System

Abstract

A system according to an embodiment aims to concretize dreams and goals and provide concrete steps to achieve them.SOLUTION: A system according to an embodiment includes a dream or goal concretization unit, a small goal setting unit, a skill learning period presentation unit, and an advice providing unit. The dream / goal concretization unit concretizes a dream or a goal. The small goal setting unit sets a small goal for achieving the goal concretized by the dream or goal concretization unit. The skill learning period presentation unit presents a skill learning period and a method for achieving the small objective set by the small objective setting unit. The advice provision unit provides specific and rational advice on the basis of the skill learning period and the method presented by the skill learning period presentation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately materialize dreams and goals and provide concrete steps to achieve them, and there is room for improvement.

[0005] The system according to the embodiment aims to make dreams and goals concrete and provide specific steps to achieve them. [Means for solving the problem]

[0006] The system according to the embodiment includes a dream or goal realization unit, a small goal setting unit, a skill acquisition period presentation unit, and an advice providing unit. The dream or goal realization unit makes dreams and goals concrete. The small goal setting unit sets small goals for achieving the goals realized by the dream or goal realization unit. The skill acquisition period presentation unit presents a skill acquisition period and method for achieving the small goals set by the small goal setting unit. The advice providing unit provides specific and reasonable advice based on the skill acquisition period and method presented by the skill acquisition period presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment can make dreams and goals concrete and provide specific steps to achieve them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The KISPA2 system according to an embodiment of the present invention is a system that helps children translate their dreams and goals into concrete actions. This system clarifies children's dreams and goals, sets small goals, suggests the time and method for acquiring skills, and provides specific and rational advice. In this way, the KISPA2 system can help children translate their dreams and goals into concrete actions and achieve them.

[0029] The KISPA2 system according to the embodiment includes a dream / goal concretization unit, a small goal setting unit, a skill acquisition period presentation unit, and an advice providing unit. The dream / goal concretization unit concretizes a child's dreams and goals. For example, the generation AI generates a specific action plan based on information about the child's dreams and goals. The generation AI can also ask the child specific questions and propose an action plan based on the questions. The small goal setting unit sets small goals to achieve the concretized goal. For example, the generation AI suggests small steps necessary to achieve the goal and indicates specific actions the child should take based on the steps. The skill acquisition period presentation unit suggests a period and method for acquiring the skill. For example, the generation AI specifies a period and method for acquiring the necessary skills and suggests how the child should proceed with their studies. The advice providing unit provides specific and rational advice. For example, the generation AI explains to the child the importance of studying a little bit every day and provides advice for achieving long-term results. This allows the KISPA2 system according to the embodiment to help children translate their dreams and goals into concrete actions and achieve them. For example, the KISPA2 system watches over children's growth, provides advice at the appropriate time, and supports them in moving towards their dreams and goals.

[0030] The dream and goal concretization unit analyzes behavioral history and interests and can suggest specific dreams and goals based on that. For example, the dream and goal concretization unit collects a child's past behavioral history, and the generation AI analyzes that data. For example, it suggests related occupations and goals based on past club activities and hobbies. The dream and goal concretization unit also identifies a child's interests and suggests specific dreams and goals based on that. For example, it identifies areas in which a child is interested based on survey results and past elective subjects, and suggests goals related to those. This makes it possible to suggest more specific dreams and goals based on a child's past behavioral history and interests.

[0031] The dream and goal realization unit presents success stories and role models related to the dreams and goals, and can create an action plan based on them. For example, the dream and goal realization unit uses the generative AI to collect success stories related to a child's dreams and goals and create a specific action plan based on them. For example, if a child wants to become a doctor, it will show them the career path of a successful doctor. The dream and goal realization unit also presents role models and creates an action plan based on them. For example, it shows how a child should act based on the success stories of industry leaders and well-known people. This allows a specific action plan to be created based on success stories and role models.

[0032] The small goal setting unit can set small goals that are individually customized to suit the learning style and pace. For example, the small goal setting unit uses a generative AI to analyze a child's learning style and set small goals that are individually customized based on that. For example, goals using visual learning materials are set for visual learners. The small goal setting unit also sets goals that suit the child's learning pace. For example, goals can be set on a weekly or monthly basis so that the child can achieve them without difficulty. This makes it possible to set small goals that suit the child's learning style and pace.

[0033] The small goal setting unit can monitor progress in real time and dynamically adjust goals and methods of achieving them as needed. In the small goal setting unit, for example, the generation AI monitors a child's progress in real time and dynamically adjusts goals as needed. For example, if goal achievement is lagging behind, the goal will be divided and set. The small goal setting unit also dynamically adjusts the method of achievement. For example, it changes the learning method according to progress to make it easier for the child to achieve the goal. This makes it possible to dynamically adjust goals and methods of achievement according to the child's progress.

[0034] The skill acquisition period presentation unit can evaluate the skill level in detail and, based on that, suggest the optimal skill acquisition period and method. For example, the generation AI evaluates a child's current skill level in detail and, based on that, suggests the optimal skill acquisition period. For example, it evaluates the English skill level and presents a six-month study plan. The skill acquisition period presentation unit also suggests the optimal skill acquisition method. For example, it suggests a learning method suitable for the child, such as an online course or on-the-job training. This makes it possible to suggest the optimal skill acquisition period and method based on the child's current skill level.

[0035] The skill acquisition period presentation unit can analyze learning history and performance data, predict skill acquisition progress, and dynamically adjust the period and method. For example, the skill acquisition period presentation unit uses a generation AI to analyze a child's learning history and predict skill acquisition progress based on that. For example, it suggests the next step based on past learning data. The skill acquisition period presentation unit also analyzes performance data and dynamically adjusts the period and method based on that. For example, it adjusts the learning plan based on practical assessments and project results. This makes it possible to predict skill acquisition progress based on a child's learning history and performance data, and dynamically adjust the period and method.

[0036] The advice providing unit can analyze successful and unsuccessful experiences and provide specific and rational advice based on the results. For example, the generation AI analyzes a child's past successful experiences and provides specific and rational advice based on the results. For example, it may suggest a learning method that was successful in the past. The advice providing unit also analyzes unsuccessful experiences and provides advice based on the results. For example, it may identify the cause of past failures and provide advice to avoid them. This makes it possible to provide specific and rational advice based on a child's past successful and unsuccessful experiences.

[0037] The advice providing unit can provide personalized advice according to the needs and goals. For example, the generative AI analyzes the individual needs of a child and provides personalized advice based on the results. For example, the advice providing unit suggests study methods for a specific subject. The advice providing unit also provides advice according to the child's goals. For example, for a child aiming to obtain a specific qualification, the advice providing unit provides advice on how to acquire the skills and knowledge required for that qualification. This makes it possible to provide personalized advice according to the individual needs and goals of a child.

[0038] The advice providing unit can provide specific and reasonable advice that can be applied to children of different age groups and grades. For example, the generation AI provides specific and reasonable advice that can be applied to children of different age groups. For example, it provides advice that covers a wide range of ages from elementary school students to high school students. The advice providing unit also provides advice that can be applied to children of different grades. For example, it provides advice on study methods and goal setting appropriate for each grade from first grade to third grade. This makes it possible to provide specific and reasonable advice that can be applied to children of different age groups and grades.

[0039] The advice providing unit can provide specific advice for working with friends and family to jointly achieve a goal. For example, the generation AI can work with a child's friends and family to provide specific advice for working together to achieve a goal. For example, the whole family can share study time. The advice providing unit can also provide advice for working with friends to achieve a goal. For example, it can suggest ways to increase motivation by studying together with friends. This makes it possible to provide specific advice for working with a child's friends and family to achieve a goal.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The KISPA2 system can also be equipped with an inspiration section, which provides stimulation for children to discover new dreams and goals. For example, it can introduce success stories and interesting projects in different fields. The inspiration section can also provide information on workshops and events that allow children to broaden their interests. Furthermore, the inspiration section can provide self-analysis tools to help children discover their own dreams and goals. This allows children to receive a variety of stimulation to discover new dreams and goals.

[0042] The KISPA2 system can further include a feedback providing unit. The feedback providing unit regularly provides children with feedback on their progress toward the goals they have set. For example, it can generate a progress report every week and provide it to them. The feedback providing unit can also suggest areas for improvement and next steps toward achieving the goals. Furthermore, the feedback providing unit can provide children with tools to self-evaluate their own progress. This allows children to understand their own progress and take concrete action toward achieving their goals.

[0043] The KISPA2 system can also be equipped with a communication support unit, which helps children communicate effectively with other children and mentors. For example, it can provide online forums and chat functions, giving children a place to share information with each other. The communication support unit can also set up regular online meetings with mentors, providing children with an opportunity to receive direct advice. The communication support unit can also provide training programs for children to learn effective communication skills. This allows children to receive support toward achieving their goals through communication with others.

[0044] The KISPA2 system can also be equipped with a self-assessment support section, which provides tools for children to self-assess their progress and skill level. For example, it can conduct regular self-assessment surveys to allow children to objectively understand their own growth. The self-assessment support section can also suggest next steps based on the self-assessment results. Furthermore, the self-assessment support section can provide feedback to help children gain confidence through self-assessment. This allows children to realize their own growth and increases their motivation to achieve their goals.

[0045] The KISPA2 system can also include a career exploration module. This module helps children think concretely about their future careers. For example, it can provide detailed information about different occupations and the skills required. The career exploration module can also introduce internship and volunteer opportunities related to occupations that interest children. The career exploration module can also provide tools for children to plan specific steps toward their future careers. This allows children to think concretely about their future careers and set goals.

[0046] The KISPA2 system can further include a resource provider. The resource provider provides children with the resources they need to achieve their goals. For example, it can provide a list of study materials and reference books. The resource provider can also provide information on online courses and workshops. The resource provider can also provide information on scholarships and grants that children can use to achieve their goals. This allows children to effectively utilize the resources they need to achieve their goals.

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

[0048] Step 1: The dream and goal realization unit makes the child's dreams and goals concrete. For example, the generation AI generates a specific action plan based on information about the child's dreams and goals. The generation AI can also pose specific questions to the child and suggest an action plan based on those questions. Step 2: The small goal setting unit sets small goals to achieve the specified goal. For example, the generation AI suggests small steps necessary to achieve the goal and shows the child specific actions to take based on them. Step 3: The skill acquisition period suggestion unit suggests the period and method for acquiring the skill. For example, the generation AI may specifically indicate the period and method for acquiring the necessary skills and suggest how the child should proceed with their studies. Step 4: The advice provider provides specific and rational advice. For example, the generator AI explains to the child the importance of studying a little bit every day and provides advice for achieving long-term results.

[0049] (Example 2) The KISPA2 system according to an embodiment of the present invention is a system that helps children translate their dreams and goals into concrete actions. This system clarifies children's dreams and goals, sets small goals, suggests the time and method for acquiring skills, and provides specific and rational advice. In this way, the KISPA2 system can help children translate their dreams and goals into concrete actions and achieve them.

[0050] The KISPA2 system according to the embodiment includes a dream / goal concretization unit, a small goal setting unit, a skill acquisition period presentation unit, and an advice providing unit. The dream / goal concretization unit concretizes a child's dreams and goals. For example, the generation AI generates a specific action plan based on information about the child's dreams and goals. The generation AI can also ask the child specific questions and propose an action plan based on the questions. The small goal setting unit sets small goals to achieve the concretized goal. For example, the generation AI suggests small steps necessary to achieve the goal and indicates specific actions the child should take based on the steps. The skill acquisition period presentation unit suggests a period and method for acquiring the skill. For example, the generation AI specifies a period and method for acquiring the necessary skills and suggests how the child should proceed with their studies. The advice providing unit provides specific and rational advice. For example, the generation AI explains to the child the importance of studying a little bit every day and provides advice for achieving long-term results. This allows the KISPA2 system according to the embodiment to help children translate their dreams and goals into concrete actions and achieve them. For example, the KISPA2 system watches over children's growth, provides advice at the appropriate time, and supports them in moving towards their dreams and goals.

[0051] The dream and goal concretization unit analyzes behavioral history and interests and can suggest specific dreams and goals based on that. For example, the dream and goal concretization unit collects a child's past behavioral history, and the generation AI analyzes that data. For example, it suggests related occupations and goals based on past club activities and hobbies. The dream and goal concretization unit also identifies a child's interests and suggests specific dreams and goals based on that. For example, it identifies areas in which a child is interested based on survey results and past elective subjects, and suggests goals related to those. This makes it possible to suggest more specific dreams and goals based on a child's past behavioral history and interests.

[0052] The dream and goal realization unit presents success stories and role models related to the dreams and goals, and can create an action plan based on them. For example, the dream and goal realization unit uses the generative AI to collect success stories related to a child's dreams and goals and create a specific action plan based on them. For example, if a child wants to become a doctor, it will show them the career path of a successful doctor. The dream and goal realization unit also presents role models and creates an action plan based on them. For example, it shows how a child should act based on the success stories of industry leaders and well-known people. This allows a specific action plan to be created based on success stories and role models.

[0053] The dream and goal concretization unit can use the emotion estimation function to analyze the emotions a child has toward their dreams and goals and generate questions to elicit positive emotions. The dream and goal concretization unit, for example, uses the emotion estimation function to analyze the emotions a child has toward their dreams and goals in real time. For example, if a child is feeling anxious about their dreams, it generates questions to alleviate that anxiety. The dream and goal concretization unit also generates questions to elicit positive emotions. For example, it generates questions that make a child feel excited and excited about their dreams and suggests an action plan based on those questions. This can lead to a child having positive emotions toward their dreams and goals.

[0054] The small goal setting unit can set small goals that are individually customized to suit the learning style and pace. For example, the small goal setting unit uses a generative AI to analyze a child's learning style and set small goals that are individually customized based on that. For example, goals using visual learning materials are set for visual learners. The small goal setting unit also sets goals that suit the child's learning pace. For example, goals can be set on a weekly or monthly basis so that the child can achieve them without difficulty. This makes it possible to set small goals that suit the child's learning style and pace.

[0055] The small goal setting unit can monitor progress in real time and dynamically adjust goals and methods of achieving them as needed. In the small goal setting unit, for example, the generation AI monitors a child's progress in real time and dynamically adjusts goals as needed. For example, if goal achievement is lagging behind, the goal will be divided and set. The small goal setting unit also dynamically adjusts the method of achievement. For example, it changes the learning method according to progress to make it easier for the child to achieve the goal. This makes it possible to dynamically adjust goals and methods of achievement according to the child's progress.

[0056] The small goal setting unit can use the emotion estimation function to analyze emotions when achieving small goals and provide advice to maintain motivation. The small goal setting unit, for example, uses the emotion estimation function to analyze emotions in real time when a child achieves a small goal. For example, if the child feels a sense of accomplishment, it provides advice to maintain that emotion. The small goal setting unit also provides advice to maintain motivation. For example, it suggests encouraging words or a reward system to help the child continue working hard toward achieving the goal. This makes it possible to provide specific advice to maintain motivation when a child achieves a small goal.

[0057] The skill acquisition period presentation unit can evaluate the skill level in detail and, based on that, suggest the optimal skill acquisition period and method. For example, the generation AI evaluates a child's current skill level in detail and, based on that, suggests the optimal skill acquisition period. For example, it evaluates the English skill level and presents a six-month study plan. The skill acquisition period presentation unit also suggests the optimal skill acquisition method. For example, it suggests a learning method suitable for the child, such as an online course or on-the-job training. This makes it possible to suggest the optimal skill acquisition period and method based on the child's current skill level.

[0058] The skill acquisition period presentation unit can analyze learning history and performance data, predict skill acquisition progress, and dynamically adjust the period and method. For example, the skill acquisition period presentation unit uses a generation AI to analyze a child's learning history and predict skill acquisition progress based on that. For example, it suggests the next step based on past learning data. The skill acquisition period presentation unit also analyzes performance data and dynamically adjusts the period and method based on that. For example, it adjusts the learning plan based on practical assessments and project results. This makes it possible to predict skill acquisition progress based on a child's learning history and performance data, and dynamically adjust the period and method.

[0059] The skill acquisition period presentation unit can use the emotion estimation function to analyze emotions felt toward skill acquisition and suggest a learning method to elicit positive emotions. The skill acquisition period presentation unit, for example, uses the emotion estimation function to analyze in real time the emotions felt by a child toward skill acquisition. For example, if a child feels anxious about learning, the unit suggests a learning method to alleviate that anxiety. The skill acquisition period presentation unit also suggests a learning method to elicit positive emotions. For example, the unit can use gamified learning or interactive teaching materials to help children learn in a fun way. This makes it possible to suggest a learning method that induces children to have positive emotions toward skill acquisition.

[0060] The advice providing unit can analyze successful and unsuccessful experiences and provide specific and rational advice based on the results. For example, the generation AI analyzes a child's past successful experiences and provides specific and rational advice based on the results. For example, it may suggest a learning method that was successful in the past. The advice providing unit also analyzes unsuccessful experiences and provides advice based on the results. For example, it may identify the cause of past failures and provide advice to avoid them. This makes it possible to provide specific and rational advice based on a child's past successful and unsuccessful experiences.

[0061] The advice providing unit can provide personalized advice according to the needs and goals. For example, the generative AI analyzes the individual needs of a child and provides personalized advice based on the results. For example, the advice providing unit suggests study methods for a specific subject. The advice providing unit also provides advice according to the child's goals. For example, for a child aiming to obtain a specific qualification, the advice providing unit provides advice on how to acquire the skills and knowledge required for that qualification. This makes it possible to provide personalized advice according to the individual needs and goals of a child.

[0062] The advice providing unit can use the emotion estimation function to analyze the emotion felt toward the advice and provide advice to elicit positive emotions. The advice providing unit, for example, uses the emotion estimation function to analyze in real time the emotion felt by the child toward the advice. For example, if the child feels anxious about the advice, it provides advice to alleviate that anxiety. The advice providing unit also provides advice to elicit positive emotions. For example, it provides words of encouragement or shares successful experiences so that the child will have a positive attitude toward the advice. This makes it possible to provide specific advice that will induce the child to have positive emotions toward the advice.

[0063] The advice providing unit can provide specific and reasonable advice that can be applied to children of different age groups and grades. For example, the generation AI provides specific and reasonable advice that can be applied to children of different age groups. For example, it provides advice that covers a wide range of ages from elementary school students to high school students. The advice providing unit also provides advice that can be applied to children of different grades. For example, it provides advice on study methods and goal setting appropriate for each grade from first grade to third grade. This makes it possible to provide specific and reasonable advice that can be applied to children of different age groups and grades.

[0064] The advice providing unit can provide specific advice for working with friends and family to jointly achieve a goal. For example, the generation AI can work with a child's friends and family to provide specific advice for working together to achieve a goal. For example, the whole family can share study time. The advice providing unit can also provide advice for working with friends to achieve a goal. For example, it can suggest ways to increase motivation by studying together with friends. This makes it possible to provide specific advice for working with a child's friends and family to achieve a goal.

[0065] The advice providing unit can use the emotion estimation function to monitor in real time the emotions felt toward the advice, and adjust the advice according to changes in emotions. The advice providing unit, for example, uses the emotion estimation function to monitor in real time the emotions felt by the child toward the advice. For example, if the child feels anxious about the advice, the advice providing unit provides advice to alleviate the anxiety. The advice providing unit also adjusts the advice according to changes in emotions. For example, if the child's emotions change, the content of the advice is changed according to the change. In this way, the advice can be adjusted according to changes in the emotions felt by the child toward the advice.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The KISPA2 system can also be equipped with an inspiration section, which provides stimulation for children to discover new dreams and goals. For example, it can introduce success stories and interesting projects in different fields. The inspiration section can also provide information on workshops and events that allow children to broaden their interests. Furthermore, the inspiration section can provide self-analysis tools to help children discover their own dreams and goals. This allows children to receive a variety of stimulation to discover new dreams and goals.

[0068] The KISPA2 system can further include a feedback providing unit. The feedback providing unit regularly provides children with feedback on their progress toward the goals they have set. For example, it can generate a progress report every week and provide it to them. The feedback providing unit can also suggest areas for improvement and next steps toward achieving the goals. Furthermore, the feedback providing unit can provide children with tools to self-evaluate their own progress. This allows children to understand their own progress and take concrete action toward achieving their goals.

[0069] The KISPA2 system can also use its emotion estimation function to monitor children's stress levels and suggest appropriate relaxation methods. For example, if children are feeling high levels of stress, it can suggest breathing exercises or meditation techniques for relaxation. It can also use emotion estimation to suggest music or activities that will help children relax. It can also use emotion estimation to suggest exercises or hobbies that will help children reduce stress. This allows children to effectively manage stress and maintain motivation to achieve their goals.

[0070] The KISPA2 system can also be equipped with a communication support unit, which helps children communicate effectively with other children and mentors. For example, it can provide online forums and chat functions, giving children a place to share information with each other. The communication support unit can also set up regular online meetings with mentors, providing children with an opportunity to receive direct advice. The communication support unit can also provide training programs for children to learn effective communication skills. This allows children to receive support toward achieving their goals through communication with others.

[0071] The KISPA2 system can also use emotion estimation to provide a reward system to increase children's motivation. For example, children can earn badges or points every time they achieve a small goal. The emotion estimation function can also be used to suggest rewards that children find particularly enjoyable. Furthermore, the emotion estimation function can be used to design a reward system that gives children a sense of accomplishment. This makes it easier for children to maintain their motivation to achieve their goals.

[0072] The KISPA2 system can also be equipped with a self-assessment support section, which provides tools for children to self-assess their progress and skill level. For example, it can conduct regular self-assessment surveys to allow children to objectively understand their own growth. The self-assessment support section can also suggest next steps based on the self-assessment results. Furthermore, the self-assessment support section can provide feedback to help children gain confidence through self-assessment. This allows children to realize their own growth and increases their motivation to achieve their goals.

[0073] The KISPA2 system can also use its emotion estimation function to analyze children's feelings about learning and provide advice to optimize their learning style. For example, if children feel stressed about a particular learning method, it can suggest an alternative learning method. The emotion estimation function can also be used to suggest an environment in which children can learn most relaxedly. Furthermore, the emotion estimation function can also be used to adjust the learning content so that children have positive feelings about learning. This allows children to study in the learning style that is best suited to them.

[0074] The KISPA2 system can also include a career exploration module. This module helps children think concretely about their future careers. For example, it can provide detailed information about different occupations and the skills required. The career exploration module can also introduce internship and volunteer opportunities related to occupations that interest children. The career exploration module can also provide tools for children to plan specific steps toward their future careers. This allows children to think concretely about their future careers and set goals.

[0075] The KISPA2 system can also use its emotion estimation function to suggest activities to boost children's self-esteem. For example, it can suggest journaling activities for children to reflect on their strengths and successes. It can also use emotion estimation to provide children with positive affirmations to boost their self-esteem. It can also use emotion estimation to suggest group activities to boost children's self-esteem. This helps children to boost their self-esteem and move towards their goals with confidence.

[0076] The KISPA2 system can further include a resource provider. The resource provider provides children with the resources they need to achieve their goals. For example, it can provide a list of study materials and reference books. The resource provider can also provide information on online courses and workshops. The resource provider can also provide information on scholarships and grants that children can use to achieve their goals. This allows children to effectively utilize the resources they need to achieve their goals.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The dream and goal realization unit makes the child's dreams and goals concrete. For example, the generation AI generates a specific action plan based on information about the child's dreams and goals. The generation AI can also pose specific questions to the child and suggest an action plan based on those questions. Step 2: The small goal setting unit sets small goals to achieve the specified goal. For example, the generation AI suggests small steps necessary to achieve the goal and shows the child specific actions to take based on them. Step 3: The skill acquisition period suggestion unit suggests the period and method for acquiring the skill. For example, the generation AI may specifically indicate the period and method for acquiring the necessary skills and suggest how the child should proceed with their studies. Step 4: The advice provider provides specific and rational advice. For example, the generator AI explains to the child the importance of studying a little bit every day and provides advice for achieving long-term results.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A department that embodies dreams and goals, a small goal setting unit that sets small goals to achieve the goals that have been realized by the dream or goal realization unit; a skill acquisition period presentation unit that presents a skill acquisition period and a method for achieving the small goal set by the small goal setting unit; an advice providing unit that provides specific and reasonable advice based on the skill acquisition period and method presented by the skill acquisition period presenting unit; A system characterized by:

2. The part that embodies dreams and goals is: Analyzing behavioral history and interests, and based on that, making specific suggestions for achieving the dreams and goals mentioned above 2. The system of claim 1.

3. The small goal setting unit Set these mini-goals that are individually tailored to your learning style and pace 2. The system of claim 1.

4. The skill acquisition period presentation unit A detailed assessment of skill levels is conducted, and the optimal period and method for acquiring said skills are proposed based on the assessment.

2. The system of claim 1.

5. The advice providing unit Analyze successful and unsuccessful experiences and provide specific and rational advice based on those experiences 2. The system of claim 1.

6. The part that embodies dreams and goals is: Analyze the emotions you have about your dreams and goals and generate questions to elicit those positive emotions.

2. The system of claim 1.

7. The small goal setting unit Analyze how you feel when achieving the small goal and provide advice on how to stay motivated 2. The system of claim 1.

8. The skill acquisition period presentation unit Analyze the emotions felt when learning a skill and suggest learning methods to elicit those positive emotions 2. The system of claim 1.

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