System

Abstract

To provide a system that provides suggestions on health based on daily health data.SOLUTION: A system according to the embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit inputs health data. The analysis unit analyzes the data input by the reception unit. The suggestion unit makes suggestions on health based on the analysis result obtained by the analysis 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] Existing technology lacks specific health suggestions based on daily health data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide health-related suggestions based on daily health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit inputs health data. The analysis unit analyzes the data input by the reception unit. The suggestion unit makes health-related suggestions based on the analysis results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can provide health-related suggestions based on daily health data. [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 a 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 suggestion system according to the embodiment of the present invention is a system that suggests foods, health drinks, and supplements to be eaten on a given day based on health data obtained from health checkups, daily weight measurements, and the like. In the health suggestion system, a user inputs health data, an AI analyzes the data, and makes health-related suggestions based on the analysis results. This suggestion is made with a light touch, like daily fortune telling. For example, a user inputs data such as weight, blood pressure, and blood sugar level. This data is input to the AI. The AI ​​then analyzes the input data. The AI ​​evaluates the user's health condition based on past data and statistical information. For example, if weight is increasing, a suggestion is made to limit calorie intake. Based on the analysis results, the AI ​​makes health-related suggestions. For example, it suggests foods, health drinks, and supplements to be eaten on that day. This allows the user to easily manage their daily health. This system makes suggestions with a light touch, like daily fortune telling, so it places less of a burden on the user. In addition, the content of the suggestions is customized to suit the health condition of each individual user, enabling effective health management. This allows the health suggestion system to make individually customized health suggestions based on the user's health data.

[0029] The health suggestion system according to the embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit is a unit for a user to input health data. The health data includes, for example, weight, blood pressure, heart rate, and the like, but is not limited to such examples. The reception unit allows, for example, a user to input health data using a smartphone or a tablet. The reception unit can also automatically acquire data from a wearable device. The analysis unit is a unit that analyzes the data input by the reception unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit evaluates the health condition of the user based on past data and statistical information. The analysis unit can detect, for example, a tendency of weight gain, and make a suggestion to reduce calorie intake. The suggestion unit is a unit that makes health-related suggestions based on the analysis results obtained by the analysis unit. The suggestions include, for example, recommendations for improving diet and exercise, but are not limited to such examples. For example, the suggestion unit suggests ingredients, health drinks, and supplements to be eaten that day. The suggestion unit can customize the suggestions according to the user's health condition. As a result, the health suggestion system according to the embodiment can provide individually customized health suggestions based on the user's health data. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that receives the analysis results obtained by the analysis unit as input and outputs suggestions.

[0030] The reception unit is a part for the user to input health data. The health data includes, for example, weight, blood pressure, heart rate, etc., but is not limited to such examples. For example, the reception unit allows the user to input health data using a smartphone or tablet. The reception unit can also automatically acquire data from a wearable device. Specifically, the user can manually input daily health data through a dedicated application on the smartphone or tablet. For example, an interface for inputting daily weight and blood pressure is provided, and the user can easily record the data. In addition, when acquiring data from a wearable device, data is automatically synchronized via Bluetooth (registered trademark) or Wi-Fi. For example, a smart watch or fitness tracker continuously monitors the user's heart rate, number of steps, sleep pattern, etc., and the data is automatically transmitted to the reception unit. In addition, the reception unit also has a security function for safely managing the user's health data. For example, data encryption and user authentication functions are introduced to prevent unauthorized access by third parties. This allows the user to input and manage his or her health data with peace of mind. The reception unit also supports data input using voice input and image recognition technology to increase user convenience. For example, if a user reports their weight or blood pressure by voice, the system can automatically recognize and record that data. In addition, a function is provided that takes a picture of the display of a blood pressure monitor or weight scale with a camera, analyzes the image, and automatically inputs the data. This allows the reception unit to efficiently collect users' health data in a variety of ways, improving the data accuracy and user experience of the entire system.

[0031] The analysis unit is a part that analyzes the data input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit evaluates the user's health condition based on past data and statistical information. Specifically, the statistical analysis can analyze the fluctuation pattern of the user's weight and blood pressure, and issue a warning if there is an abnormal fluctuation. By using a machine learning algorithm, more advanced analysis is possible. For example, a model that has learned the user's past health data can be used to predict future health risks. For example, the analysis unit can detect a tendency for weight gain and make a suggestion to reduce calorie intake. Furthermore, the analysis unit performs analysis taking into account the user's lifestyle and environmental factors. For example, data such as the user's amount of exercise, dietary content, and stress level can be integrated to perform a comprehensive health evaluation. As a result, the analysis unit does not only simply analyze data, but also provides a foundation for grasping the user's overall health condition and making individually customized health suggestions. In addition, the analysis unit can process data in real time and quickly provide analysis results. For example, the heart rate data acquired from the wearable device is analyzed in real time, and if an abnormal heart rate is detected, the user is immediately notified. This allows the user to address health risks at an early stage. Furthermore, the analysis unit can perform large-scale data analysis by utilizing cloud computing. This makes it possible to simultaneously analyze data from multiple users and identify health trends and risk factors at a population level. This allows the analysis unit to provide beneficial health suggestions not only to individual users but also to a broad user group.

[0032] The suggestion unit is a part that makes suggestions regarding health based on the analysis results obtained by the analysis unit. The suggestions include, but are not limited to, recommendations for improving diet and exercise. For example, the suggestion unit makes suggestions about ingredients, health drinks, and supplements to be eaten on that day. Specifically, if the analysis unit detects a tendency for the user to gain weight, the suggestion unit suggests low-calorie ingredients and recipes to suppress calorie intake. In addition, if the user is not getting enough exercise, the suggestion unit can provide an exercise plan and exercise videos that are easy to incorporate into daily life. The suggestion unit can customize the suggestions according to the user's health condition. For example, if the user has a specific allergy, ingredients and supplements that correspond to the allergy are excluded from the suggestions. In addition, the suggestions can be flexibly adjusted according to the user's preferences and lifestyle. This allows the suggestion unit to provide feasible and effective health suggestions for the user. Furthermore, the suggestion unit can generate the suggestions using AI. For example, the suggestion unit can make suggestions using an AI model that takes the analysis results obtained by the analysis unit as input and outputs suggestions. This AI model can learn from past data and user feedback to continuously improve the accuracy and effectiveness of the suggestions. For example, by inputting the results of the user's execution of a proposed meal plan as feedback, the AI ​​model can evaluate its effectiveness and reflect it in the next proposal. As a result, the suggestion unit can provide suggestions optimized for the user's health condition and support the user's health maintenance and improvement. In addition, the suggestion unit also has a function for increasing the user's motivation. For example, the suggestion unit supports the user's continuous efforts toward their health goals by providing a reward system according to goal achievement and a dashboard that visualizes progress. As a result, the suggestion unit can comprehensively support the user's health management and promote long-term health maintenance.

[0033] The suggestion unit includes a provision unit that provides the suggested content to the user. The provision unit is a part for providing the suggested content to the user. The suggestion content is provided, for example, by a method such as a notification, an e-mail, or an in-app message, but is not limited to such examples. For example, the provision unit notifies the user of the suggested content using a notification function of a smartphone. The provision unit can also transmit the suggested content through an e-mail. Furthermore, the provision unit can also display the suggested content using an in-app message. This makes it possible to provide the suggested content to the user. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide the suggested content using an AI model that inputs the suggested content generated by the suggestion unit and outputs a notification or e-mail.

[0034] The suggestion unit includes a customization unit that customizes the suggestion by using past data or statistical information. The customization unit is a part for customizing the suggestion based on the past data or statistical information of the user. The past data includes, for example, health data from the past year and data on specific health indicators, but is not limited to such examples. The statistical information includes, for example, national health statistics and local health data, but is not limited to such examples. The customization unit can make optimal suggestions to the user by using these data. For example, the customization unit makes suggestions regarding weight management based on the user's past weight data. In addition, the customization unit can also make suggestions regarding local health risks based on local health data. This makes it possible to customize the suggestion. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can customize the suggestion by using an AI model that inputs past data and statistical information and outputs a customized suggestion.

[0035] The analysis unit can evaluate the health condition of the user based on past data and statistical information. The analysis unit is a part for evaluating the health condition of the user based on past data and statistical information. The evaluation is performed, for example, by a method such as a health score or a risk evaluation, but is not limited to such examples. For example, the analysis unit evaluates the status of the user's weight management based on past weight data. The analysis unit can also evaluate the user's health risk based on statistical information. This makes it possible to evaluate the user's health condition. A 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 perform the evaluation using an AI model that inputs past data and statistical information and outputs an evaluation of the health condition.

[0036] The suggestion unit can make a suggestion to refrain from calorie intake when weight is increasing. The suggestion unit is a part for making a suggestion to refrain from calorie intake when weight is increasing. The criteria for weight gain include, for example, a weight gain rate within a certain period, but are not limited to such examples. For example, the suggestion unit analyzes the weight data of the user and makes a suggestion to refrain from calorie intake when weight is increasing. The suggestion unit can also provide a specific method of calorie restriction and a proposal for improving diet. This makes it possible to make an appropriate suggestion when weight is increasing. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs weight data and outputs a suggestion for calorie restriction.

[0037] The suggestion unit can suggest food ingredients, health drinks, and supplements to be eaten on that day. The suggestion unit is a part for suggesting food ingredients, health drinks, and supplements to be eaten on that day. Food ingredients include, for example, vegetables, fruits, protein sources, etc., but are not limited to such examples. Health drinks include, for example, smoothies, protein drinks, etc., but are not limited to such examples. Supplements include, for example, vitamins, minerals, etc., but are not limited to such examples. The suggestion unit can suggest these food ingredients, drinks, and supplements according to the user's health condition. For example, the suggestion unit suggests food ingredients to be eaten on that day based on the user's weight data and blood pressure data. The suggestion unit can also suggest appropriate supplements according to the user's health goals. This makes it possible to suggest food ingredients, health drinks, and supplements to be eaten on that day. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs health data and outputs suggestions for food ingredients, drinks, and supplements.

[0038] The reception unit can analyze the user's past health data input history and select the optimal input method. The reception unit is a part for analyzing the user's past health data input history and selecting the optimal input method. The optimal input method includes, for example, voice input, touch input, etc., but is not limited to such examples. For example, the reception unit preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze a time period in which the user has made an input in the past and send a reminder during that time period. Furthermore, the reception unit can automatically complete a specific data item from the user's past input history. This makes it possible to analyze the user's past health data input history and select 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 past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0039] The reception unit can filter the health data based on the user's current living situation and field of interest when the health data is input. The reception unit is a part for filtering the health data based on the user's current living situation and field of interest when the health data is input. The living situation includes, for example, work situation, home environment, etc., but is not limited to such examples. The field of interest includes, for example, fitness, nutrition, etc., but is not limited to such examples. For example, if the user is on a diet, the reception unit prioritizes data input related to calories. In addition, if the user has a specific health problem, the reception unit can also prompt the user to input data related to the problem. Furthermore, the reception unit can also suggest related data input based on the user's field of interest. This makes it possible to filter the input of health data based on the user's current living situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the living situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0040] The reception unit can preferentially input highly relevant data based on the geographical location information of the user when inputting health data. The reception unit is a part for preferentially inputting highly relevant data based on the geographical location information of the user when inputting health data. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, when the user is at high altitude, the reception unit prompts the input of health data related to altitude. In addition, when the user is in an urban area, the reception unit can also prioritize the input of data related to air quality. Furthermore, when the user is traveling, the reception unit can also suggest the input of data related to the climate and environment of the travel destination. This makes it possible to preferentially input highly relevant data 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 geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0041] The reception unit can input related data based on the user's social media activity when inputting health data. The reception unit is a part for inputting related data based on the user's social media activity when inputting health data. Social media activity includes, for example, the content of the post and the number of likes, but is not limited to such examples. For example, when the user posts a photo of a meal on social media, the reception unit inputs data related to the meal. In addition, when the user posts about exercise, the reception unit can also input data related to the exercise. Furthermore, when the user posts about stress, the reception unit can also input data related to stress management. This makes it possible to analyze the user's social media activity and input related data. 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 social media data to the generation AI and cause the generation AI to select related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit is a part for adjusting the level of detail of the analysis based on the importance of the health data during the analysis. The level of importance includes, for example, health risk and urgency, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on important health data (for example, blood pressure and blood glucose level). The analysis unit can also perform a simple analysis on general health data (for example, weight and number of steps). Furthermore, the analysis unit can provide a detailed analysis on data in which the user is highly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the health data. 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 perform the analysis using an AI model that inputs health data and adjusts the level of detail of the analysis based on the importance.

[0043] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit is a part for applying different analysis algorithms depending on the category of health data during analysis. The categories include, for example, dietary data, exercise data, etc., but are not limited to such examples. For example, the analysis unit applies a nutritional balance analysis algorithm to nutrition data. In addition, the analysis unit can also apply an exercise effect analysis algorithm to exercise data. Furthermore, the analysis unit can also apply a stress level analysis algorithm to stress data. This makes it possible to apply different analysis algorithms depending on the category of health data. 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 perform analysis using an AI model that inputs health data and applies an analysis algorithm depending on the category.

[0044] The analysis unit can determine the priority of analysis based on the time of submission of health data during analysis. The analysis unit is a part for determining the priority of analysis based on the time of submission of health data during analysis. The submission time includes, for example, the submission date and the submission time, but is not limited to such examples. For example, the analysis unit preferentially analyzes recently submitted health data. The analysis unit can also preferentially analyze health data that is submitted periodically. Furthermore, the analysis unit can also preferentially analyze data submitted by a user in relation to a specific event (for example, a health check). This makes it possible to determine the priority of analysis based on the time of submission of health data. A 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 data on the submission time to the generation AI and cause the generation AI to execute the determination of the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit is a part for adjusting the order of analysis based on the relevance of the health data during analysis. The relevance includes, for example, correlation and co-occurrence of data, but is not limited to such examples. For example, the analysis unit analyzes health data related to each other together. The analysis unit can also preferentially analyze data that has a large effect on the user's health condition. Furthermore, the analysis unit can also preferentially analyze data in which the user is highly interested. This makes it possible to adjust the order of analysis based on the relevance of the health data. A 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 data of the relevance to the generation AI and cause the generation AI to execute the order of analysis.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health data when making a proposal. The suggestion unit is a part for adjusting the level of detail of the proposal based on the importance of the health data when making a proposal. The level of importance includes, for example, health risk and urgency, but is not limited to such examples. For example, the suggestion unit makes a detailed proposal for important health data (for example, blood pressure and blood glucose level). The suggestion unit can also make a simple proposal for general health data (for example, weight and number of steps). Furthermore, the suggestion unit can provide a detailed proposal for data in which the user is highly interested. This makes it possible to adjust the level of detail of the proposal based on the importance of the health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs health data and adjusts the level of detail of the proposal based on the importance.

[0047] The suggestion unit can apply different proposal algorithms according to the category of health data at the time of proposal. The suggestion unit is a part for applying different proposal algorithms according to the category of health data at the time of proposal. The categories include, for example, dietary data, exercise data, etc., but are not limited to such examples. For example, the suggestion unit applies a nutritional balance proposal algorithm to nutrition data. In addition, the suggestion unit can also apply an exercise effect proposal algorithm to exercise data. Furthermore, the suggestion unit can also apply a stress management proposal algorithm to stress data. In this way, different proposal algorithms can be applied according to the category of health data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs health data and applies a proposal algorithm according to the category.

[0048] The suggestion unit can determine the priority of the proposal based on the time of submission of the health data at the time of proposal. The suggestion unit is a part for determining the priority of the proposal based on the time of submission of the health data at the time of proposal. The submission time includes, for example, a submission date and a submission time, but is not limited to such examples. For example, the suggestion unit makes a proposal based on recently submitted health data. In addition, the suggestion unit can also make a proposal based on health data that is periodically submitted. Furthermore, the suggestion unit can also make a proposal based on data submitted by a user in relation to a specific event (for example, a health check). This makes it possible to determine the priority of the proposal based on the time of submission of the health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data on the submission time to the generation AI and cause the generation AI to execute the determination of the priority.

[0049] The suggestion unit can adjust the order of the suggestions based on the relevance of the health data at the time of suggestion. The suggestion unit is a part for adjusting the order of the suggestions based on the relevance of the health data at the time of suggestion. The relevance includes, for example, correlation and co-occurrence of data, but is not limited to such examples. For example, the suggestion unit makes a suggestion based on health data related to each other. In addition, the suggestion unit can also make a suggestion based on data that has a large effect on the user's health condition. Furthermore, the suggestion unit can also make a suggestion based on data in which the user is highly interested. This makes it possible to adjust the order of the suggestions based on the relevance of the health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data of the relevance to the generation AI and cause the generation AI to execute the order of the suggestions.

[0050] The providing unit can select the optimal display method by referring to the user's past operation history when displaying the proposed content. The providing unit is a part for selecting the optimal display method by referring to the user's past operation history when displaying the proposed content. The optimal display method includes, for example, a screen layout and a font size, but is not limited to such examples. For example, the providing unit preferentially provides a display method that the user has used in the past. In addition, the providing unit can eliminate a display method that the user has avoided in the past and suggest another method. Furthermore, the providing unit can select the most efficient display method from the user's operation history. This makes it possible to select the optimal display method by referring to the user's past operation 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 operation history data to the generating AI and cause the generating AI to select the optimal display method.

[0051] The providing unit can select the optimal display method in consideration of the device information of the user when displaying the proposed content. The providing unit is a part for selecting the optimal display method in consideration of the device information of the user when displaying the proposed content. The device information includes, for example, the type of device and the screen size, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the providing unit can also provide a display method that is simple and has high visibility. This makes it possible to select the optimal display method in consideration of the device 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 device information to the generating AI and cause the generating AI to select the optimal display method.

[0052] The customization unit can select the optimal customization method by analyzing the user's past health data at the time of customization. The customization unit is a part for analyzing the user's past health data at the time of customization and selecting the optimal customization method. The optimal customization method includes, for example, the user's preferences and past behavior patterns, but is not limited to such examples. For example, the customization unit proposes an optimal customization option based on the user's past health data. The customization unit can also preferentially provide a specific customization method from the user's past health data. Furthermore, the customization unit can analyze the user's past health data and select the most effective customization method. This makes it possible to select the optimal customization method by analyzing the user's past health data. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can input past health data to the generation AI and cause the generation AI to select the optimal customization method.

[0053] The customization unit can select the optimal customization method in consideration of the geographical location information of the user at the time of customization. The customization unit is a part for selecting the optimal customization method in consideration of the geographical location information of the user at the time of customization. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, when the user is at high altitude, the customization unit provides a customization option related to altitude. In addition, when the user is in an urban area, the customization unit can also provide a customization option related to air quality. Furthermore, when the user is traveling, the customization unit can also provide a customization option related to the climate and environment of the travel destination. This makes it possible to select the optimal customization method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can input geographical location information to the generation AI and cause the generation AI to select the optimal customization method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health recommendation system can further acquire the user's sleep data and analyze it in the analysis unit. For example, the user's sleep time and sleep quality can be evaluated, and the recommendation unit can make a suggestion to adjust the amount of activity and dietary content for that day. In addition, based on the sleep data, it can also suggest appropriate relaxation methods and supplements to improve sleep to the user. This allows for comprehensive health management of the user.

[0056] The health recommendation system can further acquire the user's exercise data and analyze it in the analysis unit. For example, the number of steps and exercise time of the user can be evaluated, and the suggestion unit can make suggestions for adjusting the meal contents and rest time of the day. In addition, it can also suggest appropriate exercise plans and stretching methods to the user based on the exercise data. This allows for comprehensive health management of the user.

[0057] The health recommendation system can further acquire the user's dietary data and analyze it in the analysis unit. For example, the user's dietary content and calorie intake can be evaluated, and the suggestion unit can suggest the amount of exercise and supplements for that day. In addition, the system can suggest appropriate meal plans and methods for improving nutritional balance to the user based on the dietary data. This allows for comprehensive health management of the user.

[0058] The health recommendation system can further measure the user's stress level and analyze it in the analysis unit. For example, the user's heart rate and breathing pattern can be evaluated, and the recommendation unit can make suggestions for stress management. In addition, the system can suggest appropriate relaxation methods and supplements for stress reduction to the user based on the stress level. This allows for comprehensive health management of the user.

[0059] The health recommendation system can further acquire the user's water intake data and analyze it in the analysis unit. For example, the amount of water intake of the user can be evaluated, and the recommendation unit can make a recommendation for hydration for that day. In addition, the system can suggest appropriate hydration methods and timing for hydration to the user based on the water intake data. This allows for comprehensive health management of the user.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit is a section for a user to input health data. The health data includes, but is not limited to, for example, weight, blood pressure, and heart rate. The reception unit allows a user to input health data using, for example, a smartphone or a tablet. The reception unit can also automatically obtain data from a wearable device. Step 2: The analysis unit is a part that analyzes the data input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit evaluates the health condition of the user based on past data and statistical information. For example, the analysis unit can detect a tendency for weight gain and make a suggestion to reduce calorie intake. Step 3: The suggestion unit is a part that makes health-related suggestions based on the analysis results obtained by the analysis unit. The suggestions include, but are not limited to, recommendations for improving diet and exercise. For example, the suggestion unit makes suggestions about ingredients, health drinks, and supplements to be eaten that day. The suggestion unit can customize the suggestions according to the user's health condition. As a result, the health suggestion system according to the embodiment can make individually customized health suggestions based on the user's health data. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the analysis results obtained by the analysis unit and outputs suggestions.

[0062] (Example 2) The health suggestion system according to the embodiment of the present invention is a system that suggests foods, health drinks, and supplements to be eaten on a given day based on health data obtained from health checkups, daily weight measurements, and the like. In the health suggestion system, a user inputs health data, an AI analyzes the data, and makes health-related suggestions based on the analysis results. This suggestion is made with a light touch, like daily fortune telling. For example, a user inputs data such as weight, blood pressure, and blood sugar level. This data is input to the AI. The AI ​​then analyzes the input data. The AI ​​evaluates the user's health condition based on past data and statistical information. For example, if weight is increasing, a suggestion is made to limit calorie intake. Based on the analysis results, the AI ​​makes health-related suggestions. For example, it suggests foods, health drinks, and supplements to be eaten on that day. This allows the user to easily manage their daily health. This system makes suggestions with a light touch, like daily fortune telling, so it places less of a burden on the user. In addition, the content of the suggestions is customized to suit the health condition of each individual user, enabling effective health management. This allows the health suggestion system to make individually customized health suggestions based on the user's health data.

[0063] The health suggestion system according to the embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit is a unit for a user to input health data. The health data includes, for example, weight, blood pressure, heart rate, and the like, but is not limited to such examples. The reception unit allows, for example, a user to input health data using a smartphone or a tablet. The reception unit can also automatically acquire data from a wearable device. The analysis unit is a unit that analyzes the data input by the reception unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit evaluates the health condition of the user based on past data and statistical information. The analysis unit can detect, for example, a tendency of weight gain, and make a suggestion to reduce calorie intake. The suggestion unit is a unit that makes health-related suggestions based on the analysis results obtained by the analysis unit. The suggestions include, for example, recommendations for improving diet and exercise, but are not limited to such examples. For example, the suggestion unit suggests ingredients, health drinks, and supplements to be eaten that day. The suggestion unit can customize the suggestions according to the user's health condition. As a result, the health suggestion system according to the embodiment can provide individually customized health suggestions based on the user's health data. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that receives the analysis results obtained by the analysis unit as input and outputs suggestions.

[0064] The reception unit is a part for the user to input health data. The health data includes, for example, weight, blood pressure, heart rate, etc., but is not limited to such examples. For example, the reception unit allows the user to input health data using a smartphone or tablet. The reception unit can also automatically acquire data from a wearable device. Specifically, the user can manually input daily health data through a dedicated application on the smartphone or tablet. For example, an interface for inputting daily weight and blood pressure is provided, and the user can easily record the data. In addition, when acquiring data from a wearable device, data is automatically synchronized via Bluetooth or Wi-Fi. For example, a smart watch or fitness tracker continuously monitors the user's heart rate, number of steps, sleep pattern, etc., and the data is automatically transmitted to the reception unit. In addition, the reception unit also has a security function for safely managing the user's health data. For example, data encryption and user authentication functions are introduced to prevent unauthorized access by third parties. This allows the user to input and manage his or her health data with peace of mind. The reception unit also supports data input using voice input and image recognition technology to enhance user convenience. For example, if a user reports their weight or blood pressure by voice, the system can automatically recognize and record that data. In addition, a function is provided that takes a picture of the display of a blood pressure monitor or weight scale with a camera, analyzes the image, and automatically inputs the data. This allows the reception unit to efficiently collect users' health data in a variety of ways, improving the data accuracy and user experience of the entire system.

[0065] The analysis unit is a part that analyzes the data input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit evaluates the user's health condition based on past data and statistical information. Specifically, the statistical analysis can analyze the fluctuation pattern of the user's weight and blood pressure, and issue a warning if there is an abnormal fluctuation. By using a machine learning algorithm, more advanced analysis is possible. For example, a model that has learned the user's past health data can be used to predict future health risks. For example, the analysis unit can detect a tendency for weight gain and make a suggestion to reduce calorie intake. Furthermore, the analysis unit performs analysis taking into account the user's lifestyle and environmental factors. For example, data such as the user's amount of exercise, dietary content, and stress level can be integrated to perform a comprehensive health evaluation. As a result, the analysis unit does not only simply analyze data, but also provides a foundation for grasping the user's overall health condition and making individually customized health suggestions. In addition, the analysis unit can process data in real time and quickly provide analysis results. For example, the heart rate data acquired from the wearable device is analyzed in real time, and if an abnormal heart rate is detected, the user is immediately notified. This allows the user to address health risks at an early stage. Furthermore, the analysis unit can perform large-scale data analysis by utilizing cloud computing. This makes it possible to simultaneously analyze data from multiple users and identify health trends and risk factors at a population level. This allows the analysis unit to provide beneficial health suggestions not only to individual users but also to a broad user group.

[0066] The suggestion unit is a part that makes suggestions regarding health based on the analysis results obtained by the analysis unit. The suggestions include, but are not limited to, recommendations for improving diet and exercise. For example, the suggestion unit makes suggestions about ingredients, health drinks, and supplements to be eaten on that day. Specifically, if the analysis unit detects a tendency for the user to gain weight, the suggestion unit suggests low-calorie ingredients and recipes to suppress calorie intake. In addition, if the user is not getting enough exercise, the suggestion unit can provide an exercise plan and exercise videos that are easy to incorporate into daily life. The suggestion unit can customize the suggestions according to the user's health condition. For example, if the user has a specific allergy, ingredients and supplements that correspond to the allergy are excluded from the suggestions. In addition, the suggestions can be flexibly adjusted according to the user's preferences and lifestyle. This allows the suggestion unit to provide feasible and effective health suggestions for the user. Furthermore, the suggestion unit can generate the suggestions using AI. For example, the suggestion unit can make suggestions using an AI model that takes the analysis results obtained by the analysis unit as input and outputs suggestions. This AI model can learn from past data and user feedback to continuously improve the accuracy and effectiveness of the suggestions. For example, by inputting the results of the user's execution of a proposed meal plan as feedback, the AI ​​model can evaluate its effectiveness and reflect it in the next proposal. As a result, the suggestion unit can provide suggestions optimized for the user's health condition and support the user's health maintenance and improvement. In addition, the suggestion unit also has a function for increasing the user's motivation. For example, the suggestion unit supports the user's continuous efforts toward their health goals by providing a reward system according to goal achievement and a dashboard that visualizes progress. As a result, the suggestion unit can comprehensively support the user's health management and promote long-term health maintenance.

[0067] The suggestion unit includes a provision unit that provides the suggested content to the user. The provision unit is a part for providing the suggested content to the user. The suggestion content is provided, for example, by a method such as a notification, an e-mail, or an in-app message, but is not limited to such examples. For example, the provision unit notifies the user of the suggested content using a notification function of a smartphone. The provision unit can also transmit the suggested content through an e-mail. Furthermore, the provision unit can also display the suggested content using an in-app message. This makes it possible to provide the suggested content to the user. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide the suggested content using an AI model that inputs the suggested content generated by the suggestion unit and outputs a notification or e-mail.

[0068] The suggestion unit includes a customization unit that customizes the suggestion by using past data or statistical information. The customization unit is a part for customizing the suggestion based on the past data or statistical information of the user. The past data includes, for example, health data from the past year and data on specific health indicators, but is not limited to such examples. The statistical information includes, for example, national health statistics and local health data, but is not limited to such examples. The customization unit can make optimal suggestions to the user by using these data. For example, the customization unit makes suggestions regarding weight management based on the user's past weight data. In addition, the customization unit can also make suggestions regarding local health risks based on local health data. This makes it possible to customize the suggestion. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can customize the suggestion by using an AI model that inputs past data and statistical information and outputs a customized suggestion.

[0069] The analysis unit can evaluate the health condition of the user based on past data and statistical information. The analysis unit is a part for evaluating the health condition of the user based on past data and statistical information. The evaluation is performed, for example, by a method such as a health score or a risk evaluation, but is not limited to such examples. For example, the analysis unit evaluates the status of the user's weight management based on past weight data. The analysis unit can also evaluate the user's health risk based on statistical information. This makes it possible to evaluate the user's health condition. A 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 perform the evaluation using an AI model that inputs past data and statistical information and outputs an evaluation of the health condition.

[0070] The suggestion unit can make a suggestion to refrain from calorie intake when weight is increasing. The suggestion unit is a part for making a suggestion to refrain from calorie intake when weight is increasing. The criteria for weight gain include, for example, a weight gain rate within a certain period, but are not limited to such examples. For example, the suggestion unit analyzes the weight data of the user and makes a suggestion to refrain from calorie intake when weight is increasing. The suggestion unit can also provide a specific method of calorie restriction and a proposal for improving diet. This makes it possible to make an appropriate suggestion when weight is increasing. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs weight data and outputs a suggestion for calorie restriction.

[0071] The suggestion unit can suggest food ingredients, health drinks, and supplements to be eaten on that day. The suggestion unit is a part for suggesting food ingredients, health drinks, and supplements to be eaten on that day. Food ingredients include, for example, vegetables, fruits, protein sources, etc., but are not limited to such examples. Health drinks include, for example, smoothies, protein drinks, etc., but are not limited to such examples. Supplements include, for example, vitamins, minerals, etc., but are not limited to such examples. The suggestion unit can suggest these food ingredients, drinks, and supplements according to the user's health condition. For example, the suggestion unit suggests food ingredients to be eaten on that day based on the user's weight data and blood pressure data. The suggestion unit can also suggest appropriate supplements according to the user's health goals. This makes it possible to suggest food ingredients, health drinks, and supplements to be eaten on that day. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs health data and outputs suggestions for food ingredients, drinks, and supplements.

[0072] The reception unit can estimate the user's emotion and adjust the timing of inputting the health data based on the estimated emotion of the user. The reception unit is a part for estimating the user's emotion and adjusting the timing of inputting the health data based on the estimated emotion. The emotion estimation is performed by, for example, a method such as facial expression recognition or voice analysis, but is not limited to such examples. For example, when the user is feeling stressed, the reception unit prompts the user to input health data during a time when the user can relax. In addition, when the user is relaxed, the reception unit can also send a reminder to prompt the user to input health data. Furthermore, when the user is busy, the reception unit can also provide a simple interface that allows the user to input data in a short time. This makes it possible to adjust the timing of inputting the health data based on the user's emotion. The emotion estimation is realized by 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 by using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0073] The reception unit can analyze the user's past health data input history and select the optimal input method. The reception unit is a part for analyzing the user's past health data input history and selecting the optimal input method. The optimal input method includes, for example, voice input, touch input, etc., but is not limited to such examples. For example, the reception unit preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze a time period in which the user has made an input in the past and send a reminder during that time period. Furthermore, the reception unit can automatically complete a specific data item from the user's past input history. This makes it possible to analyze the user's past health data input history and select 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 past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0074] The reception unit can filter the health data based on the user's current living situation and field of interest when the health data is input. The reception unit is a part for filtering the health data based on the user's current living situation and field of interest when the health data is input. The living situation includes, for example, work situation, home environment, etc., but is not limited to such examples. The field of interest includes, for example, fitness, nutrition, etc., but is not limited to such examples. For example, if the user is on a diet, the reception unit prioritizes data input related to calories. In addition, if the user has a specific health problem, the reception unit can also prompt the user to input data related to the problem. Furthermore, the reception unit can also suggest related data input based on the user's field of interest. This makes it possible to filter the input of health data based on the user's current living situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the living situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0075] The reception unit can estimate the user's emotion and determine the priority of the health data to be input based on the estimated user's emotion. The reception unit is a part for estimating the user's emotion and determining the priority of the health data to be input based on the estimated emotion. The emotion estimation is performed by, for example, facial expression recognition or voice analysis, but is not limited to such examples. For example, the reception unit prioritizes data input related to stress management when the user is stressed. In addition, the reception unit can also prompt the user to input overall health data when the user is relaxed. Furthermore, the reception unit can also prioritize simple data input and postpone detailed data input when the user is tired. This makes it possible to determine the priority of the health data 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, 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 facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0076] The reception unit can preferentially input highly relevant data based on the geographical location information of the user when inputting health data. The reception unit is a part for preferentially inputting highly relevant data based on the geographical location information of the user when inputting health data. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, when the user is at high altitude, the reception unit prompts the input of health data related to altitude. In addition, when the user is in an urban area, the reception unit can also prioritize the input of data related to air quality. Furthermore, when the user is traveling, the reception unit can also suggest the input of data related to the climate and environment of the travel destination. This makes it possible to preferentially input highly relevant data 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 geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0077] The reception unit can input related data based on the user's social media activity when inputting health data. The reception unit is a part for inputting related data based on the user's social media activity when inputting health data. Social media activity includes, for example, the content of the post and the number of likes, but is not limited to such examples. For example, when the user posts a photo of a meal on social media, the reception unit inputs data related to the meal. In addition, when the user posts about exercise, the reception unit can also input data related to the exercise. Furthermore, when the user posts about stress, the reception unit can also input data related to stress management. This makes it possible to analyze the user's social media activity and input related data. 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 social media data to the generation AI and cause the generation AI to select related data.

[0078] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit is a part for estimating the user's emotion and adjusting the expression method of the analysis based on the estimated emotion. The emotion estimation is performed by, for example, facial expression recognition or voice analysis, but is not limited to such examples. For example, the analysis unit provides a simple and highly visible analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a concise analysis result that covers the main points when the user is in a hurry. This makes it possible to adjust the expression method of the analysis 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, 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 can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit is a part for adjusting the level of detail of the analysis based on the importance of the health data during the analysis. The level of importance includes, for example, health risk and urgency, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on important health data (for example, blood pressure and blood glucose level). The analysis unit can also perform a simple analysis on general health data (for example, weight and number of steps). Furthermore, the analysis unit can provide a detailed analysis on data in which the user is highly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the health data. 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 perform the analysis using an AI model that inputs health data and adjusts the level of detail of the analysis based on the importance.

[0080] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit is a part for applying different analysis algorithms depending on the category of health data during analysis. The categories include, for example, dietary data, exercise data, etc., but are not limited to such examples. For example, the analysis unit applies a nutritional balance analysis algorithm to nutrition data. In addition, the analysis unit can also apply an exercise effect analysis algorithm to exercise data. Furthermore, the analysis unit can also apply a stress level analysis algorithm to stress data. This makes it possible to apply different analysis algorithms depending on the category of health data. 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 perform analysis using an AI model that inputs health data and applies an analysis algorithm depending on the category.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit is a part for estimating the user's emotion and adjusting the length of the analysis based on the estimated emotion. The emotion estimation is performed by, for example, facial expression recognition, voice analysis, or other methods, but is not limited to such examples. For example, the analysis unit provides a short and to the point analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide an analysis result with a visually stimulating effect when the user is excited. This makes it possible to adjust the length of the analysis 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, 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 can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The analysis unit can determine the priority of analysis based on the time of submission of health data during analysis. The analysis unit is a part for determining the priority of analysis based on the time of submission of health data during analysis. The submission time includes, for example, the submission date and the submission time, but is not limited to such examples. For example, the analysis unit preferentially analyzes recently submitted health data. The analysis unit can also preferentially analyze health data that is submitted periodically. Furthermore, the analysis unit can also preferentially analyze data submitted by a user in relation to a specific event (for example, a health check). This makes it possible to determine the priority of analysis based on the time of submission of health data. A 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 data on the submission time to the generation AI and cause the generation AI to execute the determination of the priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit is a part for adjusting the order of analysis based on the relevance of the health data during analysis. The relevance includes, for example, correlation and co-occurrence of data, but is not limited to such examples. For example, the analysis unit analyzes health data related to each other together. The analysis unit can also preferentially analyze data that has a large effect on the user's health condition. Furthermore, the analysis unit can also preferentially analyze data in which the user is highly interested. This makes it possible to adjust the order of analysis based on the relevance of the health data. A 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 data of the relevance to the generation AI and cause the generation AI to execute the order of analysis.

[0084] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated emotion of the user. The suggestion unit is a part for estimating the user's emotion and adjusting the way of expressing the proposal based on the estimated emotion. The estimation of the emotion is performed by, for example, a method such as facial expression recognition or voice analysis, but is not limited to such examples. For example, the suggestion unit provides a simple and highly visible proposal when the user is nervous. The suggestion unit can also provide a detailed proposal when the user is relaxed. Furthermore, the suggestion unit can also provide a concise proposal that covers the main points when the user is in a hurry. This makes it possible to adjust the way of expressing the proposal based on the user's emotion. The estimation of the 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the emotion.

[0085] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health data when making a proposal. The suggestion unit is a part for adjusting the level of detail of the proposal based on the importance of the health data when making a proposal. The level of importance includes, for example, health risk and urgency, but is not limited to such examples. For example, the suggestion unit makes a detailed proposal for important health data (for example, blood pressure and blood glucose level). The suggestion unit can also make a simple proposal for general health data (for example, weight and number of steps). Furthermore, the suggestion unit can provide a detailed proposal for data in which the user is highly interested. This makes it possible to adjust the level of detail of the proposal based on the importance of the health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs health data and adjusts the level of detail of the proposal based on the importance.

[0086] The suggestion unit can apply different proposal algorithms according to the category of health data at the time of proposal. The suggestion unit is a part for applying different proposal algorithms according to the category of health data at the time of proposal. The categories include, for example, dietary data, exercise data, etc., but are not limited to such examples. For example, the suggestion unit applies a nutritional balance proposal algorithm to nutrition data. In addition, the suggestion unit can also apply an exercise effect proposal algorithm to exercise data. Furthermore, the suggestion unit can also apply a stress management proposal algorithm to stress data. In this way, different proposal algorithms can be applied according to the category of health data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs health data and applies a proposal algorithm according to the category.

[0087] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit is a part for estimating the user's emotion and adjusting the length of the proposal based on the estimated emotion. The estimation of the emotion is performed by, for example, a method such as facial expression recognition or voice analysis, but is not limited to such examples. For example, the suggestion unit provides a short and to the point proposal when the user is in a hurry. The suggestion unit can also provide a detailed proposal when the user is relaxed. Furthermore, the suggestion unit can also provide a proposal with a visually stimulating effect when the user is excited. This makes it possible to adjust the length of the proposal based on the user's emotion. The estimation of the 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0088] The suggestion unit can determine the priority of the proposal based on the time of submission of the health data at the time of proposal. The suggestion unit is a part for determining the priority of the proposal based on the time of submission of the health data at the time of proposal. The submission time includes, for example, a submission date and a submission time, but is not limited to such examples. For example, the suggestion unit makes a proposal based on recently submitted health data. In addition, the suggestion unit can also make a proposal based on health data that is periodically submitted. Furthermore, the suggestion unit can also make a proposal based on data submitted by a user in relation to a specific event (for example, a health check). This makes it possible to determine the priority of the proposal based on the time of submission of the health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data on the submission time to the generation AI and cause the generation AI to execute the determination of the priority.

[0089] The suggestion unit can adjust the order of the suggestions based on the relevance of the health data at the time of suggestion. The suggestion unit is a part for adjusting the order of the suggestions based on the relevance of the health data at the time of suggestion. The relevance includes, for example, correlation and co-occurrence of data, but is not limited to such examples. For example, the suggestion unit makes a suggestion based on health data related to each other. In addition, the suggestion unit can also make a suggestion based on data that has a large effect on the user's health condition. Furthermore, the suggestion unit can also make a suggestion based on data in which the user is highly interested. This makes it possible to adjust the order of the suggestions based on the relevance of the health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data of the relevance to the generation AI and cause the generation AI to execute the order of the suggestions.

[0090] The providing unit can estimate the user's emotion and adjust the display method of the proposed content based on the estimated user's emotion. The providing unit is a part for estimating the user's emotion and adjusting the display method of the proposed content based on the estimated emotion. The emotion estimation is performed by, for example, facial expression recognition or voice analysis, but is not limited to such examples. For example, when the user is nervous, the providing unit provides a display method that is simple and highly visible. Furthermore, when the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the proposed content 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, 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 facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] The providing unit can select the optimal display method by referring to the user's past operation history when displaying the proposed content. The providing unit is a part for selecting the optimal display method by referring to the user's past operation history when displaying the proposed content. The optimal display method includes, for example, a screen layout and a font size, but is not limited to such examples. For example, the providing unit preferentially provides a display method that the user has used in the past. In addition, the providing unit can eliminate a display method that the user has avoided in the past and suggest another method. Furthermore, the providing unit can select the most efficient display method from the user's operation history. This makes it possible to select the optimal display method by referring to the user's past operation 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 operation history data to the generating AI and cause the generating AI to select the optimal display method.

[0092] The providing unit can estimate the user's emotion and adjust the operation procedure of the proposed content based on the estimated user's emotion. The providing unit is a part for estimating the user's emotion and adjusting the operation procedure of the proposed content based on the estimated emotion. The emotion estimation is performed by, for example, a method such as facial expression recognition or voice analysis, but is not limited to such examples. For example, the providing unit provides a simple and intuitive operation procedure when the user is nervous. In addition, the providing unit can also provide a detailed operation procedure when the user is relaxed. Furthermore, the providing unit can also provide a procedure that can be operated quickly when the user is in a hurry. This makes it possible to adjust the operation procedure of the proposed content 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, 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 facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The providing unit can select the optimal display method in consideration of the device information of the user when displaying the proposed content. The providing unit is a part for selecting the optimal display method in consideration of the device information of the user when displaying the proposed content. The device information includes, for example, the type of device and the screen size, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the providing unit can also provide a display method that is simple and has high visibility. This makes it possible to select the optimal display method in consideration of the device 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 device information to the generating AI and cause the generating AI to select the optimal display method.

[0094] The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. The customization unit is a part for estimating the user's emotion and adjusting the customization method based on the estimated emotion. The emotion estimation is performed by, for example, facial expression recognition or voice analysis, but is not limited to such examples. For example, the customization unit provides a simple and intuitive customization method when the user is nervous. The customization unit can also provide detailed customization options when the user is relaxed. Furthermore, the customization unit can also provide a method that allows quick customization when the user is in a hurry. This makes it possible to adjust the customization 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, 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 customization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the customization unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] The customization unit can select the optimal customization method by analyzing the user's past health data at the time of customization. The customization unit is a part for analyzing the user's past health data at the time of customization and selecting the optimal customization method. The optimal customization method includes, for example, the user's preferences and past behavior patterns, but is not limited to such examples. For example, the customization unit proposes an optimal customization option based on the user's past health data. The customization unit can also preferentially provide a specific customization method from the user's past health data. Furthermore, the customization unit can analyze the user's past health data and select the most effective customization method. This makes it possible to select the optimal customization method by analyzing the user's past health data. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can input past health data to the generation AI and cause the generation AI to select the optimal customization method.

[0096] The customization unit can estimate the user's emotion and determine the priority of customization based on the estimated user's emotion. The customization unit is a part for estimating the user's emotion and determining the priority of customization based on the estimated emotion. The estimation of emotion is performed by, for example, a method such as facial expression recognition or voice analysis, but is not limited to such examples. For example, the customization unit prioritizes simple and intuitive customization when the user is nervous. The customization unit can also prioritize detailed customization when the user is relaxed. Furthermore, the customization unit can also prioritize options that can be customized quickly when the user is in a hurry. This makes it possible to determine the priority of customization 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 customization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the customization unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0097] The customization unit can select the optimal customization method in consideration of the geographical location information of the user at the time of customization. The customization unit is a part for selecting the optimal customization method in consideration of the geographical location information of the user at the time of customization. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, when the user is at high altitude, the customization unit provides a customization option related to altitude. In addition, when the user is in an urban area, the customization unit can also provide a customization option related to air quality. Furthermore, when the user is traveling, the customization unit can also provide a customization option related to the climate and environment of the travel destination. This makes it possible to select the optimal customization method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can input geographical location information to the generation AI and cause the generation AI to select the optimal customization method.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The health recommendation system can further acquire the user's sleep data and analyze it in the analysis unit. For example, the user's sleep time and sleep quality can be evaluated, and the recommendation unit can make a suggestion to adjust the amount of activity and dietary content for that day. In addition, based on the sleep data, it can also suggest appropriate relaxation methods and supplements to improve sleep to the user. This allows for comprehensive health management of the user.

[0100] The suggestion unit can estimate the user's emotions and adjust the tone of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can convey the suggestions in gentle words. If the user is relaxed, the suggestion unit can provide suggestions including detailed information. Furthermore, if the user is excited, the suggestion unit can provide suggestions that will increase the user's motivation. This makes it possible to provide suggestions that correspond to the user's emotions.

[0101] The health recommendation system can further acquire the user's exercise data and analyze it in the analysis unit. For example, the number of steps and exercise time of the user can be evaluated, and the suggestion unit can make suggestions for adjusting the meal contents and rest time of the day. In addition, it can also suggest appropriate exercise plans and stretching methods to the user based on the exercise data. This allows for comprehensive health management of the user.

[0102] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a concise analysis result that focuses on the main points can be provided. In this way, the display method of the analysis results can be adjusted based on the user's emotions.

[0103] The health recommendation system can further acquire the user's dietary data and analyze it in the analysis unit. For example, the user's dietary content and calorie intake can be evaluated, and the suggestion unit can suggest the amount of exercise and supplements for that day. In addition, the system can suggest appropriate meal plans and methods for improving nutritional balance to the user based on the dietary data. This allows for comprehensive health management of the user.

[0104] The suggestion unit can estimate the user's emotions and adjust the priority of the suggested contents based on the estimated emotions. For example, if the user is stressed, the suggestion unit can prioritize suggestions related to stress management. If the user is relaxed, the suggestion unit can also prioritize suggestions related to overall health management. Furthermore, if the user is busy, the suggestion unit can prioritize suggestions that are simple and easy to implement. In this way, the priority of the suggested contents can be adjusted based on the user's emotions.

[0105] The health recommendation system can further measure the user's stress level and analyze it in the analysis unit. For example, the user's heart rate and breathing pattern can be evaluated, and the recommendation unit can make suggestions for stress management. In addition, the system can suggest appropriate relaxation methods and supplements for stress reduction to the user based on the stress level. This allows for comprehensive health management of the user.

[0106] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is busy, a simple analysis can be performed. Furthermore, if the user is feeling stressed, the analysis of data related to stress management can be prioritized. This makes it possible to adjust the timing of the analysis based on the user's emotions.

[0107] The health recommendation system can further acquire the user's water intake data and analyze it in the analysis unit. For example, the amount of water intake of the user can be evaluated, and the recommendation unit can make a recommendation for hydration for that day. In addition, the system can suggest appropriate hydration methods and timing for hydration to the user based on the water intake data. This allows for comprehensive health management of the user.

[0108] The suggestion unit can estimate the user's emotions and adjust the level of detail of the suggested content based on the estimated emotions. For example, if the user is nervous, a simple and highly visible suggestion can be provided. Also, if the user is relaxed, a detailed suggestion can be provided. Furthermore, if the user is in a hurry, a concise suggestion that focuses on the main points can be provided. In this way, the level of detail of the suggested content can be adjusted based on the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit is a section for a user to input health data. The health data includes, but is not limited to, for example, weight, blood pressure, and heart rate. The reception unit allows a user to input health data using, for example, a smartphone or a tablet. The reception unit can also automatically obtain data from a wearable device. Step 2: The analysis unit is a part that analyzes the data input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit evaluates the health condition of the user based on past data and statistical information. For example, the analysis unit can detect a tendency for weight gain and make a suggestion to reduce calorie intake. Step 3: The suggestion unit is a part that makes health-related suggestions based on the analysis results obtained by the analysis unit. The suggestions include, but are not limited to, recommendations for improving diet and exercise. For example, the suggestion unit makes suggestions about ingredients, health drinks, and supplements to be eaten that day. The suggestion unit can customize the suggestions according to the user's health condition. As a result, the health suggestion system according to the embodiment can make individually customized health suggestions based on the user's health data. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the analysis results obtained by the analysis unit and outputs suggestions.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] For example, the reception unit allows the user to input health data using the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input data. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes health-related suggestions based on the analysis results. For example, the provision unit provides the suggested content to the user using the output device 40 of the smart device 14. For example, the customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the suggestion using past data and statistical information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] For example, the reception unit allows the user to input health data using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input data. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes health-related suggestions based on the analysis results. For example, the provision unit provides the suggested content to the user using the speaker 240 of the smart glasses 214. For example, the customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the suggestion using past data and statistical information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] For example, the reception unit allows the user to input health data using the microphone 238 of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and makes health-related suggestions based on the analysis results. For example, the provision unit provides the suggested content to the user using the speaker 240 of the headset type terminal 314. For example, the customization unit is realized by the specific processing unit 290 of the data processing device 12, and customizes the suggestion using past data and statistical information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] For example, the reception unit allows the user to input health data using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and makes health-related suggestions based on the analysis results. For example, the provision unit provides the suggested content to the user using the speaker 240 of the robot 414. For example, the customization unit is realized by the specific processing unit 290 of the data processing device 12, and customizes the suggestion using past data and statistical information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] These emotions are distributed in the three 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) A reception desk for inputting health data; an analysis unit that analyzes the data input by the reception unit; a suggestion unit that makes health-related suggestions based on the analysis results obtained by the analysis unit; Equipped A system characterized by: (Appendix 2) The suggestion unit, A provision unit is provided to provide the proposal contents to the user. 2. The system of claim 1, (Appendix 3) The suggestion unit, Equipped with a customization section that uses past data or statistical information to customize suggestions 2. The system of claim 1, (Appendix 4) The analysis unit is Evaluate the user's health status based on past data and statistics 2. The system of claim 1, (Appendix 5) The suggestion unit, If you are gaining weight, we suggest you reduce your calorie intake. 2. The system of claim 1, (Appendix 6) The suggestion unit, Suggest foods to eat, health drinks, or supplements for that day 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust the timing of inputting health data based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past health data entry history and select the optimal entry method 2. The system of claim 1, (Appendix 9) The reception unit is Filtering health data input based on the user's current life situation or areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize the health data to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When entering health data, prioritize the most relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is When entering health data, populate relevant data based on the user's social media activity 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the health data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of health data. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when health data was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of health data 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the health data 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of health data. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, When submitting proposals, prioritize proposals based on when the health data was submitted 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of your health data 2. The system of claim 1, (Appendix 25) The providing unit is Infer user emotions and adjust how suggestions are displayed based on the inferred user emotions 3. The system of claim 2, (Appendix 26) The providing unit is When displaying suggestions, the system selects the optimal display method by referring to the user's past operation history. 3. The system of claim 2, (Appendix 27) The providing unit is Inferring user emotions and adjusting suggested operation procedures based on the estimated user emotions 3. The system of claim 2, (Appendix 28) The providing unit is When displaying suggestions, the optimal display method is selected taking into account the user's device information. 3. The system of claim 2, (Appendix 29) The customization unit includes: Inferring a user's emotions and adjusting the customization method based on the inferred user's emotions 4. The system of claim 3. (Appendix 30) The customization unit includes: When customizing, the system analyzes the user's past health data to select the optimal customization method. 4. The system of claim 3. (Appendix 31) The customization unit includes: To estimate a user's emotion and determine a priority of customization based on the estimated user's emotion. 4. The system of claim 3. (Appendix 32) The customization unit includes: During customization, select the optimal customization method by taking into account the user's geographic location information 4. The system of claim 3. [Explanation of symbols]

[0183] 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 desk for inputting health data; an analysis unit that analyzes the data input by the reception unit; a suggestion unit that makes health-related suggestions based on the analysis results obtained by the analysis unit; Equipped A system characterized by:

2. The suggestion unit, A provision unit is provided to provide the proposal contents to the user.

2. The system of claim 1 .

3. The suggestion unit, Equipped with a customization section that uses past data or statistical information to customize suggestions 2. The system of claim 1 .

4. The analysis unit is Evaluate the user's health status based on past data and statistics 2. The system of claim 1 .

5. The suggestion unit, If you are gaining weight, we suggest you reduce your calorie intake.

2. The system of claim 1 .

6. The suggestion unit, Suggest foods to eat that day, health drinks, or supplements 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust the timing of inputting health data based on the estimated emotion of the user.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past health data entry history and select the optimal entry method 2. The system of claim 1 .

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