System

Abstract

An object of a system according to an embodiment is to analyze a user's behavior and lifestyle and provide appropriate advice and an appropriate insulin dose.SOLUTION: A system includes a behavior analysis unit, a chat advice unit, and an insulin calculation unit. The behavior analysis unit analyzes the user's behavior and lifestyle. The chat advice unit provides advice on meals and exercise on the basis of the result analyzed by the behavior analysis unit. The insulin calculation unit analyzes the blood glucose level data and the image of the meal to calculate an appropriate amount of insulin.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide appropriate advice or calculate insulin dosages based on the behavior and lifestyle of diabetic patients, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's behavior and lifestyle habits and provide appropriate advice and insulin dosage. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior analysis unit, a chat advice unit, and an insulin calculation unit. The behavior analysis unit analyzes the user's behavior and lifestyle habits. The chat advice unit provides dietary and exercise advice based on the results of the analysis by the behavior analysis unit. The insulin calculation unit analyzes blood glucose level data and meal images to calculate an appropriate amount of insulin. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's behavior and lifestyle habits and provide appropriate advice and insulin dosage. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The therapeutic app according to the embodiment of the present invention is a system that analyzes the user's behavior and lifestyle, generates AI to provide dietary and exercise advice, and calculates the appropriate amount of insulin by analyzing blood glucose level data and meal images. This allows the therapeutic app to comprehensively support the treatment and prevention of diabetes patients.

[0029] A therapeutic app according to an embodiment includes a behavior analysis unit, a chat advice unit, and an insulin calculation unit. The behavior analysis unit analyzes a user's behavior and lifestyle habits. For example, the behavior analysis unit collects and analyzes data such as the user's number of steps, sleep time, and dietary content. The behavior analysis unit can also evaluate the user's health status based on the lifestyle habit data. For example, the behavior analysis unit collects and analyzes data from a smartphone or a wearable device. The chat advice unit provides dietary and exercise advice based on the results of the analysis by the behavior analysis unit. For example, when a user asks, "What should I eat for dinner tonight?", the chat advice unit suggests an appropriate meal menu. The chat advice unit can also provide exercise advice. For example, the chat advice unit suggests an appropriate exercise plan based on the user's exercise data. The insulin calculation unit analyzes blood glucose level data and meal images to calculate an appropriate insulin dose. For example, the insulin calculation unit connects to an external blood glucose measuring device to acquire blood glucose level data in real time. Furthermore, when a user uploads an image of a meal to the app, the insulin calculation unit analyzes the image and evaluates the content and calories of the meal. Based on this, the insulin calculation unit automatically calculates the appropriate amount of insulin and suggests it to the user. This allows the therapeutic app according to the embodiment to comprehensively support the treatment and prevention of diabetes patients. For example, by receiving advice on how to make healthy choices in daily life, the user can reduce the risk of diabetes, and for patients who already have diabetes, support can be provided to help them receive appropriate treatment.

[0030] The behavior analysis unit collects data such as the user's number of steps, sleep time, and dietary content, automatically generates an individual lifestyle improvement plan, and evaluates progress on a weekly basis. The behavior analysis unit collects data such as the user's number of steps, sleep time, and dietary content, and AI automatically generates an individual lifestyle improvement plan. For example, if the user's step count is low, a daily walking goal is set and progress is evaluated on a weekly basis. The behavior analysis unit can also analyze the user's sleep time and suggest an appropriate sleep time. For example, if the user's sleep time is short, it can suggest the habit of going to bed early and getting up early. The behavior analysis unit can also analyze the user's diet and suggest a balanced meal plan. For example, if the user's diet is unbalanced, it can suggest a nutritionally balanced meal menu. This allows for a detailed understanding of the user's lifestyle habits and provides an individual improvement plan, thereby improving the user's health.

[0031] The behavior analysis unit can analyze the user's lifestyle data and quantitatively evaluate the impact of specific behavioral patterns on diabetes risk. The behavior analysis unit, for example, collects the user's dietary and exercise data and uses AI to quantitatively evaluate the impact on diabetes risk. For example, if the user eats a lot of high-calorie meals, the risk is displayed numerically. The behavior analysis unit can also analyze the user's exercise data and evaluate the impact of lack of exercise on diabetes risk. For example, it can show that continued lack of exercise increases the risk of diabetes. The behavior analysis unit can also comprehensively evaluate the impact of specific behavioral patterns on diabetes risk based on the user's lifestyle data. For example, it can combine dietary and exercise data to evaluate diabetes risk. This enables risk management by quantitatively evaluating the impact of specific behavioral patterns on diabetes risk.

[0032] The chat advice unit can analyze the user's past chat history and provide dietary advice that takes into consideration individual preferences and allergy information. The chat advice unit, for example, analyzes the user's past chat history and provides dietary advice that takes into consideration preferences and allergy information. For example, if the user has a nut allergy, it can suggest a meal that does not contain nuts. The chat advice unit can also analyze the user's preferences and suggest a meal menu that suits the user's preferences. For example, if the user likes Japanese food, it can suggest a Japanese meal menu. The chat advice unit can also suggest a safe meal menu based on the user's allergy information. For example, if the user has a dairy allergy, it can suggest a meal that does not contain dairy products. In this way, individual needs can be met by providing dietary advice that takes into consideration the user's preferences and allergy information.

[0033] The insulin calculation unit can analyze blood glucose level data and meal images to evaluate in detail the impact of specific ingredients or dishes on blood glucose levels. The insulin calculation unit, for example, analyzes blood glucose level data and meal images to evaluate the impact of specific ingredients on blood glucose levels. For example, it shows that meals high in carbohydrates raise blood glucose levels. The insulin calculation unit can also evaluate the impact of specific dishes on blood glucose levels. For example, it shows that fried foods raise blood glucose levels. The insulin calculation unit can also comprehensively evaluate the impact of combinations of ingredients and dishes on blood glucose levels. For example, it shows that meals high in carbohydrates and fats raise blood glucose levels. This enables appropriate dietary management by evaluating in detail the impact of specific ingredients and dishes on blood glucose levels.

[0034] The insulin calculation unit takes into account the user's past blood glucose fluctuation patterns when calculating the insulin amount, allowing it to propose a more accurate insulin amount. The insulin calculation unit, for example, analyzes the user's past blood glucose data and identifies the fluctuation pattern. For example, it calculates the insulin amount based on the pattern of blood glucose level rise after meals. The insulin calculation unit can also adjust the insulin amount based on the user's blood glucose level fluctuation pattern. For example, if the blood glucose level rises sharply, it increases the insulin amount. The insulin calculation unit can also comprehensively evaluate the user's past blood glucose level data and optimize the insulin amount. For example, it fine-tunes the insulin amount based on the past data. In this way, by taking into account the past blood glucose level fluctuation patterns, it is possible to propose a more accurate insulin amount.

[0035] The insulin calculation unit can analyze the user's eating and exercise patterns based on the blood glucose level data and suggest optimal meal timings and exercise plans. The insulin calculation unit can, for example, analyze the user's eating patterns based on the blood glucose level data and suggest optimal meal timings. For example, it can suggest eating meals during times when blood glucose levels are stable. The insulin calculation unit can also analyze the user's exercise patterns and suggest optimal exercise plans. For example, it can suggest exercising during times when blood glucose levels are low. The insulin calculation unit can also comprehensively evaluate the user's eating and exercise patterns and suggest optimal lifestyle rhythms. For example, it can adjust the timing of meals and exercise to stabilize blood glucose levels. This allows for effective blood glucose management by suggesting optimal meal timings and exercise plans based on blood glucose level data.

[0036] The insulin calculation unit can provide an individualized insulin administration schedule by taking into account the user's lifestyle rhythm and daily activity level. The insulin calculation unit, for example, analyzes the user's lifestyle rhythm and proposes an insulin administration schedule. For example, it adjusts insulin administration to match the user's wake-up time and bedtime. The insulin calculation unit can also analyze the user's daily activity level and adjust the insulin administration schedule. For example, it can reduce the amount of insulin on days when the user exercises a lot. The insulin calculation unit can also comprehensively evaluate the user's lifestyle rhythm and activity level and provide an optimal insulin administration schedule. For example, it can adjust the timing and amount of insulin administration based on the lifestyle rhythm and activity level. This makes it possible to meet individual needs by providing an insulin administration schedule that takes into account the user's lifestyle rhythm and activity level.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] Therapeutic apps can also collect data on a user's social activity to evaluate the impact of social connections on health. For example, if a user has few interactions with friends and family, the app can evaluate the impact of loneliness on health. It can also provide advice on increasing social connections based on the user's social activity data. For example, it can suggest participation in local events or online communities. It can also comprehensively evaluate the user's social activity data and quantitatively indicate the impact of social connections on health. This suggests that increasing social connections can improve the user's health.

[0039] Therapeutic apps can also suggest healthy activities based on a user's hobbies and interests. For example, if a user likes music, they can suggest exercises to do while listening to music. Or, if a user enjoys cooking, they can provide healthy recipes. Furthermore, based on a user's hobbies and interests, they can suggest activities to relieve stress. For example, they can suggest activities such as gardening or art therapy. This allows them to promote healthy lifestyle habits by utilizing the user's hobbies and interests.

[0040] Therapeutic apps can further analyze a user's sleep data and provide advice to improve sleep quality. For example, they can analyze the user's sleep duration and depth and suggest an appropriate sleeping environment. They can also suggest routines to improve sleep quality based on the user's sleep data. For example, they can suggest relaxation methods before bedtime or how to select appropriate bedding. They can also comprehensively evaluate the user's sleep data and quantitatively indicate the impact of sleep quality on health. This allows users to improve their overall health by improving their sleep quality.

[0041] Therapeutic apps can also analyze a user's dietary data and evaluate the nutritional balance of their meals. For example, they can analyze the user's dietary content and evaluate whether there are any nutrient deficiencies or excesses. They can also suggest nutritionally balanced meal menus based on the user's dietary data. For example, if there is a vitamin or mineral deficiency, they can suggest meal menus that supplement that deficiency. They can also comprehensively evaluate the user's dietary data and quantitatively indicate the impact that nutritional balance has on health. This allows them to evaluate the nutritional balance of their meals and promote healthy eating.

[0042] Therapeutic apps can further analyze the user's exercise data and evaluate the effects of exercise. For example, they can analyze the user's exercise data and evaluate the impact of exercise on health. They can also provide advice to maximize the effects of exercise based on the user's exercise data. For example, they can provide advice on adjusting the frequency and intensity of exercise. They can also comprehensively evaluate the user's exercise data and quantitatively indicate the impact of exercise on health. This makes it possible to evaluate the effects of the user's exercise and promote healthy exercise habits.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The behavior analysis unit analyzes the user's behavior and lifestyle habits. For example, the behavior analysis unit collects and analyzes data such as the user's number of steps, sleep time, and dietary habits. The behavior analysis unit can also evaluate the user's health condition based on the user's lifestyle habits data. For example, the behavior analysis unit collects and analyzes data from smartphones and wearable devices. Step 2: The chat advice unit provides dietary and exercise advice based on the results of the analysis by the behavior analysis unit. For example, when the user asks, "What should I eat for dinner tonight?", the chat advice unit suggests an appropriate meal menu. The chat advice unit can also provide advice on exercise. For example, the chat advice unit suggests an appropriate exercise plan based on the user's exercise data. Step 3: The insulin calculation unit analyzes the blood glucose data and meal images to calculate the appropriate amount of insulin. For example, the insulin calculation unit connects to an external blood glucose measuring device to obtain blood glucose data in real time. When the user uploads an image of their meal to the app, the insulin calculation unit analyzes the image and evaluates the content and calories of the meal. Based on this, the insulin calculation unit automatically calculates the appropriate amount of insulin and suggests it to the user.

[0045] (Example 2) The therapeutic app according to the embodiment of the present invention is a system that analyzes the user's behavior and lifestyle, generates AI to provide dietary and exercise advice, and calculates the appropriate amount of insulin by analyzing blood glucose level data and meal images. This allows the therapeutic app to comprehensively support the treatment and prevention of diabetes patients.

[0046] A therapeutic app according to an embodiment includes a behavior analysis unit, a chat advice unit, and an insulin calculation unit. The behavior analysis unit analyzes a user's behavior and lifestyle habits. For example, the behavior analysis unit collects and analyzes data such as the user's number of steps, sleep time, and dietary content. The behavior analysis unit can also evaluate the user's health status based on the lifestyle habit data. For example, the behavior analysis unit collects and analyzes data from a smartphone or a wearable device. The chat advice unit provides dietary and exercise advice based on the results of the analysis by the behavior analysis unit. For example, when a user asks, "What should I eat for dinner tonight?", the chat advice unit suggests an appropriate meal menu. The chat advice unit can also provide exercise advice. For example, the chat advice unit suggests an appropriate exercise plan based on the user's exercise data. The insulin calculation unit analyzes blood glucose level data and meal images to calculate an appropriate insulin dose. For example, the insulin calculation unit connects to an external blood glucose measuring device to acquire blood glucose level data in real time. Furthermore, when a user uploads an image of a meal to the app, the insulin calculation unit analyzes the image and evaluates the content and calories of the meal. Based on this, the insulin calculation unit automatically calculates the appropriate amount of insulin and suggests it to the user. This allows the therapeutic app according to the embodiment to comprehensively support the treatment and prevention of diabetes patients. For example, by receiving advice on how to make healthy choices in daily life, the user can reduce the risk of diabetes, and for patients who already have diabetes, support can be provided to help them receive appropriate treatment.

[0047] The behavior analysis unit collects data such as the user's number of steps, sleep time, and dietary content, automatically generates an individual lifestyle improvement plan, and evaluates progress on a weekly basis. The behavior analysis unit collects data such as the user's number of steps, sleep time, and dietary content, and AI automatically generates an individual lifestyle improvement plan. For example, if the user's step count is low, a daily walking goal is set and progress is evaluated on a weekly basis. The behavior analysis unit can also analyze the user's sleep time and suggest an appropriate sleep time. For example, if the user's sleep time is short, it can suggest the habit of going to bed early and getting up early. The behavior analysis unit can also analyze the user's diet and suggest a balanced meal plan. For example, if the user's diet is unbalanced, it can suggest a nutritionally balanced meal menu. This allows for a detailed understanding of the user's lifestyle habits and provides an individual improvement plan, thereby improving the user's health.

[0048] The behavior analysis unit can analyze the user's lifestyle data and quantitatively evaluate the impact of specific behavioral patterns on diabetes risk. The behavior analysis unit, for example, collects the user's dietary and exercise data and uses AI to quantitatively evaluate the impact on diabetes risk. For example, if the user eats a lot of high-calorie meals, the risk is displayed numerically. The behavior analysis unit can also analyze the user's exercise data and evaluate the impact of lack of exercise on diabetes risk. For example, it can show that continued lack of exercise increases the risk of diabetes. The behavior analysis unit can also comprehensively evaluate the impact of specific behavioral patterns on diabetes risk based on the user's lifestyle data. For example, it can combine dietary and exercise data to evaluate diabetes risk. This enables risk management by quantitatively evaluating the impact of specific behavioral patterns on diabetes risk.

[0049] The behavior analysis unit can use the emotion estimation function to analyze the user's emotional state, evaluate the impact of stress and anxiety on lifestyle habits, and suggest appropriate countermeasures. For example, the behavior analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact if stress or anxiety is high. For example, it can indicate that high stress leads to a decline in diet quality. The behavior analysis unit can also comprehensively evaluate the impact of stress and anxiety on lifestyle habits based on the user's emotional state. For example, it can indicate that high stress leads to continued lack of exercise. The behavior analysis unit can also analyze the user's emotional state and suggest appropriate countermeasures. For example, if stress is high, it can suggest relaxation methods. In this way, by analyzing the user's emotional state, evaluating the impact of stress and anxiety on lifestyle habits, and suggesting appropriate countermeasures, health management can be improved.

[0050] The chat advice unit can analyze the user's past chat history and provide dietary advice that takes into consideration individual preferences and allergy information. The chat advice unit, for example, analyzes the user's past chat history and provides dietary advice that takes into consideration preferences and allergy information. For example, if the user has a nut allergy, it can suggest a meal that does not contain nuts. The chat advice unit can also analyze the user's preferences and suggest a meal menu that suits the user's preferences. For example, if the user likes Japanese food, it can suggest a Japanese meal menu. The chat advice unit can also suggest a safe meal menu based on the user's allergy information. For example, if the user has a dairy allergy, it can suggest a meal that does not contain dairy products. In this way, individual needs can be met by providing dietary advice that takes into consideration the user's preferences and allergy information.

[0051] The chat advice unit can analyze the chat content and automatically generate an encouraging message to increase the user's motivation and enthusiasm. The chat advice unit, for example, analyzes the chat content and automatically generates an encouraging message to increase the user's motivation. For example, it sends a message encouraging the user to continue exercising. The chat advice unit can also provide a message to increase the user's motivation. For example, it sends a message encouraging the user to continue eating a healthy diet. The chat advice unit can also provide a message to maintain the user's motivation. For example, it sends a message congratulating the user on achieving a goal. In this way, by providing an encouraging message to increase the user's motivation and enthusiasm, it is possible to support continuous health management.

[0052] The insulin calculation unit can analyze blood glucose level data and meal images to evaluate in detail the impact of specific ingredients or dishes on blood glucose levels. The insulin calculation unit, for example, analyzes blood glucose level data and meal images to evaluate the impact of specific ingredients on blood glucose levels. For example, it shows that meals high in carbohydrates raise blood glucose levels. The insulin calculation unit can also evaluate the impact of specific dishes on blood glucose levels. For example, it shows that fried foods raise blood glucose levels. The insulin calculation unit can also comprehensively evaluate the impact of combinations of ingredients and dishes on blood glucose levels. For example, it shows that meals high in carbohydrates and fats raise blood glucose levels. This enables appropriate dietary management by evaluating in detail the impact of specific ingredients and dishes on blood glucose levels.

[0053] The insulin calculation unit takes into account the user's past blood glucose fluctuation patterns when calculating the insulin amount, allowing it to propose a more accurate insulin amount. The insulin calculation unit, for example, analyzes the user's past blood glucose data and identifies the fluctuation pattern. For example, it calculates the insulin amount based on the pattern of blood glucose level rise after meals. The insulin calculation unit can also adjust the insulin amount based on the user's blood glucose level fluctuation pattern. For example, if the blood glucose level rises sharply, it increases the insulin amount. The insulin calculation unit can also comprehensively evaluate the user's past blood glucose level data and optimize the insulin amount. For example, it fine-tunes the insulin amount based on the past data. In this way, by taking into account the past blood glucose level fluctuation patterns, it is possible to propose a more accurate insulin amount.

[0054] The insulin calculation unit can use the emotion estimation function to evaluate the effect of the user's emotional state on blood glucose levels and suggest an appropriate amount of insulin. The insulin calculation unit, for example, uses the emotion estimation function to analyze the user's emotional state and evaluate the effect on blood glucose levels. For example, it can indicate that blood glucose levels rise when stress is high. The insulin calculation unit can also adjust the amount of insulin based on the user's emotional state. For example, it can increase the amount of insulin when stress is high. The insulin calculation unit can also use the emotion estimation function to comprehensively evaluate the user's emotional state and optimize the amount of insulin. For example, it can fine-tune the amount of insulin based on the emotional state. This enables more effective blood glucose management by evaluating the effect of the emotional state on blood glucose levels and suggesting an appropriate amount of insulin.

[0055] The insulin calculation unit can analyze the user's eating and exercise patterns based on the blood glucose level data and suggest optimal meal timings and exercise plans. The insulin calculation unit can, for example, analyze the user's eating patterns based on the blood glucose level data and suggest optimal meal timings. For example, it can suggest eating meals during times when blood glucose levels are stable. The insulin calculation unit can also analyze the user's exercise patterns and suggest optimal exercise plans. For example, it can suggest exercising during times when blood glucose levels are low. The insulin calculation unit can also comprehensively evaluate the user's eating and exercise patterns and suggest optimal lifestyle rhythms. For example, it can adjust the timing of meals and exercise to stabilize blood glucose levels. This allows for effective blood glucose management by suggesting optimal meal timings and exercise plans based on blood glucose level data.

[0056] The insulin calculation unit can provide an individualized insulin administration schedule by taking into account the user's lifestyle rhythm and daily activity level. The insulin calculation unit, for example, analyzes the user's lifestyle rhythm and proposes an insulin administration schedule. For example, it adjusts insulin administration to match the user's wake-up time and bedtime. The insulin calculation unit can also analyze the user's daily activity level and adjust the insulin administration schedule. For example, it can reduce the amount of insulin on days when the user exercises a lot. The insulin calculation unit can also comprehensively evaluate the user's lifestyle rhythm and activity level and provide an optimal insulin administration schedule. For example, it can adjust the timing and amount of insulin administration based on the lifestyle rhythm and activity level. This makes it possible to meet individual needs by providing an insulin administration schedule that takes into account the user's lifestyle rhythm and activity level.

[0057] The insulin calculation unit can use the emotion estimation function to suggest the timing and method of insulin administration according to the user's emotional state, thereby reducing stress. The insulin calculation unit, for example, uses the emotion estimation function to analyze the user's emotional state and suggest the timing of insulin administration. For example, the administration timing is adjusted when stress is high. The insulin calculation unit can also suggest the method of insulin administration based on the user's emotional state. For example, the administration method is changed when stress is high. The insulin calculation unit can also use the emotion estimation function to comprehensively evaluate the user's emotional state and optimize the timing and method of insulin administration. For example, the timing and method of insulin administration are fine-tuned based on the emotional state. In this way, by suggesting the timing and method of insulin administration according to the emotional state, stress can be reduced and effective blood sugar management can be achieved.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] Therapeutic apps can also collect data on a user's social activity to evaluate the impact of social connections on health. For example, if a user has few interactions with friends and family, the app can evaluate the impact of loneliness on health. It can also provide advice on increasing social connections based on the user's social activity data. For example, it can suggest participation in local events or online communities. It can also comprehensively evaluate the user's social activity data and quantitatively indicate the impact of social connections on health. This suggests that increasing social connections can improve the user's health.

[0060] Therapeutic apps can also suggest healthy activities based on a user's hobbies and interests. For example, if a user likes music, they can suggest exercises to do while listening to music. Or, if a user enjoys cooking, they can provide healthy recipes. Furthermore, based on a user's hobbies and interests, they can suggest activities to relieve stress. For example, they can suggest activities such as gardening or art therapy. This allows them to promote healthy lifestyle habits by utilizing the user's hobbies and interests.

[0061] Therapeutic apps can further analyze a user's sleep data and provide advice to improve sleep quality. For example, they can analyze the user's sleep duration and depth and suggest an appropriate sleeping environment. They can also suggest routines to improve sleep quality based on the user's sleep data. For example, they can suggest relaxation methods before bedtime or how to select appropriate bedding. They can also comprehensively evaluate the user's sleep data and quantitatively indicate the impact of sleep quality on health. This allows users to improve their overall health by improving their sleep quality.

[0062] The therapeutic app can also analyze the user's emotional state and provide dietary advice based on the emotion. For example, if the user is feeling stressed, it can suggest a meal menu to relieve stress. Also, if the user is feeling tired, it can suggest a meal menu to replenish energy. Furthermore, it can suggest meal timings based on the user's emotional state. For example, it can suggest eating a light snack when stress is high. In this way, it can support health management by providing dietary advice based on the user's emotional state.

[0063] Therapeutic apps can also analyze a user's emotional state and provide exercise advice based on the emotion. For example, if the user is feeling anxious, an exercise plan to reduce anxiety can be suggested. Also, if the user is feeling depressed, an exercise plan to improve mood can be suggested. Furthermore, based on the user's emotional state, it can suggest the timing of exercise according to the emotion. For example, it can suggest exercise to relax when stress is high. In this way, health management can be supported by providing exercise advice according to the user's emotional state.

[0064] The therapeutic app can also analyze the user's emotional state and suggest relaxation methods based on the emotion. For example, if the user is feeling stressed, it can suggest relaxation methods to reduce stress. Also, if the user is feeling anxious, it can suggest relaxation methods to reduce anxiety. Furthermore, it can suggest the timing of relaxation based on the user's emotional state. For example, it can suggest relaxation when stress is high. In this way, it can support health management by providing relaxation methods that correspond to the user's emotional state.

[0065] The therapeutic app can also analyze the user's emotional state and provide sleep advice based on the emotion. For example, if the user is feeling stressed, it can suggest a sleep environment to reduce stress. Also, if the user is feeling anxious, it can suggest a sleep routine to reduce anxiety. Furthermore, it can suggest sleep timing based on the user's emotional state. For example, it can suggest going to bed earlier when stress is high. In this way, it can support health management by providing sleep advice based on the user's emotional state.

[0066] The therapeutic app can further analyze the user's emotional state and suggest the timing and method of insulin administration based on the emotion. For example, if the user is feeling stressed, the app can suggest the timing of insulin administration to reduce stress. Also, if the user is feeling anxious, the app can suggest the method of insulin administration to reduce anxiety. Furthermore, the app can optimize the timing and method of insulin administration based on the emotion of the user. For example, the app can fine-tune the timing and method of insulin administration based on the emotional state. In this way, by suggesting the timing and method of insulin administration based on the emotional state, stress can be reduced and effective blood sugar management can be achieved.

[0067] Therapeutic apps can also analyze a user's dietary data and evaluate the nutritional balance of their meals. For example, they can analyze the user's dietary content and evaluate whether there are any nutrient deficiencies or excesses. They can also suggest nutritionally balanced meal menus based on the user's dietary data. For example, if there is a vitamin or mineral deficiency, they can suggest meal menus that supplement that deficiency. They can also comprehensively evaluate the user's dietary data and quantitatively indicate the impact that nutritional balance has on health. This allows them to evaluate the nutritional balance of their meals and promote healthy eating.

[0068] Therapeutic apps can further analyze the user's exercise data and evaluate the effects of exercise. For example, they can analyze the user's exercise data and evaluate the impact of exercise on health. They can also provide advice to maximize the effects of exercise based on the user's exercise data. For example, they can provide advice on adjusting the frequency and intensity of exercise. They can also comprehensively evaluate the user's exercise data and quantitatively indicate the impact of exercise on health. This makes it possible to evaluate the effects of the user's exercise and promote healthy exercise habits.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The behavior analysis unit analyzes the user's behavior and lifestyle habits. For example, the behavior analysis unit collects and analyzes data such as the user's number of steps, sleep time, and dietary habits. The behavior analysis unit can also evaluate the user's health condition based on the user's lifestyle habits data. For example, the behavior analysis unit collects and analyzes data from smartphones and wearable devices. Step 2: The chat advice unit provides dietary and exercise advice based on the results of the analysis by the behavior analysis unit. For example, when the user asks, "What should I eat for dinner tonight?", the chat advice unit suggests an appropriate meal menu. The chat advice unit can also provide advice on exercise. For example, the chat advice unit suggests an appropriate exercise plan based on the user's exercise data. Step 3: The insulin calculation unit analyzes the blood glucose data and meal images to calculate the appropriate amount of insulin. For example, the insulin calculation unit connects to an external blood glucose measuring device to obtain blood glucose data in real time. When the user uploads an image of their meal to the app, the insulin calculation unit analyzes the image and evaluates the content and calories of the meal. Based on this, the insulin calculation unit automatically calculates the appropriate amount of insulin and suggests it to the user.

[0071] 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0084] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0085] 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.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

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

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 7, the 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

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

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

[0115] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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. [Explanation of symbols]

[0138] 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 behavior analysis unit that analyzes the user's behavior and lifestyle; a chat advice unit that provides advice on diet and exercise based on the results of the analysis by the behavior analysis unit; and an insulin calculation unit that analyzes blood glucose level data and meal images to calculate an appropriate amount of insulin. A system characterized by:

2. The behavior analysis unit The system collects data such as the user's number of steps, sleep time, and dietary details, automatically generates an individual lifestyle improvement plan, and evaluates progress on a weekly basis.

2. The system of claim 1.

3. The chat advice unit Analyze the user's past chat history and provide dietary advice that takes into account individual preferences and allergy information.

2. The system of claim 1.

4. The insulin calculation unit The blood glucose level data and meal images are analyzed to evaluate in detail the effects of specific ingredients and dishes on blood glucose levels.

2. The system of claim 1.

5. The behavior analysis unit Analyzing the emotional state of the user, evaluating the impact of stress and anxiety on the lifestyle habits, and proposing appropriate measures 2. The system of claim 1.

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