system

Abstract

The system according to the embodiment aims to facilitate the management of uric acid levels and the presentation of improvement measures by having the user input daily diet and exercise data. [Solution] A system according to an embodiment includes an input unit, a transmission unit, an analysis unit, and a presentation unit. The input unit allows a user to input daily diet and exercise data. The transmission unit transmits the data input by the input unit. The analysis unit analyzes the data transmitted by the transmission unit. The presentation unit presents improvement measures based on the results of the analysis by the analysis 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, managing uric acid levels and suggesting improvement measures was time-consuming, making it difficult for users to continue working on it.

[0005] The system according to the embodiment aims to facilitate the management of uric acid levels and the presentation of improvement measures by having the user input daily diet and exercise data. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a transmission unit, an analysis unit, and a presentation unit. The input unit allows a user to input daily dietary or exercise data. The transmission unit transmits the data input by the input unit. The analysis unit analyzes the data transmitted by the transmission unit. The presentation unit presents improvement measures based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to input daily diet and exercise data, making it easier to manage uric acid levels and provide suggestions for improvement. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention targets users with high uric acid levels (primarily men in their 30s to 50s). This health management system automatically analyzes factors such as fluid intake, purine intake balance, and whether exercise is excessive or insufficient, based on the user's daily diet and exercise data. The AI ​​then provides recommendations and coaching. For example, a user inputs their daily diet and exercise data into a dedicated app, including the contents of their meals, fluid intake, and the type and duration of their exercise. This data is then sent to the AI, which automatically analyzes it. Based on the analysis results, the AI ​​then presents specific recommendations to the user. For example, if fluid intake is insufficient, the AI ​​displays advice such as "Drink more water." If purine intake is high, the AI ​​provides specific suggestions such as "Choose foods with low purines." Furthermore, the AI ​​analyzes the user's exercise level and suggests appropriate exercise amounts. For example, if the user is not exercising enough, the AI ​​displays advice such as "Add 30 minutes of walking each day." Conversely, if the user is exercising too much, the AI ​​displays suggestions such as "Reduce exercise and take more rest." This service can be offered at a low monthly fee because AI coaching reduces labor costs. Users can easily manage their health, which will help prevent and improve gout. In addition, to dispel the negative image of gout, the company plans to incorporate gamification into the UX. For example, a system will be introduced in which users will accumulate points and receive rewards each time they achieve a goal. This will allow users to manage their health while having fun, helping to maintain their motivation. In this way, providing users with high uric acid levels with a health management service that utilizes AI will support the prevention and improvement of gout. This will enable the health management system to efficiently analyze users' diet and exercise data and suggest specific improvement measures.

[0029] A health management system according to an embodiment includes an input unit, a transmission unit, an analysis unit, and a presentation unit. The input unit allows a user to input daily dietary or exercise data. The data input by the user includes, but is not limited to, meal content, calorie intake, type of exercise, and exercise duration. The input unit can input data via, for example, a smartphone app or a web application. The input unit can also input data using voice input or image recognition technology. For example, a user can take a photo of a meal and automatically analyze the meal content using image recognition technology. The transmission unit transmits the data input by the input unit to the analysis unit. The transmission unit can transmit the data via, for example, the Internet. The transmission unit can also ensure data security using encryption technology when transmitting the data. The analysis unit analyzes the data transmitted by the transmission unit. The analysis unit can analyze the data using, for example, AI. The analysis unit can analyze the data using statistical analysis or machine learning algorithms to evaluate the user's health status. For example, the analysis unit can analyze the balance of fluid intake, purine intake, and whether the user is exercising too much or too little. The analysis unit uses AI to evaluate the user's health status based on their diet and exercise data and derive specific improvement measures. The presentation unit presents the improvement measures based on the analysis results. The presentation unit can present the improvement measures to the user, for example, through a smartphone app or web application. The presentation unit presents specific improvement measures to the user based on the analysis results. For example, if the user's water intake is insufficient, the presentation unit displays advice such as "Drink more water." If the user's purine intake is high, the presentation unit makes specific suggestions such as "Choose foods with low purines." Furthermore, the presentation unit analyzes the user's exercise level and suggests appropriate exercise amounts. For example, if the user is not exercising enough, the presentation unit displays advice such as "Add 30 minutes of walking each day." Conversely, if the user is exercising too much, the presentation unit makes suggestions such as "Reduce the amount of exercise and take more rest."This allows the health management system according to the embodiment to efficiently analyze the diet and exercise data of the user and present specific measures for improvement.

[0030] The analysis unit can analyze the amount of water intake, the balance of purine intake, and whether the user is getting enough exercise. The analysis unit, for example, analyzes the amount of water intake. To analyze the amount of water intake, the analysis unit evaluates the daily intake and the recommended intake based on the data on the amount of water consumed input by the user. For example, the analysis unit can calculate the amount of water consumed by the user in a day and compare it with the recommended intake. The analysis unit can also analyze the balance of purine intake. To analyze the amount of purine intake, the analysis unit evaluates the purine content of food based on the dietary data input by the user. For example, the analysis unit can calculate the amount of purine consumed by the user in a day and compare it with the recommended intake. The analysis unit can also analyze whether the user is getting enough exercise. To analyze whether the user is getting enough exercise, the analysis unit evaluates the recommended amount of exercise and the criteria for excessive exercise based on the exercise data input by the user. For example, the analysis unit can calculate the type and duration of exercise the user performed in a day and compare it with the recommended amount of exercise. This allows the analysis unit to analyze the user's health in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's diet and exercise data into AI, which may then output the analysis results.

[0031] The presentation unit can present specific improvement measures based on the analysis results. The presentation unit presents specific improvement measures based on the analysis results, for example. Specific improvement measures include, but are not limited to, recommendations for dietary changes and exercise. For example, the presentation unit can display advice such as "Drink more water" if the user's water intake is insufficient. Furthermore, the presentation unit can provide specific suggestions such as "Choose foods with low purines" if the user's purine intake is high. Furthermore, the presentation unit can display advice such as "Add 30 minutes of walking each day" if the user is not exercising enough. Conversely, the presentation unit can provide suggestions such as "Reduce the amount of exercise and take more rest" if the user is exercising too much. This allows the presentation unit to provide specific improvement measures to the user. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the analysis results into AI, which then outputs specific improvement measures.

[0032] The presentation unit can display advice to the user, such as "Try to increase your water intake." The presentation unit can display advice to the user, such as "Try to increase your water intake." The content of the advice includes, but is not limited to, increasing the amount of water consumed or changing one's diet. For example, the presentation unit can display advice such as "Try to drink more water" if the user's daily water intake is insufficient. The presentation unit can also suggest specific methods for the user to increase their water intake. For example, the presentation unit can display specific advice such as "Try to drink a glass of water after each meal." This allows the presentation unit to provide the user with specific advice on water intake. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input analysis results into AI, which then outputs specific advice.

[0033] The presentation unit can suggest low-purine foods. For example, the presentation unit suggests to the user to choose low-purine foods. The content of the suggestion may include, but is not limited to, specific food names and purine content standards. For example, the presentation unit may suggest, "Choose low-purine foods" if the user consumes a high amount of purine per day. The presentation unit can also suggest specific ways for the user to choose low-purine foods. For example, the presentation unit may make a specific suggestion such as, "Avoid seafood and organ meats and eat more vegetables and fruits." This allows the presentation unit to provide the user with specific dietary improvements. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit may input analysis results into AI, which then outputs specific suggestions.

[0034] The presentation unit can suggest increasing the amount of exercise. For example, the presentation unit suggests to the user to increase the amount of exercise. The content of the suggestion includes, but is not limited to, recommended types and durations of exercise. For example, if the amount of exercise the user has done each day is insufficient, the presentation unit can suggest, "Add 30 minutes of walking every day." The presentation unit can also suggest specific ways for the user to increase the amount of exercise. For example, the presentation unit can make specific suggestions such as, "Try taking the stairs instead of the elevator." This allows the presentation unit to provide the user with specific exercise improvement measures. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input analysis results into AI, which then outputs specific suggestions.

[0035] The presentation unit can suggest to the user to reduce the amount of exercise and take a rest. For example, the presentation unit suggests to the user to reduce the amount of exercise and take a rest. The content of the suggestion includes, but is not limited to, the recommended rest time and the frequency of exercise. For example, if the amount of exercise the user has performed in one day is excessive, the presentation unit can suggest, "Try to reduce the amount of exercise and take a rest." The presentation unit can also suggest specific ways for the user to take a rest. For example, the presentation unit can make a specific suggestion such as, "Try to take complete rest one day a week." This allows the presentation unit to provide the user with specific measures to improve rest. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the analysis results into AI, which can output specific suggestions.

[0036] The health management system includes a gamification point unit, and points are accumulated each time a user achieves a goal. The point unit, for example, awards points each time the user achieves a goal. Methods of awarding points include, but are not limited to, goal achievement criteria and point types. For example, the point unit can award points when a user achieves a set amount of water intake per day. The point unit can also award points when a user achieves a set exercise goal. For example, the point unit can award points when a user walks for 30 minutes every day. This allows the point unit to maintain the user's motivation and enable health management while having fun. Some or all of the above-described processing in the point unit may be performed using, for example, AI, or may be performed without AI. For example, the point unit can input the user's goal achievement data into AI, which then awards points.

[0037] The health management system includes a ranking unit, allowing users to compete against each other. The ranking unit, for example, displays a ranking for the users to compete against each other. Ranking criteria include, but are not limited to, competition rules and ranking update frequency. For example, the ranking unit may display a ranking based on the number of goals achieved by users or the total points. The ranking unit may also periodically update the ranking to promote competition between users. For example, the ranking unit may display a weekly ranking so that users can check their ranking. This allows the ranking unit to promote competition between users and increase motivation for health management. Some or all of the above-described processing in the ranking unit may be performed using, for example, AI, or may be performed without AI. For example, the ranking unit may input users' goal achievement data into AI, which then generates rankings.

[0038] The input unit can analyze the user's past input history and suggest an optimal input method. The input unit, for example, analyzes the user's past input history and suggests an optimal input method. Criteria for the optimal input method include, but are not limited to, the design of the input interface and ease of input. For example, the input unit can automatically display data about food and drink or exercise that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (such as voice or text) that the user has used in the past. Furthermore, the input unit can predict and suggest data to be input during a specific time period based on the user's past input history. This allows the input unit to provide an optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's past input data into AI, which then suggests an optimal input method.

[0039] The input unit can customize input items based on the user's current health condition and lifestyle habits at the time of input. For example, the input unit customizes the input items based on the user's current health condition and lifestyle habits at the time of input. Methods for customizing input items include, but are not limited to, methods of selecting items based on the user's health condition and lifestyle habits. For example, the input unit can display only simple input items when the user is tired. The input unit can also display detailed input items when the user has a healthy lifestyle. Furthermore, if the user has a specific health condition (e.g., high blood pressure), the input unit can preferentially display input items related to that condition. This allows the input unit to provide input items according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the user's health data into AI, which can then customize the input items.

[0040] The input unit can prioritize displaying highly relevant input items during input, taking into account the user's geographical location information. For example, the input unit can prioritize displaying highly relevant input items during input, taking into account the user's geographical location information. Methods of using geographical location information include, but are not limited to, using GPS data and selecting input items based on location information. For example, when the user is in a specific area, the input unit can prioritize displaying food and drink data related to that area. Furthermore, when the user is traveling, the input unit can display input items related to meals and exercise at the user's travel destination. Furthermore, when the user is at home, the input unit can prioritize displaying daily food and drink and exercise data. This allows the input unit to provide input items based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location data to AI, which can select highly relevant input items.

[0041] The input unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the input unit can analyze the user's social media activity at the time of input and suggest related input items. Methods for analyzing social media activity include, but are not limited to, analyzing posted content and suggesting related input items. For example, the input unit can automatically input food and drink data based on photos of meals shared by the user on social media. The input unit can also suggest exercise data based on events the user has participated in on social media. Furthermore, the input unit can customize input items based on health-related accounts the user follows on social media. This allows the input unit to provide input items based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media data into AI, which then suggests related input items.

[0042] The transmitting unit can select the optimal transmission method by analyzing the user's past transmission history when transmitting data. For example, the transmitting unit can select the optimal transmission method by analyzing the user's past transmission history when transmitting data. Criteria for selecting the optimal transmission method include, but are not limited to, a method for selecting a transmission method based on the past transmission history. For example, the transmitting unit can prioritize a transmission method (Wi-Fi, mobile data, etc.) that the user has used in the past. The transmitting unit can also suggest the most efficient transmission method based on the user's past transmission history. Furthermore, the transmitting unit can select the optimal transmission method based on the amount of data the user has transmitted in the past. This allows the transmitting unit to provide the optimal transmission method based on the user's past transmission history. Some or all of the above-described processing in the transmitting unit can be performed using, for example, AI, or can be performed without using AI. For example, the transmitting unit can input the user's past transmission data into AI, which can then select the optimal transmission method.

[0043] The transmitting unit can customize the transmission method based on the user's current network conditions when transmitting data. For example, the transmitting unit customizes the transmission method based on the user's current network conditions when transmitting data. Methods of using the network conditions include, but are not limited to, measuring network speed and customizing the transmission method. For example, the transmitting unit can transmit a large amount of data when the user is connected to Wi-Fi. The transmitting unit can also transmit a reduced amount of data when the user is using mobile data. Furthermore, the transmitting unit can temporarily suspend transmission when the user's network connection is unstable. This allows the transmitting unit to provide a transmission method that suits the user's network conditions. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the user's network data into AI, which can then customize the transmission method.

[0044] When transmitting data, the transmission unit can prioritize transmitting highly relevant data in consideration of the user's geographical location information. For example, when transmitting data, the transmission unit prioritizes transmitting highly relevant data in consideration of the user's geographical location information. Criteria for selecting highly relevant data include, but are not limited to, a method of selecting data based on geographical location information. For example, when the user is in a specific area, the transmission unit can prioritize transmitting data related to that area. Furthermore, when the user is traveling, the transmission unit can prioritize transmitting data about the user's travel destination. Furthermore, when the user is at home, the transmission unit can prioritize transmitting everyday data. This allows the transmission unit to provide data transmission based on the user's geographical location information. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the user's geographical location data into AI, which can select highly relevant data.

[0045] The transmitting unit may analyze the user's social media activity and transmit related data when transmitting data. For example, the transmitting unit may analyze the user's social media activity and transmit related data when transmitting data. Methods for analyzing social media activity include, but are not limited to, analyzing posted content and transmitting related data. For example, the transmitting unit may transmit related data based on data shared by the user on social media. The transmitting unit may also transmit related data based on events the user participated in on social media. Furthermore, the transmitting unit may transmit related data based on health-related accounts the user follows on social media. This allows the transmitting unit to provide data transmission based on the user's social media activity. Some or all of the above-described processing in the transmitting unit may be performed using, or without, AI. For example, the transmitting unit may input the user's social media data into AI, which then transmits the related data.

[0046] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past health data during analysis. Methods of referring to past health data include, but are not limited to, methods of referring to past data and methods of improving the accuracy of analysis. For example, the analysis unit can analyze current data based on the user's past dietary data. The analysis unit can also analyze current data based on the user's past exercise data. Furthermore, the analysis unit can analyze current data based on the user's past health condition. This allows the analysis unit to provide an analysis based on the user's past health data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past health data into AI, which can improve the accuracy of the analysis.

[0047] The analysis unit can customize the analysis method based on the user's lifestyle habits and eating patterns during analysis. For example, the analysis unit customizes the analysis method based on the user's lifestyle habits and eating patterns during analysis. Methods for acquiring lifestyle habits and eating patterns include, but are not limited to, methods for analyzing daily behavioral data and food records. For example, if the user has a specific eating pattern, the analysis unit can perform analysis based on the pattern. Furthermore, if the user has a specific lifestyle habit, the analysis unit can perform analysis based on the habit. Furthermore, if the user has a specific health condition, the analysis unit can perform analysis based on the condition. This allows the analysis unit to provide an analysis based on the user's lifestyle habits and eating patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's lifestyle habits and eating patterns into AI, which can customize the analysis method.

[0048] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. Methods of using geographical location information include, but are not limited to, using GPS data and location-based analysis methods. For example, when the user is in a specific area, the analysis unit can perform analysis based on data related to that area. Furthermore, when the user is traveling, the analysis unit can perform analysis based on travel destination data. Furthermore, when the user is at home, the analysis unit can perform analysis based on everyday data. This allows the analysis unit to provide analysis based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location data into AI, which can improve the accuracy of the analysis.

[0049] The analysis unit may analyze the user's social media activities during the analysis and use the associated data for the analysis. For example, the analysis unit may analyze the user's social media activities during the analysis and use the associated data for the analysis. Methods for analyzing social media activities include, but are not limited to, analyzing posted content and associated data. For example, the analysis unit may perform analysis based on data shared by the user on social media. The analysis unit may also perform analysis based on events the user participated in on social media. Furthermore, the analysis unit may perform analysis based on health-related accounts the user follows on social media. This allows the analysis unit to provide analysis based on the user's social media activities. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's social media data into AI, and the AI ​​may use the associated data for analysis.

[0050] The presentation unit can suggest an optimal improvement measure by referring to the user's past improvement history when presenting the improvement measure. For example, the presentation unit can suggest an optimal improvement measure by referring to the user's past improvement history when presenting the improvement measure. Methods of referring to the past improvement history include, but are not limited to, a method of referring to past history and a method of suggesting an optimal improvement measure. For example, the presentation unit can suggest a current improvement measure based on the user's past improvement history. The presentation unit can also suggest the most effective improvement measure from the user's past improvement history. Furthermore, the presentation unit can analyze the user's past improvement history and suggest the most appropriate improvement measure. This allows the presentation unit to provide an optimal improvement measure based on the user's past improvement history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past improvement history data into AI, which then suggests the optimal improvement measure.

[0051] The presentation unit can customize the improvement measures based on the user's current health condition when presenting the improvement measures. For example, the presentation unit customizes the improvement measures based on the user's current health condition when presenting the improvement measures. Methods for acquiring the current health condition include, but are not limited to, health checkup results and daily health data usage. For example, the presentation unit can suggest simple improvement measures when the user is tired. The presentation unit can also suggest detailed improvement measures when the user is in good health. Furthermore, the presentation unit can suggest improvement measures related to a specific health condition (e.g., high blood pressure) when the user has that condition. This allows the presentation unit to provide improvement measures according to the user's health condition. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI, or may be performed without AI. For example, the presentation unit can input the user's health data into AI, which can then customize the improvement measures.

[0052] The presentation unit may prioritize displaying highly relevant improvements in consideration of the user's geographical location information when presenting the improvements. For example, the presentation unit may prioritize displaying highly relevant improvements in consideration of the user's geographical location information when presenting the improvements. Criteria for selecting highly relevant improvements include, but are not limited to, a method for selecting improvements based on geographical location information. For example, when the user is in a specific area, the presentation unit may prioritize displaying improvements related to that area. Furthermore, when the user is traveling, the presentation unit may prioritize displaying improvements for travel destinations. Furthermore, when the user is at home, the presentation unit may prioritize displaying everyday improvements. This allows the presentation unit to provide improvements based on the user's geographical location information. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input the user's geographical location data into AI, which may select highly relevant improvements.

[0053] The presentation unit may analyze the user's social media activity and suggest related improvement measures at the time of presentation. For example, the presentation unit may analyze the user's social media activity and suggest related improvement measures at the time of presentation. Methods for analyzing social media activity include, but are not limited to, analyzing posted content and suggesting related improvement measures. For example, the presentation unit may suggest related improvement measures based on data shared by the user on social media. The presentation unit may also suggest related improvement measures based on events the user participated in on social media. Furthermore, the presentation unit may suggest related improvement measures based on health-related accounts the user follows on social media. This allows the presentation unit to provide improvement measures based on the user's social media activity. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit may input the user's social media data into AI, which then suggests related improvement measures.

[0054] The point unit can select the optimal point awarding method by referring to the user's past point history when awarding points. For example, the point unit can select the optimal awarding method by referring to the user's past point history when awarding points. Methods of referring to past point history include, but are not limited to, methods of referring to past history and methods of selecting the optimal awarding method. For example, the point unit can select the current point awarding method based on the user's past point history. The point unit can also select the most effective point awarding method from the user's past point history. Furthermore, the point unit can analyze the user's past point history and select the most appropriate point awarding method. This allows the point unit to provide the optimal point awarding method based on the user's past point history. Some or all of the above-described processing in the point unit may be performed using, for example, AI, or may be performed without AI. For example, the point unit can input the user's past point history data into AI, which then selects the optimal point awarding method.

[0055] The point unit can select the optimal point awarding method by taking into account the user's geographical location information when awarding points. For example, the point unit selects the optimal point awarding method by taking into account the user's geographical location information when awarding points. Methods of using geographical location information include, but are not limited to, using GPS data and selecting an awarding method based on location information. For example, when a user is in a specific area, the point unit can prioritize points related to that area. Furthermore, when a user is traveling, the point unit can prioritize points for travel destinations. Furthermore, when a user is at home, the point unit can prioritize points for everyday activities. This allows the point unit to provide a point awarding method based on the user's geographical location information. Some or all of the above-described processing in the point unit may be performed using, for example, AI, or may be performed without using AI. For example, the point unit can input the user's geographical location data into AI, which can select the optimal awarding method.

[0056] The ranking unit can select the optimal display method by referring to the user's past ranking history when displaying the rankings. For example, the ranking unit can select the optimal display method by referring to the user's past ranking history when displaying the rankings. Methods of referring to past ranking history include, but are not limited to, methods of referring to past history and methods of selecting the optimal display method. For example, the ranking unit can select the current ranking display method based on the user's past ranking history. The ranking unit can also select the most effective ranking display method from the user's past ranking history. Furthermore, the ranking unit can analyze the user's past ranking history and select the most appropriate ranking display method. This allows the ranking unit to provide the optimal display method based on the user's past ranking history. Some or all of the above-described processing in the ranking unit can be performed, for example, using AI or without AI. For example, the ranking unit can input the user's past ranking history data into AI, which can select the optimal display method.

[0057] The ranking unit can select an optimal display method by taking into consideration the user's geographical location information when displaying rankings. For example, the ranking unit selects an optimal display method by taking into consideration the user's geographical location information when displaying rankings. Methods of using geographical location information include, but are not limited to, using GPS data and selecting a display method based on location information. For example, when a user is in a specific area, the ranking unit can prioritize displaying rankings related to that area. Furthermore, when a user is traveling, the ranking unit can prioritize displaying rankings of travel destinations. Furthermore, when a user is at home, the ranking unit can prioritize displaying everyday rankings. This allows the ranking unit to provide a ranking display method based on the user's geographical location information. Some or all of the above-described processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input the user's geographical location data into AI, which can select an optimal display method.

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

[0059] The health management system can also acquire the user's sleep data and analyze it in the analysis unit. For example, if the user inputs the amount of sleep and the quality of sleep, the analysis unit can evaluate the user's health condition in more detail based on this data. The analysis unit can also evaluate the impact of lack of sleep on uric acid levels and suggest appropriate sleep durations. The analysis unit can also analyze the user's sleep patterns and identify causes of stress and fatigue. This allows the user to perform comprehensive health management.

[0060] The presentation unit can suggest regional health information and meals taking into account the user's geographical location information. For example, if the user is traveling, it can suggest local specialties and healthy ingredients. If the user is in a specific region, the presentation unit can also suggest exercise methods suited to the climate and environment of that region. Furthermore, if the user is at home, the presentation unit can suggest recipes and exercise methods using local ingredients. This allows the user to manage their health according to their geographical conditions.

[0061] The health management system can further analyze the user's social media activity and perform analysis using the analysis unit. For example, the analysis unit can evaluate the user's health condition based on the diet and exercise data the user shares on social media. The analysis unit can also incorporate the latest health information and trends into the analysis based on the health-related accounts the user follows. Furthermore, the analysis unit can analyze emotional data obtained from the user's social media activity to evaluate the user's stress level and motivation. This allows the user to perform more personalized health management.

[0062] The health management system can also acquire the user's purchasing history and analyze it in the analysis unit. For example, the analysis unit can evaluate the user's nutritional balance based on data on the foods and supplements purchased by the user. The analysis unit can also suggest healthy foods and supplements based on the user's purchasing history. Furthermore, the analysis unit can provide advice on reducing unnecessary expenses based on the user's purchasing history. This allows the user to live a financially healthy life.

[0063] The health management system can also acquire health data from the user's family and friends and analyze it in the analysis unit. For example, the analysis unit can evaluate the user's health condition based on the health data shared by the user's family and friends. The analysis unit can also provide appropriate advice to the user based on the health data of family and friends. Furthermore, the analysis unit can encourage the sharing of health data with family and friends, creating an environment where they can support each other. This allows the user to manage their health while receiving social support.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The input unit allows the user to input daily food and drink or exercise data. The data input by the user includes the contents of meals, calorie intake, type of exercise, and exercise time. The input unit can input data via a smartphone app or web application, or it can also input data using voice input or image recognition technology. For example, the user can take a photo of their meal, and the contents of the meal can be automatically analyzed using image recognition technology. Step 2: The transmitting unit transmits the data input by the input unit to the analyzing unit. The transmitting unit can transmit the data via the Internet and can also ensure the security of the data by using encryption technology when transmitting the data. Step 3: The analysis unit analyzes the data sent by the transmission unit. The analysis unit uses AI to analyze the data and evaluates the user's health using statistical analysis and machine learning algorithms. For example, it analyzes the balance of fluid intake, purine intake, and whether the user is exercising too much or too little, evaluates the user's health based on the user's diet and exercise data, and derives specific improvement measures. Step 4: The presentation unit presents improvement measures based on the results of the analysis by the analysis unit. The presentation unit presents the improvement measures to the user through a smartphone app or web application, displaying specific improvement measures. For example, if the user's fluid intake is insufficient, the unit may suggest "drink more water," or if the user's purine intake is high, the unit may suggest "choose foods with low purines." The unit also suggests the appropriate amount of exercise depending on whether the user is exercising too much or too little.

[0066] (Example 2) A health management system according to an embodiment of the present invention targets users with high uric acid levels (primarily men in their 30s to 50s). This health management system automatically analyzes factors such as fluid intake, purine intake balance, and whether exercise is excessive or insufficient, based on the user's daily diet and exercise data. The AI ​​then provides recommendations and coaching. For example, a user inputs their daily diet and exercise data into a dedicated app, including the contents of their meals, fluid intake, and the type and duration of their exercise. This data is then sent to the AI, which automatically analyzes it. Based on the analysis results, the AI ​​then presents specific recommendations to the user. For example, if fluid intake is insufficient, the AI ​​displays advice such as "Drink more water." If purine intake is high, the AI ​​provides specific suggestions such as "Choose foods with low purines." Furthermore, the AI ​​analyzes the user's exercise level and suggests appropriate exercise amounts. For example, if the user is not exercising enough, the AI ​​displays advice such as "Add 30 minutes of walking each day." Conversely, if the user is exercising too much, the AI ​​displays suggestions such as "Reduce exercise and take more rest." This service can be offered at a low monthly fee because AI coaching reduces labor costs. Users can easily manage their health, which will help prevent and improve gout. In addition, to dispel the negative image of gout, the company plans to incorporate gamification into the UX. For example, a system will be introduced in which users will accumulate points and receive rewards each time they achieve a goal. This will allow users to manage their health while having fun, helping to maintain their motivation. In this way, providing users with high uric acid levels with a health management service that utilizes AI will support the prevention and improvement of gout. This will enable the health management system to efficiently analyze users' diet and exercise data and suggest specific improvement measures.

[0067] A health management system according to an embodiment includes an input unit, a transmission unit, an analysis unit, and a presentation unit. The input unit allows a user to input daily dietary or exercise data. The data input by the user includes, but is not limited to, meal content, calorie intake, type of exercise, and exercise duration. The input unit can input data via, for example, a smartphone app or a web application. The input unit can also input data using voice input or image recognition technology. For example, a user can take a photo of a meal and automatically analyze the meal content using image recognition technology. The transmission unit transmits the data input by the input unit to the analysis unit. The transmission unit can transmit the data via, for example, the Internet. The transmission unit can also ensure data security using encryption technology when transmitting the data. The analysis unit analyzes the data transmitted by the transmission unit. The analysis unit can analyze the data using, for example, AI. The analysis unit can analyze the data using statistical analysis or machine learning algorithms to evaluate the user's health status. For example, the analysis unit can analyze the balance of fluid intake, purine intake, and whether the user is exercising too much or too little. The analysis unit uses AI to evaluate the user's health status based on their diet and exercise data and derive specific improvement measures. The presentation unit presents the improvement measures based on the analysis results. The presentation unit can present the improvement measures to the user, for example, through a smartphone app or web application. The presentation unit presents specific improvement measures to the user based on the analysis results. For example, if the user's water intake is insufficient, the presentation unit displays advice such as "Drink more water." If the user's purine intake is high, the presentation unit makes specific suggestions such as "Choose foods with low purines." Furthermore, the presentation unit analyzes the user's exercise level and suggests appropriate exercise amounts. For example, if the user is not exercising enough, the presentation unit displays advice such as "Add 30 minutes of walking each day." Conversely, if the user is exercising too much, the presentation unit makes suggestions such as "Reduce the amount of exercise and take more rest."This allows the health management system according to the embodiment to efficiently analyze the diet and exercise data of the user and present specific measures for improvement.

[0068] The analysis unit can analyze the amount of water intake, the balance of purine intake, and whether the user is getting enough exercise. The analysis unit, for example, analyzes the amount of water intake. To analyze the amount of water intake, the analysis unit evaluates the daily intake and the recommended intake based on the data on the amount of water consumed input by the user. For example, the analysis unit can calculate the amount of water consumed by the user in a day and compare it with the recommended intake. The analysis unit can also analyze the balance of purine intake. To analyze the amount of purine intake, the analysis unit evaluates the purine content of food based on the dietary data input by the user. For example, the analysis unit can calculate the amount of purine consumed by the user in a day and compare it with the recommended intake. The analysis unit can also analyze whether the user is getting enough exercise. To analyze whether the user is getting enough exercise, the analysis unit evaluates the recommended amount of exercise and the criteria for excessive exercise based on the exercise data input by the user. For example, the analysis unit can calculate the type and duration of exercise the user performed in a day and compare it with the recommended amount of exercise. This allows the analysis unit to analyze the user's health in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's diet and exercise data into AI, which may then output the analysis results.

[0069] The presentation unit can present specific improvement measures based on the analysis results. The presentation unit presents specific improvement measures based on the analysis results, for example. Specific improvement measures include, but are not limited to, recommendations for dietary changes and exercise. For example, the presentation unit can display advice such as "Drink more water" if the user's water intake is insufficient. Furthermore, the presentation unit can provide specific suggestions such as "Choose foods with low purines" if the user's purine intake is high. Furthermore, the presentation unit can display advice such as "Add 30 minutes of walking each day" if the user is not exercising enough. Conversely, the presentation unit can provide suggestions such as "Reduce the amount of exercise and take more rest" if the user is exercising too much. This allows the presentation unit to provide specific improvement measures to the user. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the analysis results into AI, which then outputs specific improvement measures.

[0070] The presentation unit can display advice to the user, such as "Try to increase your water intake." The presentation unit can display advice to the user, such as "Try to increase your water intake." The content of the advice includes, but is not limited to, increasing the amount of water consumed or changing one's diet. For example, the presentation unit can display advice such as "Try to drink more water" if the user's daily water intake is insufficient. The presentation unit can also suggest specific methods for the user to increase their water intake. For example, the presentation unit can display specific advice such as "Try to drink a glass of water after each meal." This allows the presentation unit to provide the user with specific advice on water intake. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input analysis results into AI, which then outputs specific advice.

[0071] The presentation unit can suggest low-purine foods. For example, the presentation unit suggests to the user to choose low-purine foods. The content of the suggestion may include, but is not limited to, specific food names and purine content standards. For example, the presentation unit may suggest, "Choose low-purine foods" if the user consumes a high amount of purine per day. The presentation unit can also suggest specific ways for the user to choose low-purine foods. For example, the presentation unit may make a specific suggestion such as, "Avoid seafood and organ meats and eat more vegetables and fruits." This allows the presentation unit to provide the user with specific dietary improvements. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit may input analysis results into AI, which then outputs specific suggestions.

[0072] The presentation unit can suggest increasing the amount of exercise. For example, the presentation unit suggests to the user to increase the amount of exercise. The content of the suggestion includes, but is not limited to, recommended types and durations of exercise. For example, if the amount of exercise the user has done each day is insufficient, the presentation unit can suggest, "Add 30 minutes of walking every day." The presentation unit can also suggest specific ways for the user to increase the amount of exercise. For example, the presentation unit can make specific suggestions such as, "Try taking the stairs instead of the elevator." This allows the presentation unit to provide the user with specific exercise improvement measures. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input analysis results into AI, which then outputs specific suggestions.

[0073] The presentation unit can suggest to the user to reduce the amount of exercise and take a rest. For example, the presentation unit suggests to the user to reduce the amount of exercise and take a rest. The content of the suggestion includes, but is not limited to, the recommended rest time and the frequency of exercise. For example, if the amount of exercise the user has performed in one day is excessive, the presentation unit can suggest, "Try to reduce the amount of exercise and take a rest." The presentation unit can also suggest specific ways for the user to take a rest. For example, the presentation unit can make a specific suggestion such as, "Try to take complete rest one day a week." This allows the presentation unit to provide the user with specific measures to improve rest. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the analysis results into AI, which can output specific suggestions.

[0074] The health management system includes a gamification point unit, and points are accumulated each time a user achieves a goal. The point unit, for example, awards points each time the user achieves a goal. Methods of awarding points include, but are not limited to, goal achievement criteria and point types. For example, the point unit can award points when a user achieves a set amount of water intake per day. The point unit can also award points when a user achieves a set exercise goal. For example, the point unit can award points when a user walks for 30 minutes every day. This allows the point unit to maintain the user's motivation and enable health management while having fun. Some or all of the above-described processing in the point unit may be performed using, for example, AI, or may be performed without AI. For example, the point unit can input the user's goal achievement data into AI, which then awards points.

[0075] The health management system includes a ranking unit, allowing users to compete against each other. The ranking unit, for example, displays a ranking for the users to compete against each other. Ranking criteria include, but are not limited to, competition rules and ranking update frequency. For example, the ranking unit may display a ranking based on the number of goals achieved by users or the total points. The ranking unit may also periodically update the ranking to promote competition between users. For example, the ranking unit may display a weekly ranking so that users can check their ranking. This allows the ranking unit to promote competition between users and increase motivation for health management. Some or all of the above-described processing in the ranking unit may be performed using, for example, AI, or may be performed without AI. For example, the ranking unit may input users' goal achievement data into AI, which then generates rankings.

[0076] The input unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user emotion. The input unit, for example, estimates the user's emotion and adjusts the design of the input interface based on the estimated emotion. Methods for emotion estimation include, but are not limited to, emotion recognition algorithms and data used. For example, the input unit can provide a simple interface and minimize input steps when the user is stressed. The input unit can also provide detailed input options and suggest customizable input methods when the user is relaxed. Furthermore, the input unit can prioritize voice input when the user is in a hurry, allowing the user to quickly enter data about food, drink, and exercise. This allows the input unit to provide an input interface that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 input unit may be performed using, for example, AI, or without AI. For example, the input unit can input the user's emotional data into the AI, which can then adjust the design of the input interface.

[0077] The input unit can analyze the user's past input history and suggest an optimal input method. The input unit, for example, analyzes the user's past input history and suggests an optimal input method. Criteria for the optimal input method include, but are not limited to, the design of the input interface and ease of input. For example, the input unit can automatically display data about food and drink or exercise that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (such as voice or text) that the user has used in the past. Furthermore, the input unit can predict and suggest data to be input during a specific time period based on the user's past input history. This allows the input unit to provide an optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's past input data into AI, which then suggests an optimal input method.

[0078] The input unit can customize input items based on the user's current health condition and lifestyle habits at the time of input. For example, the input unit customizes the input items based on the user's current health condition and lifestyle habits at the time of input. Methods for customizing input items include, but are not limited to, methods of selecting items based on the user's health condition and lifestyle habits. For example, the input unit can display only simple input items when the user is tired. The input unit can also display detailed input items when the user has a healthy lifestyle. Furthermore, if the user has a specific health condition (e.g., high blood pressure), the input unit can preferentially display input items related to that condition. This allows the input unit to provide input items according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the user's health data into AI, which can then customize the input items.

[0079] The input unit can estimate the user's emotions and determine the priority of inputs based on the estimated user emotions. The input unit, for example, estimates the user's emotions and determines the priority of inputs based on the estimated emotions. Methods for determining the priority of inputs include, but are not limited to, methods for setting priorities based on emotion estimation results. For example, the input unit can prioritize displaying important input items when the user is stressed. The input unit can also display detailed input items when the user is relaxed. Furthermore, the input unit can display only the most important input items when the user is in a hurry. This allows the input unit to provide input priorities 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the input unit may be performed using an AI, for example, or without an AI. For example, the input unit can input the user's emotion data into an AI, which can then determine the priority of inputs.

[0080] The input unit can prioritize displaying highly relevant input items during input, taking into account the user's geographical location information. For example, the input unit can prioritize displaying highly relevant input items during input, taking into account the user's geographical location information. Methods of using geographical location information include, but are not limited to, using GPS data and selecting input items based on location information. For example, when the user is in a specific area, the input unit can prioritize displaying food and drink data related to that area. Furthermore, when the user is traveling, the input unit can display input items related to meals and exercise at the user's travel destination. Furthermore, when the user is at home, the input unit can prioritize displaying daily food and drink and exercise data. This allows the input unit to provide input items based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location data to AI, which can select highly relevant input items.

[0081] The input unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the input unit can analyze the user's social media activity at the time of input and suggest related input items. Methods for analyzing social media activity include, but are not limited to, analyzing posted content and suggesting related input items. For example, the input unit can automatically input food and drink data based on photos of meals shared by the user on social media. The input unit can also suggest exercise data based on events the user has participated in on social media. Furthermore, the input unit can customize input items based on health-related accounts the user follows on social media. This allows the input unit to provide input items based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media data into AI, which then suggests related input items.

[0082] The transmission unit can estimate the user's emotion and adjust the timing of data transmission based on the estimated user's emotion. For example, the transmission unit estimates the user's emotion and adjusts the timing of data transmission based on the estimated emotion. Methods for adjusting the timing of data transmission include, but are not limited to, methods for setting the transmission timing based on emotion estimation results. For example, the transmission unit can immediately transmit data when the user is relaxed. The transmission unit can also postpone data transmission when the user is stressed. Furthermore, the transmission unit can quickly transmit data when the user is in a hurry. This allows the transmission unit to provide timing for data transmission that corresponds 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI. For example, the transmission unit can input user's emotion data into an AI, which can then adjust the timing of data transmission.

[0083] The transmitting unit can select the optimal transmission method by analyzing the user's past transmission history when transmitting data. For example, the transmitting unit can select the optimal transmission method by analyzing the user's past transmission history when transmitting data. Criteria for selecting the optimal transmission method include, but are not limited to, a method for selecting a transmission method based on the past transmission history. For example, the transmitting unit can prioritize a transmission method (Wi-Fi, mobile data, etc.) that the user has used in the past. The transmitting unit can also suggest the most efficient transmission method based on the user's past transmission history. Furthermore, the transmitting unit can select the optimal transmission method based on the amount of data the user has transmitted in the past. This allows the transmitting unit to provide the optimal transmission method based on the user's past transmission history. Some or all of the above-described processing in the transmitting unit can be performed using, for example, AI, or can be performed without using AI. For example, the transmitting unit can input the user's past transmission data into AI, which can then select the optimal transmission method.

[0084] The transmitting unit can customize the transmission method based on the user's current network conditions when transmitting data. For example, the transmitting unit customizes the transmission method based on the user's current network conditions when transmitting data. Methods of using the network conditions include, but are not limited to, measuring network speed and customizing the transmission method. For example, the transmitting unit can transmit a large amount of data when the user is connected to Wi-Fi. The transmitting unit can also transmit a reduced amount of data when the user is using mobile data. Furthermore, the transmitting unit can temporarily suspend transmission when the user's network connection is unstable. This allows the transmitting unit to provide a transmission method that suits the user's network conditions. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the user's network data into AI, which can then customize the transmission method.

[0085] The transmission unit can estimate the user's emotion and determine the priority of the transmission data based on the estimated user's emotion. The transmission unit, for example, estimates the user's emotion and determines the priority of the transmission data based on the estimated emotion. Methods for determining the priority of the transmission data include, but are not limited to, methods for setting priority based on emotion estimation results. For example, the transmission unit can prioritize important data when the user is stressed. The transmission unit can also transmit detailed data when the user is relaxed. Furthermore, the transmission unit can transmit only the most important data when the user is in a hurry. This allows the transmission unit to provide a priority of the transmission 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI. For example, the transmission unit can input the user's emotion data into an AI, which can then determine the priority of the transmission data.

[0086] When transmitting data, the transmission unit can prioritize transmitting highly relevant data in consideration of the user's geographical location information. For example, when transmitting data, the transmission unit prioritizes transmitting highly relevant data in consideration of the user's geographical location information. Criteria for selecting highly relevant data include, but are not limited to, a method of selecting data based on geographical location information. For example, when the user is in a specific area, the transmission unit can prioritize transmitting data related to that area. Furthermore, when the user is traveling, the transmission unit can prioritize transmitting data about the user's travel destination. Furthermore, when the user is at home, the transmission unit can prioritize transmitting everyday data. This allows the transmission unit to provide data transmission based on the user's geographical location information. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the user's geographical location data into AI, which can select highly relevant data.

[0087] The transmitting unit may analyze the user's social media activity and transmit related data when transmitting data. For example, the transmitting unit may analyze the user's social media activity and transmit related data when transmitting data. Methods for analyzing social media activity include, but are not limited to, analyzing posted content and transmitting related data. For example, the transmitting unit may transmit related data based on data shared by the user on social media. The transmitting unit may also transmit related data based on events the user participated in on social media. Furthermore, the transmitting unit may transmit related data based on health-related accounts the user follows on social media. This allows the transmitting unit to provide data transmission based on the user's social media activity. Some or all of the above-described processing in the transmitting unit may be performed using, or without, AI. For example, the transmitting unit may input the user's social media data into AI, which then transmits the related data.

[0088] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, the analysis unit estimates the user's emotion and adjusts the analysis algorithm based on the estimated emotion. Methods for adjusting the analysis algorithm include, but are not limited to, methods for adjusting the algorithm based on emotion estimation results. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is stressed. Furthermore, the analysis unit can also perform a quick analysis when the user is in a hurry. This allows the analysis unit to provide an analysis algorithm that corresponds to the user's emotion. 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 these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI, which then adjusts the analysis algorithm.

[0089] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past health data during analysis. Methods of referring to past health data include, but are not limited to, methods of referring to past data and methods of improving the accuracy of analysis. For example, the analysis unit can analyze current data based on the user's past dietary data. The analysis unit can also analyze current data based on the user's past exercise data. Furthermore, the analysis unit can analyze current data based on the user's past health condition. This allows the analysis unit to provide an analysis based on the user's past health data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past health data into AI, which can improve the accuracy of the analysis.

[0090] The analysis unit can customize the analysis method based on the user's lifestyle habits and eating patterns during analysis. For example, the analysis unit customizes the analysis method based on the user's lifestyle habits and eating patterns during analysis. Methods for acquiring lifestyle habits and eating patterns include, but are not limited to, methods for analyzing daily behavioral data and food records. For example, if the user has a specific eating pattern, the analysis unit can perform analysis based on the pattern. Furthermore, if the user has a specific lifestyle habit, the analysis unit can perform analysis based on the habit. Furthermore, if the user has a specific health condition, the analysis unit can perform analysis based on the condition. This allows the analysis unit to provide an analysis based on the user's lifestyle habits and eating patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's lifestyle habits and eating patterns into AI, which can customize the analysis method.

[0091] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated emotion. Methods for adjusting the display method of the analysis results include, but are not limited to, setting the display method based on the emotion estimation result. For example, the analysis unit can provide a simple, highly visible display method when the user is nervous. The analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can provide a display method that focuses on the main points when the user is in a hurry. This allows the analysis unit to provide a display method of the analysis results that corresponds to the user's emotion. 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 these examples. 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 emotional data into the AI, which can then adjust how the analysis results are displayed.

[0092] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. Methods of using geographical location information include, but are not limited to, using GPS data and location-based analysis methods. For example, when the user is in a specific area, the analysis unit can perform analysis based on data related to that area. Furthermore, when the user is traveling, the analysis unit can perform analysis based on travel destination data. Furthermore, when the user is at home, the analysis unit can perform analysis based on everyday data. This allows the analysis unit to provide analysis based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location data into AI, which can improve the accuracy of the analysis.

[0093] The analysis unit may analyze the user's social media activities during the analysis and use the associated data for the analysis. For example, the analysis unit may analyze the user's social media activities during the analysis and use the associated data for the analysis. Methods for analyzing social media activities include, but are not limited to, analyzing posted content and associated data. For example, the analysis unit may perform analysis based on data shared by the user on social media. The analysis unit may also perform analysis based on events the user participated in on social media. Furthermore, the analysis unit may perform analysis based on health-related accounts the user follows on social media. This allows the analysis unit to provide analysis based on the user's social media activities. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's social media data into AI, and the AI ​​may use the associated data for analysis.

[0094] The presentation unit can estimate the user's emotion and adjust the presentation method of the improvement measures based on the estimated emotion. For example, the presentation unit can estimate the user's emotion and adjust the presentation method of the improvement measures based on the estimated emotion. Methods for adjusting the presentation method of the improvement measures include, but are not limited to, setting the presentation method based on the emotion estimation result. For example, the presentation unit can provide a simple and highly visible presentation method when the user is nervous. The presentation unit can also provide a presentation method including detailed information when the user is relaxed. Furthermore, the presentation unit can provide a presentation method that focuses on the main points when the user is in a hurry. This allows the presentation unit to provide a presentation method of the improvement measures according to the user's emotion. 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 these examples. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or without an AI. For example, the presentation unit can input the user's emotional data into the AI, which can then adjust the way the improvement measures are presented.

[0095] The presentation unit can suggest an optimal improvement measure by referring to the user's past improvement history when presenting the improvement measure. For example, the presentation unit can suggest an optimal improvement measure by referring to the user's past improvement history when presenting the improvement measure. Methods of referring to the past improvement history include, but are not limited to, a method of referring to past history and a method of suggesting an optimal improvement measure. For example, the presentation unit can suggest a current improvement measure based on the user's past improvement history. The presentation unit can also suggest the most effective improvement measure from the user's past improvement history. Furthermore, the presentation unit can analyze the user's past improvement history and suggest the most appropriate improvement measure. This allows the presentation unit to provide an optimal improvement measure based on the user's past improvement history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past improvement history data into AI, which then suggests the optimal improvement measure.

[0096] The presentation unit can customize the improvement measures based on the user's current health condition when presenting the improvement measures. For example, the presentation unit customizes the improvement measures based on the user's current health condition when presenting the improvement measures. Methods for acquiring the current health condition include, but are not limited to, health checkup results and daily health data usage. For example, the presentation unit can suggest simple improvement measures when the user is tired. The presentation unit can also suggest detailed improvement measures when the user is in good health. Furthermore, the presentation unit can suggest improvement measures related to a specific health condition (e.g., high blood pressure) when the user has that condition. This allows the presentation unit to provide improvement measures according to the user's health condition. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI, or may be performed without AI. For example, the presentation unit can input the user's health data into AI, which can then customize the improvement measures.

[0097] The presentation unit can estimate the user's emotions and prioritize the improvement measures based on the estimated user emotions. The presentation unit, for example, estimates the user's emotions and prioritizes the improvement measures based on the estimated emotions. Methods for prioritizing the improvement measures include, but are not limited to, methods for setting priorities based on emotion estimation results. For example, the presentation unit can prioritize important improvement measures when the user is feeling stressed. The presentation unit can also display detailed improvement measures when the user is relaxed. Furthermore, the presentation unit can display only the most important improvement measures when the user is in a hurry. This allows the presentation unit to prioritize the improvement measures according to the user's emotions. The emotion estimation is achieved 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 these examples. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit can input the user's emotion data into an AI, which then prioritizes the improvement measures.

[0098] The presentation unit may prioritize displaying highly relevant improvements in consideration of the user's geographical location information when presenting the improvements. For example, the presentation unit may prioritize displaying highly relevant improvements in consideration of the user's geographical location information when presenting the improvements. Criteria for selecting highly relevant improvements include, but are not limited to, a method for selecting improvements based on geographical location information. For example, when the user is in a specific area, the presentation unit may prioritize displaying improvements related to that area. Furthermore, when the user is traveling, the presentation unit may prioritize displaying improvements for travel destinations. Furthermore, when the user is at home, the presentation unit may prioritize displaying everyday improvements. This allows the presentation unit to provide improvements based on the user's geographical location information. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input the user's geographical location data into AI, which may select highly relevant improvements.

[0099] The presentation unit may analyze the user's social media activity and suggest related improvement measures at the time of presentation. For example, the presentation unit may analyze the user's social media activity and suggest related improvement measures at the time of presentation. Methods for analyzing social media activity include, but are not limited to, analyzing posted content and suggesting related improvement measures. For example, the presentation unit may suggest related improvement measures based on data shared by the user on social media. The presentation unit may also suggest related improvement measures based on events the user participated in on social media. Furthermore, the presentation unit may suggest related improvement measures based on health-related accounts the user follows on social media. This allows the presentation unit to provide improvement measures based on the user's social media activity. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit may input the user's social media data into AI, which then suggests related improvement measures.

[0100] The point unit can estimate the user's emotion and adjust the point awarding method based on the estimated user's emotion. The point unit, for example, estimates the user's emotion and adjusts the point awarding method based on the estimated emotion. Methods for adjusting the point awarding method include, but are not limited to, methods for setting the awarding method based on emotion estimation results. For example, the point unit can award more points when the user is relaxed. The point unit can also award fewer points when the user is stressed. Furthermore, the point unit can award points quickly when the user is in a hurry. This allows the point unit to provide a point awarding method that corresponds 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the point unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the point unit can input the user's emotion data into an AI, and the AI ​​can adjust the point awarding method.

[0101] The point unit can select the optimal point awarding method by referring to the user's past point history when awarding points. For example, the point unit can select the optimal awarding method by referring to the user's past point history when awarding points. Methods of referring to past point history include, but are not limited to, methods of referring to past history and methods of selecting the optimal awarding method. For example, the point unit can select the current point awarding method based on the user's past point history. The point unit can also select the most effective point awarding method from the user's past point history. Furthermore, the point unit can analyze the user's past point history and select the most appropriate point awarding method. This allows the point unit to provide the optimal point awarding method based on the user's past point history. Some or all of the above-described processing in the point unit may be performed using, for example, AI, or may be performed without AI. For example, the point unit can input the user's past point history data into AI, which then selects the optimal point awarding method.

[0102] The point unit can estimate the user's emotions and determine the priority of points based on the estimated user emotions. For example, the point unit can estimate the user's emotions and determine the priority of points based on the estimated emotions. Methods for determining the priority of points include, but are not limited to, methods for setting priorities based on emotion estimation results. For example, the point unit can prioritize important points when the user is stressed. The point unit can also assign detailed points when the user is relaxed. Furthermore, the point unit can assign only the most important points when the user is in a hurry. This allows the point unit to provide a priority of points 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the point unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the point unit can input the user's emotion data into an AI, which can then determine the priority of points.

[0103] The point unit can select the optimal point awarding method by taking into account the user's geographical location information when awarding points. For example, the point unit selects the optimal point awarding method by taking into account the user's geographical location information when awarding points. Methods of using geographical location information include, but are not limited to, using GPS data and selecting an awarding method based on location information. For example, when a user is in a specific area, the point unit can prioritize points related to that area. Furthermore, when a user is traveling, the point unit can prioritize points for travel destinations. Furthermore, when a user is at home, the point unit can prioritize points for everyday activities. This allows the point unit to provide a point awarding method based on the user's geographical location information. Some or all of the above-described processing in the point unit may be performed using, for example, AI, or may be performed without using AI. For example, the point unit can input the user's geographical location data into AI, which can select the optimal awarding method.

[0104] The ranking unit can estimate the user's emotions and adjust the ranking display method based on the estimated user emotions. For example, the ranking unit estimates the user's emotions and adjusts the ranking display method based on the estimated emotions. Methods for adjusting the ranking display method include, but are not limited to, setting the display method based on emotion estimation results. For example, the ranking unit can provide a simple, highly visible display method when the user is nervous. The ranking unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the ranking unit can provide a display method that focuses on the main points when the user is in a hurry. This allows the ranking unit to provide a ranking display method that corresponds 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, an AI. For example, the ranking unit can input user emotion data into an AI, which can then adjust the ranking display method.

[0105] The ranking unit can select the optimal display method by referring to the user's past ranking history when displaying the rankings. For example, the ranking unit can select the optimal display method by referring to the user's past ranking history when displaying the rankings. Methods of referring to past ranking history include, but are not limited to, methods of referring to past history and methods of selecting the optimal display method. For example, the ranking unit can select the current ranking display method based on the user's past ranking history. The ranking unit can also select the most effective ranking display method from the user's past ranking history. Furthermore, the ranking unit can analyze the user's past ranking history and select the most appropriate ranking display method. This allows the ranking unit to provide the optimal display method based on the user's past ranking history. Some or all of the above-described processing in the ranking unit can be performed, for example, using AI or without AI. For example, the ranking unit can input the user's past ranking history data into AI, which can select the optimal display method.

[0106] The ranking unit can estimate the user's emotions and determine the ranking priority based on the estimated user emotions. For example, the ranking unit estimates the user's emotions and determines the ranking priority based on the estimated emotions. Methods for determining the ranking priority include, but are not limited to, methods for setting priority based on emotion estimation results. For example, the ranking unit can prioritize and display important rankings when the user is stressed. The ranking unit can also display detailed rankings when the user is relaxed. Furthermore, the ranking unit can display only the most important rankings when the user is in a hurry. This allows the ranking unit to provide ranking priorities 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the ranking unit can input the user's emotion data into an AI, which then determines the ranking priority.

[0107] The ranking unit can select an optimal display method by taking into consideration the user's geographical location information when displaying rankings. For example, the ranking unit selects an optimal display method by taking into consideration the user's geographical location information when displaying rankings. Methods of using geographical location information include, but are not limited to, using GPS data and selecting a display method based on location information. For example, when a user is in a specific area, the ranking unit can prioritize displaying rankings related to that area. Furthermore, when a user is traveling, the ranking unit can prioritize displaying rankings of travel destinations. Furthermore, when a user is at home, the ranking unit can prioritize displaying everyday rankings. This allows the ranking unit to provide a ranking display method based on the user's geographical location information. Some or all of the above-described processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input the user's geographical location data into AI, which can select an optimal display method. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, transmission unit, analysis unit, presentation unit, point unit, ranking unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the smart device 14, allowing the user to input daily food and exercise data. The transmission unit transmits data to the data processing device 12 via the communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the data using AI. The presentation unit is implemented by the control unit 46A of the smart device 14, and presents improvement measures to the user based on the analysis results. The point unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and awards points each time the user achieves a goal. The ranking unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and displays a ranking for users to compete against each other. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the input interface and point allocation method. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, transmission unit, analysis unit, presentation unit, point unit, ranking unit, and emotion estimation function, is realized by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214, and allows the user to input daily eating and drinking and exercise data. The transmission unit transmits data to the data processing device 12 via the communication I / F 44 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the data using AI. The presentation unit is realized by the control unit 46A of the smart glasses 214, and presents improvement measures to the user based on the analysis results. The point unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and awards points each time the user achieves a goal. The ranking unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and displays a ranking for users to compete against each other. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the input interface and point allocation method. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, transmission unit, analysis unit, presentation unit, point unit, ranking unit, and emotion estimation function, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the headset-type terminal 314, allowing the user to input daily diet and exercise data. The transmission unit transmits data to the data processing device 12 via the communication I / F 44 of the headset-type terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the data using AI. The presentation unit is implemented by the control unit 46A of the headset-type terminal 314, and presents improvement measures to the user based on the analysis results. The point unit is implemented by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and awards points each time the user achieves a goal. The ranking unit is implemented by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and displays rankings for user competition. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the input interface and point allocation method. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, transmission unit, analysis unit, presentation unit, point unit, ranking unit, and emotion estimation function, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the robot 414, and allows the user to input daily diet and exercise data. The transmission unit transmits data to the data processing device 12 via the communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the data using AI. The presentation unit is implemented by the control unit 46A of the robot 414, and presents improvement measures to the user based on the analysis results. The point unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and awards points each time the user achieves a goal. The ranking unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and displays a ranking for users to compete against each other. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the input interface and point allocation method.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The health management system can also acquire the user's sleep data and analyze it in the analysis unit. For example, if the user inputs the amount of sleep and the quality of sleep, the analysis unit can evaluate the user's health condition in more detail based on this data. The analysis unit can also evaluate the impact of lack of sleep on uric acid levels and suggest appropriate sleep durations. The analysis unit can also analyze the user's sleep patterns and identify causes of stress and fatigue. This allows the user to perform comprehensive health management.

[0110] The analysis unit can estimate the user's emotions and evaluate the reliability of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can evaluate the reliability of the data as low and prompt the user to re-enter the data. On the other hand, if the user is relaxed, the analysis unit can evaluate the reliability of the data as high and reflect this in the analysis results. Furthermore, the analysis unit can adjust the display method of the analysis results based on the user's emotions and provide the results in a format that is easy for the user to understand. This allows the analysis unit to perform flexible analysis according to the user's emotions.

[0111] The presentation unit can suggest regional health information and meals taking into account the user's geographical location information. For example, if the user is traveling, it can suggest local specialties and healthy ingredients. If the user is in a specific region, the presentation unit can also suggest exercise methods suited to the climate and environment of that region. Furthermore, if the user is at home, the presentation unit can suggest recipes and exercise methods using local ingredients. This allows the user to manage their health according to their geographical conditions.

[0112] The presentation unit can estimate the user's emotions and display a message to increase motivation based on the estimated emotions. For example, if the user is feeling stressed, it can display an encouraging message or suggest a relaxation method. If the user is relaxed, it can display a message encouraging further goal achievement. Furthermore, if the user is in a hurry, it can provide concise, to-the-point advice. In this way, the presentation unit can provide appropriate support according to the user's emotions.

[0113] The health management system can further analyze the user's social media activity and perform analysis using the analysis unit. For example, the analysis unit can evaluate the user's health condition based on the diet and exercise data the user shares on social media. The analysis unit can also incorporate the latest health information and trends into the analysis based on the health-related accounts the user follows. Furthermore, the analysis unit can analyze emotional data obtained from the user's social media activity to evaluate the user's stress level and motivation. This allows the user to perform more personalized health management.

[0114] The presentation unit can estimate the user's emotions and adjust gamification elements based on the estimated emotions. For example, if the user is feeling stressed, it can suggest easy tasks or relaxing games. If the user is feeling relaxed, it can suggest challenging tasks or games with competitive elements. Furthermore, if the user is in a hurry, it can provide tasks or games that can be completed in a short time. In this way, the presentation unit can provide appropriate gamification according to the user's emotions.

[0115] The health management system can also acquire the user's purchasing history and analyze it in the analysis unit. For example, the analysis unit can evaluate the user's nutritional balance based on data on the foods and supplements purchased by the user. The analysis unit can also suggest healthy foods and supplements based on the user's purchasing history. Furthermore, the analysis unit can provide advice on reducing unnecessary expenses based on the user's purchasing history. This allows the user to live a financially healthy life.

[0116] The presentation unit can estimate the user's emotions and adjust the frequency and content of notifications based on the estimated emotions. For example, if the user is feeling stressed, the frequency of notifications can be reduced and content that helps the user relax can be provided. Also, if the user is relaxed, the frequency of notifications can be increased and proactive advice can be provided. Furthermore, if the user is in a hurry, only important notifications can be displayed and concise content can be provided. In this way, the presentation unit can provide appropriate notifications according to the user's emotions.

[0117] The health management system can also acquire health data from the user's family and friends and analyze it in the analysis unit. For example, the analysis unit can evaluate the user's health condition based on the health data shared by the user's family and friends. The analysis unit can also provide appropriate advice to the user based on the health data of family and friends. Furthermore, the analysis unit can encourage the sharing of health data with family and friends, creating an environment where they can support each other. This allows the user to manage their health while receiving social support.

[0118] The health management system can further estimate the user's emotions and adjust the reward system based on the estimated emotions. For example, if the user is feeling stressed, it can provide a reward that helps the user relax or a benefit to refresh the user. If the user is relaxed, it can provide a reward or benefit that encourages the user to further achieve a goal. Furthermore, if the user is in a hurry, it can provide a reward or benefit that can be received quickly. In this way, the health management system can provide an appropriate reward system according to the user's emotions.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The input unit allows the user to input daily food and drink or exercise data. The data input by the user includes the contents of meals, calorie intake, type of exercise, and exercise time. The input unit can input data via a smartphone app or web application, or it can also input data using voice input or image recognition technology. For example, the user can take a photo of their meal, and the contents of the meal can be automatically analyzed using image recognition technology. Step 2: The transmitting unit transmits the data input by the input unit to the analyzing unit. The transmitting unit can transmit the data via the Internet and can also ensure the security of the data by using encryption technology when transmitting the data. Step 3: The analysis unit analyzes the data sent by the transmission unit. The analysis unit uses AI to analyze the data and evaluates the user's health using statistical analysis and machine learning algorithms. For example, it analyzes the balance of fluid intake, purine intake, and whether the user is exercising too much or too little, evaluates the user's health based on the user's diet and exercise data, and derives specific improvement measures. Step 4: The presentation unit presents improvement measures based on the results of the analysis by the analysis unit. The presentation unit presents the improvement measures to the user through a smartphone app or web application, displaying specific improvement measures. For example, if the user's fluid intake is insufficient, the unit may suggest "drink more water," or if the user's purine intake is high, the unit may suggest "choose foods with low purines." The unit also suggests the appropriate amount of exercise depending on whether the user is exercising too much or too little.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] 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.

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

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the 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.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

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

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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. an input unit for inputting daily dietary or exercise data by a user; a transmitting unit that transmits the data input by the input unit; an analysis unit that analyzes the data transmitted by the transmission unit; a presentation unit that presents an improvement measure based on the result of the analysis by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Analyze the balance of fluid intake, purine intake, and whether exercise is excessive or insufficient The system of claim 1 .

3. The presentation unit Based on the analysis results, we will present specific improvement measures. The system of claim 1 .

4. The presentation unit Suggest choosing foods low in purines The system of claim 1 .

5. The presentation unit Suggesting increased exercise The system of claim 1 .

6. The presentation unit Suggest reducing exercise and getting more rest The system of claim 1 .

7. Equipped with a gamification point section, Points are accumulated each time the user achieves a goal. The system of claim 1 .

8. Equipped with a ranking department, Users can compete with each other The system of claim 1 .

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