's relevance to a generation AI and cause the generation AI to adjust the order of suggestions.

[0042] During a conversation, the conversation unit can analyze the user's past conversation history and select optimal conversation content. The conversation unit, for example, selects related topics based on content the user has previously spoken about. For example, the conversation unit selects related topics based on content the user has previously spoken about. The conversation unit can also prioritize selecting topics in which the user has previously shown interest. For example, the conversation unit prioritizes selecting topics in which the user has previously shown interest. The conversation unit can also select the most frequently discussed content from the user's past conversation history. For example, the conversation unit selects the most frequently discussed content from the user's past conversation history. This makes it possible to provide more appropriate conversation content by analyzing the user's past conversation history. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's past conversation history into a generation AI and cause the generation AI to select optimal conversation content.

[0043] The conversation unit can customize the conversation content based on the user's current situation and areas of interest during the conversation. For example, if the user is interested in their current health condition, the conversation unit can provide health-related topics. For example, if the user is interested in their current health condition, the conversation unit can provide health-related topics. Furthermore, if the user is interested in their job, the conversation unit can provide job-related topics. For example, if the user is interested in their job, the conversation unit can provide job-related topics. Furthermore, if the user is interested in a particular hobby, the conversation unit can provide topics related to the hobby. For example, if the user is interested in a particular hobby, the conversation unit can provide topics related to the hobby. In this way, by customizing the conversation content based on the user's current situation and areas of interest, more appropriate conversation can be provided. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's current situation and areas of interest into the generation AI and cause the generation AI to customize the conversation content.

[0044] During a conversation, the conversation unit can prioritize providing highly relevant conversation content by taking into account the user's geographical location information. For example, when the user is in a specific area, the conversation unit provides topics related to that area. For example, when the user is in a specific area, the conversation unit provides topics related to that area. Furthermore, when the user is traveling, the conversation unit can also provide topics related to the travel destination. For example, when the user is traveling, the conversation unit provides topics related to the travel destination. Furthermore, when the user is at home, the conversation unit can also provide topics related to the area around the user's home. For example, when the user is at home, the conversation unit provides topics related to the area around the user's home. In this way, by taking the user's geographical location information into account, more relevant conversation content can be provided. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information to the generation AI and cause the generation AI to provide highly relevant conversation content.

[0045] The conversation unit can analyze the user's social media activity during a conversation and provide relevant conversation content. For example, if the user posts about health on social media, the conversation unit can provide health-related topics. For example, if the user posts about health on social media, the conversation unit can provide health-related topics. Furthermore, if the user posts about work, the conversation unit can provide work-related topics. For example, if the user posts about work, the conversation unit can provide work-related topics. Furthermore, if the user posts about a specific event, the conversation unit can provide topics related to the event. For example, if the user posts about a specific event, the conversation unit can provide topics related to the event. In this way, by analyzing the user's social media activity, more relevant conversation content can be provided. Some or all of the above-described processing in the conversation unit can be performed using AI, for example, or without AI. For example, the conversation unit can input the user's social media activity into a generation AI and cause the generation AI to provide related conversation content.

[0046] At the time of collaboration, the collaboration unit can analyze the user's past collaboration history and select the optimal collaboration method. For example, the collaboration unit selects a similar method based on collaboration methods used by the user in the past. For example, the collaboration unit selects a similar method based on collaboration methods used by the user in the past. The collaboration unit can also select the most effective collaboration method from the user's past collaboration history. For example, the collaboration unit selects the most effective collaboration method from the user's past collaboration history. The collaboration unit can also analyze the user's past collaboration history and select the most frequently used collaboration method. For example, the collaboration unit analyzes the user's past collaboration history and selects the most frequently used collaboration method. In this way, by analyzing the user's past collaboration history, a more appropriate collaboration method can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past collaboration history to the generation AI and cause the generation AI to select the optimal collaboration method.

[0047] The collaboration unit can customize the collaboration method based on the user's current situation during collaboration. For example, if the user has a health problem, the collaboration unit prioritizes collaboration with a medical institution. For example, if the user has a health problem, the collaboration unit prioritizes collaboration with a medical institution. The collaboration unit can also prioritize collaboration with workplace industrial health staff if the user has a work-related problem. For example, if the user has a work-related problem, the collaboration unit prioritizes collaboration with workplace industrial health staff. The collaboration unit can also prioritize collaboration with a psychological counselor if the user has a mental health problem. For example, if the user has a mental health problem, the collaboration unit prioritizes collaboration with a psychological counselor. This allows for more appropriate collaboration by customizing the collaboration method based on the user's current situation. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's current situation into the generation AI and cause the generation AI to customize the collaboration method.

[0048] When linking, the linking unit can select the optimal linking method by taking into account the user's geographical location information. For example, when the user is in a specific area, the linking unit prioritizes linking with medical institutions in that area. For example, when the user is in a specific area, the linking unit prioritizes linking with medical institutions in that area. Furthermore, when the user is traveling, the linking unit can prioritize linking with medical institutions at the user's travel destination. For example, when the user is traveling, the linking unit prioritizes linking with medical institutions at the user's travel destination. Furthermore, when the user is at home, the linking unit can prioritize linking with medical institutions near the user's home. For example, when the user is at home, the linking unit prioritizes linking with medical institutions near the user's home. This makes it possible to provide a more appropriate linking method by taking the user's geographical location information into account. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal linking method.

[0049] The linking unit can analyze the user's social media activity during linking and suggest optimal linking means. For example, if the user posts about health on social media, the linking unit can suggest health-related linking means. For example, if the user posts about health on social media, the linking unit can suggest health-related linking means. Furthermore, if the user posts about work, the linking unit can suggest work-related linking means. For example, if the user posts about work, the linking unit can suggest work-related linking means. Furthermore, if the user posts about a specific event, the linking unit can suggest linking means related to the event. For example, if the user posts about a specific event, the linking unit can suggest linking means related to the event. In this way, by analyzing the user's social media activity, more appropriate linking means can be provided. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's social media activity into the generation AI and cause the generation AI to suggest optimal linking means.

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

[0051] The suggestion unit can analyze the user's past suggestion history and make optimal suggestions. For example, if the user has accepted many suggestions related to health management in the past, the suggestion unit can prioritize new suggestions related to health management. Also, if the user has accepted many work-related suggestions in the past, the suggestion unit can prioritize new work-related suggestions. Furthermore, if the user has accepted many mental health suggestions in the past, the suggestion unit can also prioritize new mental health suggestions. This allows more appropriate suggestions to be made based on the user's past suggestion history.

[0052] The conversation unit can grasp the user's current activity status in real time and start a conversation at an appropriate timing. For example, if the user is at work, the conversation unit can ask a short greeting or a simple question. If the user is taking a break, the conversation unit can offer relaxing topics. Furthermore, if the user is exercising, the conversation unit can offer topics related to exercise. This makes it possible to offer a more appropriate conversation depending on the user's current activity status.

[0053] The linking unit can suggest optimal linking destinations in consideration of the user's geographical location information. For example, if the user is in a specific area, medical institutions and counseling services in that area can be suggested preferentially. Also, if the user is traveling, medical institutions and counseling services in the travel destination can be suggested preferentially. Furthermore, if the user is at home, medical institutions and counseling services near the user's home can be suggested preferentially. This makes it possible to suggest more appropriate linking destinations based on the user's geographical location information.

[0054] The suggestion unit can adjust the frequency of suggestions based on the user's health condition and work situation. For example, if the user's health condition is good, the suggestion unit can lower the frequency of suggestions. Also, if the user's health condition is deteriorating, the suggestion unit can increase the frequency of suggestions. Furthermore, if the user's work situation is busy, the suggestion unit can also lower the frequency of suggestions. This makes it possible to adjust the frequency of suggestions more appropriately based on the user's health condition and work situation.

[0055] The collaboration unit can analyze the user's past collaboration history and propose the optimal collaboration method. For example, it can propose a similar collaboration method based on the collaboration method the user has used in the past. It can also propose the most effective collaboration method based on the user's past collaboration history. It can also analyze the user's past collaboration history and propose the most frequently used collaboration method. This makes it possible to propose a more appropriate collaboration method based on the user's past collaboration history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit prompts the user to select the use of their personal information. Personal information includes, for example, name, address, telephone number, email address, etc. Depending on the user's state, the reception unit prompts the user to select the use of their personal information immediately if they are relaxed, after a short delay if they are stressed, or quickly with a brief explanation if they are in a hurry. Step 2: The suggestion unit makes personalized suggestions based on the personal information received by the reception unit. The suggestion unit uses data from health checkups, medical history, and work history to provide appropriate advice based on the user's health condition and work situation. It also estimates the user's emotions and makes detailed suggestions if they are relaxed, concise suggestions if they are stressed, and suggestions that focus on the main points if they are in a hurry. Step 3: The conversation unit begins the conversation when the user reports that they are starting work. Depending on the user's state, the conversation unit will begin the conversation immediately if the user is relaxed, after a short delay if the user is stressed, or with a short greeting if the user is in a hurry. Step 4: The coordination unit detects trends of stress and overwork and coordinates with industrial health staff. Depending on the user's condition, the coordination unit provides detailed coordination methods if the user is relaxed, simple coordination methods if the user is stressed, and quick coordination methods if the user is in a hurry.

[0058] (Example 2) An AI mentor system according to an embodiment of the present invention provides mental health support that users can consult with anytime, anywhere. This AI mentor system utilizes data such as health checkups, medical history, and work history, allowing users to select the use of their personal information and providing personalized suggestions and dialogue. The AI ​​proactively engages in conversations, triggering daily conversations (small talk) when the user reports to work, for example, to make conversations a part of daily life and lower the psychological barrier to interaction. Furthermore, if trends such as stress or overwork are detected, the system also connects with industrial health staff, allowing users to select the appropriate contact point. For example, when a user selects the use of their personal information, data such as health checkups, medical history, and work history is input into the AI. The AI ​​then analyzes this data and provides personalized suggestions and dialogue to the user. For example, it provides appropriate advice based on the user's health condition and work situation. Furthermore, the AI ​​proactively engages in conversations. For example, by triggering daily conversations (small talk) when the user reports to work, the system creates an environment in which the user is psychologically comfortable talking. This allows users to converse with the AI ​​on a daily basis, lowering the psychological barrier to interaction. The AI ​​also has the ability to detect trends such as stress and overwork. For example, it analyzes the user's conversation content and behavioral patterns to detect signs of stress or overwork. If signs are detected, the AI ​​will connect with industrial health staff. The user can choose who to connect with and receive appropriate support. This allows the user's mental health to be effectively managed and their health condition to be maintained. This allows the AI ​​mentor system to make personalized suggestions and dialogue based on the user's personal information, detect tendencies toward stress and overwork, and connect appropriately.

[0059] The AI ​​mentor system according to the embodiment includes a reception unit, a proposal unit, a conversation unit, and a collaboration unit. The reception unit allows a user to select how their personal information is to be used. Examples of personal information include, but are not limited to, a name, address, telephone number, and email address. For example, if the user is relaxed, the reception unit prompts the user to select how their personal information is to be used immediately. If the user is stressed, the reception unit may prompt the user to select how their personal information is to be used after a short delay. If the user is in a hurry, the reception unit may prompt the user to select how their personal information is to be used quickly with a concise explanation. The proposal unit makes personalized proposals and dialogues using data from health checkups, medical history, and work history. The proposal unit provides appropriate advice based on, for example, the user's health condition and work situation. The proposal unit may also estimate the user's emotions and adjust the way the proposal is presented based on the estimated user's emotions. For example, if the user is relaxed, the proposal unit may make detailed proposals. If the user is stressed, the proposal unit may make concise proposals. If the user is in a hurry, the proposal unit may make proposals that focus on the main points. The conversation unit starts a conversation for the day when the user reports that they have started work. For example, if the user is relaxed, the conversation unit starts the conversation immediately. If the user is feeling stressed, the conversation unit can also start the conversation after a short delay. If the user is in a hurry, the conversation unit can also start the conversation with a short greeting. The collaboration unit detects a tendency toward stress or overwork and collaborates with industrial health staff. For example, if the user is relaxed, the collaboration unit provides a detailed collaboration method. If the user is feeling stressed, the collaboration unit can also provide a simple collaboration method. If the user is in a hurry, the collaboration unit can also provide a quick collaboration method. As a result, the AI ​​mentor system according to the embodiment can make personalized suggestions and dialogue based on the user's personal information, detect a tendency toward stress or overwork, and collaborate appropriately.

[0060] The suggestion unit can make personalized suggestions and dialogues using data on health checkups, medical history, and work history. The suggestion unit provides appropriate advice based on, for example, the user's health condition and work situation. For example, the suggestion unit provides health management advice based on the results of the user's health checkup. The suggestion unit can also refer the user to an appropriate medical institution based on the user's medical history. The suggestion unit can also provide career advice based on the user's work history. This enables more personalized suggestions and dialogues by using data on health checkups, medical history, and work history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the results of the user's health checkup into the generation AI and cause the generation AI to provide health management advice.

[0061] The conversation unit can start a conversation for the day using the start of work report as a trigger. For example, if the user is relaxed, the conversation unit can start a conversation immediately. For example, the conversation unit can start small talk immediately after the user reports the start of work. Also, if the user is feeling stressed, the conversation unit can start a conversation after a short delay. For example, the conversation unit can start a conversation a few minutes after the user reports the start of work. Also, if the user is in a hurry, the conversation unit can start a conversation with a short greeting. For example, the conversation unit can make a simple greeting immediately after the user reports the start of work, followed by a detailed conversation. In this way, starting a conversation using the start of work report as a trigger can create an environment in which the user feels psychologically more comfortable talking. Some or all of the above-mentioned processing in the conversation unit can be performed using, for example, AI, or can be performed without AI. For example, the conversation unit can input the user's start of work report into a generation AI and cause the generation AI to start a conversation.

[0062] The linking unit can link to industrial health staff when it detects a tendency toward stress or overwork. The linking unit, for example, analyzes the user's conversation content and behavioral patterns to detect signs of stress or overwork. For example, the linking unit can detect signs of stress from the user's conversation content and link to industrial health staff. The linking unit can also detect signs of overwork from the user's behavioral patterns and link to industrial health staff. For example, the linking unit can analyze the user's behavioral patterns to detect signs of overwork. The linking unit can also estimate the user's emotions and adjust the linking method based on the estimated user emotions. For example, the linking unit can provide a detailed linking method when the user is relaxed. The linking unit can also provide a simple linking method when the user is stressed. The linking unit can also provide a quick linking method when the user is in a hurry. This makes it possible to effectively manage the user's mental health by performing appropriate linking when a tendency toward stress or overwork is detected. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or without AI. For example, the collaboration unit can input the content of a user's conversation into the generation AI and have the generation AI detect signs of stress.

[0063] The linking unit can provide the user with a choice of linking destinations. For example, when the user is relaxed, the linking unit provides the user with detailed options for linking destinations. For example, the linking unit presents the user with options for multiple medical institutions or counseling services. The linking unit can also provide the user with concise options for linking destinations when the user is feeling stressed. For example, the linking unit presents the user with only one most appropriate linking destination. The linking unit can also enable the user to quickly select a linking destination when the user is in a hurry. For example, the linking unit presents the user with concise options for linking destinations, allowing the user to quickly select one. This allows the user to receive appropriate support by selecting a linking destination. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's emotions into the generation AI and cause the generation AI to present options for linking destinations.

[0064] The reception unit can estimate the user's emotions and adjust the timing of the selection of the use of personal information based on the estimated user's emotions. For example, when the user is relaxed, the reception unit immediately prompts the user to select the use of personal information. For example, the reception unit prompts the user to select the use of personal information when the user is relaxed. Furthermore, when the user is feeling stressed, the reception unit can prompt the user to select the use of personal information after a short delay. For example, when the user is feeling stressed, the reception unit prompts the user to select the use of personal information after a short delay. Furthermore, when the user is in a hurry, the reception unit can prompt the user to select the use of personal information quickly with a concise explanation. For example, when the user is in a hurry, the reception unit prompts the user to select the use of personal information quickly with a concise explanation. In this way, by adjusting the timing of the selection of the use of personal information according to the user's emotions, the selection can be prompted at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotions into the generation AI and have the generation AI adjust the timing of the selection of use of personal information.

[0065] The reception unit can analyze the user's past selection history and present the optimal selection method. The reception unit, for example, presents similar options based on the method of use of personal information selected by the user in the past. For example, the reception unit presents similar options based on the method of use of personal information selected by the user in the past. The reception unit can also prompt the user to make a selection at the optimal timing based on the timing of the user's past selection. For example, the reception unit prompts the user to make a selection at the optimal timing based on the timing of the user's past selection. The reception unit can also prioritize presenting the most frequently selected method from the user's past selection history. For example, the reception unit prioritizes presenting the most frequently selected method from the user's past selection history. In this way, the optimal selection method can be presented to the user by analyzing the past selection history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past selection history to a generation AI and cause the generation AI to present the optimal selection method.

[0066] When selecting the use of personal information, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, if the user is interested in their current health condition, the reception unit can prioritize presenting health-related information. For example, if the user is interested in their current health condition, the reception unit can prioritize presenting health-related information. Furthermore, if the user is interested in their work history, the reception unit can prioritize presenting work-related information. For example, if the user is interested in their work history, the reception unit can prioritize presenting work-related information. Furthermore, if the user is interested in a specific medical history, the reception unit can prioritize presenting information related to that medical history. For example, if the user is interested in a specific medical history, the reception unit can prioritize presenting information related to that medical history. In this way, by filtering based on the user's current situation and areas of interest, more relevant information can be provided. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and areas of interest into the generation AI and cause the generation AI to perform filtering.

[0067] The reception unit can estimate the user's emotions and determine the priority of usage selection based on the estimated user's emotions. For example, when the user is relaxed, the reception unit prioritizes presenting detailed options. For example, when the user is relaxed, the reception unit prioritizes presenting detailed options. The reception unit can also prioritize presenting concise options when the user is stressed. For example, when the user is stressed, the reception unit prioritizes presenting concise options. The reception unit can also prioritize presenting the most important options when the user is in a hurry. For example, when the user is in a hurry, the reception unit prioritizes presenting the most important options. This allows the user to prioritize usage selection according to the user's emotions, thereby providing more appropriate options. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's emotions into the generation AI and have the generation AI determine the priority of usage selection.

[0068] When selecting the use of personal information, the reception unit can prioritize presenting highly relevant options taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes presenting information related to that area. For example, when the user is in a specific area, the reception unit prioritizes presenting information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize presenting information related to the travel destination. For example, when the user is traveling, the reception unit prioritizes presenting information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize presenting information around the user's home. For example, when the user is at home, the reception unit prioritizes presenting information around the user's home. This makes it possible to provide more relevant options by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant options.

[0069] When selecting the use of personal information, the reception unit can analyze the user's social media activity and present relevant options. For example, if the user posts about health on social media, the reception unit can prioritize presenting health-related information. For example, if the user posts about health on social media, the reception unit can prioritize presenting health-related information. Also, if the user posts about work, the reception unit can prioritize presenting work-related information. For example, if the user posts about work, the reception unit can prioritize presenting work-related information. Also, if the user posts about a specific event, the reception unit can prioritize presenting information related to the event. For example, if the user posts about a specific event, the reception unit can prioritize presenting information related to the event. In this way, by analyzing the user's social media activity, more relevant options can be provided. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generation AI and cause the generation AI to present relevant options.

[0070] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit makes a detailed suggestion. For example, when the user is relaxed, the suggestion unit makes a detailed suggestion. Furthermore, when the user is stressed, the suggestion unit can make a concise suggestion. For example, when the user is stressed, the suggestion unit can make a concise suggestion. Furthermore, when the user is in a hurry, the suggestion unit can make a suggestion that focuses on the main points. For example, when the user is in a hurry, the suggestion unit makes a suggestion that focuses on the main points. In this way, by adjusting the way the suggestions are expressed according to the user's emotions, more appropriate suggestions can be made. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotions into the generation AI and have the generation AI adjust the way the suggestions are expressed.

[0071] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the user's health condition and work situation. For example, if the user's health condition is good, the suggestion unit provides detailed health advice. For example, if the user's health condition is good, the suggestion unit provides detailed health advice. The suggestion unit can also provide concise work advice if the user's work situation is busy. For example, if the user's health condition is busy, the suggestion unit can also provide specific improvement measures if the user's health condition is poor. For example, if the user's health condition is poor, the suggestion unit provides specific improvement measures. This allows for more appropriate proposals to be made by adjusting the level of detail of the proposal based on the user's health condition and work situation. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition and work situation into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0072] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the user's category. For example, if the user belongs to a health category, the suggestion unit applies a health-related proposal algorithm. For example, if the user belongs to a health category, the suggestion unit applies a health-related proposal algorithm. Furthermore, the suggestion unit can also apply a job-related proposal algorithm if the user belongs to a job category. For example, if the user belongs to a job category, the suggestion unit applies a job-related proposal algorithm. Furthermore, the suggestion unit can also apply a mental health-related proposal algorithm if the user belongs to a mental health category. For example, if the user belongs to a mental health category, the suggestion unit applies a mental health-related proposal algorithm. In this way, by applying different proposal algorithms depending on the user's category, more appropriate proposals can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's category to a generation AI and cause the generation AI to apply the proposal algorithm.

[0073] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit makes a longer suggestion. For example, when the user is relaxed, the suggestion unit makes a longer suggestion. Furthermore, when the user is stressed, the suggestion unit can make a shorter suggestion. For example, when the user is stressed, the suggestion unit can make a shorter suggestion that focuses on the main points. Furthermore, when the user is in a hurry, the suggestion unit can make a short suggestion that focuses on the main points. For example, when the user is in a hurry, the suggestion unit makes a short suggestion that focuses on the main points. In this way, by adjusting the length of the suggestion according to the user's emotion, more appropriate suggestions can be made. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotions into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0074] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of the user's submission. For example, if the user submits early in the morning, the suggestion unit prioritizes the proposal. For example, if the user submits early in the morning, the suggestion unit can prioritize the proposal for the next day. Furthermore, if the user submits late at night, the suggestion unit can prioritize the proposal for the next day. For example, if the user submits late at night, the suggestion unit can prioritize the proposal for the next day. Furthermore, if the user submits within a specific deadline, the suggestion unit can determine the priority of the proposal based on the deadline. For example, if the user submits within a specific deadline, the suggestion unit determines the priority of the proposal based on the deadline. In this way, by determining the priority of the proposal based on the time of the user's submission, more appropriate proposals can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's submission time into the generation AI and cause the generation AI to determine the priority of the proposals.

[0075] When making a suggestion, the suggestion unit can adjust the order of the suggestions based on the user's relevance. For example, if the user frequently uses health-related information, the suggestion unit prioritizes health-related suggestions. For example, if the user frequently uses health-related information, the suggestion unit prioritizes health-related suggestions. Furthermore, if the user frequently uses work-related information, the suggestion unit can also prioritize work-related suggestions. For example, if the user frequently uses work-related information, the suggestion unit prioritizes work-related suggestions. Furthermore, if the user frequently uses mental health-related information, the suggestion unit can also prioritize mental health-related suggestions. For example, if the user frequently uses mental health-related information, the suggestion unit prioritizes mental health-related suggestions. This allows for more appropriate suggestions to be made by adjusting the order of suggestions based on the user's relevance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's relevance to a generation AI and cause the generation AI to adjust the order of suggestions.

[0076] The conversation unit can estimate the user's emotions and adjust the timing of starting a conversation based on the estimated user's emotions. For example, if the user is relaxed, the conversation unit can start the conversation immediately. For example, if the user is relaxed, the conversation unit can start the conversation immediately. Furthermore, if the user is feeling stressed, the conversation unit can wait a short time before starting the conversation. For example, if the user is feeling stressed, the conversation unit can wait a short time before starting the conversation. Furthermore, if the user is in a hurry, the conversation unit can start the conversation with a short greeting. For example, if the user is in a hurry, the conversation unit can start the conversation with a short greeting. In this way, by adjusting the timing of starting the conversation according to the user's emotions, it is possible to start the conversation at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotions into the generation AI and have the generation AI adjust the timing of when the conversation starts.

[0077] During a conversation, the conversation unit can analyze the user's past conversation history and select optimal conversation content. The conversation unit, for example, selects related topics based on content the user has previously spoken about. For example, the conversation unit selects related topics based on content the user has previously spoken about. The conversation unit can also prioritize selecting topics in which the user has previously shown interest. For example, the conversation unit prioritizes selecting topics in which the user has previously shown interest. The conversation unit can also select the most frequently discussed content from the user's past conversation history. For example, the conversation unit selects the most frequently discussed content from the user's past conversation history. This makes it possible to provide more appropriate conversation content by analyzing the user's past conversation history. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's past conversation history into a generation AI and cause the generation AI to select optimal conversation content.

[0078] The conversation unit can customize the conversation content based on the user's current situation and areas of interest during the conversation. For example, if the user is interested in their current health condition, the conversation unit can provide health-related topics. For example, if the user is interested in their current health condition, the conversation unit can provide health-related topics. Furthermore, if the user is interested in their job, the conversation unit can provide job-related topics. For example, if the user is interested in their job, the conversation unit can provide job-related topics. Furthermore, if the user is interested in a particular hobby, the conversation unit can provide topics related to the hobby. For example, if the user is interested in a particular hobby, the conversation unit can provide topics related to the hobby. In this way, by customizing the conversation content based on the user's current situation and areas of interest, more appropriate conversation can be provided. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's current situation and areas of interest into the generation AI and cause the generation AI to customize the conversation content.

[0079] The conversation unit can estimate the user's emotions and determine the priority of conversations based on the estimated user's emotions. For example, if the user is relaxed, the conversation unit prioritizes detailed conversations. For example, if the user is relaxed, the conversation unit prioritizes detailed conversations. The conversation unit can also prioritize concise conversations if the user is stressed. For example, if the user is stressed, the conversation unit prioritizes concise conversations. The conversation unit can also prioritize conversations that focus on the main points if the user is in a hurry. For example, if the user is in a hurry, the conversation unit prioritizes conversations that focus on the main points. This allows for determining the priority of conversations according to the user's emotions, thereby providing more appropriate conversations. 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 conversation unit can be performed, for example, using AI or without AI. For example, the conversation unit can input the user's emotions into the generation AI and have the generation AI determine the priority of the conversation.

[0080] During a conversation, the conversation unit can prioritize providing highly relevant conversation content by taking into account the user's geographical location information. For example, when the user is in a specific area, the conversation unit provides topics related to that area. For example, when the user is in a specific area, the conversation unit provides topics related to that area. Furthermore, when the user is traveling, the conversation unit can also provide topics related to the travel destination. For example, when the user is traveling, the conversation unit provides topics related to the travel destination. Furthermore, when the user is at home, the conversation unit can also provide topics related to the area around the user's home. For example, when the user is at home, the conversation unit provides topics related to the area around the user's home. In this way, by taking the user's geographical location information into account, more relevant conversation content can be provided. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information to the generation AI and cause the generation AI to provide highly relevant conversation content.

[0081] The conversation unit can analyze the user's social media activity during a conversation and provide relevant conversation content. For example, if the user posts about health on social media, the conversation unit can provide health-related topics. For example, if the user posts about health on social media, the conversation unit can provide health-related topics. Furthermore, if the user posts about work, the conversation unit can provide work-related topics. For example, if the user posts about work, the conversation unit can provide work-related topics. Furthermore, if the user posts about a specific event, the conversation unit can provide topics related to the event. For example, if the user posts about a specific event, the conversation unit can provide topics related to the event. In this way, by analyzing the user's social media activity, more relevant conversation content can be provided. Some or all of the above-described processing in the conversation unit can be performed using AI, for example, or without AI. For example, the conversation unit can input the user's social media activity into a generation AI and cause the generation AI to provide related conversation content.

[0082] The collaboration unit can estimate the user's emotion and adjust the collaboration method based on the estimated user's emotion. For example, when the user is relaxed, the collaboration unit provides a detailed collaboration method. For example, when the user is relaxed, the collaboration unit provides a detailed collaboration method. The collaboration unit can also provide a simple collaboration method when the user is stressed. For example, when the user is stressed, the collaboration unit provides a simple collaboration method. The collaboration unit can also provide a quick collaboration method when the user is in a hurry. For example, when the user is in a hurry, the collaboration unit provides a quick collaboration method. This allows for more appropriate collaboration by adjusting the collaboration method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collaboration unit can be performed using an AI, for example, or without an AI. For example, the collaboration unit can input the user's emotion into the generation AI and cause the generation AI to adjust the collaboration method.

[0083] At the time of collaboration, the collaboration unit can analyze the user's past collaboration history and select the optimal collaboration method. For example, the collaboration unit selects a similar method based on collaboration methods used by the user in the past. For example, the collaboration unit selects a similar method based on collaboration methods used by the user in the past. The collaboration unit can also select the most effective collaboration method from the user's past collaboration history. For example, the collaboration unit selects the most effective collaboration method from the user's past collaboration history. The collaboration unit can also analyze the user's past collaboration history and select the most frequently used collaboration method. For example, the collaboration unit analyzes the user's past collaboration history and selects the most frequently used collaboration method. In this way, by analyzing the user's past collaboration history, a more appropriate collaboration method can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past collaboration history to the generation AI and cause the generation AI to select the optimal collaboration method.

[0084] The collaboration unit can customize the collaboration method based on the user's current situation during collaboration. For example, if the user has a health problem, the collaboration unit prioritizes collaboration with a medical institution. For example, if the user has a health problem, the collaboration unit prioritizes collaboration with a medical institution. The collaboration unit can also prioritize collaboration with workplace industrial health staff if the user has a work-related problem. For example, if the user has a work-related problem, the collaboration unit prioritizes collaboration with workplace industrial health staff. The collaboration unit can also prioritize collaboration with a psychological counselor if the user has a mental health problem. For example, if the user has a mental health problem, the collaboration unit prioritizes collaboration with a psychological counselor. This allows for more appropriate collaboration by customizing the collaboration method based on the user's current situation. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's current situation into the generation AI and cause the generation AI to customize the collaboration method.

[0085] The linking unit can estimate the user's emotions and determine a priority order for linking based on the estimated user's emotions. For example, when the user is relaxed, the linking unit prioritizes a detailed linking method. For example, when the user is relaxed, the linking unit prioritizes a detailed linking method. The linking unit can also prioritize a concise linking method when the user is stressed. For example, when the user is stressed, the linking unit prioritizes a concise linking method. The linking unit can also prioritize a quick linking method when the user is in a hurry. For example, when the user is in a hurry, the linking unit prioritizes a quick linking method. This allows for more appropriate linking by determining the priority order for linking based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the linking unit can be performed using, for example, an AI, or without using an AI. For example, the collaboration unit can input the user's emotions into the generation AI and have the generation AI determine collaboration priorities.

[0086] When linking, the linking unit can select the optimal linking method by taking into account the user's geographical location information. For example, when the user is in a specific area, the linking unit prioritizes linking with medical institutions in that area. For example, when the user is in a specific area, the linking unit prioritizes linking with medical institutions in that area. Furthermore, when the user is traveling, the linking unit can prioritize linking with medical institutions at the user's travel destination. For example, when the user is traveling, the linking unit prioritizes linking with medical institutions at the user's travel destination. Furthermore, when the user is at home, the linking unit can prioritize linking with medical institutions near the user's home. For example, when the user is at home, the linking unit prioritizes linking with medical institutions near the user's home. This makes it possible to provide a more appropriate linking method by taking the user's geographical location information into account. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal linking method.

[0087] The linking unit can analyze the user's social media activity during linking and suggest optimal linking means. For example, if the user posts about health on social media, the linking unit can suggest health-related linking means. For example, if the user posts about health on social media, the linking unit can suggest health-related linking means. Furthermore, if the user posts about work, the linking unit can suggest work-related linking means. For example, if the user posts about work, the linking unit can suggest work-related linking means. Furthermore, if the user posts about a specific event, the linking unit can suggest linking means related to the event. For example, if the user posts about a specific event, the linking unit can suggest linking means related to the event. In this way, by analyzing the user's social media activity, more appropriate linking means can be provided. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's social media activity into the generation AI and cause the generation AI to suggest optimal linking means. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, suggestion unit, conversation unit, and linking 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 reception unit is realized by the control unit 46A of the smart device 14, and allows the user to select the use of personal information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes personalized suggestions and dialogues using data on health checkups, medical history, and work history. The conversation unit is realized, for example, by the control unit 46A of the smart device 14, and starts a daily conversation triggered by the start of work report. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects tendencies toward stress and overwork and links to industrial health staff. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, suggestion unit, conversation unit, and linkage 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 reception unit is realized by the control unit 46A of the smart glasses 214, and allows the user to select the use of personal information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes personalized suggestions and dialogues using data on health checkups, medical history, and work history. The conversation unit is realized, for example, by the control unit 46A of the smart glasses 214, and starts a daily conversation triggered by the start of work report. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects tendencies toward stress and overwork and links to industrial health staff. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, proposal unit, conversation unit, and linkage 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 reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows the user to select the use of personal information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes personalized proposals and dialogues using data from health checkups, medical history, and work history. The conversation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and starts a daily conversation triggered by the start of work report. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects tendencies toward stress and overwork and links to industrial health staff. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, proposal unit, conversation unit, and linkage unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the user to select the use of personal information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes personalized proposals and dialogues using data on health checkups, medical history, and work history. The conversation unit is realized, for example, by the control unit 46A of the robot 414, and starts a daily conversation triggered by the start of work report. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects tendencies toward stress and overwork and links to industrial health staff.

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

[0089] The reception unit can analyze the user's tone of voice and speaking style to estimate the user's emotional state. For example, if the user speaks in a calm voice, the reception unit can estimate that the user is relaxed and provide a detailed explanation. If the user speaks quickly, the reception unit can estimate that the user is in a hurry and provide a concise explanation. Furthermore, if the user's voice is trembling, the reception unit can estimate that the user is stressed and provide an explanation in a gentler tone. This allows for more appropriate responses to be made based on the user's tone of voice and speaking style.

[0090] The suggestion unit can analyze the user's past suggestion history and make optimal suggestions. For example, if the user has accepted many suggestions related to health management in the past, the suggestion unit can prioritize new suggestions related to health management. Also, if the user has accepted many work-related suggestions in the past, the suggestion unit can prioritize new work-related suggestions. Furthermore, if the user has accepted many mental health suggestions in the past, the suggestion unit can also prioritize new mental health suggestions. This allows more appropriate suggestions to be made based on the user's past suggestion history.

[0091] The conversation unit can grasp the user's current activity status in real time and start a conversation at an appropriate timing. For example, if the user is at work, the conversation unit can ask a short greeting or a simple question. If the user is taking a break, the conversation unit can offer relaxing topics. Furthermore, if the user is exercising, the conversation unit can offer topics related to exercise. This makes it possible to offer a more appropriate conversation depending on the user's current activity status.

[0092] The collaboration unit can estimate the user's emotions and adjust the timing of collaboration based on the estimated user's emotions. For example, if the user is relaxed, the collaboration unit can start collaboration immediately. If the user is feeling stressed, the collaboration unit can start collaboration after a short delay. Furthermore, if the user is in a hurry, the collaboration unit can start collaboration quickly. In this way, by adjusting the timing of collaboration according to the user's emotions, more appropriate collaboration can be achieved.

[0093] The linking unit can suggest optimal linking destinations in consideration of the user's geographical location information. For example, if the user is in a specific area, medical institutions and counseling services in that area can be suggested preferentially. Also, if the user is traveling, medical institutions and counseling services in the travel destination can be suggested preferentially. Furthermore, if the user is at home, medical institutions and counseling services near the user's home can be suggested preferentially. This makes it possible to suggest more appropriate linking destinations based on the user's geographical location information.

[0094] The reception unit can estimate the user's emotions and adjust the method of selecting the use of personal information based on the estimated user's emotions. For example, if the user is relaxed, the reception unit can prompt the user to select the use of personal information while providing a detailed explanation. If the user is stressed, the reception unit can prompt the user to select the use of personal information while providing a brief explanation. Furthermore, if the user is in a hurry, the reception unit can prompt the user to quickly select the use of personal information. In this way, the reception unit can prompt the user to make a more appropriate selection by adjusting the method of selecting the use of personal information according to the user's emotions.

[0095] The suggestion unit can adjust the frequency of suggestions based on the user's health condition and work situation. For example, if the user's health condition is good, the suggestion unit can lower the frequency of suggestions. Also, if the user's health condition is deteriorating, the suggestion unit can increase the frequency of suggestions. Furthermore, if the user's work situation is busy, the suggestion unit can also lower the frequency of suggestions. This makes it possible to adjust the frequency of suggestions more appropriately based on the user's health condition and work situation.

[0096] The conversation unit can estimate the user's emotions and adjust the content of the conversation based on the estimated user's emotions. For example, if the user is relaxed, detailed topics can be provided. If the user is stressed, concise topics can be provided. Furthermore, if the user is in a hurry, topics that focus on the main points can be provided. In this way, by adjusting the content of the conversation according to the user's emotions, more appropriate conversation can be provided.

[0097] The collaboration unit can analyze the user's past collaboration history and propose the optimal collaboration method. For example, it can propose a similar collaboration method based on the collaboration method the user has used in the past. It can also propose the most effective collaboration method based on the user's past collaboration history. It can also analyze the user's past collaboration history and propose the most frequently used collaboration method. This makes it possible to propose a more appropriate collaboration method based on the user's past collaboration history.

[0098] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can make a suggestion immediately. If the user is stressed, the suggestion unit can make a suggestion after a short delay. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion quickly. In this way, by adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit prompts the user to select the use of their personal information. Personal information includes, for example, name, address, telephone number, email address, etc. Depending on the user's state, the reception unit prompts the user to select the use of their personal information immediately if they are relaxed, after a short delay if they are stressed, or quickly with a brief explanation if they are in a hurry. Step 2: The suggestion unit makes personalized suggestions based on the personal information received by the reception unit. The suggestion unit uses data from health checkups, medical history, and work history to provide appropriate advice based on the user's health condition and work situation. It also estimates the user's emotions and makes detailed suggestions if they are relaxed, concise suggestions if they are stressed, and suggestions that focus on the main points if they are in a hurry. Step 3: The conversation unit begins the conversation when the user reports that they are starting work. Depending on the user's state, the conversation unit will begin the conversation immediately if the user is relaxed, after a short delay if the user is stressed, or with a short greeting if the user is in a hurry. Step 4: The coordination unit detects trends of stress and overwork and coordinates with industrial health staff. Depending on the user's condition, the coordination unit provides detailed coordination methods if the user is relaxed, simple coordination methods if the user is stressed, and quick coordination methods if the user is in a hurry.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

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

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

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

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

[0110] 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).

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

[0112] 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.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0115] 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.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

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

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

[0126] 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).

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

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

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0131] 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

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

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

[0142] 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).

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

[0144] 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.

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

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0148] 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.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

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

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a 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.

[0163] 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.

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

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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, in order to avoid confusion and to 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.

[0171] 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.

[0172] [Explanation of symbols]

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

Claims

1. a reception unit that receives selections for use of personal information; a proposal unit that makes personalized proposals based on the personal information received by the reception unit; A conversation section that starts a conversation when the start of work is reported, The liaison department detects stress trends and coordinates with industrial health staff. Equipped with A system characterized by:

2. The proposal unit Use health checkups, medical history, and work history data to provide personalized recommendations and conversations 2. The system of claim 1.

3. The conversation unit is Start the day's conversations with the start of work report as a trigger 2. The system of claim 1.

4. The linking unit is If a tendency toward stress or overwork is detected, contact industrial health staff.

2. The system of claim 1.

5. The linking unit is Providing users with a choice of partners 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the timing of selection of use of personal information based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past selection history and suggest the optimal selection method 2. The system of claim 1.

8. The reception unit Filtering your personal information based on your current circumstances and interests when choosing to use it 2. The system of claim 1.

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