System

Abstract

An object of a system according to an embodiment is to propose, by a parent, play for making a child enjoy learning a lifestyle.SOLUTION: A system includes a reception unit, a generation unit, and a proposal unit. The reception unit inputs a lifestyle habit that the parent wants the child to wear. The generation unit analyzes the information input by the reception unit and devises a play. The suggestion section suggests the play devised by the generation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is a burden for parents to come up with games that will help their children develop fun lifestyle habits.

[0005] The system according to the embodiment aims to enable parents to suggest games that will help their children acquire fun lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a suggestion unit. The reception unit inputs lifestyle habits that parents want their children to acquire. The generation unit analyzes the information input by the reception unit and devise games. The suggestion unit proposes the games devised by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows parents to suggest games that will help their children acquire good lifestyle habits in a fun way. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention devise and propose games that encourage children to acquire fun lifestyle habits simply by inputting the desired habits. This system utilizes generative AI technology to devise and propose different games each time, preventing children from falling into a rut while fostering independence and cooperation. This is expected to reduce the burden on parents and deepen family bonds. For example, parents input the lifestyle habits they want their children to acquire into the system, such as "early to bed, early to rise" or "washing their hands and gargling." This information is then input into the generative AI. The generative AI then analyzes the input information and devise and proposes games that children can enjoy and acquire. For example, to encourage "early to bed, early to rise," we propose a game in which points are earned by going to bed at a set time every night and rewards players upon reaching a certain number of points. Another game to encourage "hand washing and gargling" is proposed, in which the time spent washing hands is measured and whether the child has washed their hands correctly. Because the generative AI devise and proposes different games each time, children can acquire these lifestyle habits without getting bored. For example, even if a child has the same habit of "early to bed, early to rise," different rewards or point systems can be offered depending on the day, keeping the child interested. This reduces the burden on parents. Parents do not need to think of ways to help their children develop good habits; they can leave it to the system. Having the whole family cooperate through play can also be expected to deepen family bonds. For example, by having the whole family participate in a "hand washing and gargling" game and competing to see who can wash their hands the most correctly, the whole family can have fun while developing good habits. In this way, the whole family can spend time together in a fun and cooperative manner. This reduces the burden on parents and deepens family bonds.

[0029] A lifestyle habit suggestion system according to an embodiment includes a reception unit, a generation unit, and a suggestion unit. The reception unit inputs lifestyle habits that parents want their children to acquire. For example, parents can input lifestyle habits such as "early to bed, early to rise" and "washing hands and gargling." The reception unit can accept, for example, text input or voice input. The reception unit can also store the information input by parents in a database. The generation unit uses a generation AI to analyze the information input by the reception unit and devise games. For example, the generation AI devise games that children can enjoy and acquire based on the input lifestyle habits. The generation unit can generate content for games using, for example, a text generation AI (e.g., LLM). The generation unit can also generate content for games using a multimodal generation AI. For example, the generation AI can devise a game to help children acquire the habit of "early to bed, early to rise." Points are earned by going to bed at a set time every night, and a reward is awarded when a certain number of points are accumulated. The suggestion unit suggests the games devised by the generation unit. For example, the suggestion unit can notify the parent of the game devised by the generation AI. The suggestion unit can suggest the content of the game to the parent, for example, by using a text message or a voice message. The suggestion unit can also provide guidelines for the parent to play the suggested game. For example, the suggestion unit can explain the steps and rules of the game to the parent. In this way, the lifestyle habit suggestion system according to the embodiment can devise and suggest games that allow parents to have fun and help their children acquire the lifestyle habits they want them to acquire.

[0030] The lifestyle habit suggestion system includes a data collection unit that collects past play history or child reaction data. The data collection unit collects past play history. For example, the data collection unit can collect information such as date and time, content of play, and results. The data collection unit also collects child reaction data. For example, the data collection unit can collect information such as the child's facial expressions, behavior, and feedback. The data collection unit can store the collected data in a database. In this way, by collecting past play history and child reaction data, it is possible to reflect the collected data in suggesting the next play. For example, the data collection unit can identify patterns of play that the child enjoyed based on the past play history and reflect the identified patterns in suggesting the next play. In addition, the data collection unit can evaluate the effectiveness of play based on the child's reaction data and reflect the identified patterns in suggesting the next play. In this way, the lifestyle habit suggestion system can suggest more effective play.

[0031] The lifestyle habit suggestion system includes an evaluation unit that evaluates the effectiveness of play and reflects the evaluation in the next play suggestion. The evaluation unit evaluates the effectiveness of play. For example, the evaluation unit can evaluate the effectiveness of play based on criteria such as the degree of habit establishment and the child's satisfaction. The evaluation unit can evaluate the effectiveness of play using, for example, a questionnaire or observation. The evaluation unit can also evaluate the effectiveness of play based on collected data. For example, the evaluation unit can evaluate the effectiveness of play based on the child's reaction data. The evaluation unit can store the evaluation results in a database. This allows the evaluation of the effectiveness of play and reflects the evaluation in the next play suggestion, thereby making it possible to suggest more effective play. For example, the evaluation unit can optimize the next play suggestion based on past evaluation data. The evaluation unit can also adjust the content and difficulty of the play based on the evaluation results. This allows the lifestyle habit suggestion system to suggest more effective play.

[0032] The reception unit can analyze the parent's past input history and select an appropriate input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the parent has frequently used in the past. The reception unit can also analyze lifestyle patterns input by the parent in the past and suggest the optimal input method. The reception unit can also predict and suggest the input method to be used during a specific time period from the parent's past input history. In this way, the optimal input method can be suggested by analyzing the parent's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's past input history data into a generation AI and have the generation AI select the optimal input method.

[0033] When inputting lifestyle habits, the reception unit can filter the lifestyle habits based on the parent's current living situation and areas of interest. For example, if the parent is interested in health, the reception unit can suggest that health-related lifestyle habits be input first. Furthermore, if the parent is busy with work, the reception unit can suggest lifestyle habits that can be implemented in a short time. Furthermore, if the parent is interested in their child's education, the reception unit can suggest that education-related lifestyle habits be input first. This reduces the burden on the parent by suggesting lifestyle habits based on the parent's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the parent's living situation data into the generation AI and cause the generation AI to perform filtering.

[0034] When inputting lifestyle habits, the reception unit can select the optimal input means depending on the parent's input method (voice, text, image, etc.). For example, if the parent prefers voice input, the reception unit can preferentially suggest voice input. Also, if the parent prefers text input, the reception unit can preferentially suggest text input. Also, if the parent prefers image input, the reception unit can preferentially suggest image input. This allows the optimal input means to be selected depending on the parent's input method, thereby reducing the burden on the parent. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0035] When inputting lifestyle habits, the reception unit can prioritize inputting highly relevant information taking into account the parent's geographical location information. For example, if the parent lives in a specific area, the reception unit can suggest that lifestyle habits related to that area be prioritized. Furthermore, if the parent is traveling, the reception unit can suggest that lifestyle habits related to the travel destination be prioritized. Furthermore, if the parent frequently visits a specific facility, the reception unit can suggest that lifestyle habits related to the facility be prioritized. This reduces the burden on the parent by suggesting lifestyle habits taking into account the parent's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the parent's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant information.

[0036] The reception unit can analyze the parent's social media activity and input related information when inputting lifestyle habits. For example, the reception unit can suggest inputting related lifestyle habits based on information shared by the parent on social media. The reception unit can also analyze the parent's social media activity and suggest inputting related lifestyle habits. The reception unit can also suggest inputting related lifestyle habits by referring to the activity of the parent's friends on social media. In this way, related lifestyle habits can be suggested by analyzing the parent's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data to the generation AI and cause the generation AI to input related information.

[0037] The reception unit can customize the input method by reflecting the parent's past feedback when inputting lifestyle habits. The reception unit can, for example, suggest an optimal input method based on feedback provided by the parent in the past. The reception unit can also preferentially suggest a specific input method based on the parent's past feedback. The reception unit can also analyze the parent's past feedback and customize the input method. This makes it possible to suggest an optimal input method by reflecting the parent's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the parent's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0038] When generating activities, the generation unit can adjust the level of detail of the activities based on the importance of the lifestyle habits. For example, the generation unit can suggest detailed activities for important lifestyle habits. Furthermore, the generation unit can suggest easy activities for lifestyle habits with low importance. Furthermore, the generation unit can adjust the difficulty of the activities according to the importance of the lifestyle habits. In this way, by adjusting the level of detail of the activities according to the importance of the lifestyle habits, children can acquire lifestyle habits in a fun way. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input lifestyle habit importance data into the generation AI and cause the generation AI to adjust the level of detail of the activities.

[0039] When generating activities, the generation unit can apply different generation algorithms depending on the lifestyle habit category. For example, the generation unit can suggest activities that incorporate exercise for health-related lifestyle habits. Furthermore, the generation unit can suggest activities that incorporate learning for education-related lifestyle habits. Furthermore, the generation unit can suggest activities that encourage cooperation for social-related lifestyle habits. In this way, by applying different generation algorithms depending on the lifestyle habit category, children can acquire lifestyle habits in a fun way. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input lifestyle habit category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0040] When generating a game, the generation unit can improve the accuracy of the game by referring to past game results. The generation unit, for example, analyzes past game results and incorporates successful game patterns. The generation unit can also preferentially suggest games that the child enjoyed based on past game results. The generation unit can also adjust the difficulty of the game based on past game results. In this way, the accuracy of the game can be improved by referring to past game results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past game result data into the generation AI and cause the generation AI to improve the accuracy of the game.

[0041] When generating activities, the generation unit can determine the priority of activities based on the time when lifestyle habits were input. For example, if the time when lifestyle habits were input is early, the generation unit can preferentially suggest activities. Furthermore, if the time when lifestyle habits were input is late, the generation unit can suggest activities to be postponed. Furthermore, the generation unit can adjust the priority of activities according to the time when lifestyle habits were input. In this way, by determining the priority of activities based on the time when lifestyle habits were input, children can acquire lifestyle habits in a fun way. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when lifestyle habits were input into the generation AI and cause the generation AI to determine the priority of activities.

[0042] When generating games, the generation unit can adjust the order of games based on the relevance of lifestyle habits. For example, the generation unit prioritizes incorporating highly relevant lifestyle habits into games. The generation unit can also postpone incorporating less relevant lifestyle habits into games. The generation unit can also adjust the order of games according to the relevance of lifestyle habits. In this way, by adjusting the order of games based on the relevance of lifestyle habits, children can acquire lifestyle habits in a fun way. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input lifestyle habit relevance data into the generation AI and cause the generation AI to adjust the order of games.

[0043] When generating a game, the generation unit can customize the content of the game according to the child's age and interests. The generation unit, for example, can suggest appropriate games according to the child's age. The generation unit can also suggest games that will attract the child's attention according to the child's interests. The generation unit can also customize the content of the game based on the child's age and interests. This allows the child to acquire fun lifestyle habits by customizing the content of the game according to the child's age and interests. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's age and interest data into the generation AI and cause the generation AI to customize the content of the game.

[0044] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the activity. For example, the suggestion unit makes a detailed suggestion for an important activity. The suggestion unit can also make a simple suggestion for an activity with a low importance. The suggestion unit can also adjust the level of detail of the suggestion according to the importance of the activity. This makes it easier for parents to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input importance data of the activity into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0045] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of play. For example, the suggestion unit can make suggestions that incorporate exercise for health-related play. Furthermore, the suggestion unit can make suggestions that incorporate learning for education-related play. Furthermore, the suggestion unit can make suggestions that encourage cooperation for social play. In this way, applying different suggestion algorithms depending on the category of play makes it easier for parents to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input play category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0046] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to past suggestion results. For example, the suggestion unit analyzes past suggestion results and incorporates successful suggestion patterns. The suggestion unit can also prioritize suggestions that parents preferred based on past suggestion results. The suggestion unit can also adjust the level of detail of the suggestion based on past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0047] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the activity was generated. For example, if the activity was generated early, the suggestion unit can prioritize the suggestion. Also, if the activity was generated late, the suggestion unit can postpone the suggestion. Also, the suggestion unit can adjust the priority of the suggestion depending on the time when the activity was generated. In this way, by determining the priority of the suggestion based on the time when the activity was generated, parents are more likely to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the time when the activity was generated into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0048] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the activities. For example, the suggestion unit prioritizes suggesting highly relevant activities. The suggestion unit can also postpone suggesting less relevant activities. The suggestion unit can also adjust the order of suggestions according to the relevance of the activities. In this way, adjusting the order of suggestions based on the relevance of the activities makes it easier for parents to accept the suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the activities into a generation AI and cause the generation AI to adjust the order of suggestions.

[0049] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise. For example, if the parent has technical expertise, the suggestion unit can make the suggestion using technical terminology. Also, if the parent does not have technical expertise, the suggestion unit can make the suggestion in simple language. Also, the suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise. This makes it easier for the parent to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the parent's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0050] When collecting data, the data collection unit can improve the accuracy of the collected data by referring to past play history. The data collection unit improves the accuracy of the collected data based on, for example, past play history. The data collection unit can also collect successful play patterns from past play history. The data collection unit can also analyze past play history and improve the accuracy of the collected data. In this way, the accuracy of the collected data can be improved by referring to past play history. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past play history data into a generation AI and cause the generation AI to improve the accuracy of the collected data.

[0051] The data collection unit can analyze the child's reaction data during data collection and optimize the collection method. The data collection unit, for example, optimizes the collection method based on the child's reaction data. The data collection unit can also propose an optimal collection method from the child's reaction data. The data collection unit can also analyze the child's reaction data and optimize the collection method. In this way, the collection method can be optimized by analyzing the child's reaction data. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, for example, or may be performed without using AI. For example, the data collection unit can input the child's reaction data into the generation AI and cause the generation AI to optimize the collection method.

[0052] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific area, the data collection unit can prioritize collecting data related to that area. Furthermore, if the child is traveling, the data collection unit can prioritize collecting data related to the travel destination. Furthermore, if the child is in a specific facility, the data collection unit can also prioritize collecting data related to the facility. In this way, by collecting data by taking the child's geographical location information into account, the accuracy of data collection can be improved. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the child's geographical location information data to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0053] During data collection, the data collection unit can analyze the child's social media activities and collect related data. For example, the data collection unit collects related data based on information shared by the child on social media. The data collection unit can also analyze the child's social media activities and collect related data. The data collection unit can also collect related data by referring to the activities of the child's friends on social media. In this way, related data can be collected by analyzing the child's social media activities. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the child's social media activity data into the generation AI and cause the generation AI to collect related data.

[0054] When evaluating play, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data. The evaluation unit, for example, optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also incorporate successful play patterns from the past evaluation data. The evaluation unit can also analyze past evaluation data and optimize the evaluation algorithm. In this way, the evaluation algorithm can be optimized by referring to the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation data into a generation AI and cause the generation AI to optimize the evaluation algorithm.

[0055] The evaluation unit can analyze the child's reaction data when evaluating play and improve the evaluation method. The evaluation unit can improve the evaluation method based on, for example, the child's reaction data. The evaluation unit can also propose an optimal evaluation method from the child's reaction data. The evaluation unit can also analyze the child's reaction data and improve the evaluation method. In this way, the evaluation method can be improved by analyzing the child's reaction data. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without using AI. For example, the evaluation unit can input the child's reaction data into a generation AI and cause the generation AI to improve the evaluation method.

[0056] When evaluating play, the evaluation unit can make the evaluation taking into account the child's geographical location information. For example, if the child is in a specific area, the evaluation unit can make an evaluation related to that area. Furthermore, if the child is traveling, the evaluation unit can make an evaluation related to the travel destination. Furthermore, if the child is at a specific facility, the evaluation unit can make an evaluation related to the facility. In this way, by making an evaluation taking into account the child's geographical location information, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the child's geographical location information data into the generation AI and have the generation AI perform the evaluation.

[0057] The evaluation unit can analyze the child's social media activity when evaluating play and improve the accuracy of the evaluation. The evaluation unit can improve the accuracy of the evaluation, for example, based on information shared by the child on social media. The evaluation unit can also analyze the child's social media activity and improve the accuracy of the evaluation. The evaluation unit can also improve the accuracy of the evaluation by referring to the activity of the child's friends on social media. In this way, the accuracy of the evaluation can be improved by analyzing the child's social media activity. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the child's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can monitor in real time how the child responds to the lifestyle habits entered by the parent and provide feedback as needed. For example, if a parent enters "Early to bed, early to rise," the reception unit will monitor the child's sleep patterns and provide appropriate advice. Also, if a parent enters "Hand washing / gargling," the reception unit can monitor the frequency and duration of the child's hand washing and suggest areas for improvement. This allows parents to understand the progress of their child's lifestyle habits in real time and provide appropriate support.

[0060] The evaluator can collect parental feedback and reflect it in the next game suggestion. For example, if a parent provides feedback that "this game was too difficult for my child," the evaluator can adjust the difficulty level in the next suggestion. Also, if a parent provides feedback that "my child really enjoyed this game," the evaluator can suggest a similar game. Furthermore, if a parent provides feedback that "this game was not effective," the evaluator can try a different approach. In this way, parental feedback can be utilized to suggest more effective games.

[0061] The reception unit can analyze the parent's past input history and suggest the optimal input timing. For example, if the parent has previously input data at night, the reception unit can send a notification prompting the parent to input data at night. Also, if the parent has previously input data on weekends, the reception unit can send a notification prompting the parent to input data on weekends. Furthermore, if the parent has previously input data during a specific time period, the reception unit can also send a notification prompting the parent to input data during that time period. In this way, the parent's past input history can be utilized to suggest the optimal input timing.

[0062] The reception unit can customize the lifestyle habit input content based on the parent's current living situation and areas of interest. For example, if the parent is interested in health, health-related lifestyle habits can be prioritized. Also, if the parent is busy with work, lifestyle habits that can be implemented in a short time can be suggested. Furthermore, if the parent is interested in their child's education, education-related lifestyle habits can be prioritized. This reduces the burden on parents by suggesting lifestyle habits based on the parent's living situation and areas of interest.

[0063] The reception unit can select the optimal input means depending on the parent's input method (voice, text, image, etc.). For example, if the parent prefers voice input, it can preferentially suggest voice input. Also, if the parent prefers text input, it can preferentially suggest text input. Furthermore, if the parent prefers image input, it can preferentially suggest image input. In this way, by selecting the optimal input means depending on the parent's input method, it is possible to reduce the burden on the parent.

[0064] The reception unit can prioritize input of highly relevant information taking into account the geographical location information of the parent. For example, if the parent lives in a specific area, it can suggest that lifestyle habits related to that area be input first. Also, if the parent is traveling, it can suggest that lifestyle habits related to the travel destination be input first. Furthermore, if the parent frequently visits a specific facility, it can suggest that lifestyle habits related to that facility be input first. In this way, by suggesting lifestyle habits taking into account the geographical location information of the parent, it is possible to reduce the burden on the parent.

[0065] The reception unit can analyze the parent's social media activity and input relevant information. For example, it can suggest that the parent input relevant lifestyle habits based on information shared by the parent on social media. It can also analyze the parent's social media activity and suggest that the parent input relevant lifestyle habits. It can also suggest that the parent input relevant lifestyle habits based on the activities of the parent's friends on social media. In this way, it is possible to suggest relevant lifestyle habits by analyzing the parent's social media activity.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs the lifestyle habits that parents want their children to acquire. For example, parents can input lifestyle habits such as "early to bed, early to rise" or "washing their hands and gargling." The reception unit can accept text input and voice input, and can also store the information entered by parents in a database. Step 2: The generation unit uses a generation AI to analyze the information input by the reception unit and devise games. For example, the generation AI devise games that children can enjoy and learn based on the input lifestyle habits. The generation unit can generate the content of games using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The suggestion unit suggests the game devised by the generation unit. For example, the suggestion unit can notify the parent of the game devised by the generation AI. The suggestion unit can suggest the content of the game to the parent using a text message or a voice message. The suggestion unit can also provide guidelines for the parent to carry out the suggested game. For example, the suggestion unit can explain the steps and rules of the game to the parent.

[0068] (Example 2) A system according to an embodiment of the present invention devise and propose games that encourage children to acquire fun lifestyle habits simply by inputting the desired habits. This system utilizes generative AI technology to devise and propose different games each time, preventing children from falling into a rut while fostering independence and cooperation. This is expected to reduce the burden on parents and deepen family bonds. For example, parents input the lifestyle habits they want their children to acquire into the system, such as "early to bed, early to rise" or "washing their hands and gargling." This information is then input into the generative AI. The generative AI then analyzes the input information and devise and proposes games that children can enjoy and acquire. For example, to encourage "early to bed, early to rise," we propose a game in which points are earned by going to bed at a set time every night and rewards players upon reaching a certain number of points. Another game to encourage "hand washing and gargling" is proposed, in which the time spent washing hands is measured and whether the child has washed their hands correctly. Because the generative AI devise and proposes different games each time, children can acquire these lifestyle habits without getting bored. For example, even if a child has the same habit of "early to bed, early to rise," different rewards or point systems can be offered depending on the day, keeping the child interested. This reduces the burden on parents. Parents do not need to think of ways to help their children develop good habits; they can leave it to the system. Having the whole family cooperate through play can also be expected to deepen family bonds. For example, by having the whole family participate in a "hand washing and gargling" game and competing to see who can wash their hands the most correctly, the whole family can have fun while developing good habits. In this way, the whole family can spend time together in a fun and cooperative manner. This reduces the burden on parents and deepens family bonds.

[0069] A lifestyle habit suggestion system according to an embodiment includes a reception unit, a generation unit, and a suggestion unit. The reception unit inputs lifestyle habits that parents want their children to acquire. For example, parents can input lifestyle habits such as "early to bed, early to rise" and "washing hands and gargling." The reception unit can accept, for example, text input or voice input. The reception unit can also store the information input by parents in a database. The generation unit uses a generation AI to analyze the information input by the reception unit and devise games. For example, the generation AI devise games that children can enjoy and acquire based on the input lifestyle habits. The generation unit can generate content for games using, for example, a text generation AI (e.g., LLM). The generation unit can also generate content for games using a multimodal generation AI. For example, the generation AI can devise a game to help children acquire the habit of "early to bed, early to rise." Points are earned by going to bed at a set time every night, and a reward is awarded when a certain number of points are accumulated. The suggestion unit suggests the games devised by the generation unit. For example, the suggestion unit can notify the parent of the game devised by the generation AI. The suggestion unit can suggest the content of the game to the parent, for example, by using a text message or a voice message. The suggestion unit can also provide guidelines for the parent to play the suggested game. For example, the suggestion unit can explain the steps and rules of the game to the parent. In this way, the lifestyle habit suggestion system according to the embodiment can devise and suggest games that allow parents to have fun and help their children acquire the lifestyle habits they want them to acquire.

[0070] The lifestyle habit suggestion system includes a data collection unit that collects past play history or child reaction data. The data collection unit collects past play history. For example, the data collection unit can collect information such as date and time, content of play, and results. The data collection unit also collects child reaction data. For example, the data collection unit can collect information such as the child's facial expressions, behavior, and feedback. The data collection unit can store the collected data in a database. In this way, by collecting past play history and child reaction data, it is possible to reflect the collected data in suggesting the next play. For example, the data collection unit can identify patterns of play that the child enjoyed based on the past play history and reflect the identified patterns in suggesting the next play. In addition, the data collection unit can evaluate the effectiveness of play based on the child's reaction data and reflect the identified patterns in suggesting the next play. In this way, the lifestyle habit suggestion system can suggest more effective play.

[0071] The lifestyle habit suggestion system includes an evaluation unit that evaluates the effectiveness of play and reflects the evaluation in the next play suggestion. The evaluation unit evaluates the effectiveness of play. For example, the evaluation unit can evaluate the effectiveness of play based on criteria such as the degree of habit establishment and the child's satisfaction. The evaluation unit can evaluate the effectiveness of play using, for example, a questionnaire or observation. The evaluation unit can also evaluate the effectiveness of play based on collected data. For example, the evaluation unit can evaluate the effectiveness of play based on the child's reaction data. The evaluation unit can store the evaluation results in a database. This allows the evaluation of the effectiveness of play and reflects the evaluation in the next play suggestion, thereby making it possible to suggest more effective play. For example, the evaluation unit can optimize the next play suggestion based on past evaluation data. The evaluation unit can also adjust the content and difficulty of the play based on the evaluation results. This allows the lifestyle habit suggestion system to suggest more effective play.

[0072] The reception unit can estimate the parent's emotions and adjust the timing of lifestyle habit input based on the estimated parent's emotions. For example, if the reception unit is stressed, the system refrains from notifying the parent to input information and instead prompts the parent to input information at a time when the parent is relaxed. Furthermore, if the parent is relaxed, the reception unit can immediately prompt the parent to input information, supporting smooth operation. Furthermore, if the parent is busy, the reception unit can set a reminder to input information later and notify the parent at an appropriate time. This reduces the burden on the parent by adjusting the timing of lifestyle habit input according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the parent's facial expression data into the generation AI and cause the generation AI to estimate the parent's emotions.

[0073] The reception unit can analyze the parent's past input history and select an appropriate input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the parent has frequently used in the past. The reception unit can also analyze lifestyle patterns input by the parent in the past and suggest the optimal input method. The reception unit can also predict and suggest the input method to be used during a specific time period from the parent's past input history. In this way, the optimal input method can be suggested by analyzing the parent's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's past input history data into a generation AI and have the generation AI select the optimal input method.

[0074] When inputting lifestyle habits, the reception unit can filter the lifestyle habits based on the parent's current living situation and areas of interest. For example, if the parent is interested in health, the reception unit can suggest that health-related lifestyle habits be input first. Furthermore, if the parent is busy with work, the reception unit can suggest lifestyle habits that can be implemented in a short time. Furthermore, if the parent is interested in their child's education, the reception unit can suggest that education-related lifestyle habits be input first. This reduces the burden on the parent by suggesting lifestyle habits based on the parent's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the parent's living situation data into the generation AI and cause the generation AI to perform filtering.

[0075] When inputting lifestyle habits, the reception unit can select the optimal input means depending on the parent's input method (voice, text, image, etc.). For example, if the parent prefers voice input, the reception unit can preferentially suggest voice input. Also, if the parent prefers text input, the reception unit can preferentially suggest text input. Also, if the parent prefers image input, the reception unit can preferentially suggest image input. This allows the optimal input means to be selected depending on the parent's input method, thereby reducing the burden on the parent. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0076] The reception unit can estimate the parent's emotions and determine the priority of lifestyle habits to be input based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit can suggest that the parent prioritizes inputting lifestyle habits that help reduce stress. Furthermore, if the parent is relaxed, the reception unit can suggest that the parent prioritizes inputting lifestyle habits related to long-term goals. Furthermore, if the parent is busy, the reception unit can suggest that the parent prioritizes inputting lifestyle habits that can be implemented in the short term. This reduces the burden on the parent by prioritizing lifestyle habits according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the parent's facial expression data into the generation AI and cause the generation AI to estimate the parent's emotions.

[0077] When inputting lifestyle habits, the reception unit can prioritize inputting highly relevant information taking into account the parent's geographical location information. For example, if the parent lives in a specific area, the reception unit can suggest that lifestyle habits related to that area be prioritized. Furthermore, if the parent is traveling, the reception unit can suggest that lifestyle habits related to the travel destination be prioritized. Furthermore, if the parent frequently visits a specific facility, the reception unit can suggest that lifestyle habits related to the facility be prioritized. This reduces the burden on the parent by suggesting lifestyle habits taking into account the parent's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the parent's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant information.

[0078] The reception unit can analyze the parent's social media activity and input related information when inputting lifestyle habits. For example, the reception unit can suggest inputting related lifestyle habits based on information shared by the parent on social media. The reception unit can also analyze the parent's social media activity and suggest inputting related lifestyle habits. The reception unit can also suggest inputting related lifestyle habits by referring to the activity of the parent's friends on social media. In this way, related lifestyle habits can be suggested by analyzing the parent's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data to the generation AI and cause the generation AI to input related information.

[0079] The reception unit can customize the input method by reflecting the parent's past feedback when inputting lifestyle habits. The reception unit can, for example, suggest an optimal input method based on feedback provided by the parent in the past. The reception unit can also preferentially suggest a specific input method based on the parent's past feedback. The reception unit can also analyze the parent's past feedback and customize the input method. This makes it possible to suggest an optimal input method by reflecting the parent's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the parent's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0080] The generation unit can estimate the child's emotions and adjust the content of the play based on the estimated child's emotions. For example, if the child is excited, the generation unit can suggest active play. If the child is relaxed, the generation unit can suggest quiet play. If the child is tired, the generation unit can also suggest play that can be completed in a short time. By adjusting the content of play according to the child's emotions, the child can acquire fun lifestyle habits. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the child's emotions.

[0081] When generating activities, the generation unit can adjust the level of detail of the activities based on the importance of the lifestyle habits. For example, the generation unit can suggest detailed activities for important lifestyle habits. Furthermore, the generation unit can suggest easy activities for lifestyle habits with low importance. Furthermore, the generation unit can adjust the difficulty of the activities according to the importance of the lifestyle habits. In this way, by adjusting the level of detail of the activities according to the importance of the lifestyle habits, children can acquire lifestyle habits in a fun way. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input lifestyle habit importance data into the generation AI and cause the generation AI to adjust the level of detail of the activities.

[0082] When generating activities, the generation unit can apply different generation algorithms depending on the lifestyle habit category. For example, the generation unit can suggest activities that incorporate exercise for health-related lifestyle habits. Furthermore, the generation unit can suggest activities that incorporate learning for education-related lifestyle habits. Furthermore, the generation unit can suggest activities that encourage cooperation for social-related lifestyle habits. In this way, by applying different generation algorithms depending on the lifestyle habit category, children can acquire lifestyle habits in a fun way. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input lifestyle habit category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0083] When generating a game, the generation unit can improve the accuracy of the game by referring to past game results. The generation unit, for example, analyzes past game results and incorporates successful game patterns. The generation unit can also preferentially suggest games that the child enjoyed based on past game results. The generation unit can also adjust the difficulty of the game based on past game results. In this way, the accuracy of the game can be improved by referring to past game results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past game result data into the generation AI and cause the generation AI to improve the accuracy of the game.

[0084] The generation unit can estimate the child's emotions and adjust the length of the play based on the estimated child's emotions. For example, if the child is excited, the generation unit can suggest a longer play session. If the child is tired, the generation unit can also suggest a shorter play session. If the child is relaxed, the generation unit can also suggest a moderate length of play session. By adjusting the length of play according to the child's emotions, the child can acquire fun lifestyle habits. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the child's emotions.

[0085] When generating activities, the generation unit can determine the priority of activities based on the time when lifestyle habits were input. For example, if the time when lifestyle habits were input is early, the generation unit can preferentially suggest activities. Furthermore, if the time when lifestyle habits were input is late, the generation unit can suggest activities to be postponed. Furthermore, the generation unit can adjust the priority of activities according to the time when lifestyle habits were input. In this way, by determining the priority of activities based on the time when lifestyle habits were input, children can acquire lifestyle habits in a fun way. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when lifestyle habits were input into the generation AI and cause the generation AI to determine the priority of activities.

[0086] When generating games, the generation unit can adjust the order of games based on the relevance of lifestyle habits. For example, the generation unit prioritizes incorporating highly relevant lifestyle habits into games. The generation unit can also postpone incorporating less relevant lifestyle habits into games. The generation unit can also adjust the order of games according to the relevance of lifestyle habits. In this way, by adjusting the order of games based on the relevance of lifestyle habits, children can acquire lifestyle habits in a fun way. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input lifestyle habit relevance data into the generation AI and cause the generation AI to adjust the order of games.

[0087] When generating a game, the generation unit can customize the content of the game according to the child's age and interests. The generation unit, for example, can suggest appropriate games according to the child's age. The generation unit can also suggest games that will attract the child's attention according to the child's interests. The generation unit can also customize the content of the game based on the child's age and interests. This allows the child to acquire fun lifestyle habits by customizing the content of the game according to the child's age and interests. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's age and interest data into the generation AI and cause the generation AI to customize the content of the game.

[0088] The suggestion unit can estimate the parent's emotions and adjust the way the suggestion is expressed based on the estimated parent's emotions. For example, if the parent is stressed, the suggestion unit can make a simple and easy-to-understand suggestion. Furthermore, if the parent is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the parent is busy, the suggestion unit can make a suggestion that can be understood in a short time. By adjusting the way the suggestion is expressed based on the parent's emotions, the parent is more likely to accept the suggestion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input facial expression data of the parent into the generation AI and cause the generation AI to estimate the parent's emotions.

[0089] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the activity. For example, the suggestion unit makes a detailed suggestion for an important activity. The suggestion unit can also make a simple suggestion for an activity with a low importance. The suggestion unit can also adjust the level of detail of the suggestion according to the importance of the activity. This makes it easier for parents to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input importance data of the activity into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0090] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of play. For example, the suggestion unit can make suggestions that incorporate exercise for health-related play. Furthermore, the suggestion unit can make suggestions that incorporate learning for education-related play. Furthermore, the suggestion unit can make suggestions that encourage cooperation for social play. In this way, applying different suggestion algorithms depending on the category of play makes it easier for parents to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input play category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0091] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to past suggestion results. For example, the suggestion unit analyzes past suggestion results and incorporates successful suggestion patterns. The suggestion unit can also prioritize suggestions that parents preferred based on past suggestion results. The suggestion unit can also adjust the level of detail of the suggestion based on past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0092] The suggestion unit can estimate the parent's emotions and adjust the length of the suggestion based on the estimated parent's emotions. For example, if the parent is stressed, the suggestion unit can make short, concise suggestions. If the parent is relaxed, the suggestion unit can make detailed suggestions. If the parent is busy, the suggestion unit can make suggestions that can be understood in a short amount of time. This makes it easier for the parent to accept the suggestion by adjusting the length of the suggestion according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the parent's facial expression data into the generation AI and cause the generation AI to estimate the parent's emotions.

[0093] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the activity was generated. For example, if the activity was generated early, the suggestion unit can prioritize the suggestion. Also, if the activity was generated late, the suggestion unit can postpone the suggestion. Also, the suggestion unit can adjust the priority of the suggestion depending on the time when the activity was generated. In this way, by determining the priority of the suggestion based on the time when the activity was generated, parents are more likely to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the time when the activity was generated into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0094] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the activities. For example, the suggestion unit prioritizes suggesting highly relevant activities. The suggestion unit can also postpone suggesting less relevant activities. The suggestion unit can also adjust the order of suggestions according to the relevance of the activities. In this way, adjusting the order of suggestions based on the relevance of the activities makes it easier for parents to accept the suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the activities into a generation AI and cause the generation AI to adjust the order of suggestions.

[0095] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise. For example, if the parent has technical expertise, the suggestion unit can make the suggestion using technical terminology. Also, if the parent does not have technical expertise, the suggestion unit can make the suggestion in simple language. Also, the suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise. This makes it easier for the parent to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the parent's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The data collection unit can estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, if the child is relaxed, the data collection unit can immediately collect data. Furthermore, if the child is excited, the data collection unit can postpone data collection. Furthermore, if the child is tired, the data collection unit can refrain from collecting data and collect it after the child has rested. This allows for adjusting the timing of data collection according to the child's emotions, thereby improving the accuracy of data collection. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data collection unit can input data of the child's facial expression into the generation AI and cause the generation AI to estimate the child's emotions.

[0097] When collecting data, the data collection unit can improve the accuracy of the collected data by referring to past play history. The data collection unit improves the accuracy of the collected data based on, for example, past play history. The data collection unit can also collect successful play patterns from past play history. The data collection unit can also analyze past play history and improve the accuracy of the collected data. In this way, the accuracy of the collected data can be improved by referring to past play history. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past play history data into a generation AI and cause the generation AI to improve the accuracy of the collected data.

[0098] The data collection unit can analyze the child's reaction data during data collection and optimize the collection method. The data collection unit, for example, optimizes the collection method based on the child's reaction data. The data collection unit can also propose an optimal collection method from the child's reaction data. The data collection unit can also analyze the child's reaction data and optimize the collection method. In this way, the collection method can be optimized by analyzing the child's reaction data. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, for example, or may be performed without using AI. For example, the data collection unit can input the child's reaction data into the generation AI and cause the generation AI to optimize the collection method.

[0099] The data collection unit can estimate the child's emotions and determine the priority of data to be collected based on the estimated child's emotions. For example, if the child is excited, the data collection unit can prioritize collecting data related to excitement. Furthermore, if the child is relaxed, the data collection unit can prioritize collecting data related to relaxation. Furthermore, if the child is tired, the data collection unit can prioritize collecting data related to fatigue. This allows for determining the priority of data to be collected according to the child's emotions, thereby improving the accuracy of data collection. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input data of the child's facial expression into the generation AI and cause the generation AI to estimate the child's emotions.

[0100] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific area, the data collection unit can prioritize collecting data related to that area. Furthermore, if the child is traveling, the data collection unit can prioritize collecting data related to the travel destination. Furthermore, if the child is in a specific facility, the data collection unit can also prioritize collecting data related to the facility. In this way, by collecting data by taking the child's geographical location information into account, the accuracy of data collection can be improved. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the child's geographical location information data to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0101] During data collection, the data collection unit can analyze the child's social media activities and collect related data. For example, the data collection unit collects related data based on information shared by the child on social media. The data collection unit can also analyze the child's social media activities and collect related data. The data collection unit can also collect related data by referring to the activities of the child's friends on social media. In this way, related data can be collected by analyzing the child's social media activities. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the child's social media activity data into the generation AI and cause the generation AI to collect related data.

[0102] The evaluation unit can estimate the child's emotions and evaluate the effectiveness of the play based on the estimated child's emotions. For example, if the child is enjoying the play, the evaluation unit can rate the effectiveness of the play highly. If the child is bored, the evaluation unit can rate the effectiveness of the play low. If the child is excited, the evaluation unit can also rate the effectiveness of the play highly. In this way, by evaluating the effectiveness of the play according to the child's emotions, the evaluation can be reflected in the next play suggestion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using an AI, for example, or without an AI. For example, the evaluation unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the child's emotions.

[0103] When evaluating play, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data. The evaluation unit, for example, optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also incorporate successful play patterns from the past evaluation data. The evaluation unit can also analyze past evaluation data and optimize the evaluation algorithm. In this way, the evaluation algorithm can be optimized by referring to the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation data into a generation AI and cause the generation AI to optimize the evaluation algorithm.

[0104] The evaluation unit can analyze the child's reaction data when evaluating play and improve the evaluation method. The evaluation unit can improve the evaluation method based on, for example, the child's reaction data. The evaluation unit can also propose an optimal evaluation method from the child's reaction data. The evaluation unit can also analyze the child's reaction data and improve the evaluation method. In this way, the evaluation method can be improved by analyzing the child's reaction data. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without using AI. For example, the evaluation unit can input the child's reaction data into a generation AI and cause the generation AI to improve the evaluation method.

[0105] The evaluation unit can estimate the child's emotions and determine the priority of the evaluation based on the estimated child's emotions. For example, if the child is having fun, the evaluation unit can prioritize evaluations related to enjoyment. Also, if the child is bored, the evaluation unit can prioritize evaluations related to boredom. Also, if the child is excited, the evaluation unit can prioritize evaluations related to excitement. This allows the accuracy of the evaluations to be improved by determining the priority of the evaluations according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input child's facial expression data into the generation AI and cause the generation AI to estimate the child's emotions.

[0106] When evaluating play, the evaluation unit can make the evaluation taking into account the child's geographical location information. For example, if the child is in a specific area, the evaluation unit can make an evaluation related to that area. Furthermore, if the child is traveling, the evaluation unit can make an evaluation related to the travel destination. Furthermore, if the child is at a specific facility, the evaluation unit can make an evaluation related to the facility. In this way, by making an evaluation taking into account the child's geographical location information, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the child's geographical location information data into the generation AI and have the generation AI perform the evaluation.

[0107] The evaluation unit can analyze the child's social media activity when evaluating play and improve the accuracy of the evaluation. The evaluation unit can improve the accuracy of the evaluation, for example, based on information shared by the child on social media. The evaluation unit can also analyze the child's social media activity and improve the accuracy of the evaluation. The evaluation unit can also improve the accuracy of the evaluation by referring to the activity of the child's friends on social media. In this way, the accuracy of the evaluation can be improved by analyzing the child's social media activity. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the child's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the evaluation. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, suggestion unit, data collection unit, and evaluation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows parents to input lifestyle habits they want their children to acquire. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generative AI to create games. The suggestion unit is realized, for example, by the output device 40 of the smart device 14, and notifies the parent of the generated games. The data collection unit collects reaction data from the child using, for example, the camera 42 or microphone 38B of the smart device 14. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of the games based on the collected data. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, suggestion unit, data collection unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing parents to voice-input lifestyle habits they want their children to acquire. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generative AI to create games. The suggestion unit is realized, for example, by the speaker 240 of the smart glasses 214, and notifies the parent of the generated games. The data collection unit collects reaction data of the child using, for example, the camera 42 of the smart glasses 214. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of the games based on the collected data. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, suggestion unit, data collection unit, and evaluation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and allows parents to voice-input lifestyle habits they want their children to acquire. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and devise games using a generative AI. The suggestion unit is realized, for example, by the display 343 of the headset-type terminal 314, and notifies the parent of the generated games. The data collection unit collects reaction data of the child using, for example, the camera 42 of the headset-type terminal 314. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of the games based on the collected data. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, suggestion unit, data collection unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and allows parents to voice-input lifestyle habits they want their child to acquire. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and devise games using a generative AI. The suggestion unit is realized, for example, by the speaker 240 of the robot 414, and notifies the parent of the generated games. The data collection unit is realized, for example, by the camera 42 of the robot 414, and collects reaction data from the child. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of the games based on the collected data.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit can monitor in real time how the child responds to the lifestyle habits entered by the parent and provide feedback as needed. For example, if a parent enters "Early to bed, early to rise," the reception unit will monitor the child's sleep patterns and provide appropriate advice. Also, if a parent enters "Hand washing / gargling," the reception unit can monitor the frequency and duration of the child's hand washing and suggest areas for improvement. This allows parents to understand the progress of their child's lifestyle habits in real time and provide appropriate support.

[0110] The data collection unit can estimate a child's emotions and customize play suggestions based on the estimated emotions. For example, if a child is feeling stressed, it can suggest games that will help them relax. If a child is excited, it can suggest games that will help them release energy. Furthermore, if a child is bored, it can suggest games that offer a new challenge. This allows for more effective lifestyle habits to be developed by suggesting games that correspond to the child's emotions.

[0111] The evaluator can collect parental feedback and reflect it in the next game suggestion. For example, if a parent provides feedback that "this game was too difficult for my child," the evaluator can adjust the difficulty level in the next suggestion. Also, if a parent provides feedback that "my child really enjoyed this game," the evaluator can suggest a similar game. Furthermore, if a parent provides feedback that "this game was not effective," the evaluator can try a different approach. In this way, parental feedback can be utilized to suggest more effective games.

[0112] The reception unit can estimate the parent's emotions and customize the lifestyle habit entry content based on the estimated emotions. For example, if the parent is feeling stressed, the system can suggest simple and easy-to-follow lifestyle habits. If the parent is relaxed, the system can encourage the parent to enter more detailed lifestyle habits. Furthermore, if the parent is busy, the system can suggest lifestyle habits that can be entered in a short amount of time. In this way, the burden on the parent can be reduced by customizing the lifestyle habit entry content according to the parent's emotions.

[0113] The reception unit can analyze the parent's past input history and suggest the optimal input timing. For example, if the parent has previously input data at night, the reception unit can send a notification prompting the parent to input data at night. Also, if the parent has previously input data on weekends, the reception unit can send a notification prompting the parent to input data on weekends. Furthermore, if the parent has previously input data during a specific time period, the reception unit can also send a notification prompting the parent to input data during that time period. In this way, the parent's past input history can be utilized to suggest the optimal input timing.

[0114] The reception unit can customize the lifestyle habit input content based on the parent's current living situation and areas of interest. For example, if the parent is interested in health, health-related lifestyle habits can be prioritized. Also, if the parent is busy with work, lifestyle habits that can be implemented in a short time can be suggested. Furthermore, if the parent is interested in their child's education, education-related lifestyle habits can be prioritized. This reduces the burden on parents by suggesting lifestyle habits based on the parent's living situation and areas of interest.

[0115] The reception unit can select the optimal input means depending on the parent's input method (voice, text, image, etc.). For example, if the parent prefers voice input, it can preferentially suggest voice input. Also, if the parent prefers text input, it can preferentially suggest text input. Furthermore, if the parent prefers image input, it can preferentially suggest image input. In this way, by selecting the optimal input means depending on the parent's input method, it is possible to reduce the burden on the parent.

[0116] The reception unit can estimate the parent's emotions and determine the priority of lifestyle habits to be input based on the estimated emotions. For example, if the parent is feeling stressed, the reception unit can suggest that the parent prioritizes inputting lifestyle habits that help reduce stress. Also, if the parent is relaxed, the reception unit can suggest that the parent prioritizes inputting lifestyle habits related to long-term goals. Furthermore, if the parent is busy, the reception unit can suggest that the parent prioritizes inputting lifestyle habits that can be implemented in the short term. In this way, the burden on the parent can be reduced by prioritizing lifestyle habits according to the parent's emotions.

[0117] The reception unit can prioritize input of highly relevant information taking into account the geographical location information of the parent. For example, if the parent lives in a specific area, it can suggest that lifestyle habits related to that area be input first. Also, if the parent is traveling, it can suggest that lifestyle habits related to the travel destination be input first. Furthermore, if the parent frequently visits a specific facility, it can suggest that lifestyle habits related to that facility be input first. In this way, by suggesting lifestyle habits taking into account the geographical location information of the parent, it is possible to reduce the burden on the parent.

[0118] The reception unit can analyze the parent's social media activity and input relevant information. For example, it can suggest that the parent input relevant lifestyle habits based on information shared by the parent on social media. It can also analyze the parent's social media activity and suggest that the parent input relevant lifestyle habits. It can also suggest that the parent input relevant lifestyle habits based on the activities of the parent's friends on social media. In this way, it is possible to suggest relevant lifestyle habits by analyzing the parent's social media activity.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit inputs the lifestyle habits that parents want their children to acquire. For example, parents can input lifestyle habits such as "early to bed, early to rise" or "washing their hands and gargling." The reception unit can accept text input and voice input, and can also store the information entered by parents in a database. Step 2: The generation unit uses a generation AI to analyze the information input by the reception unit and devise games. For example, the generation AI devise games that children can enjoy and learn based on the input lifestyle habits. The generation unit can generate the content of games using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The suggestion unit suggests the game devised by the generation unit. For example, the suggestion unit can notify the parent of the game devised by the generation AI. The suggestion unit can suggest the content of the game to the parent using a text message or a voice message. The suggestion unit can also provide guidelines for the parent to carry out the suggested game. For example, the suggestion unit can explain the steps and rules of the game to the parent.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

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

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] 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.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where parents can input the lifestyle habits they want their children to acquire, a generation unit that analyzes the information input by the reception unit and devises a game; a suggestion unit that suggests the play devised by the creation unit; Equipped with A system characterized by:

2. Equipped with a data collection unit that collects past play history or child reaction data 2. The system of claim 1.

3. Equipped with an evaluation department that evaluates the effectiveness of play and reflects it in future play proposals 2. The system of claim 1.

4. The reception unit Estimates the parent's emotions and adjusts the timing of lifestyle habit input based on the estimated parent's emotions 2. The system of claim 1.

5. The reception unit Analyze the parent's past input history and select the appropriate input method 2. The system of claim 1.

6. The reception unit When entering lifestyle habits, filtering is performed based on the parent's current living situation and areas of interest.

2. The system of claim 1.

7. The reception unit When entering lifestyle habits, select the most appropriate input method based on the parent's input method.

2. The system of claim 1.

8. The reception unit Estimate the parent's emotions and determine the priority of lifestyle habits to be input based on the estimated parent's emotions 2. The system of claim 1.

9. The reception unit When entering lifestyle habits, the system takes into account the parent's geographic location information to prioritize the most relevant information.

2. The system of claim 1.

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