System

Abstract

An object of a system according to an embodiment is to provide personalized advice based on lifestyle data of a user.SOLUTION: A system includes a collection unit, a generation unit, and an advice provision unit. The collection unit collects lifestyle data of a user. The generation unit predicts a future appearance based on the data collected by the collection unit, and generates an avatar. The advice providing unit provides personalized advice on the basis of the avatar generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] Conventional technologies do not adequately provide personalized advice based on a user's lifestyle habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized advice based on lifestyle habit data of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and an advice providing unit. The collection unit collects lifestyle habit data of a user. The generation unit predicts a future appearance of the user based on the data collected by the collection unit and generates an avatar. The advice providing unit provides personalized advice based on the avatar generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized advice based on lifestyle habit data of the user. [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) An AI butler system according to an embodiment of the present invention predicts a user's future appearance based on their lifestyle data and provides personalized advice. The AI ​​butler system collects the user's lifestyle data, and a generation AI predicts their future appearance and generates an avatar. The generated avatar provides personalized advice in the form of a chatbot based on the user's selection. For example, the AI ​​butler system collects data such as the user's current age, exercise, diet, and sleep. Then, based on the collected data, it generates an avatar that realistically depicts what the user will look like in five or ten years' time using real-time image generation technology. The generated avatar provides personalized advice in the form of a chatbot based on the user's selection. For example, the AI ​​butler system may suggest healthy meal plans based on the user's dietary data as dietary advice. It may also suggest appropriate exercise programs based on the user's exercise data as health advice. Furthermore, the AI ​​butler system analyzes the user's stress level and mood fluctuations to provide appropriate advice for mental health. This allows the user to obtain useful information for their daily lives. The AI ​​butler system also uses a generation AI to create individual fitness routines and exercises based on the user's exercise data, providing an exercise program perfectly tailored to the user's goals and physical condition. Daily information is collected in conjunction with smartphones and IoT devices, and dietary information is identified from photos by the AI ​​generator and collected as data. This allows for a detailed understanding of the user's lifestyle data. By living a healthy lifestyle, the avatar's appearance will also become younger, and visual feedback is provided. For example, if the user continues to eat healthy and exercise, the avatar's appearance will become younger, motivating the user. This allows the user to maintain a healthy lifestyle. This allows the AI ​​butler system to predict the user's future appearance based on the user's lifestyle data and provide personalized advice. For example, if the user continues to eat healthy and exercise, the avatar's appearance will become younger, motivating the user. This allows the user to maintain a healthy lifestyle.

[0029] The AI ​​butler system according to the embodiment includes a collection unit, a generation unit, and an advice provision unit. The collection unit collects lifestyle data of a user. The lifestyle data includes, but is not limited to, diet, exercise, sleep, and stress levels. The collection unit collects daily lifestyle data of the user in cooperation with, for example, a smartphone or an IoT device. For example, a smartwatch is used to collect exercise data, and a smartphone app is used to take photos of meals. The generation AI analyzes the photos and determines the contents of the meals. The generation unit uses the generation AI to predict the user's future appearance based on the data collected by the collection unit and generate an avatar. The future appearance includes, but is not limited to, health status, body shape, and lifestyle changes. For example, the generation unit analyzes the user's current data and predicts the user's future appearance. The generation AI can realistically recreate the user's appearance five or ten years from now based on the user's current data. The advice provision unit provides personalized advice based on the avatar generated by the generation unit. The advice includes, but is not limited to, dietary advice, health advice, and mental health advice. The advice providing unit provides advice, for example, in the form of a chatbot. For example, as dietary advice, it suggests a healthy meal menu based on the user's dietary data. As health advice, it suggests an appropriate exercise program based on the user's exercise data. Furthermore, for mental health, it analyzes the user's stress level and mood fluctuations and provides appropriate advice. This allows the AI ​​butler system according to the embodiment to predict the user's future appearance based on the user's lifestyle habit data and provide personalized advice. For example, if the user continues to eat healthily and exercise, the avatar's appearance will become younger, which will motivate the user. This allows the user to maintain a healthy lifestyle.

[0030] The collection unit can collect daily life data of a user in cooperation with a smartphone or an IoT device. Examples of smartphones or IoT devices include, but are not limited to, smartwatches, fitness trackers, and smart speakers. The collection unit can collect exercise data using, for example, a smartwatch. For example, the collection unit can collect data such as the number of steps, heart rate, and calories burned. The collection unit can also take photos of meals using a smartphone app and have the generation AI analyze the photos to determine the contents of the meals. For example, the collection unit can determine the details of the meals using the resolution of the photos and an analysis algorithm. This allows for efficient collection of daily life data of a user by cooperation with a smartphone or an IoT device. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input exercise data acquired by a smartwatch into the generation AI and have the generation AI analyze the exercise data.

[0031] The collection unit can collect exercise data using a smartwatch, take photos of meals using a smartphone app, and have the generation AI analyze the photos to determine the contents of the meals. Exercise data includes, but is not limited to, the number of steps, heart rate, and calories burned. The collection unit can collect exercise data using, for example, a smartwatch. For example, the collection unit collects data such as the number of steps, heart rate, and calories burned. The collection unit can also take photos of meals using a smartphone app, and have the generation AI analyze the photos to determine the contents of the meals. For example, the collection unit can determine the details of the meals using the resolution of the photos and an analysis algorithm. This allows detailed collection of exercise data and dietary data using the smartwatch and smartphone app. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input exercise data acquired by the smartwatch into the generation AI and have the generation AI analyze the exercise data.

[0032] The generation unit can analyze the user's current data and predict their future appearance. Current data includes, but is not limited to, for example, their current health status, lifestyle habits, and past data. The generation unit, for example, analyzes the user's current data and predicts their future appearance. The generation AI can realistically recreate what the user will look like in five or ten years based on the user's current data. For example, the generation unit can realistically recreate what the user will look like in five or ten years if they continue their current lifestyle habits. This allows the user to visually confirm what they will look like in the future. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's current data into the generation AI and have the generation AI predict their future appearance.

[0033] The advice providing unit can provide dietary advice, health advice, and mental health advice in the form of a chatbot. Examples of chatbots include, but are not limited to, text-based, voice-based, and dialogue-based advice. The advice providing unit provides advice in the form of a chatbot. For example, as dietary advice, it may suggest a healthy meal menu based on the user's dietary data. As health advice, it may suggest an appropriate exercise program based on the user's exercise data. Furthermore, for mental health, it may analyze the user's stress level and mood fluctuations and provide appropriate advice. Thus, by providing advice in the form of a chatbot, the user can easily receive advice. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit may input the user's dietary data into a generation AI and cause the generation AI to generate dietary advice.

[0034] The advice providing unit can use the generation AI to create individual fitness routines and exercises based on the user's exercise data, providing an exercise program suited to the user's goals and condition. The exercise program can include, but is not limited to, fitness routines, exercise types, and training frequency. The advice providing unit can create individual fitness routines and exercises based on the user's exercise data, providing an exercise program perfectly suited to the user's goals and condition. For example, if the user is trying to lose weight, the generation AI can analyze the user's exercise data and suggest an effective exercise program. This allows the user to practice an exercise program that suits them. Some or all of the above-described processing in the advice providing unit can be performed using, for example, AI, or without AI. For example, the advice providing unit can input the user's exercise data into the generation AI and have the generation AI generate an exercise program.

[0035] The generation unit can provide visual feedback that rejuvenates the appearance of the avatar as the user lives a healthy lifestyle. Examples of visual feedback include, but are not limited to, avatar changes, graph displays, and animations. For example, the generation unit can provide visual feedback that rejuvenates the appearance of the avatar as the user continues to eat healthy and exercise. For example, the generation unit can update the appearance of the avatar in real time based on the user's lifestyle data and visually show the effects of a healthy lifestyle. This can motivate the user to maintain a healthy lifestyle. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's lifestyle data into the generation AI and cause the generation AI to update the avatar.

[0036] The collection unit can analyze the user's past lifestyle data and select an appropriate collection method. Examples of past lifestyle data include, but are not limited to, past health checkup results, exercise history, and diet records. For example, the collection unit collects data by preferentially using a device that the user has used in the past. The collection unit can also select the most efficient collection method based on the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection patterns and determine the optimal collection timing. This allows the optimal collection method to be selected by analyzing the past data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past data into a generation AI and have the generation AI select the optimal collection method.

[0037] When collecting lifestyle data, the collection unit can filter the data based on the user's current health condition and lifestyle rhythm. Filtering methods include, but are not limited to, data importance, relevance, and timestamps. For example, if the user is in poor health, the collection unit can reduce the amount of data collected to reduce the burden on the user. The collection unit can also adjust the type of data collected to match the user's lifestyle rhythm. Furthermore, the collection unit can prioritize the collection of specific data based on the user's health condition. This improves the quality of the collected data by filtering data based on the user's health condition and lifestyle rhythm. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's health condition data into a generation AI and have the generation AI perform filtering.

[0038] When collecting life data, the collection unit can select an appropriate collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the user prefers text input, the collection unit can preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can preferentially collect image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0039] When collecting lifestyle data, the collection unit can prioritize collecting relevant data based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting lifestyle data related to the user's home. This improves the usefulness of the data by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect relevant data.

[0040] When collecting lifestyle data, the collection unit can analyze the user's social media activities and collect related data. Social media activities include, but are not limited to, post content, number of likes, and comment content. For example, the collection unit can collect data related to locations where the user checked in on social media. The collection unit can also analyze the user's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0041] When collecting lifestyle data, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal collection method. Furthermore, the collection unit can customize the collection timing and method based on the user's feedback. This allows the collection method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0042] When generating an avatar, the generation unit can adjust the specificity of the future appearance based on the user's lifestyle data. The specificity of the future appearance can include, but is not limited to, detailed health predictions, lifestyle changes, and visual accuracy. For example, if the user has a healthy lifestyle, the generation unit can generate a detailed future appearance. Furthermore, if the user has an irregular lifestyle, the generation unit can generate a simplified future appearance. Furthermore, the generation unit can generate a future appearance with an optimal level of detail based on the user's lifestyle data. By adjusting the level of detail based on the lifestyle data, a more realistic future appearance can be generated. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's lifestyle data into the generation AI and cause the generation AI to adjust the specificity of the future appearance.

[0043] When generating an avatar, the generation unit can apply an appropriate generation algorithm depending on the user's health data. Examples of generation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, if the user's health data is good, the generation unit can apply a generation algorithm that emphasizes a healthy appearance. Furthermore, if the user's health data has deteriorated, the generation unit can apply a generation algorithm that indicates room for improvement. Furthermore, the generation unit can select an optimal generation algorithm based on the user's health data. This allows for the generation of a more appropriate avatar by adjusting the generation algorithm depending on the health data. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's health data into the generation AI and cause the generation AI to apply the generation algorithm.

[0044] When generating an avatar, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. Past generation results include, but are not limited to, past avatar images and the accuracy of generated data. For example, the generation unit can analyze the user's past generation results and incorporate feedback to improve accuracy. The generation unit can also adjust optimal generation parameters based on the user's past generation results. Furthermore, the generation unit can improve the generation algorithm by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] When generating avatars, the generation unit can determine the order of generation based on changes in the user's lifestyle. Criteria for determining the order of generation include, but are not limited to, data importance, urgency, and relevance. For example, if the user's lifestyle changes significantly, the generation unit can prioritize generating an avatar that reflects the change. Furthermore, if the user's lifestyle remains stable, the generation unit can periodically update the avatar. Furthermore, the generation unit can determine the optimal generation timing based on changes in the user's lifestyle. This allows avatars to be generated at more appropriate times by determining the generation priority based on changes in the lifestyle. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's lifestyle data into the generation AI and have the generation AI determine the order of generation.

[0046] When generating avatars, the generation unit can adjust the generation order by referring to the user's related data. Related data includes, but is not limited to, past generation results and user feedback. For example, the generation unit can prioritize reference to the user's health data to determine the avatar generation order. The generation unit can also adjust the avatar generation order based on the user's exercise data. Furthermore, the generation unit can optimize the avatar generation order by referring to the user's dietary data. This allows the generation order to be optimized by referring to the related data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's related data into the generation AI and have the generation AI adjust the generation order.

[0047] When generating an avatar, the generation unit can adjust the specificity of the avatar according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the generation unit generates a detailed avatar if the user has expertise. The generation unit can also generate a simplified avatar if the user is a beginner. Furthermore, the generation unit can generate an avatar with an optimal level of detail based on the user's level of expertise. By adjusting the level of detail according to the level of expertise, an optimal avatar for the user can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the specificity of the avatar.

[0048] When providing advice, the advice providing unit can adjust the specificity of the advice based on the user's lifestyle data. Examples of the specificity of the advice include, but are not limited to, detailed explanations, concise suggestions, and step-by-step guides. For example, the advice providing unit can provide detailed advice if the user's lifestyle is healthy. Furthermore, the advice providing unit can provide simplified advice if the user's lifestyle is irregular. Furthermore, the advice providing unit can provide advice with an optimal level of detail based on the user's lifestyle data. This allows for more appropriate advice to be provided by adjusting the level of detail based on the lifestyle data. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's lifestyle data into a generation AI and cause the generation AI to adjust the specificity of the advice.

[0049] When providing advice, the advice providing unit can apply an appropriate advice algorithm depending on the user's health data. Examples of advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, if the user's health data is good, the advice providing unit can apply an algorithm that provides advice for maintaining health. Furthermore, if the user's health data is deteriorating, the advice providing unit can also apply an algorithm that provides advice for improvement. Furthermore, the advice providing unit can select an optimal advice algorithm based on the user's health data. This allows for more appropriate advice to be provided by adjusting the advice algorithm depending on the health data. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's health data into a generation AI and cause the generation AI to apply an advice algorithm.

[0050] When providing advice, the advice providing unit can improve the accuracy of the advice by referring to the user's past advice results. Past advice results include, but are not limited to, for example, user feedback and the effect of the advice. For example, the advice providing unit analyzes the user's past advice results and reflects feedback to improve accuracy. The advice providing unit can also adjust optimal advice parameters based on the user's past advice results. Furthermore, the advice providing unit can also improve 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 past advice results. Some or all of the above-mentioned processing in the advice providing unit may be performed, for example, using AI or without AI. For example, the advice providing unit can input the user's past advice results into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0051] When providing advice, the advice providing unit can determine the order of advice based on changes in the user's lifestyle. Criteria for determining the order of advice include, but are not limited to, data importance, urgency, and relevance. For example, if the user's lifestyle changes significantly, the advice providing unit can prioritize providing advice that reflects the change. The advice providing unit can also periodically update the advice if the user's lifestyle is stable. Furthermore, the advice providing unit can determine the optimal timing for providing advice based on changes in the user's lifestyle. This allows advice to be provided at more appropriate times by determining the priority of advice based on changes in lifestyle. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's lifestyle data into a generation AI and have the generation AI determine the order of advice.

[0052] When providing advice, the advice providing unit can adjust the order of advice by referring to the user's related data. Related data includes, for example, past advice results and user feedback, but is not limited to these examples. For example, the advice providing unit can preferentially refer to the user's health data to determine the order of advice. The advice providing unit can also adjust the order of advice based on the user's exercise data. Furthermore, the advice providing unit can also optimize the order of advice by referring to the user's dietary data. In this way, the order of advice can be optimized by referring to the related data. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's related data into the generation AI and cause the generation AI to adjust the order of advice.

[0053] When providing advice, the advice providing unit can adjust the use of technical terminology in the advice depending on the user's level of expertise. Examples of technical terminology include, but are not limited to, technical terms, industry jargon, and abbreviations. For example, if the user has specialized knowledge, the advice providing unit can provide advice using detailed technical terminology. Furthermore, if the user is a beginner, the advice providing unit can also provide advice using simplified terminology. Furthermore, the advice providing unit can provide advice using optimal terminology based on the user's level of expertise. By adjusting the use of technical terminology depending on the level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

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

[0055] The AI ​​butler system can also analyze the influence of a user's social network based on the user's lifestyle data and provide advice. For example, the collection unit collects data on the user's social media activities and friendships, and the generation unit predicts social influences on the user's lifestyle based on this data. The advice provision unit can suggest what kind of social support the user needs to live a healthy lifestyle. For example, if the user is motivated by exercising with friends, the system can suggest an exercise plan with those friends. Also, if the user is feeling stressed, the system can recommend interacting with friends who can help them relax. This allows the user to utilize their social network to maintain a healthier lifestyle.

[0056] The AI ​​butler system can also suggest activities related to the user's hobbies and interests based on the user's lifestyle data. For example, the collection unit collects data on the user's hobbies and interests, and the generation unit predicts activities the user will enjoy based on this data. The advice provision unit can suggest activities related to the user's hobbies and interests to help the user live a healthy lifestyle. For example, if the user likes outdoor activities, the system can suggest hiking and camping plans. Also, if the user enjoys cooking, the system can suggest healthy recipes. This allows the user to maintain a healthier lifestyle by utilizing their hobbies and interests.

[0057] The AI ​​butler system can also analyze the user's sleep patterns based on the user's lifestyle data and provide advice for improvement. For example, the collection unit collects the user's sleep data, and the generation unit predicts the user's sleep patterns based on this data. The advice provision unit can provide specific advice to help the user get better sleep. For example, if the user tends to stay up late, the system can suggest getting into the habit of going to bed early and getting up early. Also, if the user is unable to sleep due to stress, the system can suggest ways to relax. This allows the user to improve the quality of their sleep and maintain a healthy lifestyle.

[0058] The AI ​​butler system can also analyze the impact of the user's work environment based on the user's lifestyle data and provide advice. For example, the collection unit collects data about the user's work environment, and the generation unit predicts the impact of the work environment on the user's lifestyle based on this data. The advice provision unit can suggest ways to improve the work environment so that the user can live a healthy lifestyle. For example, if the user sits for long periods of time, the system can suggest that the user stand up and stretch regularly. Also, if the user feels stressed at work, the system can suggest ways to relax. This allows the user to improve their work environment and maintain a healthy lifestyle.

[0059] The AI ​​butler system can also support the user's seasonal health management based on the user's lifestyle data. For example, the collection unit collects the user's seasonal lifestyle data, and the generation unit predicts seasonal health risks based on this data. The advice provision unit can offer specific advice to help the user maintain their health for each season. For example, in winter, it can suggest diet and exercise to prevent colds, and in summer, it can suggest measures to prevent heatstroke. It can also suggest measures to prevent pollen during hay fever season. This allows the user to respond to seasonal health risks and maintain a healthy lifestyle.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects the user's lifestyle data. This lifestyle data includes diet, exercise, sleep, stress levels, etc. The collection unit works with smartphones and IoT devices to collect the user's daily lifestyle data. For example, a smartwatch can be used to collect exercise data, and a smartphone app can be used to take photos of meals. The generation AI then analyzes the photos to determine the contents of the meal. Step 2: The generator uses AI to predict the user's future appearance based on the data collected by the collector, generating an avatar. The future appearance includes changes in health, body shape, and lifestyle habits. The generator analyzes the user's current data and realistically recreates what the user will look like in five and ten years. Step 3: The advice providing unit provides personalized advice based on the avatar generated by the generation unit. The advice includes dietary advice, health advice, mental health advice, etc. The advice providing unit provides advice in the form of a chatbot. For example, as dietary advice, it suggests a healthy meal menu based on the user's dietary data, and as health advice, it suggests an appropriate exercise program based on the user's exercise data. Furthermore, with regard to mental health, it analyzes the user's stress level and mood fluctuations and provides appropriate advice.

[0062] (Example 2) An AI butler system according to an embodiment of the present invention predicts a user's future appearance based on their lifestyle data and provides personalized advice. The AI ​​butler system collects the user's lifestyle data, and a generation AI predicts their future appearance and generates an avatar. The generated avatar provides personalized advice in the form of a chatbot based on the user's selection. For example, the AI ​​butler system collects data such as the user's current age, exercise, diet, and sleep. Then, based on the collected data, it generates an avatar that realistically depicts what the user will look like in five or ten years' time using real-time image generation technology. The generated avatar provides personalized advice in the form of a chatbot based on the user's selection. For example, the AI ​​butler system may suggest healthy meal plans based on the user's dietary data as dietary advice. It may also suggest appropriate exercise programs based on the user's exercise data as health advice. Furthermore, the AI ​​butler system analyzes the user's stress level and mood fluctuations to provide appropriate advice for mental health. This allows the user to obtain useful information for their daily lives. The AI ​​butler system also uses a generation AI to create individual fitness routines and exercises based on the user's exercise data, providing an exercise program perfectly tailored to the user's goals and physical condition. Daily information is collected in conjunction with smartphones and IoT devices, and dietary information is identified from photos by the AI ​​generator and collected as data. This allows for a detailed understanding of the user's lifestyle data. By living a healthy lifestyle, the avatar's appearance will also become younger, and visual feedback is provided. For example, if the user continues to eat healthy and exercise, the avatar's appearance will become younger, motivating the user. This allows the user to maintain a healthy lifestyle. This allows the AI ​​butler system to predict the user's future appearance based on the user's lifestyle data and provide personalized advice. For example, if the user continues to eat healthy and exercise, the avatar's appearance will become younger, motivating the user. This allows the user to maintain a healthy lifestyle.

[0063] The AI ​​butler system according to the embodiment includes a collection unit, a generation unit, and an advice provision unit. The collection unit collects lifestyle data of a user. The lifestyle data includes, but is not limited to, diet, exercise, sleep, and stress levels. The collection unit collects daily lifestyle data of the user in cooperation with, for example, a smartphone or an IoT device. For example, a smartwatch is used to collect exercise data, and a smartphone app is used to take photos of meals. The generation AI analyzes the photos and determines the contents of the meals. The generation unit uses the generation AI to predict the user's future appearance based on the data collected by the collection unit and generate an avatar. The future appearance includes, but is not limited to, health status, body shape, and lifestyle changes. For example, the generation unit analyzes the user's current data and predicts the user's future appearance. The generation AI can realistically recreate the user's appearance five or ten years from now based on the user's current data. The advice provision unit provides personalized advice based on the avatar generated by the generation unit. The advice includes, but is not limited to, dietary advice, health advice, and mental health advice. The advice providing unit provides advice, for example, in the form of a chatbot. For example, as dietary advice, it suggests a healthy meal menu based on the user's dietary data. As health advice, it suggests an appropriate exercise program based on the user's exercise data. Furthermore, for mental health, it analyzes the user's stress level and mood fluctuations and provides appropriate advice. This allows the AI ​​butler system according to the embodiment to predict the user's future appearance based on the user's lifestyle habit data and provide personalized advice. For example, if the user continues to eat healthily and exercise, the avatar's appearance will become younger, which will motivate the user. This allows the user to maintain a healthy lifestyle.

[0064] The collection unit can collect daily life data of a user in cooperation with a smartphone or an IoT device. Examples of smartphones or IoT devices include, but are not limited to, smartwatches, fitness trackers, and smart speakers. The collection unit can collect exercise data using, for example, a smartwatch. For example, the collection unit can collect data such as the number of steps, heart rate, and calories burned. The collection unit can also take photos of meals using a smartphone app and have the generation AI analyze the photos to determine the contents of the meals. For example, the collection unit can determine the details of the meals using the resolution of the photos and an analysis algorithm. This allows for efficient collection of daily life data of a user by cooperation with a smartphone or an IoT device. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input exercise data acquired by a smartwatch into the generation AI and have the generation AI analyze the exercise data.

[0065] The collection unit can collect exercise data using a smartwatch, take photos of meals using a smartphone app, and have the generation AI analyze the photos to determine the contents of the meals. Exercise data includes, but is not limited to, the number of steps, heart rate, and calories burned. The collection unit can collect exercise data using, for example, a smartwatch. For example, the collection unit collects data such as the number of steps, heart rate, and calories burned. The collection unit can also take photos of meals using a smartphone app, and have the generation AI analyze the photos to determine the contents of the meals. For example, the collection unit can determine the details of the meals using the resolution of the photos and an analysis algorithm. This allows detailed collection of exercise data and dietary data using the smartwatch and smartphone app. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input exercise data acquired by the smartwatch into the generation AI and have the generation AI analyze the exercise data.

[0066] The generation unit can analyze the user's current data and predict their future appearance. Current data includes, but is not limited to, for example, their current health status, lifestyle habits, and past data. The generation unit, for example, analyzes the user's current data and predicts their future appearance. The generation AI can realistically recreate what the user will look like in five or ten years based on the user's current data. For example, the generation unit can realistically recreate what the user will look like in five or ten years if they continue their current lifestyle habits. This allows the user to visually confirm what they will look like in the future. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's current data into the generation AI and have the generation AI predict their future appearance.

[0067] The advice providing unit can provide dietary advice, health advice, and mental health advice in the form of a chatbot. Examples of chatbots include, but are not limited to, text-based, voice-based, and dialogue-based advice. The advice providing unit provides advice in the form of a chatbot. For example, as dietary advice, it may suggest a healthy meal menu based on the user's dietary data. As health advice, it may suggest an appropriate exercise program based on the user's exercise data. Furthermore, for mental health, it may analyze the user's stress level and mood fluctuations and provide appropriate advice. Thus, by providing advice in the form of a chatbot, the user can easily receive advice. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit may input the user's dietary data into a generation AI and cause the generation AI to generate dietary advice.

[0068] The advice providing unit can use the generation AI to create individual fitness routines and exercises based on the user's exercise data, providing an exercise program suited to the user's goals and condition. The exercise program can include, but is not limited to, fitness routines, exercise types, and training frequency. The advice providing unit can create individual fitness routines and exercises based on the user's exercise data, providing an exercise program perfectly suited to the user's goals and condition. For example, if the user is trying to lose weight, the generation AI can analyze the user's exercise data and suggest an effective exercise program. This allows the user to practice an exercise program that suits them. Some or all of the above-described processing in the advice providing unit can be performed using, for example, AI, or without AI. For example, the advice providing unit can input the user's exercise data into the generation AI and have the generation AI generate an exercise program.

[0069] The generation unit can provide visual feedback that rejuvenates the appearance of the avatar as the user lives a healthy lifestyle. Examples of visual feedback include, but are not limited to, avatar changes, graph displays, and animations. For example, the generation unit can provide visual feedback that rejuvenates the appearance of the avatar as the user continues to eat healthy and exercise. For example, the generation unit can update the appearance of the avatar in real time based on the user's lifestyle data and visually show the effects of a healthy lifestyle. This can motivate the user to maintain a healthy lifestyle. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's lifestyle data into the generation AI and cause the generation AI to update the avatar.

[0070] The collection unit can estimate the user's emotions and adjust the timing of collecting life data based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is feeling stressed, the collection unit can reduce the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also increase the collection timing to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can shorten the collection timing to quickly collect the minimum amount of data necessary. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] The collection unit can analyze the user's past lifestyle data and select an appropriate collection method. Examples of past lifestyle data include, but are not limited to, past health checkup results, exercise history, and diet records. For example, the collection unit collects data by preferentially using a device that the user has used in the past. The collection unit can also select the most efficient collection method based on the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection patterns and determine the optimal collection timing. This allows the optimal collection method to be selected by analyzing the past data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past data into a generation AI and have the generation AI select the optimal collection method.

[0072] When collecting lifestyle data, the collection unit can filter the data based on the user's current health condition and lifestyle rhythm. Filtering methods include, but are not limited to, data importance, relevance, and timestamps. For example, if the user is in poor health, the collection unit can reduce the amount of data collected to reduce the burden on the user. The collection unit can also adjust the type of data collected to match the user's lifestyle rhythm. Furthermore, the collection unit can prioritize the collection of specific data based on the user's health condition. This improves the quality of the collected data by filtering data based on the user's health condition and lifestyle rhythm. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's health condition data into a generation AI and have the generation AI perform filtering.

[0073] When collecting life data, the collection unit can select an appropriate collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the user prefers text input, the collection unit can preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can preferentially collect image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0074] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. Criteria for determining data priority include, but are not limited to, the importance, urgency, and relevance of the data. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed lifestyle data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting the minimum amount of data necessary. This allows important data to be collected preferentially by determining the priority of data 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0075] When collecting lifestyle data, the collection unit can prioritize collecting relevant data based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting lifestyle data related to the user's home. This improves the usefulness of the data by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect relevant data.

[0076] When collecting lifestyle data, the collection unit can analyze the user's social media activities and collect related data. Social media activities include, but are not limited to, post content, number of likes, and comment content. For example, the collection unit can collect data related to locations where the user checked in on social media. The collection unit can also analyze the user's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0077] When collecting lifestyle data, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal collection method. Furthermore, the collection unit can customize the collection timing and method based on the user's feedback. This allows the collection method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0078] The generation unit can estimate the user's emotions and adjust the avatar generation method based on the estimated user's emotions. Avatar generation methods include, but are not limited to, 3D modeling, image generation, and text generation. For example, if the user is relaxed, the generation unit can generate an avatar that moves at a leisurely pace. If the user is in a hurry, the generation unit can also generate an avatar that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an avatar with visually stimulating effects. This allows for the generation of a more personalized avatar by adjusting the avatar generation method according to the user's emotions. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the avatar generation method.

[0079] When generating an avatar, the generation unit can adjust the specificity of the future appearance based on the user's lifestyle data. The specificity of the future appearance can include, but is not limited to, detailed health predictions, lifestyle changes, and visual accuracy. For example, if the user has a healthy lifestyle, the generation unit can generate a detailed future appearance. Furthermore, if the user has an irregular lifestyle, the generation unit can generate a simplified future appearance. Furthermore, the generation unit can generate a future appearance with an optimal level of detail based on the user's lifestyle data. By adjusting the level of detail based on the lifestyle data, a more realistic future appearance can be generated. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's lifestyle data into the generation AI and cause the generation AI to adjust the specificity of the future appearance.

[0080] When generating an avatar, the generation unit can apply an appropriate generation algorithm depending on the user's health data. Examples of generation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, if the user's health data is good, the generation unit can apply a generation algorithm that emphasizes a healthy appearance. Furthermore, if the user's health data has deteriorated, the generation unit can apply a generation algorithm that indicates room for improvement. Furthermore, the generation unit can select an optimal generation algorithm based on the user's health data. This allows for the generation of a more appropriate avatar by adjusting the generation algorithm depending on the health data. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's health data into the generation AI and cause the generation AI to apply the generation algorithm.

[0081] When generating an avatar, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. Past generation results include, but are not limited to, past avatar images and the accuracy of generated data. For example, the generation unit can analyze the user's past generation results and incorporate feedback to improve accuracy. The generation unit can also adjust optimal generation parameters based on the user's past generation results. Furthermore, the generation unit can improve the generation algorithm by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0082] The generation unit can estimate the user's emotions and adjust the appearance of the avatar based on the estimated user's emotions. The avatar's appearance includes, but is not limited to, facial features, body shape, and clothing. For example, if the user is relaxed, the generation unit can generate an avatar with a calm expression. Furthermore, if the user is excited, the generation unit can generate an avatar with a lively expression. Furthermore, if the user is stressed, the generation unit can generate an avatar with a calm expression. This allows the avatar's appearance to be adjusted according to the user's emotions, resulting in a more personalized avatar. The emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the avatar's appearance.

[0083] When generating avatars, the generation unit can determine the order of generation based on changes in the user's lifestyle. Criteria for determining the order of generation include, but are not limited to, data importance, urgency, and relevance. For example, if the user's lifestyle changes significantly, the generation unit can prioritize generating an avatar that reflects the change. Furthermore, if the user's lifestyle remains stable, the generation unit can periodically update the avatar. Furthermore, the generation unit can determine the optimal generation timing based on changes in the user's lifestyle. This allows avatars to be generated at more appropriate times by determining the generation priority based on changes in the lifestyle. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's lifestyle data into the generation AI and have the generation AI determine the order of generation.

[0084] When generating avatars, the generation unit can adjust the generation order by referring to the user's related data. Related data includes, but is not limited to, past generation results and user feedback. For example, the generation unit can prioritize reference to the user's health data to determine the avatar generation order. The generation unit can also adjust the avatar generation order based on the user's exercise data. Furthermore, the generation unit can optimize the avatar generation order by referring to the user's dietary data. This allows the generation order to be optimized by referring to the related data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's related data into the generation AI and have the generation AI adjust the generation order.

[0085] When generating an avatar, the generation unit can adjust the specificity of the avatar according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the generation unit generates a detailed avatar if the user has expertise. The generation unit can also generate a simplified avatar if the user is a beginner. Furthermore, the generation unit can generate an avatar with an optimal level of detail based on the user's level of expertise. By adjusting the level of detail according to the level of expertise, an optimal avatar for the user can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the specificity of the avatar.

[0086] The advice providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. The way the advice is expressed can include, but is not limited to, text, audio, visuals, and the like. For example, if the user is nervous, the advice providing unit can provide the advice in a calm manner. Furthermore, if the user is relaxed, the advice providing unit can provide the advice in a friendly manner. Furthermore, if the user is in a hurry, the advice providing unit can provide the advice in a concise and quick manner. This allows for more effective advice to be provided by adjusting the way the advice is expressed based on 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 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 advice providing unit can be performed using, for example, an AI, or without an AI. For example, the advice providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0087] When providing advice, the advice providing unit can adjust the specificity of the advice based on the user's lifestyle data. Examples of the specificity of the advice include, but are not limited to, detailed explanations, concise suggestions, and step-by-step guides. For example, the advice providing unit can provide detailed advice if the user's lifestyle is healthy. Furthermore, the advice providing unit can provide simplified advice if the user's lifestyle is irregular. Furthermore, the advice providing unit can provide advice with an optimal level of detail based on the user's lifestyle data. This allows for more appropriate advice to be provided by adjusting the level of detail based on the lifestyle data. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's lifestyle data into a generation AI and cause the generation AI to adjust the specificity of the advice.

[0088] When providing advice, the advice providing unit can apply an appropriate advice algorithm depending on the user's health data. Examples of advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, if the user's health data is good, the advice providing unit can apply an algorithm that provides advice for maintaining health. Furthermore, if the user's health data is deteriorating, the advice providing unit can also apply an algorithm that provides advice for improvement. Furthermore, the advice providing unit can select an optimal advice algorithm based on the user's health data. This allows for more appropriate advice to be provided by adjusting the advice algorithm depending on the health data. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's health data into a generation AI and cause the generation AI to apply an advice algorithm.

[0089] When providing advice, the advice providing unit can improve the accuracy of the advice by referring to the user's past advice results. Past advice results include, but are not limited to, for example, user feedback and the effect of the advice. For example, the advice providing unit analyzes the user's past advice results and reflects feedback to improve accuracy. The advice providing unit can also adjust optimal advice parameters based on the user's past advice results. Furthermore, the advice providing unit can also improve 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 past advice results. Some or all of the above-mentioned processing in the advice providing unit may be performed, for example, using AI or without AI. For example, the advice providing unit can input the user's past advice results into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0090] The advice providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. Examples of the length of the advice include, but are not limited to, short advice and detailed advice. For example, if the user is in a hurry, the advice providing unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the advice providing unit can provide longer advice with detailed explanations. Furthermore, if the user is excited, the advice providing unit can provide advice with visually stimulating effects. This allows for more effective advice to be provided by adjusting the length of the advice according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the advice providing unit may be performed using, for example, an AI. For example, the advice providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0091] When providing advice, the advice providing unit can determine the order of advice based on changes in the user's lifestyle. Criteria for determining the order of advice include, but are not limited to, data importance, urgency, and relevance. For example, if the user's lifestyle changes significantly, the advice providing unit can prioritize providing advice that reflects the change. The advice providing unit can also periodically update the advice if the user's lifestyle is stable. Furthermore, the advice providing unit can determine the optimal timing for providing advice based on changes in the user's lifestyle. This allows advice to be provided at more appropriate times by determining the priority of advice based on changes in lifestyle. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's lifestyle data into a generation AI and have the generation AI determine the order of advice.

[0092] When providing advice, the advice providing unit can adjust the order of advice by referring to the user's related data. Related data includes, for example, past advice results and user feedback, but is not limited to these examples. For example, the advice providing unit can preferentially refer to the user's health data to determine the order of advice. The advice providing unit can also adjust the order of advice based on the user's exercise data. Furthermore, the advice providing unit can also optimize the order of advice by referring to the user's dietary data. In this way, the order of advice can be optimized by referring to the related data. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's related data into the generation AI and cause the generation AI to adjust the order of advice.

[0093] When providing advice, the advice providing unit can adjust the use of technical terminology in the advice depending on the user's level of expertise. Examples of technical terminology include, but are not limited to, technical terms, industry jargon, and abbreviations. For example, if the user has specialized knowledge, the advice providing unit can provide advice using detailed technical terminology. Furthermore, if the user is a beginner, the advice providing unit can also provide advice using simplified terminology. Furthermore, the advice providing unit can provide advice using optimal terminology based on the user's level of expertise. By adjusting the use of technical terminology depending on the level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and advice providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized by the specific processing unit 290 of the data processing device 12, predicts the user's future appearance based on the collected data, and generates an avatar. The advice providing unit is realized, for example, by the control unit 46A of the smart device 14, and provides personalized advice in the form of a chatbot based on the generated avatar. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and advice providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's future appearance based on the collected data and generates an avatar. The advice providing unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides personalized advice in the form of a chatbot based on the generated avatar. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and advice providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the user's future appearance based on the collected data and generates an avatar. The advice providing unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides personalized advice in the form of a chatbot based on the generated avatar. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and advice provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects lifestyle habit data of the user using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the future appearance of the user based on the collected data and generates an avatar. The advice provision unit is realized, for example, by the control unit 46A of the robot 414, and provides personalized advice in the form of a chatbot based on the generated avatar.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The AI ​​butler system can also analyze the influence of a user's social network based on the user's lifestyle data and provide advice. For example, the collection unit collects data on the user's social media activities and friendships, and the generation unit predicts social influences on the user's lifestyle based on this data. The advice provision unit can suggest what kind of social support the user needs to live a healthy lifestyle. For example, if the user is motivated by exercising with friends, the system can suggest an exercise plan with those friends. Also, if the user is feeling stressed, the system can recommend interacting with friends who can help them relax. This allows the user to utilize their social network to maintain a healthier lifestyle.

[0096] The AI ​​butler system can also suggest activities related to the user's hobbies and interests based on the user's lifestyle data. For example, the collection unit collects data on the user's hobbies and interests, and the generation unit predicts activities the user will enjoy based on this data. The advice provision unit can suggest activities related to the user's hobbies and interests to help the user live a healthy lifestyle. For example, if the user likes outdoor activities, the system can suggest hiking and camping plans. Also, if the user enjoys cooking, the system can suggest healthy recipes. This allows the user to maintain a healthier lifestyle by utilizing their hobbies and interests.

[0097] The AI ​​butler system can also analyze the user's sleep patterns based on the user's lifestyle data and provide advice for improvement. For example, the collection unit collects the user's sleep data, and the generation unit predicts the user's sleep patterns based on this data. The advice provision unit can provide specific advice to help the user get better sleep. For example, if the user tends to stay up late, the system can suggest getting into the habit of going to bed early and getting up early. Also, if the user is unable to sleep due to stress, the system can suggest ways to relax. This allows the user to improve the quality of their sleep and maintain a healthy lifestyle.

[0098] The AI ​​butler system can also analyze the impact of the user's work environment based on the user's lifestyle data and provide advice. For example, the collection unit collects data about the user's work environment, and the generation unit predicts the impact of the work environment on the user's lifestyle based on this data. The advice provision unit can suggest ways to improve the work environment so that the user can live a healthy lifestyle. For example, if the user sits for long periods of time, the system can suggest that the user stand up and stretch regularly. Also, if the user feels stressed at work, the system can suggest ways to relax. This allows the user to improve their work environment and maintain a healthy lifestyle.

[0099] The AI ​​butler system can also support the user's seasonal health management based on the user's lifestyle data. For example, the collection unit collects the user's seasonal lifestyle data, and the generation unit predicts seasonal health risks based on this data. The advice provision unit can offer specific advice to help the user maintain their health for each season. For example, in winter, it can suggest diet and exercise to prevent colds, and in summer, it can suggest measures to prevent heatstroke. It can also suggest measures to prevent pollen during hay fever season. This allows the user to respond to seasonal health risks and maintain a healthy lifestyle.

[0100] The AI ​​butler system can estimate the user's emotions and provide advice to improve the user's motivation based on the estimated user emotions. For example, the collection unit collects the user's emotional data, and the generation unit predicts the user's emotional state based on this data. The advice provision unit can provide specific advice to help the user maintain their motivation. For example, if the user is feeling down, the system can provide an encouraging message. Also, if the user is losing motivation, the system can suggest goal setting or tasks that will give the user a sense of accomplishment. This allows the user to receive support according to their emotions and maintain their motivation.

[0101] The AI ​​butler system can estimate a user's emotions and support the user's stress management based on the estimated user emotions. For example, the collection unit collects the user's emotional data, and the generation unit predicts the user's stress level based on this data. The advice provision unit can provide specific advice to help the user reduce stress. For example, if the user is feeling high stress, the system can suggest ways to relax or activities to relieve stress. If the user cannot identify the cause of stress, the system can also suggest keeping a stress diary. This allows the user to effectively manage stress and maintain a healthy lifestyle.

[0102] The AI ​​butler system can estimate a user's emotions and provide advice to support the user's mental health based on the estimated emotions. For example, the collection unit collects the user's emotional data, and the generation unit predicts the user's mental health state based on this data. The advice provision unit can provide the user with specific advice to maintain their mental health. For example, if the user is feeling anxious, the system can suggest breathing exercises or meditation to help them relax. Also, if the user is feeling lonely, the system can encourage them to interact with friends and family. This allows the user to effectively manage their mental health and maintain a healthy lifestyle.

[0103] The AI ​​butler system can estimate a user's emotions and support the user in choosing meals based on the estimated user emotions. For example, the collection unit collects the user's emotional data, and the generation unit predicts the influence of emotions on the user's meal choices based on this data. The advice provision unit can provide specific advice to help the user choose healthy meals. For example, if the user is feeling stressed, the system can suggest ingredients and recipes that will help relieve stress. Also, if the user is tired, the system can suggest meals to replenish energy. This allows the user to make meal choices according to their emotions and maintain a healthy lifestyle.

[0104] The AI ​​butler system can estimate the user's emotions and adjust the user's exercise program based on the estimated user emotions. For example, the collection unit collects the user's emotional data, and the generation unit predicts the impact of emotions on the user's exercise based on this data. The advice provision unit can provide specific advice to help the user exercise effectively. For example, if the user is tired, the system can suggest light stretching or relaxing exercises. Also, if the user is energetic, the system can suggest a more strenuous exercise program. This allows the user to implement an exercise program that suits their emotions and maintain a healthy lifestyle.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The collection unit collects the user's lifestyle data. This lifestyle data includes diet, exercise, sleep, stress levels, etc. The collection unit works with smartphones and IoT devices to collect the user's daily lifestyle data. For example, a smartwatch can be used to collect exercise data, and a smartphone app can be used to take photos of meals. The generation AI then analyzes the photos to determine the contents of the meal. Step 2: The generator uses AI to predict the user's future appearance based on the data collected by the collector, generating an avatar. The future appearance includes changes in health, body shape, and lifestyle habits. The generator analyzes the user's current data and realistically recreates what the user will look like in five and ten years. Step 3: The advice providing unit provides personalized advice based on the avatar generated by the generation unit. The advice includes dietary advice, health advice, mental health advice, etc. The advice providing unit provides advice in the form of a chatbot. For example, as dietary advice, it suggests a healthy meal menu based on the user's dietary data, and as health advice, it suggests an appropriate exercise program based on the user's exercise data. Furthermore, with regard to mental health, it analyzes the user's stress level and mood fluctuations and provides appropriate advice.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects lifestyle habit data of a user; a generation unit that predicts a future appearance based on the data collected by the collection unit and generates an avatar; an advice providing unit that provides personalized advice based on the avatar generated by the generation unit. A system characterized by:

2. The collecting unit Collecting data on users' daily lives in collaboration with smartphones or IoT devices 2. The system of claim 1.

3. The collecting unit Exercise data is collected using a smartwatch, photos of meals are taken using a smartphone app, and the AI ​​analyzes the photos to determine the contents of the meal.

2. The system of claim 1.

4. The generation unit Analyze current user data and predict future behavior 2. The system of claim 1.

5. The advice providing unit Providing dietary, health and mental health advice in the form of a chatbot 2. The system of claim 1.

6. The advice providing unit Generative AI creates personalized fitness routines and exercises based on the user's exercise data, providing an exercise program suited to the user's goals and condition.

2. The system of claim 1.

7. The generation unit Provides visual feedback that encourages users to lead a healthier lifestyle and rejuvenate their avatar's appearance 2. The system of claim 1.

8. The collecting unit Estimates user emotions and adjusts the timing of collecting life data based on the estimated user emotions.

2. The system of claim 1.

Images