system

Abstract

The system according to this embodiment aims to provide appropriate advice and programs to resolve worries and anxieties related to child-rearing. [Solution] The system according to this embodiment comprises a setting reception unit, an advice provision unit, an activity provision unit, and a learning unit. The setting reception unit receives the user's personal settings. The advice provision unit provides advice based on the personal settings received by the setting reception unit. The activity provision unit provides a daily activity program based on the advice provided by the advice provision unit. The learning unit studies books that the user wishes to refer to based on the advice provided by the advice provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully done to provide appropriate advice and programs for solving the troubles and anxieties regarding child-rearing, and there is room for improvement. <�

[0005] The system according to the embodiment aims to provide appropriate advice and programs for solving the troubles and anxieties regarding child-rearing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a settings reception unit, an advice provision unit, an activity provision unit, and a learning unit. The settings reception unit receives the user's personal settings. The advice provision unit provides advice based on the personal settings received by the settings reception unit. The activity provision unit provides a daily activity program based on the advice provided by the advice provision unit. The learning unit studies books that the user wishes to refer to based on the advice provided by the advice provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate advice and programs to resolve worries and anxieties related to child-rearing. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that helps solve worries and anxieties about child-rearing. This AI agent system is a child-rearing teacher that provides necessary advice at the right time for modern parents, where the number of dual-income households is increasing and the time available to spend with children and learn about child-rearing methods is limited. Based on the user's personal settings (child's age, gender, hobbies, etc.), the AI ​​agent system provides advice based on famous child-rearing theories such as Montessori education and Steiner education. The AI ​​agent system is always present as a companion in child-rearing, learns books that the user wants to refer to, and engages in conversation based on their content. Furthermore, the AI ​​agent system provides a daily activity program for children up to 3 years old, and the AI ​​recommends the next activity based on the results of the daily activities. The business model of the AI ​​agent system is based on three revenue models: service provision, advertising revenue, and collaboration. The service provision employs a subscription and freemium model, with free services including conversation with the AI ​​agent system, messages from the AI ​​agent system, and daily programs (up to 3). Paid services include book reading, unlimited daily programs, and no advertisements. In the advertising revenue model, when users ask the AI ​​agent system for recommendations on toys, play areas, or extracurricular activities, advertisements will be displayed in addition to the regular answers. In the collaboration model, collaborative projects will be implemented with companies that provide products for children, including sponsored articles and giveaway campaigns. As part of the expansion strategy for the AI ​​agent system, there are plans to increase the number of users by offering it free of charge for one year to families. Furthermore, for overseas expansion, there are plans to develop the same system for business people and people who want to lose weight. With this, the AI ​​agent system will provide advice based on the user's personal settings, offer daily activity programs, and learn from books that the user may find helpful.

[0029] The AI ​​agent system according to this embodiment comprises a settings reception unit, an advice provision unit, an activity provision unit, and a learning unit. The settings reception unit receives the user's personal settings. The user's personal settings include, but are not limited to, the child's age, gender, hobbies, health status, and lifestyle. The settings reception unit can, for example, store the information entered by the user in a database for later reference. The settings reception unit can also customize the user's input method. For example, if the user selects voice input, the settings reception unit uses speech recognition technology to convert the input into text data. The advice provision unit provides advice based on the personal settings received by the settings reception unit. The advice provision unit provides advice based on, for example, child-rearing theories such as Montessori education and Steiner education. The advice provision unit can also provide advice tailored to the user's individual needs. For example, if the user asks a question about a specific problem, the advice provision unit provides a specific solution to that problem. The activity provision unit provides a daily activity program based on the advice provided by the advice provision unit. The activity provision unit suggests daily activities for children up to 3 years old. Furthermore, the activity provider unit can recommend the next activity based on the results of the daily activities. For example, after a child completes a specific activity, the activity provider unit evaluates the results and suggests the next activity. The learning unit learns about books that the user may want to refer to based on the advice provided by the advice provider unit. For example, the learning unit reads a book specified by the user and analyzes its contents. The learning unit can also provide appropriate advice to the user based on the analysis results. For example, the learning unit extracts the key points of a book specified by the user and provides advice based on those key points. Thus, the AI ​​agent system according to this embodiment can provide advice based on the user's personal settings, provide a daily activity program, and learn about books that the user may want to refer to.

[0030] The settings reception unit accepts user personal settings. These settings include, but are not limited to, a child's age, gender, hobbies, health status, and lifestyle. The settings reception unit can, for example, store user-entered information in a database for later reference. Specifically, information entered by users through a web interface or mobile application is stored in a secure database for quick access when needed. The settings reception unit can also customize user input methods. For example, if a user chooses voice input, the settings reception unit uses speech recognition technology to convert the input into text data. Natural language processing (NLP) algorithms are used in the speech recognition technology to accurately analyze the user's speech and store it as text data. Furthermore, the settings reception unit can analyze user input and ask supplementary questions as needed to gather more detailed information. For example, if a user enters "sports" as a child's hobby, the settings reception unit might ask additional questions such as, "What specific sports does your child like?" to gather more detailed information. This allows the settings reception unit to collect detailed settings information tailored to the user's individual needs and circumstances, improving the overall accuracy and effectiveness of the system.

[0031] The advice service provides advice based on the user's personal settings received by the settings reception service. The advice service provides advice based on parenting theories such as Montessori and Steiner education. Specifically, it suggests appropriate educational methods and activities based on the user's child's age, hobbies, and health condition. For example, for a 3-year-old child, it suggests activities that develop fine motor skills based on Montessori principles, and for a 5-year-old child, it suggests creative play incorporating a Steiner approach. The advice service can also provide advice tailored to the user's individual needs. For example, if a user asks about a specific problem, the advice service provides concrete solutions. For instance, if a child cries at night, the advice service identifies the cause and suggests appropriate countermeasures. Furthermore, the advice service can use AI to analyze the user's past data and behavioral patterns to provide more personalized advice. For example, it can evaluate the effectiveness of previously provided advice and prioritize suggesting the most effective advice. This allows the advice service to provide users with optimal advice and support their parenting.

[0032] The Activity Provider Department provides daily activity programs based on advice provided by the Advice Provider Department. For example, the Activity Provider Department suggests daily activities for children up to 3 years old. Specifically, the Activity Provider Department selects activities appropriate to the child's age and developmental stage and suggests them to parents. For example, for a 3-year-old child, it might suggest block play to develop fine motor skills or story time to stimulate creativity. The Activity Provider Department can also recommend the next activity based on the results of the daily activity. For example, after a child completes a particular activity, the Activity Provider Department evaluates the results and suggests the next activity. The evaluation can use feedback from parents or data on the child's behavior. For example, parents can provide feedback on the effectiveness of an activity, and the next activity can be adjusted based on that feedback. The Activity Provider Department can also use AI to analyze the child's behavior data and suggest the most suitable activity. For example, if a child shows interest in a particular activity, it can suggest activities to further stimulate that interest. In this way, the Activity Provider Department can support the child's development and provide parents with an effective activity program.

[0033] The learning unit learns about books that users want to refer to based on the advice provided by the advice-providing unit. For example, the learning unit reads a book specified by the user and analyzes its content. Specifically, the learning unit uses natural language processing (NLP) technology to analyze the book's content and extract important information and key points. For example, it reads parenting or educational books specified by the user and extracts information and activity ideas useful for child-rearing. The learning unit can also provide appropriate advice to the user based on the analysis results. For example, the learning unit extracts the key points of a book specified by the user and provides advice based on those points. Furthermore, the learning unit can provide more personalized advice by considering the user's past advice history and behavioral data. For example, it can evaluate the effectiveness of previously provided advice and prioritize suggesting effective advice. The learning unit can also quickly analyze newly specified books by the user and provide advice based on the latest information. In this way, the learning unit can always provide users with the latest and most appropriate advice and support them in child-rearing.

[0034] The activity provision unit includes a recommendation unit that recommends the next activity based on the results of daily activities. The recommendation unit, for example, evaluates the results of activities performed by the user and suggests the next activity based on that evaluation. For example, if the user exercises, the recommendation unit suggests the next exercise program based on the degree of achievement and feedback from that exercise. The recommendation unit can also evaluate the results of a meal plan if the user has followed a meal plan and suggest the next meal plan. For example, the recommendation unit suggests the next meal plan based on the nutrients and calories consumed by the user. This makes it possible to recommend the next activity based on the results of daily activities. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's activity data into a generative AI and have the generative AI suggest the next activity.

[0035] The advice-providing unit includes an advertising display unit that displays advertisements based on the advice. The advertising display unit displays relevant advertisements when a user receives advice, for example. For example, when a user receives advice on childcare, the advertising display unit displays advertisements for recommended toys and play areas. The advertising display unit can also display advertisements for health foods and fitness products when a user receives advice on health. For example, when a user receives specific health advice, the advertising display unit displays advertisements for products and services related to that advice. This allows advertisements to be displayed based on the advice. Some or all of the above processing in the advertising display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advertising display unit can input user advice data into a generative AI and have the generative AI display relevant advertisements.

[0036] The Activity Provisioning Unit includes a Collaboration Unit that implements collaborative projects based on the activities. The Collaboration Unit proposes collaborative projects with relevant companies and organizations based on the results of the activities performed by the user. For example, if the user completes a specific exercise program, the Collaboration Unit proposes collaborative projects with fitness gyms and sports clubs related to that exercise program. The Collaboration Unit can also propose collaborative projects with restaurants and food manufacturers related to a specific meal plan if the user completes that meal plan. For example, based on the meal plan the user has completed, the Collaboration Unit proposes special menus at specific restaurants or new products from food manufacturers. This enables the implementation of collaborative projects based on activities. Some or all of the above processing in the Collaboration Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Collaboration Unit can input the user's activity data into a generative AI and have the generative AI execute the proposal of collaborative projects.

[0037] The settings reception unit analyzes the user's past settings history and suggests the most suitable settings. For example, the settings reception unit automatically displays items that the user has frequently set in the past as candidates. The settings reception unit can also prioritize suggesting settings methods that the user has used in the past (voice, text, etc.). Furthermore, the settings reception unit can predict and suggest settings that the user will use during specific time periods based on the user's past settings history. This allows the system to analyze the user's past settings history and suggest the most suitable settings. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's past settings data into a generative AI and have the generative AI suggest the most suitable settings.

[0038] The settings reception unit customizes the settings based on the user's current lifestyle and areas of interest when settings are received. For example, if the user is in a dual-income household, the settings reception unit will prioritize displaying settings related to time management. Furthermore, if the user is interested in a particular parenting theory, the settings reception unit can suggest settings based on that theory. In addition, if the user has specific hobbies or interests, the settings reception unit can customize and display settings related to those interests. This allows for the customization of settings based on the user's current lifestyle and areas of interest. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's lifestyle and interest data into a generative AI and have the generative AI perform the customization of settings.

[0039] The settings reception unit, upon receiving a setting request, prioritizes displaying settings that are highly relevant to the user, taking into account the user's geographical location. For example, if the user lives in a specific region, the settings reception unit prioritizes displaying settings related to that region. Furthermore, if the user is traveling, the settings reception unit can prioritize displaying settings related to the travel destination. Additionally, if the user is participating in a specific event, the settings reception unit can prioritize displaying settings related to that event. This allows for the display of highly relevant settings, taking into account the user's geographical location. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without one. For example, the settings reception unit can input the user's geographical location data into a generative AI and have the generative AI display highly relevant settings.

[0040] The settings reception unit analyzes the user's social media activity and suggests relevant settings when a settings request is received. For example, the settings reception unit suggests settings based on the content the user frequently posts on social media. It can also suggest settings based on the accounts the user follows on social media. Furthermore, it can suggest settings based on the groups the user participates in on social media. This allows the system to analyze the user's social media activity and suggest relevant settings. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's social media data into a generative AI and have the generative AI suggest relevant settings.

[0041] The advice-providing unit adjusts the level of detail of the advice based on the child's age and gender. For example, if the child is a toddler, the advice-providing unit provides simple and easy-to-understand advice. If the child is a primary school student, the advice-providing unit can also provide advice that includes specific examples. Furthermore, if the child is a middle school student or older, the advice-providing unit can provide advice that explains detailed theories and backgrounds. This allows the level of detail of the advice to be adjusted based on the child's age and gender. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input the child's age and gender data into a generative AI and have the generative AI perform the adjustment of the level of detail of the advice.

[0042] The advice-providing unit applies different advice algorithms depending on the parenting theory when providing advice. For example, the advice-providing unit can provide advice based on Montessori education. It can also provide advice based on Steiner education. Furthermore, it can provide advice based on Piaget's developmental theory. This allows for the application of different advice algorithms depending on the parenting theory. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input parenting theory data into a generative AI and have the generative AI perform the application of the advice algorithm.

[0043] The advice-providing unit prioritizes advice based on the child's developmental stage. For example, if the child is an infant, the advice-providing unit prioritizes advice on basic childcare methods. If the child is a toddler, the advice-providing unit may also prioritize advice on education and play. Furthermore, if the child is of elementary school age or older, the advice-providing unit may also prioritize advice on learning and social skills. This allows the advice-providing unit to prioritize advice based on the child's developmental stage. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input child developmental stage data into a generative AI and have the generative AI determine the priority of advice.

[0044] The advice-providing unit provides optimal advice by referring to the user's past advice history when providing advice. For example, the advice-providing unit provides relevant advice based on advice the user has received in the past. The advice-providing unit can also prioritize providing effective advice from the user's past advice history. Furthermore, the advice-providing unit can analyze the user's past advice history and provide the most appropriate advice. This allows the advice-providing unit to provide optimal advice by referring to the user's past advice history. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input the user's past advice data into a generative AI and have the generative AI perform the task of providing optimal advice.

[0045] The activity provider adjusts the level of detail of an activity based on the child's interests and preferences when providing it. For example, if the child is interested in animals, the activity provider will provide an activity related to animals. It can also provide a music-related activity if the child is interested in music. Furthermore, if the child is interested in science, it can provide a science experiment activity. This allows the level of detail of an activity to be adjusted based on the child's interests and preferences. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider can input data on the child's interests and preferences into a generative AI and have the generative AI adjust the level of detail of the activity.

[0046] The activity provider optimizes the next activity by referring to past activity results when providing an activity. For example, the activity provider suggests the next activity based on the results of activities the user has performed in the past. The activity provider can also prioritize providing effective activities based on the user's past activity results. Furthermore, the activity provider can analyze the user's past activity results and provide the most appropriate activity. This allows for the optimization of the next activity by referring to past activity results. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider can input the user's past activity data into a generative AI and have the generative AI perform the optimization of the next activity.

[0047] The activity provider unit prioritizes providing highly relevant activities, taking into account the child's geographical location when providing activities. For example, if the child lives in a specific area, the activity provider unit will prioritize activities related to that area. Furthermore, if the child is traveling, the activity provider unit can prioritize activities related to the travel destination. Additionally, if the child is participating in a specific event, the activity provider unit can prioritize activities related to that event. This allows for the prioritization of highly relevant activities, taking into account the child's geographical location. Some or all of the above processing in the activity provider unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider unit can input the child's geographical location data into a generative AI and have the generative AI provide highly relevant activities.

[0048] The activity provider analyzes the user's social media activity and suggests relevant activities when providing activities. For example, the activity provider may suggest activities based on the content the user frequently posts on social media. It can also suggest activities based on the accounts the user follows on social media. Furthermore, it can suggest activities based on the groups the user participates in on social media. This allows the activity provider to analyze the user's social media activity and suggest relevant activities. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider can input the user's social media data into a generative AI and have the generative AI suggest relevant activities.

[0049] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit provides relevant learning content based on what the user has learned in the past. The learning unit can also prioritize providing effective learning methods based on the user's past learning data. Furthermore, the learning unit can analyze the user's past learning data and provide the most appropriate learning content. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0050] The learning unit customizes learning content based on the user's interests and preferences during the learning process. For example, if the user is interested in a particular parenting theory, the learning unit will provide learning content based on that theory. The learning unit can also provide learning content related to specific hobbies or interests if the user has them. Furthermore, if the user is facing a particular problem, the learning unit can provide learning content related to that problem. This allows for the customization of learning content based on the user's interests and preferences. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user interest data into a generative AI and have the generative AI perform the customization of learning content.

[0051] The learning unit prioritizes providing highly relevant learning content during the learning process, taking into account the user's geographical location. For example, if the user lives in a specific region, the learning unit prioritizes providing learning content related to that region. Furthermore, if the user is traveling, the learning unit can prioritize providing learning content related to their travel destination. Additionally, if the user is participating in a specific event, the learning unit can prioritize providing learning content related to that event. This allows the learning unit to prioritize providing highly relevant learning content while considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location data into a generative AI and have the generative AI provide highly relevant learning content.

[0052] The learning unit analyzes the user's social media activity during learning and suggests relevant learning content. For example, the learning unit suggests learning content based on the content the user frequently posts on social media. It can also suggest learning content based on the accounts the user follows on social media. Furthermore, the learning unit can suggest learning content based on the groups the user participates in on social media. This allows the learning unit to analyze the user's social media activity and suggest relevant learning content. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's social media data into a generative AI and have the generative AI suggest relevant learning content.

[0053] The recommendation unit optimizes the next activity by referring to past activity results when making recommendations. For example, the recommendation unit suggests the next activity based on the results of activities the user has performed in the past. The recommendation unit can also prioritize providing effective activities based on the user's past activity results. Furthermore, the recommendation unit can analyze the user's past activity results and provide the most appropriate activity. This allows the next activity to be optimized by referring to past activity results. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input the user's past activity data into a generative AI and have the generative AI perform the optimization of the next activity.

[0054] The recommendation unit customizes recommendations based on the child's interests and preferences. For example, if the child is interested in animals, the recommendation unit will recommend activities related to animals. It can also recommend music-related activities if the child is interested in music. Furthermore, if the child is interested in science, the recommendation unit can recommend activities related to science experiments. This allows for the customization of recommendations based on the child's interests and preferences. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit can input data on the child's interests and preferences into a generative AI and have the generative AI customize the recommendations.

[0055] The recommendation unit prioritizes recommending activities that are highly relevant to the child, taking into account the child's geographical location. For example, if the child lives in a specific area, the recommendation unit will prioritize recommending activities related to that area. Furthermore, if the child is traveling, the recommendation unit can prioritize recommending activities related to the travel destination. Additionally, if the child is participating in a specific event, the recommendation unit can prioritize recommending activities related to that event. This allows the recommendation unit to prioritize recommending activities that are highly relevant, taking into account the child's geographical location. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit can input the child's geographical location data into a generative AI and have the generative AI recommend highly relevant activities.

[0056] The recommendation unit analyzes the user's social media activity and recommends relevant activities. For example, the recommendation unit may recommend activities based on the content the user frequently posts on social media. It can also recommend activities based on the accounts the user follows on social media. Furthermore, the recommendation unit may recommend activities based on the groups the user participates in on social media. This allows the recommendation unit to analyze the user's social media activity and recommend relevant activities. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input the user's social media data into a generative AI and have the generative AI perform the recommendation of relevant activities.

[0057] The ad display unit displays the most suitable ads by referring to the user's past purchase history when displaying ads. For example, the ad display unit displays ads related to products the user has previously purchased. The ad display unit can also prioritize advertising products that the user is likely to be interested in based on their past purchase history. Furthermore, the ad display unit can analyze the user's past purchase history and display the most appropriate ads. This allows the ad display unit to display the most suitable ads by referring to the user's past purchase history. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input the user's past purchase data into a generative AI and have the generative AI execute the display of the most suitable ads.

[0058] The ad display unit customizes ad content based on the user's interests when displaying ads. For example, if the user has a particular hobby or interest, the ad display unit will display ads related to that. Furthermore, if the user is interested in a particular parenting theory, the ad display unit can display ads related to that theory. In addition, if the user is facing a particular problem, the ad display unit can display ads related to that problem. This allows for the customization of ad content based on the user's interests. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input user interest data into a generative AI and have the generative AI perform the customization of ad content.

[0059] The ad display unit prioritizes displaying highly relevant ads by considering the user's geographical location when displaying ads. For example, if the user lives in a specific region, the ad display unit will prioritize displaying ads related to that region. Furthermore, if the user is traveling, the ad display unit can prioritize displaying ads related to their travel destination. Additionally, if the user is participating in a specific event, the ad display unit can prioritize displaying ads related to that event. This allows for the prioritization of highly relevant ads by considering the user's geographical location. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input the user's geographical location data into a generative AI and have the generative AI display highly relevant ads.

[0060] The ad display unit analyzes the user's social media activity when displaying ads and displays relevant ads. For example, the ad display unit displays ads based on the content the user frequently posts on social media. It can also display ads based on the accounts the user follows on social media. Furthermore, the ad display unit can display ads based on the groups the user participates in on social media. This allows for the analysis of the user's social media activity and the display of relevant ads. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input the user's social media data into a generative AI and have the generative AI display relevant ads.

[0061] The collaboration department provides optimal collaboration plans by referring to the user's past activity results. For example, the collaboration department proposes the most suitable collaboration plan based on the results of the user's past activities. The collaboration department can also prioritize providing effective plans based on the user's past activity results. Furthermore, the collaboration department can analyze the user's past activity results and provide the most appropriate collaboration plan. This allows the collaboration department to provide optimal plans by referring to the user's past activity results. Some or all of the above processing in the collaboration department may be performed using, for example, a generative AI, or without a generative AI. For example, the collaboration department can input the user's past activity data into a generative AI and have the generative AI provide the optimal collaboration plan.

[0062] The Collaboration Department customizes project content based on the user's interests and preferences when planning collaborations. For example, if a user has a specific hobby or interest, the Collaboration Department can provide a collaboration project related to that. Furthermore, if a user is interested in a particular parenting theory, the Collaboration Department can provide a collaboration project related to that theory. In addition, if a user is facing a specific problem, the Collaboration Department can provide a collaboration project related to that problem. This allows for the customization of project content based on the user's interests and preferences. Some or all of the above processing in the Collaboration Department may be performed using, for example, a generative AI, or not. For example, the Collaboration Department can input user interest data into a generative AI and have the generative AI customize the project content.

[0063] The Collaboration Department prioritizes providing highly relevant projects to users when planning collaborations, taking into account their geographical location. For example, if a user lives in a specific region, the Collaboration Department will prioritize providing collaboration projects related to that region. Furthermore, if a user is traveling, the Collaboration Department can prioritize providing collaboration projects related to their travel destination. Additionally, if a user is participating in a specific event, the Collaboration Department can prioritize providing collaboration projects related to that event. This allows for the prioritization of highly relevant projects by considering the user's geographical location. Some or all of the above processing in the Collaboration Department may be performed using, for example, a generative AI, or without one. For instance, the Collaboration Department can input the user's geographical location data into a generative AI and have the AI ​​provide highly relevant projects.

[0064] The Collaboration Department analyzes users' social media activity and proposes relevant projects when planning collaborations. For example, the Collaboration Department may propose collaboration projects based on the content users frequently post on social media. It can also propose collaboration projects based on the accounts users follow on social media. Furthermore, the Collaboration Department may propose collaboration projects based on the groups users participate in on social media. This allows for the analysis of users' social media activity and the proposal of relevant projects. Some or all of the above processing in the Collaboration Department may be performed using, for example, generative AI, or not. For example, the Collaboration Department can input user social media data into a generative AI and have the generative AI generate proposals for relevant projects.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The settings reception unit can analyze the user's past settings history and suggest the most suitable settings. For example, it can automatically display items that the user has frequently set in the past as candidates. It can also prioritize suggesting settings methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest settings that the user will use during specific time periods based on their past settings history. This allows the system to analyze the user's past settings history and suggest the most suitable settings. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's past settings data into a generative AI and have the generative AI suggest the most suitable settings.

[0067] The advice-providing unit can adjust the level of detail of the advice based on the child's age and gender. For example, if the child is a toddler, it can provide simple and easy-to-understand advice. If the child is an elementary school student, it can provide advice that includes specific examples. Furthermore, if the child is a middle school student or older, it can provide advice that explains detailed theories and backgrounds. This allows the level of detail of the advice to be adjusted based on the child's age and gender. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input the child's age and gender data into a generative AI and have the generative AI perform the adjustment of the level of detail of the advice.

[0068] The activity provider can adjust the level of detail of an activity based on the child's interests and preferences when providing an activity. For example, if the child is interested in animals, it can provide an activity related to animals. If the child is interested in music, it can also provide an activity related to music. Furthermore, if the child is interested in science, it can provide an activity related to science experiments. This allows the level of detail of the activity to be adjusted based on the child's interests and preferences. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the activity provider can input data on the child's interests and preferences into a generative AI and have the generative AI perform the adjustment of the level of detail of the activity.

[0069] The recommendation unit can optimize the next activity by referring to past activity results when making recommendations. For example, it can suggest the next activity based on the results of activities the user has performed in the past. It can also prioritize providing activities that were effective based on the user's past activity results. Furthermore, it can analyze the user's past activity results and provide the most appropriate activity. This allows the recommendation unit to optimize the next activity by referring to past activity results. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's past activity data into a generative AI and have the generative AI perform the optimization of the next activity.

[0070] The ad display unit can display the most suitable ads by referring to the user's past purchase history when displaying ads. For example, it can display ads related to products the user has previously purchased. It can also prioritize advertising products that the user is likely to be interested in based on their past purchase history. Furthermore, it can analyze the user's past purchase history and display the most appropriate ads. This allows the ad display unit to display the most suitable ads by referring to the user's past purchase history. Some or all of the above processing in the ad display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the ad display unit can input the user's past purchase data into a generation AI and have the generation AI execute the display of the most suitable ads.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The settings reception unit receives the user's personal settings. These settings include the child's age, gender, hobbies, health status, and lifestyle. The settings reception unit saves the user's input information to a database for later reference. It also allows users to customize their input methods; for example, if voice input is selected, speech recognition technology is used to convert the input into text data. Step 2: The advice provider provides advice based on the personal settings received by the settings reception unit. The advice provider provides advice based on parenting theories such as Montessori education and Steiner education. It also provides advice tailored to the user's individual needs and, if asked about a specific problem, provides concrete solutions to that problem. Step 3: The Activity Provider Department provides a daily activity program based on the advice provided by the Advice Provider Department. For example, the Activity Provider Department suggests daily activities for children up to 3 years old and recommends the next activity based on the results of the daily activities. After the child completes a particular activity, the results are evaluated and the next activity is suggested. Step 4: The learning unit studies the book the user wants to refer to based on the advice provided by the advice-providing unit. The learning unit reads the book specified by the user and analyzes its contents. Based on the analysis results, it provides appropriate advice to the user, extracts the key points of the specified book, and provides advice based on those key points.

[0073] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that helps solve worries and anxieties about child-rearing. This AI agent system is a child-rearing teacher that provides necessary advice at the right time for modern parents, where the number of dual-income households is increasing and the time available to spend with children and learn about child-rearing methods is limited. Based on the user's personal settings (child's age, gender, hobbies, etc.), the AI ​​agent system provides advice based on famous child-rearing theories such as Montessori education and Steiner education. The AI ​​agent system is always present as a companion in child-rearing, learns books that the user wants to refer to, and engages in conversation based on their content. Furthermore, the AI ​​agent system provides a daily activity program for children up to 3 years old, and the AI ​​recommends the next activity based on the results of the daily activities. The business model of the AI ​​agent system is based on three revenue models: service provision, advertising revenue, and collaboration. The service provision employs a subscription and freemium model, with free services including conversation with the AI ​​agent system, messages from the AI ​​agent system, and daily programs (up to 3). Paid services include book reading, unlimited daily programs, and no advertisements. In the advertising revenue model, when users ask the AI ​​agent system for recommendations on toys, play areas, or extracurricular activities, advertisements will be displayed in addition to the regular answers. In the collaboration model, collaborative projects will be implemented with companies that provide products for children, including sponsored articles and giveaway campaigns. As part of the expansion strategy for the AI ​​agent system, there are plans to increase the number of users by offering it free of charge for one year to families. Furthermore, for overseas expansion, there are plans to develop the same system for business people and people who want to lose weight. With this, the AI ​​agent system will provide advice based on the user's personal settings, offer daily activity programs, and learn from books that the user may find helpful.

[0074] The AI ​​agent system according to this embodiment comprises a settings reception unit, an advice provision unit, an activity provision unit, and a learning unit. The settings reception unit receives the user's personal settings. The user's personal settings include, but are not limited to, the child's age, gender, hobbies, health status, and lifestyle. The settings reception unit can, for example, store the information entered by the user in a database for later reference. The settings reception unit can also customize the user's input method. For example, if the user selects voice input, the settings reception unit uses speech recognition technology to convert the input into text data. The advice provision unit provides advice based on the personal settings received by the settings reception unit. The advice provision unit provides advice based on, for example, child-rearing theories such as Montessori education and Steiner education. The advice provision unit can also provide advice tailored to the user's individual needs. For example, if the user asks a question about a specific problem, the advice provision unit provides a specific solution to that problem. The activity provision unit provides a daily activity program based on the advice provided by the advice provision unit. The activity provision unit suggests daily activities for children up to 3 years old. Furthermore, the activity provider unit can recommend the next activity based on the results of the daily activities. For example, after a child completes a specific activity, the activity provider unit evaluates the results and suggests the next activity. The learning unit learns about books that the user may want to refer to based on the advice provided by the advice provider unit. For example, the learning unit reads a book specified by the user and analyzes its contents. The learning unit can also provide appropriate advice to the user based on the analysis results. For example, the learning unit extracts the key points of a book specified by the user and provides advice based on those key points. Thus, the AI ​​agent system according to this embodiment can provide advice based on the user's personal settings, provide a daily activity program, and learn about books that the user may want to refer to.

[0075] The settings reception unit accepts user personal settings. These settings include, but are not limited to, a child's age, gender, hobbies, health status, and lifestyle. The settings reception unit can, for example, store user-entered information in a database for later reference. Specifically, information entered by users through a web interface or mobile application is stored in a secure database for quick access when needed. The settings reception unit can also customize user input methods. For example, if a user chooses voice input, the settings reception unit uses speech recognition technology to convert the input into text data. Natural language processing (NLP) algorithms are used in the speech recognition technology to accurately analyze the user's speech and store it as text data. Furthermore, the settings reception unit can analyze user input and ask supplementary questions as needed to gather more detailed information. For example, if a user enters "sports" as a child's hobby, the settings reception unit might ask additional questions such as, "What specific sports does your child like?" to gather more detailed information. This allows the settings reception unit to collect detailed settings information tailored to the user's individual needs and circumstances, improving the overall accuracy and effectiveness of the system.

[0076] The advice service provides advice based on the user's personal settings received by the settings reception service. The advice service provides advice based on parenting theories such as Montessori and Steiner education. Specifically, it suggests appropriate educational methods and activities based on the user's child's age, hobbies, and health condition. For example, for a 3-year-old child, it suggests activities that develop fine motor skills based on Montessori principles, and for a 5-year-old child, it suggests creative play incorporating a Steiner approach. The advice service can also provide advice tailored to the user's individual needs. For example, if a user asks about a specific problem, the advice service provides concrete solutions. For instance, if a child cries at night, the advice service identifies the cause and suggests appropriate countermeasures. Furthermore, the advice service can use AI to analyze the user's past data and behavioral patterns to provide more personalized advice. For example, it can evaluate the effectiveness of previously provided advice and prioritize suggesting the most effective advice. This allows the advice service to provide users with optimal advice and support their parenting.

[0077] The Activity Provider Department provides daily activity programs based on advice provided by the Advice Provider Department. For example, the Activity Provider Department suggests daily activities for children up to 3 years old. Specifically, the Activity Provider Department selects activities appropriate to the child's age and developmental stage and suggests them to parents. For example, for a 3-year-old child, it might suggest block play to develop fine motor skills or story time to stimulate creativity. The Activity Provider Department can also recommend the next activity based on the results of the daily activity. For example, after a child completes a particular activity, the Activity Provider Department evaluates the results and suggests the next activity. The evaluation can use feedback from parents or data on the child's behavior. For example, parents can provide feedback on the effectiveness of an activity, and the next activity can be adjusted based on that feedback. The Activity Provider Department can also use AI to analyze the child's behavior data and suggest the most suitable activity. For example, if a child shows interest in a particular activity, it can suggest activities to further stimulate that interest. In this way, the Activity Provider Department can support the child's development and provide parents with an effective activity program.

[0078] The learning unit learns about books that users want to refer to based on the advice provided by the advice-providing unit. For example, the learning unit reads a book specified by the user and analyzes its content. Specifically, the learning unit uses natural language processing (NLP) technology to analyze the book's content and extract important information and key points. For example, it reads parenting or educational books specified by the user and extracts information and activity ideas useful for child-rearing. The learning unit can also provide appropriate advice to the user based on the analysis results. For example, the learning unit extracts the key points of a book specified by the user and provides advice based on those points. Furthermore, the learning unit can provide more personalized advice by considering the user's past advice history and behavioral data. For example, it can evaluate the effectiveness of previously provided advice and prioritize suggesting effective advice. The learning unit can also quickly analyze newly specified books by the user and provide advice based on the latest information. In this way, the learning unit can always provide users with the latest and most appropriate advice and support them in child-rearing.

[0079] The activity provision unit includes a recommendation unit that recommends the next activity based on the results of daily activities. The recommendation unit, for example, evaluates the results of activities performed by the user and suggests the next activity based on that evaluation. For example, if the user exercises, the recommendation unit suggests the next exercise program based on the degree of achievement and feedback from that exercise. The recommendation unit can also evaluate the results of a meal plan if the user has followed a meal plan and suggest the next meal plan. For example, the recommendation unit suggests the next meal plan based on the nutrients and calories consumed by the user. This makes it possible to recommend the next activity based on the results of daily activities. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's activity data into a generative AI and have the generative AI suggest the next activity.

[0080] The advice-providing unit includes an advertising display unit that displays advertisements based on the advice. The advertising display unit displays relevant advertisements when a user receives advice, for example. For example, when a user receives advice on childcare, the advertising display unit displays advertisements for recommended toys and play areas. The advertising display unit can also display advertisements for health foods and fitness products when a user receives advice on health. For example, when a user receives specific health advice, the advertising display unit displays advertisements for products and services related to that advice. This allows advertisements to be displayed based on the advice. Some or all of the above processing in the advertising display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advertising display unit can input user advice data into a generative AI and have the generative AI display relevant advertisements.

[0081] The Activity Provisioning Unit includes a Collaboration Unit that implements collaborative projects based on the activities. The Collaboration Unit proposes collaborative projects with relevant companies and organizations based on the results of the activities performed by the user. For example, if the user completes a specific exercise program, the Collaboration Unit proposes collaborative projects with fitness gyms and sports clubs related to that exercise program. The Collaboration Unit can also propose collaborative projects with restaurants and food manufacturers related to a specific meal plan if the user completes that meal plan. For example, based on the meal plan the user has completed, the Collaboration Unit proposes special menus at specific restaurants or new products from food manufacturers. This enables the implementation of collaborative projects based on activities. Some or all of the above processing in the Collaboration Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Collaboration Unit can input the user's activity data into a generative AI and have the generative AI execute the proposal of collaborative projects.

[0082] The settings reception unit estimates the user's emotions and adjusts the input method for personal settings based on the estimated emotions. For example, if the user is stressed, the settings reception unit provides a simple interface and minimizes the input steps. If the user is relaxed, the settings reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the settings reception unit can prioritize voice input to allow for quick input of personal settings. This allows the input method for personal settings to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings reception unit may be performed using a generative AI, or not. For example, the settings reception unit can input the user's emotion data into a generative AI and have the generative AI adjust the input method for personal settings.

[0083] The settings reception unit analyzes the user's past settings history and suggests the most suitable settings. For example, the settings reception unit automatically displays items that the user has frequently set in the past as candidates. The settings reception unit can also prioritize suggesting settings methods that the user has used in the past (voice, text, etc.). Furthermore, the settings reception unit can predict and suggest settings that the user will use during specific time periods based on the user's past settings history. This allows the system to analyze the user's past settings history and suggest the most suitable settings. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's past settings data into a generative AI and have the generative AI suggest the most suitable settings.

[0084] The settings reception unit customizes the settings based on the user's current lifestyle and areas of interest when settings are received. For example, if the user is in a dual-income household, the settings reception unit will prioritize displaying settings related to time management. Furthermore, if the user is interested in a particular parenting theory, the settings reception unit can suggest settings based on that theory. In addition, if the user has specific hobbies or interests, the settings reception unit can customize and display settings related to those interests. This allows for the customization of settings based on the user's current lifestyle and areas of interest. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's lifestyle and interest data into a generative AI and have the generative AI perform the customization of settings.

[0085] The settings reception unit estimates the user's emotions and determines the priority of settings items based on the estimated emotions. For example, if the user is stressed, the settings reception unit will prioritize displaying settings that promote relaxation. It can also prioritize displaying detailed settings items if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize displaying items that can be configured quickly. This allows the settings items to be prioritized based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or not. For example, the settings reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of settings items.

[0086] The settings reception unit, upon receiving a setting request, prioritizes displaying settings that are highly relevant to the user, taking into account the user's geographical location. For example, if the user lives in a specific region, the settings reception unit prioritizes displaying settings related to that region. Furthermore, if the user is traveling, the settings reception unit can prioritize displaying settings related to the travel destination. Additionally, if the user is participating in a specific event, the settings reception unit can prioritize displaying settings related to that event. This allows for the display of highly relevant settings, taking into account the user's geographical location. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without one. For example, the settings reception unit can input the user's geographical location data into a generative AI and have the generative AI display highly relevant settings.

[0087] The settings reception unit analyzes the user's social media activity and suggests relevant settings when a settings request is received. For example, the settings reception unit suggests settings based on the content the user frequently posts on social media. It can also suggest settings based on the accounts the user follows on social media. Furthermore, it can suggest settings based on the groups the user participates in on social media. This allows the system to analyze the user's social media activity and suggest relevant settings. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's social media data into a generative AI and have the generative AI suggest relevant settings.

[0088] The advice-providing unit estimates the user's emotions and adjusts the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the advice-providing unit will provide advice in gentle language. If the user is relaxed, the advice-providing unit can also provide detailed advice. Furthermore, if the user is in a hurry, the advice-providing unit can provide concise and to-the-point advice. This allows the advice-providing unit to adjust the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using a generative AI, or not. For example, the advice-providing unit can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0089] The advice-providing unit adjusts the level of detail of the advice based on the child's age and gender. For example, if the child is a toddler, the advice-providing unit provides simple and easy-to-understand advice. If the child is a primary school student, the advice-providing unit can also provide advice that includes specific examples. Furthermore, if the child is a middle school student or older, the advice-providing unit can provide advice that explains detailed theories and backgrounds. This allows the level of detail of the advice to be adjusted based on the child's age and gender. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input the child's age and gender data into a generative AI and have the generative AI perform the adjustment of the level of detail of the advice.

[0090] The advice-providing unit applies different advice algorithms depending on the parenting theory when providing advice. For example, the advice-providing unit can provide advice based on Montessori education. It can also provide advice based on Steiner education. Furthermore, it can provide advice based on Piaget's developmental theory. This allows for the application of different advice algorithms depending on the parenting theory. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input parenting theory data into a generative AI and have the generative AI perform the application of the advice algorithm.

[0091] The advice-providing unit estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice-providing unit provides short, concise advice. If the user is relaxed, the advice-providing unit may provide longer advice with more detailed explanations. Furthermore, if the user is in a hurry, the advice-providing unit may provide brief and quick advice. This allows the length of the advice to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using or without a generative AI. For example, the advice-providing unit can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0092] The advice-providing unit prioritizes advice based on the child's developmental stage. For example, if the child is an infant, the advice-providing unit prioritizes advice on basic childcare methods. If the child is a toddler, the advice-providing unit may also prioritize advice on education and play. Furthermore, if the child is of elementary school age or older, the advice-providing unit may also prioritize advice on learning and social skills. This allows the advice-providing unit to prioritize advice based on the child's developmental stage. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input child developmental stage data into a generative AI and have the generative AI determine the priority of advice.

[0093] The advice-providing unit provides optimal advice by referring to the user's past advice history when providing advice. For example, the advice-providing unit provides relevant advice based on advice the user has received in the past. The advice-providing unit can also prioritize providing effective advice from the user's past advice history. Furthermore, the advice-providing unit can analyze the user's past advice history and provide the most appropriate advice. This allows the advice-providing unit to provide optimal advice by referring to the user's past advice history. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input the user's past advice data into a generative AI and have the generative AI perform the task of providing optimal advice.

[0094] The activity provider unit estimates the user's emotions and adjusts the content of the activity based on the estimated emotions. For example, if the user is feeling stressed, the activity provider unit provides a relaxing activity. If the user is relaxed, the activity provider unit can also provide a challenging activity. Furthermore, if the user is in a hurry, the activity provider unit can provide an activity that can be completed in a short time. This allows the content of the activity to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the activity provider unit may be performed using a generative AI, or not using a generative AI. For example, the activity provider unit can input user emotion data into a generative AI and have the generative AI adjust the content of the activity.

[0095] The activity provider adjusts the level of detail of an activity based on the child's interests and preferences when providing it. For example, if the child is interested in animals, the activity provider will provide an activity related to animals. It can also provide a music-related activity if the child is interested in music. Furthermore, if the child is interested in science, it can provide a science experiment activity. This allows the level of detail of an activity to be adjusted based on the child's interests and preferences. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider can input data on the child's interests and preferences into a generative AI and have the generative AI adjust the level of detail of the activity.

[0096] The activity provider optimizes the next activity by referring to past activity results when providing an activity. For example, the activity provider suggests the next activity based on the results of activities the user has performed in the past. The activity provider can also prioritize providing effective activities based on the user's past activity results. Furthermore, the activity provider can analyze the user's past activity results and provide the most appropriate activity. This allows for the optimization of the next activity by referring to past activity results. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider can input the user's past activity data into a generative AI and have the generative AI perform the optimization of the next activity.

[0097] The activity provider estimates the user's emotions and prioritizes activities based on the estimated emotions. For example, if the user is stressed, the activity provider may prioritize relaxing activities. If the user is relaxed, the activity provider may also prioritize challenging activities. Furthermore, if the user is in a hurry, the activity provider may prioritize activities that can be completed quickly. This allows for the prioritization of activities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the activity provider may be performed using or without a generative AI. For example, the activity provider can input user emotion data into a generative AI and have the generative AI determine the priority of activities.

[0098] The activity provider unit prioritizes providing highly relevant activities, taking into account the child's geographical location when providing activities. For example, if the child lives in a specific area, the activity provider unit will prioritize activities related to that area. Furthermore, if the child is traveling, the activity provider unit can prioritize activities related to the travel destination. Additionally, if the child is participating in a specific event, the activity provider unit can prioritize activities related to that event. This allows for the prioritization of highly relevant activities, taking into account the child's geographical location. Some or all of the above processing in the activity provider unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider unit can input the child's geographical location data into a generative AI and have the generative AI provide highly relevant activities.

[0099] The activity provider analyzes the user's social media activity and suggests relevant activities when providing activities. For example, the activity provider may suggest activities based on the content the user frequently posts on social media. It can also suggest activities based on the accounts the user follows on social media. Furthermore, it can suggest activities based on the groups the user participates in on social media. This allows the activity provider to analyze the user's social media activity and suggest relevant activities. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or without a generative AI. For example, the activity provider can input the user's social media data into a generative AI and have the generative AI suggest relevant activities.

[0100] The learning unit estimates the user's emotions and adjusts the learning content based on the estimated emotions. For example, if the user is stressed, the learning unit provides relaxing learning content. It can also provide detailed learning content if the user is relaxed. Furthermore, if the user is in a hurry, the learning unit can provide concise and to-the-point learning content. This allows the learning content to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning content.

[0101] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit provides relevant learning content based on what the user has learned in the past. The learning unit can also prioritize providing effective learning methods based on the user's past learning data. Furthermore, the learning unit can analyze the user's past learning data and provide the most appropriate learning content. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0102] The learning unit customizes learning content based on the user's interests and preferences during the learning process. For example, if the user is interested in a particular parenting theory, the learning unit will provide learning content based on that theory. The learning unit can also provide learning content related to specific hobbies or interests if the user has them. Furthermore, if the user is facing a particular problem, the learning unit can provide learning content related to that problem. This allows for the customization of learning content based on the user's interests and preferences. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user interest data into a generative AI and have the generative AI perform the customization of learning content.

[0103] The learning unit estimates the user's emotions and determines learning priorities based on the estimated emotions. For example, if the user is stressed, the learning unit prioritizes providing relaxing learning content. If the user is relaxed, the learning unit can also prioritize providing detailed learning content. Furthermore, if the user is in a hurry, the learning unit can prioritize providing concise and to-the-point learning content. This allows the learning priority to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI determine the learning priority.

[0104] The learning unit prioritizes providing highly relevant learning content during the learning process, taking into account the user's geographical location. For example, if the user lives in a specific region, the learning unit prioritizes providing learning content related to that region. Furthermore, if the user is traveling, the learning unit can prioritize providing learning content related to their travel destination. Additionally, if the user is participating in a specific event, the learning unit can prioritize providing learning content related to that event. This allows the learning unit to prioritize providing highly relevant learning content while considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location data into a generative AI and have the generative AI provide highly relevant learning content.

[0105] The learning unit analyzes the user's social media activity during learning and suggests relevant learning content. For example, the learning unit suggests learning content based on the content the user frequently posts on social media. It can also suggest learning content based on the accounts the user follows on social media. Furthermore, the learning unit can suggest learning content based on the groups the user participates in on social media. This allows the learning unit to analyze the user's social media activity and suggest relevant learning content. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's social media data into a generative AI and have the generative AI suggest relevant learning content.

[0106] The recommendation unit estimates the user's emotions and adjusts the recommendation content based on the estimated emotions. For example, if the user is stressed, the recommendation unit provides relaxing recommendations. If the user is relaxed, the recommendation unit can also provide detailed recommendations. Furthermore, if the user is in a hurry, the recommendation unit can provide concise and to-the-point recommendations. This allows the recommendation content to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the recommendation content.

[0107] The recommendation unit optimizes the next activity by referring to past activity results when making recommendations. For example, the recommendation unit suggests the next activity based on the results of activities the user has performed in the past. The recommendation unit can also prioritize providing effective activities based on the user's past activity results. Furthermore, the recommendation unit can analyze the user's past activity results and provide the most appropriate activity. This allows the next activity to be optimized by referring to past activity results. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input the user's past activity data into a generative AI and have the generative AI perform the optimization of the next activity.

[0108] The recommendation unit customizes recommendations based on the child's interests and preferences. For example, if the child is interested in animals, the recommendation unit will recommend activities related to animals. It can also recommend music-related activities if the child is interested in music. Furthermore, if the child is interested in science, the recommendation unit can recommend activities related to science experiments. This allows for the customization of recommendations based on the child's interests and preferences. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit can input data on the child's interests and preferences into a generative AI and have the generative AI customize the recommendations.

[0109] The recommendation unit estimates the user's emotions and determines the priority of recommendations based on the estimated emotions. For example, if the user is stressed, the recommendation unit will prioritize providing relaxing recommendations. If the user is relaxed, the recommendation unit may also prioritize providing detailed recommendations. Furthermore, if the user is in a hurry, the recommendation unit may prioritize providing concise and to-the-point recommendations. This allows the recommendation unit to determine the priority of recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI determine the priority of recommendations.

[0110] The recommendation unit prioritizes recommending activities that are highly relevant to the child, taking into account the child's geographical location. For example, if the child lives in a specific area, the recommendation unit will prioritize recommending activities related to that area. Furthermore, if the child is traveling, the recommendation unit can prioritize recommending activities related to the travel destination. Additionally, if the child is participating in a specific event, the recommendation unit can prioritize recommending activities related to that event. This allows the recommendation unit to prioritize recommending activities that are highly relevant, taking into account the child's geographical location. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit can input the child's geographical location data into a generative AI and have the generative AI recommend highly relevant activities.

[0111] The recommendation unit analyzes the user's social media activity and recommends relevant activities. For example, the recommendation unit may recommend activities based on the content the user frequently posts on social media. It can also recommend activities based on the accounts the user follows on social media. Furthermore, the recommendation unit may recommend activities based on the groups the user participates in on social media. This allows the recommendation unit to analyze the user's social media activity and recommend relevant activities. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input the user's social media data into a generative AI and have the generative AI perform the recommendation of relevant activities.

[0112] The ad display unit estimates the user's emotions and adjusts how ads are displayed based on the estimated emotions. For example, if the user is stressed, the ad display unit may display relaxing ads. It may also display detailed ads if the user is relaxed. Furthermore, if the user is in a hurry, the ad display unit may display concise, to-the-point ads. This allows the ad display method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad display unit may be performed using, for example, generative AI, or without generative AI. For example, the ad display unit can input user emotion data into a generative AI and have the generative AI adjust how ads are displayed.

[0113] The ad display unit displays the most suitable ads by referring to the user's past purchase history when displaying ads. For example, the ad display unit displays ads related to products the user has previously purchased. The ad display unit can also prioritize advertising products that the user is likely to be interested in based on their past purchase history. Furthermore, the ad display unit can analyze the user's past purchase history and display the most appropriate ads. This allows the ad display unit to display the most suitable ads by referring to the user's past purchase history. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input the user's past purchase data into a generative AI and have the generative AI execute the display of the most suitable ads.

[0114] The ad display unit customizes ad content based on the user's interests when displaying ads. For example, if the user has a particular hobby or interest, the ad display unit will display ads related to that. Furthermore, if the user is interested in a particular parenting theory, the ad display unit can display ads related to that theory. In addition, if the user is facing a particular problem, the ad display unit can display ads related to that problem. This allows for the customization of ad content based on the user's interests. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input user interest data into a generative AI and have the generative AI perform the customization of ad content.

[0115] The ad display unit estimates the user's emotions and determines ad priorities based on the estimated emotions. For example, if the user is stressed, the ad display unit will prioritize displaying relaxing ads. It can also prioritize displaying detailed ads if the user is relaxed. Furthermore, if the user is in a hurry, the ad display unit can prioritize displaying concise and to-the-point ads. This allows for ad prioritization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad display unit may be performed using, for example, generative AI, or not. For example, the ad display unit can input user emotion data into a generative AI and have the generative AI determine ad priorities.

[0116] The ad display unit prioritizes displaying highly relevant ads by considering the user's geographical location when displaying ads. For example, if the user lives in a specific region, the ad display unit will prioritize displaying ads related to that region. Furthermore, if the user is traveling, the ad display unit can prioritize displaying ads related to their travel destination. Additionally, if the user is participating in a specific event, the ad display unit can prioritize displaying ads related to that event. This allows for the prioritization of highly relevant ads by considering the user's geographical location. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input the user's geographical location data into a generative AI and have the generative AI display highly relevant ads.

[0117] The ad display unit analyzes the user's social media activity when displaying ads and displays relevant ads. For example, the ad display unit displays ads based on the content the user frequently posts on social media. It can also display ads based on the accounts the user follows on social media. Furthermore, the ad display unit can display ads based on the groups the user participates in on social media. This allows for the analysis of the user's social media activity and the display of relevant ads. Some or all of the above processing in the ad display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ad display unit can input the user's social media data into a generative AI and have the generative AI display relevant ads.

[0118] The collaboration unit estimates the user's emotions and adjusts the content of the collaboration project based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit can provide a relaxing collaboration project. Conversely, if the user is relaxed, the collaboration unit can also provide a challenging collaboration project. Furthermore, if the user is in a hurry, the collaboration unit can provide a collaboration project that can be completed quickly. This allows the content of the collaboration project to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using, for example, generative AI, or not using generative AI. For example, the collaboration unit can input user emotion data into a generative AI and have the generative AI adjust the content of the collaboration project.

[0119] The collaboration department provides optimal collaboration plans by referring to the user's past activity results. For example, the collaboration department proposes the most suitable collaboration plan based on the results of the user's past activities. The collaboration department can also prioritize providing effective plans based on the user's past activity results. Furthermore, the collaboration department can analyze the user's past activity results and provide the most appropriate collaboration plan. This allows the collaboration department to provide optimal plans by referring to the user's past activity results. Some or all of the above processing in the collaboration department may be performed using, for example, a generative AI, or without a generative AI. For example, the collaboration department can input the user's past activity data into a generative AI and have the generative AI provide the optimal collaboration plan.

[0120] The Collaboration Department customizes project content based on the user's interests and preferences when planning collaborations. For example, if a user has a specific hobby or interest, the Collaboration Department can provide a collaboration project related to that. Furthermore, if a user is interested in a particular parenting theory, the Collaboration Department can provide a collaboration project related to that theory. In addition, if a user is facing a specific problem, the Collaboration Department can provide a collaboration project related to that problem. This allows for the customization of project content based on the user's interests and preferences. Some or all of the above processing in the Collaboration Department may be performed using, for example, a generative AI, or not. For example, the Collaboration Department can input user interest data into a generative AI and have the generative AI customize the project content.

[0121] The collaboration unit estimates the user's emotions and prioritizes collaboration projects based on those emotions. For example, if the user is stressed, the collaboration unit will prioritize providing relaxing collaboration projects. Conversely, if the user is relaxed, the collaboration unit may prioritize providing challenging collaboration projects. Furthermore, if the user is in a hurry, the collaboration unit may prioritize providing collaboration projects that can be completed quickly. This allows for the prioritization of collaboration projects based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using, for example, generative AI, or not. For example, the collaboration unit can input user emotion data into a generative AI and have the generative AI determine the priority of collaboration projects.

[0122] The Collaboration Department prioritizes providing highly relevant projects to users when planning collaborations, taking into account their geographical location. For example, if a user lives in a specific region, the Collaboration Department will prioritize providing collaboration projects related to that region. Furthermore, if a user is traveling, the Collaboration Department can prioritize providing collaboration projects related to their travel destination. Additionally, if a user is participating in a specific event, the Collaboration Department can prioritize providing collaboration projects related to that event. This allows for the prioritization of highly relevant projects by considering the user's geographical location. Some or all of the above processing in the Collaboration Department may be performed using, for example, a generative AI, or without one. For instance, the Collaboration Department can input the user's geographical location data into a generative AI and have the AI ​​provide highly relevant projects.

[0123] The Collaboration Department analyzes users' social media activity and proposes relevant projects when planning collaborations. For example, the Collaboration Department may propose collaboration projects based on the content users frequently post on social media. It can also propose collaboration projects based on the accounts users follow on social media. Furthermore, the Collaboration Department may propose collaboration projects based on the groups users participate in on social media. This allows for the analysis of users' social media activity and the proposal of relevant projects. Some or all of the above processing in the Collaboration Department may be performed using, for example, generative AI, or not. For example, the Collaboration Department can input user social media data into a generative AI and have the generative AI generate proposals for relevant projects.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The advice-providing unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is stressed, it can provide relaxing advice. If the user is relaxed, it can also provide challenging advice. Furthermore, if the user is in a hurry, it can provide concise and to-the-point advice. This allows the content of the advice to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using a generative AI, or not. For example, the advice-providing unit can input user emotion data into a generative AI and have the generative AI adjust the content of the advice.

[0126] The activity provider can estimate the user's emotions and adjust the content of the activity based on the estimated emotions. For example, if the user is stressed, it can provide a relaxing activity. If the user is relaxed, it can provide a challenging activity. Furthermore, if the user is in a hurry, it can provide an activity that can be completed in a short time. In this way, the content of the activity can be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the activity provider may be performed using a generative AI, or not using a generative AI. For example, the activity provider can input user emotion data into a generative AI and have the generative AI adjust the content of the activity.

[0127] The settings reception unit can estimate the user's emotions and adjust the input method for personal settings based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick personal setting input. This allows the input method for personal settings to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings reception unit may be performed using a generative AI, or not. For example, the settings reception unit can input user emotion data into a generative AI and have the generative AI adjust the input method for personal settings.

[0128] The learning unit can estimate the user's emotions and adjust the learning content based on those emotions. For example, if the user is stressed, it can provide relaxing learning content. If the user is relaxed, it can provide detailed learning content. Furthermore, if the user is in a hurry, it can provide concise and to-the-point learning content. This allows the learning content to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning content.

[0129] The ad display unit can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is stressed, it can display relaxing ads. If the user is relaxed, it can display more detailed ads. Furthermore, if the user is in a hurry, it can display concise and to-the-point ads. This allows the ad display method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad display unit may be performed using a generative AI, or not. For example, the ad display unit can input user emotion data into a generative AI and have the generative AI adjust how ads are displayed.

[0130] The settings reception unit can analyze the user's past settings history and suggest the most suitable settings. For example, it can automatically display items that the user has frequently set in the past as candidates. It can also prioritize suggesting settings methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest settings that the user will use during specific time periods based on their past settings history. This allows the system to analyze the user's past settings history and suggest the most suitable settings. Some or all of the above processing in the settings reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings reception unit can input the user's past settings data into a generative AI and have the generative AI suggest the most suitable settings.

[0131] The advice-providing unit can adjust the level of detail of the advice based on the child's age and gender. For example, if the child is a toddler, it can provide simple and easy-to-understand advice. If the child is an elementary school student, it can provide advice that includes specific examples. Furthermore, if the child is a middle school student or older, it can provide advice that explains detailed theories and backgrounds. This allows the level of detail of the advice to be adjusted based on the child's age and gender. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input the child's age and gender data into a generative AI and have the generative AI perform the adjustment of the level of detail of the advice.

[0132] The activity provider can adjust the level of detail of an activity based on the child's interests and preferences when providing an activity. For example, if the child is interested in animals, it can provide an activity related to animals. If the child is interested in music, it can also provide an activity related to music. Furthermore, if the child is interested in science, it can provide an activity related to science experiments. This allows the level of detail of the activity to be adjusted based on the child's interests and preferences. Some or all of the above processing in the activity provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the activity provider can input data on the child's interests and preferences into a generative AI and have the generative AI perform the adjustment of the level of detail of the activity.

[0133] The recommendation unit can optimize the next activity by referring to past activity results when making recommendations. For example, it can suggest the next activity based on the results of activities the user has performed in the past. It can also prioritize providing activities that were effective based on the user's past activity results. Furthermore, it can analyze the user's past activity results and provide the most appropriate activity. This allows the recommendation unit to optimize the next activity by referring to past activity results. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's past activity data into a generative AI and have the generative AI perform the optimization of the next activity.

[0134] The ad display unit can display the most suitable ads by referring to the user's past purchase history when displaying ads. For example, it can display ads related to products the user has previously purchased. It can also prioritize advertising products that the user is likely to be interested in based on their past purchase history. Furthermore, it can analyze the user's past purchase history and display the most appropriate ads. This allows the ad display unit to display the most suitable ads by referring to the user's past purchase history. Some or all of the above processing in the ad display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the ad display unit can input the user's past purchase data into a generation AI and have the generation AI execute the display of the most suitable ads.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The settings reception unit receives the user's personal settings. These settings include the child's age, gender, hobbies, health status, and lifestyle. The settings reception unit saves the user's input information to a database for later reference. It also allows users to customize their input methods; for example, if voice input is selected, speech recognition technology is used to convert the input into text data. Step 2: The advice provider provides advice based on the personal settings received by the settings reception unit. The advice provider provides advice based on parenting theories such as Montessori education and Steiner education. It also provides advice tailored to the user's individual needs and, if asked about a specific problem, provides concrete solutions to that problem. Step 3: The Activity Provider Department provides a daily activity program based on the advice provided by the Advice Provider Department. For example, the Activity Provider Department suggests daily activities for children up to 3 years old and recommends the next activity based on the results of the daily activities. After the child completes a particular activity, the results are evaluated and the next activity is suggested. Step 4: The learning unit studies the book the user wants to refer to based on the advice provided by the advice-providing unit. The learning unit reads the book specified by the user and analyzes its contents. Based on the analysis results, it provides appropriate advice to the user, extracts the key points of the specified book, and provides advice based on those key points.

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0140] Each of the multiple elements described above, including the setting reception unit, advice provision unit, activity provision unit, learning unit, recommendation unit, advertisement display unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the setting reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's personal settings. The advice provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice based on the user's personal settings. The activity provision unit is implemented by the control unit 46A of the smart device 14 and provides a daily activity program. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns books that the user wants to refer to. The recommendation unit is implemented by the control unit 46A of the smart device 14 and recommends the next activity based on the results of the daily activity. The advertisement display unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays advertisements based on the advice. The collaboration unit is implemented, for example, by the control unit 46A of the smart device 14, and carries out collaboration plans based on activities. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] As shown in Figure 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the setting reception unit, advice provision unit, activity provision unit, learning unit, recommendation unit, advertisement display unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the setting reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's personal settings. The advice provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice based on the user's personal settings. The activity provision unit is implemented by the control unit 46A of the smart glasses 214 and provides a daily activity program. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns books that the user may want to refer to. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and recommends the next activity based on the results of the daily activity. The advertisement display unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays advertisements based on the advice. The collaboration section is implemented, for example, by the control unit 46A of the smart glasses 214, and carries out collaboration plans based on activities. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the setting reception unit, advice provision unit, activity provision unit, learning unit, recommendation unit, advertisement display unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the setting reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's personal settings. The advice provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice based on the user's personal settings. The activity provision unit is implemented by the control unit 46A of the headset terminal 314 and provides a daily activity program. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows the user to learn about books they wish to refer to. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and recommends the next activity based on the results of the daily activity. The advertisement display unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays advertisements based on the advice. The collaboration unit is implemented, for example, by the control unit 46A of the headset terminal 314, and carries out collaboration plans based on activities. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] As shown in Figure 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.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the setting reception unit, advice provision unit, activity provision unit, learning unit, recommendation unit, advertisement display unit, and collaboration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the setting reception unit is implemented by the control unit 46A of the robot 414 and receives the user's personal settings. The advice provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides advice based on the user's personal settings. The activity provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides a daily activity program. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and learns books that the user wants to refer to. The recommendation unit is implemented by, for example, the control unit 46A of the robot 414 and recommends the next activity based on the results of the daily activity. The advertisement display unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and displays advertisements based on the advice. The collaboration unit is implemented, for example, by the control unit 46A of robot 414, and carries out collaborative projects based on activities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0190] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] 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.

[0200] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A settings reception section that accepts user personal settings, An advice provision unit provides advice based on the personal settings received by the aforementioned setting reception unit, An activity provision unit provides a daily activity program based on the advice provided by the aforementioned advice provision unit, The system includes a learning unit that studies books that the user wishes to refer to based on the advice provided by the aforementioned advice-providing unit. A system characterized by the following features. (Note 2) The aforementioned activity provision unit, It includes a recommendation section that recommends the next activity based on the results of daily activities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice-providing unit, It includes an ad display section that displays ads based on advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned activity provision unit, The department has a collaboration section that implements collaborative projects based on activities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned setting reception unit, It estimates the user's emotions and adjusts how personal settings are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned setting reception unit, It analyzes the user's past settings history and suggests the optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned setting reception unit, When the user submits their settings, the system customizes the settings based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned setting reception unit, It estimates the user's emotions and determines the priority of settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned setting reception unit, When receiving configuration requests, the system prioritizes displaying highly relevant settings based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned setting reception unit, When a user submits a configuration request, the system analyzes their social media activity and suggests relevant configuration options. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned advice-providing unit, When providing advice, adjust the level of detail based on the child's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned advice-providing unit, When providing advice, different advice algorithms are applied depending on the parenting theory. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice-providing unit, When providing advice, prioritize the advice based on the child's developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice-providing unit, When providing advice, we refer to the user's past advice history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned activity provision unit, It estimates the user's emotions and adjusts the content of the activity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned activity provision unit, When providing activities, adjust the level of detail based on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned activity provision unit, When providing an activity, we optimize the next activity by referring to past activity results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned activity provision unit, It estimates the user's emotions and prioritizes activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned activity provision unit, When providing activities, we prioritize offering activities that are highly relevant to the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned activity provision unit, When providing activities, the system analyzes the user's social media activity and suggests relevant activities. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, During learning, the learning content is customized based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During learning, the system prioritizes providing highly relevant learning content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, During learning, the system analyzes the user's social media activity and suggests relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recommendation unit is, It estimates the user's emotions and adjusts the recommendation content based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The recommendation unit is, When making recommendations, we refer to past activity results to optimize the next activity. The system described in Appendix 2, characterized by the features described herein. (Note 31) The recommendation unit is, When making recommendations, customize the recommendations based on the child's interests and preferences. The system described in Appendix 2, characterized by the features described herein. (Note 32) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The recommendation unit is, When making recommendations, the system prioritizes recommending highly relevant activities by considering the child's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The recommendation unit is, When making recommendations, the system analyzes the user's social media activity and recommends relevant activities. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned advertising display unit is It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned advertising display unit is When displaying ads, the system refers to the user's past purchase history to display the most relevant ads. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned advertising display unit is When displaying ads, the ad content is customized based on the user's interests and preferences. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned advertising display unit is It estimates user sentiment and prioritizes ads based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned advertising display unit is When displaying ads, the system prioritizes showing the most relevant ads by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned advertising display unit is When displaying ads, we analyze the user's social media activity and display relevant ads. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned collaboration department, We estimate the user's emotions and adjust the content of the collaboration project based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned collaboration department, When planning collaborative projects, we provide the most suitable plan by referring to the user's past activity results. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned collaboration department, When planning a collaboration, customize the project content based on the user's interests and preferences. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned collaboration department, It estimates user sentiment and prioritizes collaboration projects based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned collaboration department, When planning collaborations, we prioritize providing highly relevant projects by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned collaboration department, When planning collaborations, we analyze users' social media activity and propose relevant projects. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A settings reception section that accepts user personal settings, An advice provision unit provides advice based on the personal settings received by the aforementioned setting reception unit, An activity provision unit provides a daily activity program based on the advice provided by the aforementioned advice provision unit, The system includes a learning unit that studies books that the user wishes to refer to based on the advice provided by the aforementioned advice-providing unit. A system characterized by the following features.

2. The aforementioned activity provision unit, It includes a recommendation section that recommends the next activity based on the results of daily activities. The system according to feature 1.

3. The aforementioned advice-providing unit, It includes an ad display section that displays ads based on advice. The system according to feature 1.

4. The aforementioned activity provision unit, The department has a collaboration section that implements collaborative projects based on activities. The system according to feature 1.

5. The aforementioned setting reception unit, It estimates the user's emotions and adjusts how personal settings are entered based on those estimated emotions. The system according to feature 1.

6. The aforementioned setting reception unit, It analyzes the user's past settings history and suggests the optimal settings. The system according to feature 1.

7. The aforementioned setting reception unit, When the user submits their settings, the system customizes the settings based on their current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned setting reception unit, It estimates the user's emotions and determines the priority of settings based on those estimated emotions. The system according to feature 1.

9. The aforementioned setting reception unit, When receiving configuration requests, the system prioritizes displaying highly relevant settings based on the user's geographical location. The system according to feature 1.

10. The aforementioned setting reception unit, When a user submits a configuration request, the system analyzes their social media activity and suggests relevant configuration options. The system according to feature 1.

11. The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system according to feature 1.

Images