system

Abstract

The system according to the embodiment aims to comprehensively improve the three axes of diet, exercise, and motivation in blood glucose level management. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a reception unit, and a generation unit. The collection unit collects blood glucose level data. The analysis unit analyzes the blood glucose level data collected by the collection unit. The provision unit provides real-time advice based on the results of the analysis by the analysis unit. The reception unit accepts photos of meals. The generation unit analyzes the photos accepted by the reception unit, and generates and records data on ingested carbohydrates. The collection unit collects exercise data. The analysis unit analyzes the exercise data collected by the collection unit, and generates and records calories consumed. The generation unit generates a game with a reward. The provision unit provides the game generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to manage blood sugar levels in an integrated manner, including diet and exercise data, and maintain motivation.

[0005] The system according to the embodiment aims to comprehensively improve the three axes of diet, exercise, and motivation in blood glucose level management. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a reception unit, and a generation unit. The collection unit collects blood glucose level data. The analysis unit analyzes the blood glucose level data collected by the collection unit. The provision unit provides real-time advice based on the results of the analysis by the analysis unit. The reception unit accepts photos of meals. The generation unit analyzes the photos accepted by the reception unit, and generates and records data on ingested carbohydrates. The collection unit collects exercise data. The analysis unit analyzes the exercise data collected by the collection unit, and generates and records calories consumed. The generation unit generates a game with a reward. The provision unit provides the game generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively improve the three axes of diet, exercise, and motivation in blood glucose level management. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system of this embodiment of the present invention is based on blood glucose measurement using existing wearable sensors and reading devices. It uses image recognition and real-time content provision by generative AI to assist in improving three areas: diet, exercise, and motivation. The system measures a patient's blood glucose level in real time, analyzes the data, and provides real-time advice. For example, if blood glucose levels are high, the system recommends dietary changes and exercise. Next, the patient takes and posts photos of each meal, similar to a social media account. These photos are analyzed by generative AI to generate and record carbohydrate intake data. For example, the amount of carbohydrate intake is calculated from the food photos, and appropriate dietary advice is provided to the patient. Furthermore, simply by wearing the device, the patient generates and records calorie intake data via GPS. For example, by measuring walking or running distance and time and calculating calories consumed, exercise advice is provided to the patient. Finally, a reward-based game is generated using participation fees. Patients can earn rewards by completing game-like missions. This allows patients to improve their lifestyles and maintain normal blood glucose levels without feeling any burden. This system will enable patients to improve their lifestyles independently without drug therapy, and is expected to contribute to reducing medical costs worldwide.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a reception unit, and a generation unit. The collection unit collects blood glucose data. The collection unit collects blood glucose data, for example, from a blood glucose measurement device. The collection unit can also collect blood glucose data using a continuous blood glucose monitoring device. The collection unit can also collect blood glucose data using a fingertip blood sampling device. The analysis unit analyzes the collected blood glucose data. The analysis unit analyzes the blood glucose data, for example, using a generation AI to generate real-time advice. The analysis unit can also analyze fluctuation patterns in the blood glucose data using the generation AI. The analysis unit can also detect abnormal values ​​in the blood glucose data using the generation AI. The provision unit provides real-time advice based on the results of the analysis by the analysis unit. The provision unit, for example, makes dietary suggestions and exercise recommendations. The provision unit can also advise on the timing of taking medication. The provision unit can also provide advice to encourage lifestyle improvements. The reception unit accepts photos of meals. The reception unit, for example, receives photos of meals taken by patients. The reception unit can also receive photos of meals posted on social media, for example. The reception unit can also build a system that automatically receives photos of meals. The generation unit analyzes the received photos and generates and records ingested carbohydrate data. The generation unit can, for example, use a generation AI to analyze the photos of meals and calculate the amount of ingested carbohydrates. The generation unit can also use the generation AI to extract food components from the photos of meals. The generation unit can also use the generation AI to analyze the photos of meals and calculate ingested calories. As a result, the health management system according to the embodiment can support the user's health management by collecting and analyzing blood glucose level data, dietary data, and exercise data, and providing real-time advice and games with rewards.

[0030] The collection unit can collect blood glucose level data from a blood glucose measuring device. Examples of blood glucose measuring devices include, but are not limited to, fingertip blood sampling devices and continuous blood glucose monitoring devices. The collection unit collects blood glucose level data using, for example, a fingertip blood sampling device. The collection unit can also collect blood glucose level data using a continuous blood glucose monitoring device. The collection unit can also collect blood glucose level data using a non-invasive blood glucose measuring device. This allows accurate blood glucose level data to be obtained by collecting data from the blood glucose measuring device. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the blood glucose measuring device into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected blood glucose level data and generate real-time advice. The analysis unit can analyze the blood glucose level data using, for example, a generation AI and generate real-time advice. For example, the analysis unit can analyze fluctuation patterns in the blood glucose level data and make dietary suggestions. The analysis unit can also detect abnormal values ​​in the blood glucose level data and recommend exercise. The analysis unit can also advise on the timing of taking medicine based on the blood glucose level data. In this way, generating real-time advice can encourage the user to take immediate action. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected blood glucose level data to the generation AI and cause the generation AI to generate real-time advice.

[0032] The reception unit can accept photos of meals. For example, the reception unit accepts photos of meals taken by the patient. For example, the reception unit can accept photos of meals taken using a smartphone camera. The reception unit can also accept photos of meals posted, for example, on social media. The reception unit can also build a system that automatically accepts photos of meals. In this way, by accepting photos of meals, data on meals consumed can be collected. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input photos of meals that have been taken into a generation AI and have the generation AI analyze the photos.

[0033] The generation unit can analyze the received photos and generate and record ingested carbohydrate data. The generation unit can, for example, use a generation AI to analyze a photo of a meal and calculate the amount of ingested carbohydrates. For example, the generation unit can extract food components from a photo of a meal and calculate the amount of ingested carbohydrates. The generation unit can also use a generation AI to analyze a photo of a meal and calculate the calories ingested. The generation unit can also use a generation AI to analyze a photo of a meal and record the nutrients ingested. This makes it easier to manage diet by generating and recording ingested carbohydrate data. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input a received photo of a meal into the generation AI and have the generation AI generate carbohydrate data.

[0034] The collection unit can collect exercise data through GPS. The collection unit collects exercise data using, for example, GPS. For example, the collection unit measures the distance and time of walking or running and collects exercise data. The collection unit can also use GPS to collect exercise data for cycling or hiking. The collection unit can also use GPS to record the exercise route. In this way, accurate exercise data can be obtained by collecting exercise data through GPS. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI analyze the exercise data.

[0035] The analysis unit can analyze the collected exercise data and generate and record the calorie burn. The analysis unit can analyze the exercise data using, for example, a generation AI and generate the calorie burn. For example, the analysis unit can calculate the calorie burn based on the intensity and duration of exercise. The analysis unit can also calculate the calorie burn based on the type and frequency of exercise. The analysis unit can also calculate the calorie burn based on the exercise data, taking into account the individual's basal metabolic rate. In this way, by generating and recording the calorie burn, the effects of exercise can be understood. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the collected exercise data into the generation AI and have the generation AI generate the calorie burn.

[0036] The generation unit can generate a reward-provided game. The generation unit generates the reward-provided game using, for example, a generation AI. For example, the generation unit generates a game that includes missions related to health management. The generation unit can also generate a game in which a player can earn a reward by achieving exercise or dietary goals. The generation unit can also build a system that provides rewards according to the progress of the game. In this way, generating a reward-provided game improves the user's motivation. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the content of the reward-provided game into the generation AI and causes the generation AI to generate the game.

[0037] The providing unit can provide the generated game. The providing unit, for example, provides the generated game to a user. For example, the providing unit provides the game through a web application or a mobile application. The providing unit can also build a system that displays the progress of the game in real time. The providing unit can also build a system that provides rewards based on the results of the game. In this way, by providing the generated game, the user can manage their health while enjoying the game. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a method for providing the generated game to a generating AI and cause the generating AI to optimize the method of providing.

[0038] When collecting blood glucose data, the collection unit can adjust the collection frequency taking into account the user's past blood glucose fluctuation pattern. The collection unit, for example, optimizes the collection frequency taking into account the user's past blood glucose fluctuation pattern. For example, the collection unit increases the collection frequency when the user's past blood glucose fluctuation is large. The collection unit can also decrease the collection frequency when the user's past blood glucose fluctuation is stable. The collection unit can also increase the collection frequency during a specific time period when the user's past blood glucose fluctuation is concentrated in that time period. This allows the collection frequency to be optimized by taking into account the past blood glucose fluctuation pattern, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past blood glucose data into the generation AI and cause the generation AI to optimize the collection frequency.

[0039] When collecting blood glucose level data, the collection unit can customize the collection method by referring to the user's diet and exercise history. The collection unit customizes the collection method by referring to the user's diet and exercise history, for example. For example, the collection unit collects blood glucose level data immediately after the user eats a meal. The collection unit can also collect blood glucose level data immediately after the user exercises. The collection unit can also set the optimal collection timing based on the user's diet and exercise history. This allows the collection method to be customized by referring to the diet and exercise history, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input diet and exercise history data into the generation AI and have the generation AI customize the collection method.

[0040] The collection unit can adjust the collection timing based on the user's lifestyle rhythm when collecting blood glucose level data. The collection unit adjusts the collection timing based on the user's lifestyle rhythm, for example. For example, the collection unit collects blood glucose level data after the user has breakfast. The collection unit can also collect blood glucose level data after the user has lunch. The collection unit can also collect blood glucose level data after the user has dinner. By adjusting the collection timing based on the lifestyle rhythm, data collection that is tailored to the user's lifestyle becomes possible. Some or all of the above-described processing in 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 user's lifestyle rhythm data to the generation AI and cause the generation AI to adjust the collection timing.

[0041] When collecting blood glucose level data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is out, the collection unit prioritizes collecting blood glucose level data from the user's location. Furthermore, when the user is at home, the collection unit can also prioritize collecting blood glucose level data from the user's location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting blood glucose level data from that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. 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 can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the data.

[0042] The collection unit can analyze the user's social media activity and collect related data when collecting blood glucose level data. The collection unit, for example, analyzes the user's social media activity and collects related data. For example, if the user posts a photo of a meal on social media, the collection unit collects blood glucose level data immediately thereafter. The collection unit can also collect blood glucose level data immediately thereafter if the user posts about exercise. The collection unit can also analyze the user's social media activity and collect blood glucose level data at relevant times. This allows for the collection of relevant data by analyzing social media activity, enabling more accurate data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to adjust the timing of data collection.

[0043] The collection unit can customize the collection method by reflecting the user's past feedback when collecting blood glucose level data. The collection unit, for example, customizes the collection method by reflecting the user's past feedback. For example, if the user has previously been dissatisfied with the collection method, the collection unit can improve the collection method by reflecting that feedback. Furthermore, if the user has previously been satisfied with the collection method, the collection unit can continue using that method. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal collection method. In this way, the collection method can be customized by reflecting the past feedback, enabling optimal data collection for the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0044] During analysis, the analysis unit can adjust the analysis algorithm taking into account the fluctuation pattern of the blood glucose level data. The analysis unit, for example, optimizes the analysis algorithm taking into account the fluctuation pattern of the blood glucose level data. For example, when the fluctuation of the blood glucose level data is large, the analysis unit applies an algorithm that smooths the fluctuation. Furthermore, when the fluctuation of the blood glucose level data is small, the analysis unit can also apply an algorithm that performs a detailed analysis. Furthermore, the analysis unit can select an optimal analysis algorithm based on the fluctuation pattern of the blood glucose level data. In this way, by taking the fluctuation pattern of the blood glucose level data into consideration, the analysis algorithm can be optimized and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the analysis unit can input the fluctuation pattern of the blood glucose level data to the generation AI and have the generation AI optimize the analysis algorithm.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's diet and exercise history. The analysis unit can improve the accuracy of the analysis by, for example, referring to the user's diet and exercise history. For example, the analysis unit can refer to the user's diet history to perform an analysis that takes into account the influence of diet. The analysis unit can also refer to the user's exercise history to perform an analysis that takes into account the influence of exercise. The analysis unit can also improve the accuracy of the analysis by comprehensively referring to the user's diet and exercise history. In this way, by referring to the diet and exercise history, the analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's diet and exercise history data into the generation AI and have the generation AI improve the analysis accuracy.

[0046] During analysis, the analysis unit can customize the analysis results based on the user's lifestyle rhythm. The analysis unit customizes the analysis results based on, for example, the user's lifestyle rhythm. For example, the analysis unit analyzes blood glucose level fluctuations after breakfast based on the user's lifestyle rhythm. The analysis unit can also analyze blood glucose level fluctuations after lunch based on the user's lifestyle rhythm. The analysis unit can also analyze blood glucose level fluctuations after dinner based on the user's lifestyle rhythm. By customizing the analysis results based on the user's lifestyle rhythm, it is possible to provide optimal analysis results for the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the analysis results.

[0047] During analysis, the analysis unit can customize the analysis results by taking into account the user's geographical location information. The analysis unit customizes the analysis results by taking into account, for example, the user's geographical location information. For example, when the user is out, the analysis unit prioritizes analyzing blood glucose level data from outside the home. Furthermore, when the user is at home, the analysis unit can prioritize analyzing blood glucose level data from home. Furthermore, when the user is in a specific location, the analysis unit can prioritize analyzing blood glucose level data from that location. In this way, by taking into account the geographical location information, it is possible to provide analysis results that are highly relevant to the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0048] During the analysis, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. The analysis unit, for example, analyzes the user's social media activity and reflects related data in the analysis. For example, if the user posts a photo of their meal on social media, the analysis unit reflects that data in the analysis. In addition, if the user posts about exercise, the analysis unit can also reflect that data in the analysis. In addition, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. In this way, by analyzing social media activity, related data can be reflected in the analysis, and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI reflect the analysis results.

[0049] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, adjusts the analysis algorithm by reflecting the user's past feedback. For example, if the user was dissatisfied with the analysis results in the past, the analysis unit improves the analysis algorithm by reflecting that feedback. The analysis unit can also continue the algorithm if the user was satisfied with the analysis results in the past. The analysis unit can also analyze the user's past feedback and propose an optimal analysis algorithm. In this way, the analysis algorithm can be adjusted by reflecting the past feedback, and optimal analysis results can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0050] When providing advice, the providing unit can optimize the advice content by taking into account the fluctuation pattern of the blood glucose level data. The providing unit, for example, optimizes the advice content by taking into account the fluctuation pattern of the blood glucose level data. For example, when the fluctuation of the blood glucose level data is large, the providing unit provides advice to smooth the fluctuation. Furthermore, when the fluctuation of the blood glucose level data is small, the providing unit can also provide detailed advice. Furthermore, the providing unit can also provide optimal advice content based on the fluctuation pattern of the blood glucose level data. In this way, by taking the fluctuation pattern of the blood glucose level data into account, the advice content can be optimized and more effective advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit can input the fluctuation pattern of the blood glucose level data to the generation AI and cause the generation AI to optimize the advice content.

[0051] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's diet and exercise history. The providing unit, for example, improves the accuracy of the advice by referring to the user's diet and exercise history. For example, the providing unit can provide advice that takes into account the influence of diet by referring to the user's diet history. The providing unit can also provide advice that takes into account the influence of exercise by referring to the user's exercise history. The providing unit can also improve the accuracy of the advice by comprehensively referring to the user's diet and exercise history. In this way, by referring to the diet and exercise history, the accuracy of the advice can be improved and more accurate advice can be provided. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's diet and exercise history data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0052] The providing unit can customize the advice content based on the user's lifestyle rhythm when providing advice. The providing unit customizes the advice content based on, for example, the user's lifestyle rhythm. For example, the providing unit provides post-breakfast advice based on the user's lifestyle rhythm. The providing unit can also provide post-lunch advice based on the user's lifestyle rhythm. The providing unit can also provide post-dinner advice based on the user's lifestyle rhythm. In this way, by customizing the advice content based on the user's lifestyle rhythm, it is possible to provide optimal advice for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the advice content.

[0053] When providing advice, the providing unit can customize the advice content by taking into account the user's geographical location information. The providing unit customizes the advice content by taking into account, for example, the user's geographical location information. For example, when the user is out, the providing unit provides advice for the user while out. Furthermore, when the user is at home, the providing unit can also provide advice for the user at home. Furthermore, when the user is in a specific location, the providing unit can also provide advice for that location. In this way, by taking into account the geographical location information, it is possible to provide advice that is highly relevant to the user. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the user's geographical location information to the generation AI and cause the generation AI to customize the advice content.

[0054] When providing advice, the providing unit can analyze the user's social media activity and provide relevant advice. The providing unit, for example, analyzes the user's social media activity and provides relevant advice. For example, if the user posts a photo of a meal on social media, the providing unit can provide advice based on that data. In addition, if the user posts about exercise, the providing unit can also provide advice based on that data. In addition, the providing unit can analyze the user's social media activity and provide relevant advice. In this way, by analyzing social media activity, relevant advice can be provided, and more accurate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide advice content.

[0055] When providing advice, the providing unit can adjust the advice content by reflecting the user's past feedback. The providing unit, for example, adjusts the advice content by reflecting the user's past feedback. For example, if the user was dissatisfied with the advice content in the past, the providing unit can improve the advice content by reflecting that feedback. Furthermore, if the user was satisfied with the advice content in the past, the providing unit can continue that content. Furthermore, the providing unit can analyze the user's past feedback and suggest optimal advice content. In this way, the advice content can be adjusted by reflecting the past feedback, and optimal advice can be provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the advice content.

[0056] The reception unit can optimize the reception method by referring to the user's past meal history when receiving a photo. The reception unit, for example, optimizes the reception method by referring to the user's past meal history. For example, the reception unit may refer to the user's past meal history and automatically accept similar meals if there are any. The reception unit may also refer to the user's past meal history and request detailed information if there are any different meals. The reception unit may also suggest the optimal reception method by referring to the user's past meal history. This allows the reception method to be optimized by referring to the past meal history, enabling more accurate data collection. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit may input the user's past meal history data into the generation AI and cause the generation AI to optimize the reception method.

[0057] The reception unit can customize the reception method by taking into account the user's eating patterns when receiving photos. The reception unit customizes the reception method by taking into account the user's eating patterns, for example. For example, the reception unit may accept photos according to the time of day when the user eats breakfast. The reception unit may also accept photos according to the time of day when the user eats lunch. The reception unit may also accept photos according to the time of day when the user eats dinner. This allows the reception method to be customized by taking eating patterns into account, enabling more accurate data collection. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input the user's eating pattern data into the generation AI and cause the generation AI to customize the reception method.

[0058] The reception unit can adjust the reception timing based on the user's lifestyle rhythm when receiving a photo. The reception unit adjusts the reception timing based on the user's lifestyle rhythm, for example. For example, the reception unit may receive a photo after breakfast based on the user's lifestyle rhythm. The reception unit may also receive a photo after lunch based on the user's lifestyle rhythm. The reception unit may also receive a photo after dinner based on the user's lifestyle rhythm. By adjusting the reception timing based on the user's lifestyle rhythm, data collection tailored to the user's lifestyle becomes possible. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's lifestyle rhythm data into the generation AI and cause the generation AI to adjust the reception timing.

[0059] When accepting photos, the reception unit can prioritize accepting highly relevant photos in consideration of the user's geographical location information. The reception unit, for example, prioritizes accepting highly relevant photos in consideration of the user's geographical location information. For example, when the user is out, the reception unit can prioritize accepting photos of meals eaten while out. Furthermore, when the user is at home, the reception unit can prioritize accepting photos of meals eaten at home. Furthermore, when the user is in a specific location, the reception unit can prioritize accepting photos of meals eaten at that location. In this way, by taking geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the photos.

[0060] The reception unit can analyze the user's social media activity and accept related photos when accepting photos. The reception unit, for example, analyzes the user's social media activity and accepts related photos. For example, if the user posts a photo of a meal on social media, the reception unit accepts the photo. Furthermore, if the user posts a photo related to a meal on social media, the reception unit can also accept the photo. Furthermore, the reception unit can analyze the user's social media activity and accept related photos. This allows for the collection of related data by analyzing social media activity, enabling more accurate data collection. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to accept photos.

[0061] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a photo. The reception unit, for example, customizes the reception method by reflecting the user's past feedback. For example, if the user was dissatisfied with the reception method in the past, the reception unit can improve the reception method by reflecting that feedback. Furthermore, if the user was satisfied with the reception method in the past, the reception unit can continue that method. Furthermore, the reception unit can analyze the user's past feedback and suggest an optimal reception method. In this way, the reception method can be customized by reflecting past feedback, enabling optimal data collection for the user. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0062] The generation unit can optimize the analysis algorithm during analysis, taking into account the type and amount of food. The generation unit optimizes the analysis algorithm, for example, taking into account the type and amount of food. For example, the generation unit applies an algorithm that performs a detailed analysis when there are many types of food. The generation unit can also apply an algorithm that performs a detailed analysis when the amount of food is large. The generation unit can also select an optimal analysis algorithm based on the type and amount of food. In this way, by taking into account the type and amount of food, the analysis algorithm can be optimized and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the type and amount of food into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0063] During analysis, the generation unit can improve analysis accuracy by referring to the user's past meal history. The generation unit, for example, improves analysis accuracy by referring to the user's past meal history. For example, the generation unit refers to the user's past meal history and performs a detailed analysis if similar meals are present. The generation unit can also refer to the user's past meal history and perform a detailed analysis if different meals are present. The generation unit can also refer to the user's past meal history to suggest an optimal analysis method. In this way, by referring to the past meal history, analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past meal history data into the generation AI and cause the generation AI to improve analysis accuracy.

[0064] The generation unit can customize the analysis results based on the user's lifestyle rhythm during analysis. The generation unit customizes the analysis results based on, for example, the user's lifestyle rhythm. For example, the generation unit provides analysis results after breakfast based on the user's lifestyle rhythm. The generation unit can also provide analysis results after lunch based on the user's lifestyle rhythm. The generation unit can also provide analysis results after dinner based on the user's lifestyle rhythm. In this way, by customizing the analysis results based on the user's lifestyle rhythm, it is possible to provide optimal analysis results for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the analysis results.

[0065] During analysis, the generation unit can customize the analysis results by taking into account the user's geographical location information. The generation unit customizes the analysis results by taking into account, for example, the user's geographical location information. For example, when the user is out, the generation unit can prioritize providing analysis results of meals eaten while out. Furthermore, when the user is at home, the generation unit can prioritize providing analysis results of meals eaten at home. Furthermore, when the user is in a specific location, the generation unit can prioritize providing analysis results of meals eaten at that location. In this way, by taking into account the geographical location information, it is possible to provide analysis results that are highly relevant to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0066] During analysis, the generation unit can analyze the user's social media activity and reflect related data in the analysis. The generation unit, for example, analyzes the user's social media activity and reflects related data in the analysis. For example, if the user posts a photo of a meal on social media, the generation unit performs analysis based on that data. In addition, if the user posts something about a meal, the generation unit can also perform analysis based on that data. In addition, the generation unit can analyze the user's social media activity and reflect related data in the analysis. In this way, by analyzing social media activity, related data can be reflected in the analysis, and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to reflect the analysis results.

[0067] During analysis, the generation unit can adjust the analysis algorithm by reflecting the user's past feedback. The generation unit, for example, adjusts the analysis algorithm by reflecting the user's past feedback. For example, if the user was dissatisfied with the analysis results in the past, the generation unit improves the analysis algorithm by reflecting that feedback. The generation unit can also continue the algorithm if the user was satisfied with the analysis results in the past. The generation unit can also analyze the user's past feedback and propose an optimal analysis algorithm. In this way, the analysis algorithm can be adjusted by reflecting the past feedback, and optimal analysis results can be provided to the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0068] When collecting exercise data, the collection unit can optimize the collection method by referring to the user's past exercise history. The collection unit, for example, optimizes the collection method by referring to the user's past exercise history. For example, the collection unit refers to the user's past exercise history and automatically collects similar exercise data if there is a similar exercise. The collection unit can also refer to the user's past exercise history to request detailed information if there is a different exercise. The collection unit can also suggest the optimal collection method by referring to the user's past exercise history. This allows the collection method to be optimized by referring to the past exercise history, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past exercise history data into the generation AI and have the generation AI optimize the collection method.

[0069] When collecting exercise data, the collection unit can customize the collection method by taking into account the user's exercise pattern. The collection unit customizes the collection method by taking into account, for example, the user's exercise pattern. For example, the collection unit collects exercise data according to the time of day when the user walks. The collection unit can also collect exercise data according to the time of day when the user runs. The collection unit can also collect exercise data according to the time of day when the user trains at the gym. This allows the collection method to be customized by taking into account the exercise pattern, enabling more accurate data collection. 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 user's exercise pattern data into the generation AI and cause the generation AI to customize the collection method.

[0070] The collection unit can adjust the collection timing based on the user's lifestyle rhythm when collecting exercise data. The collection unit adjusts the collection timing based on, for example, the user's lifestyle rhythm. For example, the collection unit collects morning exercise data based on the user's lifestyle rhythm. The collection unit can also collect daytime exercise data based on the user's lifestyle rhythm. The collection unit can also collect evening exercise data based on the user's lifestyle rhythm. By adjusting the collection timing based on the user's lifestyle rhythm, data collection tailored to the user's lifestyle becomes possible. Some or all of the above-described processing in 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 user's lifestyle rhythm data to the generation AI and cause the generation AI to adjust the collection timing.

[0071] When collecting exercise data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is out, the collection unit prioritizes collecting exercise data from outside the home. Furthermore, when the user is at home, the collection unit can prioritize collecting exercise data from the home. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting exercise data from that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the exercise data.

[0072] The collection unit can analyze the user's social media activity and collect related data when collecting exercise data. The collection unit, for example, analyzes the user's social media activity and collects related data. For example, if the user posts about exercise on social media, the collection unit collects exercise data based on that data. Furthermore, if the user posts a photo about exercise, the collection unit can also collect exercise data based on that data. Furthermore, the collection unit can analyze the user's social media activity and collect related data. By analyzing social media activity, related data can be collected, enabling more accurate data collection. 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 user's social media activity data into a generation AI and cause the generation AI to collect exercise data.

[0073] When collecting exercise data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, customizes the collection method by reflecting the user's past feedback. For example, if the user was dissatisfied with the collection method in the past, the collection unit can improve the collection method by reflecting that feedback. Furthermore, if the user was satisfied with the collection method in the past, the collection unit can continue that method. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal collection method. In this way, the collection method can be customized by reflecting the past feedback, enabling optimal data collection for the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0074] During analysis, the analysis unit can optimize the analysis algorithm by taking into account the type and intensity of exercise. The analysis unit optimizes the analysis algorithm by taking into account, for example, the type and intensity of exercise. For example, the analysis unit applies an algorithm that performs a detailed analysis when there are many types of exercise. The analysis unit can also apply an algorithm that performs a detailed analysis when the intensity of exercise is high. The analysis unit can also select an optimal analysis algorithm based on the type and intensity of exercise. This allows the analysis algorithm to be optimized by taking into account the type and intensity of exercise, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the type and intensity of exercise into the generation AI and have the generation AI optimize the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past exercise history. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past exercise history. For example, the analysis unit refers to the user's past exercise history to perform a detailed analysis when there is a similar exercise. The analysis unit can also refer to the user's past exercise history to perform a detailed analysis when there is a different exercise. The analysis unit can also refer to the user's past exercise history to suggest an optimal analysis method. By referring to the past exercise history, analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past exercise history data into the generation AI and have the generation AI improve the analysis accuracy.

[0076] During analysis, the analysis unit can customize the analysis results based on the user's lifestyle rhythm. The analysis unit customizes the analysis results based on, for example, the user's lifestyle rhythm. For example, the analysis unit analyzes morning exercise data based on the user's lifestyle rhythm. The analysis unit can also analyze daytime exercise data based on the user's lifestyle rhythm. The analysis unit can also analyze evening exercise data based on the user's lifestyle rhythm. This allows the analysis results to be customized based on the user's lifestyle rhythm, thereby providing optimal analysis results for the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the analysis results.

[0077] During analysis, the analysis unit can customize the analysis results by taking into account the user's geographical location information. The analysis unit customizes the analysis results by taking into account, for example, the user's geographical location information. For example, when the user is out, the analysis unit prioritizes analyzing exercise data from outside the home. Furthermore, when the user is at home, the analysis unit can prioritize analyzing exercise data from home. Furthermore, when the user is in a specific location, the analysis unit can prioritize analyzing exercise data from that location. In this way, by taking into account the geographical location information, it is possible to provide analysis results that are highly relevant to the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0078] During the analysis, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. The analysis unit, for example, analyzes the user's social media activity and reflects related data in the analysis. For example, if the user posts about exercise on social media, the analysis unit can perform analysis based on that data. Also, if the user posts a photo about exercise, the analysis unit can perform analysis based on that data. Also, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. In this way, by analyzing social media activity, related data can be reflected in the analysis, and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI reflect the analysis results.

[0079] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, adjusts the analysis algorithm by reflecting the user's past feedback. For example, if the user was dissatisfied with the analysis results in the past, the analysis unit improves the analysis algorithm by reflecting that feedback. The analysis unit can also continue the algorithm if the user was satisfied with the analysis results in the past. The analysis unit can also analyze the user's past feedback and propose an optimal analysis algorithm. In this way, the analysis algorithm can be adjusted by reflecting the past feedback, and optimal analysis results can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0080] When generating a game, the generation unit can optimize the game content by referring to the user's past game history. The generation unit, for example, optimizes the game content by referring to the user's past game history. For example, the generation unit may suggest optimal game content based on data on games the user has played in the past. The generation unit may also analyze the user's preferred genres from the user's past game history and generate a related game. The generation unit may also generate a game with an adjusted difficulty level by referring to the user's past game history. This allows the game content to be optimized by referring to the past game history, providing the user with an optimal gaming experience. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's past game history data into the generation AI and cause the generation AI to optimize the game content.

[0081] The generation unit can customize game content by taking into account the user's interests and concerns when generating a game. The generation unit customizes game content by taking into account the user's interests and concerns, for example. For example, the generation unit generates a game based on a theme that the user is interested in. The generation unit can also generate a game featuring a character that the user is interested in. The generation unit can also suggest optimal game content based on the user's interests and concerns. This allows the game content to be customized by taking into account the user's interests and concerns, thereby providing the user with an optimal gaming experience. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user interest and concern data into the generation AI and have the generation AI customize the game content.

[0082] The generation unit can adjust the progress of the game based on the user's lifestyle rhythm when generating the game. The generation unit adjusts the progress of the game based on, for example, the user's lifestyle rhythm. For example, the generation unit generates a game suitable for a morning time period based on the user's lifestyle rhythm. The generation unit can also generate a game suitable for a daytime time period based on the user's lifestyle rhythm. The generation unit can also generate a game suitable for an evening time period based on the user's lifestyle rhythm. By adjusting the progress of the game based on the user's lifestyle rhythm, it is possible to provide an optimal gaming experience for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to adjust the progress of the game.

[0083] The generation unit can customize game content by taking into account the user's geographical location information when generating a game. The generation unit customizes game content by taking into account the user's geographical location information, for example. For example, when the user is out, the generation unit generates a game that can be played while out. Furthermore, when the user is at home, the generation unit can generate a game that can be played at home. Furthermore, when the user is in a specific location, the generation unit can generate a game related to that location. In this way, by taking into account the geographical location information, a highly relevant game experience can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI and causes the generation AI to customize the game content.

[0084] The generation unit can analyze the user's social media activities and provide related game content when generating a game. The generation unit can, for example, analyze the user's social media activities and provide related game content. For example, the generation unit can generate a game based on a theme in which the user has shown interest on social media. The generation unit can also generate a game featuring characters the user follows on social media. The generation unit can also analyze the user's social media activities and provide related game content. In this way, by analyzing social media activities, related game content can be provided, providing an optimal gaming experience for the user. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide game content.

[0085] The generation unit can adjust the game content by reflecting the user's past feedback when generating a game. The generation unit, for example, adjusts the game content by reflecting the user's past feedback. For example, if the user was dissatisfied with the game content in the past, the generation unit can improve the game content by reflecting that feedback. Furthermore, if the user was satisfied with the game content in the past, the generation unit can continue that content. Furthermore, the generation unit can analyze the user's past feedback and suggest optimal game content. In this way, the game content can be adjusted by reflecting the past feedback, and the user can be provided with an optimal gaming experience. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the game content.

[0086] When providing a game, the providing unit can optimize the providing method by referring to the user's past game history. The providing unit, for example, optimizes the providing method by referring to the user's past game history. For example, the providing unit can provide a similar providing method by referring to the user's past game history. The providing unit can also provide a different providing method by referring to the user's past game history. The providing unit can also suggest an optimal providing method by referring to the user's past game history. In this way, by referring to the past game history, the providing method can be optimized and the optimal game experience can be provided for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past game history data into the generation AI and cause the generation AI to optimize the providing method.

[0087] The providing unit can customize the delivery method by taking into account the user's interests and concerns when providing a game. The providing unit customizes the delivery method by taking into account the user's interests and concerns, for example. For example, the providing unit provides a delivery method based on a theme in which the user is interested. The providing unit can also provide a delivery method that features a character in which the user is interested. The providing unit can also suggest the optimal delivery method based on the user's interests and concerns. In this way, the delivery method can be customized by taking into account the user's interests and concerns, and the optimal game experience can be provided for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's interest and concern data into the generation AI and cause the generation AI to customize the delivery method.

[0088] The providing unit can adjust the timing of providing a game based on the user's lifestyle rhythm when providing the game. The providing unit adjusts the timing of providing a game based on, for example, the user's lifestyle rhythm. For example, the providing unit provides a providing method suitable for a morning time period based on the user's lifestyle rhythm. The providing unit can also provide a providing method suitable for a daytime time period based on the user's lifestyle rhythm. The providing unit can also provide a providing method suitable for an evening time period based on the user's lifestyle rhythm. In this way, by adjusting the timing of providing a game based on the user's lifestyle rhythm, it is possible to provide an optimal game experience for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit may input the user's lifestyle rhythm data into the generation AI and cause the generation AI to adjust the timing of providing a game.

[0089] The providing unit can customize the provision method when providing a game by taking into account the user's geographical location information. The providing unit customizes the provision method by taking into account, for example, the user's geographical location information. For example, when the user is out, the providing unit can provide a game that can be played while out. Furthermore, when the user is at home, the providing unit can also provide a game that can be played at home. Furthermore, when the user is in a specific location, the providing unit can also provide a game related to that location. In this way, by taking into account the geographical location information, a highly relevant gaming experience can be provided to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the provision method.

[0090] The providing unit can analyze the user's social media activity and provide related game content when providing a game. The providing unit, for example, analyzes the user's social media activity and provides related game content. For example, the providing unit can provide a game based on a theme in which the user has shown interest on social media. The providing unit can also provide a game featuring a character the user follows on social media. The providing unit can also analyze the user's social media activity and provide related game content. In this way, by analyzing social media activity, related game content can be provided, providing an optimal gaming experience for the user. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide game content.

[0091] The providing unit can adjust the game providing method by reflecting the user's past feedback when providing a game. The providing unit, for example, adjusts the game providing method by reflecting the user's past feedback. For example, if the user was dissatisfied with the game providing method in the past, the providing unit can improve the game providing method by reflecting that feedback. Furthermore, if the user was satisfied with the game providing method in the past, the providing unit can continue that method. Furthermore, the providing unit can analyze the user's past feedback and propose an optimal game providing method. In this way, the game providing method can be adjusted by reflecting the past feedback, and an optimal game experience can be provided to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the game providing method.

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

[0093] The collection unit can also collect the user's heart rate data and analyze it in combination with blood glucose level data. For example, the collection unit can monitor heart rate fluctuations in real time and evaluate the effects of stress and exercise. The collection unit can also estimate the intensity and duration of exercise based on the heart rate data. Furthermore, the collection unit can use the heart rate data to evaluate the user's overall health and provide appropriate advice. Thus, collecting heart rate data enables more comprehensive health management.

[0094] The analysis unit can also analyze the user's sleep data and evaluate the correlation with blood glucose level data. For example, the analysis unit can analyze the user's sleep patterns and evaluate the impact of lack of sleep on blood glucose levels. The analysis unit can also evaluate the quality of sleep and provide advice for improvement. Furthermore, the analysis unit can make suggestions for adjusting the user's lifestyle based on the sleep data. This allows for more accurate health management by analyzing sleep data.

[0095] The reception unit can also evaluate the nutritional balance of a meal when accepting a photo of the user's meal. For example, the reception unit can evaluate the balance of vegetables, protein, and carbohydrates from the photo of the meal and suggest improvements to the nutritional balance. The reception unit can also calculate calorie intake from the photo of the meal and point out excess or deficiency. Furthermore, the reception unit can detect allergens from the photo of the meal and suggest allergy countermeasures. In this way, accepting photo of meals enables more detailed dietary management.

[0096] When collecting the user's exercise data, the collection unit can automatically identify the type and intensity of exercise. For example, the collection unit can automatically identify exercise such as walking, running, cycling, etc. and classify the data. The collection unit can also evaluate the intensity of the exercise and suggest an appropriate amount of exercise. Furthermore, the collection unit can measure the duration of the exercise and evaluate the effect of the exercise. This allows for more detailed exercise management by collecting exercise data.

[0097] The providing unit can also provide region-specific health information by taking into account the user's geographical location information. For example, the providing unit can provide appropriate dietary and exercise advice based on the climate and food culture of the region in which the user lives. The providing unit can also provide information on local medical institutions and fitness facilities. Furthermore, the providing unit can provide information on local events and health programs. This allows for more personalized health management by taking into account the geographical location information.

[0098] The providing unit can also analyze the user's social media activity and provide related health information. For example, the providing unit can provide appropriate advice based on health topics the user has shown interest in on social media. The providing unit can also provide information on health experts the user follows. Furthermore, the providing unit can analyze the user's social media activity and provide information on related health events and programs. This allows for more personalized health management by analyzing social media activity.

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

[0100] Step 1: The collection unit collects blood glucose level data. The collection unit collects blood glucose level data using, for example, a blood glucose measurement device, a continuous blood glucose monitoring device, or a fingertip blood sampling device. Step 2: The analysis unit analyzes the collected blood glucose data. The analysis unit uses the generation AI to analyze the blood glucose data and generate real-time advice. It can also analyze fluctuation patterns and abnormal values ​​in the blood glucose data. Step 3: The provider provides real-time advice based on the results of the analysis by the analyzer, such as dietary suggestions, exercise recommendations, advice on medication timing, and advice to improve lifestyle habits. Step 4: The reception unit accepts photos of meals. The reception unit accepts photos of meals taken by patients or posted on social media. It is also possible to build a system that automatically accepts photos of meals. Step 5: The generator analyzes the received photo and generates and records the ingested carbohydrate data. The generator uses the generation AI to analyze the food photo and calculate the amount of ingested carbohydrates, food components, and calories. Step 6: The collection unit collects the exercise data. The collection unit collects the exercise data using, for example, a sensor of a wearable device or a smartphone. Step 7: The analysis unit analyzes the collected exercise data, generates and records the calorie consumption. The analysis unit analyzes the exercise data using the generation AI and calculates the calorie consumption. Step 8: The generator generates a reward-provided game. The generator generates, for example, a game to encourage the user to manage their health. Step 9: The providing unit provides the game generated by the generating unit. The providing unit provides a game that allows the user to manage their health while having fun.

[0101] (Example 2) The system of this embodiment of the present invention is based on blood glucose measurement using existing wearable sensors and reading devices. It uses image recognition and real-time content provision by generative AI to assist in improving three areas: diet, exercise, and motivation. The system measures a patient's blood glucose level in real time, analyzes the data, and provides real-time advice. For example, if blood glucose levels are high, the system recommends dietary changes and exercise. Next, the patient takes and posts photos of each meal, similar to a social media account. These photos are analyzed by generative AI to generate and record carbohydrate intake data. For example, the amount of carbohydrate intake is calculated from the food photos, and appropriate dietary advice is provided to the patient. Furthermore, simply by wearing the device, the patient generates and records calorie intake data via GPS. For example, by measuring walking or running distance and time and calculating calories consumed, exercise advice is provided to the patient. Finally, a reward-based game is generated using participation fees. Patients can earn rewards by completing game-like missions. This allows patients to improve their lifestyles and maintain normal blood glucose levels without feeling any burden. This system will enable patients to improve their lifestyles independently without drug therapy, and is expected to contribute to reducing medical costs worldwide.

[0102] A health management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a reception unit, and a generation unit. The collection unit collects blood glucose data. The collection unit collects blood glucose data, for example, from a blood glucose measurement device. The collection unit can also collect blood glucose data using a continuous blood glucose monitoring device. The collection unit can also collect blood glucose data using a fingertip blood sampling device. The analysis unit analyzes the collected blood glucose data. The analysis unit analyzes the blood glucose data, for example, using a generation AI to generate real-time advice. The analysis unit can also analyze fluctuation patterns in the blood glucose data using the generation AI. The analysis unit can also detect abnormal values ​​in the blood glucose data using the generation AI. The provision unit provides real-time advice based on the results of the analysis by the analysis unit. The provision unit, for example, makes dietary suggestions and exercise recommendations. The provision unit can also advise on the timing of taking medication. The provision unit can also provide advice to encourage lifestyle improvements. The reception unit accepts photos of meals. The reception unit, for example, receives photos of meals taken by patients. The reception unit can also receive photos of meals posted on social media, for example. The reception unit can also build a system that automatically receives photos of meals. The generation unit analyzes the received photos and generates and records ingested carbohydrate data. The generation unit can, for example, use a generation AI to analyze the photos of meals and calculate the amount of ingested carbohydrates. The generation unit can also use the generation AI to extract food components from the photos of meals. The generation unit can also use the generation AI to analyze the photos of meals and calculate ingested calories. As a result, the health management system according to the embodiment can support the user's health management by collecting and analyzing blood glucose level data, dietary data, and exercise data, and providing real-time advice and games with rewards.

[0103] The collection unit can collect blood glucose level data from a blood glucose measuring device. Examples of blood glucose measuring devices include, but are not limited to, fingertip blood sampling devices and continuous blood glucose monitoring devices. The collection unit collects blood glucose level data using, for example, a fingertip blood sampling device. The collection unit can also collect blood glucose level data using a continuous blood glucose monitoring device. The collection unit can also collect blood glucose level data using a non-invasive blood glucose measuring device. This allows accurate blood glucose level data to be obtained by collecting data from the blood glucose measuring device. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the blood glucose measuring device into a generation AI and have the generation AI analyze the data.

[0104] The analysis unit can analyze the collected blood glucose level data and generate real-time advice. The analysis unit can analyze the blood glucose level data using, for example, a generation AI and generate real-time advice. For example, the analysis unit can analyze fluctuation patterns in the blood glucose level data and make dietary suggestions. The analysis unit can also detect abnormal values ​​in the blood glucose level data and recommend exercise. The analysis unit can also advise on the timing of taking medicine based on the blood glucose level data. In this way, generating real-time advice can encourage the user to take immediate action. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected blood glucose level data to the generation AI and cause the generation AI to generate real-time advice.

[0105] The reception unit can accept photos of meals. For example, the reception unit accepts photos of meals taken by the patient. For example, the reception unit can accept photos of meals taken using a smartphone camera. The reception unit can also accept photos of meals posted, for example, on social media. The reception unit can also build a system that automatically accepts photos of meals. In this way, by accepting photos of meals, data on meals consumed can be collected. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input photos of meals that have been taken into a generation AI and have the generation AI analyze the photos.

[0106] The generation unit can analyze the received photos and generate and record ingested carbohydrate data. The generation unit can, for example, use a generation AI to analyze a photo of a meal and calculate the amount of ingested carbohydrates. For example, the generation unit can extract food components from a photo of a meal and calculate the amount of ingested carbohydrates. The generation unit can also use a generation AI to analyze a photo of a meal and calculate the calories ingested. The generation unit can also use a generation AI to analyze a photo of a meal and record the nutrients ingested. This makes it easier to manage diet by generating and recording ingested carbohydrate data. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input a received photo of a meal into the generation AI and have the generation AI generate carbohydrate data.

[0107] The collection unit can collect exercise data through GPS. The collection unit collects exercise data using, for example, GPS. For example, the collection unit measures the distance and time of walking or running and collects exercise data. The collection unit can also use GPS to collect exercise data for cycling or hiking. The collection unit can also use GPS to record the exercise route. In this way, accurate exercise data can be obtained by collecting exercise data through GPS. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI analyze the exercise data.

[0108] The analysis unit can analyze the collected exercise data and generate and record the calorie burn. The analysis unit can analyze the exercise data using, for example, a generation AI and generate the calorie burn. For example, the analysis unit can calculate the calorie burn based on the intensity and duration of exercise. The analysis unit can also calculate the calorie burn based on the type and frequency of exercise. The analysis unit can also calculate the calorie burn based on the exercise data, taking into account the individual's basal metabolic rate. In this way, by generating and recording the calorie burn, the effects of exercise can be understood. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the collected exercise data into the generation AI and have the generation AI generate the calorie burn.

[0109] The generation unit can generate a reward-provided game. The generation unit generates the reward-provided game using, for example, a generation AI. For example, the generation unit generates a game that includes missions related to health management. The generation unit can also generate a game in which a player can earn a reward by achieving exercise or dietary goals. The generation unit can also build a system that provides rewards according to the progress of the game. In this way, generating a reward-provided game improves the user's motivation. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the content of the reward-provided game into the generation AI and causes the generation AI to generate the game.

[0110] The providing unit can provide the generated game. The providing unit, for example, provides the generated game to a user. For example, the providing unit provides the game through a web application or a mobile application. The providing unit can also build a system that displays the progress of the game in real time. The providing unit can also build a system that provides rewards based on the results of the game. In this way, by providing the generated game, the user can manage their health while enjoying the game. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a method for providing the generated game to a generating AI and cause the generating AI to optimize the method of providing.

[0111] The collection unit can estimate the user's emotion and adjust the timing of collecting blood glucose level data based on the estimated user emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting blood glucose level data based on the estimated emotion. For example, if the user is feeling stressed, the collection unit collects blood glucose level data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can also collect blood glucose level data at a normal collection timing. Furthermore, if the user is excited, the collection unit can delay collection until the user's excitement subsides. This enables more appropriate data collection by adjusting the timing of collecting blood glucose level data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0112] When collecting blood glucose data, the collection unit can adjust the collection frequency taking into account the user's past blood glucose fluctuation pattern. The collection unit, for example, optimizes the collection frequency taking into account the user's past blood glucose fluctuation pattern. For example, the collection unit increases the collection frequency when the user's past blood glucose fluctuation is large. The collection unit can also decrease the collection frequency when the user's past blood glucose fluctuation is stable. The collection unit can also increase the collection frequency during a specific time period when the user's past blood glucose fluctuation is concentrated in that time period. This allows the collection frequency to be optimized by taking into account the past blood glucose fluctuation pattern, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past blood glucose data into the generation AI and cause the generation AI to optimize the collection frequency.

[0113] When collecting blood glucose level data, the collection unit can customize the collection method by referring to the user's diet and exercise history. The collection unit customizes the collection method by referring to the user's diet and exercise history, for example. For example, the collection unit collects blood glucose level data immediately after the user eats a meal. The collection unit can also collect blood glucose level data immediately after the user exercises. The collection unit can also set the optimal collection timing based on the user's diet and exercise history. This allows the collection method to be customized by referring to the diet and exercise history, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input diet and exercise history data into the generation AI and have the generation AI customize the collection method.

[0114] The collection unit can adjust the collection timing based on the user's lifestyle rhythm when collecting blood glucose level data. The collection unit adjusts the collection timing based on the user's lifestyle rhythm, for example. For example, the collection unit collects blood glucose level data after the user has breakfast. The collection unit can also collect blood glucose level data after the user has lunch. The collection unit can also collect blood glucose level data after the user has dinner. By adjusting the collection timing based on the lifestyle rhythm, data collection that is tailored to the user's lifestyle becomes possible. Some or all of the above-described processing in 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 user's lifestyle rhythm data to the generation AI and cause the generation AI to adjust the collection timing.

[0115] The collection unit can estimate the user's emotions and determine the priority of blood glucose level data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of blood glucose level data based on the estimated emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting data on the impact of stress on blood glucose levels. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting normal blood glucose level data. Furthermore, if the user is excited, the collection unit can also prioritize collecting data on the impact of excitement on blood glucose levels. Thus, by determining the priority of data based on the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0116] When collecting blood glucose level data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is out, the collection unit prioritizes collecting blood glucose level data from the user's location. Furthermore, when the user is at home, the collection unit can also prioritize collecting blood glucose level data from the user's location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting blood glucose level data from that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. 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 can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the data.

[0117] The collection unit can analyze the user's social media activity and collect related data when collecting blood glucose level data. The collection unit, for example, analyzes the user's social media activity and collects related data. For example, if the user posts a photo of a meal on social media, the collection unit collects blood glucose level data immediately thereafter. The collection unit can also collect blood glucose level data immediately thereafter if the user posts about exercise. The collection unit can also analyze the user's social media activity and collect blood glucose level data at relevant times. This allows for the collection of relevant data by analyzing social media activity, enabling more accurate data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to adjust the timing of data collection.

[0118] The collection unit can customize the collection method by reflecting the user's past feedback when collecting blood glucose level data. The collection unit, for example, customizes the collection method by reflecting the user's past feedback. For example, if the user has previously been dissatisfied with the collection method, the collection unit can improve the collection method by reflecting that feedback. Furthermore, if the user has previously been satisfied with the collection method, the collection unit can continue using that method. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal collection method. In this way, the collection method can be customized by reflecting the past feedback, enabling optimal data collection for the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0119] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand by adjusting the presentation method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis results.

[0120] During analysis, the analysis unit can adjust the analysis algorithm taking into account the fluctuation pattern of the blood glucose level data. The analysis unit, for example, optimizes the analysis algorithm taking into account the fluctuation pattern of the blood glucose level data. For example, when the fluctuation of the blood glucose level data is large, the analysis unit applies an algorithm that smooths the fluctuation. Furthermore, when the fluctuation of the blood glucose level data is small, the analysis unit can also apply an algorithm that performs a detailed analysis. Furthermore, the analysis unit can select an optimal analysis algorithm based on the fluctuation pattern of the blood glucose level data. In this way, by taking the fluctuation pattern of the blood glucose level data into consideration, the analysis algorithm can be optimized and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the analysis unit can input the fluctuation pattern of the blood glucose level data to the generation AI and have the generation AI optimize the analysis algorithm.

[0121] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's diet and exercise history. The analysis unit can improve the accuracy of the analysis by, for example, referring to the user's diet and exercise history. For example, the analysis unit can refer to the user's diet history to perform an analysis that takes into account the influence of diet. The analysis unit can also refer to the user's exercise history to perform an analysis that takes into account the influence of exercise. The analysis unit can also improve the accuracy of the analysis by comprehensively referring to the user's diet and exercise history. In this way, by referring to the diet and exercise history, the analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's diet and exercise history data into the generation AI and have the generation AI improve the analysis accuracy.

[0122] During analysis, the analysis unit can customize the analysis results based on the user's lifestyle rhythm. The analysis unit customizes the analysis results based on, for example, the user's lifestyle rhythm. For example, the analysis unit analyzes blood glucose level fluctuations after breakfast based on the user's lifestyle rhythm. The analysis unit can also analyze blood glucose level fluctuations after lunch based on the user's lifestyle rhythm. The analysis unit can also analyze blood glucose level fluctuations after dinner based on the user's lifestyle rhythm. By customizing the analysis results based on the user's lifestyle rhythm, it is possible to provide optimal analysis results for the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the analysis results.

[0123] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analyzing the impact of stress on blood glucose levels. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing normal blood glucose level data. Furthermore, if the user is excited, the analysis unit can prioritize analyzing the impact of excitement on blood glucose levels. Thus, by prioritizing the analysis results based on the user's emotions, important analysis results can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0124] During analysis, the analysis unit can customize the analysis results by taking into account the user's geographical location information. The analysis unit customizes the analysis results by taking into account, for example, the user's geographical location information. For example, when the user is out, the analysis unit prioritizes analyzing blood glucose level data from outside the home. Furthermore, when the user is at home, the analysis unit can prioritize analyzing blood glucose level data from home. Furthermore, when the user is in a specific location, the analysis unit can prioritize analyzing blood glucose level data from that location. In this way, by taking into account the geographical location information, it is possible to provide analysis results that are highly relevant to the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0125] During the analysis, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. The analysis unit, for example, analyzes the user's social media activity and reflects related data in the analysis. For example, if the user posts a photo of their meal on social media, the analysis unit reflects that data in the analysis. In addition, if the user posts about exercise, the analysis unit can also reflect that data in the analysis. In addition, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. In this way, by analyzing social media activity, related data can be reflected in the analysis, and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI reflect the analysis results.

[0126] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, adjusts the analysis algorithm by reflecting the user's past feedback. For example, if the user was dissatisfied with the analysis results in the past, the analysis unit improves the analysis algorithm by reflecting that feedback. The analysis unit can also continue the algorithm if the user was satisfied with the analysis results in the past. The analysis unit can also analyze the user's past feedback and propose an optimal analysis algorithm. In this way, the analysis algorithm can be adjusted by reflecting the past feedback, and optimal analysis results can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0127] The providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. If the user is relaxed, the providing unit can also provide detailed advice. If the user is in a hurry, the providing unit can also provide advice that focuses on the main points. This allows the advice to be presented in a way that is easy for the user to understand by adjusting the way the advice is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is presented.

[0128] When providing advice, the providing unit can optimize the advice content by taking into account the fluctuation pattern of the blood glucose level data. The providing unit, for example, optimizes the advice content by taking into account the fluctuation pattern of the blood glucose level data. For example, when the fluctuation of the blood glucose level data is large, the providing unit provides advice to smooth the fluctuation. Furthermore, when the fluctuation of the blood glucose level data is small, the providing unit can also provide detailed advice. Furthermore, the providing unit can also provide optimal advice content based on the fluctuation pattern of the blood glucose level data. In this way, by taking the fluctuation pattern of the blood glucose level data into account, the advice content can be optimized and more effective advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit can input the fluctuation pattern of the blood glucose level data to the generation AI and cause the generation AI to optimize the advice content.

[0129] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's diet and exercise history. The providing unit, for example, improves the accuracy of the advice by referring to the user's diet and exercise history. For example, the providing unit can provide advice that takes into account the influence of diet by referring to the user's diet history. The providing unit can also provide advice that takes into account the influence of exercise by referring to the user's exercise history. The providing unit can also improve the accuracy of the advice by comprehensively referring to the user's diet and exercise history. In this way, by referring to the diet and exercise history, the accuracy of the advice can be improved and more accurate advice can be provided. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's diet and exercise history data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0130] The providing unit can customize the advice content based on the user's lifestyle rhythm when providing advice. The providing unit customizes the advice content based on, for example, the user's lifestyle rhythm. For example, the providing unit provides post-breakfast advice based on the user's lifestyle rhythm. The providing unit can also provide post-lunch advice based on the user's lifestyle rhythm. The providing unit can also provide post-dinner advice based on the user's lifestyle rhythm. In this way, by customizing the advice content based on the user's lifestyle rhythm, it is possible to provide optimal advice for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the advice content.

[0131] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can prioritize advice about the impact of stress on blood glucose levels. Furthermore, if the user is relaxed, the providing unit can prioritize providing normal advice. Furthermore, if the user is excited, the providing unit can prioritize advice about the impact of excitement on blood glucose levels. In this way, by determining the priority of advice based on the user's emotions, important advice can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of advice.

[0132] When providing advice, the providing unit can customize the advice content by taking into account the user's geographical location information. The providing unit customizes the advice content by taking into account, for example, the user's geographical location information. For example, when the user is out, the providing unit provides advice for the user while out. Furthermore, when the user is at home, the providing unit can also provide advice for the user at home. Furthermore, when the user is in a specific location, the providing unit can also provide advice for that location. In this way, by taking into account the geographical location information, it is possible to provide advice that is highly relevant to the user. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the user's geographical location information to the generation AI and cause the generation AI to customize the advice content.

[0133] When providing advice, the providing unit can analyze the user's social media activity and provide relevant advice. The providing unit, for example, analyzes the user's social media activity and provides relevant advice. For example, if the user posts a photo of a meal on social media, the providing unit can provide advice based on that data. In addition, if the user posts about exercise, the providing unit can also provide advice based on that data. In addition, the providing unit can analyze the user's social media activity and provide relevant advice. In this way, by analyzing social media activity, relevant advice can be provided, and more accurate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide advice content.

[0134] When providing advice, the providing unit can adjust the advice content by reflecting the user's past feedback. The providing unit, for example, adjusts the advice content by reflecting the user's past feedback. For example, if the user was dissatisfied with the advice content in the past, the providing unit can improve the advice content by reflecting that feedback. Furthermore, if the user was satisfied with the advice content in the past, the providing unit can continue that content. Furthermore, the providing unit can analyze the user's past feedback and suggest optimal advice content. In this way, the advice content can be adjusted by reflecting the past feedback, and optimal advice can be provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the advice content.

[0135] The reception unit can estimate the user's emotion and adjust the timing of photo reception based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of photo reception based on the estimated emotion. For example, if the user is feeling stressed, the reception unit may accept photos at a relaxed timing. Furthermore, if the user is relaxed, the reception unit may accept photos at a normal timing. Furthermore, if the user is excited, the reception unit may delay photo reception until the user's excitement subsides. This allows the timing of photo reception to be adjusted according to the user's emotion, thereby allowing photos to be received at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of photo reception.

[0136] The reception unit can optimize the reception method by referring to the user's past meal history when receiving a photo. The reception unit, for example, optimizes the reception method by referring to the user's past meal history. For example, the reception unit may refer to the user's past meal history and automatically accept similar meals if there are any. The reception unit may also refer to the user's past meal history and request detailed information if there are any different meals. The reception unit may also suggest the optimal reception method by referring to the user's past meal history. This allows the reception method to be optimized by referring to the past meal history, enabling more accurate data collection. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit may input the user's past meal history data into the generation AI and cause the generation AI to optimize the reception method.

[0137] The reception unit can customize the reception method by taking into account the user's eating patterns when receiving photos. The reception unit customizes the reception method by taking into account the user's eating patterns, for example. For example, the reception unit may accept photos according to the time of day when the user eats breakfast. The reception unit may also accept photos according to the time of day when the user eats lunch. The reception unit may also accept photos according to the time of day when the user eats dinner. This allows the reception method to be customized by taking eating patterns into account, enabling more accurate data collection. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input the user's eating pattern data into the generation AI and cause the generation AI to customize the reception method.

[0138] The reception unit can adjust the reception timing based on the user's lifestyle rhythm when receiving a photo. The reception unit adjusts the reception timing based on the user's lifestyle rhythm, for example. For example, the reception unit may receive a photo after breakfast based on the user's lifestyle rhythm. The reception unit may also receive a photo after lunch based on the user's lifestyle rhythm. The reception unit may also receive a photo after dinner based on the user's lifestyle rhythm. By adjusting the reception timing based on the user's lifestyle rhythm, data collection tailored to the user's lifestyle becomes possible. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's lifestyle rhythm data into the generation AI and cause the generation AI to adjust the reception timing.

[0139] The reception unit can estimate the user's emotions and determine the priority of photos to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of photos based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize receiving photos showing the impact of stress on eating. Furthermore, if the user is relaxed, the reception unit can prioritize receiving normal photos. Furthermore, if the user is excited, the reception unit can prioritize receiving photos showing the impact of excitement on eating. This allows important data to be collected preferentially by determining the priority of photos based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of photos.

[0140] When accepting photos, the reception unit can prioritize accepting highly relevant photos in consideration of the user's geographical location information. The reception unit, for example, prioritizes accepting highly relevant photos in consideration of the user's geographical location information. For example, when the user is out, the reception unit can prioritize accepting photos of meals eaten while out. Furthermore, when the user is at home, the reception unit can prioritize accepting photos of meals eaten at home. Furthermore, when the user is in a specific location, the reception unit can prioritize accepting photos of meals eaten at that location. In this way, by taking geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the photos.

[0141] The reception unit can analyze the user's social media activity and accept related photos when accepting photos. The reception unit, for example, analyzes the user's social media activity and accepts related photos. For example, if the user posts a photo of a meal on social media, the reception unit accepts the photo. Furthermore, if the user posts a photo related to a meal on social media, the reception unit can also accept the photo. Furthermore, the reception unit can analyze the user's social media activity and accept related photos. This allows for the collection of related data by analyzing social media activity, enabling more accurate data collection. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to accept photos.

[0142] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a photo. The reception unit, for example, customizes the reception method by reflecting the user's past feedback. For example, if the user was dissatisfied with the reception method in the past, the reception unit can improve the reception method by reflecting that feedback. Furthermore, if the user was satisfied with the reception method in the past, the reception unit can continue that method. Furthermore, the reception unit can analyze the user's past feedback and suggest an optimal reception method. In this way, the reception method can be customized by reflecting past feedback, enabling optimal data collection for the user. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0143] The generation unit can estimate the user's emotion and adjust the presentation method of the analysis results based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis results based on the estimated emotion. For example, if the user is nervous, the generation unit provides simple, highly visible analysis results. The generation unit can also provide detailed analysis results if the user is relaxed. The generation unit can also provide analysis results that focus on the main points if the user is in a hurry. By adjusting the presentation method of the analysis results according to the user's emotion, it is possible to provide analysis results that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the analysis results.

[0144] The generation unit can optimize the analysis algorithm during analysis, taking into account the type and amount of food. The generation unit optimizes the analysis algorithm, for example, taking into account the type and amount of food. For example, the generation unit applies an algorithm that performs a detailed analysis when there are many types of food. The generation unit can also apply an algorithm that performs a detailed analysis when the amount of food is large. The generation unit can also select an optimal analysis algorithm based on the type and amount of food. In this way, by taking into account the type and amount of food, the analysis algorithm can be optimized and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the type and amount of food into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0145] During analysis, the generation unit can improve analysis accuracy by referring to the user's past meal history. The generation unit, for example, improves analysis accuracy by referring to the user's past meal history. For example, the generation unit refers to the user's past meal history and performs a detailed analysis if similar meals are present. The generation unit can also refer to the user's past meal history and perform a detailed analysis if different meals are present. The generation unit can also refer to the user's past meal history to suggest an optimal analysis method. In this way, by referring to the past meal history, analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past meal history data into the generation AI and cause the generation AI to improve analysis accuracy.

[0146] The generation unit can customize the analysis results based on the user's lifestyle rhythm during analysis. The generation unit customizes the analysis results based on, for example, the user's lifestyle rhythm. For example, the generation unit provides analysis results after breakfast based on the user's lifestyle rhythm. The generation unit can also provide analysis results after lunch based on the user's lifestyle rhythm. The generation unit can also provide analysis results after dinner based on the user's lifestyle rhythm. In this way, by customizing the analysis results based on the user's lifestyle rhythm, it is possible to provide optimal analysis results for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the analysis results.

[0147] The generation unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the user is stressed, the generation unit prioritizes analyzing the impact of stress on eating. Furthermore, if the user is relaxed, the generation unit can prioritize providing normal analysis results. Furthermore, if the user is excited, the generation unit can prioritize analyzing the impact of excitement on eating. Thus, by prioritizing the analysis results based on the user's emotions, important data can be analyzed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0148] During analysis, the generation unit can customize the analysis results by taking into account the user's geographical location information. The generation unit customizes the analysis results by taking into account, for example, the user's geographical location information. For example, when the user is out, the generation unit can prioritize providing analysis results of meals eaten while out. Furthermore, when the user is at home, the generation unit can prioritize providing analysis results of meals eaten at home. Furthermore, when the user is in a specific location, the generation unit can prioritize providing analysis results of meals eaten at that location. In this way, by taking into account the geographical location information, it is possible to provide analysis results that are highly relevant to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0149] During analysis, the generation unit can analyze the user's social media activity and reflect related data in the analysis. The generation unit, for example, analyzes the user's social media activity and reflects related data in the analysis. For example, if the user posts a photo of a meal on social media, the generation unit performs analysis based on that data. In addition, if the user posts something about a meal, the generation unit can also perform analysis based on that data. In addition, the generation unit can analyze the user's social media activity and reflect related data in the analysis. In this way, by analyzing social media activity, related data can be reflected in the analysis, and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to reflect the analysis results.

[0150] During analysis, the generation unit can adjust the analysis algorithm by reflecting the user's past feedback. The generation unit, for example, adjusts the analysis algorithm by reflecting the user's past feedback. For example, if the user was dissatisfied with the analysis results in the past, the generation unit improves the analysis algorithm by reflecting that feedback. The generation unit can also continue the algorithm if the user was satisfied with the analysis results in the past. The generation unit can also analyze the user's past feedback and propose an optimal analysis algorithm. In this way, the analysis algorithm can be adjusted by reflecting the past feedback, and optimal analysis results can be provided to the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0151] The collection unit can estimate the user's emotions and adjust the timing of collecting exercise data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting exercise data based on the estimated emotions. For example, if the user is feeling stressed, the collection unit collects exercise data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can also collect exercise data at a normal collection timing. Furthermore, if the user is excited, the collection unit can delay collection until the user's excitement subsides. This allows for more appropriate data collection by adjusting the timing of collecting exercise data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of collecting exercise data.

[0152] When collecting exercise data, the collection unit can optimize the collection method by referring to the user's past exercise history. The collection unit, for example, optimizes the collection method by referring to the user's past exercise history. For example, the collection unit refers to the user's past exercise history and automatically collects similar exercise data if there is a similar exercise. The collection unit can also refer to the user's past exercise history to request detailed information if there is a different exercise. The collection unit can also suggest the optimal collection method by referring to the user's past exercise history. This allows the collection method to be optimized by referring to the past exercise history, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past exercise history data into the generation AI and have the generation AI optimize the collection method.

[0153] When collecting exercise data, the collection unit can customize the collection method by taking into account the user's exercise pattern. The collection unit customizes the collection method by taking into account, for example, the user's exercise pattern. For example, the collection unit collects exercise data according to the time of day when the user walks. The collection unit can also collect exercise data according to the time of day when the user runs. The collection unit can also collect exercise data according to the time of day when the user trains at the gym. This allows the collection method to be customized by taking into account the exercise pattern, enabling more accurate data collection. 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 user's exercise pattern data into the generation AI and cause the generation AI to customize the collection method.

[0154] The collection unit can adjust the collection timing based on the user's lifestyle rhythm when collecting exercise data. The collection unit adjusts the collection timing based on, for example, the user's lifestyle rhythm. For example, the collection unit collects morning exercise data based on the user's lifestyle rhythm. The collection unit can also collect daytime exercise data based on the user's lifestyle rhythm. The collection unit can also collect evening exercise data based on the user's lifestyle rhythm. By adjusting the collection timing based on the user's lifestyle rhythm, data collection tailored to the user's lifestyle becomes possible. Some or all of the above-described processing in 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 user's lifestyle rhythm data to the generation AI and cause the generation AI to adjust the collection timing.

[0155] The collection unit can estimate the user's emotions and determine the priority of the exercise data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the exercise data based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data on the impact of stress on exercise. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting normal exercise data. Furthermore, if the user is excited, the collection unit can also prioritize collecting data on the impact of excitement on exercise. Thus, by determining the priority of data based on the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the exercise data.

[0156] When collecting exercise data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is out, the collection unit prioritizes collecting exercise data from outside the home. Furthermore, when the user is at home, the collection unit can prioritize collecting exercise data from the home. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting exercise data from that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the exercise data.

[0157] The collection unit can analyze the user's social media activity and collect related data when collecting exercise data. The collection unit, for example, analyzes the user's social media activity and collects related data. For example, if the user posts about exercise on social media, the collection unit collects exercise data based on that data. Furthermore, if the user posts a photo about exercise, the collection unit can also collect exercise data based on that data. Furthermore, the collection unit can analyze the user's social media activity and collect related data. By analyzing social media activity, related data can be collected, enabling more accurate data collection. 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 user's social media activity data into a generation AI and cause the generation AI to collect exercise data.

[0158] When collecting exercise data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, customizes the collection method by reflecting the user's past feedback. For example, if the user was dissatisfied with the collection method in the past, the collection unit can improve the collection method by reflecting that feedback. Furthermore, if the user was satisfied with the collection method in the past, the collection unit can continue that method. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal collection method. In this way, the collection method can be customized by reflecting the past feedback, enabling optimal data collection for the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0159] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand by adjusting the presentation method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis results.

[0160] During analysis, the analysis unit can optimize the analysis algorithm by taking into account the type and intensity of exercise. The analysis unit optimizes the analysis algorithm by taking into account, for example, the type and intensity of exercise. For example, the analysis unit applies an algorithm that performs a detailed analysis when there are many types of exercise. The analysis unit can also apply an algorithm that performs a detailed analysis when the intensity of exercise is high. The analysis unit can also select an optimal analysis algorithm based on the type and intensity of exercise. This allows the analysis algorithm to be optimized by taking into account the type and intensity of exercise, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the type and intensity of exercise into the generation AI and have the generation AI optimize the analysis algorithm.

[0161] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past exercise history. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past exercise history. For example, the analysis unit refers to the user's past exercise history to perform a detailed analysis when there is a similar exercise. The analysis unit can also refer to the user's past exercise history to perform a detailed analysis when there is a different exercise. The analysis unit can also refer to the user's past exercise history to suggest an optimal analysis method. By referring to the past exercise history, analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past exercise history data into the generation AI and have the generation AI improve the analysis accuracy.

[0162] During analysis, the analysis unit can customize the analysis results based on the user's lifestyle rhythm. The analysis unit customizes the analysis results based on, for example, the user's lifestyle rhythm. For example, the analysis unit analyzes morning exercise data based on the user's lifestyle rhythm. The analysis unit can also analyze daytime exercise data based on the user's lifestyle rhythm. The analysis unit can also analyze evening exercise data based on the user's lifestyle rhythm. This allows the analysis results to be customized based on the user's lifestyle rhythm, thereby providing optimal analysis results for the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the analysis results.

[0163] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analyzing the impact of stress on exercise. Furthermore, if the user is relaxed, the analysis unit can prioritize providing normal analysis results. Furthermore, if the user is excited, the analysis unit can prioritize analyzing the impact of excitement on exercise. Thus, by prioritizing the analysis results based on the user's emotions, important data can be analyzed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0164] During analysis, the analysis unit can customize the analysis results by taking into account the user's geographical location information. The analysis unit customizes the analysis results by taking into account, for example, the user's geographical location information. For example, when the user is out, the analysis unit prioritizes analyzing exercise data from outside the home. Furthermore, when the user is at home, the analysis unit can prioritize analyzing exercise data from home. Furthermore, when the user is in a specific location, the analysis unit can prioritize analyzing exercise data from that location. In this way, by taking into account the geographical location information, it is possible to provide analysis results that are highly relevant to the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0165] During the analysis, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. The analysis unit, for example, analyzes the user's social media activity and reflects related data in the analysis. For example, if the user posts about exercise on social media, the analysis unit can perform analysis based on that data. Also, if the user posts a photo about exercise, the analysis unit can perform analysis based on that data. Also, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. In this way, by analyzing social media activity, related data can be reflected in the analysis, and more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI reflect the analysis results.

[0166] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, adjusts the analysis algorithm by reflecting the user's past feedback. For example, if the user was dissatisfied with the analysis results in the past, the analysis unit improves the analysis algorithm by reflecting that feedback. The analysis unit can also continue the algorithm if the user was satisfied with the analysis results in the past. The analysis unit can also analyze the user's past feedback and propose an optimal analysis algorithm. In this way, the analysis algorithm can be adjusted by reflecting the past feedback, and optimal analysis results can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0167] The generation unit can estimate the user's emotions and adjust the game content based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the game content based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a game that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a game that can be completed in a short time. Furthermore, if the user is excited, the generation unit can generate a game that adds visually stimulating effects. By adjusting the game content according to the user's emotions, an optimal gaming experience can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the game content.

[0168] When generating a game, the generation unit can optimize the game content by referring to the user's past game history. The generation unit, for example, optimizes the game content by referring to the user's past game history. For example, the generation unit may suggest optimal game content based on data on games the user has played in the past. The generation unit may also analyze the user's preferred genres from the user's past game history and generate a related game. The generation unit may also generate a game with an adjusted difficulty level by referring to the user's past game history. This allows the game content to be optimized by referring to the past game history, providing the user with an optimal gaming experience. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's past game history data into the generation AI and cause the generation AI to optimize the game content.

[0169] The generation unit can customize game content by taking into account the user's interests and concerns when generating a game. The generation unit customizes game content by taking into account the user's interests and concerns, for example. For example, the generation unit generates a game based on a theme that the user is interested in. The generation unit can also generate a game featuring a character that the user is interested in. The generation unit can also suggest optimal game content based on the user's interests and concerns. This allows the game content to be customized by taking into account the user's interests and concerns, thereby providing the user with an optimal gaming experience. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user interest and concern data into the generation AI and have the generation AI customize the game content.

[0170] The generation unit can adjust the progress of the game based on the user's lifestyle rhythm when generating the game. The generation unit adjusts the progress of the game based on, for example, the user's lifestyle rhythm. For example, the generation unit generates a game suitable for a morning time period based on the user's lifestyle rhythm. The generation unit can also generate a game suitable for a daytime time period based on the user's lifestyle rhythm. The generation unit can also generate a game suitable for an evening time period based on the user's lifestyle rhythm. By adjusting the progress of the game based on the user's lifestyle rhythm, it is possible to provide an optimal gaming experience for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to adjust the progress of the game.

[0171] The generation unit can estimate the user's emotions and prioritize games based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and prioritizes games based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can prioritize generating games that help relieve stress. Furthermore, if the user is relaxed, the generation unit can prioritize generating normal games. Furthermore, if the user is excited, the generation unit can prioritize generating games that maintain excitement. Thus, by prioritizing games based on the user's emotions, important games can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the game priorities.

[0172] The generation unit can customize game content by taking into account the user's geographical location information when generating a game. The generation unit customizes game content by taking into account the user's geographical location information, for example. For example, when the user is out, the generation unit generates a game that can be played while out. Furthermore, when the user is at home, the generation unit can generate a game that can be played at home. Furthermore, when the user is in a specific location, the generation unit can generate a game related to that location. In this way, by taking into account the geographical location information, a highly relevant game experience can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI and causes the generation AI to customize the game content.

[0173] The generation unit can analyze the user's social media activities and provide related game content when generating a game. The generation unit can, for example, analyze the user's social media activities and provide related game content. For example, the generation unit can generate a game based on a theme in which the user has shown interest on social media. The generation unit can also generate a game featuring characters the user follows on social media. The generation unit can also analyze the user's social media activities and provide related game content. In this way, by analyzing social media activities, related game content can be provided, providing an optimal gaming experience for the user. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide game content.

[0174] The generation unit can adjust the game content by reflecting the user's past feedback when generating a game. The generation unit, for example, adjusts the game content by reflecting the user's past feedback. For example, if the user was dissatisfied with the game content in the past, the generation unit can improve the game content by reflecting that feedback. Furthermore, if the user was satisfied with the game content in the past, the generation unit can continue that content. Furthermore, the generation unit can analyze the user's past feedback and suggest optimal game content. In this way, the game content can be adjusted by reflecting the past feedback, and the user can be provided with an optimal gaming experience. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the game content.

[0175] The providing unit can estimate the user's emotions and adjust the game presentation method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the game presentation method based on the estimated emotions. For example, if the user is nervous, the providing unit provides a presentation method that allows the user to relax. Furthermore, if the user is relaxed, the providing unit can provide a normal presentation method. Furthermore, if the user is excited, the providing unit can provide a presentation method that maintains the user's excitement. This allows the game presentation method to be adjusted according to the user's emotions, thereby providing an optimal game experience for the user. Emotion estimation is achieved 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 such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the game presentation method.

[0176] When providing a game, the providing unit can optimize the providing method by referring to the user's past game history. The providing unit, for example, optimizes the providing method by referring to the user's past game history. For example, the providing unit can provide a similar providing method by referring to the user's past game history. The providing unit can also provide a different providing method by referring to the user's past game history. The providing unit can also suggest an optimal providing method by referring to the user's past game history. In this way, by referring to the past game history, the providing method can be optimized and the optimal game experience can be provided for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past game history data into the generation AI and cause the generation AI to optimize the providing method.

[0177] The providing unit can customize the delivery method by taking into account the user's interests and concerns when providing a game. The providing unit customizes the delivery method by taking into account the user's interests and concerns, for example. For example, the providing unit provides a delivery method based on a theme in which the user is interested. The providing unit can also provide a delivery method that features a character in which the user is interested. The providing unit can also suggest the optimal delivery method based on the user's interests and concerns. In this way, the delivery method can be customized by taking into account the user's interests and concerns, and the optimal game experience can be provided for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's interest and concern data into the generation AI and cause the generation AI to customize the delivery method.

[0178] The providing unit can adjust the timing of providing a game based on the user's lifestyle rhythm when providing the game. The providing unit adjusts the timing of providing a game based on, for example, the user's lifestyle rhythm. For example, the providing unit provides a providing method suitable for a morning time period based on the user's lifestyle rhythm. The providing unit can also provide a providing method suitable for a daytime time period based on the user's lifestyle rhythm. The providing unit can also provide a providing method suitable for an evening time period based on the user's lifestyle rhythm. In this way, by adjusting the timing of providing a game based on the user's lifestyle rhythm, it is possible to provide an optimal game experience for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit may input the user's lifestyle rhythm data into the generation AI and cause the generation AI to adjust the timing of providing a game.

[0179] The providing unit can estimate the user's emotions and determine the priority of games to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of games based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing games that are useful for stress relief. Furthermore, if the user is relaxed, the providing unit can prioritize providing normal games. Furthermore, if the user is excited, the providing unit can prioritize providing games that maintain excitement. In this way, by determining the priority of games based on the user's emotions, important games can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of games.

[0180] The providing unit can customize the provision method when providing a game by taking into account the user's geographical location information. The providing unit customizes the provision method by taking into account, for example, the user's geographical location information. For example, when the user is out, the providing unit can provide a game that can be played while out. Furthermore, when the user is at home, the providing unit can also provide a game that can be played at home. Furthermore, when the user is in a specific location, the providing unit can also provide a game related to that location. In this way, by taking into account the geographical location information, a highly relevant gaming experience can be provided to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the provision method.

[0181] The providing unit can analyze the user's social media activity and provide related game content when providing a game. The providing unit, for example, analyzes the user's social media activity and provides related game content. For example, the providing unit can provide a game based on a theme in which the user has shown interest on social media. The providing unit can also provide a game featuring a character the user follows on social media. The providing unit can also analyze the user's social media activity and provide related game content. In this way, by analyzing social media activity, related game content can be provided, providing an optimal gaming experience for the user. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide game content.

[0182] The providing unit can adjust the game providing method by reflecting the user's past feedback when providing a game. The providing unit, for example, adjusts the game providing method by reflecting the user's past feedback. For example, if the user was dissatisfied with the game providing method in the past, the providing unit can improve the game providing method by reflecting that feedback. Furthermore, if the user was satisfied with the game providing method in the past, the providing unit can continue that method. Furthermore, the providing unit can analyze the user's past feedback and propose an optimal game providing method. In this way, the game providing method can be adjusted by reflecting the past feedback, and an optimal game experience can be provided to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the game providing method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, reception unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects blood glucose data using a blood glucose measurement device or a continuous blood glucose monitoring device of the smart device 14. The analysis unit analyzes the blood glucose data using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides real-time advice based on the analysis results by the specific processing unit 290 of the data processing device 12. The reception unit accepts photos of meals by the control unit 46A of the smart device 14. The generation unit analyzes the photos of meals using a generation AI by the specific processing unit 290 of the data processing device 12, and generates and records ingested carbohydrate data. The collection unit can estimate the user's emotions and adjust the timing of blood glucose data collection based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, provision unit, reception unit, and generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects blood glucose data using a blood glucose measurement device or a continuous blood glucose monitoring device of the smart glasses 214. The analysis unit analyzes the blood glucose data using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides real-time advice based on the analysis results by the specific processing unit 290 of the data processing device 12. The reception unit accepts photos of meals by the control unit 46A of the smart glasses 214. The generation unit analyzes the photos of meals using a generation AI by the specific processing unit 290 of the data processing device 12, and generates and records ingested carbohydrate data. The collection unit can estimate the user's emotions and adjust the timing of blood glucose data collection based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, reception unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects blood glucose data using a blood glucose measurement device or a continuous blood glucose monitoring device of the headset-type terminal 314. The analysis unit analyzes the blood glucose data using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides real-time advice based on the analysis results by the specific processing unit 290 of the data processing device 12. The reception unit accepts photos of meals by the control unit 46A of the headset-type terminal 314. The generation unit analyzes the photos of meals using a generation AI by the specific processing unit 290 of the data processing device 12, and generates and records ingested carbohydrate data. The collection unit can estimate the user's emotions and adjust the timing of blood glucose data collection based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, reception unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects blood glucose data using a blood glucose measurement device or a continuous blood glucose monitoring device of the robot 414. The analysis unit analyzes the blood glucose data using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides real-time advice based on the analysis results by the specific processing unit 290 of the data processing device 12. The reception unit accepts photos of meals by the control unit 46A of the robot 414. The generation unit analyzes the photos of meals using a generation AI by the specific processing unit 290 of the data processing device 12, and generates and records ingested carbohydrate data. The collection unit can estimate the user's emotions and adjust the timing of collection of blood glucose data based on the estimated emotions.

[0183] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0184] The collection unit can also collect the user's heart rate data and analyze it in combination with blood glucose level data. For example, the collection unit can monitor heart rate fluctuations in real time and evaluate the effects of stress and exercise. The collection unit can also estimate the intensity and duration of exercise based on the heart rate data. Furthermore, the collection unit can use the heart rate data to evaluate the user's overall health and provide appropriate advice. Thus, collecting heart rate data enables more comprehensive health management.

[0185] The analysis unit can also analyze the user's sleep data and evaluate the correlation with blood glucose level data. For example, the analysis unit can analyze the user's sleep patterns and evaluate the impact of lack of sleep on blood glucose levels. The analysis unit can also evaluate the quality of sleep and provide advice for improvement. Furthermore, the analysis unit can make suggestions for adjusting the user's lifestyle based on the sleep data. This allows for more accurate health management by analyzing sleep data.

[0186] The providing unit can also estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide advice to relax. Also, if the user is relaxed, the providing unit can provide advice to encourage proactive behavior. Furthermore, if the user is excited, the providing unit can provide advice to stay calm. This allows for more effective health management by providing advice according to the user's emotions.

[0187] The reception unit can also evaluate the nutritional balance of a meal when accepting a photo of the user's meal. For example, the reception unit can evaluate the balance of vegetables, protein, and carbohydrates from the photo of the meal and suggest improvements to the nutritional balance. The reception unit can also calculate calorie intake from the photo of the meal and point out excess or deficiency. Furthermore, the reception unit can detect allergens from the photo of the meal and suggest allergy countermeasures. In this way, accepting photo of meals enables more detailed dietary management.

[0188] The generation unit can also estimate the user's emotions and suggest meals based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can suggest foods that have a relaxing effect. Also, if the user is relaxed, the generation unit can suggest foods that are suitable for replenishing energy. Furthermore, if the user is excited, the generation unit can suggest foods that are easy to digest. This allows for more effective dietary management by suggesting meals according to the user's emotions.

[0189] When collecting the user's exercise data, the collection unit can automatically identify the type and intensity of exercise. For example, the collection unit can automatically identify exercise such as walking, running, cycling, etc. and classify the data. The collection unit can also evaluate the intensity of the exercise and suggest an appropriate amount of exercise. Furthermore, the collection unit can measure the duration of the exercise and evaluate the effect of the exercise. This allows for more detailed exercise management by collecting exercise data.

[0190] The analysis unit can also estimate the user's emotions and suggest exercises based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can suggest exercises that have a relaxing effect. Also, if the user is relaxed, the analysis unit can suggest exercises that consume a lot of energy. Furthermore, if the user is excited, the analysis unit can suggest exercises to calm the user down. This allows for more effective exercise management by suggesting exercises that correspond to the user's emotions.

[0191] The providing unit can also provide region-specific health information by taking into account the user's geographical location information. For example, the providing unit can provide appropriate dietary and exercise advice based on the climate and food culture of the region in which the user lives. The providing unit can also provide information on local medical institutions and fitness facilities. Furthermore, the providing unit can provide information on local events and health programs. This allows for more personalized health management by taking into account the geographical location information.

[0192] The generation unit can also estimate the user's emotions and adjust the difficulty level of the reward-provided game based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can provide a game with a low level of difficulty. If the user is relaxed, the generation unit can also provide a game with a normal level of difficulty. Furthermore, if the user is excited, the generation unit can also provide a game with a high level of difficulty. This allows for more effective motivation management by adjusting the difficulty level of the game according to the user's emotions.

[0193] The providing unit can also analyze the user's social media activity and provide related health information. For example, the providing unit can provide appropriate advice based on health topics the user has shown interest in on social media. The providing unit can also provide information on health experts the user follows. Furthermore, the providing unit can analyze the user's social media activity and provide information on related health events and programs. This allows for more personalized health management by analyzing social media activity.

[0194] The processing flow of the second embodiment will be briefly explained below.

[0195] Step 1: The collection unit collects blood glucose level data. The collection unit collects blood glucose level data using, for example, a blood glucose measurement device, a continuous blood glucose monitoring device, or a fingertip blood sampling device. Step 2: The analysis unit analyzes the collected blood glucose data. The analysis unit uses the generation AI to analyze the blood glucose data and generate real-time advice. It can also analyze fluctuation patterns and abnormal values ​​in the blood glucose data. Step 3: The provider provides real-time advice based on the results of the analysis by the analyzer, such as dietary suggestions, exercise recommendations, advice on medication timing, and advice to improve lifestyle habits. Step 4: The reception unit accepts photos of meals. The reception unit accepts photos of meals taken by patients or posted on social media. It is also possible to build a system that automatically accepts photos of meals. Step 5: The generator analyzes the received photo and generates and records the ingested carbohydrate data. The generator uses the generation AI to analyze the food photo and calculate the amount of ingested carbohydrates, food components, and calories. Step 6: The collection unit collects the exercise data. The collection unit collects the exercise data using, for example, a sensor of a wearable device or a smartphone. Step 7: The analysis unit analyzes the collected exercise data, generates and records the calorie consumption. The analysis unit analyzes the exercise data using the generation AI and calculates the calorie consumption. Step 8: The generator generates a reward-provided game. The generator generates, for example, a game to encourage the user to manage their health. Step 9: The providing unit provides the game generated by the generating unit. The providing unit provides a game that allows the user to manage their health while having fun.

[0196] 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.

[0197] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0232] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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."

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0266] 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.

[0267] [Explanation of symbols]

[0268] 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 blood glucose level data; an analysis unit that analyzes the blood glucose level data collected by the collection unit; a providing unit that provides real-time advice based on the results of the analysis by the analyzing unit; a reception desk that accepts photos of meals; a generation unit that analyzes the photograph received by the reception unit, generates ingested carbohydrate data, and records the data; a collection unit that collects exercise data; an analysis unit that analyzes the exercise data collected by the collection unit, and generates and records a calorie intake; a generator for generating a rewarded game; a providing unit that provides the game generated by the generation unit; Equipped with A system characterized by:

2. The collecting unit Collecting blood glucose data from blood glucose measuring devices 2. The system of claim 1.

3. The analysis unit Analyzes collected blood glucose data and generates real-time advice 2. The system of claim 1.

4. The reception unit Accept photos of meals 2. The system of claim 1.

5. The generation unit Analyze the received photos, generate and record ingested carbohydrate data 2. The system of claim 1.

6. The collecting unit Collecting exercise data via GPS 2. The system of claim 1.

7. The analysis unit Analyzes the collected exercise data, calculates and records the calories burned 2. The system of claim 1.

8. The generation unit Generate a rewarded game 2. The system of claim 1.

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