System

Abstract

An object of the system according to the embodiment is to propose an appropriate occupation or department based on the personality or thought of an individual and provide an experience in a virtual space.SOLUTION: A system according to an embodiment includes a personal data generator, a suggestion unit, an avatar generator, and a virtual space opening unit. A personal data generation part generates fixed personal data in which each individual inputs his or her own character and direction of thinking. The proposal unit proposes an appropriate occupation and an appropriate department based on the personal data. The avatar creator creates its own avatar on the Web. The virtual space opening unit opens the avatar in the virtual space.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 technology does not adequately develop a system that suggests suitable occupations or majors based on an individual's personality and thoughts, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest suitable occupations and subjects based on an individual's personality and thoughts, and to provide experiences in a virtual space. [Means for solving the problem]

[0006] The system according to the embodiment comprises a personal data creation unit, a suggestion unit, an avatar creation unit, and a virtual space opening unit. The personal data creation unit creates fixed personal data in which each individual inputs their own personality and way of thinking. The suggestion unit suggests suitable occupations and subjects based on the personal data. The avatar creation unit creates an avatar of the individual on the web. The virtual space opening unit opens the avatar in the virtual space. [Effects of the Invention]

[0007] The system according to the embodiment can suggest suitable occupations and subjects based on an individual's personality and thoughts, and provide experiences in a virtual space. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The aptitude suggestion system according to an embodiment of the present invention is a system in which each individual creates fixed personal data by inputting their own personality and way of thinking, and companies and schools suggest suitable occupations and suitable majors based on that data. As a result, the aptitude suggestion system can grasp the individual's characteristics and aptitudes in detail and suggest the most suitable occupations and majors.

[0029] The aptitude suggestion system according to the embodiment includes a personal data creation unit, a suggestion unit, an avatar creation unit, and a virtual space release unit. The personal data creation unit creates fixed personal data in which each individual inputs their own personality and thought patterns. For example, the individual's personality and thought patterns are recorded in detail by answering personality diagnostic tests or thought pattern questions. The personal data creation unit also converts the data into a format that is easy for the generation AI to analyze. The suggestion unit suggests suitable occupations and subjects based on the personal data. For example, the generation AI may make suggestions such as, "Based on your personality and interests, the following occupations are suitable." The suggestion unit also allows the generation AI to analyze the personal data and select the occupation or subject that best suits the individual's characteristics. The avatar creation unit creates a personal avatar on the web. For example, the individual's appearance and behavior can be customized. The avatar creation unit also allows the generation AI to generate an avatar that reflects the individual's personality and thought patterns. The virtual space release unit releases the avatar in a virtual space. For example, a user can interact with other avatars in a virtual space, and the generated AI analyzes the content of the interaction and provides feedback. The virtual space release unit also allows companies to create virtual schools and companies in the virtual space and have their avatars undergo temporary work experience or enroll. This allows the aptitude suggestion system according to the embodiment to gain a detailed understanding of an individual's characteristics and aptitudes and suggest optimal occupations and majors. For example, through work experience at a virtual company, an individual's work aptitude and skills can be evaluated and the optimal career path can be suggested. This can support the growth and development of individuals.

[0030] The personal data creation unit can analyze an individual's past behavioral history and social media postings to complement their personality and thinking direction. The personal data creation unit, for example, analyzes a user's past behavioral history to complement their personality and thinking direction. For example, based on past purchase history and browsing history, interests and concerns are reflected in the personal data. The personal data creation unit also analyzes the content of social media posts to complement the user's personality and thinking direction. For example, it performs sentiment analysis on the posted content to clarify the personality of users who post many positive comments. The personal data creation unit also integrates past behavioral history and social media data to analyze the user's personality and thinking direction in detail. For example, it complements the personal data based on behavioral patterns and posting frequency. In this way, the personality and thinking direction can be complemented by analyzing past behavioral history and social media postings.

[0031] The personal data creation unit collects personal physiological data and can reflect the user's stress and excitement state. For example, when creating personal data, the personal data creation unit monitors the user's heart rate in real time and records the user's stress and excitement state. For example, the personal data creation unit calculates the intensity of emotion based on fluctuations in heart rate. The personal data creation unit also analyzes electrodermal activity and reflects the user's stress and excitement state in the personal data. For example, the intensity of emotion is evaluated based on changes in electrodermal activity. The personal data creation unit also collects physiological data and analyzes the user's stress and excitement state in detail. For example, data on heart rate and electrodermal activity is integrated to reflect the intensity of emotion in the personal data. In this way, stress and excitement state can be reflected by collecting physiological data.

[0032] The suggestion unit collects user feedback on the proposed content, allowing the generation AI to continuously learn and improve the accuracy of the suggestions. The suggestion unit, for example, collects user feedback on proposed occupations or subjects, and the generation AI improves the accuracy of the suggestions based on that data. For example, it analyzes user ratings and comments to improve the proposal content. The suggestion unit also collects user feedback in real time, building a system in which the generation AI continuously learns to improve the accuracy of the suggestions. For example, it adjusts the proposal algorithm based on user opinions. The suggestion unit also analyzes user feedback on the proposed content, allowing the generation AI to improve the accuracy of the suggestions based on the results. For example, it prioritizes the adoption of suggestions that receive a lot of positive feedback. In this way, the generation AI can collect user feedback and continuously learn, thereby improving the accuracy of the suggestions.

[0033] The avatar creation unit can generate a more realistic avatar by reflecting the user's physical characteristics and vocal characteristics. The avatar creation unit, for example, scans the user's physical characteristics and reflects them in the avatar. For example, the avatar is generated based on the shape of the face and body type. The avatar creation unit also analyzes the user's vocal characteristics and reflects them in the avatar's voice. For example, the avatar's voice is generated based on the tone and pitch of the voice. The avatar creation unit also combines the physical characteristics and vocal characteristics to generate a more realistic avatar. For example, the user's facial expression and vocal tone are integrated and reflected in the avatar. In this way, a more realistic avatar can be generated by reflecting the user's physical characteristics and vocal characteristics.

[0034] The virtual space opening unit analyzes the avatar's behavioral history and learns the user's thought patterns and behavioral tendencies, thereby enabling more natural conversations. The virtual space opening unit, for example, analyzes the avatar's behavioral history and learns the user's thought patterns and behavioral tendencies. For example, it adjusts the avatar's responses based on the content of past conversations. The virtual space opening unit also develops an algorithm based on the user's behavioral history to enable the avatar to have more natural conversations. For example, it realizes conversations that reflect the user's preferences and interests. The virtual space opening unit also analyzes the avatar's behavioral history in detail and builds a system that learns the user's thought patterns and behavioral tendencies. For example, it optimizes the avatar's responses based on the user's past choices and reactions. In this way, by analyzing the avatar's behavioral history, it is possible to learn the user's thought patterns and behavioral tendencies and enable more natural conversations.

[0035] The virtual space opening unit can analyze experience data within the virtual environment and provide feedback on the user's growth and skill improvement in real time. The virtual space opening unit, for example, analyzes experience data within the virtual environment in real time and provides feedback on the user's growth and skill improvement. For example, it displays the user's progress in a graph. The virtual space opening unit also analyzes the user's experience data in detail and builds a system that provides feedback on the user's growth and skill improvement in real time. For example, it evaluates the user's skill level and suggests the next step. The virtual space opening unit also provides feedback on the user's growth and skill improvement in real time based on the experience data within the virtual environment. For example, it provides advice based on the user's successful and unsuccessful experiences. In this way, by analyzing the experience data within the virtual environment, it is possible to provide feedback on the user's growth and skill improvement in real time.

[0036] The virtual space opening unit can analyze the content of the dialogue and evaluate the user's communication skills and dialogue patterns in detail. The virtual space opening unit, for example, analyzes the content of the dialogue and evaluates the user's communication skills in detail. For example, it evaluates the clarity and logic of the user's statements. The virtual space opening unit also analyzes the user's dialogue patterns and builds a system for evaluating communication skills. For example, it evaluates the frequency of the user's statements and the speed of their responses. The virtual space opening unit also analyzes the content of the dialogue in detail and evaluates the user's communication skills and dialogue patterns. For example, it evaluates the consistency and persuasiveness of the user's statements. In this way, by analyzing the content of the dialogue, the user's communication skills and dialogue patterns can be evaluated in detail.

[0037] The virtual space opening unit can provide a customized training program for improving the user's communication skills based on the dialogue history. The virtual space opening unit, for example, analyzes the dialogue history and provides a customized training program for improving the user's communication skills. For example, it suggests specific exercises to compensate for the user's weaknesses. The virtual space opening unit also builds a system that customizes a training program for improving communication skills based on the user's dialogue history. For example, it points out areas for improvement in the user's utterances. The virtual space opening unit also analyzes the dialogue history in detail and provides a customized training program for improving the user's communication skills. For example, it provides specific advice to improve the quality of the user's utterances. In this way, a customized training program for improving the user's communication skills can be provided based on the dialogue history.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The appropriate suggestion system may further include a hobby / special skill input unit for inputting the user's hobbies and special skills. For example, by inputting the user's hobbies such as sports or music, or special skills such as handicrafts or cooking, the suggestion unit can suggest occupations or academic subjects related to the user's hobbies and special skills based on this information. The hobby / special skill input unit can also input the results of events or contests that the user has participated in in the past. This allows for more specific suggestions based on the user's hobbies and special skills. Furthermore, the hobby / special skill input unit can also input new hobbies or special skills that the user would like to try in the future. This allows for suggestions that broaden the user's range of interests and concerns.

[0040] The personal data creation unit can analyze the user's past learning history and grade data to complement their personality and thinking direction. For example, by inputting the user's past test scores and learning progress, the suggestion unit can use this data to understand the user's learning tendencies and areas of expertise and suggest suitable occupations and majors. The personal data creation unit can also analyze the history of online courses and workshops the user has participated in to complement their learning interests. This enables more specific suggestions based on the user's learning history. Furthermore, the personal data creation unit can input a list of books and papers the user has read in the past. This enables suggestions that reflect the depth and breadth of the user's learning.

[0041] The suggestion unit can analyze the user's lifestyle and health condition and suggest suitable occupations and majors. For example, by inputting the user's daily exercise and dietary habits, the suggestion unit can suggest occupations and majors that are suitable for the user's health condition and lifestyle based on this data. The suggestion unit can also analyze the user's sleep patterns and stress levels to complement the health condition. This allows for more specific suggestions based on the user's lifestyle. Furthermore, the suggestion unit can also input the results of health checkups the user has taken in the past. This allows for a detailed understanding of the user's health condition and suggests suitable occupations and majors.

[0042] The suggestion unit can analyze the user's cultural background and values ​​to suggest suitable occupations and majors. For example, by inputting information about the region where the user grew up, their family environment, religion, and beliefs, the suggestion unit can use this data to suggest occupations and majors that are suitable for the user's cultural background and values. The suggestion unit can also analyze the user's history of past participation in cultural events and volunteer activities to complement the values. This allows for more specific suggestions based on the user's cultural background. Furthermore, the suggestion unit can also input the values ​​and beliefs that the user holds dear. This allows for suggestions that reflect the user's values.

[0043] The suggestion unit can analyze the user's future goals and dreams and suggest suitable occupations and majors. For example, by inputting the goals and dreams the user wants to achieve in the future, the suggestion unit can suggest occupations and majors that are suitable for the user's goals and dreams based on this data. The suggestion unit can also analyze the goals and achievements the user has set in the past and complement the future goals. This makes it possible to make more specific suggestions based on the user's goals. Furthermore, the suggestion unit can also input new goals and dreams that the user wants to challenge in the future. This makes it possible to make suggestions that reflect the user's goals and dreams.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The personal data creation unit creates fixed personal data in which each individual inputs their own personality and thought patterns. For example, by answering personality diagnostic tests and thought pattern questions, the unit records each individual's personality and thought patterns in detail. The personal data creation unit also converts the data into a format that is easy for the generation AI to analyze. Step 2: The suggestion unit suggests suitable occupations and subjects based on the personal data. For example, the generation AI might suggest, "Based on your personality and interests, the following occupations are suitable." The suggestion unit also uses the generation AI to analyze the personal data and select the occupations and subjects that best suit the individual's characteristics. Step 3: The avatar creator creates an avatar for the user on the web. For example, the user can customize the user's appearance and behavior. The avatar creator also uses a generation AI to create an avatar that reflects the user's personality and thought patterns. Step 4: The virtual space release department releases the avatar into the virtual space. For example, the avatar can interact with other avatars in the virtual space, and the generated AI will analyze the content of that interaction and provide feedback. The virtual space release department also allows companies to create virtual schools and companies in the virtual space, allowing their avatars to undergo work experience or enroll in the school.

[0046] (Example 2) The aptitude suggestion system according to an embodiment of the present invention is a system in which each individual creates fixed personal data by inputting their own personality and way of thinking, and companies and schools suggest suitable occupations and suitable majors based on that data. As a result, the aptitude suggestion system can grasp the individual's characteristics and aptitudes in detail and suggest the most suitable occupations and majors.

[0047] The aptitude suggestion system according to the embodiment includes a personal data creation unit, a suggestion unit, an avatar creation unit, and a virtual space release unit. The personal data creation unit creates fixed personal data in which each individual inputs their own personality and thought patterns. For example, the individual's personality and thought patterns are recorded in detail by answering personality diagnostic tests or thought pattern questions. The personal data creation unit also converts the data into a format that is easy for the generation AI to analyze. The suggestion unit suggests suitable occupations and subjects based on the personal data. For example, the generation AI may make suggestions such as, "Based on your personality and interests, the following occupations are suitable." The suggestion unit also allows the generation AI to analyze the personal data and select the occupation or subject that best suits the individual's characteristics. The avatar creation unit creates a personal avatar on the web. For example, the individual's appearance and behavior can be customized. The avatar creation unit also allows the generation AI to generate an avatar that reflects the individual's personality and thought patterns. The virtual space release unit releases the avatar in a virtual space. For example, a user can interact with other avatars in a virtual space, and the generated AI analyzes the content of the interaction and provides feedback. The virtual space release unit also allows companies to create virtual schools and companies in the virtual space and have their avatars undergo temporary work experience or enroll. This allows the aptitude suggestion system according to the embodiment to gain a detailed understanding of an individual's characteristics and aptitudes and suggest optimal occupations and majors. For example, through work experience at a virtual company, an individual's work aptitude and skills can be evaluated and the optimal career path can be suggested. This can support the growth and development of individuals.

[0048] The personal data creation unit analyzes emotions when answering personality diagnostic tests and thought pattern questions, thereby generating more accurate personal data. For example, the personal data creation unit analyzes the user's facial expressions and voice when answering personality diagnostic tests and records emotional changes in real time. For example, it calculates an emotion score based on the user's reaction to the questions and reflects this in the personal data. The personal data creation unit also collects physiological data (heart rate, electrodermal activity, etc.) of the user when answering thought pattern questions and analyzes the intensity of the emotion. For example, it incorporates stress and excitement into the personal data. The personal data creation unit also uses an emotion estimation function to analyze in detail the emotional fluctuations when the user answers questions, thereby more accurately reflecting the user's personality and thought direction. For example, it places emphasis on answers with strong positive emotions. This allows for more accurate personal data to be generated through emotion analysis.

[0049] The personal data creation unit can analyze an individual's past behavioral history and social media postings to complement their personality and thinking direction. The personal data creation unit, for example, analyzes a user's past behavioral history to complement their personality and thinking direction. For example, based on past purchase history and browsing history, interests and concerns are reflected in the personal data. The personal data creation unit also analyzes the content of social media posts to complement the user's personality and thinking direction. For example, it performs sentiment analysis on the posted content to clarify the personality of users who post many positive comments. The personal data creation unit also integrates past behavioral history and social media data to analyze the user's personality and thinking direction in detail. For example, it complements the personal data based on behavioral patterns and posting frequency. In this way, the personality and thinking direction can be complemented by analyzing past behavioral history and social media postings.

[0050] The personal data creation unit collects personal physiological data and can reflect the user's stress and excitement state. For example, when creating personal data, the personal data creation unit monitors the user's heart rate in real time and records the user's stress and excitement state. For example, the personal data creation unit calculates the intensity of emotion based on fluctuations in heart rate. The personal data creation unit also analyzes electrodermal activity and reflects the user's stress and excitement state in the personal data. For example, the intensity of emotion is evaluated based on changes in electrodermal activity. The personal data creation unit also collects physiological data and analyzes the user's stress and excitement state in detail. For example, data on heart rate and electrodermal activity is integrated to reflect the intensity of emotion in the personal data. In this way, stress and excitement state can be reflected by collecting physiological data.

[0051] The suggestion unit can analyze the user's emotional response to the proposed occupations or departments and prioritize suggestions that show the most positive response. For example, the suggestion unit can analyze the user's emotional response to the proposed occupations or departments in real time and prioritize suggestions that show a positive response. For example, it can prioritize suggestions that make the user feel joyful or excited. The suggestion unit can also use an emotion estimation function to calculate the user's emotional score for the proposed occupations or departments and select suggestions that show the most positive response. For example, it can prioritize suggestions with high emotional scores. The suggestion unit can also dynamically adjust the content of the suggestion based on the user's emotional response and suggest occupations or departments that show the most positive response. For example, it can update the content of the suggestion every time the user's emotion changes. In this way, by analyzing the user's emotional response, it can prioritize suggestions that show the most positive response.

[0052] The suggestion unit collects user feedback on the proposed content, allowing the generation AI to continuously learn and improve the accuracy of the suggestions. The suggestion unit, for example, collects user feedback on proposed occupations or subjects, and the generation AI improves the accuracy of the suggestions based on that data. For example, it analyzes user ratings and comments to improve the proposal content. The suggestion unit also collects user feedback in real time, building a system in which the generation AI continuously learns to improve the accuracy of the suggestions. For example, it adjusts the proposal algorithm based on user opinions. The suggestion unit also analyzes user feedback on the proposed content, allowing the generation AI to improve the accuracy of the suggestions based on the results. For example, it prioritizes the adoption of suggestions that receive a lot of positive feedback. In this way, the generation AI can collect user feedback and continuously learn, thereby improving the accuracy of the suggestions.

[0053] The avatar creation unit can generate a more realistic avatar by reflecting the user's physical characteristics and vocal characteristics. The avatar creation unit, for example, scans the user's physical characteristics and reflects them in the avatar. For example, the avatar is generated based on the shape of the face and body type. The avatar creation unit also analyzes the user's vocal characteristics and reflects them in the avatar's voice. For example, the avatar's voice is generated based on the tone and pitch of the voice. The avatar creation unit also combines the physical characteristics and vocal characteristics to generate a more realistic avatar. For example, the user's facial expression and vocal tone are integrated and reflected in the avatar. In this way, a more realistic avatar can be generated by reflecting the user's physical characteristics and vocal characteristics.

[0054] The virtual space opening unit analyzes the avatar's behavioral history and learns the user's thought patterns and behavioral tendencies, thereby enabling more natural conversations. The virtual space opening unit, for example, analyzes the avatar's behavioral history and learns the user's thought patterns and behavioral tendencies. For example, it adjusts the avatar's responses based on the content of past conversations. The virtual space opening unit also develops an algorithm based on the user's behavioral history to enable the avatar to have more natural conversations. For example, it realizes conversations that reflect the user's preferences and interests. The virtual space opening unit also analyzes the avatar's behavioral history in detail and builds a system that learns the user's thought patterns and behavioral tendencies. For example, it optimizes the avatar's responses based on the user's past choices and reactions. In this way, by analyzing the avatar's behavioral history, it is possible to learn the user's thought patterns and behavioral tendencies and enable more natural conversations.

[0055] The virtual space opening unit uses the emotion estimation function to analyze the emotions of an avatar when it converses with another avatar, thereby improving the quality of the conversation. For example, the virtual space opening unit uses the emotion estimation function to analyze the emotions of an avatar when it converses with another avatar in real time, thereby improving the quality of the conversation. For example, the avatar's responses are adjusted according to changes in emotion. The virtual space opening unit also analyzes emotions in conversations between avatars in detail and develops an algorithm to improve the quality of the conversation. For example, it introduces conversation patterns that elicit positive emotions. The virtual space opening unit also uses the emotion estimation function to build a system that improves the quality of conversations between avatars. For example, it adjusts the topic of the conversation according to changes in emotion. In this way, the emotion estimation function can be used to improve the quality of conversations between avatars.

[0056] The virtual space opening unit creates virtual schools and companies and provides virtual work experiences, and can use an emotion estimation function to analyze the user's emotions during the virtual work experience and evaluate their stress and satisfaction. The virtual space opening unit, for example, uses the emotion estimation function to analyze the user's emotions during the virtual work experience in real time and evaluate their stress and satisfaction. For example, if the user is feeling stressed, the cause is identified. The virtual space opening unit also analyzes the user's emotions during the virtual work experience in detail and builds a system to evaluate their stress and satisfaction. For example, the virtual space opening unit evaluates the user's experience based on an emotion score. The virtual space opening unit also uses the emotion estimation function to analyze the user's emotions during the virtual work experience and provide feedback on their stress and satisfaction in real time. For example, if the user is satisfied, the cause is identified. In this way, the user's emotions during the virtual work experience can be analyzed to evaluate their stress and satisfaction.

[0057] The virtual space opening unit can analyze experience data within the virtual environment and provide feedback on the user's growth and skill improvement in real time. The virtual space opening unit, for example, analyzes experience data within the virtual environment in real time and provides feedback on the user's growth and skill improvement. For example, it displays the user's progress in a graph. The virtual space opening unit also analyzes the user's experience data in detail and builds a system that provides feedback on the user's growth and skill improvement in real time. For example, it evaluates the user's skill level and suggests the next step. The virtual space opening unit also provides feedback on the user's growth and skill improvement in real time based on the experience data within the virtual environment. For example, it provides advice based on the user's successful and unsuccessful experiences. In this way, by analyzing the experience data within the virtual environment, it is possible to provide feedback on the user's growth and skill improvement in real time.

[0058] The virtual space opening unit can use the emotion estimation function to monitor the user's emotions during the experience in the virtual environment and dynamically adjust the experience content. For example, the virtual space opening unit can use the emotion estimation function to monitor the user's emotions during the experience in the virtual environment in real time and dynamically adjust the experience content. For example, if the user is feeling stressed, the difficulty of the task can be lowered. The virtual space opening unit can also analyze the user's emotional reactions in real time and build a system that dynamically adjusts the experience content in the virtual environment. For example, if the user is excited, a challenging task can be provided. The virtual space opening unit can also use the emotion estimation function to analyze the user's emotions during the experience in the virtual environment in detail and dynamically adjust the experience content. For example, if the user is relaxed, the learning content can be increased. In this way, the experience content can be dynamically adjusted by monitoring the user's emotions during the experience in the virtual environment.

[0059] The virtual space opening unit can use the emotion estimation function to analyze the emotions of a user during a conversation in real time and provide appropriate feedback. The virtual space opening unit, for example, uses the emotion estimation function to analyze the emotions of a user during a conversation in real time and provide appropriate feedback. For example, if the user is feeling anxious, reassuring advice is provided. The virtual space opening unit also builds a system that analyzes the user's emotions in real time and provides feedback to improve the quality of the conversation. For example, if the user is excited, advice to stay calm is provided. The virtual space opening unit also uses the emotion estimation function to analyze the user's emotions in detail during a conversation and provide appropriate feedback. For example, if the user is happy, advice to maintain that emotion is provided. In this way, appropriate feedback can be provided by analyzing the user's emotions during a conversation in real time.

[0060] The virtual space opening unit can analyze the content of the dialogue and evaluate the user's communication skills and dialogue patterns in detail. The virtual space opening unit, for example, analyzes the content of the dialogue and evaluates the user's communication skills in detail. For example, it evaluates the clarity and logic of the user's statements. The virtual space opening unit also analyzes the user's dialogue patterns and builds a system for evaluating communication skills. For example, it evaluates the frequency of the user's statements and the speed of their responses. The virtual space opening unit also analyzes the content of the dialogue in detail and evaluates the user's communication skills and dialogue patterns. For example, it evaluates the consistency and persuasiveness of the user's statements. In this way, by analyzing the content of the dialogue, the user's communication skills and dialogue patterns can be evaluated in detail.

[0061] The virtual space opening unit can provide a customized training program for improving the user's communication skills based on the dialogue history. The virtual space opening unit, for example, analyzes the dialogue history and provides a customized training program for improving the user's communication skills. For example, it suggests specific exercises to compensate for the user's weaknesses. The virtual space opening unit also builds a system that customizes a training program for improving communication skills based on the user's dialogue history. For example, it points out areas for improvement in the user's utterances. The virtual space opening unit also analyzes the dialogue history in detail and provides a customized training program for improving the user's communication skills. For example, it provides specific advice to improve the quality of the user's utterances. In this way, a customized training program for improving the user's communication skills can be provided based on the dialogue history.

[0062] The virtual space opening unit can use the emotion estimation function to monitor the user's emotions during a conversation in real time and provide advice to improve the quality of the conversation. For example, the virtual space opening unit can use the emotion estimation function to monitor the user's emotions during a conversation in real time and provide advice to improve the quality of the conversation. For example, if the user is nervous, advice to relax is provided. The virtual space opening unit can also analyze the user's emotions in real time and build a system that provides specific advice to improve the quality of the conversation. For example, if the user is excited, advice to stay calm is provided. The virtual space opening unit can also use the emotion estimation function to analyze the user's emotions during a conversation in detail and provide advice to improve the quality of the conversation. For example, if the user is happy, advice to maintain that emotion is provided. In this way, by monitoring the user's emotions during a conversation in real time, advice to improve the quality of the conversation can be provided.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The appropriate suggestion system may further include a hobby / special skill input unit for inputting the user's hobbies and special skills. For example, by inputting the user's hobbies such as sports or music, or special skills such as handicrafts or cooking, the suggestion unit can suggest occupations or academic subjects related to the user's hobbies and special skills based on this information. The hobby / special skill input unit can also input the results of events or contests that the user has participated in in the past. This allows for more specific suggestions based on the user's hobbies and special skills. Furthermore, the hobby / special skill input unit can also input new hobbies or special skills that the user would like to try in the future. This allows for suggestions that broaden the user's range of interests and concerns.

[0065] The personal data creation unit can analyze the user's past learning history and grade data to complement their personality and thinking direction. For example, by inputting the user's past test scores and learning progress, the suggestion unit can use this data to understand the user's learning tendencies and areas of expertise and suggest suitable occupations and majors. The personal data creation unit can also analyze the history of online courses and workshops the user has participated in to complement their learning interests. This enables more specific suggestions based on the user's learning history. Furthermore, the personal data creation unit can input a list of books and papers the user has read in the past. This enables suggestions that reflect the depth and breadth of the user's learning.

[0066] The suggestion unit can analyze the user's lifestyle and health condition and suggest suitable occupations and majors. For example, by inputting the user's daily exercise and dietary habits, the suggestion unit can suggest occupations and majors that are suitable for the user's health condition and lifestyle based on this data. The suggestion unit can also analyze the user's sleep patterns and stress levels to complement the health condition. This allows for more specific suggestions based on the user's lifestyle. Furthermore, the suggestion unit can also input the results of health checkups the user has taken in the past. This allows for a detailed understanding of the user's health condition and suggests suitable occupations and majors.

[0067] The suggestion unit can analyze the user's cultural background and values ​​to suggest suitable occupations and majors. For example, by inputting information about the region where the user grew up, their family environment, religion, and beliefs, the suggestion unit can use this data to suggest occupations and majors that are suitable for the user's cultural background and values. The suggestion unit can also analyze the user's history of past participation in cultural events and volunteer activities to complement the values. This allows for more specific suggestions based on the user's cultural background. Furthermore, the suggestion unit can also input the values ​​and beliefs that the user holds dear. This allows for suggestions that reflect the user's values.

[0068] The suggestion unit can analyze the user's future goals and dreams and suggest suitable occupations and majors. For example, by inputting the goals and dreams the user wants to achieve in the future, the suggestion unit can suggest occupations and majors that are suitable for the user's goals and dreams based on this data. The suggestion unit can also analyze the goals and achievements the user has set in the past and complement the future goals. This makes it possible to make more specific suggestions based on the user's goals. Furthermore, the suggestion unit can also input new goals and dreams that the user wants to challenge in the future. This makes it possible to make suggestions that reflect the user's goals and dreams.

[0069] The suggestion unit can analyze the user's emotional response and dynamically adjust the content of the suggestions. For example, the suggestion unit can analyze the user's emotional response to proposed occupations or departments in real time and prioritize suggestions that show a positive response. For example, it can prioritize suggestions that make the user feel joyful or excited. The suggestion unit can also use an emotion estimation function to calculate the user's emotional score for proposed occupations or departments and select suggestions that show the most positive response. For example, it can prioritize suggestions with high emotional scores. The suggestion unit can also dynamically adjust the content of the suggestions based on the user's emotional response and suggest occupations or departments that show the most positive response. For example, it can update the content of the suggestions each time the user's emotions change. In this way, by analyzing the user's emotional response, it is possible to prioritize suggestions that show the most positive response.

[0070] The suggestion unit can personalize the suggested content using the user's emotion estimation function. For example, the suggestion unit analyzes the user's emotional response to suggested occupations or departments in real time and adjusts the suggested content based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit suggests occupations or departments that will help them relax. The suggestion unit also uses the emotion estimation function to calculate the user's emotion score and personalize the suggested content based on their emotions. For example, suggestions with a high emotion score are prioritized. The suggestion unit also dynamically adjusts the suggested content based on the user's emotional response and suggests occupations or departments that best suit the user's emotions. For example, the suggested content is updated each time the user's emotions change. In this way, the suggested content can be personalized by using the user's emotion estimation function.

[0071] The suggestion unit can use the user's emotion estimation function to collect feedback on the proposed content and improve the accuracy of the suggestions. For example, the suggestion unit can analyze the user's emotional response to proposed occupations or subjects in real time and collect feedback. For example, it can prioritize and collect suggestions for which the user expresses positive emotions. The suggestion unit can also use the emotion estimation function to calculate the user's emotion score and improve the accuracy of the suggestions based on the feedback. For example, it can prioritize suggestions with high emotion scores. The suggestion unit can also dynamically adjust the proposed content based on the user's emotional response and collect feedback. For example, it can update the proposed content every time the user's emotions change. In this way, by using the user's emotion estimation function, it is possible to collect feedback on the proposed content and improve the accuracy of the suggestions.

[0072] The suggestion unit can evaluate the effectiveness of the proposed content using the user's emotion estimation function. For example, the suggestion unit analyzes the user's emotional response to the proposed occupation or subject in real time to evaluate the effectiveness of the proposed content. For example, a suggestion in which the user expresses positive emotion is evaluated as effective. The suggestion unit also uses the emotion estimation function to calculate the user's emotion score and evaluate the effectiveness of the proposed content. For example, a suggestion with a high emotion score is evaluated as effective. The suggestion unit also dynamically evaluates the effectiveness of the proposed content based on the user's emotional response. For example, the effectiveness of the proposed content is evaluated every time the user's emotion changes. In this way, the effectiveness of the proposed content can be evaluated by using the user's emotion estimation function.

[0073] The suggestion unit can evaluate the satisfaction level of the proposed content using the user's emotion estimation function. For example, the suggestion unit analyzes the user's emotional response to the proposed occupation or subject in real time and evaluates the satisfaction level of the proposed content. For example, a suggestion in which the user expresses positive emotions is evaluated as having a high level of satisfaction. The suggestion unit also uses the emotion estimation function to calculate the user's emotion score and evaluates the satisfaction level of the proposed content. For example, a suggestion with a high emotion score is evaluated as having a high level of satisfaction. The suggestion unit also dynamically evaluates the satisfaction level of the proposed content based on the user's emotional response. For example, the suggestion unit evaluates the satisfaction level of the proposed content every time the user's emotions change. In this way, the suggestion unit can evaluate the satisfaction level of the proposed content by using the user's emotion estimation function.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The personal data creation unit creates fixed personal data in which each individual inputs their own personality and thought patterns. For example, by answering personality diagnostic tests and thought pattern questions, the unit records each individual's personality and thought patterns in detail. The personal data creation unit also converts the data into a format that is easy for the generation AI to analyze. Step 2: The suggestion unit suggests suitable occupations and subjects based on the personal data. For example, the generation AI might suggest, "Based on your personality and interests, the following occupations are suitable." The suggestion unit also uses the generation AI to analyze the personal data and select the occupations and subjects that best suit the individual's characteristics. Step 3: The avatar creator creates an avatar for the user on the web. For example, the user can customize the user's appearance and behavior. The avatar creator also uses a generation AI to create an avatar that reflects the user's personality and thought patterns. Step 4: The virtual space release department releases the avatar into the virtual space. For example, the avatar can interact with other avatars in the virtual space, and the generated AI will analyze the content of that interaction and provide feedback. The virtual space release department also allows companies to create virtual schools and companies in the virtual space, allowing their avatars to undergo work experience or enroll in the school.

[0076] 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.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0142] 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]

[0143] 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 personal data creation unit that creates fixed personal data for each individual, including inputting their own personality and way of thinking; A proposal unit that proposes suitable occupations and suitable subjects based on the personal data; An avatar creation section where you can create your own avatar on the web, a virtual space opening unit that opens the avatar in a virtual space. A system characterized by:

2. The personal data creation unit Analyzing emotions when answering questions about personality tests and thought patterns to generate more accurate personal data 2. The system of claim 1.

3. The personal data creation unit Analyzing an individual's past behavioral history and social media posts to complement the personality and direction of thought 2. The system of claim 1.

4. The personal data creation unit Collects individual physiological data to reflect stress and excitement 2. The system of claim 1.

5. The proposal unit Analyze users' emotional reactions to suggested jobs and subjects and prioritize suggestions that generate the most positive reactions.

2. The system of claim 1.

6. The proposal unit Collect user feedback on the suggestions, and the generative AI will continuously learn to improve the accuracy of the suggestions.

2. The system of claim 1.

7. The avatar creation unit Generate a more realistic avatar by reflecting the user's physical and vocal characteristics.

2. The system of claim 1.

8. The virtual space opening section is The avatar's behavioral history is analyzed, and the user's thought patterns and behavioral tendencies are learned to enable more natural conversations.

2. The system of claim 1.

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