System

Abstract

An object of a system according to an embodiment is to provide career support and a learning curriculum based on characteristics and personality of a user.SOLUTION: A system includes a generation AI, a conversation analysis unit, a characteristic analysis unit, and a career support unit. The conversation analysis unit talks and asks the user in a conversation form. The characteristic analysis unit analyzes a characteristic or personality of the user on the basis of the conversation data analyzed by the conversation analysis unit. The carrier support unit supports a carrier path and a learning curriculum based on the characteristics and personalities analyzed by the characteristic analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide career support or learning curricula based on the user's characteristics and personality, and there is room for improvement.

[0005] The system according to the embodiment aims to provide career support and learning curriculum based on the characteristics and personality of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a conversation analysis unit, a characteristic analysis unit, and a career support unit. The conversation analysis unit speaks to the user in a conversational format and asks questions. The characteristic analysis unit analyzes the user's characteristics and personality based on the conversation data analyzed by the conversation analysis unit. The career support unit supports career paths and learning curricula based on the characteristics and personality analyzed by the characteristic analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide career support and learning curriculum based on the characteristics and personality of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​coaching system according to an embodiment of the present invention is a system that uses generative AI to talk to and ask questions to a user in a conversational format, thereby cultivating the user's problem-solving ability and providing support for individually customized career and learning plans.

[0029] An AI coaching system according to an embodiment includes a generation AI, a conversation analysis unit, a characteristic analysis unit, and a career support unit. The generation AI speaks to a user in a conversational format and asks questions. For example, the generation AI asks questions such as, "What problems have you been facing recently?" and "What methods have you tried to solve those problems?" The conversation analysis unit analyzes conversation data generated by the generation AI. For example, the conversation analysis unit analyzes the user's responses to understand the user's thought patterns and emotional state. The characteristic analysis unit analyzes the user's characteristics and personality based on the conversation data analyzed by the conversation analysis unit. For example, the characteristic analysis unit analyzes the user's past conversation data and profile information to identify the user's characteristics and personality. The career support unit supports a career path and learning curriculum based on the characteristics and personality analyzed by the characteristic analysis unit. For example, the career support unit analyzes the user's goals and progress and generates optimal questions. As a result, the AI ​​coaching system according to an embodiment can develop the user's problem-solving ability and provide support for individually customized career and learning plans.

[0030] The conversation analysis unit analyzes the user's past conversation data and learns the user's thought patterns, allowing it to ask more appropriate questions. For example, the generation AI analyzes the user's past conversation data and learns what solutions the user has tried to the problems they have. As a result, in the next conversation, the conversation analysis unit can propose new solutions based on the methods the user has tried in the past. The conversation analysis unit also analyzes the user's thought patterns based on the user's past conversation data. For example, for a user who prefers logical thinking, the conversation analysis unit asks questions that show specific steps. The generation AI also analyzes the user's past conversation data and learns under what circumstances the user can most effectively solve problems. As a result, the conversation analysis unit asks questions that recreate the situations in which the user can most effectively solve problems. In this way, by analyzing the user's past conversation data and learning their thought patterns, more appropriate questions can be asked.

[0031] The characteristic analysis unit can analyze the user's psychological test results and provide customized questions based on their personality. For example, the generation AI analyzes the user's psychological test results and, for an introverted user, provides questions encouraging them to spend time alone thinking. For example, the characteristic analysis unit may ask, "Do you take time to relax alone?" Based on the user's psychological test results, the characteristic analysis unit may provide questions encouraging social activities to an extroverted user. For example, the characteristic analysis unit may ask, "Have you met anyone new recently?" The generation AI may analyze the user's psychological test results and provide advice to avoid risks to a cautious user. For example, the characteristic analysis unit may ask, "What preparations do you make before trying something new?" This allows the system to analyze the user's psychological test results and provide customized questions based on their personality, thereby providing more appropriate support.

[0032] The characteristic analysis unit can analyze a user's past experiences of success and failure and provide advice based on that. For example, the characteristic analysis unit allows the generative AI to analyze a user's past experiences of success and provide advice for success in similar situations. For example, the characteristic analysis unit makes a suggestion such as, "Why don't you try again the method that was successful in the past?" The characteristic analysis unit can also analyze a user's past experiences of failure and provide advice to prevent the same failure from occurring again. For example, the characteristic analysis unit makes a suggestion such as, "Why don't you try a new method, making use of what you learned from your past failure?" The characteristic analysis unit can also allow the generative AI to analyze a user's past experiences of success and failure and suggest specific steps to recreate the successful experience. For example, the unit asks a question such as, "What preparations are you making to recreate the situation when you were successful?" This allows the system to provide more appropriate support by analyzing a user's past experiences of success and failure and providing advice based on that.

[0033] The career support unit can analyze a user's past learning history and suggest an optimal learning curriculum. For example, the generation AI in the career support unit analyzes the user's past learning history to determine the level of knowledge the user has in each field. This is used to suggest what the user should learn next. For example, it may make a suggestion such as, "Why don't you try learning the basics of this field next?" The career support unit also understands the user's learning progress based on the user's learning history and suggests what the user should learn next. For example, it may make a suggestion such as, "Why don't you try using this teaching material next to learn the applications of this field?" The career support unit also analyzes the user's past learning history using the generation AI to suggest a curriculum to maximize the effectiveness of learning. For example, it may make a suggestion such as, "It would be a good idea to review the basics of this field before moving on to the next one." In this way, the effectiveness of learning can be maximized by analyzing the user's past learning history and suggesting an optimal learning curriculum.

[0034] The career support unit can analyze the required skill set based on the user's career goals and ask questions based on that. For example, the generation AI in the career support unit analyzes the user's career goals and identifies the skill set required to achieve those goals. For example, it asks a question such as, "What skills do you need to achieve that goal?" The career support unit also analyzes the required skill set based on the user's career goals and proposes a specific learning plan. For example, it makes a suggestion such as, "Why not try using this learning material next to acquire this skill?" The generation AI in the career support unit also analyzes the user's career goals and grasps the acquisition status of the skill set. Based on this, it suggests the next skill to learn. For example, it makes a suggestion such as, "After you have acquired this skill, why not try learning this skill next?" In this way, it is possible to support career growth by analyzing the required skill set based on the user's career goals and asking questions based on that.

[0035] The career support unit can analyze the user's industry trends and provide career advice based on the latest information. For example, the generation AI in the career support unit analyzes the user's industry trends and provides career advice based on the latest information. For example, it asks questions such as, "Are you aware of the latest trends in this field?" The career support unit also analyzes the user's industry trends and provides the latest information related to their career path. For example, it makes suggestions such as, "This skill may be useful for your future career." The generation AI in the career support unit also analyzes the user's industry trends and provides specific career advice based on the latest information. For example, it makes suggestions such as, "Why not try learning the latest technology in this field?" In this way, it is possible to support career growth by analyzing the user's industry trends and providing career advice based on the latest information.

[0036] The career support unit can analyze a user's learning style and suggest the optimal learning method. For example, the generation AI in the career support unit analyzes a user's learning style and suggests the optimal learning method. For example, it may make a suggestion such as, "If you're good at visual learning, why not try using visual learning materials?" The career support unit also suggests ways to maximize the effectiveness of learning based on the user's learning style. For example, it may make a suggestion such as, "If you're good at auditory learning, why not try using audio learning materials?" The generation AI in the career support unit also analyzes a user's learning style and grasps their learning progress. This allows it to suggest what they should learn next. For example, it may make a suggestion such as, "It would be a good idea to review the basics of this subject before moving on to the next one." In this way, the effectiveness of learning can be maximized by analyzing a user's learning style and suggesting the optimal learning method.

[0037] The generative AI can analyze a user's growth data over the long term, grasp growth trends, and ask appropriate questions. For example, the generative AI can analyze a user's growth data over the long term to grasp growth trends. This allows it to suggest what to learn next. For example, it can make a suggestion such as, "Based on your recent growth, why not try learning the applications of this field?" The generative AI can also grasp growth trends based on the user's growth data and suggest what to learn next. For example, it can make a suggestion such as, "It would be a good idea to review the basics of this field before moving on to the next field." The generative AI can also analyze a user's growth data over the long term to grasp growth trends. This allows it to suggest what to learn next. For example, it can make a suggestion such as, "It would be a good idea to review the basics of this field before moving on to the next field." In this way, the generative AI can analyze a user's growth data over the long term to grasp growth trends and ask appropriate questions, thereby supporting the user's growth.

[0038] The generative AI can analyze user feedback and ask continuously improved questions. The generative AI can, for example, analyze user feedback and ask continuously improved questions. For example, it can ask questions such as, "What happened when you tried this method?" The generative AI can also suggest what to learn next based on user feedback. For example, it can suggest, "It would be a good idea to review the basics of this field before moving on to the next step." The generative AI can also analyze user feedback and ask continuously improved questions. For example, it can ask questions such as, "What happened when you tried this method?" In this way, the generative AI can support the user's growth by analyzing user feedback and asking continuously improved questions.

[0039] The generative AI can analyze the user's life events and provide support accordingly. For example, it might ask, "What kind of support do you need when you first start a new job?" The generative AI might also suggest what the user should learn next based on the user's life events. For example, it might suggest, "When you first start a new job, why not try learning the basics of this field?" The generative AI might also analyze the user's life events and provide support accordingly. For example, it might ask, "What kind of support do you need when you first start a new job?" In this way, the generative AI can analyze the user's life events and provide support accordingly, thereby supporting the user's growth.

[0040] The generative AI can analyze the user's health data and ask questions based on their health condition. For example, the generative AI can analyze the user's health data and ask questions based on their health condition. For example, it can ask questions such as, "Tell me about your health condition recently." The generative AI can also suggest what to learn next based on the user's health data. For example, it can suggest, "When you're in good health, why not try learning the basics of this field?" The generative AI can also analyze the user's health data and provide support based on their health condition. For example, it can ask questions such as, "How do you relax when you're not feeling well?" In this way, the generative AI can support the user's health by analyzing their health data and asking questions based on their health condition.

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

[0042] The AI ​​coaching system can also obtain the user's physical activity data and provide advice based on their health condition. For example, it can analyze the user's step count and heart rate, and if they are not getting enough exercise, it can make suggestions such as, "Why not take a short walk today?" It can also analyze the user's sleep data and, if they are not getting enough sleep, it can offer advice such as, "Why not try going to bed earlier today?" It can also analyze the user's dietary data and, if their nutritional balance is unbalanced, it can make suggestions such as, "Why not try eating more vegetables?" This allows it to provide comprehensive support for the user's health condition.

[0043] The AI ​​coaching system can also provide recommendations based on the user's hobbies and interests. For example, if the user likes music, it can suggest, "Why don't you listen to this recently released album?". If the user likes reading, it can recommend, "This book might suit your interests." If the user likes movies, it can suggest, "This movie might suit your mood." This allows the system to provide personalized support based on the user's hobbies and interests.

[0044] The AI ​​coaching system can also analyze the user's learning style and suggest the optimal learning method. For example, for a user who is good at visual learning, the system can suggest, "Why don't you try studying using visual learning materials?". For a user who is good at auditory learning, the system can also provide advice such as, "Why don't you try studying using audio learning materials?". Furthermore, for a user who is good at practical learning, the system can suggest, "Why don't you try learning by actually doing it?". This makes it possible to provide the optimal learning method based on the user's learning style.

[0045] The AI ​​coaching system can also analyze the required skill sets based on the user's career goals and ask questions based on them. For example, it can identify the skill sets necessary to achieve the user's career goals and ask questions such as, "What skills do you need to achieve that goal?" It can also propose specific learning plans. For example, it can suggest, "Why not try using this learning material next to acquire this skill?" It can also grasp the skill set acquisition status and make suggestions such as, "After acquiring this skill, why not try learning this skill next?" In this way, the system can support career growth by analyzing the required skill sets based on the user's career goals and asking questions based on them.

[0046] The AI ​​coach system can also analyze the user's life events and provide support accordingly. For example, if the user has just started a new job, it can ask questions such as, "What kind of support do you need when you first start a new job?". Also, if the user experiences a life event such as marriage or childbirth, it can provide advice such as, "What preparations are you making for this new stage in your life?". Furthermore, if the user experiences a major change such as moving or changing jobs, it can make suggestions such as, "What kind of support do you need to get used to your new environment?" This makes it possible to provide personalized support according to the user's life events.

[0047] The AI ​​coaching system can also analyze the user's health data and provide advice based on their health condition. For example, it can analyze the user's step count and heart rate, and if they are not getting enough exercise, it can make suggestions such as, "Why not take a short walk today?" It can also analyze the user's sleep data and, if they are not getting enough sleep, it can offer advice such as, "Why not go to bed earlier today?" It can also analyze the user's dietary data and, if their nutritional balance is unbalanced, it can make suggestions such as, "Why not try eating more vegetables?" This allows it to provide comprehensive support for the user's health condition.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The generative AI speaks to the user in a conversational format and asks questions, such as, "What problems have you been facing recently?" and "What methods have you tried to solve those problems?" Step 2: The conversation analysis unit analyzes the conversation data generated by the generation AI. For example, it analyzes the user's responses to understand the user's thought patterns and emotional state. Step 3: The characteristic analysis unit analyzes the user's characteristics and personality based on the conversation data analyzed by the conversation analysis unit. For example, it analyzes past conversation data and profile information to identify the user's characteristics and personality. Step 4: The career support unit supports career paths and learning curricula based on the characteristics and personality analyzed by the characteristics analysis unit. For example, it analyzes the user's goals and progress and generates optimal questions.

[0050] (Example 2) The AI ​​coaching system according to an embodiment of the present invention is a system that uses generative AI to talk to and ask questions to a user in a conversational format, thereby cultivating the user's problem-solving ability and providing support for individually customized career and learning plans.

[0051] An AI coaching system according to an embodiment includes a generation AI, a conversation analysis unit, a characteristic analysis unit, and a career support unit. The generation AI speaks to a user in a conversational format and asks questions. For example, the generation AI asks questions such as, "What problems have you been facing recently?" and "What methods have you tried to solve those problems?" The conversation analysis unit analyzes conversation data generated by the generation AI. For example, the conversation analysis unit analyzes the user's responses to understand the user's thought patterns and emotional state. The characteristic analysis unit analyzes the user's characteristics and personality based on the conversation data analyzed by the conversation analysis unit. For example, the characteristic analysis unit analyzes the user's past conversation data and profile information to identify the user's characteristics and personality. The career support unit supports a career path and learning curriculum based on the characteristics and personality analyzed by the characteristic analysis unit. For example, the career support unit analyzes the user's goals and progress and generates optimal questions. As a result, the AI ​​coaching system according to an embodiment can develop the user's problem-solving ability and provide support for individually customized career and learning plans.

[0052] The conversation analysis unit analyzes the user's past conversation data and learns the user's thought patterns, allowing it to ask more appropriate questions. For example, the generation AI analyzes the user's past conversation data and learns what solutions the user has tried to the problems they have. As a result, in the next conversation, the conversation analysis unit can propose new solutions based on the methods the user has tried in the past. The conversation analysis unit also analyzes the user's thought patterns based on the user's past conversation data. For example, for a user who prefers logical thinking, the conversation analysis unit asks questions that show specific steps. The generation AI also analyzes the user's past conversation data and learns under what circumstances the user can most effectively solve problems. As a result, the conversation analysis unit asks questions that recreate the situations in which the user can most effectively solve problems. In this way, by analyzing the user's past conversation data and learning their thought patterns, more appropriate questions can be asked.

[0053] The conversation analysis unit can analyze the user's tone of voice and speaking style and ask questions that correspond to their emotional state. For example, the generation AI can analyze the user's tone of voice and, if the user is feeling stressed, ask questions to help them relax. For example, it can suggest, "Why don't you take a short break?" The conversation analysis unit can also analyze the user's speaking style and, if the user is excited, ask questions to help them calm down. For example, it can ask, "Tell me more about that problem." The generation AI can also analyze the user's tone of voice and speaking style and, if the user is feeling down, it can offer words of encouragement. For example, it can send a positive message such as, "I'm sure you can solve this." This makes it possible to provide more appropriate support by analyzing the user's tone of voice and speaking style and asking questions that correspond to their emotional state.

[0054] The conversation analysis unit can use the emotion estimation function to estimate the user's emotional state in real time and ask questions to elicit positive emotions. For example, if the user is feeling negative emotions, the conversation analysis unit uses the emotion estimation function to ask questions to elicit positive emotions. For example, the conversation analysis unit asks a question such as, "Has anything good happened to you recently?" The conversation analysis unit can also estimate the user's emotional state in real time and suggest specific actions to elicit positive emotions. For example, the conversation analysis unit suggests, "Why don't you listen to your favorite music?" The conversation analysis unit can also use the emotion estimation function to ask questions to further reinforce positive emotions if the user is feeling positive emotions. For example, the conversation analysis unit asks a question such as, "Why don't you share your success story with others?" In this way, the emotion estimation function can be used to estimate the user's emotional state in real time and elicit positive emotions, thereby increasing the user's motivation.

[0055] The characteristic analysis unit can analyze the user's psychological test results and provide customized questions based on their personality. For example, the generation AI analyzes the user's psychological test results and, for an introverted user, provides questions encouraging them to spend time alone thinking. For example, the characteristic analysis unit may ask, "Do you take time to relax alone?" Based on the user's psychological test results, the characteristic analysis unit may provide questions encouraging social activities to an extroverted user. For example, the characteristic analysis unit may ask, "Have you met anyone new recently?" The generation AI may analyze the user's psychological test results and provide advice to avoid risks to a cautious user. For example, the characteristic analysis unit may ask, "What preparations do you make before trying something new?" This allows the system to analyze the user's psychological test results and provide customized questions based on their personality, thereby providing more appropriate support.

[0056] The characteristic analysis unit can analyze a user's past experiences of success and failure and provide advice based on that. For example, the characteristic analysis unit allows the generative AI to analyze a user's past experiences of success and provide advice for success in similar situations. For example, the characteristic analysis unit makes a suggestion such as, "Why don't you try again the method that was successful in the past?" The characteristic analysis unit can also analyze a user's past experiences of failure and provide advice to prevent the same failure from occurring again. For example, the characteristic analysis unit makes a suggestion such as, "Why don't you try a new method, making use of what you learned from your past failure?" The characteristic analysis unit can also allow the generative AI to analyze a user's past experiences of success and failure and suggest specific steps to recreate the successful experience. For example, the unit asks a question such as, "What preparations are you making to recreate the situation when you were successful?" This allows the system to provide more appropriate support by analyzing a user's past experiences of success and failure and providing advice based on that.

[0057] The characteristic analysis unit can use the emotion estimation function to ask questions that take into account personality traits according to the user's emotional state. For example, if the user is feeling stressed, the characteristic analysis unit uses the emotion estimation function to ask questions that will help the user relax. For example, the characteristic analysis unit asks a question such as, "What have you been doing to relax lately?" The characteristic analysis unit also analyzes the user's emotional state in real time and asks questions that take into account personality traits to elicit positive emotions. For example, the characteristic analysis unit asks a question such as, "Tell me about a recent success story." The characteristic analysis unit also uses the emotion estimation function to offer words of encouragement if the user is feeling down. For example, the characteristic analysis unit sends a positive message such as, "I'm sure you can solve this." In this way, the emotion estimation function can be used to ask questions that take into account personality traits according to the user's emotional state, thereby providing more appropriate support.

[0058] The career support unit can analyze a user's past learning history and suggest an optimal learning curriculum. For example, the generation AI in the career support unit analyzes the user's past learning history to determine the level of knowledge the user has in each field. This is used to suggest what the user should learn next. For example, it may make a suggestion such as, "Why don't you try learning the basics of this field next?" The career support unit also understands the user's learning progress based on the user's learning history and suggests what the user should learn next. For example, it may make a suggestion such as, "Why don't you try using this teaching material next to learn the applications of this field?" The career support unit also analyzes the user's past learning history using the generation AI to suggest a curriculum to maximize the effectiveness of learning. For example, it may make a suggestion such as, "It would be a good idea to review the basics of this field before moving on to the next one." In this way, the effectiveness of learning can be maximized by analyzing the user's past learning history and suggesting an optimal learning curriculum.

[0059] The career support unit can analyze the required skill set based on the user's career goals and ask questions based on that. For example, the generation AI in the career support unit analyzes the user's career goals and identifies the skill set required to achieve those goals. For example, it asks a question such as, "What skills do you need to achieve that goal?" The career support unit also analyzes the required skill set based on the user's career goals and proposes a specific learning plan. For example, it makes a suggestion such as, "Why not try using this learning material next to acquire this skill?" The generation AI in the career support unit also analyzes the user's career goals and grasps the acquisition status of the skill set. Based on this, it suggests the next skill to learn. For example, it makes a suggestion such as, "After you have acquired this skill, why not try learning this skill next?" In this way, it is possible to support career growth by analyzing the required skill set based on the user's career goals and asking questions based on that.

[0060] The career support unit can use the emotion estimation function to provide positive feedback to increase the user's motivation to learn. For example, if the user uses the emotion estimation function to feel positive about learning, the career support unit provides feedback to further reinforce those feelings. For example, the career support unit may offer encouraging words such as, "Keep up the good work." The career support unit also analyzes the user's emotional state in real time and provides positive feedback to increase motivation to learn. For example, the career support unit may ask, "Tell us about your recent learning results." The career support unit also uses the emotion estimation function to provide feedback to elicit positive feelings if the user feels negative about learning. For example, the career support unit may suggest, "Why don't you take a short break and refresh yourself?" In this way, the effect of learning can be maximized by using the emotion estimation function to provide positive feedback to increase the user's motivation to learn.

[0061] The career support unit can analyze the user's industry trends and provide career advice based on the latest information. For example, the generation AI in the career support unit analyzes the user's industry trends and provides career advice based on the latest information. For example, it asks questions such as, "Are you aware of the latest trends in this field?" The career support unit also analyzes the user's industry trends and provides the latest information related to their career path. For example, it makes suggestions such as, "This skill may be useful for your future career." The generation AI in the career support unit also analyzes the user's industry trends and provides specific career advice based on the latest information. For example, it makes suggestions such as, "Why not try learning the latest technology in this field?" In this way, it is possible to support career growth by analyzing the user's industry trends and providing career advice based on the latest information.

[0062] The career support unit can analyze a user's learning style and suggest the optimal learning method. For example, the generation AI in the career support unit analyzes a user's learning style and suggests the optimal learning method. For example, it may make a suggestion such as, "If you're good at visual learning, why not try using visual learning materials?" The career support unit also suggests ways to maximize the effectiveness of learning based on the user's learning style. For example, it may make a suggestion such as, "If you're good at auditory learning, why not try using audio learning materials?" The generation AI in the career support unit also analyzes a user's learning style and grasps their learning progress. This allows it to suggest what they should learn next. For example, it may make a suggestion such as, "It would be a good idea to review the basics of this subject before moving on to the next one." In this way, the effectiveness of learning can be maximized by analyzing a user's learning style and suggesting the optimal learning method.

[0063] The career support unit can use the emotion estimation function to suggest a study curriculum for the time period when the user can concentrate best. For example, the career support unit uses the emotion estimation function to identify the time period when the user can concentrate best and suggest a study curriculum for that time period. For example, it makes a suggestion such as, "Why don't you try studying using this learning material in the morning?" The career support unit also analyzes the user's emotional state in real time and suggests a study curriculum that is optimal for the time period when the user can concentrate best. For example, it makes a suggestion such as, "Why don't you try studying using this learning material in the evening while you are relaxing?" The career support unit also uses the emotion estimation function to suggest a curriculum for maximizing the effectiveness of learning for the time period when the user can concentrate best. For example, it makes a suggestion such as, "Why don't you try learning the basics of this field during this time period?" In this way, the effectiveness of learning can be maximized by using the emotion estimation function to suggest a study curriculum for the time period when the user can concentrate best.

[0064] The generative AI can analyze a user's growth data over the long term, grasp growth trends, and ask appropriate questions. For example, the generative AI can analyze a user's growth data over the long term to grasp growth trends. This allows it to suggest what to learn next. For example, it can make a suggestion such as, "Based on your recent growth, why not try learning the applications of this field?" The generative AI can also grasp growth trends based on the user's growth data and suggest what to learn next. For example, it can make a suggestion such as, "It would be a good idea to review the basics of this field before moving on to the next field." The generative AI can also analyze a user's growth data over the long term to grasp growth trends. This allows it to suggest what to learn next. For example, it can make a suggestion such as, "It would be a good idea to review the basics of this field before moving on to the next field." In this way, the generative AI can analyze a user's growth data over the long term to grasp growth trends and ask appropriate questions, thereby supporting the user's growth.

[0065] The generative AI can analyze user feedback and ask continuously improved questions. The generative AI can, for example, analyze user feedback and ask continuously improved questions. For example, it can ask questions such as, "What happened when you tried this method?" The generative AI can also suggest what to learn next based on user feedback. For example, it can suggest, "It would be a good idea to review the basics of this field before moving on to the next step." The generative AI can also analyze user feedback and ask continuously improved questions. For example, it can ask questions such as, "What happened when you tried this method?" In this way, the generative AI can support the user's growth by analyzing user feedback and asking continuously improved questions.

[0066] The generation AI can use the emotion estimation function to analyze the user's emotional response to their growth and ask questions to maintain their motivation. For example, the generation AI can use the emotion estimation function to analyze the user's emotional response to their growth and ask questions to maintain their motivation. For example, it can ask a question such as, "How do you feel about your recent growth?" The generation AI can also analyze the user's emotional state in real time and ask questions to elicit positive emotions about their growth. For example, it can ask a question such as, "Tell us about a recent success story." The generation AI can also use the emotion estimation function to analyze the user's emotional response to their growth and provide feedback to maintain their motivation. For example, it can say encouraging words such as, "Keep it up." In this way, the generation AI can support the user's growth by using the emotion estimation function to analyze the user's emotional response to their growth and ask questions to maintain their motivation.

[0067] The generative AI can analyze the user's life events and provide support accordingly. For example, it might ask, "What kind of support do you need when you first start a new job?" The generative AI might also suggest what the user should learn next based on the user's life events. For example, it might suggest, "When you first start a new job, why not try learning the basics of this field?" The generative AI might also analyze the user's life events and provide support accordingly. For example, it might ask, "What kind of support do you need when you first start a new job?" In this way, the generative AI can analyze the user's life events and provide support accordingly, thereby supporting the user's growth.

[0068] The generative AI can analyze the user's health data and ask questions based on their health condition. For example, the generative AI can analyze the user's health data and ask questions based on their health condition. For example, it can ask questions such as, "Tell me about your health condition recently." The generative AI can also suggest what to learn next based on the user's health data. For example, it can suggest, "When you're in good health, why not try learning the basics of this field?" The generative AI can also analyze the user's health data and provide support based on their health condition. For example, it can ask questions such as, "How do you relax when you're not feeling well?" In this way, the generative AI can support the user's health by analyzing their health data and asking questions based on their health condition.

[0069] The generative AI can use its emotion estimation function to provide positive feedback at the moment when the user feels most grateful. For example, the generative AI can use its emotion estimation function to identify the moment when the user feels most grateful and provide positive feedback at that moment. For example, it can ask questions such as, "Tell us about a recent success story." The generative AI can also analyze the user's emotional state in real time and provide positive feedback at the moment when the user feels grateful. For example, it can say encouraging words such as, "Keep it up." The generative AI can also use its emotion estimation function to provide feedback to elicit gratitude at the moment when the user feels most grateful. For example, it can send a positive message such as, "Your efforts are paying off." This can increase the user's motivation by providing positive feedback at the moment when the user feels most grateful using the emotion estimation function.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The AI ​​coaching system can also obtain the user's physical activity data and provide advice based on their health condition. For example, it can analyze the user's step count and heart rate, and if they are not getting enough exercise, it can make suggestions such as, "Why not take a short walk today?" It can also analyze the user's sleep data and, if they are not getting enough sleep, it can offer advice such as, "Why not try going to bed earlier today?" It can also analyze the user's dietary data and, if their nutritional balance is unbalanced, it can make suggestions such as, "Why not try eating more vegetables?" This allows it to provide comprehensive support for the user's health condition.

[0072] The AI ​​coaching system can also provide recommendations based on the user's hobbies and interests. For example, if the user likes music, it can suggest, "Why don't you listen to this recently released album?". If the user likes reading, it can recommend, "This book might suit your interests." If the user likes movies, it can suggest, "This movie might suit your mood." This allows the system to provide personalized support based on the user's hobbies and interests.

[0073] The AI ​​coaching system can also estimate the user's emotional state and provide advice for stress management. For example, if the user is feeling stressed, it can suggest, "Why don't you take a deep breath and relax?". If the user is feeling anxious, it can also offer advice such as, "Why don't you take a short walk to change your mood?". Furthermore, if the user is tired, it can suggest, "Why don't you take an early rest today?". This makes it possible to provide stress management support tailored to the user's emotional state.

[0074] The AI ​​coaching system can also analyze the user's learning style and suggest the optimal learning method. For example, for a user who is good at visual learning, the system can suggest, "Why don't you try studying using visual learning materials?". For a user who is good at auditory learning, the system can also provide advice such as, "Why don't you try studying using audio learning materials?". Furthermore, for a user who is good at practical learning, the system can suggest, "Why don't you try learning by actually doing it?". This makes it possible to provide the optimal learning method based on the user's learning style.

[0075] The AI ​​coaching system can also estimate the user's emotional state and provide positive feedback to increase motivation. For example, if the user feels positive about learning, it can offer encouraging words such as "Keep it up." If the user feels negative about learning, it can suggest, "Why don't you take a short break and refresh yourself?" Furthermore, if the user feels a sense of accomplishment, it can offer advice such as, "Why don't you share your achievements with others?" This makes it possible to provide support to increase motivation according to the user's emotional state.

[0076] The AI ​​coaching system can also analyze the required skill sets based on the user's career goals and ask questions based on them. For example, it can identify the skill sets necessary to achieve the user's career goals and ask questions such as, "What skills do you need to achieve that goal?" It can also propose specific learning plans. For example, it can suggest, "Why not try using this learning material next to acquire this skill?" It can also grasp the skill set acquisition status and make suggestions such as, "After acquiring this skill, why not try learning this skill next?" In this way, the system can support career growth by analyzing the required skill sets based on the user's career goals and asking questions based on them.

[0077] The AI ​​coaching system can also estimate the user's emotional state and ask questions to elicit positive emotions. For example, if the user is feeling negative, it might ask, "Has anything good happened to you recently?" It can also suggest specific actions to elicit positive emotions. For example, it might suggest, "Why don't you listen to your favorite music?" Furthermore, if the user is feeling positive, it can ask questions to further reinforce those emotions. For example, it might ask, "Why don't you share your success story with others?" This allows the emotion estimation function to estimate the user's emotional state in real time and elicit positive emotions, thereby increasing the user's motivation.

[0078] The AI ​​coach system can also analyze the user's life events and provide support accordingly. For example, if the user has just started a new job, it can ask questions such as, "What kind of support do you need when you first start a new job?". Also, if the user experiences a life event such as marriage or childbirth, it can provide advice such as, "What preparations are you making for this new stage in your life?". Furthermore, if the user experiences a major change such as moving or changing jobs, it can make suggestions such as, "What kind of support do you need to get used to your new environment?" This makes it possible to provide personalized support according to the user's life events.

[0079] The AI ​​coaching system can also estimate the user's emotional state and provide positive feedback to increase motivation to learn. For example, if the user has positive feelings about learning, it can offer encouraging words such as "Keep it up." If the user has negative feelings about learning, it can suggest, "Why don't you take a short break and refresh yourself?" Furthermore, if the user feels a sense of accomplishment, it can offer advice such as, "Why don't you share your achievements with others?" This makes it possible to provide support that increases motivation to learn according to the user's emotional state.

[0080] The AI ​​coaching system can also analyze the user's health data and provide advice based on their health condition. For example, it can analyze the user's step count and heart rate, and if they are not getting enough exercise, it can make suggestions such as, "Why not take a short walk today?" It can also analyze the user's sleep data and, if they are not getting enough sleep, it can offer advice such as, "Why not go to bed earlier today?" It can also analyze the user's dietary data and, if their nutritional balance is unbalanced, it can make suggestions such as, "Why not try eating more vegetables?" This allows it to provide comprehensive support for the user's health condition.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The generative AI speaks to the user in a conversational format and asks questions, such as, "What problems have you been facing recently?" and "What methods have you tried to solve those problems?" Step 2: The conversation analysis unit analyzes the conversation data generated by the generation AI. For example, it analyzes the user's responses to understand the user's thought patterns and emotional state. Step 3: The characteristic analysis unit analyzes the user's characteristics and personality based on the conversation data analyzed by the conversation analysis unit. For example, it analyzes past conversation data and profile information to identify the user's characteristics and personality. Step 4: The career support unit supports career paths and learning curricula based on the characteristics and personality analyzed by the characteristics analysis unit. For example, it analyzes the user's goals and progress and generates optimal questions.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. Equipped with generative AI, The generated AI is a conversation analysis unit that speaks to the user in a conversational format and asks questions; a characteristic analysis unit that analyzes the characteristics and personality of a user based on the conversation data analyzed by the conversation analysis unit; a career support unit that supports a career path and a learning curriculum based on the characteristics and personality analyzed by the characteristic analysis unit. A system characterized by:

2. The conversation analysis unit Analyzes the user's past conversation data, learns the user's thought patterns, and asks more appropriate questions 2. The system of claim 1.

3. The conversation analysis unit Analyze the user's tone of voice and speaking style and ask questions according to their emotional state.

2. The system of claim 1.

4. The conversation analysis unit The emotional state of the user is estimated in real time, and questions are asked to elicit positive emotions.

2. The system of claim 1.

5. The characteristic analysis unit Analyzing the user's psychological test results and providing customized questions based on the user's personality 2. The system of claim 1.

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