System

Abstract

An object of a system according to an embodiment is to propose play for making a child pleasantly learn a lifestyle.SOLUTION: A system includes a lifestyle habit input part, a play devising part, and a play proposing part. The lifestyle habit input unit inputs a lifestyle habit. The play devising unit devises play on the basis of the lifestyle input by the lifestyle input unit. The play suggestion section suggests the play devised by the play devising 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] With conventional technology, it was difficult to come up with games that would help children develop good lifestyle habits in a fun way, which placed a heavy burden on parents.

[0005] The system according to the embodiment aims to suggest games that will help children acquire good lifestyle habits in a fun way. [Means for solving the problem]

[0006] The system according to the embodiment includes a lifestyle input unit, a game idea unit, and a game suggestion unit. The lifestyle input unit inputs lifestyle habits. The game idea unit devise games based on the lifestyle habits input by the lifestyle input unit. The game suggestion unit suggests games devised by the game idea unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest games that children can play to help them 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The lifestyle habit suggestion system according to an embodiment of the present invention is a system in which parents simply input the lifestyle habits they want their children to acquire into the system, and a generating AI devise and suggest games that will help children acquire those habits in a fun way. This lifestyle habit suggestion system is expected to help children acquire lifestyle habits in a fun way, reduce the burden on parents, and deepen family bonds.

[0029] A lifestyle habit suggestion system according to an embodiment includes a lifestyle habit input unit, a game idea unit, and a game suggestion unit. The lifestyle habit input unit inputs lifestyle habits that parents want their children to acquire. For example, specific habits such as getting up early every morning, washing hands before meals, and putting away toys are input. This input can be easily performed through the system's interface. The game idea unit devise games that children can enjoy and acquire based on the lifestyle habits input by the lifestyle habit input unit. For example, to help children acquire the habit of getting up early every morning, the system may suggest a game that rewards points for waking up early. The generation AI generates game ideas using a text generation AI (e.g., LLM) or a multimodal generation AI. The game suggestion unit suggests games devised by the game idea unit. For example, even for the same habit of tidying up toys, the system may suggest a variety of games, such as a time trial game today and a story game with a tidying-up theme tomorrow. This allows children to acquire lifestyle habits in a fun way, reduces the burden on parents, and is expected to deepen family bonds.

[0030] The lifestyle habit input unit can input lifestyle habits using voice recognition technology. For example, when a parent inputs lifestyle habits, the system can be built using voice recognition technology to complete the input simply by speaking. For example, if a parent simply says that they wake up at 7 o'clock every morning, that information will be automatically entered. The voice recognition technology uses deep learning-based voice recognition and keyword spotting. This allows parents to easily input lifestyle habits simply by speaking.

[0031] The lifestyle habit input unit can provide an auto-completion function based on past input history. For example, when a parent inputs lifestyle habits, the lifestyle habit input unit builds a system that provides an auto-completion function based on past input history. For example, when a parent re-enters "I wake up at 7am every morning," which was previously input, it is automatically displayed as a candidate. The auto-completion function is realized by using a method for using past input data and a completion algorithm. This reduces the effort required for input by providing an auto-completion function based on past input history.

[0032] The lifestyle habit input unit can allow children to input their lifestyle habits. The lifestyle habit input unit provides, for example, an interface that allows children to input their lifestyle habits by themselves. For example, a simple input screen designed for children can be prepared, allowing the child to input that they get up at 7 o'clock every morning. To enable children to input their lifestyle habits, an intuitive interface, voice input, game-style input, or the like can be used. This allows children to input their lifestyle habits themselves, thereby encouraging their independence.

[0033] The lifestyle habit input unit can enable all family members to share their lifestyle habits. The lifestyle habit input unit, for example, creates a system that allows all family members to input and share their lifestyle habits. For example, all family members can log in with the same account and input and share their lifestyle habits. To enable sharing among all family members, cloud-based sharing, a home network, a dedicated app, etc. can be used. By sharing lifestyle habits among all family members, the whole family can set goals and work together to achieve them.

[0034] The game idea unit can learn from the history of past games and devise new games based on patterns of games that children particularly enjoyed. For example, the game idea unit constructs a system in which a generative AI learns from the history of past games and devise new games based on patterns of games that children particularly enjoyed. For example, it can suggest new games that incorporate elements of games that have been highly rated in the past. Data such as the type of game, duration, frequency, and children's reactions is saved as the history of past games. This allows the system to learn from the history of past games and devise new games based on patterns of games that children particularly enjoyed, thereby maintaining a child's interest.

[0035] The game ideation department can adjust the difficulty and content of games based on parental feedback, and suggest more effective games. For example, the game ideation department will build a system in which the generation AI adjusts the difficulty and content of games based on parental feedback. For example, if a parent gives feedback that a game is too difficult, the difficulty of the next game will be lowered. Feedback will include parental comments, evaluation scores, and areas for improvement. This will allow the department to suggest more effective games by adjusting the difficulty and content of games based on parental feedback.

[0036] The play creation department can create games for different age groups and developmental stages, making it possible to accommodate a wide range of children. For example, the play creation department will build a system in which a generative AI can create games for different age groups and developmental stages. For example, it can accommodate a wide range of games, from simple games for toddlers to complex games for elementary school students. Different age groups and developmental stages include toddlers, school children, and adolescents. This makes it possible to create games for different age groups and developmental stages, making it possible to accommodate a wide range of children.

[0037] The game ideation department can devise special games according to the season or event, allowing users to enjoy a sense of the season or event. For example, the game ideation department will build a system in which a generation AI devise special games according to the season or event. For example, it will propose special games to coincide with Christmas or Halloween. Examples of seasons or events include summer vacation, Christmas, and Halloween. In this way, by devising special games according to the season or event, users can enjoy a sense of the season or event.

[0038] The play suggestion unit can reflect parental feedback in the next play suggestion. For example, the play suggestion unit builds a system in which the generation AI reflects parental feedback in the next play suggestion. For example, if a parent provides feedback that they would like their child to get more exercise, an exercise element will be incorporated into the next play. Feedback includes parental comments, evaluation scores, and areas for improvement. By incorporating parental feedback, the next play suggestion will be more effective.

[0039] The play suggestion unit can take into account a child's learning progress and provide play that is appropriate for their development. For example, the play suggestion unit builds a system in which a generation AI monitors a child's learning progress in real time and, based on the results, provides play that is appropriate for their development. For example, if a child acquires a specific skill, it can suggest new play that makes use of that skill. To take learning progress into account, it uses the collection and analysis of learning data, progress evaluation criteria, etc. This makes it possible to provide play that is appropriate for a child's development by taking into account their learning progress.

[0040] The game suggestion unit can incorporate games from different cultures and regions to propose a variety of games from an international perspective. For example, the game suggestion unit will build a system in which the generation AI incorporates games from different cultures and regions to propose a variety of games from an international perspective. For example, it will propose traditional Japanese games and popular American games. Examples of different cultures and regions include Asia, Europe, and Africa. This makes it possible to propose a variety of games from an international perspective by incorporating games from different cultures and regions.

[0041] The play suggestion unit suggests collaborative games that parents and children can enjoy together, deepening the bond between them. For example, the play suggestion unit builds a system in which a generative AI suggests collaborative games that parents and children can enjoy together. For example, it suggests games in which parents and children work together to complete a mission. Examples of collaborative games include team-building games and collaborative creative activities. This allows the bond between parents and children to be deepened by suggesting collaborative games that parents and children can enjoy together.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The lifestyle habit input unit can monitor the parent's health condition and prompt the parent to input lifestyle habits at appropriate times. For example, it can suggest refraining from inputting information when the parent is tired and prompting input when the parent is relaxed. To monitor the parent's health condition, sensors in wearable devices and smartphones are used. This allows the parent to input lifestyle habits at timings that take into consideration the parent's health condition, reducing stress and making it easier for the parent to input lifestyle habits.

[0044] The lifestyle habit input unit can learn the parent's daily rhythm and suggest the optimal timing for input. For example, it can learn that the parent relaxes at a specific time each night and prompt them to input at that time. To learn the parent's daily rhythm, an algorithm is used to analyze the parent's behavioral patterns and find the optimal timing. This allows for input timing that matches the parent's daily rhythm, reducing stress and enabling efficient input of lifestyle habits.

[0045] The lifestyle habit input unit can provide a customizable input interface according to the parent's preferences. For example, an interface can be provided that allows the parent to select their favorite color or design. To realize a customizable input interface, a system is constructed that creates a user profile and saves settings according to the parent's preferences. This allows the parent to input lifestyle habits using an interface that suits their preferences, improving their motivation to input information.

[0046] The play idea department can change the theme of the play based on parental feedback. For example, if a parent shows interest in a particular theme, it will suggest a play based on that theme. Feedback includes comments and evaluation scores from parents. This allows the department to change the theme of the play based on parental feedback, providing play that both parents and children can enjoy.

[0047] The Play Ideas Department can propose collaborative play that parents and children can enjoy together. For example, they can propose games where parents and children work together to complete a mission, or activities where parents and children create artwork together. Examples of collaborative play include team-building games and collaborative creative activities. By proposing collaborative play that parents and children can enjoy together, they can deepen the bond between parents and children.

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

[0049] Step 1: The lifestyle habit input section allows parents to input the lifestyle habits they want their children to acquire. For example, specific habits such as getting up early every morning, washing hands before meals, and putting away toys can be input. This input can be easily done through the system interface. Step 2: The game ideation unit creates games that children can enjoy and learn based on the lifestyle habits input by the lifestyle habit input unit. For example, to help children develop the habit of waking up early every morning, the unit suggests a game in which they can earn points if they wake up early. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate game ideas. Step 3: The Play Suggestion Department proposes games devised by the Play Creation Department. For example, even if it is the same habit of tidying up toys, they can suggest a variety of games, such as a time trial game today and a story game with a tidying up theme tomorrow.

[0050] (Example 2) The lifestyle habit suggestion system according to an embodiment of the present invention is a system in which parents simply input the lifestyle habits they want their children to acquire into the system, and a generating AI devise and suggest games that will help children acquire those habits in a fun way. This lifestyle habit suggestion system is expected to help children acquire lifestyle habits in a fun way, reduce the burden on parents, and deepen family bonds.

[0051] A lifestyle habit suggestion system according to an embodiment includes a lifestyle habit input unit, a game idea unit, and a game suggestion unit. The lifestyle habit input unit inputs lifestyle habits that parents want their children to acquire. For example, specific habits such as getting up early every morning, washing hands before meals, and putting away toys are input. This input can be easily performed through the system's interface. The game idea unit devise games that children can enjoy and acquire based on the lifestyle habits input by the lifestyle habit input unit. For example, to help children acquire the habit of getting up early every morning, the system may suggest a game that rewards points for waking up early. The generation AI generates game ideas using a text generation AI (e.g., LLM) or a multimodal generation AI. The game suggestion unit suggests games devised by the game idea unit. For example, even for the same habit of tidying up toys, the system may suggest a variety of games, such as a time trial game today and a story game with a tidying-up theme tomorrow. This allows children to acquire lifestyle habits in a fun way, reduces the burden on parents, and is expected to deepen family bonds.

[0052] The lifestyle habit input unit can analyze the parent's emotional state using an emotion estimation function and suggest a less stressful input method. For example, when a parent inputs their lifestyle habits, the lifestyle habit input unit uses a camera or microphone to analyze the parent's facial expressions and tone of voice in real time to estimate their emotional state. For example, if it determines that the parent is under high stress, it will suggest simplifying the input interface. The emotion estimation function estimates emotions using technologies such as facial expression recognition, voice analysis, and biometrics. This reduces stress for parents and allows them to input their lifestyle habits more comfortably.

[0053] The lifestyle habit input unit can input lifestyle habits using voice recognition technology. For example, when a parent inputs lifestyle habits, the system can be built using voice recognition technology to complete the input simply by speaking. For example, if a parent simply says that they wake up at 7 o'clock every morning, that information will be automatically entered. The voice recognition technology uses deep learning-based voice recognition and keyword spotting. This allows parents to easily input lifestyle habits simply by speaking.

[0054] The lifestyle habit input unit can provide an auto-completion function based on past input history. For example, when a parent inputs lifestyle habits, the lifestyle habit input unit builds a system that provides an auto-completion function based on past input history. For example, when a parent re-enters "I wake up at 7am every morning," which was previously input, it is automatically displayed as a candidate. The auto-completion function is realized by using a method for using past input data and a completion algorithm. This reduces the effort required for input by providing an auto-completion function based on past input history.

[0055] The lifestyle habit input unit can allow children to input their lifestyle habits. The lifestyle habit input unit provides, for example, an interface that allows children to input their lifestyle habits by themselves. For example, a simple input screen designed for children can be prepared, allowing the child to input that they get up at 7 o'clock every morning. To enable children to input their lifestyle habits, an intuitive interface, voice input, game-style input, or the like can be used. This allows children to input their lifestyle habits themselves, thereby encouraging their independence.

[0056] The lifestyle habit input unit can enable all family members to share their lifestyle habits. The lifestyle habit input unit, for example, creates a system that allows all family members to input and share their lifestyle habits. For example, all family members can log in with the same account and input and share their lifestyle habits. To enable sharing among all family members, cloud-based sharing, a home network, a dedicated app, etc. can be used. By sharing lifestyle habits among all family members, the whole family can set goals and work together to achieve them.

[0057] The lifestyle habit input unit can use the emotion estimation function to analyze a child's emotional response to lifestyle habits and make suggestions that will elicit a positive response. The lifestyle habit input unit, for example, uses the emotion estimation function to build a system that analyzes a child's emotional response to input lifestyle habits in real time. For example, a camera or microphone can be used to analyze a child's facial expression and tone of voice to measure the emotional response. To analyze the emotional response, techniques such as facial expression analysis, voice tone analysis, and behavior pattern analysis are used. This analyzes a child's emotional response and elicits a positive response, thereby promoting the establishment of lifestyle habits.

[0058] The play idea unit can analyze a child's emotional state in real time and suggest the most appropriate play. For example, when the generative AI devise a play, it uses a camera and microphone to analyze the child's facial expressions and tone of voice in real time to estimate their emotional state. For example, if it determines that the child is having fun, it will suggest a play that will maintain that emotion. To perform analysis in real time, it uses technologies such as streaming data analysis and real-time feedback systems. This allows the system to analyze a child's emotional state in real time and suggest the most appropriate play, thereby maintaining the child's interest.

[0059] The game idea unit can learn from the history of past games and devise new games based on patterns of games that children particularly enjoyed. For example, the game idea unit constructs a system in which a generative AI learns from the history of past games and devise new games based on patterns of games that children particularly enjoyed. For example, it can suggest new games that incorporate elements of games that have been highly rated in the past. Data such as the type of game, duration, frequency, and children's reactions is saved as the history of past games. This allows the system to learn from the history of past games and devise new games based on patterns of games that children particularly enjoyed, thereby maintaining a child's interest.

[0060] The game ideation department can adjust the difficulty and content of games based on parental feedback, and suggest more effective games. For example, the game ideation department will build a system in which the generation AI adjusts the difficulty and content of games based on parental feedback. For example, if a parent gives feedback that a game is too difficult, the difficulty of the next game will be lowered. Feedback will include parental comments, evaluation scores, and areas for improvement. This will allow the department to suggest more effective games by adjusting the difficulty and content of games based on parental feedback.

[0061] The play creation department can create games for different age groups and developmental stages, making it possible to accommodate a wide range of children. For example, the play creation department will build a system in which a generative AI can create games for different age groups and developmental stages. For example, it can accommodate a wide range of games, from simple games for toddlers to complex games for elementary school students. Different age groups and developmental stages include toddlers, school children, and adolescents. This makes it possible to create games for different age groups and developmental stages, making it possible to accommodate a wide range of children.

[0062] The game ideation department can devise special games according to the season or event, allowing users to enjoy a sense of the season or event. For example, the game ideation department will build a system in which a generation AI devise special games according to the season or event. For example, it will propose special games to coincide with Christmas or Halloween. Examples of seasons or events include summer vacation, Christmas, and Halloween. In this way, by devising special games according to the season or event, users can enjoy a sense of the season or event.

[0063] The game idea unit can use the emotion estimation function to monitor a child's emotional reactions in real time when suggesting a game, and continuously provide optimal games. The game idea unit, for example, uses the emotion estimation function to build a system that monitors a child's emotional reactions in real time when suggesting a game. For example, a camera or microphone can be used to analyze a child's facial expressions and tone of voice to measure their emotional state. Technologies such as wearable devices, camera systems, and voice analysis can be used to monitor emotional reactions in real time. This allows the emotion estimation function to monitor a child's emotional reactions in real time and continuously provide optimal games, thereby maintaining the child's interest.

[0064] The play suggestion unit can reflect parental feedback in the next play suggestion. For example, the play suggestion unit builds a system in which the generation AI reflects parental feedback in the next play suggestion. For example, if a parent provides feedback that they would like their child to get more exercise, an exercise element will be incorporated into the next play. Feedback includes parental comments, evaluation scores, and areas for improvement. By incorporating parental feedback, the next play suggestion will be more effective.

[0065] The play suggestion unit can take into account a child's learning progress and provide play that is appropriate for their development. For example, the play suggestion unit builds a system in which a generation AI monitors a child's learning progress in real time and, based on the results, provides play that is appropriate for their development. For example, if a child acquires a specific skill, it can suggest new play that makes use of that skill. To take learning progress into account, it uses the collection and analysis of learning data, progress evaluation criteria, etc. This makes it possible to provide play that is appropriate for a child's development by taking into account their learning progress.

[0066] The game suggestion unit can incorporate games from different cultures and regions to propose a variety of games from an international perspective. For example, the game suggestion unit will build a system in which the generation AI incorporates games from different cultures and regions to propose a variety of games from an international perspective. For example, it will propose traditional Japanese games and popular American games. Examples of different cultures and regions include Asia, Europe, and Africa. This makes it possible to propose a variety of games from an international perspective by incorporating games from different cultures and regions.

[0067] The play suggestion unit suggests collaborative games that parents and children can enjoy together, deepening the bond between them. For example, the play suggestion unit builds a system in which a generative AI suggests collaborative games that parents and children can enjoy together. For example, it suggests games in which parents and children work together to complete a mission. Examples of collaborative games include team-building games and collaborative creative activities. This allows the bond between parents and children to be deepened by suggesting collaborative games that parents and children can enjoy together.

[0068] The game suggestion unit can use the emotion estimation function to monitor the child's emotional reactions in real time when suggesting games and continuously provide optimal games. The game suggestion unit, for example, uses the emotion estimation function to build a system that monitors the child's emotional reactions in real time when suggesting games. For example, a camera or microphone can be used to analyze the child's facial expressions and tone of voice to measure the emotional state. Technologies such as wearable devices, camera systems, and voice analysis can be used to monitor the emotional reactions in real time. This allows the emotion estimation function to monitor the child's emotional reactions in real time and continuously provide optimal games, thereby maintaining the child's interest.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The lifestyle habit input unit can monitor the parent's health condition and prompt the parent to input lifestyle habits at appropriate times. For example, it can suggest refraining from inputting information when the parent is tired and prompting input when the parent is relaxed. To monitor the parent's health condition, sensors in wearable devices and smartphones are used. This allows the parent to input lifestyle habits at timings that take into consideration the parent's health condition, reducing stress and making it easier for the parent to input lifestyle habits.

[0071] The lifestyle input unit can estimate the parent's emotional state and suggest relaxing music or aromas. For example, if it determines that the parent is feeling stressed, it can play relaxing music or activate an aroma diffuser. The emotion estimation function estimates emotions using facial expression recognition and voice analysis. This allows the parent to reduce stress by offering relaxation methods that correspond to their emotional state, and allows them to input their lifestyle habits in a comfortable environment.

[0072] The lifestyle habit input unit can learn the parent's daily rhythm and suggest the optimal timing for input. For example, it can learn that the parent relaxes at a specific time each night and prompt them to input at that time. To learn the parent's daily rhythm, an algorithm is used to analyze the parent's behavioral patterns and find the optimal timing. This allows for input timing that matches the parent's daily rhythm, reducing stress and enabling efficient input of lifestyle habits.

[0073] The lifestyle habit input unit can estimate the parent's emotional state and provide positive feedback. For example, if it determines that the parent is feeling stressed, it can display an encouraging message or positive feedback. The emotion estimation function estimates emotions using facial expression recognition and voice analysis. This allows the parent to receive positive feedback according to their emotional state, reducing stress and increasing motivation.

[0074] The lifestyle habit input unit can provide a customizable input interface according to the parent's preferences. For example, an interface can be provided that allows the parent to select their favorite color or design. To realize a customizable input interface, a system is constructed that creates a user profile and saves settings according to the parent's preferences. This allows the parent to input lifestyle habits using an interface that suits their preferences, improving their motivation to input information.

[0075] The lifestyle habit input unit can estimate the parent's emotional state and provide advice on the input content. For example, if it is determined that the parent is feeling anxious, it will provide specific advice and support on the input content. The emotion estimation function estimates emotions using facial expression recognition and voice analysis. This allows the parent to provide advice based on their emotional state, reducing anxiety and allowing them to input lifestyle habits more effectively.

[0076] The play idea department can change the theme of the play based on parental feedback. For example, if a parent shows interest in a particular theme, it will suggest a play based on that theme. Feedback includes comments and evaluation scores from parents. This allows the department to change the theme of the play based on parental feedback, providing play that both parents and children can enjoy.

[0077] The play ideation department can analyze a child's emotional state in real time and suggest games that will reduce stress. For example, if it determines that a child is feeling stressed, it will suggest games that have a relaxing effect. Facial expression recognition and voice analysis are used to analyze emotional states in real time. This allows it to suggest games that correspond to a child's emotional state, reducing stress and providing a fun time.

[0078] The Play Ideas Department can propose collaborative play that parents and children can enjoy together. For example, they can propose games where parents and children work together to complete a mission, or activities where parents and children create artwork together. Examples of collaborative play include team-building games and collaborative creative activities. By proposing collaborative play that parents and children can enjoy together, they can deepen the bond between parents and children.

[0079] The play ideation department can analyze a child's emotional state in real time and suggest games that bring out positive emotions. For example, if it determines that a child is having fun, it will suggest games that will maintain that emotion. Facial expression recognition and voice analysis are used to analyze emotional states in real time. This allows it to suggest games that match a child's emotional state, bringing out positive emotions and providing a fun time.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The lifestyle habit input section allows parents to input the lifestyle habits they want their children to acquire. For example, specific habits such as getting up early every morning, washing hands before meals, and putting away toys can be input. This input can be easily done through the system interface. Step 2: The game ideation unit creates games that children can enjoy and learn based on the lifestyle habits input by the lifestyle habit input unit. For example, to help children develop the habit of waking up early every morning, the unit suggests a game in which they can earn points if they wake up early. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate game ideas. Step 3: The Play Suggestion Department proposes games devised by the Play Creation Department. For example, even if it is the same habit of tidying up toys, they can suggest a variety of games, such as a time trial game today and a story game with a tidying up theme tomorrow.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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 AI 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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 AI 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 lifestyle habit input unit for inputting lifestyle habits; a game devising unit that devise games based on the lifestyle habits input by the lifestyle habit input unit; a play suggestion unit that suggests the play devised by the play devising unit; A system characterized by:

2. The lifestyle habit input unit Input the lifestyle habits using voice recognition technology 2. The system of claim 1.

3. The game idea department It learns the history of past play and creates new play based on the patterns of play that the child particularly enjoyed.

2. The system of claim 1.

4. The play suggestion unit The emotional state of the child is analyzed using an emotion estimation function, and the content of the play is dynamically changed according to the change in emotion.

2. The system of claim 1.

5. The lifestyle habit input unit The emotional state of the parent is analyzed using an emotion estimation function, and a less stressful input method is suggested.

2. The system of claim 1.

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