System

Abstract

An object of a system according to an embodiment is to promote improvement in lifestyle and eating habits by showing a future figure to a user.SOLUTION: A system includes a reception unit, an analysis unit, a provision unit, and a display unit. The receiving unit receives information on a lifestyle and an eating habit of a user. The analysis part analyzes the information received by the reception part and projects a future figure. The providing unit provides advice on lifestyle and eating habits based on the future figure projected by the analysis unit. When the lifestyle is improved according to the advice provided by the providing unit, the future appearance of the display unit changes in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, even if people understood that they needed to improve their lifestyle and diet, it was difficult to put it into practice because they lacked a sense of ownership.

[0005] The system according to the embodiment aims to encourage users to improve their lifestyle and dietary habits by showing them what they will look like in the future. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a display unit. The reception unit receives information about a user's lifestyle and dietary habits. The analysis unit analyzes the information received by the reception unit and displays a future appearance. The provision unit provides advice about lifestyle and dietary habits based on the future appearance displayed by the analysis unit. The display unit changes the future appearance in real time when the user improves their lifestyle in accordance with the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can encourage users to improve their lifestyle and eating habits by showing them what they will look like in the future. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A future vision system according to an embodiment of the present invention projects a future image based on a user's lifestyle and dietary habits, encouraging healthy lifestyle habits. The future vision system allows users to input information about their lifestyle and dietary habits, and a generation AI analyzes the information to project a future image. For example, the system projects acne and weight gain caused by a constant diet of oily foods. Furthermore, the generation AI provides the user with advice on healthy lifestyles and dietary habits. As the user improves their lifestyle in accordance with the advice, the future image changes in real time. This allows the user to visually understand how their actions will affect their future and achieve a healthier lifestyle. This allows the future vision system to project a future image based on the user's lifestyle and dietary habits, encouraging healthy lifestyle habits. For example, the system encourages the user to eat a balanced diet and exercise moderately to avoid acne and weight gain caused by a constant diet of oily foods. In this way, the future vision system protects the user's future.

[0029] A future vision system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a display unit. The reception unit receives information about a user's lifestyle and diet. The information about the user's lifestyle and diet includes, but is not limited to, meal contents, exercise frequency, and sleep duration. The reception unit can, for example, record the meal contents entered by the user, track exercise frequency, and measure sleep duration. The analysis unit uses a generation AI to analyze the information received by the reception unit and project a future appearance. The analysis unit predicts the user's future appearance based on, for example, past data and statistical information. For example, the generation AI can predict acne and weight gain that may occur if the user continues to eat only oily foods. The provision unit provides advice on healthy lifestyle and diet based on the future appearance projected by the analysis unit. The provision unit provides advice such as a balanced diet, moderate exercise, and sufficient sleep. For example, the provision unit recommends the user to eat more vegetables, exercise 30 minutes daily, and go to bed earlier. The display unit changes the future image in real time when the user improves their lifestyle habits in accordance with the advice provided by the provider. For example, the display unit displays a future image in which the user's acne is reduced and their weight is maintained at a healthy level by continuing to eat a balanced diet. This allows the future vision system according to the embodiment to display a future image based on the user's lifestyle and eating habits and promote healthy lifestyle habits. For example, the user may be encouraged to eat a balanced diet and exercise moderately to avoid acne and weight gain caused by eating only oily foods. In this way, the future vision system protects the user's future.

[0030] The reception unit can receive information including the user's dietary details, exercise frequency, and sleep duration. The reception unit, for example, records the dietary details entered by the user. For example, it can record the types of ingredients, intake amounts, calories, etc. The reception unit can also track the user's exercise frequency. For example, it can record the type, number of times, and duration of exercise. The reception unit can also measure the user's sleep duration. For example, it can record the time of bedtime, wake-up time, and quality of sleep. By receiving detailed lifestyle information about the user, a more accurate future image can be projected. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the dietary details entered by the user into AI and have the AI ​​analyze the types of ingredients and intake amounts.

[0031] The analysis unit can predict the user's future appearance based on past data and statistical information. The analysis unit can predict the user's future appearance based on past data, for example. For example, the analysis can be performed based on data from the past year or the past five years. The analysis unit can also predict the user's future appearance based on statistical information. For example, the analysis can be performed based on government health statistics or academic research data. This makes it possible to predict the user's future appearance based on past data and statistical information, thereby enabling more reliable predictions. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input past data and statistical information into the generation AI and have the generation AI predict the user's future appearance.

[0032] The providing unit can provide advice on a balanced diet, moderate exercise, and sufficient sleep. The providing unit, for example, provides advice on a balanced diet. For example, it recommends eating a lot of vegetables and consuming an appropriate amount of protein. The providing unit can also provide advice on moderate exercise. For example, it recommends exercising 30 minutes every day and aerobic exercise three times a week. The providing unit can also provide advice on sufficient sleep. For example, it recommends going to bed early at night and improving your sleeping environment. This makes it possible to provide specific advice for promoting healthy lifestyle habits. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input lifestyle information about the user into AI and cause the AI ​​to generate advice on healthy lifestyle habits.

[0033] The display unit can change the future appearance in real time as the user improves their lifestyle habits in accordance with the advice. For example, the display unit can display a future appearance in which the user's acne is reduced and their weight is maintained at a healthy level by continuing to eat a balanced diet. The display unit can also display a future appearance in which the user's physical strength is improved and a healthy figure is maintained by continuing to exercise moderately. The display unit can also display a future appearance in which the user's stress is reduced and their mental health is maintained by getting enough sleep. In this way, the future appearance can change in real time as the user improves their lifestyle habits in accordance with the advice. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the user's lifestyle improvements into AI and cause the AI ​​to generate changes in the future appearance.

[0034] The reception unit can analyze the user's past lifestyle data and select the optimal information input method. For example, the reception unit can suggest the most frequently used input method based on data previously input by the user. The reception unit can also automatically select items that need to be input from the user's lifestyle data to simplify input. The reception unit can also analyze the user's past input history and provide a customized input form to reduce the effort required for input. This allows the optimal information input method to be selected by analyzing the past lifestyle data. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past lifestyle data into a generation AI and have the generation AI select the optimal information input method.

[0035] The reception unit can filter information based on the user's current health condition and goals when inputting information. For example, when the user inputs their current health condition, the reception unit automatically filters input items according to that condition. The reception unit can also prompt the user to input only relevant information based on the user's health goals. The reception unit can also automatically exclude items that do not need to be input based on the user's health condition and goals. In this way, by filtering input items according to the user's health condition and goals, only necessary information can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition data to a generation AI and have the generation AI filter the input items.

[0036] When inputting information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input information using a keyboard or touch panel. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. This makes it easier to input information by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0037] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prompt the user to prioritize inputting information related to that area. Furthermore, if the user is traveling, the reception unit can also prompt the user to prioritize inputting information related to their travel destination. Furthermore, if the user is at home, the reception unit can also prompt the user to prioritize inputting information related to their home. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input related information when inputting information. For example, the reception unit can automatically input meal details based on photos of meals shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and input related information. The reception unit can also input related information by referring to the activity of the user's friends on social media. In this way, related information can be automatically input by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to extract related information.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. For example, the reception unit can suggest an optimal input method based on feedback previously input by the user. The reception unit can also customize the input form to simplify input based on the user's past feedback. The reception unit can also analyze the user's past feedback and make improvements to reduce the effort required for input. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle habits. For example, the analysis unit analyzes information about important lifestyle habits in detail and provides the result to the user. The analysis unit can also simplify and analyze information about less important lifestyle habits. The analysis unit can also prioritize the analysis of more important lifestyle habits based on the user's health goals. This allows important information to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the lifestyle habits. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the lifestyle habit category. For example, the analysis unit can apply an analysis algorithm that emphasizes nutritional balance to information about dietary habits. The analysis unit can also apply an analysis algorithm that emphasizes exercise effects to information about exercise habits. The analysis unit can also apply an analysis algorithm that emphasizes sleep quality to information about sleep habits. By applying different analysis algorithms depending on the lifestyle habit category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of lifestyle habit fluctuation. For example, the analysis unit may focus on periods of significant lifestyle habit fluctuation during analysis. The analysis unit may also perform a simplified analysis during periods of minimal lifestyle habit fluctuation. The analysis unit may also dynamically adjust the priority of analysis based on the time of lifestyle habit fluctuation. This enables analysis to focus on important periods by determining the priority of analysis based on the time of lifestyle habit fluctuation. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's lifestyle habit data into the generation AI and cause the generation AI to determine the priority of analysis based on the time of fluctuation.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of lifestyle habits. For example, the analysis unit prioritizes analysis of highly relevant lifestyle habits. The analysis unit can also postpone analysis of less relevant lifestyle habits. The analysis unit can also dynamically adjust the order of analysis based on the relevance of lifestyle habits. In this way, by adjusting the order of analysis based on the relevance of lifestyle habits, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0046] The providing unit can adjust the level of detail of the advice based on the importance of the lifestyle habit when providing advice. For example, the providing unit provides detailed advice regarding important lifestyle habits. The providing unit can also provide simplified advice regarding less important lifestyle habits. The providing unit can also prioritize advice regarding more important lifestyle habits based on the user's health goals. In this way, by adjusting the level of detail of the advice based on the importance of the lifestyle habit, it is possible to provide important information in detail. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habit data to the generation AI and cause the generation AI to adjust the level of detail of the advice based on the importance.

[0047] When providing advice, the providing unit can apply different advice algorithms depending on the lifestyle category. For example, the providing unit can apply an algorithm that emphasizes nutritional balance to advice regarding dietary habits. The providing unit can also apply an algorithm that emphasizes exercise effectiveness to advice regarding exercise habits. The providing unit can also apply an algorithm that emphasizes sleep quality to advice regarding sleep habits. In this way, by applying different advice algorithms depending on the lifestyle category, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to apply an advice algorithm depending on the category.

[0048] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, corrects the current advice based on the user's past advice results. The providing unit can also extract patterns for improving the accuracy of the advice from the user's past advice results. The providing unit can also optimize the advice algorithm by referring to the user's past advice results. In this way, the accuracy of the advice can be improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0049] When providing advice, the providing unit can determine the priority of advice based on the period of change in lifestyle habits. For example, the providing unit provides advice with an emphasis on periods when lifestyle habits change significantly. The providing unit can also provide simplified advice during periods when lifestyle habits change little. The providing unit can also dynamically adjust the priority of advice based on the period of change in lifestyle habits. This enables advice that focuses on important periods by determining the priority of advice based on the period of change in lifestyle habits. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to determine the priority of advice based on the period of change.

[0050] The providing unit can adjust the order of advice based on the relevance of lifestyle habits when providing advice. For example, the providing unit prioritizes advice on highly relevant lifestyle habits. The providing unit can also postpone advice on less relevant lifestyle habits. The providing unit can also dynamically adjust the order of advice based on the relevance of lifestyle habits. In this way, by adjusting the order of advice based on the relevance of lifestyle habits, highly relevant information can be given priority in advice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habit data to the generation AI and cause the generation AI to adjust the order of advice based on the relevance.

[0051] When providing advice, the providing unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide the advice using detailed technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide the advice in simple language. Furthermore, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the advice according to the user's level of expertise, it is possible to provide advice that is easy to understand. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] When displaying the future appearance, the display unit can select the optimal display method by referring to the user's past behavioral history. The display unit selects the optimal display method based on, for example, the user's past behavioral history. The display unit can also customize the display method from the user's past behavioral history. The display unit can also dynamically adjust the display method by referring to the user's past behavioral history. In this way, the optimal display method can be selected by referring to the user's past behavioral history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past behavioral history data into a generation AI and have the generation AI select the optimal display method.

[0053] When displaying the future appearance, the display unit can customize the display content according to the user's current health goals. The display unit customizes the display content of the future appearance according to, for example, the user's health goals. The display unit can also dynamically adjust the display content based on the user's health goals. The display unit can also optimize the display content according to the user's health goals. This enables a more appropriate display by customizing the display content according to the user's health goals. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's health goal data into a generation AI and have the generation AI customize the display content.

[0054] The display unit can improve the display method by reflecting user feedback when displaying the future appearance. The display unit improves the display method of the future appearance based on, for example, user feedback. The display unit can also customize the display method based on user feedback. The display unit can also dynamically adjust the display method by reflecting user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input user feedback data into a generation AI and have the generation AI improve the display method.

[0055] When displaying the future appearance, the display unit can select the optimal display method by taking into account the user's geographical location information. For example, if the user is in a specific area, the display unit can display a future appearance related to that area. Furthermore, if the user is traveling, the display unit can also display a future appearance related to the travel destination. Furthermore, if the user is at home, the display unit can display a future appearance related to the home. In this way, the optimal display method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal display method.

[0056] When displaying the future appearance, the display unit can analyze the user's social media activity and customize the display content. The display unit customizes the display content of the future appearance based on, for example, information shared by the user on social media. The display unit can also analyze the content posted by the user on social media and display the related future appearance. The display unit can also display the future appearance with reference to the activity of the user's friends on social media. In this way, the display content can be customized by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and have the generation AI customize the display content.

[0057] The display unit can customize the display method by reflecting the user's past feedback when displaying the future appearance. The display unit customizes the display method of the future appearance based on, for example, the user's past feedback. The display unit can also optimize the display method from the user's past feedback. The display unit can also dynamically adjust the display method by reflecting the user's past feedback. In this way, the display method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's feedback data into a generation AI and have the generation AI customize the display method.

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

[0059] The future vision system may further include a risk assessment unit that predicts future health risks based on the user's lifestyle habits. The risk assessment unit may assess the user's future risk of diabetes or heart disease, for example, based on the user's diet and exercise habits. The risk assessment unit may also assess the user's future mental health risk based on the user's sleep patterns. Furthermore, the risk assessment unit may assess the user's future risk of stress-related diseases based on the user's stress level. This allows the user to more specifically understand how their lifestyle habits will affect their future health and take preventative measures.

[0060] The future vision system may further include an economic impact assessment unit that predicts future economic impacts based on the user's lifestyle habits. The economic impact assessment unit may predict future increases in medical expenses based on the user's dietary and exercise habits, for example. The economic impact assessment unit may also predict future fluctuations in income based on the user's work performance. Furthermore, the economic impact assessment unit may predict future declines in labor productivity based on the user's stress level. This allows the user to understand how their lifestyle habits will affect their future economic situation and take appropriate measures.

[0061] The future vision system can further include a social impact assessment unit that predicts future social impacts based on the user's lifestyle habits. The social impact assessment unit can assess the user's risk of future social isolation based on, for example, the user's eating habits and exercise habits. The social impact assessment unit can also assess the quality of future relationships based on the user's sleep patterns. Furthermore, the social impact assessment unit can assess the risk of future relationships at work based on the user's stress level. This allows the user to understand how their lifestyle habits will affect their future social life and take appropriate measures.

[0062] The future vision system can further include an environmental impact assessment unit that predicts future environmental impacts based on the user's lifestyle habits. The environmental impact assessment unit predicts future food consumption and waste amounts based on the user's diet, for example. The environmental impact assessment unit can also predict future energy consumption based on the user's exercise habits. Furthermore, the environmental impact assessment unit can evaluate future risks of traffic congestion and air pollution based on the user's travel patterns. This allows the user to understand how their lifestyle habits will affect the environment in the future and take environmentally conscious actions.

[0063] The future vision system may further include a happiness assessment unit that predicts the user's future happiness level based on their lifestyle habits. The happiness assessment unit may assess the user's future physical health and happiness level based on, for example, their dietary habits and exercise habits. The happiness assessment unit may also assess the user's future mental health and happiness level based on their sleep patterns. Furthermore, the happiness assessment unit may assess the user's future social happiness level based on their social activities. This allows the user to understand how their lifestyle habits affect their future happiness level and take action to live a happier life.

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

[0065] Step 1: The reception unit receives information about the user's lifestyle and diet. The information about the user's lifestyle and diet includes, for example, the contents of meals, the frequency of exercise, and the amount of sleep. The reception unit can record the contents of meals entered by the user, track the frequency of exercise, and measure the amount of sleep. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and project the future. The analysis unit predicts the user's future appearance based on past data and statistical information. For example, the generation AI can predict acne and weight gain that may occur if the user continues to eat only oily foods. Step 3: The provider provides advice on healthy lifestyles and eating habits based on the future image projected by the analyzer. The provider provides advice on a balanced diet, moderate exercise, and sufficient sleep. For example, the provider recommends that the user eat more vegetables, exercise 30 minutes daily, and go to bed earlier. Step 4: The display unit changes the future image in real time as the user improves their lifestyle habits in accordance with the advice provided by the provider. The display unit shows a future image in which the user's acne is reduced and their weight is maintained at a healthy level by continuing to eat a balanced diet.

[0066] (Example 2) A future vision system according to an embodiment of the present invention projects a future image based on a user's lifestyle and dietary habits, encouraging healthy lifestyle habits. The future vision system allows users to input information about their lifestyle and dietary habits, and a generation AI analyzes the information to project a future image. For example, the system projects acne and weight gain caused by a constant diet of oily foods. Furthermore, the generation AI provides the user with advice on healthy lifestyles and dietary habits. As the user improves their lifestyle in accordance with the advice, the future image changes in real time. This allows the user to visually understand how their actions will affect their future and achieve a healthier lifestyle. This allows the future vision system to project a future image based on the user's lifestyle and dietary habits, encouraging healthy lifestyle habits. For example, the system encourages the user to eat a balanced diet and exercise moderately to avoid acne and weight gain caused by a constant diet of oily foods. In this way, the future vision system protects the user's future.

[0067] A future vision system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a display unit. The reception unit receives information about a user's lifestyle and diet. The information about the user's lifestyle and diet includes, but is not limited to, meal contents, exercise frequency, and sleep duration. The reception unit can, for example, record the meal contents entered by the user, track exercise frequency, and measure sleep duration. The analysis unit uses a generation AI to analyze the information received by the reception unit and project a future appearance. The analysis unit predicts the user's future appearance based on, for example, past data and statistical information. For example, the generation AI can predict acne and weight gain that may occur if the user continues to eat only oily foods. The provision unit provides advice on healthy lifestyle and diet based on the future appearance projected by the analysis unit. The provision unit provides advice such as a balanced diet, moderate exercise, and sufficient sleep. For example, the provision unit recommends the user to eat more vegetables, exercise 30 minutes daily, and go to bed earlier. The display unit changes the future image in real time when the user improves their lifestyle habits in accordance with the advice provided by the provider. For example, the display unit displays a future image in which the user's acne is reduced and their weight is maintained at a healthy level by continuing to eat a balanced diet. This allows the future vision system according to the embodiment to display a future image based on the user's lifestyle and eating habits and promote healthy lifestyle habits. For example, the user may be encouraged to eat a balanced diet and exercise moderately to avoid acne and weight gain caused by eating only oily foods. In this way, the future vision system protects the user's future.

[0068] The reception unit can receive information including the user's dietary details, exercise frequency, and sleep duration. The reception unit, for example, records the dietary details entered by the user. For example, it can record the types of ingredients, intake amounts, calories, etc. The reception unit can also track the user's exercise frequency. For example, it can record the type, number of times, and duration of exercise. The reception unit can also measure the user's sleep duration. For example, it can record the time of bedtime, wake-up time, and quality of sleep. By receiving detailed lifestyle information about the user, a more accurate future image can be projected. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the dietary details entered by the user into AI and have the AI ​​analyze the types of ingredients and intake amounts.

[0069] The analysis unit can predict the user's future appearance based on past data and statistical information. The analysis unit can predict the user's future appearance based on past data, for example. For example, the analysis can be performed based on data from the past year or the past five years. The analysis unit can also predict the user's future appearance based on statistical information. For example, the analysis can be performed based on government health statistics or academic research data. This makes it possible to predict the user's future appearance based on past data and statistical information, thereby enabling more reliable predictions. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input past data and statistical information into the generation AI and have the generation AI predict the user's future appearance.

[0070] The providing unit can provide advice on a balanced diet, moderate exercise, and sufficient sleep. The providing unit, for example, provides advice on a balanced diet. For example, it recommends eating a lot of vegetables and consuming an appropriate amount of protein. The providing unit can also provide advice on moderate exercise. For example, it recommends exercising 30 minutes every day and aerobic exercise three times a week. The providing unit can also provide advice on sufficient sleep. For example, it recommends going to bed early at night and improving your sleeping environment. This makes it possible to provide specific advice for promoting healthy lifestyle habits. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input lifestyle information about the user into AI and cause the AI ​​to generate advice on healthy lifestyle habits.

[0071] The display unit can change the future appearance in real time as the user improves their lifestyle habits in accordance with the advice. For example, the display unit can display a future appearance in which the user's acne is reduced and their weight is maintained at a healthy level by continuing to eat a balanced diet. The display unit can also display a future appearance in which the user's physical strength is improved and a healthy figure is maintained by continuing to exercise moderately. The display unit can also display a future appearance in which the user's stress is reduced and their mental health is maintained by getting enough sleep. In this way, the future appearance can change in real time as the user improves their lifestyle habits in accordance with the advice. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the user's lifestyle improvements into AI and cause the AI ​​to generate changes in the future appearance.

[0072] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input information during a time when the user is able to relax. Furthermore, if the user is relaxed, the reception unit can select the timing to prompt the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit can provide a simplified input form to enable the user to input information quickly. This allows the timing of information input to be adjusted according to the user's emotions, enabling the information to be input at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] The reception unit can analyze the user's past lifestyle data and select the optimal information input method. For example, the reception unit can suggest the most frequently used input method based on data previously input by the user. The reception unit can also automatically select items that need to be input from the user's lifestyle data to simplify input. The reception unit can also analyze the user's past input history and provide a customized input form to reduce the effort required for input. This allows the optimal information input method to be selected by analyzing the past lifestyle data. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past lifestyle data into a generation AI and have the generation AI select the optimal information input method.

[0074] The reception unit can filter information based on the user's current health condition and goals when inputting information. For example, when the user inputs their current health condition, the reception unit automatically filters input items according to that condition. The reception unit can also prompt the user to input only relevant information based on the user's health goals. The reception unit can also automatically exclude items that do not need to be input based on the user's health condition and goals. In this way, by filtering input items according to the user's health condition and goals, only necessary information can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition data to a generation AI and have the generation AI filter the input items.

[0075] When inputting information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input information using a keyboard or touch panel. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. This makes it easier to input information by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0076] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to prioritize inputting only important information. Furthermore, if the user is relaxed, the reception unit can also prompt the user to prioritize inputting detailed information. Furthermore, if the user is in a hurry, the reception unit can also prompt the user to prioritize inputting the most important information. Thus, by determining the priority of information to be input according to the user's emotions, important information can be input preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prompt the user to prioritize inputting information related to that area. Furthermore, if the user is traveling, the reception unit can also prompt the user to prioritize inputting information related to their travel destination. Furthermore, if the user is at home, the reception unit can also prompt the user to prioritize inputting information related to their home. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant information.

[0078] The reception unit can analyze the user's social media activity and input related information when inputting information. For example, the reception unit can automatically input meal details based on photos of meals shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and input related information. The reception unit can also input related information by referring to the activity of the user's friends on social media. In this way, related information can be automatically input by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to extract related information.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. For example, the reception unit can suggest an optimal input method based on feedback previously input by the user. The reception unit can also customize the input form to simplify input based on the user's past feedback. The reception unit can also analyze the user's past feedback and make improvements to reduce the effort required for input. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise, to-the-point analysis results. If the user is in a hurry, the analysis unit can also simplify the analysis results so that they can be quickly understood. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle habits. For example, the analysis unit analyzes information about important lifestyle habits in detail and provides the result to the user. The analysis unit can also simplify and analyze information about less important lifestyle habits. The analysis unit can also prioritize the analysis of more important lifestyle habits based on the user's health goals. This allows important information to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the lifestyle habits. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the lifestyle habit category. For example, the analysis unit can apply an analysis algorithm that emphasizes nutritional balance to information about dietary habits. The analysis unit can also apply an analysis algorithm that emphasizes exercise effects to information about exercise habits. The analysis unit can also apply an analysis algorithm that emphasizes sleep quality to information about sleep habits. By applying different analysis algorithms depending on the lifestyle habit category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. If the user is in a hurry, the analysis unit can also simplify the analysis results so that the user can understand them quickly. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time of lifestyle habit fluctuation. For example, the analysis unit may focus on periods of significant lifestyle habit fluctuation during analysis. The analysis unit may also perform a simplified analysis during periods of minimal lifestyle habit fluctuation. The analysis unit may also dynamically adjust the priority of analysis based on the time of lifestyle habit fluctuation. This enables analysis to focus on important periods by determining the priority of analysis based on the time of lifestyle habit fluctuation. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's lifestyle habit data into the generation AI and cause the generation AI to determine the priority of analysis based on the time of fluctuation.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of lifestyle habits. For example, the analysis unit prioritizes analysis of highly relevant lifestyle habits. The analysis unit can also postpone analysis of less relevant lifestyle habits. The analysis unit can also dynamically adjust the order of analysis based on the relevance of lifestyle habits. In this way, by adjusting the order of analysis based on the relevance of lifestyle habits, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0088] The providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can provide detailed advice. When the user is stressed, the providing unit can also provide concise, to-the-point advice. When the user is in a hurry, the providing unit can also simplify the advice so that it can be quickly understood. This allows for more appropriate advice to be provided by adjusting the way the advice is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The providing unit can adjust the level of detail of the advice based on the importance of the lifestyle habit when providing advice. For example, the providing unit provides detailed advice regarding important lifestyle habits. The providing unit can also provide simplified advice regarding less important lifestyle habits. The providing unit can also prioritize advice regarding more important lifestyle habits based on the user's health goals. In this way, by adjusting the level of detail of the advice based on the importance of the lifestyle habit, it is possible to provide important information in detail. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habit data to the generation AI and cause the generation AI to adjust the level of detail of the advice based on the importance.

[0090] When providing advice, the providing unit can apply different advice algorithms depending on the lifestyle category. For example, the providing unit can apply an algorithm that emphasizes nutritional balance to advice regarding dietary habits. The providing unit can also apply an algorithm that emphasizes exercise effectiveness to advice regarding exercise habits. The providing unit can also apply an algorithm that emphasizes sleep quality to advice regarding sleep habits. In this way, by applying different advice algorithms depending on the lifestyle category, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to apply an advice algorithm depending on the category.

[0091] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, corrects the current advice based on the user's past advice results. The providing unit can also extract patterns for improving the accuracy of the advice from the user's past advice results. The providing unit can also optimize the advice algorithm by referring to the user's past advice results. In this way, the accuracy of the advice can be improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0092] The providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can provide detailed advice. When the user is stressed, the providing unit can also provide concise, to-the-point advice. When the user is in a hurry, the providing unit can also simplify the advice so that it can be quickly understood. This allows for more appropriate advice to be provided by adjusting the length of the advice according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the 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 into the generation AI and cause the generation AI to estimate the emotion.

[0093] When providing advice, the providing unit can determine the priority of advice based on the period of change in lifestyle habits. For example, the providing unit provides advice with an emphasis on periods when lifestyle habits change significantly. The providing unit can also provide simplified advice during periods when lifestyle habits change little. The providing unit can also dynamically adjust the priority of advice based on the period of change in lifestyle habits. This enables advice that focuses on important periods by determining the priority of advice based on the period of change in lifestyle habits. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to determine the priority of advice based on the period of change.

[0094] The providing unit can adjust the order of advice based on the relevance of lifestyle habits when providing advice. For example, the providing unit prioritizes advice on highly relevant lifestyle habits. The providing unit can also postpone advice on less relevant lifestyle habits. The providing unit can also dynamically adjust the order of advice based on the relevance of lifestyle habits. In this way, by adjusting the order of advice based on the relevance of lifestyle habits, highly relevant information can be given priority in advice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habit data to the generation AI and cause the generation AI to adjust the order of advice based on the relevance.

[0095] When providing advice, the providing unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide the advice using detailed technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide the advice in simple language. Furthermore, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the advice according to the user's level of expertise, it is possible to provide advice that is easy to understand. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The display unit can estimate the user's emotions and adjust the display method of the future appearance based on the estimated user emotions. For example, when the user is relaxed, the display unit displays a detailed future appearance. Furthermore, when the user is stressed, the display unit can display a concise, to-the-point future appearance. Furthermore, when the user is in a hurry, the display unit can display a simplified future appearance for quick understanding. This allows for a more appropriate display by adjusting the display method of the future appearance according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI. For example, the display unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0097] When displaying the future appearance, the display unit can select the optimal display method by referring to the user's past behavioral history. The display unit selects the optimal display method based on, for example, the user's past behavioral history. The display unit can also customize the display method from the user's past behavioral history. The display unit can also dynamically adjust the display method by referring to the user's past behavioral history. In this way, the optimal display method can be selected by referring to the user's past behavioral history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past behavioral history data into a generation AI and have the generation AI select the optimal display method.

[0098] When displaying the future appearance, the display unit can customize the display content according to the user's current health goals. The display unit customizes the display content of the future appearance according to, for example, the user's health goals. The display unit can also dynamically adjust the display content based on the user's health goals. The display unit can also optimize the display content according to the user's health goals. This enables a more appropriate display by customizing the display content according to the user's health goals. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's health goal data into a generation AI and have the generation AI customize the display content.

[0099] The display unit can improve the display method by reflecting user feedback when displaying the future appearance. The display unit improves the display method of the future appearance based on, for example, user feedback. The display unit can also customize the display method based on user feedback. The display unit can also dynamically adjust the display method by reflecting user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input user feedback data into a generation AI and have the generation AI improve the display method.

[0100] The display unit can estimate the user's emotions and adjust the display order of future images based on the estimated user emotions. For example, when the user is relaxed, the display unit can prioritize displaying detailed future images. Furthermore, when the user is stressed, the display unit can prioritize displaying concise, to-the-point future images. Furthermore, when the user is in a hurry, the display unit can display simplified future images for quick understanding. This allows for more appropriate display by adjusting the display order of future images according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0101] When displaying the future appearance, the display unit can select the optimal display method by taking into account the user's geographical location information. For example, if the user is in a specific area, the display unit can display a future appearance related to that area. Furthermore, if the user is traveling, the display unit can also display a future appearance related to the travel destination. Furthermore, if the user is at home, the display unit can display a future appearance related to the home. In this way, the optimal display method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal display method.

[0102] When displaying the future appearance, the display unit can analyze the user's social media activity and customize the display content. The display unit customizes the display content of the future appearance based on, for example, information shared by the user on social media. The display unit can also analyze the content posted by the user on social media and display the related future appearance. The display unit can also display the future appearance with reference to the activity of the user's friends on social media. In this way, the display content can be customized by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and have the generation AI customize the display content.

[0103] The display unit can customize the display method by reflecting the user's past feedback when displaying the future appearance. The display unit customizes the display method of the future appearance based on, for example, the user's past feedback. The display unit can also optimize the display method from the user's past feedback. The display unit can also dynamically adjust the display method by reflecting the user's past feedback. In this way, the display method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's feedback data into a generation AI and have the generation AI customize the display method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, provision unit, and display unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information about the user's lifestyle and dietary habits. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to display a future image. For example, the provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides advice on healthy lifestyle and dietary habits based on the future image displayed by the analysis unit. For example, the display unit is implemented by the output device 40 of the smart device 14 and changes the future image in real time when the user improves their lifestyle in accordance with the advice provided by the provision unit. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and display unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives information about the user's lifestyle and dietary habits. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to display a future image. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on healthy lifestyle and dietary habits based on the future image displayed by the analysis unit. For example, the display unit is realized by the speaker 240 of the smart glasses 214 and changes the future image in real time when the user improves their lifestyle in accordance with the advice provided by the provision unit. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and display unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives information about the user's lifestyle and dietary habits. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to display a future image. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on healthy lifestyle and dietary habits based on the future image displayed by the analysis unit. For example, the display unit is realized by the display 343 of the headset terminal 314 and changes the future image in real time when the user improves their lifestyle in accordance with the advice provided by the provision unit. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and display unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives information about the user's lifestyle and dietary habits. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generative AI to project a future appearance. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on healthy lifestyle and dietary habits based on the future appearance projected by the analysis unit. For example, the display unit is realized by the speaker 240 of the robot 414 and changes the future appearance in real time when the user improves their lifestyle in accordance with the advice provided by the provision unit.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The future vision system may further include a risk assessment unit that predicts future health risks based on the user's lifestyle habits. The risk assessment unit may assess the user's future risk of diabetes or heart disease, for example, based on the user's diet and exercise habits. The risk assessment unit may also assess the user's future mental health risk based on the user's sleep patterns. Furthermore, the risk assessment unit may assess the user's future risk of stress-related diseases based on the user's stress level. This allows the user to more specifically understand how their lifestyle habits will affect their future health and take preventative measures.

[0106] The future vision system may further include an economic impact assessment unit that predicts future economic impacts based on the user's lifestyle habits. The economic impact assessment unit may predict future increases in medical expenses based on the user's dietary and exercise habits, for example. The economic impact assessment unit may also predict future fluctuations in income based on the user's work performance. Furthermore, the economic impact assessment unit may predict future declines in labor productivity based on the user's stress level. This allows the user to understand how their lifestyle habits will affect their future economic situation and take appropriate measures.

[0107] The future vision system can further include a social impact assessment unit that predicts future social impacts based on the user's lifestyle habits. The social impact assessment unit can assess the user's risk of future social isolation based on, for example, the user's eating habits and exercise habits. The social impact assessment unit can also assess the quality of future relationships based on the user's sleep patterns. Furthermore, the social impact assessment unit can assess the risk of future relationships at work based on the user's stress level. This allows the user to understand how their lifestyle habits will affect their future social life and take appropriate measures.

[0108] The future vision system can further include an environmental impact assessment unit that predicts future environmental impacts based on the user's lifestyle habits. The environmental impact assessment unit predicts future food consumption and waste amounts based on the user's diet, for example. The environmental impact assessment unit can also predict future energy consumption based on the user's exercise habits. Furthermore, the environmental impact assessment unit can evaluate future risks of traffic congestion and air pollution based on the user's travel patterns. This allows the user to understand how their lifestyle habits will affect the environment in the future and take environmentally conscious actions.

[0109] The future vision system may further include a happiness assessment unit that predicts the user's future happiness level based on their lifestyle habits. The happiness assessment unit may assess the user's future physical health and happiness level based on, for example, their dietary habits and exercise habits. The happiness assessment unit may also assess the user's future mental health and happiness level based on their sleep patterns. Furthermore, the happiness assessment unit may assess the user's future social happiness level based on their social activities. This allows the user to understand how their lifestyle habits affect their future happiness level and take action to live a happier life.

[0110] The future vision system may further include a motivation improvement unit that estimates the user's emotions and provides feedback to improve the user's motivation based on the estimated emotions. For example, if the user is feeling stressed, the motivation improvement unit may provide advice on how to relax. If the user is relaxed, the motivation improvement unit may also suggest specific steps to achieve a goal. Furthermore, if the user is tired, the motivation improvement unit may provide advice on how to take a rest. This allows the user to receive appropriate feedback according to their emotions, making it easier for them to maintain their motivation.

[0111] The future vision system may further include a stress management unit that estimates the user's emotions and supports the user's stress management based on the estimated emotions. For example, if the user feels stressed, the stress management unit may suggest relaxation techniques or breathing exercises. If the user is relaxed, the stress management unit may also provide advice on preventing stress. Furthermore, if the user is in a hurry, the stress management unit may also provide tips on time management. This allows the user to learn appropriate stress management methods according to their emotions and effectively control stress.

[0112] The future vision system can further include a communication support unit that estimates the user's emotions and supports the user's communication based on the estimated emotions. For example, the communication support unit can suggest an appropriate communication method if the user is feeling stressed. The communication support unit can also provide advice to encourage proactive communication if the user is relaxed. Furthermore, the communication support unit can suggest an efficient communication method if the user is in a hurry. This allows the user to learn appropriate communication methods according to their emotions and maintain smooth interpersonal relationships.

[0113] The future vision system can further include a learning support unit that estimates the user's emotions and provides feedback to improve the user's learning effectiveness based on the estimated emotions. For example, if the user is feeling stressed, the learning support unit can suggest a break to relax. Also, if the user is relaxed, the learning support unit can suggest a learning method to improve concentration. Furthermore, if the user is tired, the learning support unit can suggest an efficient learning schedule. This allows the user to receive appropriate feedback according to their emotions and maximize their learning effectiveness.

[0114] The future vision system can further include a health management unit that estimates the user's emotions and supports the user's health management based on the estimated emotions. For example, if the user is feeling stressed, the health management unit can provide advice on exercise and diet to reduce stress. Also, if the user is relaxed, the health management unit can suggest a specific action plan to maintain health. Furthermore, if the user is tired, the health management unit can provide advice on rest and recovery. This allows the user to learn appropriate health management methods according to their emotions, making it easier to maintain their health.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives information about the user's lifestyle and diet. The information about the user's lifestyle and diet includes, for example, the contents of meals, the frequency of exercise, and the amount of sleep. The reception unit can record the contents of meals entered by the user, track the frequency of exercise, and measure the amount of sleep. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and project the future. The analysis unit predicts the user's future appearance based on past data and statistical information. For example, the generation AI can predict acne and weight gain that may occur if the user continues to eat only oily foods. Step 3: The provider provides advice on healthy lifestyles and eating habits based on the future image projected by the analyzer. The provider provides advice on a balanced diet, moderate exercise, and sufficient sleep. For example, the provider recommends that the user eat more vegetables, exercise 30 minutes daily, and go to bed earlier. Step 4: The display unit changes the future image in real time as the user improves their lifestyle habits in accordance with the advice provided by the provider. The display unit shows a future image in which the user's acne is reduced and their weight is maintained at a healthy level by continuing to eat a balanced diet.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] 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.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about a user's lifestyle and dietary habits; an analysis unit that analyzes the information received by the reception unit and projects a future image; a providing unit that provides advice on lifestyle and dietary habits based on the future appearance projected by the analysis unit; a display unit that changes the future appearance in real time when the user improves their lifestyle in accordance with the advice provided by the providing unit. A system characterized by:

2. The reception unit Accepts information including the user's diet, exercise frequency, and sleep duration 2. The system of claim 1.

3. The analysis unit Predicting the future of users based on past data and statistical information 2. The system of claim 1.

4. The providing unit Providing advice on a balanced diet, moderate exercise, and adequate sleep 2. The system of claim 1.

5. The display unit When users follow the advice and improve their lifestyle habits, the future changes in real time.

2. The system of claim 1.

6. The reception unit Estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past lifestyle data and select the optimal information input method 2. The system of claim 1.

8. The reception unit As you enter information, it filters it based on your current health status and goals.

2. The system of claim 1.

9. The reception unit When entering information, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize the information to be input based on the estimated user emotions.

2. The system of claim 1.

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