System

Abstract

To provide a system that supports users effectively in weight management and the improvement of their dietary habits.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a provision unit, and an instruction unit. The reception unit receives information on the user's weight or dietary habits. The analysis unit analyzes the information received by the reception unit and generates daily advice. The provision unit provides the advice generated by the analysis unit to the user. The instruction unit provides exercise instructions based on the advice provided by the provision unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately provide systems that effectively support users in managing their weight and improving their dietary habits, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively support the user in managing their weight and improving their dietary habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, a providing unit, and an instruction unit. The receiving unit receives information on a user's weight or dietary habits. The analysis unit analyzes the information received by the receiving unit and generates daily advice. The providing unit provides the advice generated by the analysis unit to the user. The instruction unit gives exercise instructions based on the advice provided by the providing unit. Effect of the Invention

[0007] The system according to the embodiment can effectively support the user in managing their weight and improving their dietary habits. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The health support system according to the embodiment of the present invention is a system in which a user inputs information about his / her weight and dietary habits, and a generation AI provides daily advice. This system is provided for 300 yen per month, and the AI ​​supports the user in a conversational format until the user reaches the target weight. In addition, the system also provides instructions for exercise. For example, the user inputs information about his / her weight and dietary habits into the app. For example, the user inputs detailed information such as what he / she ate for breakfast, what he / she ate for lunch, what he / she ate for dinner, and whether or not he / she ate snacks. This information is input to the generation AI. Next, the generation AI analyzes the input information and provides daily advice to the user. For example, if the user's diet is unbalanced, the system suggests a balanced diet. In addition, if the user's weight is increasing, the system advises the user to reduce their calorie intake. In addition, the generation AI supports the user toward their target weight. For example, if the user sets a target weight, the system provides specific advice toward that goal. For example, in order to lose 1 kg in a week, the system provides detailed advice such as how much the user should limit their daily calorie intake and what kind of exercise to do. In addition, the generation AI also provides instructions for exercise. For example, if the user sets the time and type of exercise to be performed, the system provides advice to maximize the effect of the exercise. For example, when jogging, the system gives specific instructions such as how far to run and at what pace. This system enables users to efficiently manage their own health. The generation AI provides daily advice, enabling users to properly manage their weight and diet and receive support toward their target weight. In addition, by receiving exercise instructions, users can perform effective exercise. In this way, the health support system can efficiently support users' health management.

[0029] The health support system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an instruction unit. The reception unit receives information on the user's weight and dietary habits. The information on the user's weight and dietary habits includes, for example, weight measurements, meal contents, calorie intake, and the like, but is not limited to such examples. The reception unit receives, for example, information on the weight and dietary habits input by the user to the app. The reception unit can also transmit the information input by the user to the generation AI. The analysis unit uses the generation AI to analyze the information received by the reception unit and generate daily advice. The generation AI uses, for example, natural language generation technology to analyze the information input by the user and generate appropriate advice. For example, the generation AI analyzes the balance of the user's diet and suggests a balanced diet. The generation AI can also analyze the fluctuation of the user's weight and advise adjustment of calorie intake. The provision unit provides the user with the advice generated by the analysis unit. For example, the provision unit notifies the user of the advice generated by the generation AI. The provision unit can also display the advice in the app so that the user can check the advice. The instruction unit gives instructions for exercise based on the advice provided by the provision unit. The instruction unit, for example, provides the user with exercise instructions generated by the generation AI. For example, the instruction unit provides advice for maximizing the effect of exercise based on the time and type of exercise set by the user. In this way, the health support system according to the embodiment can support the user's health management by accepting and analyzing information on the user's weight and dietary habits, providing advice, and giving instructions for exercise.

[0030] The reception unit receives information on the user's weight and dietary habits. The information on the user's weight and dietary habits includes, but is not limited to, for example, weight measurements, dietary details, and calorie intake. The reception unit receives, for example, weight and dietary habits information input by the user to the app. Specifically, the user can input the daily weight and dietary details into a dedicated application using a smartphone or tablet. The weight measurements may be automatically sent to the app from a Bluetooth (registered trademark)-enabled weight scale, which can save the user time. As for the dietary details, the user can enter the types and amounts of food eaten to record the calorie intake and nutrient balance in detail. Furthermore, the reception unit can also transmit the information input by the user to the generation AI. The generation AI receives this information and uses it as data for analysis. For example, when the user inputs the types and amounts of food eaten for breakfast, the information is immediately transmitted to the generation AI and is ready for analysis. In addition, the reception unit stores the user's input information on a cloud server, allowing data backup and access from other devices. This allows the user to check and manage their health information anytime and anywhere. In addition, the reception unit also has a function of periodically checking the data entered by the user, and if there is any missing or inaccurate data, notifying the user and urging them to correct it. This enables the reception unit to collect the user's health information accurately and efficiently, and improves the reliability of the entire system.

[0031] The analysis unit uses the generation AI to analyze the information received by the reception unit and generate daily advice. The generation AI uses, for example, natural language generation technology to analyze the user's input information and generate appropriate advice. Specifically, the generation AI analyzes the balance of the user's diet and suggests a balanced diet. For example, based on the dietary content entered by the user, the generation AI analyzes the nutrients of each food and determines whether they are in excess or deficiency. If a specific nutrient is lacking, the generation AI suggests foods and recipes to supplement that nutrient. The generation AI can also analyze the user's weight fluctuations and advise adjustment of calorie intake. For example, if the user's weight is increasing, the generation AI suggests a specific meal plan to reduce calorie intake. Conversely, if the weight is decreasing, advice is given to increase calorie intake. Furthermore, the generation AI can analyze long-term health trends and predict future risks based on the user's past data. For example, by analyzing data from the past few months, if the user is exposed to a specific health risk, specific advice is provided to reduce the risk. This allows the analysis unit to comprehensively grasp the user's health condition and provide individually optimized advice. Furthermore, the analysis unit regularly updates the learning algorithm of the generating AI and incorporates the latest health information and research results, allowing it to continuously improve the accuracy and effectiveness of the advice.

[0032] The providing unit provides the user with the advice generated by the analysis unit. For example, the providing unit notifies the user of the advice generated by the generation AI. Specifically, the advice can be immediately conveyed to the user by using a pop-up notification or a push notification within the application. For example, if the generation AI makes a suggestion for a balanced lunch after breakfast, the suggestion is notified to the user's smartphone. The providing unit can also display the advice within the app so that the user can confirm the advice. The user's health information and advice are centrally managed within the app, and the user can easily refer to past advice and health data. Furthermore, the providing unit also has a function to customize the content of the advice to suit the user's preferences. For example, if the user does not like a particular food, an alternative that does not include that food can be suggested. The providing unit also has a function to monitor the effect of the advice and collect feedback from the user. By inputting the results of the user following the advice, the providing unit can evaluate the effect and reflect it in the next advice. As a result, the providing unit can always provide optimal advice to the user and maximize the effect of health management. Furthermore, the providing unit periodically updates the contents of the advice and incorporates the latest health information and research results, thereby enabling the user to always be provided with the latest information.

[0033] The instruction unit gives instructions for exercise based on the advice provided by the provision unit. The instruction unit provides the user with, for example, exercise instructions generated by the generation AI. Specifically, the instruction unit provides advice for maximizing the effect of exercise based on the time and type of exercise set by the user. For example, if the user sets to jog in the morning, the instruction unit suggests a stretching method and an appropriate pace to enhance the effect of the jogging. In addition, if the user performs strength training, the instruction unit can specifically instruct the correct form, number of times, and number of sets for various exercises. Furthermore, the instruction unit also has a function of monitoring the progress of the exercise based on the user's exercise history and adjusting the exercise plan as necessary. For example, if the progress is delayed with respect to the exercise goal set by the user, the instruction unit analyzes the cause and provides specific advice for achieving the goal. In addition, the instruction unit can collect feedback from the user and evaluate the effect and satisfaction of the exercise. As a result, the instruction unit can always provide the user with optimal exercise instructions and maximize the effect of health management. Furthermore, the instruction unit can analyze the user's exercise data using the learning algorithm of the generation AI and provide an individually optimized exercise plan. This enables the instruction unit to provide optimal exercise instructions according to the user's health condition and goals, maximizing the effectiveness of health management.

[0034] The reception unit can receive specific information on the user's weight or diet. The specific information includes, for example, details of meals, changes in weight, and the like, but is not limited to such examples. The reception unit, for example, receives detailed information on meals input by the user to the app. The reception unit can also receive weight change information input by the user. In this way, the reception unit can provide more accurate advice by receiving detailed information on the user's weight and diet. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can transmit the information input by the user to AI, and the AI ​​can analyze the information and extract specific information.

[0035] The analysis unit can analyze the user's input information by the generation AI and generate appropriate advice. The generation AI can analyze the user's input information by using, for example, natural language generation technology and generate appropriate advice. The generation AI can analyze, for example, the balance of the user's diet and suggest a balanced diet. The generation AI can also analyze the user's weight fluctuation and advise the user to adjust their calorie intake. For example, the generation AI can analyze detailed information about the diet input by the user and, if the diet is unbalanced, suggest a balanced diet. The generation AI can also analyze weight fluctuation information input by the user and, if the weight is increasing, advise the user to reduce their calorie intake. In this way, the generation AI can analyze the user's input information and generate appropriate advice. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can transmit the information input by the user to the generation AI, and the generation AI can analyze the information and generate advice.

[0036] The providing unit can provide the advice generated by the generation AI to the user. For example, the providing unit notifies the user of the advice generated by the generation AI. The providing unit can also display the advice in the app so that the user can confirm the advice. For example, the providing unit notifies the user of the advice generated by the generation AI to the user's smartphone. The providing unit can also display the advice in the app so that the user can confirm the advice. In this way, the advice generated by the generation AI can be provided to the user to support the user's health management. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the advice using an AI model for notifying the user of the advice generated by the generation AI.

[0037] The instruction unit can provide the user with exercise instructions generated by the generation AI. The instruction unit, for example, provides the user with exercise instructions generated by the generation AI. For example, the instruction unit provides advice for maximizing the effect of exercise based on the time and type of exercise set by the user. For example, the instruction unit provides specific instructions such as how far to run and at what pace to run when jogging. The instruction unit can also provide advice for maximizing the effect of exercise based on the type of exercise set by the user. This makes it possible to support the user's exercise by providing the user with exercise instructions generated by the generation AI. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can provide exercise instructions using an AI model for providing the user with exercise instructions generated by the generation AI.

[0038] The instruction unit can receive the user's feedback and adjust the next exercise instruction based on the feedback. The instruction unit, for example, receives feedback after the user has exercised. For example, the user can input the user's thoughts after exercising and the results of the exercise. The instruction unit adjusts the next exercise instruction based on the user's feedback. For example, the user can adjust the intensity and type of the next exercise based on the user's thoughts after exercising. The instruction unit can also adjust the next exercise instruction based on the results of the user's exercise. This allows the user to receive the user's feedback and adjust the next exercise instruction based on it, thereby providing more effective exercise instructions. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's feedback to AI, and the AI ​​can analyze the feedback and adjust the next exercise instruction.

[0039] The health support system includes a protection unit that protects and anonymizes the input data of the user. The protection unit, for example, encrypts the data input by the user. For example, the protection unit encrypts the weight and dietary information input by the user to prevent a third party from accessing the data. The protection unit also anonymizes the user's data. For example, the protection unit protects the user's privacy by deleting the user's personal information and anonymizing the data. This makes it possible to protect the user's privacy by protecting and anonymizing the user's input data. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can protect the data by using an AI model for encrypting the user's data.

[0040] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays meal contents that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can analyze the user's tendency to input during a specific time period from the user's past input history and suggest the optimal input timing. This improves input efficiency by analyzing the user's past input history and suggesting the optimal input method. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI, which can analyze the history and suggest the optimal input method.

[0041] The reception unit can filter the information on weight and dietary habits based on the user's current health condition and lifestyle habits when inputting the information. For example, if the user has diabetes, the reception unit displays a warning when inputting a high-sugar meal. In addition, if the user is on a diet, the reception unit can also suggest an alternative when inputting a high-calorie meal. Furthermore, if the user is not exercising enough, the reception unit can send a notification encouraging the user to exercise. This allows more appropriate information to be input by filtering based on the user's current health condition and lifestyle habits. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's health condition and lifestyle habits to AI, which can analyze the data and perform filtering.

[0042] When inputting information on weight and dietary habits, the reception unit can preferentially input information with high relevance in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially inputs information on ingredients and dishes in that area. In addition, when the user is traveling, the reception unit can also preferentially input meal information at the travel destination. Furthermore, when the user eats at a specific restaurant, the reception unit can also preferentially input menu information of the restaurant. In this way, more appropriate information can be input by preferentially inputting information with high relevance in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI, which can analyze the information and preferentially input information with high relevance.

[0043] The reception unit can analyze the user's social media activity and input related information when inputting weight and dietary information. For example, the reception unit automatically inputs meal information shared by the user on social media. The reception unit can also acquire and input information from health-related accounts that the user follows on social media. Furthermore, the reception unit can automatically input meal information of a place where the user has checked in on social media. This improves input efficiency by analyzing the user's social media activity and inputting related information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity to AI, which can analyze the activity and input related information.

[0044] The analysis unit can adjust the level of detail of the advice based on the user's health goals during the analysis. For example, if the user wants to lose weight in a short period of time, the analysis unit can provide detailed diet and exercise advice. In addition, if the user wants to maintain health in a long period of time, the analysis unit can also provide balanced advice. Furthermore, if the user has a specific health problem, the analysis unit can provide advice specific to that problem. This allows more appropriate advice to be provided by adjusting the level of detail of the advice based on the user's health goals. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's health goals into AI, and the AI ​​can adjust the level of detail of the advice based on the goals.

[0045] During the analysis, the analysis unit can apply different analysis algorithms depending on the category of the user's diet. For example, if the user is a vegetarian, the analysis unit applies an analysis algorithm for vegetarians. Furthermore, if the user has diabetes, the analysis unit can also apply an analysis algorithm for diabetes. Furthermore, if the user is an athlete, the analysis unit can also apply an analysis algorithm for athletes. In this way, by applying different analysis algorithms depending on the category of the user's diet, more appropriate advice can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the user's diet to the AI, and the AI ​​can apply the analysis algorithm depending on the category.

[0046] The analysis unit can determine the priority of advice based on the time of submission of the user's input information during the analysis. For example, the analysis unit can provide breakfast advice preferentially based on information input by the user in the morning. The analysis unit can also provide lunch advice preferentially based on information input by the user in the afternoon. Furthermore, the analysis unit can also provide dinner advice preferentially based on information input by the user in the evening. This allows more appropriate advice to be provided by determining the priority of advice based on the time of submission of the user's input information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the user's input information to AI, and the AI ​​can analyze the information to determine the priority of advice.

[0047] The analysis unit can adjust the order of advice based on the relevance of the user during analysis. For example, if the user has a specific health problem, the analysis unit can preferentially provide advice related to the problem. In addition, if the user has a specific goal, the analysis unit can also preferentially provide advice related to the goal. Furthermore, if the user has a specific eating pattern, the analysis unit can also preferentially provide advice related to the pattern. In this way, by adjusting the order of advice based on the relevance of the user, more appropriate advice can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the user to AI, and the AI ​​can analyze the information and adjust the order of advice.

[0048] When providing advice, the providing unit can select the optimal providing method by referring to the user's past advice history. For example, the providing unit preferentially provides a form of advice that the user has previously preferred. The providing unit can also provide advice that was effective for the user again. Furthermore, the providing unit can avoid advice that the user has previously rejected. In this way, the optimal advice providing method can be selected by referring to the user's past advice history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past advice history into AI, which can analyze the history and select the optimal providing method.

[0049] The providing unit can customize the means of advice based on the user's current living situation when providing advice. For example, when the user is busy, the providing unit provides advice that can be executed in a short time. In addition, the providing unit can also provide detailed advice when the user is relaxed. Furthermore, when the user is traveling, the providing unit can also provide advice that can be executed at the travel destination. In this way, by customizing the means of advice based on the user's current living situation, more appropriate advice can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current living situation into AI, and the AI ​​can analyze the situation and customize the means of advice.

[0050] The providing unit can select the optimal advice method in consideration of the geographical location information of the user when providing advice. For example, when the user is in a specific area, the providing unit provides advice on ingredients and dishes in that area. In addition, when the user is traveling, the providing unit can also provide advice that can be performed at the travel destination. Furthermore, when the user eats at a specific store, the providing unit can also provide advice based on the menu of the store. In this way, more appropriate advice can be provided by selecting the optimal advice method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information and select the optimal advice method.

[0051] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of advice. The providing unit provides advice based on, for example, food information shared by the user on social media. The providing unit can also obtain information from health-related accounts that the user follows on social media and provide advice. Furthermore, the providing unit can provide advice based on food information of a place where the user has checked in on social media. This makes it possible to provide more appropriate advice by analyzing the user's social media activity and suggesting a means of advice. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, which can analyze the activity and suggest a means of advice.

[0052] When instructing the exercise, the instruction unit can provide optimal instructions by referring to the user's past exercise history. The instruction unit, for example, analyzes the effects of exercises performed by the user in the past and suggests optimal exercises. The instruction unit can also preferentially suggest exercises that the user liked in the past. Furthermore, the instruction unit can not suggest exercises that the user avoided in the past. In this way, by providing optimal instructions by referring to the user's past exercise history, more effective exercise instructions can be provided. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's past exercise history into AI, which can analyze the history and provide optimal instructions.

[0053] The instruction unit can customize the exercise instruction means based on the user's current health condition when instructing the exercise. For example, if the user is tired, the instruction unit can suggest light exercise. Also, if the user is in a healthy state, the instruction unit can suggest hard exercise. Furthermore, if the user is in poor health, the instruction unit can suggest rest. This allows more appropriate exercise instructions to be provided by customizing the exercise instruction means based on the user's current health condition. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can input the user's health condition to AI, which can analyze the condition and customize the exercise instruction means.

[0054] The instruction unit can select an optimal exercise instruction method in consideration of the geographical location information of the user when instructing the exercise. For example, when the user is in a park, the instruction unit can suggest an exercise that can be performed in the park. Furthermore, when the user is in a gym, the instruction unit can also suggest an exercise that can be performed in the gym. Furthermore, when the user is at home, the instruction unit can also suggest an exercise that can be performed at home. This allows more appropriate exercise instructions to be provided by selecting an optimal exercise instruction method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information to select an optimal exercise instruction method.

[0055] The instruction unit can analyze the user's social media activity and suggest a means of exercise instruction when instructing the user to exercise. The instruction unit can, for example, suggest an exercise based on exercise information shared by the user on social media. The instruction unit can also obtain information from fitness-related accounts that the user follows on social media and suggest an exercise. Furthermore, the instruction unit can also suggest an exercise based on exercise information of a location where the user has checked in on social media. This makes it possible to provide more appropriate exercise instructions by analyzing the user's social media activity and suggesting a means of exercise instruction. A part or all of the above-mentioned processing in the instruction unit can be performed, for example, using AI or without using AI. For example, the instruction unit can input the user's social media activity into AI, which can analyze the activity and suggest a means of exercise instruction.

[0056] The protection unit can select the optimal protection method by referring to the user's past data protection history when protecting data. For example, the protection unit preferentially provides a data protection method that the user has previously selected. The protection unit can also avoid a data protection method that the user has previously rejected. Furthermore, the protection unit can analyze the user's past data protection history and propose an optimal protection method. As a result, more appropriate data protection can be provided by selecting the optimal protection method by referring to the user's past data protection history. A part or all of the above-mentioned processing in the protection unit can be performed, for example, using AI or can be performed without using AI. For example, the protection unit can input the user's past data protection history to AI, which can analyze the history and select the optimal protection method.

[0057] The protection unit can select the optimal protection method in consideration of the geographical location information of the user when protecting data. For example, the protection unit strengthens encryption of data when the user is in a public place. The protection unit can also provide a normal data protection method when the user is at home. Furthermore, the protection unit can prioritize data backup when the user is traveling. This allows more appropriate data protection to be provided by selecting the optimal protection method in consideration of the geographical location information of the user. Some or all of the above-mentioned processing in the protection unit may be performed, for example, using AI or may be performed without using AI. For example, the protection unit can input the geographical location information of the user to AI, which can analyze the information to select the optimal protection 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 support system may further include a sleep analysis unit that monitors the user's sleep pattern. The sleep analysis unit evaluates the user's sleep quality by collecting and analyzing the user's sleep data. For example, the user may input information about sleep time and sleep quality to the app. The sleep analysis unit may also provide advice for improving the quality of sleep based on the user's sleep data. For example, if the user is not getting enough sleep, the sleep analysis unit may advise the user to increase the amount of sleep. Also, if the user is not getting deep sleep, the sleep analysis unit may suggest a method for relaxing. This may improve the user's sleep quality and support overall health management.

[0060] The reception unit can receive photos of the user's meals and automatically recognize the meal contents using image analysis technology. For example, when the user uploads a photo of the meal to the app, the reception unit can analyze the photo and automatically extract ingredients and calories. The reception unit can also advise the user on appropriate angles and lighting conditions when taking a photo of the meal. Furthermore, the reception unit can notify the user of the timing to take a photo of the meal. This allows the user to record the meal contents without hassle and provides more accurate data.

[0061] The providing unit can evaluate the effectiveness of advice based on the user's advice history and reflect it in the next advice. For example, the providing unit can record the results of executing advice that the user has received in the past and analyze the effects. The providing unit can also check whether the user has executed the advice and send a reminder if the user has not executed the advice. Furthermore, the providing unit can receive the results of the user's execution of the advice as feedback and reflect it in the next advice. This makes it possible to provide more effective advice to the user and strengthen support for health management.

[0062] The instruction unit can evaluate the progress of the exercise based on the user's exercise history and adjust the next exercise plan. For example, the instruction unit can analyze data of the user's past exercise and evaluate the effectiveness of the exercise. The instruction unit can also check the progress toward the goal set by the user and provide specific advice for achieving the goal. Furthermore, the instruction unit can provide feedback on points to note and areas for improvement when the user exercises. This can optimize the user's exercise plan and support effective exercise.

[0063] The health support system may further include a moisture management unit that monitors the user's moisture intake. The moisture management unit allows the user to record their daily moisture intake and provides advice to encourage appropriate moisture intake. For example, when the user inputs their moisture intake into the app, the moisture management unit analyzes the data and suggests an appropriate amount of moisture intake to the user. The moisture management unit may also send reminders to the user so that the user does not forget to drink moisture. Furthermore, the moisture management unit may provide advice to adjust the amount of moisture intake according to the user's activity level and temperature. This allows the user's moisture intake to be properly managed and health maintenance to be supported.

[0064] The analysis unit can analyze the user's eating patterns and provide advice to optimize the timing and content of meals. For example, if the user tends to skip breakfast, the analysis unit can explain the importance of breakfast and suggest breakfast menus that are easy to prepare. If the user eats late at night, the analysis unit can also suggest meals that are easy to digest. Furthermore, if the user frequently snacks, the analysis unit can provide healthy snack options. This can improve the user's eating patterns and support healthy eating habits.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit receives information on the user's weight and dietary habits. The information on the user's weight and dietary habits includes, for example, weight measurements, meal contents, and calorie intake. The reception unit receives the weight and dietary habits information input by the user to the app. The reception unit can also transmit the information input by the user to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and generate daily advice. The generation AI analyzes the user's input information using, for example, natural language generation technology, and generates appropriate advice. For example, the generation AI analyzes the balance of the user's diet and suggests balanced meals. The generation AI can also analyze fluctuations in the user's weight and advise adjustments to calorie intake. Step 3: The providing unit provides the advice generated by the analysis unit to the user. For example, the providing unit notifies the user of the advice generated by the generation AI. The providing unit can also display the advice within the app so that the user can check the advice. Step 4: The instruction unit instructs the user to exercise based on the advice provided by the provision unit. The instruction unit, for example, provides the user with exercise instructions generated by the generation AI. For example, the instruction unit provides advice for maximizing the effect of the exercise based on the time and type of exercise set by the user.

[0067] (Example 2) The health support system according to the embodiment of the present invention is a system in which a user inputs information about his / her weight and dietary habits, and a generation AI provides daily advice. This system is provided for 300 yen per month, and the AI ​​supports the user in a conversational format until the user reaches the target weight. In addition, the system also provides instructions for exercise. For example, the user inputs information about his / her weight and dietary habits into the app. For example, the user inputs detailed information such as what he / she ate for breakfast, what he / she ate for lunch, what he / she ate for dinner, and whether or not he / she ate snacks. This information is input to the generation AI. Next, the generation AI analyzes the input information and provides daily advice to the user. For example, if the user's diet is unbalanced, the system suggests a balanced diet. In addition, if the user's weight is increasing, the system advises the user to reduce their calorie intake. In addition, the generation AI supports the user toward their target weight. For example, if the user sets a target weight, the system provides specific advice toward that goal. For example, in order to lose 1 kg in a week, the system provides detailed advice such as how much the user should limit their daily calorie intake and what kind of exercise to do. In addition, the generation AI also provides instructions for exercise. For example, if the user sets the time and type of exercise to be performed, the system provides advice to maximize the effect of the exercise. For example, when jogging, the system gives specific instructions such as how far to run and at what pace. This system enables users to efficiently manage their own health. The generation AI provides daily advice, enabling users to properly manage their weight and diet and receive support toward their target weight. In addition, by receiving exercise instructions, users can perform effective exercise. In this way, the health support system can efficiently support users' health management.

[0068] The health support system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an instruction unit. The reception unit receives information on the user's weight and dietary habits. The information on the user's weight and dietary habits includes, for example, weight measurements, meal contents, calorie intake, and the like, but is not limited to such examples. The reception unit receives, for example, information on the weight and dietary habits input by the user to the app. The reception unit can also transmit the information input by the user to the generation AI. The analysis unit uses the generation AI to analyze the information received by the reception unit and generate daily advice. The generation AI uses, for example, natural language generation technology to analyze the information input by the user and generate appropriate advice. For example, the generation AI analyzes the balance of the user's diet and suggests a balanced diet. The generation AI can also analyze the fluctuation of the user's weight and advise adjustment of calorie intake. The provision unit provides the user with the advice generated by the analysis unit. For example, the provision unit notifies the user of the advice generated by the generation AI. The provision unit can also display the advice in the app so that the user can check the advice. The instruction unit gives instructions for exercise based on the advice provided by the provision unit. The instruction unit, for example, provides the user with exercise instructions generated by the generation AI. For example, the instruction unit provides advice for maximizing the effect of exercise based on the time and type of exercise set by the user. In this way, the health support system according to the embodiment can support the user's health management by accepting and analyzing information on the user's weight and dietary habits, providing advice, and giving instructions for exercise.

[0069] The reception unit receives information on the user's weight and dietary habits. The information on the user's weight and dietary habits includes, but is not limited to, for example, weight measurements, dietary details, and calorie intake. The reception unit receives, for example, weight and dietary habits information input by the user to the app. Specifically, the user can input their daily weight and dietary details into a dedicated application using a smartphone or tablet. Weight measurements can be automatically sent to the app from a Bluetooth-enabled weighing scale, which can save the user time. As for the dietary details, the user can enter the types and amounts of food they ate to record the calorie intake and nutrient balance in detail. Furthermore, the reception unit can also send the information input by the user to the generation AI. The generation AI receives this information and uses it as data for analysis. For example, when the user enters the types and amounts of food they ate for breakfast, the information is immediately sent to the generation AI and is ready for analysis. In addition, the reception unit stores the user's input information on a cloud server, allowing data backup and access from other devices. This allows the user to check and manage their health information anytime and anywhere. In addition, the reception unit also has a function of periodically checking the data entered by the user, and if there is any missing or inaccurate data, notifying the user and urging them to correct it. This enables the reception unit to collect the user's health information accurately and efficiently, and improves the reliability of the entire system.

[0070] The analysis unit uses the generation AI to analyze the information received by the reception unit and generate daily advice. The generation AI uses, for example, natural language generation technology to analyze the user's input information and generate appropriate advice. Specifically, the generation AI analyzes the balance of the user's diet and suggests a balanced diet. For example, based on the dietary content entered by the user, the generation AI analyzes the nutrients of each food and determines whether they are in excess or deficiency. If a specific nutrient is lacking, the generation AI suggests foods and recipes to supplement that nutrient. The generation AI can also analyze the user's weight fluctuations and advise adjustment of calorie intake. For example, if the user's weight is increasing, the generation AI suggests a specific meal plan to reduce calorie intake. Conversely, if the weight is decreasing, advice is given to increase calorie intake. Furthermore, the generation AI can analyze long-term health trends and predict future risks based on the user's past data. For example, by analyzing data from the past few months, if the user is exposed to a specific health risk, specific advice is provided to reduce the risk. This allows the analysis unit to comprehensively grasp the user's health condition and provide individually optimized advice. Furthermore, the analysis unit regularly updates the learning algorithm of the generating AI and incorporates the latest health information and research results, allowing it to continuously improve the accuracy and effectiveness of the advice.

[0071] The providing unit provides the user with the advice generated by the analysis unit. For example, the providing unit notifies the user of the advice generated by the generation AI. Specifically, the advice can be immediately conveyed to the user by using a pop-up notification or a push notification within the application. For example, if the generation AI makes a suggestion for a balanced lunch after breakfast, the suggestion is notified to the user's smartphone. The providing unit can also display the advice within the app so that the user can confirm the advice. The user's health information and advice are centrally managed within the app, and the user can easily refer to past advice and health data. Furthermore, the providing unit also has a function to customize the content of the advice to suit the user's preferences. For example, if the user does not like a particular food, an alternative that does not include that food can be suggested. The providing unit also has a function to monitor the effect of the advice and collect feedback from the user. By inputting the results of the user following the advice, the providing unit can evaluate the effect and reflect it in the next advice. As a result, the providing unit can always provide optimal advice to the user and maximize the effect of health management. Furthermore, the providing unit can regularly update the contents of the advice and incorporate the latest health information and research results, thereby always providing the user with the latest information.

[0072] The instruction unit gives instructions for exercise based on the advice provided by the provision unit. The instruction unit provides the user with, for example, exercise instructions generated by the generation AI. Specifically, the instruction unit provides advice for maximizing the effect of exercise based on the time and type of exercise set by the user. For example, if the user sets to jog in the morning, the instruction unit suggests a stretching method and an appropriate pace to enhance the effect of the jogging. In addition, if the user performs strength training, the instruction unit can specifically instruct the correct form, number of times, and number of sets for various exercises. Furthermore, the instruction unit also has a function of monitoring the progress of the exercise based on the user's exercise history and adjusting the exercise plan as necessary. For example, if the progress is delayed with respect to the exercise goal set by the user, the instruction unit analyzes the cause and provides specific advice for achieving the goal. In addition, the instruction unit can collect feedback from the user and evaluate the effect and satisfaction of the exercise. As a result, the instruction unit can always provide the user with optimal exercise instructions and maximize the effect of health management. Furthermore, the instruction unit can analyze the user's exercise data using the learning algorithm of the generation AI and provide an individually optimized exercise plan. This enables the instruction unit to provide optimal exercise instructions according to the user's health condition and goals, maximizing the effectiveness of health management.

[0073] The reception unit can receive specific information on the user's weight or diet. The specific information includes, for example, details of meals, changes in weight, and the like, but is not limited to such examples. The reception unit, for example, receives detailed information on meals input by the user to the app. The reception unit can also receive weight change information input by the user. In this way, the reception unit can provide more accurate advice by receiving detailed information on the user's weight and diet. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can transmit the information input by the user to AI, and the AI ​​can analyze the information and extract specific information.

[0074] The analysis unit can analyze the user's input information by the generation AI and generate appropriate advice. The generation AI can analyze the user's input information by using, for example, natural language generation technology and generate appropriate advice. The generation AI can analyze, for example, the balance of the user's diet and suggest a balanced diet. The generation AI can also analyze the user's weight fluctuation and advise the user to adjust their calorie intake. For example, the generation AI can analyze detailed information about the diet input by the user and, if the diet is unbalanced, suggest a balanced diet. The generation AI can also analyze weight fluctuation information input by the user and, if the weight is increasing, advise the user to reduce their calorie intake. In this way, the generation AI can analyze the user's input information and generate appropriate advice. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can transmit the information input by the user to the generation AI, and the generation AI can analyze the information and generate advice.

[0075] The providing unit can provide the advice generated by the generation AI to the user. For example, the providing unit notifies the user of the advice generated by the generation AI. The providing unit can also display the advice in the app so that the user can confirm the advice. For example, the providing unit notifies the user of the advice generated by the generation AI to the user's smartphone. The providing unit can also display the advice in the app so that the user can confirm the advice. In this way, the advice generated by the generation AI can be provided to the user to support the user's health management. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the advice using an AI model for notifying the user of the advice generated by the generation AI.

[0076] The instruction unit can provide the user with exercise instructions generated by the generation AI. The instruction unit, for example, provides the user with exercise instructions generated by the generation AI. For example, the instruction unit provides advice for maximizing the effect of exercise based on the time and type of exercise set by the user. For example, the instruction unit provides specific instructions such as how far to run and at what pace to run when jogging. The instruction unit can also provide advice for maximizing the effect of exercise based on the type of exercise set by the user. This makes it possible to support the user's exercise by providing the user with exercise instructions generated by the generation AI. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can provide exercise instructions using an AI model for providing the user with exercise instructions generated by the generation AI.

[0077] The instruction unit can receive the user's feedback and adjust the next exercise instruction based on the feedback. The instruction unit, for example, receives feedback after the user has exercised. For example, the user can input the user's thoughts after exercising and the results of the exercise. The instruction unit adjusts the next exercise instruction based on the user's feedback. For example, the user can adjust the intensity and type of the next exercise based on the user's thoughts after exercising. The instruction unit can also adjust the next exercise instruction based on the results of the user's exercise. This allows the user to receive the user's feedback and adjust the next exercise instruction based on it, thereby providing more effective exercise instructions. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's feedback to AI, and the AI ​​can analyze the feedback and adjust the next exercise instruction.

[0078] The health support system includes a protection unit that protects and anonymizes the input data of the user. The protection unit, for example, encrypts the data input by the user. For example, the protection unit encrypts the weight and dietary information input by the user to prevent a third party from accessing the data. The protection unit also anonymizes the user's data. For example, the protection unit protects the user's privacy by deleting the user's personal information and anonymizing the data. This makes it possible to protect the user's privacy by protecting and anonymizing the user's input data. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can protect the data by using an AI model for encrypting the user's data.

[0079] The reception unit can estimate the user's emotion and adjust the input timing of weight and dietary information based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit sends a notification to prompt the user to input during a time when the user can relax. In addition, when the user is busy, the reception unit can also provide a simple form that allows the user to input information in a short time. Furthermore, when the user is relaxed, the reception unit can also send a notification to prompt the user to input detailed information. In this way, by adjusting the input timing based on the user's emotion, information can be input at a more appropriate timing. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and adjust the input timing.

[0080] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays meal contents that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can analyze the user's tendency to input during a specific time period from the user's past input history and suggest the optimal input timing. This improves input efficiency by analyzing the user's past input history and suggesting the optimal input method. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI, which can analyze the history and suggest the optimal input method.

[0081] The reception unit can filter the information on weight and dietary habits based on the user's current health condition and lifestyle habits when inputting the information. For example, if the user has diabetes, the reception unit displays a warning when inputting a high-sugar meal. In addition, if the user is on a diet, the reception unit can also suggest an alternative when inputting a high-calorie meal. Furthermore, if the user is not exercising enough, the reception unit can send a notification encouraging the user to exercise. This allows more appropriate information to be input by filtering based on the user's current health condition and lifestyle habits. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's health condition and lifestyle habits to AI, which can analyze the data and perform filtering.

[0082] The reception unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit urges the user to input only important information with priority. In addition, the reception unit can also urge the user to input detailed information when the user is relaxed. Furthermore, when the user is in a hurry, the reception unit can also urge the user to input only the most important information. In this way, important information can be input with priority by determining the priority of information to be input based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and determine the priority of information to be input.

[0083] When inputting information on weight and dietary habits, the reception unit can preferentially input information with high relevance in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially inputs information on ingredients and dishes in that area. In addition, when the user is traveling, the reception unit can also preferentially input meal information at the travel destination. Furthermore, when the user eats at a specific restaurant, the reception unit can also preferentially input menu information of the restaurant. In this way, more appropriate information can be input by preferentially inputting information with high relevance in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI, which can analyze the information and preferentially input information with high relevance.

[0084] The reception unit can analyze the user's social media activity and input related information when inputting weight and dietary information. For example, the reception unit automatically inputs meal information shared by the user on social media. The reception unit can also acquire and input information from health-related accounts that the user follows on social media. Furthermore, the reception unit can automatically input meal information of a place where the user has checked in on social media. This improves input efficiency by analyzing the user's social media activity and inputting related information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity to AI, which can analyze the activity and input related information.

[0085] The analysis unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides advice in gentle words. In addition, when the user is relaxed, the analysis unit can also provide detailed advice. Furthermore, when the user is in a hurry, the analysis unit can also provide concise advice that focuses on the main points. This allows more appropriate advice to be provided by adjusting the way of expressing advice based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 inputs the user's emotion data into the generation AI, and the generation AI can estimate the emotion and adjust the way of expressing advice.

[0086] The analysis unit can adjust the level of detail of the advice based on the user's health goals during the analysis. For example, if the user wants to lose weight in a short period of time, the analysis unit can provide detailed diet and exercise advice. In addition, if the user wants to maintain health in a long period of time, the analysis unit can also provide balanced advice. Furthermore, if the user has a specific health problem, the analysis unit can provide advice specific to that problem. This allows more appropriate advice to be provided by adjusting the level of detail of the advice based on the user's health goals. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's health goals into AI, and the AI ​​can adjust the level of detail of the advice based on the goals.

[0087] During the analysis, the analysis unit can apply different analysis algorithms depending on the category of the user's diet. For example, if the user is a vegetarian, the analysis unit applies an analysis algorithm for vegetarians. Furthermore, if the user has diabetes, the analysis unit can also apply an analysis algorithm for diabetes. Furthermore, if the user is an athlete, the analysis unit can also apply an analysis algorithm for athletes. In this way, by applying different analysis algorithms depending on the category of the user's diet, more appropriate advice can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the user's diet to the AI, and the AI ​​can apply the analysis algorithm depending on the category.

[0088] The analysis unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides short and to the point advice. In addition, the analysis unit can also provide detailed advice when the user is relaxed. Furthermore, when the user is in a hurry, the analysis unit can also provide concise and quick advice. This allows more appropriate advice to be provided by adjusting the length of the advice based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and adjust the length of the advice.

[0089] The analysis unit can determine the priority of advice based on the time of submission of the user's input information during the analysis. For example, the analysis unit can provide breakfast advice preferentially based on information input by the user in the morning. The analysis unit can also provide lunch advice preferentially based on information input by the user in the afternoon. Furthermore, the analysis unit can also provide dinner advice preferentially based on information input by the user in the evening. This allows more appropriate advice to be provided by determining the priority of advice based on the time of submission of the user's input information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the user's input information to AI, and the AI ​​can analyze the information to determine the priority of advice.

[0090] The analysis unit can adjust the order of advice based on the relevance of the user during analysis. For example, if the user has a specific health problem, the analysis unit can preferentially provide advice related to the problem. In addition, if the user has a specific goal, the analysis unit can also preferentially provide advice related to the goal. Furthermore, if the user has a specific eating pattern, the analysis unit can also preferentially provide advice related to the pattern. In this way, by adjusting the order of advice based on the relevance of the user, more appropriate advice can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the user to AI, and the AI ​​can analyze the information and adjust the order of advice.

[0091] The providing unit can estimate the user's emotion and adjust the method of providing advice based on the estimated user's emotion. For example, when the user is stressed, the providing unit provides advice in gentle words. In addition, when the user is relaxed, the providing unit can also provide detailed advice. Furthermore, when the user is in a hurry, the providing unit can also provide concise advice that focuses on the main points. This allows more appropriate advice to be provided by adjusting the method of providing advice based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and adjust the method of providing advice.

[0092] When providing advice, the providing unit can select the optimal providing method by referring to the user's past advice history. For example, the providing unit preferentially provides a form of advice that the user has previously preferred. The providing unit can also provide advice that was effective for the user again. Furthermore, the providing unit can avoid advice that the user has previously rejected. In this way, the optimal advice providing method can be selected by referring to the user's past advice history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past advice history into AI, which can analyze the history and select the optimal providing method.

[0093] The providing unit can customize the means of advice based on the user's current living situation when providing advice. For example, when the user is busy, the providing unit provides advice that can be executed in a short time. In addition, the providing unit can also provide detailed advice when the user is relaxed. Furthermore, when the user is traveling, the providing unit can also provide advice that can be executed at the travel destination. In this way, by customizing the means of advice based on the user's current living situation, more appropriate advice can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current living situation into AI, and the AI ​​can analyze the situation and customize the means of advice.

[0094] The providing unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide important advice with priority. In addition, when the user is relaxed, the providing unit can also provide detailed advice. Furthermore, when the user is in a hurry, the providing unit can also provide concise advice that covers the main points. In this way, important advice can be provided with priority by determining the priority of advice based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and determine the priority of advice.

[0095] The providing unit can select the optimal advice method in consideration of the geographical location information of the user when providing advice. For example, when the user is in a specific area, the providing unit provides advice on ingredients and dishes in that area. In addition, when the user is traveling, the providing unit can also provide advice that can be performed at the travel destination. Furthermore, when the user eats at a specific store, the providing unit can also provide advice based on the menu of the store. In this way, more appropriate advice can be provided by selecting the optimal advice method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information and select the optimal advice method.

[0096] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of advice. The providing unit provides advice based on, for example, food information shared by the user on social media. The providing unit can also obtain information from health-related accounts that the user follows on social media and provide advice. Furthermore, the providing unit can provide advice based on food information of a place where the user has checked in on social media. This makes it possible to provide more appropriate advice by analyzing the user's social media activity and suggesting a means of advice. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, which can analyze the activity and suggest a means of advice.

[0097] The instruction unit can estimate the user's emotion and adjust the method of exercise instruction based on the estimated user's emotion. For example, when the user is stressed, the instruction unit can suggest a relaxing exercise. Furthermore, when the user is relaxed, the instruction unit can also suggest a slightly harder exercise. Furthermore, when the user is in a hurry, the instruction unit can also suggest an effective exercise in a short time. This allows more appropriate exercise instruction to be provided by adjusting the method of exercise instruction based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and adjust the method of exercise instruction.

[0098] When instructing the exercise, the instruction unit can provide optimal instructions by referring to the user's past exercise history. The instruction unit, for example, analyzes the effects of exercises performed by the user in the past and suggests optimal exercises. The instruction unit can also preferentially suggest exercises that the user liked in the past. Furthermore, the instruction unit can not suggest exercises that the user avoided in the past. In this way, by providing optimal instructions by referring to the user's past exercise history, more effective exercise instructions can be provided. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's past exercise history into AI, which can analyze the history and provide optimal instructions.

[0099] The instruction unit can customize the exercise instruction means based on the user's current health condition when instructing the exercise. For example, if the user is tired, the instruction unit can suggest light exercise. Also, if the user is in a healthy state, the instruction unit can suggest hard exercise. Furthermore, if the user is in poor health, the instruction unit can suggest rest. This allows more appropriate exercise instructions to be provided by customizing the exercise instruction means based on the user's current health condition. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can input the user's health condition to AI, which can analyze the condition and customize the exercise instruction means.

[0100] The instruction unit can estimate the user's emotion and determine the priority of exercise instructions based on the estimated user's emotion. For example, when the user is feeling stressed, the instruction unit preferentially suggests relaxing exercise. Also, when the user is relaxed, the instruction unit can preferentially suggest a slightly hard exercise. Furthermore, when the user is in a hurry, the instruction unit can preferentially suggest an effective exercise in a short time. In this way, important exercise instructions can be provided preferentially by determining the priority of exercise instructions based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and determine the priority of the exercise instructions.

[0101] The instruction unit can select an optimal exercise instruction method in consideration of the geographical location information of the user when instructing the exercise. For example, when the user is in a park, the instruction unit can suggest an exercise that can be performed in the park. Furthermore, when the user is in a gym, the instruction unit can also suggest an exercise that can be performed in the gym. Furthermore, when the user is at home, the instruction unit can also suggest an exercise that can be performed at home. This allows more appropriate exercise instructions to be provided by selecting an optimal exercise instruction method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information to select an optimal exercise instruction method.

[0102] The instruction unit can analyze the user's social media activity and suggest a means of exercise instruction when instructing the user to exercise. The instruction unit can, for example, suggest an exercise based on exercise information shared by the user on social media. The instruction unit can also obtain information from fitness-related accounts that the user follows on social media and suggest an exercise. Furthermore, the instruction unit can also suggest an exercise based on exercise information of a location where the user has checked in on social media. This makes it possible to provide more appropriate exercise instructions by analyzing the user's social media activity and suggesting a means of exercise instruction. A part or all of the above-mentioned processing in the instruction unit can be performed, for example, using AI or without using AI. For example, the instruction unit can input the user's social media activity into AI, which can analyze the activity and suggest a means of exercise instruction.

[0103] The protection unit can estimate the user's emotion and adjust the data protection method based on the estimated user's emotion. The protection unit can simplify the data protection confirmation procedure when the user is stressed, for example. The protection unit can also provide a detailed data protection confirmation procedure when the user is relaxed. Furthermore, the protection unit can quickly perform the data protection confirmation procedure when the user is in a hurry. This allows more appropriate data protection to be provided by adjusting the data protection method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input the user's emotion data to the generation AI, which can estimate the emotion and adjust the data protection method.

[0104] The protection unit can select the optimal protection method by referring to the user's past data protection history when protecting data. For example, the protection unit preferentially provides a data protection method that the user has previously selected. The protection unit can also avoid a data protection method that the user has previously rejected. Furthermore, the protection unit can analyze the user's past data protection history and propose an optimal protection method. As a result, more appropriate data protection can be provided by selecting the optimal protection method by referring to the user's past data protection history. A part or all of the above-mentioned processing in the protection unit can be performed, for example, using AI or can be performed without using AI. For example, the protection unit can input the user's past data protection history to AI, which can analyze the history and select the optimal protection method.

[0105] The protection unit can estimate the user's emotion and determine the priority of data protection based on the estimated user's emotion. For example, when the user is stressed, the protection unit prioritizes the protection of important data. In addition, when the user is relaxed, the protection unit can also perform detailed data protection. Furthermore, when the user is in a hurry, the protection unit can quickly protect important data. In this way, important data protection can be provided preferentially by determining the priority of data protection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input the user's emotion data to the generation AI, and the generation AI can estimate the emotion and determine the priority of data protection.

[0106] The protection unit can select the optimal protection method in consideration of the geographical location information of the user when protecting data. For example, the protection unit strengthens encryption of data when the user is in a public place. The protection unit can also provide a normal data protection method when the user is at home. Furthermore, the protection unit can prioritize data backup when the user is traveling. This allows more appropriate data protection to be provided by selecting the optimal protection method in consideration of the geographical location information of the user. Some or all of the above-mentioned processing in the protection unit may be performed, for example, using AI or may be performed without using AI. For example, the protection unit can input the geographical location information of the user to AI, which can analyze the information to select the optimal protection method.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The health support system may further include a sleep analysis unit that monitors the user's sleep pattern. The sleep analysis unit evaluates the user's sleep quality by collecting and analyzing the user's sleep data. For example, the user may input information about sleep time and sleep quality to the app. The sleep analysis unit may also provide advice for improving the quality of sleep based on the user's sleep data. For example, if the user is not getting enough sleep, the sleep analysis unit may advise the user to increase the amount of sleep. Also, if the user is not getting deep sleep, the sleep analysis unit may suggest a method for relaxing. This may improve the user's sleep quality and support overall health management.

[0109] The reception unit can receive photos of the user's meals and automatically recognize the meal contents using image analysis technology. For example, when the user uploads a photo of the meal to the app, the reception unit can analyze the photo and automatically extract ingredients and calories. The reception unit can also advise the user on appropriate angles and lighting conditions when taking a photo of the meal. Furthermore, the reception unit can notify the user of the timing to take a photo of the meal. This allows the user to record the meal contents without hassle and provides more accurate data.

[0110] The analysis unit can estimate the user's emotions and adjust the content of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can suggest relaxing meals and exercises. If the user is feeling positive, the analysis unit can suggest challenging exercises and new meals. Furthermore, if the user is tired, the analysis unit can provide advice that is easy to follow. This makes it possible to provide appropriate advice according to the user's emotions and maintain the user's motivation.

[0111] The providing unit can evaluate the effectiveness of advice based on the user's advice history and reflect it in the next advice. For example, the providing unit can record the results of executing advice that the user has received in the past and analyze the effects. The providing unit can also check whether the user has executed the advice and send a reminder if the user has not executed the advice. Furthermore, the providing unit can receive the results of the user's execution of the advice as feedback and reflect it in the next advice. This makes it possible to provide more effective advice to the user and strengthen support for health management.

[0112] The instruction unit can estimate the user's emotions and suggest a type of exercise based on the estimated user's emotions. For example, if the user is feeling stressed, the instruction unit can suggest a relaxing exercise such as yoga or meditation. If the user is feeling energetic, the instruction unit can suggest an active exercise such as running or high intensity training (HIIT). Furthermore, if the user is tired, the instruction unit can suggest light stretching or walking. This makes it possible to suggest an appropriate exercise according to the user's emotions and maximize the effect of the exercise.

[0113] The instruction unit can evaluate the progress of the exercise based on the user's exercise history and adjust the next exercise plan. For example, the instruction unit can analyze data of the user's past exercise and evaluate the effectiveness of the exercise. The instruction unit can also check the progress toward the goal set by the user and provide specific advice for achieving the goal. Furthermore, the instruction unit can provide feedback on points to note and areas for improvement when the user exercises. This can optimize the user's exercise plan and support effective exercise.

[0114] The health support system may further include a moisture management unit that monitors the user's moisture intake. The moisture management unit allows the user to record their daily moisture intake and provides advice to encourage appropriate moisture intake. For example, when the user inputs their moisture intake into the app, the moisture management unit analyzes the data and suggests an appropriate amount of moisture intake to the user. The moisture management unit may also send reminders to the user so that the user does not forget to drink moisture. Furthermore, the moisture management unit may provide advice to adjust the amount of moisture intake according to the user's activity level and temperature. This allows the user's moisture intake to be properly managed and health maintenance to be supported.

[0115] The reception unit can estimate the user's emotion and customize the input interface based on the estimated user's emotion. For example, when the user is feeling stressed, a simple and intuitive interface can be provided. When the user is relaxed, an interface that allows detailed information to be input can be provided. Furthermore, when the user is in a hurry, a simplified form that allows input in a short time can be provided. In this way, an appropriate input interface according to the user's emotion can be provided, and input efficiency can be improved.

[0116] The analysis unit can analyze the user's eating patterns and provide advice to optimize the timing and content of meals. For example, if the user tends to skip breakfast, the analysis unit can explain the importance of breakfast and suggest breakfast menus that are easy to prepare. If the user eats late at night, the analysis unit can also suggest meals that are easy to digest. Furthermore, if the user frequently snacks, the analysis unit can provide healthy snack options. This can improve the user's eating patterns and support healthy eating habits.

[0117] The providing unit can estimate the user's emotions and adjust the timing of providing advice based on the estimated user's emotions. For example, if the user is feeling stressed, advice can be provided during a time period when the user can relax. Also, if the user is busy, advice that can be confirmed in a short time can be provided. Furthermore, if the user is relaxed, detailed advice can be provided. This makes it possible to provide advice at an appropriate timing according to the user's emotions, and to improve the ease with which the user accepts the advice.

[0118] The process flow of the second embodiment will be briefly described below.

[0119] Step 1: The reception unit receives information on the user's weight and dietary habits. The information on the user's weight and dietary habits includes, for example, weight measurements, meal contents, and calorie intake. The reception unit receives the weight and dietary habits information input by the user to the app. The reception unit can also transmit the information input by the user to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and generate daily advice. The generation AI analyzes the user's input information using, for example, natural language generation technology, and generates appropriate advice. For example, the generation AI analyzes the balance of the user's diet and suggests balanced meals. The generation AI can also analyze fluctuations in the user's weight and advise adjustments to calorie intake. Step 3: The providing unit provides the advice generated by the analysis unit to the user. For example, the providing unit notifies the user of the advice generated by the generation AI. The providing unit can also display the advice within the app so that the user can check the advice. Step 4: The instruction unit instructs the user to exercise based on the advice provided by the provision unit. The instruction unit, for example, provides the user with exercise instructions generated by the generation AI. For example, the instruction unit provides advice for maximizing the effect of the exercise based on the time and type of exercise set by the user.

[0120] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0121] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0122] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, instruction unit, and protection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and receives weight and dietary information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user of the analysis result. The instruction unit is realized, for example, by the control unit 46A of the smart device 14, and instructs exercise. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encrypts and anonymizes the user's data. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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, the RAM 30, and the 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.

[0127] 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.

[0128] 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 the voice according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0130] 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 performed in a secure state.

[0131] Fig. 4 shows an example of 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0136] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0137] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0138] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, instruction unit, and protection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives weight and dietary information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the analysis result. The instruction unit is realized, for example, by the control unit 46A of the smart glasses 214 and instructs exercise. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and encrypts and anonymizes the user's data. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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, the RAM 30, and the 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.

[0143] 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.

[0144] 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 the voice according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0146] 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 performed in a secure state.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0151] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] 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 voice indicating a user input for 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.

[0153] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, instruction unit, and protection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives weight and dietary information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user of the analysis result. The instruction unit is realized, for example, by the control unit 46A of the headset type terminal 314, and instructs exercise. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encrypts and anonymizes the user's data. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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, the RAM 30, and the 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.

[0159] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] 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 the voice according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0162] 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 performed in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0168] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] 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 a voice indicating a user input for 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.

[0170] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0172] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, instruction unit, and protection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives weight and dietary information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user of the analysis result. The instruction unit is realized, for example, by the control unit 46A of the robot 414 and instructs exercise. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and encrypts and anonymizes the user's data. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0174] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0176] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0178] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0180] 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.

[0181] 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 Universal Serial Bus (USB) 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.

[0182] In addition, 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 upon request from the data processing device 12.

[0183] 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.

[0184] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0185] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0186] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0187] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0189] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0191] (Appendix 1) A reception unit that receives information on a user's weight or dietary habits; an analysis unit that analyzes the information received by the reception unit and generates daily advice; a providing unit that provides the advice generated by the analysis unit to a user; An instruction unit that instructs the user to exercise based on the advice provided by the provision unit. A system characterized by: (Appendix 2) The system described in Appendix 1, wherein the reception unit receives specific information regarding the user's weight or eating habits. (Appendix 3) The analysis unit is Generative AI analyzes user input and generates appropriate advice 2. The system of claim 1, (Appendix 4) The providing unit is Providing advice generated by generative AI to users 2. The system of claim 1, (Appendix 5) The instruction unit is Generative AI provides generated exercise instructions to the user 2. The system of claim 1, (Appendix 6) The system described in Appendix 1, characterized in that the instruction unit accepts user feedback and adjusts next exercise instructions based on the feedback. (Appendix 7) Equipped with a protection section that protects and anonymizes user input data 2. The system of claim 1, (Appendix 8) The reception unit is The system estimates the user's emotions and adjusts the timing of inputting weight and dietary information based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1, (Appendix 10) The reception unit is When entering weight and dietary information, filtering is performed based on the user's current health status and lifestyle habits. 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the user's emotions and prioritize the information to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When entering weight and dietary information, the system takes into account the user's geographic location and prioritizes the most relevant information. 2. The system of claim 1, (Appendix 13) The reception unit is Analyzes users' social media activity and populates relevant information when they enter weight and dietary information 2. The system of claim 1, (Appendix 14) The analysis unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the level of detail in the recommendations is adjusted based on the user's health goals. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the user's diet category. 2. The system of claim 1, (Appendix 17) The analysis unit is Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, advice is prioritized based on when the user submitted their input. 2. The system of claim 1, (Appendix 19) The analysis unit is At parse time, adjust the order of advice based on user relevance 2. The system of claim 1, (Appendix 20) The providing unit is Inferring a user's emotions and adjusting the way advice is provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 21) The providing unit is When providing advice, the system selects the optimal method of providing advice by referring to the user's past advice history. 2. The system of claim 1, (Appendix 22) The providing unit is When providing advice, customize the delivery method based on the user's current life situation 2. The system of claim 1, (Appendix 23) The providing unit is Estimate the user's emotions and prioritize advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 24) The providing unit is When providing advice, the system selects the most appropriate advice method based on the user's geographic location information. 2. The system of claim 1, (Appendix 25) The providing unit is When providing advice, the system analyzes the user's social media activity to suggest ways to provide the advice. 2. The system of claim 1, (Appendix 26) The instruction unit is To estimate a user's emotion and adjust a method of instructing a movement based on the estimated user's emotion 2. The system of claim 1, (Appendix 27) The instruction unit is When providing exercise instructions, the system provides optimal instructions by referring to the user's past exercise history. 2. The system of claim 1, (Appendix 28) The instruction unit is When providing exercise instructions, customize the means of exercise instructions based on the user's current health condition. 2. The system of claim 1, (Appendix 29) The instruction unit is Estimating a user's emotion and determining the priority of movement instructions based on the estimated user's emotion 2. The system of claim 1, (Appendix 30) The instruction unit is When instructing exercise, the optimal exercise instruction method is selected taking into account the user's geographical location information. 2. The system of claim 1, (Appendix 31) The instruction unit is When instructing exercise, the system analyzes the user's social media activity to suggest exercise instruction methods. 2. The system of claim 1, (Appendix 32) The protective part is Inferring user sentiment and adjusting data protection methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 33) The protective part is When protecting data, the system selects the optimal protection method by referring to the user's past data protection history. 2. The system of claim 1, (Appendix 34) The protective part is Inferring user sentiment and determining priorities for data protection based on the inferred user sentiment 2. The system of claim 1, (Appendix 35) The protective part is When protecting data, the optimal protection method is selected taking into account the user's geographic location information 2. The system of claim 1, [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives information on a user's weight or dietary habits; an analysis unit that analyzes the information received by the reception unit and generates daily advice; a providing unit that provides the advice generated by the analysis unit to a user; An instruction unit that instructs the user to exercise based on the advice provided by the provision unit. A system characterized by:

2. The system according to claim 1 , wherein the reception unit receives specific information regarding the user's weight or eating habits.

3. The analysis unit is Generative AI analyzes user input and generates appropriate advice 2. The system of claim 1 .

4. The providing unit is Providing advice generated by generative AI to users 2. The system of claim 1 .

5. The instruction unit is Providing exercise instructions generated by the generation AI to the user 2. The system of claim 1 .

6. The system according to claim 1 , wherein the instruction unit receives feedback from a user and adjusts a next exercise instruction based on the feedback.

7. Equipped with a protection section that protects and anonymizes user input data 2. The system of claim 1 .

8. The reception unit is The system estimates the user's emotions and adjusts the timing of inputting weight and dietary information based on the estimated user emotions.

2. The system of claim 1 .

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

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