System

Abstract

An object of the system according to the embodiment is for the user to find an ideal self-image by changing self-recognition.SOLUTION: A system according to an embodiment includes a personality analysis unit, a self-image generation unit, and an advice providing unit. The personality analysis unit analyzes the personality, preferences, and values of the user. The self-image generation unit generates an ideal self-image on the basis of the result analyzed by the personality analysis unit. The advice providing unit provides daily advice on the basis of the ideal self-image generated by the self-image 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 techniques have had the problem of making it difficult for users to change their self-perception and find their ideal self-image.

[0005] The system according to the embodiment aims to help users change their self-perception and find their ideal self-image. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality analysis unit, a self-image generation unit, and an advice provision unit. The personality analysis unit analyzes the user's personality, preferences, and values. The self-image generation unit generates an ideal self-image based on the results of the analysis by the personality analysis unit. The advice provision unit provides daily advice based on the ideal self-image generated by the self-image generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to change their self-perception and find their ideal self-image. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​mirror service according to an embodiment of the present invention is a system that visualizes the ideal self-image that a user aspires to and provides daily advice on how to act like that person. This allows the AI ​​mirror service to increase self-acceptance and enable users to take social steps with confidence.

[0029] The AI ​​mirror service according to the embodiment includes a personality analysis unit, a self-image generation unit, and an advice provision unit. The personality analysis unit analyzes the user's personality, preferences, and values. For example, the personality analysis unit analyzes questionnaires, diary entries, and social media posts entered by the user to understand the user's personality, preferences, and values. The personality analysis unit can also analyze the user's personality and preferences based on behavioral data. For example, the personality analysis unit analyzes the user's purchase history and browsing history to extract latent interests and values. Furthermore, the personality analysis unit can analyze the user's tone of voice and facial expressions to grasp the user's emotional state in real time and detect changes in personality and preferences. The self-image generation unit generates an ideal self-image based on the results of the analysis by the personality analysis unit. For example, the self-image generation unit considers the user's ideal appearance and goals and generates a visual that matches them. The self-image generation unit can also analyze the user's past photos and videos to generate an ideal self-image that takes into account changes in appearance over time. Furthermore, the self-image generation unit can generate an ideal self-image that matches the user's cultural background and lifestyle. The advice provision unit provides daily advice based on the ideal self-image generated by the self-image generation unit. For example, the advice provision unit specifically suggests what actions the user should take and what habits they should develop toward their goals. The advice provision unit can also update the advice based on the user's progress and feedback to always provide optimal support. Furthermore, the advice provision unit can provide advice in real time according to the user's emotional state to maintain motivation. As a result, the AI ​​mirror service according to the embodiment can improve the user's self-acceptance and take social steps with confidence. For example, by receiving daily advice, the user can take specific actions to approach their ideal self-image. Furthermore, by improving self-acceptance, the user can gain confidence and take social steps.

[0030] The personality analysis unit can analyze a user's past purchase history and browsing history to extract latent interests and values. For example, the personality analysis unit analyzes a user's purchase history on an online shopping site to extract latent interests from the categories and brands of purchased products. For example, preferences can be identified based on purchase histories of fashion items and gadgets. The personality analysis unit can also analyze a user's browsing history to extract values ​​from websites visited and browsing time. For example, a user's interests and values ​​can be identified based on websites the user frequently visits and content that the user spends the most time viewing. This enables more accurate personality analysis by analyzing a user's past purchase history and browsing history to extract latent interests and values.

[0031] The personality analysis unit can analyze the user's physical health data and find correlations with their personality and preferences. For example, the personality analysis unit can analyze exercise data collected from the user's fitness tracker to identify their personality and preferences based on their exercise habits and activity levels. For example, it can evaluate their health orientation based on how often they run or do yoga. The personality analysis unit can also analyze the user's heart rate and sleep data and find correlations with their personality and preferences. For example, it can evaluate the user's stress level and relaxation level based on heart rate variability and sleep quality. This allows for more accurate personality analysis by analyzing the user's physical health data and finding correlations with their personality and preferences.

[0032] The personality analysis unit can collect feedback from the user's friends and family and provide a complementary evaluation of the user's personality and values ​​from a third-party perspective. The personality analysis unit, for example, conducts a survey of the user's friends and family and collects feedback on the user's personality and values. For example, it evaluates the user's strengths and weaknesses as perceived by the friends and family. The personality analysis unit can also provide a complementary evaluation of the personality and values ​​from a third-party perspective based on the feedback from the friends and family. For example, it provides a complementary evaluation of characteristics and values ​​that the user may not notice in their own evaluation. This allows for a more accurate personality analysis by collecting feedback from the user's friends and family and providing a complementary evaluation of the personality and values ​​from a third-party perspective.

[0033] The self-image generation unit can analyze the user's past photos and videos and generate an ideal self-image that takes into account changes in appearance over time. The self-image generation unit, for example, collects the user's past photos and videos and analyzes changes in appearance over time. For example, it generates an ideal self-image based on changes in facial contours and skin condition. The self-image generation unit can also predict the user's future appearance based on the user's past photos and videos and generate an ideal self-image. For example, it generates a visual by predicting the user's future appearance, taking into account the ideal figure the user is aiming for. In this way, a more realistic self-image can be provided by analyzing the user's past photos and videos and generating an ideal self-image that takes into account changes in appearance over time.

[0034] The self-image generation unit can take into account the user's cultural background and lifestyle and generate an ideal self-image that matches those. The self-image generation unit, for example, takes into account the user's cultural background and generates an ideal self-image that matches those. For example, it generates visuals that reflect the culture and traditions of the user's country or region. The self-image generation unit can also take into account the user's lifestyle and generate an ideal self-image that matches those. For example, it generates visuals that reflect the user's living habits and hobbies. In this way, by taking into account the user's cultural background and lifestyle and generating an ideal self-image that matches those, it is possible to provide a more personalized self-image.

[0035] The self-image generation unit generates a user's ideal self-image as a 3D model and allows the user to experience it in a virtual reality environment. The self-image generation unit, for example, constructs a system that generates a user's ideal self-image as a 3D model and allows the user to experience it in a VR environment. For example, the user experiences their ideal self-image by wearing a VR headset. The self-image generation unit can also generate a user's ideal self-image as a 3D model and provide an interactive experience. For example, the user can act in a virtual space as their ideal self-image. In this way, a more realistic self-image experience can be provided by generating a user's ideal self-image as a 3D model and allowing the user to experience it in a virtual reality environment.

[0036] When generating an ideal self-image, the self-image generation unit can provide variations specialized for the user's occupation or hobby. The self-image generation unit generates an ideal self-image specialized for the user's occupation, for example. For example, visuals are provided according to occupations such as doctor or engineer. The self-image generation unit can also generate an ideal self-image specialized for the user's hobby. For example, visuals are provided according to hobbies such as sports or art. Furthermore, the self-image generation unit can generate a more personalized self-image by providing variations according to the user's occupation or hobby. As a result, when generating an ideal self-image, variations are provided according to the user's occupation or hobby, making it possible to provide a more personalized self-image.

[0037] The advice providing unit can analyze the user's behavioral history and provide customized advice based on past successful and unsuccessful experiences. The advice providing unit, for example, analyzes the user's behavioral history and provides customized advice based on past successful experiences. For example, advice is provided based on diet methods or study methods that have been successful in the past. The advice providing unit can also provide advice that takes into account past unsuccessful experiences based on the user's behavioral history. For example, advice is provided to prevent the same failure from being repeated based on lessons learned from a past failed project. This enables more effective support by analyzing the user's behavioral history and providing customized advice based on past successful and unsuccessful experiences.

[0038] The advice providing unit can analyze the user's schedule and task management data and provide advice at the optimal timing. The advice providing unit can, for example, analyze the user's schedule data and provide advice at the optimal timing. For example, it can suggest ways to relax before an important meeting or event. The advice providing unit can also analyze the user's task management data and suggest efficient task management methods. For example, it can set task priorities and provide advice on how to complete tasks efficiently. In this way, by analyzing the user's schedule and task management data and providing advice at the optimal timing, it is possible to support the user's efficient actions.

[0039] The advice providing unit can provide the user with advice through a voice assistant, allowing the user to receive the advice naturally in daily life. The advice providing unit, for example, builds a system that provides the user with advice through a voice assistant. For example, the advice is received in daily life using a smart speaker. The advice providing unit can also enable the user to receive advice through the voice assistant at the timing needed. For example, when the user is getting ready in the morning, advice tailored to the day's schedule is provided. In this way, the user's advice is provided through a voice assistant, allowing the user to receive the advice naturally in daily life, thereby improving convenience for the user.

[0040] The advice providing unit can gamify the user's advice, allowing the user to get closer to their ideal self-image while feeling a sense of accomplishment. The advice providing unit, for example, builds a system that gamifies the user's advice, allowing the user to get closer to their ideal self-image while feeling a sense of accomplishment. For example, points are earned each time the advice is completed. The advice providing unit can also incorporate game elements so that the user can enjoy following the advice. For example, the user can level up or receive a reward by completing the advice. In this way, the user's advice can be gamified, allowing the user to get closer to their ideal self-image while feeling a sense of accomplishment, thereby increasing the user's motivation.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The AI ​​mirror service can also analyze a user's health data and provide advice based on their health condition. For example, it can analyze exercise data collected from the user's fitness tracker and provide advice to encourage exercise if they are not getting enough exercise. It can also analyze the user's heart rate and sleep data and suggest relaxation methods if they are under-stressed. It can also analyze the user's dietary data and suggest dietary improvements if their nutritional balance is unbalanced. This allows for more comprehensive support by providing advice based on the user's health condition.

[0043] The AI ​​mirror service can suggest new hobbies and activities based on the user's hobbies and interests. For example, if a user likes outdoor activities, it can suggest new hiking trails and campsites. If a user is interested in art, it can introduce local art galleries and workshops. It can also suggest online communities and events based on the user's interests. This can enrich the user's life by suggesting new activities based on the user's hobbies and interests.

[0044] The AI ​​mirror service can analyze a user's past behavioral data and provide advice based on their behavioral patterns. For example, it can suggest similar methods based on the user's past successful diet methods. It can also provide advice on how to avoid repeating the same mistakes based on lessons learned from a past failed project. It can also suggest efficient time management methods in daily life based on the user's behavioral patterns. This allows for more effective support by providing advice based on the user's past behavioral data.

[0045] The AI ​​mirror service can suggest environmentally friendly habits based on the user's lifestyle. For example, if the user is aiming for an eco-friendly lifestyle, it can suggest reusable products and energy-efficient home appliances. If the user is aiming for a sustainable diet, it can introduce locally produced organic foods and vegetarian recipes. Furthermore, it can suggest environmentally friendly means of transportation and recycling methods based on the user's lifestyle. In this way, it can support sustainable living by suggesting environmentally friendly habits based on the user's lifestyle.

[0046] The AI ​​mirror service can suggest personalized travel plans based on the user's cultural background and lifestyle. For example, if the user is interested in history and culture, it can suggest travel plans that include historical sites and cultural events. If the user likes nature and outdoor activities, it can also suggest travel plans that include natural parks and hiking trails. Furthermore, it can suggest travel plans to enjoy local food culture and specialties based on the user's lifestyle. This makes it possible to provide a more fulfilling travel experience by suggesting personalized travel plans based on the user's cultural background and lifestyle.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The personality analysis unit analyzes the user's personality, preferences, and values. For example, the personality analysis unit analyzes questionnaires, diary entries, and social media posts entered by the user to understand the user's personality, preferences, and values. The personality analysis unit can also analyze the user's personality and preferences based on behavioral data. For example, the personality analysis unit analyzes the user's purchase history and browsing history to extract latent interests and values. Furthermore, the personality analysis unit can analyze the user's tone of voice and facial expressions to understand their emotional state in real time and detect changes in their personality and preferences. Step 2: The self-image generation unit generates an ideal self-image based on the results of the analysis by the personality analysis unit. For example, the self-image generation unit considers the ideal appearance and goals that the user is aiming for and generates a visual that matches them. The self-image generation unit can also analyze the user's past photos and videos to generate an ideal self-image that takes into account changes in appearance over time. Furthermore, the self-image generation unit can consider the user's cultural background and lifestyle and generate an ideal self-image that matches them. Step 3: The advice providing unit provides daily advice based on the ideal self-image generated by the self-image generating unit. For example, the advice providing unit makes specific suggestions about what actions the user should take and what habits they should develop in order to achieve their goals. The advice providing unit can also update its advice based on the user's progress and feedback, providing optimal support at all times. Furthermore, the advice providing unit can provide advice in real time according to the user's emotional state to maintain motivation.

[0049] (Example 2) The AI ​​mirror service according to an embodiment of the present invention is a system that visualizes the ideal self-image that a user aspires to and provides daily advice on how to act like that person. This allows the AI ​​mirror service to increase self-acceptance and enable users to take social steps with confidence.

[0050] The AI ​​mirror service according to the embodiment includes a personality analysis unit, a self-image generation unit, and an advice provision unit. The personality analysis unit analyzes the user's personality, preferences, and values. For example, the personality analysis unit analyzes questionnaires, diary entries, and social media posts entered by the user to understand the user's personality, preferences, and values. The personality analysis unit can also analyze the user's personality and preferences based on behavioral data. For example, the personality analysis unit analyzes the user's purchase history and browsing history to extract latent interests and values. Furthermore, the personality analysis unit can analyze the user's tone of voice and facial expressions to grasp the user's emotional state in real time and detect changes in personality and preferences. The self-image generation unit generates an ideal self-image based on the results of the analysis by the personality analysis unit. For example, the self-image generation unit considers the user's ideal appearance and goals and generates a visual that matches them. The self-image generation unit can also analyze the user's past photos and videos to generate an ideal self-image that takes into account changes in appearance over time. Furthermore, the self-image generation unit can generate an ideal self-image that matches the user's cultural background and lifestyle. The advice provision unit provides daily advice based on the ideal self-image generated by the self-image generation unit. For example, the advice provision unit specifically suggests what actions the user should take and what habits they should develop toward their goals. The advice provision unit can also update the advice based on the user's progress and feedback to always provide optimal support. Furthermore, the advice provision unit can provide advice in real time according to the user's emotional state to maintain motivation. As a result, the AI ​​mirror service according to the embodiment can improve the user's self-acceptance and take social steps with confidence. For example, by receiving daily advice, the user can take specific actions to approach their ideal self-image. Furthermore, by improving self-acceptance, the user can gain confidence and take social steps.

[0051] The personality analysis unit analyzes the user's voice tone and facial expressions to grasp their emotional state in real time and detect changes in their personality and preferences. For example, when the user speaks in front of a mirror, the personality analysis unit analyzes their voice tone and facial expressions in real time using a camera and microphone. For example, it detects changes in the pitch and tempo of the voice and facial expressions to estimate their emotional state. The personality analysis unit can also detect changes in the user's personality and preferences based on changes in the user's voice tone and facial expressions. For example, if the user is feeling stressed, the personality analysis unit detects these changes and provides appropriate advice. This enables more accurate personality analysis by analyzing the user's voice tone and facial expressions to grasp their emotional state in real time and detect changes in their personality and preferences.

[0052] The personality analysis unit can analyze a user's past purchase history and browsing history to extract latent interests and values. For example, the personality analysis unit analyzes a user's purchase history on an online shopping site to extract latent interests from the categories and brands of purchased products. For example, preferences can be identified based on purchase histories of fashion items and gadgets. The personality analysis unit can also analyze a user's browsing history to extract values ​​from websites visited and browsing time. For example, a user's interests and values ​​can be identified based on websites the user frequently visits and content that the user spends the most time viewing. This enables more accurate personality analysis by analyzing a user's past purchase history and browsing history to extract latent interests and values.

[0053] The personality analysis unit can use the emotion estimation function to analyze the emotion of text entered by the user and reevaluate the personality and preferences based on the emotional fluctuations. The personality analysis unit, for example, analyzes text entered by the user into a social networking site or diary app and uses the emotion estimation function to grasp the emotional fluctuations. For example, it detects positive and negative expressions and evaluates the emotional fluctuations. The personality analysis unit can also reevaluate the personality and preferences based on the emotional fluctuations. For example, if the user is feeling stressed, the personality analysis unit reevaluates the personality and preferences based on the emotional fluctuations and provides appropriate advice. This enables more accurate personality analysis by using the emotion estimation function to analyze the emotion of text entered by the user and reevaluating the personality and preferences based on the emotional fluctuations.

[0054] The personality analysis unit can analyze the user's physical health data and find correlations with their personality and preferences. For example, the personality analysis unit can analyze exercise data collected from the user's fitness tracker to identify their personality and preferences based on their exercise habits and activity levels. For example, it can evaluate their health orientation based on how often they run or do yoga. The personality analysis unit can also analyze the user's heart rate and sleep data and find correlations with their personality and preferences. For example, it can evaluate the user's stress level and relaxation level based on heart rate variability and sleep quality. This allows for more accurate personality analysis by analyzing the user's physical health data and finding correlations with their personality and preferences.

[0055] The personality analysis unit can collect feedback from the user's friends and family and provide a complementary evaluation of the user's personality and values ​​from a third-party perspective. The personality analysis unit, for example, conducts a survey of the user's friends and family and collects feedback on the user's personality and values. For example, it evaluates the user's strengths and weaknesses as perceived by the friends and family. The personality analysis unit can also provide a complementary evaluation of the personality and values ​​from a third-party perspective based on the feedback from the friends and family. For example, it provides a complementary evaluation of characteristics and values ​​that the user may not notice in their own evaluation. This allows for a more accurate personality analysis by collecting feedback from the user's friends and family and providing a complementary evaluation of the personality and values ​​from a third-party perspective.

[0056] The personality analysis unit can use the emotion estimation function to analyze the user's emotional response to the content they watch and identify their preferences and values. The personality analysis unit can, for example, analyze the user's emotional response to the movie or drama they watch and identify their preferences and values. For example, it can analyze facial expressions and voices while watching and calculate an emotion score. The personality analysis unit can also analyze the user's emotional response to the music or video they watch and identify their preferences and values. For example, it can analyze the user's emotional response to the rhythm or melody of the music and identify the user's musical preferences. This allows for more accurate personality analysis by using the emotion estimation function to analyze the user's emotional response to the content they watch and identify their preferences and values.

[0057] The self-image generation unit can analyze the user's past photos and videos and generate an ideal self-image that takes into account changes in appearance over time. The self-image generation unit, for example, collects the user's past photos and videos and analyzes changes in appearance over time. For example, it generates an ideal self-image based on changes in facial contours and skin condition. The self-image generation unit can also predict the user's future appearance based on the user's past photos and videos and generate an ideal self-image. For example, it generates a visual by predicting the user's future appearance, taking into account the ideal figure the user is aiming for. In this way, a more realistic self-image can be provided by analyzing the user's past photos and videos and generating an ideal self-image that takes into account changes in appearance over time.

[0058] The self-image generation unit can take into account the user's cultural background and lifestyle and generate an ideal self-image that matches those. The self-image generation unit, for example, takes into account the user's cultural background and generates an ideal self-image that matches those. For example, it generates visuals that reflect the culture and traditions of the user's country or region. The self-image generation unit can also take into account the user's lifestyle and generate an ideal self-image that matches those. For example, it generates visuals that reflect the user's living habits and hobbies. In this way, by taking into account the user's cultural background and lifestyle and generating an ideal self-image that matches those, it is possible to provide a more personalized self-image.

[0059] The self-image generation unit can use the emotion estimation function to analyze the emotions the user has toward the ideal self-image and generate an ideal self-image that elicits positive emotions. The self-image generation unit, for example, analyzes the emotions the user has toward the ideal self-image and generates a self-image that elicits positive emotions. For example, it analyzes the facial expression and voice of the user when looking at the ideal self-image. The self-image generation unit can also generate a self-image that elicits positive emotions based on the user's emotional response. For example, it generates a visual that elicits positive emotions based on the emotion score when the user looks at the ideal self-image. In this way, by using the emotion estimation function to analyze the emotions the user has toward the ideal self-image and generate a self-image that elicits positive emotions, it is possible to increase user satisfaction.

[0060] The self-image generation unit generates a user's ideal self-image as a 3D model and allows the user to experience it in a virtual reality environment. The self-image generation unit, for example, constructs a system that generates a user's ideal self-image as a 3D model and allows the user to experience it in a VR environment. For example, the user experiences their ideal self-image by wearing a VR headset. The self-image generation unit can also generate a user's ideal self-image as a 3D model and provide an interactive experience. For example, the user can act in a virtual space as their ideal self-image. In this way, a more realistic self-image experience can be provided by generating a user's ideal self-image as a 3D model and allowing the user to experience it in a virtual reality environment.

[0061] When generating an ideal self-image, the self-image generation unit can provide variations specialized for the user's occupation or hobby. The self-image generation unit generates an ideal self-image specialized for the user's occupation, for example. For example, visuals are provided according to occupations such as doctor or engineer. The self-image generation unit can also generate an ideal self-image specialized for the user's hobby. For example, visuals are provided according to hobbies such as sports or art. Furthermore, the self-image generation unit can generate a more personalized self-image by providing variations according to the user's occupation or hobby. As a result, when generating an ideal self-image, variations are provided according to the user's occupation or hobby, making it possible to provide a more personalized self-image.

[0062] The advice providing unit can analyze the user's behavioral history and provide customized advice based on past successful and unsuccessful experiences. The advice providing unit, for example, analyzes the user's behavioral history and provides customized advice based on past successful experiences. For example, advice is provided based on diet methods or study methods that have been successful in the past. The advice providing unit can also provide advice that takes into account past unsuccessful experiences based on the user's behavioral history. For example, advice is provided to prevent the same failure from being repeated based on lessons learned from a past failed project. This enables more effective support by analyzing the user's behavioral history and providing customized advice based on past successful and unsuccessful experiences.

[0063] The advice providing unit can analyze the user's schedule and task management data and provide advice at the optimal timing. The advice providing unit can, for example, analyze the user's schedule data and provide advice at the optimal timing. For example, it can suggest ways to relax before an important meeting or event. The advice providing unit can also analyze the user's task management data and suggest efficient task management methods. For example, it can set task priorities and provide advice on how to complete tasks efficiently. In this way, by analyzing the user's schedule and task management data and providing advice at the optimal timing, it is possible to support the user's efficient actions.

[0064] The advice providing unit uses the emotion estimation function to provide advice in real time according to the user's emotional state, thereby maintaining motivation. The advice providing unit, for example, uses the emotion estimation function to provide advice in real time according to the user's emotional state. For example, if the user is feeling stressed, the advice providing unit may suggest a relaxation method. The advice providing unit may also provide advice to maintain motivation based on the user's emotional state. For example, if the user is working hard to achieve a goal, the advice providing unit may send an encouraging message. In this way, the emotion estimation function can be used to provide advice in real time according to the user's emotional state, maintaining motivation and supporting the user in achieving their goal.

[0065] The advice providing unit can provide the user with advice through a voice assistant, allowing the user to receive the advice naturally in daily life. The advice providing unit, for example, builds a system that provides the user with advice through a voice assistant. For example, the advice is received in daily life using a smart speaker. The advice providing unit can also enable the user to receive advice through the voice assistant at the timing needed. For example, when the user is getting ready in the morning, advice tailored to the day's schedule is provided. In this way, the user's advice is provided through a voice assistant, allowing the user to receive the advice naturally in daily life, thereby improving convenience for the user.

[0066] The advice providing unit can gamify the user's advice, allowing the user to get closer to their ideal self-image while feeling a sense of accomplishment. The advice providing unit, for example, builds a system that gamifies the user's advice, allowing the user to get closer to their ideal self-image while feeling a sense of accomplishment. For example, points are earned each time the advice is completed. The advice providing unit can also incorporate game elements so that the user can enjoy following the advice. For example, the user can level up or receive a reward by completing the advice. In this way, the user's advice can be gamified, allowing the user to get closer to their ideal self-image while feeling a sense of accomplishment, thereby increasing the user's motivation.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The AI ​​mirror service can also analyze a user's health data and provide advice based on their health condition. For example, it can analyze exercise data collected from the user's fitness tracker and provide advice to encourage exercise if they are not getting enough exercise. It can also analyze the user's heart rate and sleep data and suggest relaxation methods if they are under-stressed. It can also analyze the user's dietary data and suggest dietary improvements if their nutritional balance is unbalanced. This allows for more comprehensive support by providing advice based on the user's health condition.

[0069] The AI ​​mirror service can estimate a user's emotional state and recommend music and video content based on that emotion. For example, if a user is feeling stressed, it can recommend relaxing music. Also, if a user is feeling positive, it can recommend video content that will further uplift their mood. It can also provide encouraging messages and positive words depending on the user's emotional state. This can improve the user's mood by recommending content based on the user's emotional state.

[0070] The AI ​​mirror service can suggest new hobbies and activities based on the user's hobbies and interests. For example, if a user likes outdoor activities, it can suggest new hiking trails and campsites. If a user is interested in art, it can introduce local art galleries and workshops. It can also suggest online communities and events based on the user's interests. This can enrich the user's life by suggesting new activities based on the user's hobbies and interests.

[0071] The AI ​​mirror service can estimate a user's emotional state and provide a fitness program based on that emotion. For example, if a user is feeling stressed, it can suggest relaxing yoga or stretching. If a user is feeling energetic, it can suggest a high-intensity workout. It can also adjust the difficulty and content of the fitness program according to the user's emotional state. This allows for more effective training by providing a fitness program based on the user's emotional state.

[0072] The AI ​​mirror service can analyze a user's past behavioral data and provide advice based on their behavioral patterns. For example, it can suggest similar methods based on the user's past successful diet methods. It can also provide advice on how to avoid repeating the same mistakes based on lessons learned from a past failed project. It can also suggest efficient time management methods in daily life based on the user's behavioral patterns. This allows for more effective support by providing advice based on the user's past behavioral data.

[0073] The AI ​​mirror service can estimate the user's emotional state and suggest relaxation methods based on that emotion. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. If the user is feeling anxious, it can suggest relaxing aromatherapy or massage. It can also provide music or video content for relaxation based on the user's emotional state. This allows the user to reduce stress by suggesting relaxation methods based on their emotional state.

[0074] The AI ​​mirror service can suggest environmentally friendly habits based on the user's lifestyle. For example, if the user is aiming for an eco-friendly lifestyle, it can suggest reusable products and energy-efficient home appliances. If the user is aiming for a sustainable diet, it can introduce locally produced organic foods and vegetarian recipes. Furthermore, it can suggest environmentally friendly means of transportation and recycling methods based on the user's lifestyle. In this way, it can support sustainable living by suggesting environmentally friendly habits based on the user's lifestyle.

[0075] The AI ​​mirror service can estimate a user's emotional state and provide emotionally-based mental health advice. For example, if a user is feeling depressed, it can provide advice encouraging positive thinking. If a user is feeling anxious, it can also suggest coping strategies to reduce anxiety. Furthermore, it can also introduce users to mental health resources and experts based on their emotional state. This allows the service to support users' mental health by providing mental health advice based on their emotional state.

[0076] The AI ​​mirror service can suggest personalized travel plans based on the user's cultural background and lifestyle. For example, if the user is interested in history and culture, it can suggest travel plans that include historical sites and cultural events. If the user likes nature and outdoor activities, it can also suggest travel plans that include natural parks and hiking trails. Furthermore, it can suggest travel plans to enjoy local food culture and specialties based on the user's lifestyle. This makes it possible to provide a more fulfilling travel experience by suggesting personalized travel plans based on the user's cultural background and lifestyle.

[0077] The AI ​​mirror service can estimate a user's emotional state and provide a study plan based on that emotion. For example, if a user lacks concentration, it can suggest a method for studying effectively in a short amount of time. Also, if a user wants to increase their motivation, it can set study goals that will give them a sense of accomplishment. Furthermore, it can evaluate the user's learning progress and provide appropriate feedback based on their emotional state. This can support more effective learning by providing a study plan based on the user's emotional state.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The personality analysis unit analyzes the user's personality, preferences, and values. For example, the personality analysis unit analyzes questionnaires, diary entries, and social media posts entered by the user to understand the user's personality, preferences, and values. The personality analysis unit can also analyze the user's personality and preferences based on behavioral data. For example, the personality analysis unit analyzes the user's purchase history and browsing history to extract latent interests and values. Furthermore, the personality analysis unit can analyze the user's tone of voice and facial expressions to understand their emotional state in real time and detect changes in their personality and preferences. Step 2: The self-image generation unit generates an ideal self-image based on the results of the analysis by the personality analysis unit. For example, the self-image generation unit considers the ideal appearance and goals that the user is aiming for and generates a visual that matches them. The self-image generation unit can also analyze the user's past photos and videos to generate an ideal self-image that takes into account changes in appearance over time. Furthermore, the self-image generation unit can consider the user's cultural background and lifestyle and generate an ideal self-image that matches them. Step 3: The advice providing unit provides daily advice based on the ideal self-image generated by the self-image generating unit. For example, the advice providing unit makes specific suggestions about what actions the user should take and what habits they should develop in order to achieve their goals. The advice providing unit can also update its advice based on the user's progress and feedback, providing optimal support at all times. Furthermore, the advice providing unit can provide advice in real time according to the user's emotional state to maintain motivation.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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, in order to avoid confusion and to 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.

[0146] 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. [Explanation of symbols]

[0147] 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 personality analysis section that analyzes the user's personality, preferences, and values; a self-image generation unit that generates an ideal self-image based on the results of the analysis by the personality analysis unit; an advice providing unit that provides daily advice based on the ideal self-image generated by the self-image generating unit. A system characterized by:

2. The system according to claim 1 , wherein the personality analysis unit analyzes the user's tone of voice or facial expression to grasp the user's emotional state in real time and detect changes in personality or preferences.

3. The system of claim 1 , wherein the personality analysis unit analyzes the user's physical health data and finds associations with the personality or preferences.

4. The system of claim 1 , wherein the self-image generation unit analyzes past photos or videos of the user and generates the ideal self-image taking into account changes in appearance over time.

5. The system described in claim 1, characterized in that the self-image generation unit uses an emotion estimation function to analyze the emotions the user has toward the ideal self-image and generate an ideal self-image that elicits positive emotions.

6. The system according to claim 1 , wherein the advice providing unit uses an emotion estimation function to provide the advice according to the emotional state of the user in real time, thereby maintaining motivation.

7. The system according to claim 1 , wherein the advice providing unit provides the advice to the user through a voice assistant so that the advice can be received naturally in daily life.

8. 2. The system according to claim 1, wherein the advice providing unit turns the advice of the user into a game, allowing the user to get closer to the ideal self-image while feeling a sense of accomplishment.

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