system

Abstract

The system according to the embodiment aims to understand the mental state of a diabetic patient and to suggest specific stress management and lifestyle habits. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an education unit. The collection unit collects data including text input, voice input, and past chat history from the patient. The analysis unit analyzes the data collected by the collection unit using text analysis and emotion analysis to identify the patient's mental state. The suggestion unit suggests specific stress management methods and comfortable lifestyle habits based on the mental state identified by the analysis unit. The education unit collects reliable medical information and provides educational information to family and friends.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately grasp the mental state of diabetic patients and provide specific stress management and lifestyle suggestions.

[0005] The system according to the embodiment aims to understand the mental state of a diabetic patient and to suggest specific stress management and lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an education unit. The collection unit collects data including text input, voice input, and past chat history of the patient. The analysis unit analyzes the data collected by the collection unit using text analysis and emotion analysis to identify the patient's mental state. The suggestion unit suggests specific stress management methods and comfortable lifestyle habits based on the mental state identified by the analysis unit. The education unit collects reliable medical information and provides educational information to family and friends. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the mental state of a diabetic patient and make suggestions for specific stress management and lifestyle habits. [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) A diabetes patient support system according to an embodiment of the present invention understands the mental state of a diabetic patient through a chat function with AI and proposes specific stress management methods and comfortable lifestyle habits. In this system, a diabetic patient accesses an application and starts a chat with an AI, asking questions to understand the patient's mental state and analyzing the patient's responses. Based on the analysis results, the AI ​​proposes specific stress management methods and comfortable lifestyle habits. The system also provides educational information about diabetes to family and friends. This allows those around the patient to understand and support the patient's situation. For example, a diabetic patient accesses an application and starts a chat with an AI. If the patient types, "I've been feeling stressed lately," the AI ​​asks, "In what situations do you feel stressed?" In this way, the AI ​​collects information to understand the patient's mental state. The AI ​​then analyzes the patient's responses. For example, if the patient replies, "It's because of work pressure," the AI ​​suggests, "Try relaxing to reduce work pressure." In this way, the AI ​​proposes specific stress management methods. Furthermore, the AI ​​also suggests comfortable lifestyle habits to the patient. For example, if a patient answers, "I feel stressed because of lack of exercise," the AI ​​might suggest, "Try walking 30 minutes every day." In this way, the AI ​​suggests comfortable lifestyle habits to patients. It also provides educational information about diabetes to family and friends. For example, if a family member types, "I want to know more about diabetes," the AI ​​might explain, "Diabetes is a disease that causes high blood sugar levels. Proper diet and exercise are important." In this way, the AI ​​provides educational information about diabetes to family and friends. This application helps diabetic patients understand their mental state and offers specific stress management methods and suggestions for comfortable lifestyle habits. Family and friends can also deepen their understanding of diabetes and support patients. This allows the diabetes patient support system to understand the mental state of diabetic patients and suggest specific stress management methods and comfortable lifestyle habits.

[0029] A diabetes patient support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an education unit. The collection unit collects data including text input, voice input, and past chat history of a patient. For example, the collection unit collects text data entered by the patient into an application. The collection unit can also collect patient comments using voice input. The collection unit can also save past chat history for later analysis. For example, the collection unit stores the contents of past chats conducted by the patient in a database and retrieves and analyzes them as needed. The analysis unit analyzes the data collected by the collection unit using text analysis and emotion analysis to identify the patient's mental state. For example, the analysis unit analyzes the text data using morphological analysis to understand the meaning of the patient's comments. The analysis unit can also analyze the patient's emotions using an emotion dictionary. The analysis unit can also identify the patient's mental state using a machine learning algorithm. For example, the analysis unit uses an algorithm that estimates the patient's stress level and happiness level from the patient's comments. The suggestion unit suggests specific stress management methods and comfortable lifestyle habits based on the mental state identified by the analysis unit. The suggestion unit may suggest, for example, relaxation techniques or cognitive behavioral therapy. The suggestion unit may also suggest exercise therapy or diet therapy. Furthermore, the suggestion unit may make personalized suggestions taking into consideration information such as the patient's age, gender, and living environment. For example, the suggestion unit may suggest exercise therapy for younger patients and relaxation techniques for older patients. The education unit collects reliable medical information and provides educational information to family and friends. The education unit may collect, for example, medical papers and medical institution guidelines. The education unit may also provide information in chat format, video format, or article format. Furthermore, the education unit may customize information based on the living environment of family and friends. For example, information related to urban areas is provided to family members living in urban areas, and information related to rural areas is provided to family members living in rural areas. This allows the diabetes patient support system according to the embodiment to understand the mental state of the diabetic patient and suggest specific stress management methods and comfortable lifestyle habits.

[0030] The collection unit can analyze the patient's past health data and select an appropriate data collection method. For example, the collection unit can analyze the patient's past blood glucose level data and select a method for measuring blood glucose levels at the optimal timing. The collection unit can also analyze the patient's past exercise data and select a method for collecting the optimal amount of exercise. The collection unit can also analyze the patient's past dietary data and select an optimal dietary recording method. For example, the collection unit can obtain the patient's past blood glucose level data from a database and identify the optimal measurement timing using an analysis algorithm. The collection unit can also analyze the patient's past exercise data and identify fluctuation patterns in exercise amount. The collection unit can also analyze the patient's past dietary data and suggest methods for optimizing the content and timing of meals. In this way, the optimal data collection method can be selected by analyzing the past health data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past health data into the generation AI and cause the generation AI to select the optimal data collection method.

[0031] The collection unit can filter data based on the patient's current lifestyle and areas of interest when collecting data. For example, if the patient is at work, the collection unit can prioritize collecting work-related stress data. Furthermore, if the patient is exercising, the collection unit can prioritize collecting exercise-related data. Furthermore, if the patient is eating, the collection unit can prioritize collecting meal-related data. For example, the collection unit monitors the patient's lifestyle and collects data related to their current activities. The lifestyle monitoring can be performed, for example, by detecting the patient's activities using a sensor. The collection unit can also analyze the patient's areas of interest and collect data related to the interests. The analysis of the areas of interest can be performed, for example, by analyzing past chat history or social media activity. This allows for filtering data based on the patient's lifestyle and areas of interest, thereby collecting highly relevant data. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's lifestyle data into a generation AI and have the generation AI filter the data.

[0032] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. For example, if the patient is in a hospital, the collection unit can prioritize collecting data related to the hospital. Also, if the patient is at home, the collection unit can prioritize collecting data related to the home. Furthermore, if the patient is out, the collection unit can prioritize collecting data related to the patient's outing. For example, the collection unit acquires the patient's geographical location information using GPS data and collects data related to the patient's current location. The geographical location information can be acquired, for example, by using a location information service on a smartphone. The collection unit can also collect data related to health issues specific to the region based on the patient's location information. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0033] During data collection, the collection unit can analyze the patient's social media activity and collect relevant data. For example, if the patient feels stressed on social media, the collection unit can collect data related to stress. Furthermore, if the patient feels relaxed on social media, the collection unit can collect data related to relaxation. Furthermore, if the patient feels rushed on social media, the collection unit can collect data related to rushing. For example, the collection unit can analyze the patient's social media posts and infer their emotions using sentiment analysis technology. The sentiment analysis technology can analyze the social media posts using, for example, text analysis technology, and infer their emotions. The collection unit can also analyze the social media activity history and identify the patient's areas of interest. This allows relevant data to be collected by analyzing social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media post data into a generation AI and have the generation AI collect relevant data.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with medium importance. For example, the analysis unit identifies the importance of the data using an algorithm that evaluates the importance of the data. The evaluation of the importance of the data is performed based on criteria such as the novelty, impact, and reliability of the data. The analysis unit can also use an algorithm that adjusts the level of detail of the analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the importance of the data to adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress data. The analysis unit can also apply a relaxation analysis algorithm to relaxation data. The analysis unit can also apply a rush analysis algorithm to urgent data. For example, the analysis unit classifies the data category using an algorithm that identifies the data category. The data category is classified based on criteria such as text data, audio data, and image data. The analysis unit can also use an algorithm that applies an appropriate analysis algorithm depending on the data category. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that identifies the data category and apply an appropriate analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data submitted recently. Furthermore, the analysis unit can moderately prioritize data submitted recently. For example, the analysis unit identifies the time of data submission using an algorithm that evaluates the time of data submission. The evaluation of the time of data submission is performed based on criteria such as the submission date and time or the submission frequency. The analysis unit can also use an algorithm that determines the priority of analysis based on the time of data submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the time of data submission to determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can moderately prioritize data with medium relevance. For example, the analysis unit identifies the relevance of the data using an algorithm that evaluates the relevance of the data. The evaluation of the relevance of the data is performed based on criteria such as correlation between data or co-occurrence frequency. The analysis unit can also use an algorithm that adjusts the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the relevance of the data to adjust the order of analysis.

[0038] When making a proposal, the suggestion unit can make an individualized proposal by taking into account information including the patient's age, gender, and living environment. For example, the suggestion unit can suggest a stress management method according to the patient's age. The suggestion unit can also suggest lifestyle habits according to the patient's gender. Furthermore, the suggestion unit can make specific proposals according to the patient's living environment. For example, the suggestion unit can suggest exercise therapy to young patients and relaxation techniques to elderly patients. The suggestion unit can also make suggestions related to urban areas to patients living in urban areas and suggestions related to rural areas to patients living in rural areas. This allows for more appropriate proposals to be provided by taking into account the patient's individual information. 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 patient's individual information into the generation AI and cause the generation AI to execute an individualized proposal.

[0039] When making a proposal, the suggestion unit can provide an appropriate proposal by referring to the patient's past stress management history. For example, the suggestion unit can suggest an optimal method based on stress management methods the patient has tried in the past. The suggestion unit can also prioritize and suggest effective methods based on the patient's past history. The suggestion unit can also analyze the patient's past history and propose a new method. For example, the suggestion unit can retrieve the patient's past stress management history from a database and identify an optimal proposal using an analysis algorithm. The suggestion unit can also analyze the patient's past history and identify effective methods. In this way, the optimal proposal can be provided by referring to the past 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 patient's past stress management history into a generation AI and have the generation AI execute an optimal proposal.

[0040] When making a proposal, the suggestion unit can provide an appropriate proposal by taking into account the patient's geographical location information. For example, if the patient is at home, the suggestion unit can suggest a stress management method that can be done at home. Furthermore, if the patient is out and about, the suggestion unit can suggest a stress management method that can be done while away from home. Furthermore, if the patient is traveling, the suggestion unit can suggest a stress management method that can be done at the travel destination. For example, the suggestion unit can acquire the patient's geographical location information using GPS data and make a proposal related to the patient's current location. The geographical location information can be acquired, for example, by using a location information service on a smartphone. The suggestion unit can also make a proposal related to health issues specific to the region based on the patient's location information. This allows the proposal to be optimally provided by taking the geographical location information into consideration. 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 patient's location information data into the generation AI and cause the generation AI to execute an appropriate proposal.

[0041] When making a suggestion, the suggestion unit can analyze the patient's social media activity and make relevant suggestions. For example, if the patient feels stressed because of social media, the suggestion unit can suggest stress management methods. Furthermore, if the patient is relaxing because of social media, the suggestion unit can also suggest relaxation methods. Furthermore, if the patient is in a hurry because of social media, the suggestion unit can make suggestions that can be implemented quickly. For example, the suggestion unit can analyze the patient's social media posts and infer their emotions using sentiment analysis technology. The sentiment analysis technology can analyze the social media posts and infer their emotions using text analysis technology, for example. The suggestion unit can also analyze the social media activity history and identify the patient's areas of interest. This allows relevant suggestions to be provided by analyzing social media activity. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input social media post data into a generation AI and have the generation AI execute relevant suggestions.

[0042] When providing educational information, the education unit can provide appropriate information by referring to the past learning history of family members and friends. For example, the education unit can provide relevant information based on what the family members and friends have learned in the past. The education unit can also prioritize and provide information that was effective based on the family members and friends' past learning history. Furthermore, the education unit can analyze the family members and friends' past learning history and provide new information. For example, the education unit can retrieve the family members and friends' past learning history from a database and identify optimal information using an analysis algorithm. The education unit can also analyze the family members and friends' past learning history and identify effective information. In this way, optimal educational information can be provided by referring to the past learning history. Some or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI. For example, the education unit can input the family members and friends' past learning history into a generation AI and have the generation AI execute the optimal information.

[0043] When providing educational information, the Ministry of Education can customize the information based on the living environments of family and friends. For example, if family and friends live in urban areas, the Ministry of Education can provide information related to urban areas. Furthermore, if family and friends live in suburban areas, the Ministry of Education can provide information related to suburban areas. Furthermore, if family and friends live overseas, the Ministry of Education can provide information related to overseas locations. For example, the Ministry of Education can identify the living environments of family and friends using an algorithm that evaluates their living environments. The evaluation of the living environments is performed based on criteria such as urban areas, rural areas, and home environments. The Ministry of Education can also use an algorithm that customizes the information based on the living environments. This allows for the provision of more appropriate educational information by customizing the information based on the living environments. Some or all of the above-described processing in the Ministry of Education can be performed using, for example, AI, or without AI. For example, the Ministry of Education can input data on the living environments of family and friends into a generation AI and have the generation AI customize the information.

[0044] When providing educational information, the Ministry of Education can provide appropriate information by taking into account the geographical location information of family and friends. For example, if family and friends live in an urban area, the Ministry of Education can provide information related to urban areas. Furthermore, if family and friends live in the suburbs, the Ministry of Education can provide information related to suburban areas. Furthermore, if family and friends live overseas, the Ministry of Education can provide information related to overseas locations. For example, the Ministry of Education can obtain the geographical location information of family and friends using GPS data and provide information related to their current location. The geographical location information can be obtained, for example, by using a smartphone's location information service. Furthermore, the Ministry of Education can provide information related to region-specific health issues based on the location information of family and friends. This allows optimal educational information to be provided by taking geographical location information into consideration. Some or all of the above-described processing by the Ministry of Education can be performed, for example, using AI, or without AI. For example, the Ministry of Education can input the location information data of family and friends into the generation AI and have the generation AI execute appropriate information.

[0045] When providing educational information, the Ministry of Education can analyze the social media activities of family and friends to provide relevant information. For example, if family and friends express an interest in diabetes on social media, the Ministry of Education can provide diabetes-related information. Furthermore, if family and friends express an interest in health on social media, the Ministry of Education can provide health-related information. Furthermore, if family and friends express an interest in stress management on social media, the Ministry of Education can provide stress management-related information. For example, the Ministry of Education can analyze the content of social media posts of family and friends to identify their areas of interest. The identification of areas of interest can be performed, for example, by analyzing the content of social media posts using text analysis technology. Furthermore, the Ministry of Education can analyze social media activity history to identify the areas of interest of family and friends. This allows the analysis of social media activity to provide relevant educational information. Some or all of the above-described processing by the Ministry of Education can be performed using, for example, AI, or without AI. For example, the Ministry of Education can input social media post data into a generation AI and have the generation AI execute related information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can also monitor physiological data such as the patient's heart rate and blood pressure in real time and analyze their mental state based on this data. For example, if a patient's heart rate suddenly rises, it can determine that they are likely feeling stressed and make stress management suggestions. If their blood pressure is high, it can also suggest ways to relax. Furthermore, the analysis unit can analyze this physiological data over the long term to identify trends in the patient's health condition. This allows for a more accurate understanding of the patient's mental state and makes appropriate suggestions.

[0048] The collection unit collects the patient's sleep data, and the analysis unit analyzes it to identify the relationship between sleep quality and mental state. For example, if a patient continues to experience a lack of sleep, it can determine that stress may be increasing and suggest appropriate stress management methods. Also, if a patient is getting good quality sleep, it can suggest activities that have a relaxing effect. Furthermore, by analyzing sleep data over the long term, it is possible to understand trends in the patient's health condition and make more appropriate suggestions.

[0049] The analysis unit can analyze the patient's exercise data and evaluate the effectiveness of the exercise. For example, it can analyze changes in heart rate and blood pressure after exercise to evaluate how effective the exercise was in reducing stress. It can also analyze the frequency and intensity of exercise and suggest optimal exercise plans. Furthermore, by analyzing exercise data over the long term, it is possible to understand trends in the patient's health and make more appropriate suggestions.

[0050] The collection unit collects the patient's activity data, and the analysis unit analyzes it to identify the relationship between daily activities and mental state. For example, if a patient continues to sit at a desk for long periods of time, it can determine that stress is increasing and suggest appropriate stress management methods. Also, if a patient is doing moderate exercise, it can suggest activities that have a relaxing effect. Furthermore, by analyzing activity data over the long term, it is possible to understand trends in the patient's health condition and make more appropriate suggestions.

[0051] The education department can also suggest dietary management methods for diabetes patients to the patient's family and friends. For example, if the patient is feeling stressed, it can suggest recipes using ingredients that have a stress-reducing effect. If the patient is feeling relaxed, it can suggest recipes using ingredients that have a relaxing effect. Furthermore, if the patient is in a hurry, it can suggest healthy recipes that can be prepared in a short time. This allows family and friends to support the patient's dietary management.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection unit collects data including the patient's text input, voice input, and past chat history. For example, the collection unit collects the patient's statements using text data and voice input entered by the patient into the application, and saves the past chat history for later analysis. Step 2: The analysis unit analyzes the data collected by the collection unit using text analysis and sentiment analysis to identify the patient's mental state. For example, the analysis unit analyzes the text data using morphological analysis, and identifies the patient's emotions and mental state using a sentiment dictionary and machine learning algorithm. Step 3: The proposal unit proposes specific stress management methods and comfortable lifestyle habits based on the mental state identified by the analysis unit. For example, it suggests relaxation techniques, cognitive behavioral therapy, exercise therapy, and dietary therapy, and makes personalized proposals taking into account information such as the patient's age, gender, and living environment. Step 4: The Ministry of Education will collect reliable medical information and provide educational information to family and friends, such as collecting medical papers and guidelines from medical institutions, and providing information in the form of chats, videos, and articles, and tailoring the information to the living conditions of family and friends.

[0054] (Example 2) A diabetes patient support system according to an embodiment of the present invention understands the mental state of a diabetic patient through a chat function with AI and proposes specific stress management methods and comfortable lifestyle habits. In this system, a diabetic patient accesses an application and starts a chat with an AI, asking questions to understand the patient's mental state and analyzing the patient's responses. Based on the analysis results, the AI ​​proposes specific stress management methods and comfortable lifestyle habits. The system also provides educational information about diabetes to family and friends. This allows those around the patient to understand and support the patient's situation. For example, a diabetic patient accesses an application and starts a chat with an AI. If the patient types, "I've been feeling stressed lately," the AI ​​asks, "In what situations do you feel stressed?" In this way, the AI ​​collects information to understand the patient's mental state. The AI ​​then analyzes the patient's responses. For example, if the patient replies, "It's because of work pressure," the AI ​​suggests, "Try relaxing to reduce work pressure." In this way, the AI ​​proposes specific stress management methods. Furthermore, the AI ​​also suggests comfortable lifestyle habits to the patient. For example, if a patient answers, "I feel stressed because of lack of exercise," the AI ​​might suggest, "Try walking 30 minutes every day." In this way, the AI ​​suggests comfortable lifestyle habits to patients. It also provides educational information about diabetes to family and friends. For example, if a family member types, "I want to know more about diabetes," the AI ​​might explain, "Diabetes is a disease that causes high blood sugar levels. Proper diet and exercise are important." In this way, the AI ​​provides educational information about diabetes to family and friends. This application helps diabetic patients understand their mental state and offers specific stress management methods and suggestions for comfortable lifestyle habits. Family and friends can also deepen their understanding of diabetes and support patients. This allows the diabetes patient support system to understand the mental state of diabetic patients and suggest specific stress management methods and comfortable lifestyle habits.

[0055] A diabetes patient support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an education unit. The collection unit collects data including text input, voice input, and past chat history of a patient. For example, the collection unit collects text data entered by the patient into an application. The collection unit can also collect patient comments using voice input. The collection unit can also save past chat history for later analysis. For example, the collection unit stores the contents of past chats conducted by the patient in a database and retrieves and analyzes them as needed. The analysis unit analyzes the data collected by the collection unit using text analysis and emotion analysis to identify the patient's mental state. For example, the analysis unit analyzes the text data using morphological analysis to understand the meaning of the patient's comments. The analysis unit can also analyze the patient's emotions using an emotion dictionary. The analysis unit can also identify the patient's mental state using a machine learning algorithm. For example, the analysis unit uses an algorithm that estimates the patient's stress level and happiness level from the patient's comments. The suggestion unit suggests specific stress management methods and comfortable lifestyle habits based on the mental state identified by the analysis unit. The suggestion unit may suggest, for example, relaxation techniques or cognitive behavioral therapy. The suggestion unit may also suggest exercise therapy or diet therapy. Furthermore, the suggestion unit may make personalized suggestions taking into consideration information such as the patient's age, gender, and living environment. For example, the suggestion unit may suggest exercise therapy for younger patients and relaxation techniques for older patients. The education unit collects reliable medical information and provides educational information to family and friends. The education unit may collect, for example, medical papers and medical institution guidelines. The education unit may also provide information in chat format, video format, or article format. Furthermore, the education unit may customize information based on the living environment of family and friends. For example, information related to urban areas is provided to family members living in urban areas, and information related to rural areas is provided to family members living in rural areas. This allows the diabetes patient support system according to the embodiment to understand the mental state of the diabetic patient and suggest specific stress management methods and comfortable lifestyle habits.

[0056] The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient's emotions. For example, if the patient is stressed, the collection unit can reduce the frequency of data collection and collect data when the patient is relaxed. Furthermore, if the patient is relaxed, the collection unit can increase the frequency of data collection and collect more detailed information. Furthermore, if the patient is in a hurry, the collection unit can temporarily stop data collection and resume it later. For example, the collection unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm can estimate the emotion from the patient's facial expression using, for example, facial expression recognition technology. The collection unit can also record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology can analyze, for example, the tone and speed of the voice to estimate the emotion. Furthermore, the collection unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology can analyze the text data using, for example, an emotion dictionary to estimate the emotion. This allows for more appropriate data collection by adjusting the timing of data collection according to the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input image data of a patient captured by a camera into the generation AI and cause the generation AI to estimate the patient's emotion.

[0057] The collection unit can analyze the patient's past health data and select an appropriate data collection method. For example, the collection unit can analyze the patient's past blood glucose level data and select a method for measuring blood glucose levels at the optimal timing. The collection unit can also analyze the patient's past exercise data and select a method for collecting the optimal amount of exercise. The collection unit can also analyze the patient's past dietary data and select an optimal dietary recording method. For example, the collection unit can obtain the patient's past blood glucose level data from a database and identify the optimal measurement timing using an analysis algorithm. The collection unit can also analyze the patient's past exercise data and identify fluctuation patterns in exercise amount. The collection unit can also analyze the patient's past dietary data and suggest methods for optimizing the content and timing of meals. In this way, the optimal data collection method can be selected by analyzing the past health data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past health data into the generation AI and cause the generation AI to select the optimal data collection method.

[0058] The collection unit can filter data based on the patient's current lifestyle and areas of interest when collecting data. For example, if the patient is at work, the collection unit can prioritize collecting work-related stress data. Furthermore, if the patient is exercising, the collection unit can prioritize collecting exercise-related data. Furthermore, if the patient is eating, the collection unit can prioritize collecting meal-related data. For example, the collection unit monitors the patient's lifestyle and collects data related to their current activities. The lifestyle monitoring can be performed, for example, by detecting the patient's activities using a sensor. The collection unit can also analyze the patient's areas of interest and collect data related to the interests. The analysis of the areas of interest can be performed, for example, by analyzing past chat history or social media activity. This allows for filtering data based on the patient's lifestyle and areas of interest, thereby collecting highly relevant data. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's lifestyle data into a generation AI and have the generation AI filter the data.

[0059] The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can prioritize collecting data related to stress. Furthermore, if the patient is relaxed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the patient is in a hurry, the collection unit can prioritize collecting data related to hurry. For example, the collection unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm can estimate the emotion from the patient's facial expression using, for example, facial expression recognition technology. Furthermore, the collection unit can record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology can analyze, for example, the tone and speed of the voice to estimate the emotion. Furthermore, the collection unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology can analyze the text data using, for example, an emotion dictionary to estimate the emotion. Thus, by determining the priority of data based on the patient's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input image data of a patient captured by a camera into the generation AI and cause the generation AI to estimate the patient's emotion.

[0060] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. For example, if the patient is in a hospital, the collection unit can prioritize collecting data related to the hospital. Also, if the patient is at home, the collection unit can prioritize collecting data related to the home. Furthermore, if the patient is out, the collection unit can prioritize collecting data related to the patient's outing. For example, the collection unit acquires the patient's geographical location information using GPS data and collects data related to the patient's current location. The geographical location information can be acquired, for example, by using a location information service on a smartphone. The collection unit can also collect data related to health issues specific to the region based on the patient's location information. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0061] During data collection, the collection unit can analyze the patient's social media activity and collect relevant data. For example, if the patient feels stressed on social media, the collection unit can collect data related to stress. Furthermore, if the patient feels relaxed on social media, the collection unit can collect data related to relaxation. Furthermore, if the patient feels rushed on social media, the collection unit can collect data related to rushing. For example, the collection unit can analyze the patient's social media posts and infer their emotions using sentiment analysis technology. The sentiment analysis technology can analyze the social media posts using, for example, text analysis technology, and infer their emotions. The collection unit can also analyze the social media activity history and identify the patient's areas of interest. This allows relevant data to be collected by analyzing social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media post data into a generation AI and have the generation AI collect relevant data.

[0062] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, if the patient is stressed, the analysis unit provides simple, highly visible analysis results. Furthermore, if the patient is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm estimates the emotion from the patient's facial expression using, for example, facial expression recognition technology. The analysis unit can also record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology estimates the emotion by analyzing, for example, the tone and speed of the voice. Furthermore, the analysis unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology estimates the emotion by analyzing the text data using, for example, an emotion dictionary. This allows the analysis unit to adjust the presentation method of the analysis based on the patient's emotions, thereby providing more appropriate analysis results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a patient taken with a camera into the generation AI and cause the generation AI to estimate the patient's emotion.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with medium importance. For example, the analysis unit identifies the importance of the data using an algorithm that evaluates the importance of the data. The evaluation of the importance of the data is performed based on criteria such as the novelty, impact, and reliability of the data. The analysis unit can also use an algorithm that adjusts the level of detail of the analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the importance of the data to adjust the level of detail of the analysis.

[0064] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress data. The analysis unit can also apply a relaxation analysis algorithm to relaxation data. The analysis unit can also apply a rush analysis algorithm to urgent data. For example, the analysis unit classifies the data category using an algorithm that identifies the data category. The data category is classified based on criteria such as text data, audio data, and image data. The analysis unit can also use an algorithm that applies an appropriate analysis algorithm depending on the data category. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that identifies the data category and apply an appropriate analysis algorithm.

[0065] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, if the patient is stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the patient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the patient is in a hurry, the analysis unit can quickly provide an analysis result. For example, the analysis unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm can estimate the emotion from the patient's facial expression using, for example, facial expression recognition technology. The analysis unit can also record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology can analyze, for example, the tone and speed of the voice to estimate the emotion. Furthermore, the analysis unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology can analyze text data using, for example, an emotion dictionary to estimate the emotion. This allows the length of the analysis to be adjusted based on the patient's emotions, thereby providing an analysis result of an appropriate length. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a patient taken with a camera into the generation AI and cause the generation AI to estimate the patient's emotion.

[0066] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data submitted recently. Furthermore, the analysis unit can moderately prioritize data submitted recently. For example, the analysis unit identifies the time of data submission using an algorithm that evaluates the time of data submission. The evaluation of the time of data submission is performed based on criteria such as the submission date and time or the submission frequency. The analysis unit can also use an algorithm that determines the priority of analysis based on the time of data submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the time of data submission to determine the priority of analysis.

[0067] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can moderately prioritize data with medium relevance. For example, the analysis unit identifies the relevance of the data using an algorithm that evaluates the relevance of the data. The evaluation of the relevance of the data is performed based on criteria such as correlation between data or co-occurrence frequency. The analysis unit can also use an algorithm that adjusts the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the relevance of the data to adjust the order of analysis.

[0068] The suggestion unit can estimate the patient's emotions and adjust the way the suggestions are expressed based on the estimated patient's emotions. For example, if the patient is stressed, the suggestion unit can make simple, highly visible suggestions. Furthermore, if the patient is relaxed, the suggestion unit can also make detailed suggestions. Furthermore, if the patient is in a hurry, the suggestion unit can also make suggestions that focus on the main points. For example, the suggestion unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm can estimate the emotion from the patient's facial expression using, for example, facial expression recognition technology. The suggestion unit can also record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology can estimate the emotion by analyzing, for example, the tone and speed of the voice. Furthermore, the suggestion unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology can analyze the text data using, for example, an emotion dictionary and estimate the emotion. This allows the suggestion unit to adjust the way the suggestions are expressed based on the patient's emotions, thereby providing more appropriate suggestions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit may input image data of a patient captured by a camera into the generation AI and cause the generation AI to estimate the patient's emotion.

[0069] When making a proposal, the suggestion unit can make an individualized proposal by taking into account information including the patient's age, gender, and living environment. For example, the suggestion unit can suggest a stress management method according to the patient's age. The suggestion unit can also suggest lifestyle habits according to the patient's gender. Furthermore, the suggestion unit can make specific proposals according to the patient's living environment. For example, the suggestion unit can suggest exercise therapy to young patients and relaxation techniques to elderly patients. The suggestion unit can also make suggestions related to urban areas to patients living in urban areas and suggestions related to rural areas to patients living in rural areas. This allows for more appropriate proposals to be provided by taking into account the patient's individual information. 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 patient's individual information into the generation AI and cause the generation AI to execute an individualized proposal.

[0070] When making a proposal, the suggestion unit can provide an appropriate proposal by referring to the patient's past stress management history. For example, the suggestion unit can suggest an optimal method based on stress management methods the patient has tried in the past. The suggestion unit can also prioritize and suggest effective methods based on the patient's past history. The suggestion unit can also analyze the patient's past history and propose a new method. For example, the suggestion unit can retrieve the patient's past stress management history from a database and identify an optimal proposal using an analysis algorithm. The suggestion unit can also analyze the patient's past history and identify effective methods. In this way, the optimal proposal can be provided by referring to the past 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 patient's past stress management history into a generation AI and have the generation AI execute an optimal proposal.

[0071] The suggestion unit can estimate the patient's emotions and prioritize suggestions based on the estimated patient's emotions. For example, if the patient is feeling stressed, the suggestion unit can prioritize suggesting stress management methods. Furthermore, if the patient is relaxed, the suggestion unit can prioritize suggesting lifestyle habits. Furthermore, if the patient is in a hurry, the suggestion unit can prioritize suggestions that can be implemented quickly. For example, the suggestion unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm can estimate the emotion from the patient's facial expression using, for example, facial expression recognition technology. Furthermore, the suggestion unit can record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology can analyze, for example, the tone and speed of the voice to estimate the emotion. Furthermore, the suggestion unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology can analyze the text data using, for example, an emotion dictionary to estimate the emotion. Thus, by prioritizing suggestions based on the patient's emotions, important suggestions can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit may input image data of a patient captured by a camera into the generation AI and cause the generation AI to estimate the patient's emotion.

[0072] When making a proposal, the suggestion unit can provide an appropriate proposal by taking into account the patient's geographical location information. For example, if the patient is at home, the suggestion unit can suggest a stress management method that can be done at home. Furthermore, if the patient is out and about, the suggestion unit can suggest a stress management method that can be done while away from home. Furthermore, if the patient is traveling, the suggestion unit can suggest a stress management method that can be done at the travel destination. For example, the suggestion unit can acquire the patient's geographical location information using GPS data and make a proposal related to the patient's current location. The geographical location information can be acquired, for example, by using a location information service on a smartphone. The suggestion unit can also make a proposal related to health issues specific to the region based on the patient's location information. This allows the proposal to be optimally provided by taking the geographical location information into consideration. 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 patient's location information data into the generation AI and cause the generation AI to execute an appropriate proposal.

[0073] When making a suggestion, the suggestion unit can analyze the patient's social media activity and make relevant suggestions. For example, if the patient feels stressed because of social media, the suggestion unit can suggest stress management methods. Furthermore, if the patient is relaxing because of social media, the suggestion unit can also suggest relaxation methods. Furthermore, if the patient is in a hurry because of social media, the suggestion unit can make suggestions that can be implemented quickly. For example, the suggestion unit can analyze the patient's social media posts and infer their emotions using sentiment analysis technology. The sentiment analysis technology can analyze the social media posts and infer their emotions using text analysis technology, for example. The suggestion unit can also analyze the social media activity history and identify the patient's areas of interest. This allows relevant suggestions to be provided by analyzing social media activity. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input social media post data into a generation AI and have the generation AI execute relevant suggestions.

[0074] The education unit can estimate the patient's emotions and adjust the method of providing educational information based on the estimated patient's emotions. For example, if the patient is stressed, the education unit can provide simple, highly visible educational information. Furthermore, if the patient is relaxed, the education unit can provide detailed educational information. Furthermore, if the patient is in a hurry, the education unit can provide educational information that focuses on the key points. For example, the education unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm can estimate the emotion from the patient's facial expression using, for example, facial expression recognition technology. Furthermore, the education unit can record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology can estimate the emotion by analyzing, for example, the tone and speed of the voice. Furthermore, the education unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology can analyze the text data using, for example, an emotion dictionary and estimate the emotion. This allows the education unit to adjust the method of providing educational information based on the patient's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the education department may be performed using, for example, AI, or may be performed without using AI. For example, the education department may input image data of a patient taken with a camera into the generative AI and have the generative AI estimate the patient's emotion.

[0075] When providing educational information, the education unit can provide appropriate information by referring to the past learning history of family members and friends. For example, the education unit can provide relevant information based on what the family members and friends have learned in the past. The education unit can also prioritize and provide information that was effective based on the family members and friends' past learning history. Furthermore, the education unit can analyze the family members and friends' past learning history and provide new information. For example, the education unit can retrieve the family members and friends' past learning history from a database and identify optimal information using an analysis algorithm. The education unit can also analyze the family members and friends' past learning history and identify effective information. In this way, optimal educational information can be provided by referring to the past learning history. Some or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI. For example, the education unit can input the family members and friends' past learning history into a generation AI and have the generation AI execute the optimal information.

[0076] When providing educational information, the Ministry of Education can customize the information based on the living environments of family and friends. For example, if family and friends live in urban areas, the Ministry of Education can provide information related to urban areas. Furthermore, if family and friends live in suburban areas, the Ministry of Education can provide information related to suburban areas. Furthermore, if family and friends live overseas, the Ministry of Education can provide information related to overseas locations. For example, the Ministry of Education can identify the living environments of family and friends using an algorithm that evaluates their living environments. The evaluation of the living environments is performed based on criteria such as urban areas, rural areas, and home environments. The Ministry of Education can also use an algorithm that customizes the information based on the living environments. This allows for the provision of more appropriate educational information by customizing the information based on the living environments. Some or all of the above-described processing in the Ministry of Education can be performed using, for example, AI, or without AI. For example, the Ministry of Education can input data on the living environments of family and friends into a generation AI and have the generation AI customize the information.

[0077] The education unit can estimate the patient's emotions and prioritize educational information based on the estimated patient emotions. For example, if the patient is feeling stressed, the education unit can prioritize providing information related to stress management. Furthermore, if the patient is relaxed, the education unit can prioritize providing information related to lifestyle habits. Furthermore, if the patient is in a hurry, the education unit can prioritize providing information that can be implemented quickly. For example, the education unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm can estimate the emotion from the patient's facial expression using, for example, facial expression recognition technology. Furthermore, the education unit can record the patient's voice and estimate the emotion using voice analysis technology. The voice analysis technology can analyze, for example, the tone and speed of the voice to estimate the emotion. Furthermore, the education unit can analyze the patient's text input and estimate the emotion using text analysis technology. The text analysis technology can analyze the text data using, for example, an emotion dictionary to estimate the emotion. This allows important information to be provided preferentially by prioritizing educational information based on the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the education department may be performed using, for example, AI, or may be performed without using AI. For example, the education department may input image data of a patient taken with a camera into the generative AI and have the generative AI estimate the patient's emotion.

[0078] When providing educational information, the Ministry of Education can provide appropriate information by taking into account the geographical location information of family and friends. For example, if family and friends live in an urban area, the Ministry of Education can provide information related to urban areas. Furthermore, if family and friends live in the suburbs, the Ministry of Education can provide information related to suburban areas. Furthermore, if family and friends live overseas, the Ministry of Education can provide information related to overseas locations. For example, the Ministry of Education can obtain the geographical location information of family and friends using GPS data and provide information related to their current location. The geographical location information can be obtained, for example, by using a smartphone's location information service. Furthermore, the Ministry of Education can provide information related to region-specific health issues based on the location information of family and friends. This allows optimal educational information to be provided by taking geographical location information into consideration. Some or all of the above-described processing by the Ministry of Education can be performed, for example, using AI, or without AI. For example, the Ministry of Education can input the location information data of family and friends into the generation AI and have the generation AI execute appropriate information.

[0079] When providing educational information, the Ministry of Education can analyze the social media activities of family and friends to provide relevant information. For example, if family and friends express an interest in diabetes on social media, the Ministry of Education can provide diabetes-related information. Furthermore, if family and friends express an interest in health on social media, the Ministry of Education can provide health-related information. Furthermore, if family and friends express an interest in stress management on social media, the Ministry of Education can provide stress management-related information. For example, the Ministry of Education can analyze the content of social media posts of family and friends to identify their areas of interest. The identification of areas of interest can be performed, for example, by analyzing the content of social media posts using text analysis technology. Furthermore, the Ministry of Education can analyze social media activity history to identify the areas of interest of family and friends. This allows the analysis of social media activity to provide relevant educational information. Some or all of the above-described processing by the Ministry of Education can be performed using, for example, AI, or without AI. For example, the Ministry of Education can input social media post data into a generation AI and have the generation AI execute related information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and education unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and the specific processing unit 290 of the data processing device 12 and collects text and voice inputs from the patient. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the patient's mental state. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests specific stress management methods and comfortable lifestyle habits based on the analysis results. The education unit is realized, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12 and provides educational information to family and friends. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and education unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and collects text input and voice input from the patient. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the patient's mental state. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests specific stress management methods and comfortable lifestyle habits based on the analysis results. The education unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and provides educational information to family and friends. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and education unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12 and collects text input and voice input from the patient. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the patient's mental state. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests specific stress management methods and comfortable lifestyle habits based on the analysis results. The education unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12 and provides educational information to family and friends. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and education unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and the specific processing unit 290 of the data processing device 12 and collects text input and voice input from the patient. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the patient's mental state. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests specific stress management methods and comfortable lifestyle habits based on the analysis results. The education unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12 and provides educational information to family and friends.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can also monitor physiological data such as the patient's heart rate and blood pressure in real time and analyze their mental state based on this data. For example, if a patient's heart rate suddenly rises, it can determine that they are likely feeling stressed and make stress management suggestions. If their blood pressure is high, it can also suggest ways to relax. Furthermore, the analysis unit can analyze this physiological data over the long term to identify trends in the patient's health condition. This allows for a more accurate understanding of the patient's mental state and makes appropriate suggestions.

[0082] The suggestion unit can estimate the patient's emotions and, based on the estimated emotions, suggest stress management methods that appeal to the senses, such as music or art. For example, if the patient is feeling stressed, it can suggest music that has a relaxing effect. Also, if the patient is relaxed, it can suggest creative art activities. Furthermore, if the patient is in a hurry, it can suggest effective relaxation methods that can be done in a short amount of time. This makes it possible to provide a variety of stress management methods based on emotions.

[0083] The education department can also suggest ways for family and friends to communicate with diabetic patients. For example, if a patient is feeling stressed, the education department can provide specific suggestions for how to support the patient. If the patient is feeling relaxed, the education department can suggest activities they can enjoy together. If the patient is in a hurry, the education department can suggest quick and effective ways to communicate. This allows family and friends to support the patient more effectively.

[0084] The collection unit collects the patient's sleep data, and the analysis unit analyzes it to identify the relationship between sleep quality and mental state. For example, if a patient continues to experience a lack of sleep, it can determine that stress may be increasing and suggest appropriate stress management methods. Also, if a patient is getting good quality sleep, it can suggest activities that have a relaxing effect. Furthermore, by analyzing sleep data over the long term, it is possible to understand trends in the patient's health condition and make more appropriate suggestions.

[0085] The suggestion unit can also estimate the patient's emotions and make meal suggestions based on the estimated emotions. For example, if the patient is feeling stressed, it can suggest recipes using ingredients that have a stress-reducing effect. Also, if the patient is relaxed, it can suggest recipes using ingredients that have a relaxing effect. Furthermore, if the patient is in a hurry, it can suggest healthy recipes that can be prepared in a short time. This makes it possible to make meal suggestions based on emotions.

[0086] The analysis unit can analyze the patient's exercise data and evaluate the effectiveness of the exercise. For example, it can analyze changes in heart rate and blood pressure after exercise to evaluate how effective the exercise was in reducing stress. It can also analyze the frequency and intensity of exercise and suggest optimal exercise plans. Furthermore, by analyzing exercise data over the long term, it is possible to understand trends in the patient's health and make more appropriate suggestions.

[0087] The suggestion unit can also estimate the patient's emotions and suggest environmental settings for relaxation based on the estimated emotions. For example, if the patient is feeling stressed, the suggestion unit can suggest environmental settings such as lighting, music, and fragrance. If the patient is relaxed, the suggestion unit can also suggest environmental settings to further enhance the relaxation effect. Furthermore, if the patient is in a hurry, the suggestion unit can also suggest environmental settings that will allow the patient to relax in a short amount of time. This makes it possible to provide a relaxation environment based on emotions.

[0088] The collection unit collects the patient's activity data, and the analysis unit analyzes it to identify the relationship between daily activities and mental state. For example, if a patient continues to sit at a desk for long periods of time, it can determine that stress is increasing and suggest appropriate stress management methods. Also, if a patient is doing moderate exercise, it can suggest activities that have a relaxing effect. Furthermore, by analyzing activity data over the long term, it is possible to understand trends in the patient's health condition and make more appropriate suggestions.

[0089] The suggestion unit can estimate the patient's emotions and suggest hobbies and entertainment based on the estimated emotions. For example, if the patient is feeling stressed, it can suggest hobbies that have a relaxing effect. Also, if the patient is relaxed, it can suggest entertainment that is even more enjoyable. Furthermore, if the patient is in a hurry, it can suggest hobbies and entertainment that can be enjoyed in a short amount of time. In this way, it is possible to suggest hobbies and entertainment based on emotions.

[0090] The education department can also suggest dietary management methods for diabetes patients to the patient's family and friends. For example, if the patient is feeling stressed, it can suggest recipes using ingredients that have a stress-reducing effect. If the patient is feeling relaxed, it can suggest recipes using ingredients that have a relaxing effect. Furthermore, if the patient is in a hurry, it can suggest healthy recipes that can be prepared in a short time. This allows family and friends to support the patient's dietary management.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The collection unit collects data including the patient's text input, voice input, and past chat history. For example, the collection unit collects the patient's statements using text data and voice input entered by the patient into the application, and saves the past chat history for later analysis. Step 2: The analysis unit analyzes the data collected by the collection unit using text analysis and sentiment analysis to identify the patient's mental state. For example, the analysis unit analyzes the text data using morphological analysis, and identifies the patient's emotions and mental state using a sentiment dictionary and machine learning algorithm. Step 3: The proposal unit proposes specific stress management methods and comfortable lifestyle habits based on the mental state identified by the analysis unit. For example, it suggests relaxation techniques, cognitive behavioral therapy, exercise therapy, and dietary therapy, and makes personalized proposals taking into account information such as the patient's age, gender, and living environment. Step 4: The Ministry of Education will collect reliable medical information and provide educational information to family and friends, such as collecting medical papers and guidelines from medical institutions, and providing information in the form of chats, videos, and articles, and tailoring the information to the living conditions of family and friends.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data including text input, voice input, and past chat history of the patient; an analysis unit that analyzes the data collected by the collection unit through text analysis and emotion analysis to identify the mental state of the patient; a suggestion unit that suggests specific stress management methods and comfortable lifestyle habits based on the mental state identified by the analysis unit; and an education department that collects reliable medical information and provides educational information to family and friends. A system characterized by:

2. The collecting unit Estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions 2. The system of claim 1.

3. The collecting unit Analyze patients' past health data and select appropriate data collection methods 2. The system of claim 1.

4. The collecting unit As data is collected, filtering is performed based on the patient's current living situation and areas of interest.

2. The system of claim 1.

5. The collecting unit Estimate patient emotions and prioritize data collection based on the estimated emotions 2. The system of claim 1.

6. The collecting unit When collecting data, consider the patient's geographic location to prioritize the collection of the most relevant data.

2. The system of claim 1.

7. The collecting unit During data collection, analyze patients' social media activity and collect relevant data 2. The system of claim 1.

8. The analysis unit Estimate the patient's emotions and adjust the presentation of the analysis based on the estimated patient emotions.

2. The system of claim 1.

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